TYK2 mediates neuroinflammation in Alzheimer’s disease brains with TDP-43 pathology

Domain: biology
Published: 2026-03-14
Editorial outcome: major_revision

Abstract
Neuroinflammation is a pathological feature of neurodegenerative diseases like Alzheimer’s disease and ALS. Cytoplasmic dsRNA (cdsRNA) triggers a type-I interferon response in human neural cells, leading to their death, and is found in neurons of C9ORF72-ALS patients. Here, we report the spatial coincidence of cdsRNA and pTDP-43 inclusions in human postmortem tissue with Alzheimer’s disease pathology, and upregulated interferon response genes in affected regions. CdsRNA also accumulates in a human TDP-43 G298S iPSC cortical neuronal model. We use cryptic exon detection as a proxy for TDP-43 mislocalization and demonstrate that FDA-approved JAK inhibitors baricitinib and ruxolitinib, which block interferon signaling, show protective effects only in brains with elevated cryptic exon expression. A CRISPR screen reveals TYK2 as a top hit and TYK 2 knockdown and the selective TYK2 inhibitor deucravacitinib rescue cdsRNA-induced toxicity. We find parallel neuroinflammatory mechanisms, dependent on TYK2 - a potential disease-modifying target - for TDP-43-associated Alzheimer’s disease and C9ORF72-ALS.

Introduction
Efficacious therapies based on principles of precision medicine have impacted millions of patients suffering from heterogenous diseases such as cancer1–3. In recent years, mounting evidence indicates that clinical dementia, like cancer, is also a collection of multifaceted neurodegenerative diseases4–6. With the number of dementia patients increasing in our aging society7, it is likely that personalized treatments for specific subsets of dementia will be important to address the huge unmet need for effective therapy8.

Alzheimer’s disease (AD), a disease defined by amyloid beta (A) plaques and neurofibrillary tangles (NFTs) of hyperphosphorylated tau protein, can be associated to other proteinopathies including cytoplasmic inclusions of phosphorylated TAR DNA-binding protein 43 (pTDP-43)9. TDP-43 pathology is observed in up to 57% of AD patients10 and is one of the most prominent proteinopathies after A and tau pathology11. TDP-43 is a nuclear protein involved in RNA processing12. Ectopic cytoplasmic inclusions of this protein are thought to deprive the nucleus of TDP-43 function and dysregulate RNA processing and expression13–18. For instance, loss of function of TDP-43 triggers derepression of immunostimulatory genomic double-stranded RNA (dsRNA) that can activate pattern recognition receptors (PRRs)19,20, which results in increased cell autonomous and non-cell autonomous inflammatory processes21. Previously, we demonstrated that cytoplasmic pTDP-43 inclusions are spatially coincident with cytoplasmic dsRNA (cdsRNA) in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) patients with an intronic expansion of hexanucleotide repeats in the C9ORF72 gene22. Independent from TDP-43 pathology, cdsRNA triggers an interferon-mediated toxic innate immune response in a sequence-independent fashion and leads to neuronal cell death in in-vitro and mouse models22. Elucidating the role of cdsRNA in other neurodegenerative diseases causing dementia, such as AD, could offer a paradigm to target neuroinflammatory mechanisms using principles of precision medicine.

Here, we show that immunogenic cdsRNA and cytoplasmic pTDP-43 inclusions are spatially coincident in brains with coexisting AD pathology as well as in a cell-based model of TDP-43 pathology and demonstrate robust type-I interferon (IFN-I)-signaling in AD patients’ brains. We updated our machine learning pipeline for drug repurposing in AD (DRIAD-SP)23 to include cryptic exon (CE) expression, which is a proxy for TDP-43 pathology24–29. Here, we demonstrate a protective signal for baricitinib and ruxolitinib, both FDA-approved Janus kinase (JAK)-inhibitors that block the interferon response, in only a subset of AD patients with elevated CE expression. To evaluate the mechanism of action of neuroprotective JAK inhibitors, we conducted a genome-wide CRISPR screen that reveals that knockout of TYK2, a JAK kinase family member, robustly rescues cdsRNA-induced toxicity. In addition to baricitinib and ruxolitinib, we also found that deucravacitinib – a selective TYK2 inhibitor recently approved for moderate-to-severe plaque psoriasis30 – exhibits a neuroprotective effect in three different neural cell models with more potency compared to the other JAK inhibitors. Finally, we analyze potential inflammatory biomarkers for dsRNA-mediated neuropathology by using cell-based assays as well as conducting a human observational study of plasma levels in individuals with a TYK2-activity-reducing single nucleotide polymorphism (SNP) and find that CXCL10 is a candidate biomarker for validation in clinical trials.

Results
Presence and immunogenicity of cdsRNA in AD
To assess the presence of cdsRNA in neurons of patients with AD pathology, we stained formalin-fixed paraffin-embedded (FFPE) amygdala sections of human postmortem brains of healthy individuals (n = 9) and of patients who met the pathologic diagnosis of AD (n = 10) (Fig. 1a; Supplementary Table 1). We stratified the patients and controls based on the severity of pTDP-43 pathology and detected cdsRNA in all individuals with severe pTDP-43 pathology by immunostaining. In contrast, whenever pTDP-43 inclusions were absent, our immunostainings were negative for cdsRNA as well (Supplementary Fig. 1a,b). In individuals with mild pTDP-43 pathology, we detected cdsRNA in 33% of cases. Next, we evaluated the spatial coincidence of cdsRNA and pTDP-43 by performing cyclic immunofluorescence (CyCIF)31 which enabled multiplexed staining of pTDP-43 inclusions, cdsRNA, NFTs, A plaques, and nuclei on the same tissue section (Fig. 1b, lower panel of healthy control and AD, respectively). We compared the acquired images to luxol fast blue-hematoxylin and eosin (LHE) stainings as well as immunohistochemical stainings of cdsRNA, pTDP-43, tau, and A (Fig. 1b, upper panel of healthy control and AD, respectively). While NFTs and A plaques were detected in the tissue, neither pathology overlapped spatially with cytoplasmic pTDP-43 inclusions nor cdsRNA (Fig. 1b). However, pTDP-43 and cdsRNA were spatially coincident (enriched two-fold in AD cases; Supplementary Fig. 1c), paralleling our previous observations in ALS brains22. If this cdsRNA acts as a damage-associated molecular pattern (DAMP) in neurons, then we anticipate it would trigger an IFN-I response through the activation of the PRRs RIG-I and MDA5 (Supplementary Fig. 1d). To test whether cdsRNA is immunogenic in brains with AD pathology, we immunostained amygdala sections for phosphorylated protein kinase R (PKR), another PRR that binds to cdsRNA and activates autophosphorylation34 (Supplementary Fig. 2a). We observed phosphorylated PKR (pPKR) only in cdsRNA-positive AD brains, close to 66% of it being coincident with cdsRNA (Supplementary Fig. 2b), indicating a potential direct link between cdsRNA and the activation of PRRs.

Furthermore, we wanted to see if we can replicate the observation of increased cdsRNA levels in a more controlled setting of TDP-43 pathology. For this purpose, we used a recently established human induced pluripotent stem cell (iPSC) line that harbors a pathogenic TDP-43 mutation (TDP-43+/G298S)32 and also shows increased interferon signaling33. As a positive control for cdsRNA detection, we lipofected control iPSC-derived NGN2 cortical-like neurons with poly(I:C), a dsRNA mimetic consisting of polyinosinic-polycytidylic acid, and were able to detect an increase in cdsRNA signal (Fig. 1c). We then differentiated an isogenic pair of the mutant iPSCs into NGN2 cortical-like neurons33 and observed a significant increase in cdsRNA in cells with the familial TDP-43 mutation compared to control cells (Fig. 1d). These observations identify shared characteristics of human AD-brain cells and a smaller, better controlled model systems for TDP-43 pathology. Together these findings show a link between immunogenic cdsRNA and TDP-43 mislocalization in our model system and in AD patients with TDP-43 pathology.

Increased interferon signaling in AD
Next, we conducted a differential gene expression analysis of RNA-sequencing data from five relevant brain regions available from the Religious Order Study/Memory and Aging Project (ROSMAP)34 and Mount Sinai Brain Bank (MSBB)35 AMP-AD databases to test our hypothesis of elevated interferon signaling. We first identified differentially expressed genes between AD patients and healthy controls, then assessed enrichment of a preselected interferon stimulated gene (ISG) signature relative to gene sets from the KEGG Medicus database36, which contains pathway gene sets for normal and diseased states. The ISG gene set ranked among the top 3 most enriched gene sets across all brain regions examined (Fig. 2a). Among the other top 15 pathways, many were related to ISG signaling, including antigen processing and presentation by MHC Class II and TLR-NFκB signaling (Fig. 2b). Within the ISG set, we found 139 unique ISG that were significantly upregulated and 51 ISGs that were significantly downregulated (adjusted P-value < 0.05) in AD patients compared to healthy controls. The parahippocampal gyrus (PHG), a brain region that shows atrophy early on in disease progression37,38, had the greatest number of upregulated ISGs, followed by the dorsolateral prefrontal cortex (DPFC) and the inferior frontal gyrus (IFG). In addition, we observed upregulation of ISGs, to a lesser extent, in the superior temporal gyrus (STG) and frontal pole (FP; Fig. 2c; Supplementary Data 1).

DRIAD-SP predicts efficacy of blocking interferon signaling
To examine whether interferon signaling in brains with AD pathology was associated with pTDP-43 pathology, we extended our DRIAD-SP framework along two axes. We previously published a machine learning framework for Drug Repurposing In Alzheimer’s Disease with Systems Pharmacology (DRIAD-SP)23 that links the prediction of disease stage (early- vs. late-stage AD based on the Braak AD staging system39) to drug-induced molecular signatures in human neural cells. Importantly, it does not predict the direction of a drug’s effect – that is whether the drug rescues or exacerbates disease phenotypes – but rather nominates promising candidates for further in-vitro and in-vivo validation23. Through this pipeline, we previously identified baricitinib, ruxolitinib, and tofacitinib, which are FDA-approved JAK inhibitors that inhibit interferon signaling, as drug candidates for repurposing in AD23 (Supplementary Fig. 3a).

First, we improved DRIAD-SP’s predictive power by training and evaluating predictors of Braak disease stage in a linear manner using ordinal ridge regression (Fig. 3a), instead of the binary classification, as was done previously. This approach allows for training on the full scale of stage I to stage VI along the Braak spectrum39, without the need to stratify patients into “early” and “late” categories. Second, we used the expression of CEs as a proxy for the presence of pTDP-43 inclusions (Fig. 3b). Previous work demonstrated that the pre-mRNAs of stathmin-2 (STMN2)16–18 and UNC13A14,15 have binding sites for TDP-43 and that TDP-43 represses CE inclusion in the canonical mRNA of both STMN2 and UNC13A during splicing. pTDP-43 pathology is associated with nuclear hypofunction of TDP-43 which leads to CEs being included in the processed mRNA – resulting in a short variant of STMN2 mRNA16–18 (STMN2 short) due to premature polyadenylation, and two versions of CE-inclusive UNC13A mRNA, which we refer to as UNC13A-CE1 and UNC13A-CE214,15. Numerous studies demonstrated the expression of CE-containing mRNA and de novo proteins also in AD 24–29. Based on these findings, we posited the presence of these CEs in AD brains as a proxy for TDP-43 pathology. As a proof of concept, we tested our computational approach by checking for the presence of CEs in RNA-sequencing data40 from FACS-sorted single neuronal nuclei from the neocortex of deceased ALS patients as about 97% of ALS patients exhibit pTDP-43 inclusions41. We confirmed that neuronal nuclei with loss of nuclear TDP-43 into cytoplasmic aggregates have higher reads of the CEs STMN2 short (log2 fold change (LFC) = 3.55; P < 0.0001), UNC13A-CE1 (LFC = 4.20; P = 0.0002) and UNC13A-CE2 (LFC = 4.05; P = 0.0006) relative to control neuronal nuclei that contain TDP-43 (Supplementary Fig. 3b). By contrast, the number of reads of canonical STMN2 were reduced to a smaller degree (LFC = -1.76; P = 0.0017) and not reduced for UNC13A (LFC = 0.278; P = 0.11) mRNAs in TDP-43-deficient neuronal nuclei.

With this validation, we stratified patients’ RNA-sequencing data derived from ROSMAP and MSBB AMP-AD databases based on the presence of CEs. First, we set the threshold for the presence or absence of each individual CE above the lowest non-zero peak (essentially > 1 CE transcript detected) in the abundance histograms (transcripts per million TPM > 1.3 STMN2 short; TPM > 0.09 for UNC13A-CE1; TPM > 0.08 UNC13-CE2; Fig. 3c). We classified patients with two or more CE transcripts as TDP-43 pathology-positive (Fig. 3d). In the MSBB data, the counts for UNC13A-CE1 and UNC13A-CE2 were much lower relative to the ROSMAP data. We reasoned that the single-end sequencing protocol to generate the MSBB RNA-sequencing data was less sensitive for CE detection, relative to the paired-end reads in the ROSMAP data (Supplementary Fig. 4a). Consequently, we restricted our analysis to ROSMAP sequencing data from the posterior cingulate cortex (PCC), a brain region impaired early on in disease progression and therefore highly relevant for our study42. Of 546 total patients, no CEs were detectable in 85 patients, and one type of CE was detected in 257 patients; 183 patients had two types of CEs, and 21 patients had all three CEs present (Fig. 3d). Hence, 37% of cases were classified “TDP-43-positive”-AD patients, which is a conservative prediction considering that the clinical incidence of TDP-43-pathology in AD patients can reach 57%10. Still, we wanted to ensure that our stratification is not compromised by false positives in case of a natural predisposition of these CEs – especially UNC13A-CE1 and UNC13A-CE2 – to coincide. For this purpose, we mapped out CE expression in all 546 analyzed cases and could not identify any form of link in their occurrence (Supplementary Fig. 4b). To determine whether predicted TDP-43 positivity was associated with later Braak stages, we used an ordinal regression model and found no significant difference in Braak staging (P = 0.27) between “positive” and “negative” cases(Supplementary Fig. 5a), indicating that CE production was orthogonal to the stage of tau progression.

We assessed the predicted efficacy of baricitinib, ruxolitinib, and tofacitinib in AD patients with two or more CEs (as a proxy of TDP-43 dysfunction) relative to AD patients with one or no CEs (Fig. 3e; further replicates in Supplementary Fig. 5b). Both baricitinib and ruxolitinib, but not tofacitinib, were significant hits in the predictor trained on RNA-sequencing data from AD patients with two or more CEs. On the other hand, none of the three drugs (baricitinib P = 0.76, ruxolitinib P = 0.34, tofacitinib P = 0.16) were significant hits in the predictor trained on no / low CE-expressing AD patients. These findings and the observation that the expression of ISGs is significantly increased in cases that we deemed TDP-43-positive based on cryptic exon expression (Supplementary Fig. 5c) support our hypothesis that cdsRNA associated with TDP-43 dysfunction contributes to IFN-I-mediated neuroinflammatory processes occurring in AD patients’ brains.

CRISPR screen and validation of TYK2
To replicate cdsRNA-mediated inflammation and further determine that cdsRNA is not only immunogenic but indeed triggers neural cell death, we transfected differentiated ReN VM cells with poly(I:C). These differentiated human neural cells contain markers for neurons, astrocytes, and oligodendrocytes, but not microglia43 and are not sensitive to other common stressors such as DNA-damaging agents and reactive oxygen species (Supplementary Fig. 6), perhaps because this cell line does not express STING43. In contrast to our previous work (“One Pot” Differentiation; Supplementary Fig. 7a)22, we found that differentiating neural cells in a separate dish and then transferring differentiated cells into the assay plate reduced variability between technical replicates (“Separate Pot” Differentiation; Supplementary Fig. 7b). Both baricitinib and ruxolitinib rescued human neural cell death in this refined workflow with greater reproducibility and lower variance with nearly complete rescue at 1 µM and > 100% at 10 µM – presumably due to preventing toxicity from the transfection reagent lipofectamine 2000 present in the control. Both drugs significantly inhibited the phosphorylation of STAT1 in a dose-dependent manner (Supplementary Fig. 8a-f) as well as the downstream transcription of ISGs23.

Since the EC50 values of baricitinib (EC50 = 135.9 nM) and ruxolitinib (EC50 = 152 nM) are much greater than their affinities to their target proteins (baricitinib: IC50 (JAK1) = 5.9 nM, IC50 (JAK2) = 5.7 nM44; ruxolitinib: IC50 (JAK1) = 3.3 nM, IC50 (JAK2) = 2.8 nM45), we hypothesized that the inhibition of other kinases may be responsible for their neuroprotective effect (Supplementary Fig. 8g). To test this hypothesis, we conducted a genome-wide CRISPR screen to rescue cdsRNA-induced toxicity in differentiated human neural cells22,46. We used the Brunello library which consists of four single guide RNAs (sgRNAs) per human gene and 1,000 control sgRNAs47 (Fig. 4a) expressed in barcoded lentiviral particles to transduce ReN VM cells in their neural progenitor state. After a week-long differentiation, one replicate was harvested to identify genes that were essential for ReN cell differentiation and were not present at the initiation of either poly(I:C) or lipofectamine. Cells carrying sgRNA targeting eight different genes were significantly depleted post-differentiation (DGAT2, SIGLEC11, GSTA2, ABI1, FBXL8, AHCY, C7orf55-LUC7L2, ARHGEF12; Supplementary Data 2), indicating that these genes may be essential for neural cell survival and differentiation. These post-differentiation cells were treated for 48 hours with poly(I:C) or mock lipofectamine transfection, were harvested, genomic DNA was extracted, and the sgRNA sequences within the gDNA were PCR-amplified and sequenced to identify which sgRNA barcodes are enriched. Enriched sgRNAs in the post-poly(I:C) treatment arm versus the mock transfection arm correspond to candidate target genes whose loss-of-function enhances survival. Target genes were considered a hit if the LFC was equal or greater than 2.0 after rounding to the first decimal place.

We found interferon receptor alpha and beta subunit 2 (IFNAR2; average LFC = 2.23825, P = 0.0001), interferon regulatory factor 9 (IRF9; average LFC = 2.0625, P = 0.0002), and TYK2 (average LFC = 1.95, P = 0.0004) as the best candidates for neural cell rescue upon knockout, whereas knockout of phospholipase C gamma 1 (PLCG1; average LFC = -1.9875, P = 0.0002), hepatocyte growth factor-regulated tyrosine kinase substrate (HGS; average LFC = -1.775, P = 0.0004), and SQSTM1 (p62, average LFC = -1.7475, P = 0.0005) markedly increased the toxicity induced by poly(I:C) (Fig. 4b). A gene enrichment analysis of the top 100 hits using Enrichr48–50 and the Reactome Pathway database51 shows that these hits are strongly linked to IFN-I signaling which underlines yet again that cdsRNA toxicity is mediated by an interferon response (Fig. 4c).

Next, we independently validated IFNAR2 and TYK2 as suitable targets to achieve neuroprotection. To evaluate IFNAR2, we treated ReN VM-derived neural cells with an anti-IFNAR2 antibody before introducing poly(I:C) into the cells (Fig. 4d). The blocking antibody significantly rescued dsRNA-mediated neuronal death, but the response was not dose-dependent and plateaued at about 50% rescue (no antibody P < 0.0001; antibody 1:50 P = 0.0001; 1:25 P = 0.0002; 1:10 P = 0.0011). This is consistent with our observation that activating IFNAR2 with interferon- is not sufficient to trigger human neural cell death. We treated cells with interferon- at a range of doses. After 48 hours, there was no decrease in cell viability at any dose tested (Fig. 4e).

Subsequently, we assessed TYK2 as a potential key player in neuroprotection by generating a knockdown (KD) model in ReN VM cells. Both undifferentiated (P = 0.0003) and differentiated ReN VM-derived neural cells show an effective KD of TYK2 (Fig. 4f,g). Toxicity elicited by poly(I:C) is rescued up to 86% in TYK2 KD cells compared to wildtype cells (P < 0.0001), presumably short of 100% rescue because of some residual TYK2 expression (Fig. 4h).

Validation with deucravacitinib, a specific TYK2 inhibitor
To further evaluate TYK2, we used deucravacitinib, a potent small molecule inhibitor 30 that is highly specific for TYK2 over other JAK kinase family members (IC50 = 0.2 nM compared to IC50 (JAK1-3) > 10 µM)52–56 (Supplementary Fig. 8g). We observed that exposure to deucravacitinib rescues dsRNA toxicity fully at all previously tested concentrations from 0.01 µM to 10 µM and that dose-dependent rescue is achieved at significantly lower concentrations (10pM - 10 nM) than baricitinib and ruxolitinib (Fig. 5a; all P < 0.0001) with an EC50 = 1.483 nM. Deucravacitinib was also found to inhibit STAT1 phosphorylation (downstream protein of TYK2) in a dose-dependent manner (Fig. 5b; Supplementary Fig. 8e,f; all P < 0.0001). We validated these results in differentiated ReN CX cells (Fig. 5c,d), a cell line that shows the same characteristics as ReN VM cells but is derived from the cortex instead of the ventral mesencephalon and generates glutamatergic neurons rather than dopaminergic neurons, and in SH-SY5Y cells, a neuroblastoma cell line that unlike ReN cells expresses STING (Fig. 5e,f; Supplementary Fig. 8h). In both cell types, full rescue of cdsRNA-mediated toxicity was achieved with a 1 µM dose of deucravacitinib. Higher concentrations of deucravacitinib seem to be toxic to these cells.

We then applied deucravacitinib to the previously established ReN VM TYK2 KD cells and observed that adding as little as 0.01 µM deucravacitinib rescued the remaining cell viability deficit caused by residual interferon signaling through the few TYK2 protein that escaped the KD (Fig. 6a). Assessing the phosphorylation and therefore activation of downstream STAT1 mirrors these results (Fig. 6b).

To further evaluate TYK2 as a key signaling kinase for IFN-I signaling, we performed a deep proteomics analysis from ReN VM-derived neural cells that were treated with baricitinib and deucravacitinib at high doses (10 µM) by quantitative multiplex tandem mass tag (TMT) mass spectrometry (Fig. 6c). Here, we saw that all quantified ISGs were upregulated by poly(I:C), and this ISG induction was reversed by both baricitinib and deucravacitinib. Taken together, our results indicate that the toxic innate immune reaction to cdsRNA is dependent on TYK2. Furthermore, the action of TYK2 appears to be more complex than just mediating the IFN-I pathway alone.

CXCL10 as a biomarker for dsRNA pathology
Specific biomarkers for neuroinflammatory-based neurodegenerative disease subtypes associated with dsRNA-induced pathology would help identify patients who could benefit from treatments modulating TYK2. We screened potential biomarkers in a genome-wide association study (GWAS) using proteomics data from the UK Biobank57 and Icelandic DeCODE database58. Even though processed on different proteomics platforms (UK Biobank: Olink, DeCODE: SomaScan), we consistently found that individuals with an established partial loss-of-function polymorphism in TYK2 (rs34536443) in both cohorts have significantly lower plasma CXCL10 levels (UK Biobank: Effect: -0.12, SE = 0.02, P = 2.72 x 10-11; DeCODE: Effect: -0.10, SE = 0.02, P = 5.04 x 10-11) (Fig. 7a). This minor allele coding mutation (P1104A) leads to a near-complete (~80%) loss of TYK2 function in homozygotes, while heterozygotes have <40% reduction of function59–61, and its functional effect mimics the action of deucravacitinib62.

It has been shown that CXCL10 is elevated in patients with AD63–67. To study the chemokine CXCL10 as a potential biomarker for cdsRNA-related pathology further, we examined its levels in media from our ReN VM cell-based model system by conducting an electrochemiluminescence (ECL) immunoassay (Fig. 7b). As expected from our proteomics data showing increased expression of CXCL10 in neural cells transfected with poly(I:C) compared to control (Fig. 6c), we saw significantly increased levels of CXCL10 in media of cells treated with this dsRNA mimetic. The application of baricitinib, ruxolitinib, and deucravacitinib diminished CXCL10 expression to levels comparable to the lipofectamine-only control condition in a dose-dependent manner (P < 0.0001 in all conditions).

Next, to evaluate if this candidate biomarker can be seen in association with TDP-43 pathology, we modified our cell-based assay by treating differentiated ReN VM cells with 1 µM MG-132, a proteasome inhibitor known to induce cytoplasmic aggregation of TDP-4368,69. We confirmed translocation of TDP-43 from the nucleus to the cytoplasm by immunofluorescent staining with two different antibodies (Supplementary Fig. 9a,b) and quantified CXCL10 levels in the media by ECL immunoassay. CXCL10 levels were significantly elevated after MG-132 treatment (P < 0.005) and were reduced to control levels by 10 µM deucravacitinib (P < 0.005) but not by baricitinib and ruxolitinib treatment, indicating that selective TYK2 inhibition is most effective in reducing IFN-I mediated CXCL10 levels among the JAK kinases (Fig. 7c).

Taken together, these results nominate CXCL10 as a candidate biomarker for the assessment of dsRNA-mediated neuroinflammation using the cerebrospinal fluid (CSF) of patients with AD and could help determine whether these patients would benefit from therapeutic approaches reducing TYK2 activity.

Methods
This study complies with all relevant ethical regulations and was approved by the MGB IRB (2006P002104). Details can be found within each respective method section.

Histological Staining and Imaging
All human tissues were acquired from the MGH MADRC brain bank in accordance with protocols approved by the MGB Institutional Review Board. Informed consent, including consent to publish three or more indirect identifiers, was given by all participants. Participants were not compensated for their contribution to this study. Detailed patient data can be found in Supplementary Table 1. Sex was self-reported by participants. While an effort to achieve balanced representation was made, sex was not directly considered in the study design and data analysis to ensure that findings apply to any sex. Similar to what was previously reported22, ~5 µm thick formalin-fixed paraffin-embedded (FFPE) sections of the amygdala were dewaxed followed by antigen retrieval in a Leica Bond Fully Automated Slide Stainer. After deparaffination and rehydration by sequential incubation in Leica dewing solution (3 × 5 min), 100% ethanol (2 × 5 min), 90% ethanol (2 × 5 min), 70% ethanol (2 × 5 min), and distilled water (3 × 5 min), antigen retrieval was achieved with citrate-based antigen retrieval solution (ER2) and steam cooking for 1 hour. Afterwards, sections were cooled to room temperature and rinsed in water and 1× PBS. For permeabilization, 0.03% hydrogen peroxide solution was applied for 8 min. After blocking in tris-buffered saline (TBS)–based Odyssey blocking buffer (cat. # 927-60001, LI-COR, NE, USA) for 1 hour, for immunohistochemistry, primary antibodies (J2 for dsRNA (1:200, cat. # 76651, Cell Signaling Technology, MA, USA); TDP-43 (1:200, cat. #ab193842, abcam, MA, USA); pTDP-43 S409/410 (1:200, cat. # CAC-TIP-PTD-P02, Cosmo Bio, CA, USA); tau (1:100, cat. # NBP2-25162, Novus Biologicals, CO, USA); Aß (1:100, cat. # CST-D54D2, Cell Signaling Technology); pPKR (1:100, cat. # bs-3336R-Cy3, Bioss Antibodies, MA, USA)) diluted in blocking buffer were applied overnight at 4 °C in a humidified chamber. Following a wash with PBS (3 × 5 min), secondary antibodies were applied using the VECTASTAIN ABC Staining Kit (cat. # PK-6200, Vector Laboratories, CA, USA) and DAB Staining Kit (cat. # SK-4100, Vector Laboratories). For luxol fast blue-hematoxylin and eosin (LHE) staining, the protocol of the Luxol Fast Blue Stain Kit (cat. # ab150675, abcam) and the Sakura Prisma H&E Stain Kit (cat. # 76318-76, VWR, PA, USA) were followed. For tissue-based cyclic immunofluorescence (CyCIF)31, unspecific binding sites were blocked, slides were incubated in 3% H2O2 and 20 mM HCl in PBS at room temperature with light-emitting diode illumination for 1 hour to reduce autofluorescence, and the first primary antibody set (J2, TDP-43, and pTDP-43 S409/410) was applied. After washing, the secondary antibodies (anti-rabbit Alexa Flour 555 (cat. # A-31572, Thermo Fisher, MA, USA); anti-mouse Alexa Flour 647 (cat. # A-21237, Thermo Fisher)) 1:1000 diluted in blocking buffer were applied for 1 hour in a humidified chamber at room temperature. After a wash, SyTox Blue Nucleic Acid Stain (cat. # S11348, Thermo Fisher) was applied 1:15000 in blocking buffer for 15 min at room temperature. Following a final washing step, slides were imaged. After this first cycle, the fluorophores were inactivated by 4.5% H2O2 and 24 mM NaOH in PBS at room temperature under light exposure for 1 hour. After rinsing, the second primary antibody set (pPKR, tau and Aß) was applied and staining was continued in the same manner as described above. Images were acquired with the CyteFinder slide scanning fluorescence microscope (RareCyte Inc., WA, USA) using the Imager5 software (v. 2018, RareCyte Inc.).

Differential Gene Expression Analysis of Brain Regions
This Differential Gene Expression protocol was established based on a previously established protocol104. In brief, clinical diagnosis was used to define AD versus control conditions within the bulk RNA-sequencing data taken from the ROSMAP34 and MSBB35 databases (data obtained from the AMP-AD Knowledge Portal (doi:10.7303/syn2580853)). The analysis focused on a list of 354 pre-selected interferon-related genes. Of these genes, 309 could be found in the analyzed brain regions (from ROSMAP: dorsolateral prefrontal cortex (DPFC), from MSBB: frontal pole (FP), inferior frontal gyrus (IFG), parahippocampal gyrus (PHG), superior temporal gyrus (STG)). 139 genes were significantly upregulated (adjusted P value < 0.05) while 51 were downregulated. The full results with LFC, FC, CI, P values and adjusted P values can be found in Supplementary Data 1.

Induced Pluripotent Stem Cell differentiation and treatment
An isogenic pair of a recently established induced pluripotent stem cell (iPSC) line that harbors a pathogenic TDP-43 mutation (TDP-43+/G298S) in the FA0000011 genetic background32,105 was differentiated into NGN2 cortical-like neurons using a recently published protocol33,106 in a 384-well plate format (10,000 cells per well). Briefly, NGN2 neurons were generated by nucleofecting iPSCs with PiggyBac plasmids107–109 containing Tet-inducible transcription factors NGN2 and transposase110. After 24 hours of culture in media supplemented with CET, the cells were selected based on the presence of nuclear blue fluorescent protein 2 (BFP2) signal and using puromycin (10µg/mL; cat. # ant-pr-1, InvivoGen, CA, USA), indicating successful plasmid integration. iPSCs were plated on Matrigel-coated (cat. # 354320, Corning) plates and differentiated into post-mitotic neurons using induction media. After three days of differentiation, the cells were dissociated, frozen, and later plated for experiments, where they were cultured with neurotrophic factors and long-term media changes up to day 30 in culture. Where indicated, wildtype FA0000011 cells were transfected with poly(I:C) HMW (cat. # tlrl-pic, Invitrogen, CA, USA) by lipofection with lipofectamine 2000 (dilution factor 3:1000) cat. # 11668027, Thermo Fisher) (final dsRNA concentration: 4-8 µg/ml, depending on poly(I:C) batch effects).

ReN VM and CX cell culturing and differentiation
ReN VM (cat. # SCC008, Millipore, MA, USA) and CX cells (cat. # SCC007, Millipore) were cultured in ReNcell Maintenance media (cat. # SCM005, Millipore) containing 1:100 of Penicillin-Streptomycin (cat. #30-002-CI, Corning, NY, USA), 20 ng/ml of epidermal growth factor (cat. # GF001, Millipore) and 20 ng/ml of basic fibroblast growth factor (cat. # 03-002, Stemgent, MD, USA) and differentiated into neural cells for one week by removing the growth factors as previously described22,111, with the difference that the differentiation took place in a separate dish (Extended Figure 7) to minimize variability downstream.

TYK2 knockdown in ReN VM cells
A lentiviral vector used to express shRNA for TYK2 (target sequence: TGCAAGCCTGATGCTATATTT), pLV[shRNA]-Puro-U6>hTYK2[shRNA#1], and the control lentiviral vector used to express scramble shRNA (sequence: CCTAAGGTTAAGTCGCCCTCG), pLV[shRNA]-Puro-U6>Scramble_shRNA, was constructed and packaged by VectorBuilder Inc. (IL, USA). The vector IDs are VB250103-1179zud (TYK2 shRNA) and VB010000-9541pqu (scramble shRNA), which can be used to retrieve detailed information about the vector on vectorbuilder.com. Briefly, ReN VM cells were treated with the virus at a multiplicity of infection (MOI) of 1. After a week-long selection with puromycin at a concentration of 1 µg/ml, knockdown was confirmed by Western blot.

SH-SY5Y culturing
SH-SY5Y Neuroblastoma cells (cat. # CRL-2266, ATCC, VA, USA) were cultured in 1:1 Eagle’s Minimum Essential Medium (EMEM) (cat. # 10-009-CV, Corning) and F12 medium (cat. # 11765-047, Thermo Fisher) containing 1:100 of Penicillin-Streptomycin (cat. #30-002-CI, Corning) and 10% fetal bovine serum (FBS; cat. # 10438-026, Thermo Fisher). Prior to any treatments, cell media was changed to media containing 1% FBS instead of 10% to slow down cell division.

ReN VM, CX and SH-SY5Y cell treatments
SH-SY5Y cells as well as differentiated ReN VM and CX cells were seeded into 96-well plates (VM/CX: 20,000 cells per well with Matrigel-coating (cat. # 354320, Corning), SH-SY5Y cells: 10,000 cells per well without coating) and where applicable, respective drugs dissolved in dimethyl sulfoxide (DMSO) (baricitinib: HMS LINCS ID # 10354, ruxolitinib: HMS LINCS ID # 10138, deucravacitinib: cat. # HY-117287, MedChem Express, NJ, USA, MG-132: cat. # HY-13259, MedChem Express, etoposide: cat. # HY-13629, MedChem Express, menadione: cat. # M9429-25G, Sigma-Aldrich, MO, USA) were added using a D300e Digital Dispenser (Hewlett-Packard, CA, USA). After 1 hour of incubation at 37 °C, cells were transfected with poly(I:C) HMW where applicable (cat. # tlrl-pic, Invitrogen) by lipofection with lipofectamine 2000 (dilution factor 3:1000, cat. # 11668027, Thermo Fisher) (final dsRNA concentration: 4-8 µg/ml, depending on poly(I:C) batch effects). To test the relevance of IFNAR2, a neutralizing anti-IFNAR2 antibody (cat. # 21385-1, PBL Assay Science, NJ, USA) was added in the respective amount to each well after lipofection and without prior drug treatment. To test whether interferon  and poly(I:C) have comparable effects on neurons, interferon  (cat. # 407294-5MU, Millipore) was diluted in media and added to the cells without prior drug treatment. Western blot samples and cell media for biomarker testing were collected 24 hours (48 hours for MG-132 treatment) post-treatment (before we observe any cell death). Cell viability was assessed 72 hours (VM, SH-SY5Y) and 7 days (CX) post-treatment using the CellTiter-Glo Assay (cat. # G7572, Promega, WI, USA) following the manufacturer’s instructions. All conditions were normalized to 0.1% DMSO content. Where applicable, lipofectamine 2000 (VM, CX) or lipofectamine 2000 (dilution factor 3:1000) + 10 µM drug (SH-SY5Y) was used as the respective control. Luminescence was measured on a BioTek Synergy H1 Plate Reader (BioTek, VT, USA) using the Gen5 software (v.3.2). For mass spectrometry, two million ReN VM cells were expended in a 15-cm-dish (2 dishes per replicate) for 4 days and subsequently differentiated for 1 week, then treated with 10µM of drug, transfected with poly(I:C) after 1 hour of incubation at 37 °C, and 24 hours later, washed with ice-cold 1x PBS and collected by scraping followed by centrifugation at 500 g for 5 min. For the testing of common stressors, time-lapse images were captured every 3 hours for 33 hours using GE In-Cell 6000 Analyzer (GE HealthCare, IL, USA) with the manufacturer’s software (v.7.2) under the confocal mode. Each image's total cell surface area and average fluorescence intensity were quantified using Fiji/ImageJ-based plugin (National Institutes of Health, v.1.53c)112 and plotted using GraphPad Prism (v.9.3.1). Four biological repeats were taken for each group.

Cell Staining and Imaging
Differentiated ReN VM cells treated with 1 µM MG-132 were fixed 48 hours post-treatment and iPSC-derived NGN2 cortical-like neurons were fixed after ten days of differentiation using 4% paraformaldehyde (cat. # 28908, Thermo Fisher) at room temperature and protected from light. Additionally, after three washes with 1 x PBS, ReN VM cells were permeabilized using ice-cold methanol for 10 min. Cells were washed gently with 1 x PBS three times and then incubated with blocking solution (cat. # 927-60001, LI-COR) for 1 hour at room temperature. All blocking solution was removed, and cells were incubated with fresh blocking solution and primary antibodies overnight on a shaker at 4 °C. Primary antibodies used were anti-TUJ1 (1:250, cat. # TUJ, Aves Lab, CA, USA), K1 anti-dsRNA (1:250, cat. # 10020200, Scicons, Susteren, The Netherlands), and anti-TDP-43 (1:100, cat. # 3448, Cell Signaling Technology and 1:500, cat. #10782-2-AP, Proteintech, IL, USA). For poly(I:C) antibody incubation controls, poly(I:C) was incubated with the dsRNA primary antibodies at a ratio of 5x poly(I:C) : 1x dsRNA primary antibody for 1 hour before being added to cells. Three PBS washes were done, and cells were incubated with secondary antibodies based on species at a 1:500 (iPSCs) and 1:1000 (ReN VM) dilution in the blocking solution for 1 hour at room temperature on a shaker. Hoechst 33342 (cat. # H3570, Thermo Fisher) was added at a 1:1000 dilution to the well with iPSCs for the final ten minutes. For ReN VM cells, cells were first washed three times with 1 x PBS and then Hoechst was added to the wells in a 1:5000 dilution in PBS for 30 min at room temperature. Secondaries used were donkey anti-chicken Alexa Fluor 488 (cat. # 703-545-155, Jackson ImmunoResearch, PA, USA), donkey anti-rabbit Alexa Fluor 568 (cat. # A10042, Thermo Fisher), donkey anti-mouse Alexa Fluor 647 (cat. # A31571, Thermo Fisher), and donkey anti-rabbit Alexa Fluor 488 (cat. # A21206, Thermo Fisher). After three final washes with 1 x PBS, confocal images were acquired with an Image X-Press Micro Confocal (Molecular Devices, CA, USA) using MetaXPress (v.6.5.3.427). Images were taken with a 20x (ReN VM) or 40x (iPSC) objective with four z-stacks (1.5uM step size) per field for between 25 and 55 fields per well. All groups were imaged with the same acquisition settings within each experiment. Automatic quantifications were performed using a custom Fiji/ImageJ-based plugin (v.1.53c)112 that measures integrated intensity of K1 dsRNA antibody within TUJ1-positive cells.

DRIAD-SP: RNA-sequencing data processing and TDP-43 status prediction
RNA-sequencing reads of brain specimens from AD patients in the ROSMAP study, along with their corresponding clinical annotations, were downloaded from the AMP-AD Synapse portal at https://ad-knowledgeportal.synapse.org. RNA-sequencing reads from ALS patient neurons that were sorted for presence or absence of TDP-43 were downloaded from Sequence Read Archive (SRA) accession GSE12654240. We quantified transcripts using Salmon (v1.9.0)113 against release 107 of the hg38 human transcriptome from Ensembl. We amended the transcriptome with three transcripts that have not yet been annotated in Ensembl, but that are known to be associated with loss of nuclear TDP-4314. Specifically, we included two splice variants of UNC13A, each including an additional CE between the canonical exons 20 and 21 (hg38; chr19: 17,642,414–17,642,541 (CE1); chr19: 17,642,414–17,642,591 (CE2)), and one splice variant of STMN2, which includes an alternative exon 2 termed exon 2a (hg38; chr8:79,616,822–79,617,048).

Given our quantification of TDP-43 pathology associated transcripts, we classified patient samples as TDP-43 pathology-positive or -negative based on the number of expressed CEs. We dichotomized transcript abundance using thresholds set above the lowest non-zero peak in the abundance histograms (TPM>1.3 STMN2 short; TPM>0.09 for UNC13A-CE1; TPM>0.08 UNC13-CE2). This threshold determines whether each individual CE is present or absent. Next, patient samples expressing one or none of the three CEs (STMN short, UNC13A-CE1 or UNC13A-CE2) were considered TDP-43 pathology-negative and patient samples expressing two or all three of the CEs were considered TDP-43 pathology-positive. Using this classification, the proportion of TDP-43 pathology-positive samples is approximately 37%. Our classification is a conservative prediction, given that the clinical incidence of TDP-43 inclusions in AD patients is up to 57%10. To assess if ISGs are upregulated in cases that we deemed TDP-43 pathology-positive, a list of ISGs was curated based on Supplementary Tables 2-10 in Schoggins et al.114 and the expression of these genes was compared to the expression in TDP-43 pathology-negative cases.

DRIAD-SP: Disease stage prediction and gene set significance assessment
For assessing how well any given gene set predicts AD progression, we used an updated variant of the published DRIAD-SP methodology23. For predicting disease stage, we collapsed transcript abundances to gene-level abundances using tximport (v.1.43.0)115 and msigdbr (v.24.1). The entire transcriptional feature space was filtered down to the about ~20k protein-coding genes in the human genome to ensure that only genes with gene products that can conceivably be targeted using conventional small molecule inhibitors are included. Every AMP-AD specimen was assigned a label of disease severity based on the following mapping to the Braak annotations: A—early (Braak 1–2), B—intermediate (Braak 3–4), and C—late (Braak 5–6). We set up an ordinal regression task classifying patients according to their disease stage using the ordinalRidge R package (v.0.1; https://github.com/labsyspharm/ordinalRidge). We used the gene expression measurements from AMP-AD as predictors, filtered to only contain genes in the current gene set under investigation. This way, the performance (AUC) of a model utilizing a given gene set can be viewed as a measure of how strongly the given genes are predictive of AD progression. To address overfitting, ordinalRidge utilizes a ridge regularization term that penalizes the L2-norm of feature weights. No LASSO regularization was used, as it induces sparsity and excludes features that were specifically preselected to be included in the model.

Model performance was evaluated through leave-pair-out cross-validation. For a given regression task, each example in the dataset was associated with an example from the other classes that was the closest match in age. If there were multiple candidates for the age match, the pairing was selected uniformly at random. The resulting set of age-matched pairs was evaluated in a standard cross validation setting, by asking whether the later-stage example in each withheld pair was correctly assigned a higher score by the corresponding predictor. The fraction of correctly ranked pairs constitutes an estimate of the area under the ROC curve116. Comparing model performances across gene sets directly is not possible, as their set sizes can differ. Therefore, we normalized AUC values by relating them to performances observed on size-matched random gene sets. For each gene set of interest, 1000 random gene sets of matching sizes were sampled from a uniform distribution over the protein-coding space. After evaluating all lists through cross-validation, an empirical P value was computed as the fraction of background sets that yield higher predictor performance than the gene list of interest.

CRISPR screen
Low passage (P2) ReNcell VM cells stably expressing Cas9 were expanded and transduced with lentiviral particles containing the Brunello library47. Lentiviral transduction efficiency was tested by plating cells into two 6-well plates and varying volumes of stock preparation of Brunello library lentiviral vectors with 8 µg/ml of polybrene. One plate was treated with puromycin, the other plate remained untreated, and the proportions of cells measured for each condition using CellTiter-Glo were used to calculate the efficiency which was found to be at 84% averaged from triplicates. Transduced cells were expanded in the presence of puromycin until the required number of cells was achieved to obtain triplicates of 1000 cells per sgRNA. Then, cells were differentiated as described above. After seven days, 3 µg/ml poly(I:C) in complex with lipofectamine or lipofectamine alone (dilution factor 3:1000) as a control were added to each flask. After 3 days of treatment, genomic DNA was isolated from each group of cells. PCR and sequencing were performed according to standard protocols117,118. For analysis, the algorithm STARS was used to determine P-values117, the read counts were normalized to reads per million and then log2 transformed and the LFC of treatment and control were compared to the lipofectamine controls. Genes were considered a hit if the LFC was equal or greater than 2.0 after rounding to the first decimal place.

Pathway Enrichment Analysis
Pathway enrichment analysis of genes that rescued neural cell death was performed by selecting the 100 genes with the most significant sgRNA enrichment and querying the "Reactome_Pathways_2024" database51 using the R (v.4.4.2) package enrichR48–50 (v.3.4). Enrichment analysis of differentially expressed genes in AD samples was performed using the fgsea R package119 (v.1.32.4) with KEGG Medicus gene sets36. Genes were ranked using the metric -sign(log2FoldChange) × log10(p-value).

Mass spectrometry
First, cells were lysed in lysis buffer (2% SDS, 150 mM NaCl, 50 mM Tris pH 8.5) supplemented with protease and phosphatase inhibitor (cat. # 11873580001 and 4906845001, Millipore) and homogenized with a QIAshredder column (cat. # 79656, Qiagen, Hilden, Germany). Lysates were reduced with freshly prepared dithiothreitol (DTT) (final concentration: 5 mM) and heated at 37 °C for 1 hour. Samples were then alkylated with iodoacetamide (final concentration: 20 mM) for 25 min with 50 mM ammonium bicarbonate in the dark whereupon the reaction was stopped by adding DTT (final concentration: 50 mM). After methanol/chloroform precipitation, the purified protein was solubilized in freshly prepared 8 M urea in 200 mM EPPS, at pH 8.5 for 30 mins at 37 °C. For digestion, the urea concentration was diluted with 200 mM EPPS to 4 M final concentration and 2% acetonitrile (v/v) was added. Digestion was performed using lysyl endopeptidase® Lys-C (2 mg/ml; cat. # 121-05063; Wako, Osaka, Japan), (enzyme-to-substrate ratio: 1:50) for 3 hours at 37 °C. Following dilution of the sample with 200 mM EPPS to a final urea concentration of 1.6 M, further digestion was performed by the addition of trypsin (cat. # V5111, Promega), (enzyme-to-substrate ratio: 1:100) for 8 hours at 37 °C. A small aliquot was analyzed for its missed cleavage rate by mass spectrometry. Equal amounts of digested protein (60 µg) were removed from each sample and labeled using TMTpro 16plex Mass Tag Labelling Kit (TMT; cat. # A44520, Thermo Fisher) following the manufacturer’s instructions. Labeling efficiency of > 95% was determined along with ratio checks by mass spectrometry MSI3, while the labeling reactions were stored at -80 °C. To quench the reaction, hydroxylamine was added to a final concentration of 0.5% (v/v) for 10 mins. After acidification using formic acid, equal amounts of labelled peptide were pooled from each sample (as judged from ratio check data). Then, solvent was evaporated. The labeled peptide multiplex was desalted by solid-phase extraction (SPE; SepPak tC18 Vac RC Cartridge). The peptides were fractionated by HPLC alkaline reverse phase chromatography (Agilent 1200 Series) into 96 fractions and combined into 24 samples. Lastly, peptides were desalted over Stage Tips120 prior to MS analysis.

A MultiNotch SPS-MS3 TMT method121 was used on an Orbitrap Lumos mass spectrometer (Thermo Fisher) operated with Tune (v.3.4) as well as Xcalibur (v.4.5.445.18) and coupled to a Proxeon EASY-nLC 1200 liquid chromatography (LC) system (Thermo Fisher). Samples were injected onto a 40 cm, 100 µm (internal diameter) column packed with 2.6 µm Accucore C18 resin (flow rate of 450 nl/min). Over the course of 4 hours, the peptide fractions were separated through acidic acetonitrile gradients by the LC before being injected into the mass spectrometer. First, an MS1 spectrum (Orbitrap analysis; resolution 120,00; mass range 400-1400 Th) was taken. Then, an MS2 spectrum was collected after collision-induced dissociation (CID, CE = 35) with a maximum ion injection time of 150 ms and an isolation window of 0.7 Da. For TMT quantification of peptides, MS3 precursors were fragmented by high-energy collision-induced dissociation (HCD, CE = 40%) and analyzed in the Orbitrap at a resolution of 50,000 at 200 Th. Further details can be found in a previously published article122.

A Sequest (v.28rev.12)-based in-house software was used to search peptides against a human database with a target decoy database strategy and a false discovery rate of 1%. Oxidized methionine residues (+15.9949 Da) were dynamically searched, along with static modifications for alkylated cysteines (+57.0215 Da) and the TMTpro reagents (+304.207145 Da) on lysines and the N-termini of peptides. Relative protein quantification required a summed MS3 TMT signal/noise > 200 over all TMT channels per peptide and an isolation specificity > 70% for any given peptide. Quant tables were generated and exported to Excel for further procession with JMIP Pro (v.16), GraphPad Prism (v.9.3.1), and monocle (v.1.1). More details on the TMT intensity quantification and certain parameters can be found in another recent publication123.

Western blotting
First, 15 µl of Laemmli Sample Buffer (cat. # 1610737; Bio-Rad Laboratories, CA, USA) with 1:20 2-mercaptoethanol was added to each well after aspiration of the media. Subsequently, the lysates were boiled at 100 °C for 5 min. Then, the samples were loaded onto a precast gel (cat. # 4569036; Bio-Rad Laboratories, CA, USA) which was run in 1x running buffer diluted from a 10x Tris/Glycine/SDS stock (cat. # 1610772; Bio-Rad Laboratories) at 160 V for 40 min. The transfer was performed using a PVDF membrane (cat. # IPFL00010; Millipore) and a 1x transfer buffer diluted from a 10x stock (cat. # PI35040; Thermo Fisher, MA, USA) mixed with 20% methanol. For 90 min, the gel was blotted at 4 °C at 90 V. Finally, unspecific binding sites were blocked by phosphate-buffered saline (PBS)-based Odyssey® blocking buffer (cat. # 927-40150; LI-COR) for 1 hour at room temperature. Then, primary antibody (pSTAT1 Y701 (cat. # 9167S; Cell Signaling Technology); beta actin (ACTB, cat. # 3700; Cell Signaling Technology); TYK2 (cat. #ab303500, abcam); STING (cat. #13647, Cell Signaling Technology)) diluted 1:1000 in blocking buffer was applied overnight at 4 °C. Then, the blot was washed with tris-buffered saline (cat. # sc-262305; Santa Cruz, TX, USA) containing 0.05% Tween 20 (cat. # BP337-500; Thermo Fisher) (3 x 5min). Afterwards, secondary antibody (IRDye 800CW (cat. # 926-32211; LI-COR); IRDye 680RD (cat. # 926-68070; LI-COR)) diluted 1:2500 in blocking buffer was applied for 1 hour at room temperature. After three final washes, the blot was imaged with the Odyssey® DLx Imager (LI-COR). Images were analyzed using ImageStudioLite (v. 5.2.5, LI-COR). Unprocessed scans of all blots are provided in the source data file.

Genome-wide association tests using a TYK2 polymorphism
We used an established partial loss-of-function in TYK2 (rs34536443) to estimate the effect of pharmacological TYK2 inhibition on the circulating CXCL10 concentration59–61. Summary genetic association data was obtained from a genome-wide association study (GWAS) using the Olink proteomics platform in 54,219 participants of British ancestry in the UK Biobank57. Genome-wide association tests were performed using REGENIE (v.2.2.1) in a two-step procedure to account for population structure in a population of European ancestry (n = 34,557). Protein levels were inverse-rank normalized. Association models included the following covariates: age, age2, sex, age × sex, age2 × sex, batch, UK Biobank center, UK Biobank genetic array, time between blood sampling and measurement, and the first 20 genetic principal components of ancestry. Summary genetic association data on these measures were also obtained from a GWAS of these measures using the SomaScan platform in 35,559 individuals of Icelandic ancestry in DeCODE58. Rank-inverse normal transformed proteins were adjusted for age, sex, and sample age. Residuals were re-standardized using rank-inverse normal transformation and standardized values were used in genome-wide association testing using the linear mixed model implemented in BOLT-LMM (v.2.4.1)124.

Electrochemiluminescence (ECL) biomarker assay
To measure CXCL10 (IP-10) biomarker levels in collected cell media, we used the commercially available V-PLEX assay from Meso Scale Diagnostics (MSD), LLC (cat. # K151NND, MSD, Cambridge, MA, USA). We used the proposed procedures with the exception that a 1:100 dilution of cell media was required for the sample values to fall within the 0.37-500 pg/ml dynamic range of the assay. All plates were analyzed under the MESO QuickPlex SQ 120 (Model # 130R0) plate reader and concentrations were quantified via the Meso Scale Discovery WorkBench (v.4.0 ). Samples with concentrations reported as “NaN” were quantified as zero, as these concentrations fell below the dynamic ranges reported above.

Statistics & Reproducibility
Statistical analyses were conducted on GraphPad Prism (v.9.3.1) for unpaired t tests with two tails and ordinary one-way analysis of variance (ANOVA) with post hoc tests unless indicated otherwise in the figure legend. Correction for multiple comparison was either not conducted or done using Dunnett’s statistical hypothesis testing or the Benjamini-Hochberg procedure (see figure legend for details). Normality tests (either Shapiro-Wilk, D’Agostino-Pearson omnibus, Anderson-Darling, or Kolmogorov-Smirnov test with Dallal-Wilkinson-Lilliefor P value where appropriate and applicable based on replicate number) were conducted with a significance level of 0.05 after outlier testing and exclusion (ROUT Q = 1% and Grubbs alpha = 0.05). If there was no difference in significance with and without the respective outliers, the outliers were still included in the figure unless otherwise pointed out in the figure legend to ensure that as few values as possible were excluded for a comprehensive representation of the data. No outliers were eliminated from the dataset unless indicated in the figure legend. In the few cases where normality tests failed, a Wilcoxon Rank Sum test was conducted instead of a t test which resulted in the same significance as the t test. For image quantification, data were plotted and statistically analyzed on R-Studio software (v.4.3.3). For all other plots, ggplot (v.3.4.2), ggbeeswarm (v.0.7.2), and ComplexHeatmap (v.2.22.0) was used. For Western blot quantification, ImageStudioLite (v.5.2.5) was used to quantify signals. Boxplots and bars represent the mean and standard error of the mean, respectively. All error bars indicate the standard deviation. All replicates are biological replicates. Differences were considered significant if P < 0.05. P value style: P > 0.05 and P = 0.05 (no asterisks), P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****). Statistical details for each figure are outlined in the figure legend. Sample sizes were chosen in a way that ensures at least three (except for Fig. 4g which is a proof-of-concept and does not require statistics) and up to 24 biological replicates per condition which was sufficient as the replicates were enough to achieve robust and significant results. No statistical method was used to predetermine sample size. All attempts at replication were successful. Randomization was not relevant to our study as all grouping was done based on phenotype, differences in treatments, and/or diagnosis. Blinding was implemented in our study during the examination of human brain histological sections. This was the only experiment that required blinding in any way.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary:\nThe manuscript from König et al, presents an interesting hypothesis which is partly supported by the evidence from multiple\nmodels/databases, including their own. The hypothesis suggests that cytoplasmic dsRNA is a proxy for TDP-43 pathology in\nAD which triggers INFalpha/beta signaling and downstream JAK/STAT pathway, which upon inhibition ameliorate\nneuroinflammation and cell death in AD cell models. The proposed mechanism is compelling and findings could be provide\nsignificant for the field of neurodegeneration. However, the data presented here are mostly correlative rather than causative\nto infer mechanism. While these data are intriguing, there are several questions that arise.\nMajor points:\n• In fig 1b, authors’ claim of not finding neurofibrillary tangles (NFTs) and Abeta plaques in AD brain samples needs to be\nverified by using specific antibodies/stains that identify aggregated forms of tau and Abeta, such as MC-1 antibody (Tau\n(MC-1) | ALZFORUM) for NFTs, or stains such as methoxy-X04 (Klunk et al; 2002 PMID: 12230326) for Abeta plaques. Also,\nthe brain areas that were examined with immunostaining are not mentioned.\n• In Fig 1c, the intensity of all channels in the representative control panel is much lower than the TDP-43 mutant panel. In\nthe graph, what is the dsRNA intensity normalized to?\n• It appears that different brain regions are examined for upregulated ISGs and for CEs. Using the same brain regions would\nclarify the inference. For example, lines 137-140 state “The parahippocampal gyrus (PHG), a brain region that shows\natrophy early on in disease progression39,40, had the greatest number of upregulated ISGs, followed by the dorsolateral\nprefrontal cortex (DPFC) and the inferior frontal gyrus (IFG)”. However, while examining CEs in the same RNA seq\ndatabase, in lines 187-189, “Consequently, we restricted our analysis to ROSMAP sequencing data from the posterior\ncingulate cortex (PCC), a brain region impaired early on in disease progression and therefore highly relevant for our\nstudy45,” a different brain region is used. To make useful correlations, it will be helpful to examine these mRNA changes in\nthe same brain areas.\n• How do CEs relate to cdsRNA? Are the authors corelating them in any way? How does the statement in lines 204-206,\n“These findings support our hypothesis that cdsRNA associated with TDP-43 dysfunction contributes to IFN-I-mediated\nneuroinflammatory processes occurring in AD patients’ brains”, relates to the findings regarding CEs in the same result\nsubsection. This point should be clarified and discussed further.\n• Choice of using Ren VM cell line over iPSC-derived NGN2 neurons for the CRISPR screen should be defended/explained\nfurther. It is confusing to understand why the authors would use a “neural” line that displays markers of neurons and glial\ncells and yet be considered “differentiated”. In contrast, using iPSC-derived NGN2 neurons will help decipher a more cell-\nautonomous signaling mechanism. It will strengthen the paper to validate the main findings in iNeurons as well.\n• In fig 3c and 3d, there is more than 50% cell death in polyIC treated cells. With that in mind, in fig 3e, is equal protein\nloaded for the Western blot? It would help to normalize pSTAT1 levels over total protein and with total STAT1 than with only\nactin. It would also help to see the entire blot to make a firm assessment of the results presented. The same applies to\nextended fig 10. These are some examples of the pSTAT1/STAT/actin WB (Boriero et al, 2020 The anti‐STAT1 polyphenol\nmyricetin inhibits M1 microglia activation and counteracts neuronal death - Boriero - 2021 - The FEBS Journal - Wiley\nOnline Library; Zhao et al, 2022 STAT1 Contributes to Microglial/Macrophage Inflammation and Neurological Dysfunction in\na Mouse Model of Traumatic Brain Injury | Journal of Neuroscience (jneurosci.org))\n• In extended fig 11, it will help to see higher magnification of the images to appreciate the translocation along with\nquantification. Use of standard total TDP-43 antibody (ProteinTech, Cat# 10782 (AA 203–209 and AA near N-terminus/R\nhttps://rdcu.be/dYpSn) will also help verify this result.\n• Dose-dependent curves for JAK or Tyk2 inhibitors to show gradual changes in pSTAT1 and CXCL10 levels will be more\nconvincing.\n• Adding a pathway schematic describing the working mechanism proposed by the authors (similar to extended fig 2a) in the\nmain figures will help assimilate the findings of the paper derived from different cell lines/ databases. Careful assessment of\ncorrelation and causation needs to be made.\n• Overall, the images in the paper can be improved by using higher resolution/magnification (and quantification wherever\napplicable) as well as use of complementary antibodies such as total TDP-43 (ProteinTech, Cat# 10782 (AA 203–209 and\nAA near N-terminus/R https://rdcu.be/dYpSn), 6E10 antibody (monomeric Abeta and plaques, Biolegend, Cat# 803014) and\nTau (HT7) antibody (Thermo Fisher Cat#MN1000).\n• It will be useful for the authors to speculate on cell-autonomous and non-cell-autonomous features of their proposed\nmechanism in the disease with regard to IFN-I signaling and neuroinflammation. The proposed mechanism in this\nmanuscript could answer important questions regarding neuronal-glial crosstalk in neurodegeneration and is worth\nexploring further.\nMinor points:\n• Line 197, “tau” should be replaced with “TDP-43”", "reviewer_2": "König et al. aim to understand the role of TDP-43 inclusions in Alzheimer’s Disease, how those inclusions may drive toxicity,\nand present candidate drugs for reversing that toxicity. Another goal is to develop novel biomarkers for AD using this\ninformation.\nKönig et al. present compelling data that link TDP-43 pathology, cytosolic double-stranded RNA, and interferon induction in\nhuman patient tissue, iPSC-derived neurons, and a neuron-like cancer cell line. They then train their drug repurposing\npipeline using cryptic exon expression to identify candidate drugs for AD with TDP-43 pathology. This pipeline identified a\nnumber of tyrosine kinase inhibitors. They show that inhibition of tyrosine kinase signaling can rescue dsRNA-induced cell\ndeath by decreasing interferon signaling. Finally, they establish a link between an established TYK2 loss of function variant\nand decreased CXCL10 levels in human plasma.\nThere are two major weaknesses in the paper. First, due to reasonable technical limitations, the authors use indirect\nmeasures of cdsRNA accumulation and/or TDP-43 pathology and draw conclusions that may be consistent with their\nhypotheses, but do not rule out others in a satisfactory way. This leads to contradictions in their proposed model between the\nfirst and last parts of the text as written. Second, they draw a number of conclusions based on their drug treatments at\nconcentrations that are much higher (in some places >100-fold) than the IC50s reported in the paper. Thus, the specificity of\ntheir proposed mechanisms are not fully supported by their data. These two weaknesses can be addressed as follows:\nMajor Points:\n1. The authors stratify patients and controls by pTDP-43 pathology but do not ever tell the reader what the stratification\nstrategy is. This is especially important considering the small number of patients in this study.\n2. It is important to show that TDP43 aggregation and/or cytosolic TDP43 and/or phospho-TDP43 is co-incident with dsRNA\naccumulation in Figures 1A and 1B. Quantitation of this would provide very strong evidence for their proposed model. How\nmany TDP-43 positive cells are dsRNA negative and vice-versa? Does TDP43 aggregation/mislocalization induce dsRNA\naccumulation in cells adjacent to the cells with aggregation/mislocalization?\n3. The authors propose that cdsRNA mediates toxicity at least in part via induction of interferon signaling. However,\ninterferon induction can be due to many factors (as mentioned in lines 142-144 and 395-404 in the text), especially in AD.\nThe connection between TDP43 pathology/cdsRNA accumulation and interferon induction would be much better supported\nif the data in Extended Data Figure 2 contained cdsRNA staining and a quantification of co-localization and/or intensity of\npPKR, cdsRNA, and TDP43 pathology. Similarly, a comparison of transcriptomics (Figure 1D and Extended Data 4) from\nAD patients with and without TDP-43 pathology would make a much stronger argument in support of their model, rather than\ncomparing AD+TDP43 pathology versus healthy controls.\n4. To prove the specificity of their hits, the authors should validate their initial CRISPR-screen phenotype with knockdown or\nknockout of at least IFNAR2, IRF9, and TYK2. Using these lines, they should then show that treatment with the identified\ntyrosine kinase inhibitors does not rescue toxicity when the above genes are knocked out or knocked down. Similar\nexperiments should be done with knockdown of JAK2 and JAK1 to show specificity for TYK2. Relatedly, the authors begin\ntreatment with deucravacitinib at 10nM (50-fold higher than the reported TYK2 IC50, but also 10-fold higher than the JAK1\nIC50) and see almost complete rescue of the polyIC survival phenotype. Showing a concentration dependence for rescue\nwould provide much stronger evidence for the authors’ model.\n5. The model in Figure 4D does not take into account other possible sources of neuroinflammation or the role of TDP-43\npathology, as noted in the text (lines 395-404). The model should be updated so that it does not appear the authors are\nproposing that only cdsRNA can cause neuroinflammation to cause disease.\n6. While the data is sound, it is unclear how relevant ReN VM conditioned media are to human plasma levels of CXCL10.\nProviding some references that show that conditioned media can be extrapolated to human plasma biomarker levels would\nmake the argument for CXCL10 as an AD biomarker much stronger. To directly compare the mechanisms with Figure 5a,\nthe authors could use gene editing to make the identified SNP and measure CXCL10 levels. Alternatively, directly\nexamining CXCL10 plasma levels for AD versus healthy controls would be sufficient.\nMinor Points:\n1. For Figure 1C, it would be important to show that TDP43 is mislocalized with TDP43 IF.\n2. The heatmap in Extended Data 4 is much more informative than in Figure 1D\n3. A sentence or two introducing the RenVM cells would clarify those cells’ relevance to the reader.\n4. It is unclear how hits are called in the CRISPR screen. Please add this to the methods or in the results section.\n5. It would strengthen the authors’ argument that cdsRNA toxicity is mediated by interferon if they showed gene enrichment\nanalysis (e.g. using ENRICHR: https://maayanlab.cloud/Enrichr/) of their screen hits.\n6. It would help with clarity to have the drug targets listed with the drugs in Figure 3 and Extended Data 10.\n7. The authors mention that cGAS-STING activation can play a role in dsRNA-induced toxicity. If possible (not needed for\npublication), it would shed a huge amount of light on the relative contribution of the authors’ proposed mechanism versus\ncGAS-STING dsRNA sensing to perform those experiments in a cell line that does expression cGAS-STING.\n8. It may be worth discussing a recent paper that identified TYK2 (PMID 39528671) as a regulator of tau levels and turnover in the\ndiscussion section.\n9. Generally the paper would be improved if more of the extended data were in the main figures.\n10. I cannot offer expert feedback on the technical merits of the DRIAD-SP training or framework.", "reviewer_3": "Summary of the key results\nPrevious work by the Albers group had identified cytoplasmic double stranded RNA (cdsRNA) in the frontal and motor cortex\nin post-mortem tissue of ALS-FTD patients with C9ORF72 repeat expansion. CdsRNA was proposed to be produced in this\nmodel from antisense sense transcription of RNA from G4C2 expansion. CdsRNA was found to colocalize with\nphosphorylated TDP-43 in patient samples. (Rodriguez et al 2021 Science Translational medicine). They found cdsRNA to\nbe toxic to neural cells and this toxicity could be rescued with treatment by FDA approved JAK1/2 inhibitors Ruxolitinib and\nBaricitinib.\nSeparately the Albers group developed a machine learning model to aid the repurposing of FDA approved drugs for use in\nAlzheimer’s Disease (AD). The model compares gene expression changes at different AD severities with gene perturbations\ninduced in neural cells following treatment with FDA approved drugs. They found the gene list that comprises perturbed\ngenes follow Jak1/2 inhibition by ruxolitinib was able to reliably predict early vs late disease stages in addition to inhibitors\nfor kinases in the ULK and NEK families. (Rodriguez et al. 2021 Nature Communications).\nThe current work is an extension of the work of those two papers. They aim to find if accumulation of cdsRNA is a feature of\nTDP-43 proteinopathologies that happens beyond C9ORF72 cases. They investigated if cdsRNA occur in brains of AD\npatients that have TDP-43 pathology and show convincing data that it does. Next, they incorporated TDP-43 staging into\ntheir previously used ML model and found that genes that are associated with Jak1/2 inhibitors and had provided a signal for\nall AD in their previous work were actually specifically detecting just the TDP-43 positive cases.\nPrevious work has shown that knockdown of TDP-43 leads to accumulation of dsRNA (Saldi et al. EMBOJ 2014) in human\ncells. The presence of TDP-43 pathology in AD and the evidence of loss of function of TDP-43 has been previously shown.\nTherefore, it is somewhat expected that dsRNA would be present in AD brains with TDP-43 pathology. What was unknown\nand could not be predicted is if the presence of dsRNA in AD would elicit a significant Inteferon response that could be\ndetected between AD cases with and without TDP-43 pathology. The paper uses their ML model to suggest that this is the\ncase, but more evidence could be shown of their existing data that could make this clearer for the reader (point 4).\nPrevious work from the authors suggested that Jak inhibitors could protect from dsRNA toxicity. Using CRISPR screens they\nwere able to show that protection from toxicity is mainly elicited by the Jak family member TYK2. This is an important\ndiscovery because direct inhibition of TYK2 would probably be more tolerable to patients than inhibitors that broadly inhibit\nJak family members.\nFinally, they provide some evidence that dsRNA induce CXCL10 signalling which could potentially be used as a biomarker\nfor IFN-I signalling in AD.\nMajor points\n1. The authors demonstrate that in control cases without AD or TDP-43 pathology there is no or low presence of cdsRNA. In\nthe brains of patients with AD and TDP-43 pathology there is accumulation of cdsRNA. However, what is lacking is a\nsignificant number of AD patients without TDP-43 pathology to make a comparison on cdsRNA levels in AD with and\nwithout TDP-43. This is an important point for the authors to make because they go on to segregate AD patients by TDP-43\npathology and suggests that this is a consequence of ISG gene expression induced by cdsRNA.\n2. Line 188. The posterior cingulate cortex was the region chosen from the ROSMAP data to look for CE expression. They\nstated that this region is impaired early in AD progression and therefore is important for the study. However, accumulation of\nTDP-43 and onset of AD probably represent two distinct pathways that are co-occurring in disease and both contribute to\ndementia but are not mechanistic dependent on each other. Previous work has shown that in AD patients the amygdala is\nthe first region in the brain to have TDP-43 followed by the hippocampus. Using the sequencing data from these regions\nwould provide for more reliable detection of CE. The ROSMAP data is obtained from Rush University where there is a\nframework for staging TDP-43 pathology in patients. (https://www.radc.rush.edu/docs/var/detail.htm?\ncategory=Pathology&subcategory=TDP-43&variable=tdp_st4). Why wasn’t the Rush staging scaled used? Also, brain\nsamples were obtained from ROSMAP at RUSH. They would have TDP-43 pathology on all the samples that were\nsequence or a large fraction of them. The analysis of CE in RNA-Seq data can be useful for new datasets, but why ignore\nthe high-quality pathology data that most likely exist at Rush? The authors should include this staging data based on\npathology at Rush to validate their CE pipeline.\n3. MG132 treatment of cells is not a model of TDP-43 pathology. MG132 does lead to aggregation of TDP-43 among\ncountless other impacts on the cell. The increase in CXCL10 cannot be attributed to TDP-43 aggregation. Most of the cells\nin this treatment are probably dead at 48 hours. In extended data figure 11 there is a lack of DAPI staining after MG132\ntreatment probably because the cells have undergone apoptosis and the DNA is fragmented. Knockdown of TDP-43 has\npreviously been shown to lead to the accumulation of dsRNA. Does knockdown of TDP-43 lead to the induction of\nCXCL10?\n4. In my understanding of how the ML works, only a list of genes is used as input to the model and there is no input of the\nexpected direction of the change between groups. Given that the list of genes obtained from Jak inhibition is able to\ndistinguish TDP-43 positive and negative patients, one would expect that genes that are downregulated by Jak inhibition\nwould be upregulated in the population that has csdRNA activation of Jak signalling. This cannot be inferred from the\npredicative potential of the gene list in the ML model. It only lets you know that there is a difference in gene expression\nbetween two groups but not that it goes in the expected direction. Could the authors provide a heatmap or other graph that\nshows that Jak dependent genes are upregulated in the TDP-43 positive group and not in the TDP-43 negative group.\nThis would provide some additional evidence that the cdsRNA are not only present in the TDP-43 positive cases but induce the\nexpected interferon signalling.\n5. Figure 1c. Cell models of TDP-43 only show weak TDP-43 loss of function and barely detectable levels of aggregate\nTDP-43. Is the level of dsRNA accumulation in TDP-43 G298S cell line enough to activate PRR signalling?\n6. For statistically analysis, did the authors perform tests to ensure that datapoints are normally distributed before conducting\nt-tests and ANOVA?\n7. Figure 4 would benefit from more data. It is probably too much to ask for MSD of CXCL10 on AD patients biofluids.\nHowever, is there any indication in existing patients datasets (RNA, MassSpec) that CXCL10 is upregulated in AD?\nMinor points.\n1. Include the clinical information for all patients used for histology analysis in Extended Data Table 1\n2. Figure 1C legend states that 2/3 differentiated batches were analysed but there are three different shapes in the graph.\nShould it just state that 3 batches were used.\n3. Use of two forms of the UNC13A cryptic exon is not a good measure for TDP-43 severity. The two forms occur together\nand the detection of one vs both in sequencing data is probably more related to sequencing depth and RIN quality of\nRNA rather than degree of TDP-43 loss of function in the sample.\n4. IFN-A activates TYK2 but doesn’t cause cell death. Why does IFN-I activation of TYK2 following cdsRNA treatment lead\nto cell death?\n5. Figure 1 legend line 636 extended data fig 3 should be extended data fig 4\n6. Line 165 cryptic exon in stmn2 leads to premature polyadenylation not a premature stop codon.\n7. Line 504 capitalize ALS in the citation\n8. Figure 2b UNC13A diagram is not correct. There should be two splice junctions between exon 20 and the cryptic exon\nand one junction between the cryptic exon and exon 21.\n9. line 213, 215, and 217 References to Extended Data Figure 8 and Extended Data figure 9 are not correct. The figures\nneed to be switched.\n10. line 223. Extended data figure 10 does not show that the downstream transcription of ISGs is inhibited.\n11. Figure 3C There is an extra 2 in log2(FC)\n12. Line 260 Check the IC50 values for Jak1. The 1 nM figure is for binding of decravacitinib pseudo kinase domain of Jak1\nand not the kinase domain. Deucravacitinib binding at the pseudo kinase domain does not inhibit Jak1 activity. Using the\nbinding value of 1 nM rather than a value obtained with a cell based model of Jak1 activation makes the drug appear less\nselective than it is.\n13. Probably should have used a lower concentration of deucravaitinib for MS-TMT experiments.\n14. Wording in introduction suggests cdsRNA is a common feature in ALS but it has only been shown for C9ORF72 cases.\nPlease clarify text or provide citation that it happens in sporadic forms of the disease.\n15. Line 747 Supplemental Table 2 should be Supplemental Table 1\n16. Line 752 Brief outline of NGN2 differentiation would be useful.\n17. How much lipofectamine 2000 was used for transfections in NGN2 cortical neurons, SY5Y cells, and ReN VM cells?\n18. Line 807 is MGM2 supposed to be NGN2?", "reviewer_4": "I suggest the authors reconsider the title, as the current phrasing—particularly the segment “…in dementia with Alzheimer’s\ndisease”—is unclear and awkwardly constructed. While the latest clinical criteria for Alzheimer’s disease (AD) acknowledge\nthat a clinical diagnosis does not necessarily require the presence of dementia, incorporating this nuance into the abstract\nmay not add value to this specific study.\nThe following sentence requires rephrasing for clarity:\n\"One of the pathologic features that stratify dementia patients is Alzheimer’s disease (AD), a disease defined by amyloid\nbeta (Ab) plaques and neurofibrillary tangles (NFTs) of hyperphosphorylated tau protein, which can be associated to other\nproteinopathies, including cytoplasmic inclusions of phosphorylated TAR DNA-binding protein 43 (pTDP-43).\"\nThe sentence is overly complex and could benefit from restructuring to improve readability and precision.\nI do not believe the claim that TDP-43 pathology is the most common co-pathology in AD is entirely accurate. The frequency\nof co-pathologies depends on the population studied, and Lewy pathology is also highly prevalent among AD patients. This\npoint should be clarified.\nThe finding of CXCL10 alterations as a potential biomarker for dsRNA pathology is intriguing. However, the study would be\nsignificantly strengthened if the authors could demonstrate changes in CXCL10 levels in the cerebrospinal fluid (CSF) of AD\npatients.\nWhile this is an important study with potential translational value, it is crucial that the authors explicitly address that their\nfindings are relevant primarily for patients with concomitant TDP-43 pathology. Any therapeutic interventions discussed\nshould be contextualized as targeting effects specifically caused by TDP-43 pathology.\nIn the abstract, the authors should clarify whether JAK inhibitors influence neuroinflammation caused by increased dsRNA\nexpression/accumulation of cdsRNA or other factors that elevate IFN-I signalling.\nThe information about CERAD scoring in Extended Data Table 1 is outdated by over 20 years. The authors should use the\nupdated 2012 NIA-AA ABC scoring system to determine the level of AD neuropathological change.\nExtended Data Figure 1: I recommend specifying that the analysis was conducted on amygdala sections.\nExtended Data Figure 7: I recommend clarifying the reference to the posterior cingulate cortex. Braak & Braak staging\ncannot be determined based solely on a single region, and it is unclear how this region is reflected in the graph."}, "author_response": "POINT BY POINT RESPONSES:\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nSummary:\nThe manuscript from König et al, presents an interesting hypothesis which is partly\nsupported by the evidence from multiple models/databases, including their own. The\nhypothesis suggests that cytoplasmic dsRNA is a proxy for TDP-43 pathology in AD\nwhich triggers INFalpha/beta signaling and downstream JAK/STAT pathway, which\nupon inhibition ameliorate neuroinflammation and cell death in AD cell models. The\nproposed mechanism is compelling and findings could be provide significant for the\nfield of neurodegeneration. However, the data presented here are mostly correlative\nrather than causative to infer mechanism. While these data are intriguing, there are\nseveral questions that arise.\nMajor points:\n• In fig 1b, authors’ claim of not finding neurofibrillary tangles (NFTs) and Abeta\nplaques in AD brain samples needs to be verified by using specific antibodies/stains\nthat identify aggregated forms of tau and Abeta, such as MC-1 antibody (Tau (MC-1)\n| ALZFORUM) for NFTs, or stains such as methoxy-X04 (Klunk et al; 2002 PMID:\n12230326) for Abeta plaques. Also, the brain areas that were examined with\nimmunostaining are not mentioned.\nWe want to clarify that we did find NFTs and Abeta plaques in the amygdala\nof these patients with Alzheimer’s disease pathology (Fig. 1b), but that dsRNA\nwas not spatially coincident with plaques and tangles. We used these\nantibodies: tau (cat. # NBP2-25162, Novus Biologicals, CO, USA); Aß (cat. #\nCST-D54D2, Cell Signaling Technology) to label tangles and plaques,\nrespectively. We clarify that the tissue is amygdala, both in the main text and\nfigure legend (line 99, 117, 686, 688, 819, 1283, 1287, 1294)\n• In Fig 1c, the intensity of all channels in the representative control panel is much\nlower than the TDP-43 mutant panel. In the graph, what is the dsRNA intensity\nnormalized to?\nWe updated the images in Figure 1d to be more comparable in intensity. The\ndsRNA intensity is normalized to the dsRNA intensity in the isoTDP43+/+\ncontrol line.\n• It appears that different brain regions are examined for upregulated ISGs and for\nCEs. Using the same brain regions would clarify the inference. For example, lines\n137-140 state “The parahippocampal gyrus (PHG), a brain region that shows\natrophy early on in disease progression39,40, had the greatest number of upregulated ISGs,\nfollowed by the dorsolateral prefrontal cortex (DPFC) and the inferior frontal gyrus\n(IFG)”. However, while examining CEs in the same RNA seq database, in lines 187-\n189, “Consequently, we restricted our analysis to ROSMAP sequencing data from\nthe posterior cingulate cortex (PCC), a brain region impaired early on in disease\nprogression and therefore highly relevant for our study45,” a different brain region is\nused. To make useful correlations, it will be helpful to examine these mRNA changes\nin the same brain areas.\nThank you for this point. We used available data to analyze ISG’s and cryptic\nexons. We observe increased CE’s in the PCC, and dorsolateral prefrontal\ncortex, but not in the head of the caudate. We chose to focus on PCC since it\nis involved early in AD. Conversely, we see increased ISG’s in PCC\n(Extended Data Figure 5c).\n• How do CEs relate to cdsRNA? Are the authors corelating them in any way? How\ndoes the statement in lines 204-206, “These findings support our hypothesis that\ncdsRNA associated with TDP-43 dysfunction contributes to IFN-I-mediated\nneuroinflammatory processes occurring in AD patients’ brains”, relates to the findings\nregarding CEs in the same result subsection. This point should be clarified and\ndiscussed further.\nWe are using CE expression as a proxy of TDP-43 pathology, particularly\nnuclear hypofunction. In this study CE expression has no direct correlation\nwith cdsRNA accumulation, but CE expression (PMID: 35197628, 35197626,\n30643292, 30643298, 32790644, 28332094, 37605276, 38133681, 38175301,\nand the presence of cdsRNA (as seen in this study) are both linked to TDP-43\npathology. We added Extended Data Fig. 5c to show that interferon signaling\nis significantly upregulated in patients that we stratified TDP-43 positive which\nis likely linked to the spatially coincident cdsRNA.\n• Choice of using Ren VM cell line over iPSC-derived NGN2 neurons for the CRISPR\nscreen should be defended/explained further. It is confusing to understand why the\nauthors would use a “neural” line that displays markers of neurons and glial cells and\nyet be considered “differentiated”. In contrast, using iPSC-derived NGN2 neurons will\nhelp decipher a more cell-autonomous signaling mechanism. It will strengthen the\npaper to validate the main findings in iNeurons as well.\nWe performed this genome-wide Cas9 screen using ReN VMs, which are\nhuman neuroprogenitor cells which grow well and are amenable for screens.\nThe screen was performed after differentiation. As we published previously,\nthese cells differentiate into mixed cultures of neurons, astrocytes and\noligodendrocytes to conduct the screen. We have used 4 different cell lines\n(ReN VM, ReN CX, SH-SY5Y, and iPSC-derived NGN2 neurons) to validate\nour results from the genome-wide screen. In particular, the replication of these\nresults in NGN2 cortical neurons helps to describe the cell autonomous\nmechanism. This aligns with our previous work in the Nd1 and Nd2 mouse\nmodels, where the dsRNA is expressed exclusively in neurons.\n• In fig 3c and 3d, there is more than 50% cell death in polyIC treated cells. With that\nin mind, in fig 3e, is equal protein loaded for the Western blot? It would help to\nnormalize pSTAT1 levels over total protein and with total STAT1 than with only\nactin. It would also help to see the entire blot to make a firm assessment of the results\npresented. The same applies to extended fig 10. These are some examples of the\npSTAT1/STAT/actin WB (Boriero et al, 2020 The anti‐STAT1 polyphenol myricetin\ninhibits M1 microglia activation and counteracts neuronal death - Boriero - 2021 -\nThe FEBS Journal - Wiley Online Library; Zhao et al, 2022 STAT1 Contributes to\nMicroglial/Macrophage Inflammation and Neurological Dysfunction in a Mouse Model\nof Traumatic Brain Injury | Journal of Neuroscience (jneurosci.org))\nThank you for asking for clarification on this point. The Western Blot samples\nare collected at 24h post treatment because we do not see cell death at this\npoint. Hence we are loading the same protein amounts. This point is stated\n(line 924). Cell death occurs at 48-72h post treatment at which point we\nconduct the cell viability assay. We will enclose all uncropped western blots\nincluding the ones not shown but quantified.\n• In extended fig 11, it will help to see higher magnification of the images to\nappreciate the translocation along with quantification. Use of standard total TDP-43\nantibody (ProteinTech, Cat# 10782 (AA 203–209 and AA near N-terminus/R\nhttps://rdcu.be/dYpSn) will also help verify this result.\nThank you for this recommendation. We used the recommended antibody and\nupdated Extended Data Fig. 9 and included quantifications. For this study we\ndid not have the means to use a higher magnification.\n• Dose-dependent curves for JAK or Tyk2 inhibitors to show gradual changes in\npSTAT1 and CXCL10 levels will be more convincing.\nThank you for this suggestion. Please see Fig. 6b and Extended Data Fig.\n8e+f to see this data, which we agree, is more convincing.\n• Adding a pathway schematic describing the working mechanism proposed by the\nauthors (similar to extended fig 2a) in the main figures will help assimilate the\nfindings of the paper derived from different cell lines/ databases. Careful assessment\nof correlation and causation needs to be made.\nThank you for this comment. We opted to keep the primary data in the primary\nfigures and leave the schematic in the extended figure given the limited space\nfor each Figure. We updated the figure legend to stress that this model is a\nhypothesis based on previous work in other cell types. Also, we refer the\nreviewer to Fig. 6d as a schematic that describes the proposed mechanism\nsupported by our studies.\n• Overall, the images in the paper can be improved by using higher\nresolution/magnification (and quantification wherever applicable) as well as use of\ncomplementary antibodies such as total TDP-43 (ProteinTech, Cat# 10782 (AA 203–\n209 and AA near N-terminus/R https://rdcu.be/dYpSn), 6E10 antibody (monomeric\nAbeta and plaques, Biolegend, Cat# 803014) and Tau (HT7) antibody (Thermo\nFisher Cat#MN1000).\nPlease see the comments above where we have addressed these\nsuggestions.\n• It will be useful for the authors to speculate on cell-autonomous and non-cell-\nautonomous features of their proposed mechanism in the disease with regard to IFN-\nI signaling and neuroinflammation. The proposed mechanism in this manuscript\ncould answer important questions regarding neuronal-glial crosstalk in\nneurodegeneration and is worth exploring further.\nThank you for this suggestion. We agree with this reviewer that the proposed\nmechanism supported in this study affords opportunities for both cell-\nautonomous (neurons) and non-cell autonomous (astrocytes, microglia, and\nother neurons that are adjacent or synaptic partners in neural circuits, see Fig.\n5 in Ref. 22 in our Nd1 mouse model) activation of neuroinflammation. We\nalso address this in our introduction in line 69-72. In addition, we are currently\nwriting a review article developing these ideas by incorporating work from\nother studies that we can’t include in this paper due to space / reference\ncitation restrictions.\nMinor points:\n• Line 197, “tau” should be replaced with “TDP-43”\nThis is the sentence in the manuscript: “To determine whether predicted TDP-\n43 positivity was associated with later Braak stages, we used an ordinal\nregression model and found no significant difference in Braak staging (P =\n0.27) between “positive” and “negative” cases (Extended Data Fig. 5a),\nindicating that CE production was orthogonal to the stage of tau progression\nas quantified by Braak staging.” We intended to compare the stages of TDP-\n43 pathology with stages of tau pathology.\nReviewer #2 (Remarks to the Author):\nKönig et al. aim to understand the role of TDP-43 inclusions in Alzheimer’s Disease,\nhow those inclusions may drive toxicity, and present candidate drugs for reversing\nthat toxicity. Another goal is to develop novel biomarkers for AD using this\ninformation.\nKönig et al. present compelling data that link TDP-43 pathology, cytosolic double-\nstranded RNA, and interferon induction in human patient tissue, iPSC-derived\nneurons, and a neuron-like cancer cell line. They then train their drug repurposing\npipeline using cryptic exon expression to identify candidate drugs for AD with TDP-\n43 pathology. This pipeline identified a number of tyrosine kinase inhibitors. They\nshow that inhibition of tyrosine kinase signaling can rescue dsRNA-induced cell\ndeath by decreasing interferon signaling. Finally, they establish a link between an\nestablished TYK2 loss of function variant and decreased CXCL10 levels in human\nplasma.\nThere are two major weaknesses in the paper. First, due to reasonable technical\nlimitations, the authors use indirect measures of cdsRNA accumulation and/or TDP-\n43 pathology and draw conclusions that may be consistent with their hypotheses, but\ndo not rule out others in a satisfactory way. This leads to contradictions in their\nproposed model between the first and last parts of the text as written. Second, they\ndraw a number of conclusions based on their drug treatments at concentrations that\nare much higher (in some places >100-fold) than the IC50s reported in the paper.\nThus, the specificity of their proposed mechanisms are not fully supported by their\ndata. These two weaknesses can be addressed as follows:\nMajor Points:\n1. The authors stratify patients and controls by pTDP-43 pathology but do not ever\ntell the reader what the stratification strategy is. This is especially important\nconsidering the small number of patients in this study.\nThank you for this point. At the time of this study the neuropathology service\nonly used an antibody against TDP-43 in their clinical workflow. Our\nstratification strategy is outlined in Extended Data Figure 1.\n2. It is important to show that TDP43 aggregation and/or cytosolic TDP43 and/or\nphospho-TDP43 is co-incident with dsRNA accumulation in Figures 1A and 1B.\nQuantitation of this would provide very strong evidence for their proposed model.\nHow many TDP-43 positive cells are dsRNA negative and vice-versa? Does TDP43\naggregation/mislocalization induce dsRNA accumulation in cells adjacent to the cells\nwith aggregation/mislocalization?\nThank you for this suggestion. We have provided quantification for costaining\nof TDP-43 and dsRNA in Extended Data Fig. 1c. The staining in adjacent cells\nis part of a larger project to add cell-type specific markers along with the\nneuropathologic antibodies. Without knowing cell-type specificity, it is not clear\nif adjacent cells are due to propagation or engulfment of the dsRNA/TDP-43\nfrom dying neurons. Ultimately, those questions will be addressed in inducible\nmouse model systems or inducible brain organoid systems.\n3. The authors propose that cdsRNA mediates toxicity at least in part via induction of\ninterferon signaling. However, interferon induction can be due to many factors (as\nmentioned in lines 142-144 and 395-404 in the text), especially in AD. The\nconnection between TDP43 pathology/cdsRNA accumulation and interferon\ninduction would be much better supported if the data in Extended Data Figure 2\ncontained cdsRNA staining and a quantification of co-localization and/or intensity of\npPKR, cdsRNA, and TDP-43 pathology.\nSee updated version of Extended Data Fig. 2 where we show staining of\nphospho-PKR (activated PKR by dsRNA) is enriched in cells with cdsRNA\nand TDP-43 mislocalization. While PKR is an interferon stimulated gene\nproduct, the phosphorylated form is a marker of engagement with dsRNA.\nSimilarly, a comparison of transcriptomics (Figure 1D and Extended Data 4) from\nAD patients with and without TDP-43 pathology would make a much stronger argument\nin support of their model, rather than comparing AD+TDP43 pathology versus\nhealthy controls.\nWe have not received the Rosmap TDP-43 pathology stratification in spite of\nrequesting the DUA 6 months ago. However, we used CE expression as a\nproxy of TDP-43 pathology and read out average ISG expression in AD cases\nwith presumed TDP-43 pathology cases vs AD cases with presumed TDP-43\nnegative cases and saw a significant increase in interferon signaling\n(Extended Data Fig. 5c). The comparison group is not healthy controls but AD\ncases without presumed TDP-43 pathology\n4. To prove the specificity of their hits, the authors should validate their initial\nCRISPR-screen phenotype with knockdown or knockout of at least IFNAR2, IRF9,\nand TYK2. Using these lines, they should then show that treatment with the identified\ntyrosine kinase inhibitors does not rescue toxicity when the above genes are\nknocked out or knocked down. Similar experiments should be done with knockdown\nof JAK2 and JAK1 to show specificity for TYK2.\nThank you for this comment. We performed a TYK2 KD as we selected this hit\nas the most favorable target and the focus of this paper. See Fig. 4f-h and Fig.\n5g+h. Both JAK1 and JAK2 were not hits in the screen and the selectivity of\ndeucravacitinib at 10 nM for TYK2 over JAK1 and JAK2 (see next comment)\ndemonstrate that JAK1 and JAK2 are not contributing to cell death in this\nmodel system.\nRelatedly, the authors begin treatment with deucravacitinib at 10nM (50-fold higher\nthan the reported TYK2 IC50, but also 10-fold higher than the JAK1 IC50) and see\nalmost complete rescue of the polyIC survival phenotype. Showing a concentration\ndependence for rescue would provide much stronger evidence for the authors’\nmodel.\nSee updated Fig. 5a (Deucravacitinib calculated EC50 = 1.481 nM) and we\nalso updated the manuscript with the correct IC50 values for JAK 1-3 of\ndeucravacitinib (IC = 0.2 nM compared to IC (JAK1-3) > 10 µM).\n50 50\n5. The model in Figure 4D does not take into account other possible sources of\nneuroinflammation or the role of TDP-43 pathology, as noted in the text (lines 395-\n404). The model should be updated so that it does not appear the authors are\nproposing that only cdsRNA can cause neuroinflammation to cause disease.\nThank you for this comment, as we don’t believe that cdsRNA is the only\ncontribution of interferon signalling in the CNS or the only cause of\nneuroinflammation in the brain. See updated figure legend - “Schematic\noverview of hypothesized pathomechanisms underlying cdsRNA in neurodegenerative\ndiseases that focuses on dsRNA-mediated neuroinflammation. Other potential triggers\nfor neuroinflammation are not depicted here for simplicity’s sake.”\n6. While the data is sound, it is unclear how relevant ReN VM conditioned media are\nto human plasma levels of CXCL10. Providing some references that show that\nconditioned media can be extrapolated to human plasma biomarker levels would\nmake the argument for CXCL10 as an AD biomarker much stronger. To directly\ncompare the mechanisms with Figure 5a, the authors could use gene editing to\nmake the identified SNP and measure CXCL10 levels. Alternatively, directly\nexamining CXCL10 plasma levels for AD versus healthy controls would be sufficient.\nThank you for this comment to strengthen the connection between our in vitro\nobservations and measurements in patient samples. Numerous studies show\nelevated levels of CXCL10 in AD relative to healthy controls (PMID:\n29893515, 36967827, 28413983, 37291639, 29223680). This review (PMID:\n37863021) states evidence that deucravacitinib mimics the functional effect of\nthe SNP P1104A which is the genetic variant we present in Fig. 6a. We agree\nwith this reviewer that with this evidence of plasma levels in AD patients vs.\nhealthy controls, that creating a human REN cell line with this SNP will not\nsubstantial additional evidence to prove our point.\nMinor Points:\n1. For Figure 1C, it would be important to show that TDP43 is mislocalized with\nTDP43 IF.\nWe do not observe obvious cytoplasmic TDP-43 mislocalization in these\nmutant cells without a stressor, as is the case for other labs. We do observe\nincreased dsRNA in the mutant line, consistent with nuclear hypofunction of\nTDP-43.\n2. The heatmap in Extended Data 4 is much more informative than in Figure 1D\nWe agree and moved the heatmap to main Fig. 2a\n3. A sentence or two introducing the RenVM cells would clarify those cells’ relevance\nto the reader.\nThank you, we do this in line 220 - 223 and 285 - 288.\n4. It is unclear how hits are called in the CRISPR screen. Please add this to the\nmethods or in the results section.\nTarget genes were considered a hit if the LFC was equal or greater than 2.0\nafter rounding to the first decimal place. We added this information both to the\nmain text and to the methods.\n5. It would strengthen the authors’ argument that cdsRNA toxicity is mediated by\ninterferon if they showed gene enrichment analysis (e.g. using ENRICHR:\nhttps://maayanlab.cloud/Enrichr/) of their screen hits.\nThank you, we added this analysis to Fig. 4c.\n6. It would help with clarity to have the drug targets listed with the drugs in Figure 3\nand Extended Data 10.\nThank you, we added the primary drug targets with IC50 values to Extended\nData Fig. 8g.\n7. The authors mention that cGAS-STING activation can play a role in dsRNA-\ninduced toxicity. If possible (not needed for publication), it would shed a huge\namount of light on the relative contribution of the authors’ proposed mechanism\nversus cGAS-STING dsRNA sensing to perform those experiments in a cell line that\ndoes expression cGAS-STING.\nSH-SY5Y cells express STING which we show in the new Extended Data Fig.\n8h. As seen in Fig. 5e+f, there do not seem to be additional STING effects to\ndsRNA-mediated toxicity elicited by the dsRNA mimetic poly(I:C).\n8. It may be worth discussing a recent paper that identified TYK2 (PMID 39528671)\nas a regulator of tau levels and turnover in the discussion section.\nThank you, we cite and discuss this paper in the discussion (line 414 - 418)\n9. Generally the paper would be improved if more of the extended data were in the\nmain figures.\nThank you for highlighting our extended data. Within the limitations of space\nrequirements, we have moved figures from Extended Data Figures to the\nMain Figures.\n10. I cannot offer expert feedback on the technical merits of the DRIAD-SP training\nor framework.\nReviewer #3 (Remarks to the Author):\nSummary of the key results\nPrevious work by the Albers group had identified cytoplasmic double stranded RNA\n(cdsRNA) in the frontal and motor cortex in post-mortem tissue of ALS-FTD patients\nwith C9ORF72 repeat expansion. CdsRNA was proposed to be produced in this\nmodel from antisense sense transcription of RNA from G4C2 expansion. CdsRNA\nwas found to colocalize with phosphorylated TDP-43 in patient samples.\n(Rodriguez et al 2021 Science Translational medicine). They found cdsRNA to be\ntoxic to neural cells and this toxicity could be rescued with treatment by FDA approved\nJAK1/2 inhibitors Ruxolitinib and Baricitinib.\nSeparately the Albers group developed a machine learning model to aid the\nrepurposing of FDA approved drugs for use in Alzheimer’s Disease (AD). The model\ncompares gene expression changes at different AD severities with gene\nperturbations induced in neural cells following treatment with FDA approved drugs.\nThey found the gene list that comprises perturbed genes follow Jak1/2 inhibition by\nruxolitinib was able to reliably predict early vs late disease stages in addition to\ninhibitors for kinases in the ULK and NEK families. (Rodriguez et al. 2021 Nature\nCommunications).\nThe current work is an extension of the work of those two papers. They aim to find if\naccumulation of cdsRNA is a feature of TDP-43 proteinopathologies that happens\nbeyond C9ORF72 cases. They investigated if cdsRNA occur in brains of AD patients\nthat have TDP-43 pathology and show convincing data that it does. Next, they\nincorporated TDP-43 staging into their previously used ML model and found that\ngenes that are associated with Jak1/2 inhibitors and had provided a signal for all AD\nin their previous work were actually specifically detecting just the TDP-43 positive\ncases.\nPrevious work has shown that knockdown of TDP-43 leads to accumulation of\ndsRNA (Saldi et al. EMBOJ 2014) in human cells. The presence of TDP-43\npathology in AD and the evidence of loss of function of TDP-43 has been previously\nshown. Therefore, it is somewhat expected that dsRNA would be present in AD\nbrains with TDP-43 pathology. What was unknown and could not be predicted is if\nthe presence of dsRNA in AD would elicit a significant Inteferon response that could\nbe detected between AD cases with and without TDP-43 pathology. The paper uses\ntheir ML model to suggest that this is the case, but more evidence could be shown of\ntheir existing data that could make this clearer for the reader (point 4).\nPrevious work from the authors suggested that Jak inhibitors could protect from\ndsRNA toxicity. Using CRISPR screens they were able to show that protection from\ntoxicity is mainly elicited by the Jak family member TYK2. This is an important\ndiscovery because direct inhibition of TYK2 would probably be more tolerable to\npatients than inhibitors that broadly inhibit Jak family members.\nFinally, they provide some evidence that dsRNA induce CXCL10 signalling which\ncould potentially be used as a biomarker for IFN-I signalling in AD.\nMajor points\n1. The authors demonstrate that in control cases without AD or TDP-43 pathology\nthere is no or low presence of cdsRNA. In the brains of patients with AD and TDP-43\npathology there is accumulation of cdsRNA. However, what is lacking is a significant\nnumber of AD patients without TDP-43 pathology to make a comparison on cdsRNA\nlevels in AD with and without TDP-43. This is an important point for the authors to\nmake because they go on to segregate AD patients by TDP-43 pathology and\nsuggests that this is a consequence of ISG gene expression induced by cdsRNA.\nThank you for this point. We do not claim that TDP-43 negative cases do not\nshow dsRNA. However as seen in Extended Data Fig. 1a we observe\ncdsRNA much more frequently in AD cases with severe TDP-43 cytoplasmic\npathology in the amygdala in our study (line 103 - 104) which admittedly has a\nlimited number of assessed cases of mild and moderate TDP-43 cytoplasmic\npathology in the amygdala. CdsRNA can arise from many genetic sources\n(nucleus, mitochondria) and other mechanisms and therefore is not relying on\nTDP-43 cytoplasmic pathology (nuclear hypofunction of TDP-43 giving rise to\ncryptic exons). We added this to our discussion (line 423 - 426). In our\nintroduction we do point out that the cdsRNA-mediated innate immune-\nmediated toxicity can arise independently from TDP-43 cytoplasmic pathology\n(line 72 - 74).\n2. Line 188. The posterior cingulate cortex was the region chosen from the ROSMAP\ndata to look for CE expression. They stated that this region is impaired early in AD\nprogression and therefore is important for the study.\nWe observed an enrichment of CE’s in the PCC, but not in the head of the\ncaudate or the dorsolateral prefrontal cortex in the available ROSMAP data.\nWe chose to focus on the Rosmap data set since that data set was generated\nusing paired-end sequencing, which is more sensitive for CE expression as\ncompared to brain regions from MSBB with single end sequencing – as shown\nin Extended Data Fig. 4a.\nHowever, accumulation of TDP-43 and onset of AD probably represent two distinct\npathways that are co-occurring in disease and both contribute to dementia but are\nnot mechanistic dependent on each other. Previous work has shown that in AD\npatients the amygdala is the first region in the brain to have TDP-43 followed by the\nhippocampus. Using the sequencing data from these regions would provide for more\nreliable detection of CE.\nWe did not analyze data from the amygdala or the hippocampus, but we did\nlook at HCN and DLPFC in which we saw no enrichment of CE’s in AD cases.\nThe ROSMAP data is obtained from Rush University where there is a framework for\nstaging TDP-43 pathology in patients.\n(https://www.radc.rush.edu/docs/var/detail.htm?category=Pathology&subcategory=T\nDP-43&variable=tdp_st4). Why wasn’t the Rush staging scaled used?\nDRIAD-SP is based on Braak staging, not TDP-43 staging. CE expression is\nused to stratify input data and compare outcomes, but not to train the whole\nmodel. We look forward to building a DRIAD-TDP-43 once that data becomes\navailable to us, but the correlation with disease progression between tau\npathology and clinical outcomes is not as established for TDP-43 pathology.\nAlso, brain samples were obtained from ROSMAP at RUSH. They would have TDP-\n43 pathology on all the samples that were sequence or a large fraction of them. The\nanalysis of CE in RNA-Seq data can be useful for new datasets, but why ignore the\nhigh-quality pathology data that most likely exist at Rush? The authors should\ninclude this staging data based on pathology at Rush to validate their CE pipeline.\nThank you for this excellent suggestion. We requested the information yet still\nhave not received after 6 months due to some issues with the execution of the\nDUA between the MGH and Rush for inexplicable reasons.\n3. MG132 treatment of cells is not a model of TDP-43 pathology. MG132 does lead\nto aggregation of TDP-43 among countless other impacts on the cell. The increase in\nCXCL10 cannot be attributed to TDP-43 aggregation. Most of the cells in this\ntreatment are probably dead at 48 hours. In extended data figure 11 there is a lack of\nDAPI staining after MG132 treatment probably because the cells have undergone\napoptosis and the DNA is fragmented. Knockdown of TDP-43 has previously been\nshown to lead to the accumulation of dsRNA. Does knockdown of TDP-43 lead to the\ninduction of CXCL10?\nWe did this experiment to determine if TDP-43 mislocalization was sufficient\nto drive interferon signalling (see updated Extended Data Fig. 9) and if that\ninnate signaling was blocked by FDA-approved JAK and TYK2 inhibitors.\nOthers have used MG-132 to induce TDP-43 mislocalization (PMID:\n32040555, 21829535). We now include quantifications of TDP-43 cytoplasmic\nmislocalization after treatment with MG-132 with two different antibodies\nagainst TDP-43. We agree with the reviewer that MG-132 has pleiomorphic\neffects on these cells beyond TDP-43 mislocalization – an issue with other\nstressors used to evoke TDP-43 mislocalization. The cells are not dead within\n48h (and DAPI is observed in the nucleus), but do start dying within 72h. It\nmay take longer than 72h for cdsRNA to accumulate to detectable levels (as\nseen in the TDP43 mutant iPSC lines). We were able to show that CXCL10\nprotein was induced by MG-132 and this cytokine induction was prevented by\nthe JAK and TYK2 inhibitors in differentiated human ReN cells. TDP-43\nknockdown leads to accumulation of dsRNA and CXCL10 production in\nprimary rat astrocytes (ref. 21). We observe in our screen that 3 out of 4\ndifferent guide RNAs against mouse TARDBP slightly exacerbated cell death\n48h after poly I:C treatment, but have not validated that result in a dedicated\nexperiment.\n4. In my understanding of how the ML works, only a list of genes is used as input to\nthe model and there is no input of the expected direction of the change between\ngroups. Given that the list of genes obtained from Jak inhibition is able to distinguish\nTDP-43 positive and negative patients, one would expect that genes that are\ndownregulated by Jak inhibition would be upregulated in the population that has\ncsdRNA activation of Jak signalling. This cannot be inferred from the predicative\npotential of the gene list in the ML model. It only lets you know that there is a\ndifference in gene expression between two groups but not that it goes in the\nexpected direction.\nCorrect, results from DRIAD-SP need to be validated by in vitro and in vivo\nexperiments, which we did with the JAK inhibitors.\nCould the authors provide a heatmap or other graph that shows that Jak dependent\ngenes are upregulated in the TDP-43 positive group and not in the TDP-43 negative\ngroup. This would provide some additional evidence that the cdsRNA are not only\npresent in the TDP-43 positive cases but induce the expected interferon signalling.\nWe included a simplified version of this heatmap in Extended Data Fig. 5c in\nwhich we show that average ISG expression is significantly upregulated in\ncases that we stratified as TDP-43 positive based on the detection of cryptic\nexons.\n5. Figure 1c. Cell models of TDP-43 only show weak TDP-43 loss of function and\nbarely detectable levels of aggregate TDP-43. Is the level of dsRNA accumulation in\nTDP-43 G298S cell line enough to activate PRR signalling?\nAs noted earlier, we do not see TDP43 mislocalization in the NGN2 iPSC\nderived neural cells. However, the STING antagonist didn’t fully inhibit IFN-\nsignaling in NGN2 derived iPSCs (ref. 33), so we attribute the rest to dsRNA\naccumulation triggering RIG-I / MDA5 activation.\n6. For statistically analysis, did the authors perform tests to ensure that datapoints\nare normally distributed before conducting t-tests and ANOVA?\nYes, a normality test (either Shapiro-Wilk, D’Agostino-Pearson omnibus,\nAnderson-Darling, or Kolmogorov-Smirnov test with Dallal-Wilkinson-Lilliefor P\nvalue where appropriate and applicable based on replicate number) was\nconducted with a significance level of 0.05 after outlier testing and exclusion\n(ROUT Q = 1% and Grubbs alpha = 0.05). If there was no difference in\nsignificance with and without the respective outliers, the outliers were still\nincluded in the figure unless otherwise pointed out in the figure legend to\nensure that as few values are excluded as possible for a comprehensive\nrepresentation of the data. In the few cases where normality tests failed, a\nWilcoxon Rank Sum test (which does not assume normal distribution) was\nconducted instead of a t test which resulted in the same significance as the t\ntest.\n7. Figure 4 would benefit from more data. It is probably too much to ask for MSD of\nCXCL10 on AD patients biofluids. However, is there any indication in existing\npatients datasets (RNA, MassSpec) that CXCL10 is upregulated in AD?\nThank you for this comment. See reviewer 2 major point #6. Various studies\nshow elevated levels of CXCL10 in AD (PMID: 29893515, 36967827,\n28413983, 37291639, 29223680).\nMinor points.\n1. Include the clinical information for all patients used for histology analysis in\nExtended Data Table 1\nSee updated Extended Data Table 1\n2. Figure 1C legend states that 2/3 differentiated batches were analysed but there\nare three different shapes in the graph. Should it just state that 3 batches were used.\nCorrect, thank you. Figure legend was adjusted.\n3. Use of two forms of the UNC13A cryptic exon is not a good measure for TDP-43\nseverity. The two forms occur together and the detection of one vs both in\nsequencing data is probably more related to sequencing depth and RIN quality of\nRNA rather than degree of TDP-43 loss of function in the sample.\nBoth CE isoforms can occur in the same cell but differences in cell types may\nalter their relative distribution. We added Extended Data Fig. 4b to show that\nall three cryptic exons are detected independently in the majority of our data\nset (see small fraction of samples where CE1 and CE2 are detected together).\nOnly 7/546 (1.3%) cases were deemed positive by having only CE1 and CE2\nwithout STMN2 CE. We agree that differences in detection of the CE isoforms\ncould be related to cell type specificity, sequencing depth, or RiN. However,\nour data indicates that searching for both isoforms is important to capture the\nfull CE landscape from UNC13A.\n4. IFN-A activates TYK2 but doesn’t cause cell death. Why does IFN-I activation of\nTYK2 following cdsRNA treatment lead to cell death?\nThis is an interesting and subtle point. While TYK2 function appears to be\nnecessary for cell death mediated by dsRNA, IFN-I activation alone is not\nsufficient, suggesting that dsRNA is having additional actions beyond\ninterferon induction to mediate cell death. Ongoing experiments are\naddressing the additional mechanisms evoked by dsRNA but are beyond the\nscope of this paper. We try to capture this point by stating “in a subsequent\nvalidation it became clear that TYK2 is necessary to elicit human neural cell\ndeath” to abstract (line 43 - 44) and “The mechanism of cell death linked to\ndsRNA is TYK2-dependent, and IFN-I signaling alone (although it activates\nTYK2) is not sufficient to trigger cell death, indicating that dsRNA has broader\neffects than just triggering interferon, such as the modulation of autophagy76 -\nperhaps acting through TBK1” (line 365 - 369) to the discussion.\n5. Figure 1 legend line 636 extended data fig 3 should be extended data fig 4\nCorrect, thank you. Adjusted.\n6. Line 165 cryptic exon in stmn2 leads to premature polyadenylation not a\npremature stop codon.\nCorrect, thank you. Adjusted.\n7. Line 504 capitalize ALS in the citation\nThank you. Done.\n8. Figure 2b UNC13A diagram is not correct. There should be two splice junctions\nbetween exon 20 and the cryptic exon and one junction between the cryptic exon\nand exon 21.\nCorrect, thank you. Adjusted.\n9. line 213, 215, and 217 References to Extended Data Figure 8 and Extended Data\nfigure 9 are not correct. The figures need to be switched.\nCorrect, thank you. Adjusted.\n10. line 223. Extended data figure 10 does not show that the downstream\ntranscription of ISGs is inhibited.\nCorrect. We cited previous work showing that downstream transcription of\nISGs is inhibited.\n11. Figure 3C There is an extra 2 in log2(FC)\nThank you, corrected.\n12. Line 260 Check the IC50 values for Jak1. The 1 nM figure is for binding of\ndecravacitinib pseudo kinase domain of Jak1 and not the kinase domain.\nDeucravacitinib binding at the pseudo kinase domain does not inhibit Jak1 activity.\nUsing the binding value of 1 nM rather than a value obtained with a cell based model\nof Jak1 activation makes the drug appear less selective than it is.\nThank you for pointing this out, this was indeed a mistake. Values have been\ncorrected: IC = 0.2 nM compared to IC (JAK1-3) > 10 µM\n50 50\n13. Probably should have used a lower concentration of deucravacitinib for MS-TMT\nexperiments.\nIn retrospect, we agree that a lower dose would have likely been informative\nwith less off target effects. At the time, a previous screen in undifferentiated\ncells had suggested dose dependency at that concentration of deucravacitinib\nwhich is why this concentration was selected.\n14. Wording in introduction suggests cdsRNA is a common feature in ALS but it has\nonly been shown for C9ORF72 cases. Please clarify text or provide citation that it\nhappens in sporadic forms of the disease.\nThank you, we added this clarification in the last sentence of the abstract. In\nthe introduction we already mention that this applies to ALS and FTD patients\nwith an intronic expansion of hexanucleotide repeats in the C9ORF72 gene\n(line 69 - 72).\n15. Line 747 Supplemental Table 2 should be Supplemental Table 1\nCorrect, thank you. Adjusted.\n16. Line 752 Brief outline of NGN2 differentiation would be useful.\nAgree, we added the description to the methods (line 866 - 874).\n17. How much lipofectamine 2000 was used for transfections in NGN2 cortical\nneurons, SY5Y cells, and ReN VM cells?\nDilution factor 3:1000 - added to methods.\n18. Line 807 is MGM2 supposed to be NGN2?\nCorrect, thank you. Adjusted.\nReviewer #4 (Remarks to the Author):\nI suggest the authors reconsider the title, as the current phrasing—particularly the\nsegment “…in dementia with Alzheimer’s disease”—is unclear and awkwardly\nconstructed. While the latest clinical criteria for Alzheimer’s disease (AD)\nacknowledge that a clinical diagnosis does not necessarily require the presence of\ndementia, incorporating this nuance into the abstract may not add value to this\nspecific study.\nWe agree and have changed the title to remove the word “dementia” and\nfocused on Alzheimer’s disease pathology.\nThe following sentence requires rephrasing for clarity:\n\"One of the pathologic features that stratify dementia patients is Alzheimer’s disease\n(AD), a disease defined by amyloid beta (Ab) plaques and neurofibrillary tangles\n(NFTs) of hyperphosphorylated tau protein, which can be associated to other\nproteinopathies, including cytoplasmic inclusions of phosphorylated TAR DNA-\nbinding protein 43 (pTDP-43).\"\nThe sentence is overly complex and could benefit from restructuring to improve\nreadability and precision.\nThank you, the sentence has been adjusted to “Alzheimer’s disease (AD), a\ndisease defined by amyloid beta (Ab) plaques and neurofibrillary tangles\n(NFTs) of hyperphosphorylated tau protein, can be associated to other\nproteinopathies, including cytoplasmic inclusions of phosphorylated TAR\nDNA-binding protein 43 (pTDP-43)”\nI do not believe the claim that TDP-43 pathology is the most common co-pathology in\nAD is entirely accurate. The frequency of co-pathologies depends on the population\nstudied, and Lewy pathology is also highly prevalent among AD patients. This point\nshould be clarified.\nWe modified this sentence to state that TDP-43 is one of the most prominent\nproteinopathies in AD (line 63 - 64). We quote this source PMID: 34930382\nThe finding of CXCL10 alterations as a potential biomarker for dsRNA pathology is\nintriguing. However, the study would be significantly strengthened if the authors\ncould demonstrate changes in CXCL10 levels in the cerebrospinal fluid (CSF) of AD\npatients.\nSee response to Reviewer 2 major point #6 and reviewer 3 major point #7.\nVarious studies show elevated levels of CXCL10 in AD (PMID: 29893515,\n36967827, 28413983, 37291639, 29223680).\nWhile this is an important study with potential translational value, it is crucial that the\nauthors explicitly address that their findings are relevant primarily for patients with\nconcomitant TDP-43 pathology. Any therapeutic interventions discussed should be\ncontextualized as targeting effects specifically caused by TDP-43 pathology.\nThank you for this comment, and we agree that these insights will be relevant\nto address the heterogeneity of patients with Alzheimer’s disease pathology to\nwork towards a precision medicine approach. While our data especially our\ncell-based models with innate immune activation of IFN-I signaling does not\nrequire TDP-43 cytoplasmic inclusions, we suggest that the translational\npotential of our findings are relevant to patients with concomitant TDP-43\ncytoplasmic inclusion pathology (which is currently not accessible in living\npatients). In addtion, we suggest that TYK2 inhibition should be studied in\nany neurodegenerative disease with accumulation of immunogenic\ncytoplasmic dsRNA, possibly due to lack of TDP-43 function in the nucleus,\nwhich often correlates with TDP43 cytoplasmic pathology. Thus, at the end of\nthe abstract we focus the translational potential on neuroinflammatory\nmechanisms that are present in TDP43-associated Alzheimer’s disease\npathology and at the end of the discussion we are focused on TDP43\npathology, which is both ectopic cytoplasmic TDP43 and nuclear hypofunction\nof TDP43 as detected by cryptic exons.\nIn the abstract, the authors should clarify whether JAK inhibitors influence\nneuroinflammation caused by increased dsRNA expression/accumulation of cdsRNA\nor other factors that elevate IFN-I signalling.\nOur data cannot rule out that JAK inhibitors could block innate immune\nsignaling triggered by type I interferon. The damage associated molecular\npatterns (DAMPs) most closely associated with IFN-I are dsRNA (the focus of\nthis paper) and dsDNA (cGas/STING),which we mention in the discussion\n(lines 375 - 384). In addition, the source of dsRNA and dsDNA include the\nnuclear genome, mitochondrial genome, and viruses.\nThe information about CERAD scoring in Extended Data Table 1 is outdated by over\n20 years. The authors should use the updated 2012 NIA-AA ABC scoring system to\ndetermine the level of AD neuropathological change.\nThank you, agree, we have updated the data that was originally reported to\nthe ABC scoring system. See updated Extended Data Table 1.\nExtended Data Figure 1: I recommend specifying that the analysis was conducted on\namygdala sections.\nAgree, amygdala has been specified.\nExtended Data Figure 7: I recommend clarifying the reference to the posterior\ncingulate cortex. Braak & Braak staging cannot be determined based solely on a\nsingle region, and it is unclear how this region is reflected in the graph.\nThis is now Extended Data Fig. 5a. The figure legend clarifies that our TDP-43\nclassification is based on detectable CE’s in the PCC. We wanted to assess if\nthere were any differences in Braak staging between our binary CE (TDP-43)\nstrata, i.e., to rule out that patients with detectable CE were at later stages of\nthe disease (based on their overall Braak stage).\nNature Communications Response\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have addressed the main concerns sufficiently. The paper now better\nsupports their conclusions. I have no additional concerns that would preclude\npublication.\nThank you very much for your time and thoughtful feedback. We’re pleased to\nknow that our response addressed your concerns and that you found it\nsatisfactory.\nReviewer #2 (Remarks to the Author):\nKönig et al. aim to understand the role of TDP-43 inclusions in Alzheimer’s Disease,\nhow those inclusions may drive toxicity, and present candidate drugs for reversing\nthat toxicity. Another goal is to develop novel biomarkers for AD using this\ninformation.\nKönig et al. present compelling data that link TDP-43 pathology, cytosolic\ndouble-stranded RNA, and interferon induction in human Alzheimer’s Disease tissue,\niPSC-derived neurons, and a neuron-like cancer cell line. They then train their drug\nrepurposing pipeline using cryptic exon expression to identify candidate drugs for AD\nwith TDP-43 pathology. This pipeline identified a number of tyrosine kinase inhibitors.\nThey show that inhibition of tyrosine kinase signaling can rescue dsRNA-induced cell\ndeath by decreasing interferon signaling. Finally, they establish a link between an\nestablished TYK2 loss of function variant and decreased CXCL10 levels in human\nplasma.\nThe manuscript as revised is much improved and presents interesting data and\nhypotheses about TDP-43 pathology and its downstream pathological mechanisms\nand detection in human Alzheimer’s Disease. I only have a few minor points:\nLine 145-148: The data in figure two show that there are 190 significant IFN-I related\nDEGs between AD and non-AD brains. I cannot find anything up to this point in the\ntext or figures related to this dataset that support the idea that a specific molecular\nmechanism is causing (rather than correlated with) IFN-I induction. I would remove\nthis sentence, as the very similar sentence in lines 212-215 is supported by the\nprevious data.\nThank you for this helpful suggestion. We agree that removing this sentence\nis appropriate, as it helps avoid unnecessary repetition.\nRelated to figure 2: It would be more convincing to have GSEA or other unbiased\nenrichment analysis performed on these data to get a sense of how large of a\ntranscriptional signature ISGs are versus other changes in this AD dataset.\nAlso related to figure 2: The text is quite small on the heat map. Are there ways\nmake this figure clearer and provide some more biological meaning to this figure\npanel?\nWe performed an unbiased enrichment analysis using our list of ISGs and\ncompared it with random gene lists from the KEGG MEDICUS database. Our\nresults show that the ISG list remains the most enriched in three out of five\nbrain regions in AD patients. To add biological context to Figure 2, we\nincluded two new panels (Fig. 2a,b) illustrating this observation, which also\nhighlights that the ISG list is generally enriched across all assessed brain\nregions compared with other gene lists.\nLine 169 contains a typo “to a premature polyadenylation”\nWe corrected the mistake. Thank you!\nCode and methods for calculating LFC and p-values for the CRISPR screen should\nbe referenced and/or written up in the methods sections.\nThank you for pointing this out. We clarified it in the method section (see line\n1051 - 1052) - the code and method can be found in reference 119 which\nuses the algorithm “STARS” that can also be found under this link provided by\nthe BROAD Institute.\nFig5b-h: pSTAT1 label should be at the molecular weight of the band.\nFixed. Thank you!\nThe legends of Extended Fig 1c and 2b state that these are graphs of the proportion\nof cells, but they are graphed as the number of total cells, please update graphs or\nfigure legends (I believe graphing as proportion/percent of total cells analyzed would\nbe the most straightforward).\nThank you for this suggestion. We adjusted the figures accordingly and\nmentioned the total cell count in the figure legends.\nReviewer #3 (Remarks to the Author):\nThe authors have made improvements to their manuscript. Overall, I think that their\nclaims that there is high cdsRNA in AD patients with TDP-43 pathology, TYK2\ninhibition is protective against cell death induced by cdsRNA, and CXCL10 could be\nused as a biomarker to detect cdsRNA induced inflammation are well support and\nnovel findings.\nResponse to major points revisions.\n1. I would have like to see more cases, but the cases that they do include supports\ntheir claim that patients with high levels of pTDP-43 also have high levels of\ncdsRNAs.\nThank you for this comment. We agree that more cases are always desirable,\nbut our findings are robust.\n2. Looking at cryptic exons in the amygdala or hippocampus may have increased the\nnumber of TDP-43 positive cases. However, the authors state they were trying to be\nconservative in assigning TDP-43 positive cases and they were still able to find a\nsignificant signal in their DRIAD-SP analysis. Therefore, I think their analysis is\nsufficient.\nCorrect — we intentionally took a more conservative approach, and we\nappreciate your positive feedback.\n3. I still think MG132 is not great model of TDP-43 aggregation, but as there are no\nreliable alternatives it is sufficient. I have not detected CXCL10 expression in TDP-43\nknockdown RNAseq datasets from NGN2-cortical neurons or SH-SY5Y cells. If\nTDP-43 pathology is inducing CXCL10 expression it might be from a gain of function\nof aggregates/mislocalization rather than loss of nuclear function. The updated\nimmunofluorescence of MG132 treated cells looks very nice.\nWe are pleased that we were able to address your request, and we greatly\nappreciated your antibody recommendation in the previous round of revisions.\nThank you also for sharing your observations from NGN2-cortical neurons\nand SH-SY5Y; these insights will be very helpful in guiding future studies.\n4. The new graph (Extended Fig5 C) answers my question.\nThank you for confirming!\n5. NGN2 neurons with TDP-43 mutations have mild phenotypes, but the authors are\nstill able to detect a modest increase in cdsRNA. On the question of whether\ncdsRNA in this model can drive PPR activation, the authors point to ref 33 to state\nthat this cell line has increased expression of inflammatory response genes that is\nnot completely rescued by STING inhibitors. However, looking at ref 33 figure 7a\nthere appears to be a full rescue by STING inhibitors. The data as presented\nsuggest cytoplasmic TDP-43 could contribute to formation of cdsRNA in AD and the\nauthors make several points that the origin of cdsRNA in AD is unknown. As long as\nthere is no claim that cytoplasmic TDP-43 is the sole driver of cdsRNA, then I think\nfurther experiments in this line are not needed.\nThank you for pointing this out. Yes, we do not claim that TDP-43 is the sole\ndriver of cdsRNA. We added a clarification in line 424 - 425 and the\nsubsequent paragraph in our discussion also highlights other potential\nsources and drivers of cdsRNA.\n6. Thank you for clarifying tests of normality.\n7. Thank you for including references to data that shows increased CXCL10 levels in\nAD cases.\nHappy to!\nAll previous minor points were adequately addresses.\nThank you for your confirmation. We are glad to hear this.\nNew minor points.\nFigure 3C. There is a stray datapoint in the STMN2 violin plot potentially from figure\nre-arrangements without grouping points.\nFixed - Thank you!\nExtended Data Fig 2a. Should labels for TDP-43 (in figure and legend) be pTDP-43?\nThe labels are indeed correct and supposed to be TDP-43, but we understand\nwhy the reviewer is asking this as we used pTDP-43 in Figure 1. In Extended\nData Fig. 2, in which we analyzed immunogenicity of dsRNA, we did not apply\npTDP-43 antibody at the time. However, since TDP-43 is also detecting\ncytoplasmic pTDP-43 inclusions and since this panel shows that the TDP-43\nsignal is cytoplasmic as well, we assume that this marker is sufficiently\nshowing ectopic cytoplasmic TDP-43 and spatial coincidence with\nimmunogenic dsRNA even without specifically detecting the phosphorylated\nversion of TDP-43.\nExtended Data Fig 4a. Text for “STMN2 short” in the figure is overlapping.\nFixed - Thank you!\nReviewer #4 (Remarks to the Author):\nI am satisfied with the responses to the reviewer's comments.\nThank you very much for your time. We are glad to hear we could address\nyour comments and appreciated your valuable input.\nReviewer #5 (Remarks to the Author):\nThe main computational feature in this work seems to be the use of a previously\npublished model called DRIAD and its minor extension to DRIAD-SP in selecting\ndrugs for repurposing. First, the paper omits the explanation of DRIAD which needs\nto be entirely explained and justified in the methods section:\n--The full method should be transparently explained (in words and equations) since it\nforms an important part of the computation.\nThe methods and code for DRIAD-SP are explained in the method section in\nits entirety. However, we appreciate this comment as it became clear to us\nthat we did not label the sections transparently enough. We now included\nDRIAD-SP in the title of each method section that refers to this part of the\nmanuscript.\n--This method needs to be compared against others with different assumptions,\npotentially on synthetic data with ground truth.\nDRIAD-SP (formerly referred to as DRIAD, prior to the incorporation of ordinal\nridge regression and tailoring for TDP-43–positive patients) is a validated and\npublished machine-learning pipeline. In its original publication\n(https://doi.org/10.1038/s41467-021-21330-0), DRIAD was benchmarked\nagainst other datasets. For instance, Figure 1c illustrates how different\ndatasets were processed through DRIAD to test and compare various\nassumptions.\nDRIAD seems to be a somewhat convoluted method that works as follows:\n1) Uses gene expression measurements of cell lines perturbed by drugs to get a\ndifferentially expressed list of genes (DEG).\n2) Uses these DEG genes to train a logistic regression model (I had to go to the\nmethods section of the previous paper to find this) to predict brack stages just from\nthese gene expression levels.\n3) Uses random lists of genes to predict the braak stage to create a null distribution\nof predictiveness from which to compute significance.\nThe main improvement seems to be multi-class or ordinal rather than binary\nregression, which may seem minor but is still worth validating if treatments are\nbrack-specific.\nI have several problems with this method. First the gene expression measurements\non cell lines may not be reflective of measurements on actual tissue nor drug effects.\nWe agree with the reviewer that drug repurposing predictors such as\nDRIAD-SP do not directly predict drug effects in humans. The purpose of our\napproach is to nominate potential repurposing candidates based on gene\nexpression signatures from human cells treated with the respective\ncompounds. These candidates must then undergo validation in vitro and, if\npromising, in vivo before any conclusions can be drawn about their effects in\ntissue. We wish to emphasize that our claims are limited to this framework\nand do not extend beyond it.\nSecond, predicting Braak stage from these gene lists does not imply that the drug\nmodulates the gene in the right direction.\nCorrect. As stated in DRIAD’s original publication\n(https://doi.org/10.1038/s41467-021-21330-0), both the introduction and the\ndiscussion explicitly clarify that the direction of a nomination is not\ndetermined—that is, the approach does not predict whether a compound will\nimprove or worsen the disease phenotype. Rather, the goal is to nominate\ncandidates whose relevance must then be tested through in-vitro experiments\nand, if promising, in-vivo validation. To underscore this point, we have added\na clarifying sentence in the current manuscript (see line 159 - 161).\nThird, it is not clear to me that random gene lists construct the correct null, indeed\ngreater predictiveness could result from any genes in neurodegeneration-related\npathways. Thus I would want this method rejustified and compared in this paper\nbefore I believe it can be of actual benefit. How does it compare to, for instance\nknowledge graph methods based on known genes?\nWe thank the reviewer for this comment. Indeed our hypothesis about how\nDRIAD-SP works is based on the idea that drugs which perturb a large number\nof neurodegeneration-related pathway members will yield a screen hit when\ncompared to random gene lists. The key advantage of this approach is its\nunbiased nature. By screening compounds in differentiated ReNcell VM cells\nand comparing drug-perturbed gene signatures directly against patient\ntranscriptional data, we avoid relying solely on existing disease pathway\nannotations, which may be incomplete or biased toward well-studied\nmechanisms.\nNonetheless, we attempted to compare DRIAD-SP's results to established\nknowledge graph (KG) methods. We performed a comparison using publicly\navailable results from two sources:\n1. TxGNN: A state-of-the-art graph neural network model that predicts\ndrug-disease relationships from a large biomedical knowledge graph. We used\nits published list of 200 ranked drug candidates for AD.\n[doi:10.1038/s41591-024-03233-x]\n2. ESCARGOT on AlzKB: A LLM based method identifying drug candidates\nassociated with notes related to \"AD Pathway\" (20 drugs) and \"AD BodyPart\"\n(39 drugs) pathways in AlzKB. [doi:10.1186/s13040-025-00466-5]. Out of these\n46 unique drugs, four were identified as novel repurposing candidates.\nThe predictions from our data-driven DRIAD-SP model and the\nknowledge-based TxGNN model show very little overlap. TxGNN identifies a\nlarge number of established (approved in the early 2000’s) but largely ineffective\nAD drugs of the cholinesterase inhibitor class (e.g., donepezil), but does not\nprioritize JAK inhibitor compounds identified by DRIAD-SP. Of the top 200 drugs\nprioritized by TxGNN for AD, only one (fedratinib) is a JAK inhibitor at rank 197.\nESCARGOT on AlzKB identified four novel compounds without previous AD\nassociation: three antineoplastic agents (epirubicin, vemurafenib, fulvestrant)\nand vitamin A. There was minimal overlap between the TxGNN and\nESCARGOT drug sets, with only 1 of 20 \"AD Pathway\" drugs and 3 of 39 \"AD\nBodyPart\" drugs appearing in the TxGNN results. None of the 46 drugs\nprioritized by ESCARGOT are annotated JAK inhibitors.\nThese results suggest that KG-based methods and DRIAD-SP are\ncomplementary. KG methods identify novel compound connections to\nestablished AD-related nodes in existing knowledge networks, while DRIAD-SP\nidentifies drugs whose molecular signatures predict disease progression in\npatient tissues, independent of prior disease annotations. Notably, DRIAD-SP's\nidentification of JAK inhibitors, which were largely absent from KG predictions,\nhas motivated an ongoing clinical trial (NCT05189106).\nFourth, the authors seem to use a ridge rather than lasso regularization, and this\nlimits mechanistic interpretability and feature selection ability.\nWe agree with the author. However, we previously addressed why we did not\nuse lasso regularization in our method section (see line 1022 - 1024). Our\ngoal was to assess whether the collective perturbation of a drug predicts\ndisease progression, rather than to identify a sparse subset of the most\npredictive perturbed genes. We deliberately preselected genes based on drug\nperturbation profiles, and we wanted the model's performance to reflect\ncontributions from the entire gene set. The sparsity-inducing nature of lasso\nregularization would exclude genes that were deliberately preselected for\ninclusion in the model.\nReviewer #6 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers\nwho co-review manuscripts.\nThank you for your time and input! Please see our comments and edits\nabove.\nNature Communications Response\nREVIEWERS’ COMMENTS\nReviewer #2 (Remarks to the Author)\nAll of my concerns in the previous rounds of reviews have been addressed. I believe\nthat the data in this paper support the conclusions, and adds interesting and\nsignificant contributions to the field. Thank you to the authors for their thoughtful\nconsideration of my critiques.\nThank you very much for your constructive input that helped us improve our\nmanuscript. We appreciate your feedback and are pleased to know that we\ncould address all points.\nReviewer #5 (Remarks to the Author)\nI was asked to review the computational component of the paper and I find the\naddressing of their comments still somewhat lacking although it does not seem to be\na large part of their paper.\n---I asked them to write their method in its entirety, instead they left the short\ndescription in their methods section. I would appreciate an algorithm box or\nsomething of this sort.\n---The method still seems ad hoc to me, maybe they can give more information on all\ndrug perturbation data they used in DRIAD and which other drugs gave significant\nresults. Information on specificity and precision vs recall should be given.\n---Since their paper depends on experimental results more than computational\nresults, maybe there is an orthogonal way of deriving baricitinib and ruxolitinib for\ntesting.\nI appreciate the authors compared to knowledge graph methods.\nThank you very much for your input that helped us improve our manuscript. We\nhave provided all the details in the methods section to reproduce the work. All\nother drug perturbation data was provided in the first DRIAD paper (Rodriguez,\net. al, Nature Communications, 2021). In both this paper and the Rodriguez,\net\nal, Science Translational Medicine, 2021 paper, the orthogonal bench science\ndata is presented to support the hypothesis of baricitinib and ruxolitinib for\ntherapeutic testing.\nReviewer #6 (Remarks to the Author)\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers\nwho co-review manuscripts.\nThank you for participating in this review. Please see our comments and edits\nabove."}, {"round": 2, "reviewer_comments": {"reviewer_2": "König et al. aim to understand the role of TDP-43 inclusions in Alzheimer’s Disease, how those inclusions may drive toxicity, and present candidate drugs for reversing that toxicity. Another goal is to develop novel biomarkers for AD using this information.\nKönig et al. present compelling data that link TDP-43 pathology, cytosolic double-stranded RNA, and interferon induction in human Alzheimer’s Disease tissue, iPSC-derived neurons, and a neuron-like cancer cell line. They then train their drug repurposing pipeline using cryptic exon expression to identify candidate drugs for AD with TDP-43 pathology. This pipeline identified a number of tyrosine kinase inhibitors. They show that inhibition of tyrosine kinase signaling can rescue dsRNA-induced cell death by decreasing interferon signaling. Finally, they establish a link between an established TYK2 loss of function variant and decreased CXCL10 levels in human plasma.\nThe manuscript as revised is much improved and presents interesting data and hypotheses about TDP-43 pathology and its downstream pathological mechanisms and detection in human Alzheimer’s Disease. I only have a few minor points:\nLine 145-148: The data in figure two show that there are 190 significant IFN-I related DEGs between AD and non-AD brains. I cannot find anything up to this point in the text or figures related to this dataset that support the idea that a specific molecular mechanism is causing (rather than correlated with) IFN-I induction. I would remove this sentence, as the very similar sentence in lines 212-215 is supported by the previous data.\nRelated to figure 2: It would be more convincing to have GSEA or other unbiased enrichment analysis performed on these data to get a sense of how large of a transcriptional signature ISGs are versus other changes in this AD dataset.\nAlso related to figure 2: The text is quite small on the heat map. Are there ways make this figure clearer and provide some more biological meaning to this figure panel?\nLine 169 contains a typo “to a premature polyadenylation”\nCode and methods for calculating LFC and p-values for the CRISPR screen should be referenced and/or written up in the methods sections.\nFig5b-h: pSTAT1 label should be at the molecular weight of the band.\nThe legends of Extended Fig 1c and 2b state that these are graphs of the proportion of cells, but they are graphed as the number of total cells, please update graphs or figure legends (I believe graphing as proportion/percent of total cells analyzed would be the most straightforward).", "reviewer_3": "The authors have made improvements to their manuscript. Overall, I think that their claims that there is high cdsRNA in AD patients with TDP-43 pathology, TYK2 inhibition is protective against cell death induced by cdsRNA, and CXCL10 could be used as a biomarker to detect cdsRNA induced inflammation are well support and novel findings.\nResponse to major points revisions.\n1. I would have like to see more cases, but the cases that they do include supports their claim that patients with high levels of pTDP-43 also have high levels of cdsRNAs.\n2. Looking at cryptic exons in the amygdala or hippocampus may have increased the number of TDP-43 positive cases. However, the authors state they were trying to be conservative in assigning TDP-43 positive cases and they were still able to find a significant signal in their DRIAD-SP analysis. Therefore, I think their analysis is sufficient.\n3. I still think MG132 is not great model of TDP-43 aggregation, but as there are no reliable alternatives it is sufficient. I have not detected CXCL10 expression in TDP-43 knockdown RNAseq datasets from NGN2-cortical neurons or SH-SY5Y cells. If TDP-43 pathology is inducing CXCL10 expression it might be from a gain of function of aggregates/mislocalization rather than loss of nuclear function. The updated immunofluorescence of MG132 treated cells looks very nice.\n4. The new graph (Extended Fig5 C) answers my question.\n5. NGN2 neurons with TDP-43 mutations have mild phenotypes, but the authors are still able to detect a modest increase in cdsRNA. On the question of whether cdsRNA in this model can drive PPR activation, the authors point to ref 33 to state that this cell line has increased expression of inflammatory response genes that is not completely rescued by STING inhibitors. However, looking at ref 33 figure 7a there appears to be a full rescue by STING inhibitors. The data as presented suggest cytoplasmic TDP-43 could contribute to formation of cdsRNA in AD and the authors make several points that the origin of cdsRNA in AD is unknown. As long as there is no claim that cytoplasmic TDP-43 is the sole driver of cdsRNA, then I think further experiments in this line are not needed.\n6. Thank you for clarifying tests of normality.\n7. Thank you for including references to data that shows increased CXCL10 levels in AD cases.\nAll previous minor points were adequately addresses.\nNew minor points.\nFigure 3C. There is a stray datapoint in the STMN2 violin plot potentially from figure re-arrangements without grouping points.\nExtended Data Fig 2a. Should labels for TDP-43 (in figure and legend) be pTDP-43?\nExtended Data Fig 4a. Text for “STMN2 short” in the figure is overlapping.", "reviewer_4": "I am satisfied with the responses to the reviewer's comments.", "reviewer_5": "The main computational feature in this work seems to be the use of a previously published model called DRIAD and its minor extension to DRIAD-SP in selecting drugs for repurposing. First, the paper omits the explanation of DRIAD which needs to be entirely explained and justified in the methods section:\n--The full method should be transparently explained (in words and equations) since it forms an important part of the computation.\n--This method needs to be compared against others with different assumptions, potentially on synthetic data with ground truth.\nDRIAD seems to be a somewhat convoluted method that works as follows:\n1) Uses gene expression measurements of cell lines perturbed by drugs to get a differentially expressed list of genes (DEG).\n2) Uses these DEG genes to train a logistic regression model (I had to go to the methods section of the previous paper to find this) to predict brack stages just from these gene expression levels.\n3) Uses random lists of genes to predict the braak stage to create a null distribution of predictiveness from which to compute significance.\nThe main improvement seems to be multi-class or ordinal rather than binary regression, which may seem minor but is still worth validating if treatments are brack-specific.\nI have several problems with this method. First the gene expression measurements on cell lines may not be reflective of measurements on actual tissue nor drug effects. Second, predicting Braak stage from these gene lists does not imply that the drug modulates the gene in the right direction. Third, it is not clear to me that random gene lists construct the correct null, indeed greater predictiveness could result from any genes in neurodegeneration-related pathways. Thus I would want this method rejustified and compared in this paper before I believe it can be of actual benefit. How does it compare to, for instance knowledge graph methods based on known genes? Fourth, the authors seem to use a ridge rather than lasso regularization, and this limits mechanistic interpretability and feature selection ability.", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Nature Communications Response\nREVIEWERS’ COMMENTS\nReviewer #2 (Remarks to the Author)\nAll of my concerns in the previous rounds of reviews have been addressed. I believe\nthat the data in this paper support the conclusions, and adds interesting and\nsignificant contributions to the field. Thank you to the authors for their thoughtful\nconsideration of my critiques.\nThank you very much for your constructive input that helped us improve our\nmanuscript. We appreciate your feedback and are pleased to know that we\ncould address all points.\nReviewer #5 (Remarks to the Author)\nI was asked to review the computational component of the paper and I find the\naddressing of their comments still somewhat lacking although it does not seem to be\na large part of their paper.\n---I asked them to write their method in its entirety, instead they left the short\ndescription in their methods section. I would appreciate an algorithm box or\nsomething of this sort.\n---The method still seems ad hoc to me, maybe they can give more information on all\ndrug perturbation data they used in DRIAD and which other drugs gave significant\nresults. Information on specificity and precision vs recall should be given.\n---Since their paper depends on experimental results more than computational\nresults, maybe there is an orthogonal way of deriving baricitinib and ruxolitinib for\ntesting.\nI appreciate the authors compared to knowledge graph methods.\nThank you very much for your input that helped us improve our manuscript. We\nhave provided all the details in the methods section to reproduce the work. All\nother drug perturbation data was provided in the first DRIAD paper (Rodriguez,\net. al, Nature Communications, 2021). In both this paper and the Rodriguez,\net\nal, Science Translational Medicine, 2021 paper, the orthogonal bench science\ndata is presented to support the hypothesis of baricitinib and ruxolitinib for\ntherapeutic testing.\nReviewer #6 (Remarks to the Author)\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers\nwho co-review manuscripts.\nThank you for participating in this review. Please see our comments and edits\nabove."}, {"round": 3, "reviewer_comments": {"reviewer_2": "All of my concerns in the previous rounds of reviews have been addressed. I believe that the data in this paper support the conclusions, and adds interesting and significant contributions to the field. Thank you to the authors for their thoughtful consideration of my critiques.", "reviewer_5": "I was asked to review the computational component of the paper and I find the addressing of their comments still somewhat lacking although it does not seem to be a large part of their paper.\n---I asked them to write their method in its entirety, instead they left the short description in their methods section. I would appreciate an algorithm box or something of this sort.\n---The method still seems ad hoc to me, maybe they can give more information on all drug perturbation data they used in DRIAD and which other drugs gave significant results. Information on specificity and precision vs recall should be given.\n---Since their paper depends on experimental results more than computational results, maybe there is an orthogonal way of deriving baricitinib and ruxolitinib for testing.\nI appreciate the authors compared to knowledge graph methods.", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter