bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using big sequencing data to identify chronic SARS-Coronavirus-2 infections
Authors: Harari Sheri , Miller Danielle , Fleishon Shay , Burstein David , Stern Adi 
Published: July 2023

AbstractThe evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants, many of which may have arisen during chronic infections of immunocompromised individuals. Here, we harness a global phylogeny of ∼11.7 million SARS-CoV-2 genomes and search for clades composed of sequences with identical metadata (location, age, and sex) spanning more than 21 days. We postulate that such clades represent repeated sampling from the same chronically infected individual. A set of 271 such chronic-like clades was inferred, and displayed signatures of an elevated rate of adaptive evolution, in line with validated chronic infections. More than 70% of adaptive mutations present in currently circulating variants are found in BA.1 chronic-like clades that predate the circulating variants by months, demonstrating the predictive nature of such clades. We find that in chronic-like clades the probability of observing adaptive mutations is approximately 10-20 higher than that in global transmission chains. We next employ language models to find mutations most predictive of chronic infections and use them to infer hundreds of additional chronic-like clades in the absence of metadata and phylogenetic information. Our proposed approach presents an innovative method for mining extensive sequencing data and providing valuable insights into future evolutionary patterns.
Altered TFEB subcellular localization in nigral dopaminergic neurons of subjects with prodromal, sporadic andGBA-related Parkinson’s disease and Dementia with Lewy bodies
Authors: Moors Tim E , Morella Martino L , Bertran-Cobo Cesc , Geut Hanneke , Udayar Vinod , Timmermans-Huisman Evelien , Ingrassia Angela MT , Brevé John JP , Bol John GJM , Bonifati Vincenzo , Jagasia Ravi , van de Berg Wilma DJ 
Published: July 2023

AbstractTranscription factor EB is a master regulator of genes involved in the maintenance of autophagic and lysosomal homeostasis, processes which have been implicated in the pathogenesis ofGBA-related and sporadic Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). TFEB activation at the lysosomal level results in its translocation from the cytosol to the nucleus. Here, we aimed at investigating whether TFEB subcellular localization is altered in post-mortem human brain of aged individuals with either prodromal PD/DLB (incidental Lewy body disease, iLBD, N=3),GBA-related PD/DLB (N=9) or sPD/DLB (N=9), compared to control subjects (N=12). We scanned nigral dopaminergic neurons using high-resolution confocal and stimulated emission depletion (STED) microscopy and semi-quantitatively scored the observed TFEB subcellular localization patterns. In line with previous studies, we observed reduced nuclear TFEB immunoreactivity in PD/DLB patients compared to controls, both sporadic andGBA-related cases, as well as in iLBD cases. Nuclear depletion of TFEB was more pronounced in neurons with Ser129-phosphorylated (pSer129) aSyn cytopathology and in cases carrying pathogenicGBAvariants. Interestingly, we further observed previously unidentified TFEB-immunopositive somatic clusters in human brain dopaminergic neurons and in human embryonic stem cell (hESC)-derived neurons, which localized at the Golgi apparatus. The TFEB clustering was more frequently observed and more severe in iLBD, sPD/DLB andGBA-PD/DLB compared to controls, particularly in pSer129 aSyn-positive neurons but also in neurons without apparent cytopathology. Notably, increased frequency of cytoplasmic TFEB clusters in aSyn-negative cells correlated with reduced totalGBAenzymatic activity and higher Braak LB stage. In the studied patient population, altered TFEB distribution was accompanied by a reduction in overall mRNA expression levels of selected CLEAR genes, indicating a possible early dysfunction of lysosomal regulation. Overall, these findings suggest the early cytoplasmic TFEB retention and accumulation at the Golgi prior pSer129 aSyn accumulation in incidental,GBA-related and sporadic PD/DLB and indicate TFEB as potential as early therapeutic target for synucleinopathies
Inherited retinal degeneration: T-type voltage-gated channels, Na+/Ca2+-exchanger and calpain-2 promote photoreceptor cell death
Authors: Yan Jie , Wang Lan , Yang Qian-Lu , Yang Qian-Xi , He Xinyi , Dong Yujie , Hu Zhulin , Jiao Kangwei , Paquet-Durand François 
Published: July 2023

ABSTRACTInherited retinal degeneration (IRD) refers to a group of untreatable blinding diseases characterized by a progressive loss of photoreceptors. IRD pathology is often linked to an excessive activation of cyclic nucleotide-gated channels (CNGC) leading to Na+– and Ca2+-influx, subsequent activation of voltage-gated Ca2+-channels (VGCC), and further Ca2+influx. However, whether and how exactly intracellular Ca2+overload contributes to photoreceptor degeneration is still controversial.Here, we used whole-retina and single-cell RNA-sequencing to compare gene expression between therd1mouse model for IRD and wild-type (wt) mice. Differentially expressed genes were linked to several Ca2+–signalling related pathways. To explore this further, organotypic retinal explant cultures derived fromrd1andwtmice were treated with the intracellular Ca2+-chelator BAPTA-AM and with inhibitors for different Ca2+-permeable channels, including CNGC, L-type VGCC, T-type VGCC, Ca2+-release-activated channel (CRAC), and Na+/Ca2+exchanger (NCX). Moreover, we employed the compound NA-184 to selectively inhibit the Ca2+-dependent protease calpain-2. The overall activity of poly(ADP-ribose) polymerases (PARPs), sirtuin-type histone-deacetylases, calpains, as well as the activation of calpain-1, and –2 were analysedin situon retinal tissue sections. Cell viability was assessedviathe TUNEL assay.Whilerd1photoreceptor cell death was reduced by BAPTA-AM, the effects of Ca2+-channel blockers were ambiguous, with T-type VGCC and NCX inhibition showing protection, while blocking CNGC and CRAC was detrimental. Activity of calpains and PARPs generally followed similar trends as cell death. Remarkably, sirtuin activity and calpain-1 activation was associated with photoreceptor protection, while calpain-2 activity was linked to degeneration. Accordingly, the calpain-2 inhibitor NA-184 protectedrd1photoreceptors.Together, these results indicate that Ca2+overload inrd1photoreceptors may be triggered by T-type VGCC in conjunction with NCX. High Ca2+-levels likely suppress the protective activity of calpain-1 and promote neurodegeneration via activation of calpain-2. Our study details the complexity of Ca2+-signalling in photoreceptors and emphasizes the importance of identifying and targeting degenerative processes to achieve a therapeutic benefit for IRD.
Brightest path tracing: A Python package to trace the brightest path in 2D and 3D images
Authors: Jha Vasudha , Cudmore Robert H. 
Published: July 2023

AbstractBrightest path tracing is a widely used image processing technique in several fields including biology, geography, and geology. However, despite the availability of many image processing libraries in Python, few offer an out-of-the-box implementation of a bright-est path tracing algorithm. This paper presents a Python package,brightest-path-lib, that efficiently finds the path with maximum brightness between points in a 2D or 3D image. An example graphical user interface is provided as a Napari plugin. Taken together, the package and plugin provide a powerful and extensible tool for users to efficiently trace structures of interest in 2D or 3D images, regardless of the type of structure being analyzed.
The expression landscape and pangenome of long non-coding RNA in the fungal wheat pathogenZymoseptoria tritici
Authors: Glad Hanna M. , Tralamazza Sabina Moser , Croll Daniel 
Published: July 2023

AbstractLong non-coding RNAs (lncRNAs) are regulatory molecules interacting in a wide array of biological processes. LncRNAs in fungal pathogens can be responsive to stress and play roles in regulating growth and nutrient acquisition. Recent evidence suggests that lncRNAs may also play roles in virulence, such as regulating pathogenicity-associated enzymes and on-host reproductive cycles. Despite the importance of lncRNAs, only few model fungi have well-documented inventories of lncRNA. In this study, we apply a machine-learning based pipeline to predict high-confidence lncRNA candidates inZymoseptoria tritici,an important global pathogen of wheat impacting global food production. We analyzed genomic features of lncRNAs and the most likely associated processes through analyses of expression over a host infection cycle. We find that lncRNAs are frequently expressed during early infection, before the switch to necrotrophic growth. They are mostly located in facultative heterochromatic regions, which are known to contain many genes associated with pathogenicity. Furthermore, we find that lncRNAs are frequently co-expressed with genes that may be involved in responding to host signals, such as those responses to oxidative stress. Finally, we assess pangenome features of lncRNAs using four additional reference-quality genomes. We find evidence that the repertoire of expressed lncRNAs varies substantially between individuals, even though lncRNA loci tend to be shared at the genomic level. Overall, this study provides a repertoire and putative functions of lncRNAs inZ. triticienabling molecular genetics and functional analyses in an important pathogen.Impact statementLong non-coding RNAs (lncRNAs) serve distinct roles from messenger RNA. Despite not encoding proteins, lncRNAs can control important cellular processes such as growth and response to stress. In fungal pathogens, lncRNAs are particularly interesting because they can influence how pathogens infect and harm their hosts. Yet, only very few fungal pathogens have high-quality repertoires of lncRNA established. Here, we used machine learning to identify lncRNA in the major wheat pathogenZymoseptoria tritici.We found that lncRNAs are highly active during the early stages of infection, before the pathogen switches to necrotrophic growth. These lncRNAs are mainly located in regions of the genome associated with pathogenicity. The repertoire of expressed lncRNAs varies substantially among individuals highlighting the potential for pathogen adaptation based on variation in lncRNAs. By expanding our knowledge of lncRNAs in important pathogen models, we enable research to comprehensively investigating their roles across fungi.
PD-1/PD-L1 immune checkpoint therapy demonstrates favorable safety profile in patients with autoimmune liver disease
Authors: Kocheise Lorenz , Piseddu Ignazio , Vonderlin Joscha , Tjwa Eric T , Buescher Gustav , Meunier Lucy , Goeggelmann Pia , Fianchi Francesca , Dumortier Jérôme , Riveiro-Barciela Mar , Gevers Tom JG , Beretta-Piccoli Benedetta Terziroli , Londoño Maria-Carlota , Frankova Sona , Roesner Thomas , Joerg Vincent , Schmidt Constantin , Glaser Fabian , Sutter Jan P , Fründt Thorben W , Lohse Ansgar W , Huber Samuel , von Felden Johann , Sebode Marcial , Schulze Kornelius 
Published: July 2023

AbstractBackgroundImmune checkpoint inhibitors (ICI) have revolutionized the treatment of many malignancies in recent years. However, immune-related adverse events (irAE) are a frequent concern in clinical practice. The safety profile of ICI for the treatment of malignancies in patients diagnosed with autoimmune liver disease (AILD) remains unclear. Due to this uncertainty, these patients were excluded from ICI clinical trials and ICI are withheld from this patient group. In this retrospective multicenter study, we assessed the safety of ICI in patients with AILD.MethodsWe contacted tertiary referral hospitals for the identification of AILD patients under ICI treatment in Europe via the European Reference Network on Hepatological Diseases (ERN RARE-LIVER). Fourteen centers contributed data on AILD patients with malignancies being treated with ICI, another three centers did not treat these patients with ICI due to fear of irAEs.ResultsIn this study, 22 AILD patients under ICI treatment could be identified. Among these patients, 12 had primary biliary cholangitis (PBC), five had primary sclerosing cholangitis (PSC), four had autoimmune hepatitis (AIH), and one patient had an AIH-PSC variant syndrome. Eleven patients had hepatobiliary cancers and the other 11 patients presented with non-hepatic tumors. The applied ICIs were atezolizumab (n=7), durvalumab (n=5), pembrolizumab (n=4), nivolumab (n=4), spartalizumab (n=1), and in one case combined immunotherapy with nivolumab plus ipilimumab. Among eight patients who presented with grade 1 or 2 irAEs, three demonstrated liver irAEs. Cases with grades ≥ 3 irAEs were not reported. No significant changes in liver tests were observed during the first year after the start of ICI.ConclusionsThis European multicenter study demonstrates that PD-1/PD-L1 inhibitors appear to be safe in patients with AILD. Further studies on the safety of more potent dual immune checkpoint therapy are needed. We conclude that immunotherapy should not categorically be withheld from patients with AILD.
Predicting prenatal depression and assessing model bias using machine learning models
Authors: Huang Yongchao , Alvernaz Suzanne , Kim Sage J. , Maki Pauline , Dai Yang , Peñalver Bernabé Beatriz 
Published: July 2023

AbstractPerinatal depression (PND) is one of the most common medical complications during pregnancy and postpartum period, affecting 10-20% of pregnant individuals. Black and Latina women have higher rates of PND, yet they are less likely to be diagnosed and receive treatment. Machine learning (ML) models based on Electronic Medical Records (EMRs) have been effective in predicting postpartum depression in middle-class White women but have rarely included sufficient proportions of racial and ethnic minorities, which contributed to biases in ML models for minority women. Our goal is to determine whether ML models could serve to predict depression in early pregnancy in racial/ethnic minority women by leveraging EMR data. We extracted EMRs from a hospital in a large urban city that mostly served low-income Black and Hispanic women (N=5,875) in the U.S. Depressive symptom severity was assessed from a self-reported questionnaire, PHQ-9. We investigated multiple ML classifiers, used Shapley Additive Explanations (SHAP) for model interpretation, and determined model prediction bias with two metrics, Disparate Impact, and Equal Opportunity Difference. While ML model (Elastic Net) performance was low (ROCAUC=0.67), we identified well-known factors associated with PND, such as unplanned pregnancy and being single, as well as underexplored factors, such as self-report pain levels, lower levels of prenatal vitamin supplement intake, asthma, carrying a male fetus, and lower platelet levels blood. Our findings showed that despite being based on a sample mostly composed of 75% low-income minority women (54% Black and 27% Latina), the model performance was lower for these communities. In conclusion, ML models based on EMRs could moderately predict depression in early pregnancy, but their performance is biased against low-income minority women.
Plasma α-synuclein domain profiles across α-synucleinopathies
Authors: Pons Marie-Laure , Mohaupt Pablo , Vialaret Jérôme , Mondesert Etienne , Vignon Margaux , Coppens Salomé , Stéphane Moreau , Lehmann Sylvain , Hirtz Christophe 
Published: July 2023

AbstractBackgroundThe differential diagnosis of α-synucleinopathies, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), is challenging due to overlapping clinical features and the current lack of reliable biomarkers. The primary diagnostic approach remains clinical, underscoring the need for objective biomarkers that can distinguish between these diseases. This study profiles α-synuclein peptides in plasma to explore potential disease-specific patterns.MethodsWe developed a targeted mass spectrometry-based assay to profile α-synuclein in plasma samples from PD (n=82), DLB (n=32), MSA (n=8), and controls (n=21). The assay quantifies non-modified peptides specifically derived from the N-terminus and NAC domain, regions implicated in aggregate formation, to assess potential disease-specific peptide patterns.ResultsNo significant differences in peptide levels were observed between the disease groups, indicating consistent N-terminus and NAC domain profiles among α-synucleinopathies. However, a peptide within the NAC domain showed distinct patterns in MSA compared to other groups, which may reflect unique pathological processes.ConclusionsThis study provides the first blood-based assessment of α-synuclein peptide profiles, establishing a basis for future research into α-synucleinopathies. Refining the assay to include post-translationally modified peptides may enhance understanding of disease mechanisms and improve biomarker development.
Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data
Authors: Pope Quintin , Varma Rohan , Tataru Chritine , David Maude , Fern Xiaoli 
Published: July 2023

AbstractWe use open source human gut microbiome data to learn a microbial “language” model by adapting techniques from Natural Language Processing (NLP). Our microbial “language” model is trained in a self-supervised fashion (i.e., without additional external labels) to capture the interactions among different microbial taxa and the common compositional patterns in microbial communities. The learned model produces contextualized taxa representations that allow a single microbial taxon to be represented differently according to the specific microbial environment it appears in. The model further provides a sample representation by collectively interpreting different microbial taxa in the sample and their interactions as a whole. We show that, compared to baseline representations, our sample representation consistently leads to improved performance for multiple prediction tasks including predicting Irritable Bowel Disease (IBD) and diet patterns. Coupled with a simple ensemble strategy, it produces a highly robust IBD prediction model that generalizes well to microbiome data independently collected from different populations with substantial distribution shift.We visualize the contextualized taxa representations and find that they exhibit meaningful phylum-level structure, despite never exposing the model to such a signal. Finally, we apply an interpretation method to highlight microbial taxa that are particularly influential in driving our model’s predictions for IBD.Author summaryHuman microbiomes and their interactions with various body systems have been linked to a wide range of diseases and lifestyle variables. To understand these links, citizen science projects such as the American Gut Project (AGP) have provided large open-source datasets for microbiome investigation. In this work we leverage such open-source data and learn a “language” model for human gut microbiomes using techniques derived from natural language processing. We train the “language” model to capture the interactions among different microbial taxa and the common compositional patterns that shape gut microbiome communities. By considering the entirety of taxa within a sample and their interactions, our model produces a representation that enables contextualized interpretation of individual microbial taxa within their microbial environment. We demonstrate that our sample representation enhances prediction performance compared to baseline methods across multiple microbiome tasks including prediction of Irritable Bowel Disease (IBD) and diet patterns. Furthermore, our learned representation yields a robust IBD prediction model that generalizes well to independent data collected from different populations. To gain insight into our model’s workings, we present interpretation results that showcase its ability to learn biologically meaningful representations.
Identification of anti-pathogenic activity amongin silicopredicted small molecule inhibitors ofPseudomonas aeruginosaLasR or Nitric Oxide Reductase (NOR)
Authors: Gajera Gemini , Henriksen Niel , Cox Bryan , Kothari Vijay 
Published: July 2023

AbstractAntibiotic resistantPseudomonas aeruginosastrains cause considerable morbidity and mortality. Identification of novel targets in this notorious pathogen is urgently warranted to facilitate discovery of new anti-pathogenic agents acting against it. Attacking non-essential targets is believed to be a potential anti-virulence strategy. This study attempted to identify small molecule inhibitors of two important proteins LasR and nitric oxide reductase (NOR) inP. aeruginosa. This bacterial pathogen possesses multiple quorum sensing (QS) systems to regulate expression of many of its genes including those associated with virulence. Among these QS systems, ‘Las’ system can be said to be the ‘master’ regulator, whose receptor protein is LasR. Similarly, NOR plays crucial role in detoxification of reactive nitrogen species. This study attemptedin silicoidentification of potential LasR or NOR inhibitors through a virtual screen employing AtomNet®, a proprietary deep learning neural network. Following virtual screening of a large number of compounds for their affinity to LasR or NOR, a final subset of <100 compounds was created by clustering and filtering the top scoring compounds. These compounds were evaluated for theirin vivoanti-pathogenic activity by challenging the model hostCaenorhabditis eleganswithP. aeruginosain presence or absence of test compounds. Survival of the worm population in 24-well assay plates was monitored over a period of 5 days microscopically. Of the 96 predicted LasR inhibitors, 11 exhibited anti-Pseudomonasactivity (23-96% inhibition of bacterial virulence as per third-day end point) at 25-50 µg/ml. Of the 85 predicted NOR inhibitors, 8 exhibited anti-Pseudomonasactivity (40-85% inhibition of bacterial virulence as per second-day end point) at 25-50 µg/ml. Further investigation on molecular mode of action of active compounds is warranted.
Voltage tunes mGlu5 receptor function, impacting synaptic transmission
Authors: Boutonnet Marin , Carpena Camille , Bouquier Nathalie , Chastagnier Yan , Font-Ingles Joan , Moutin Enora , Tricoire Ludovic , Chemin Jean , Perroy Julie 
Published: July 2023

AbstractVoltage sensitivity is a common feature of many membrane proteins, including some G-protein coupled receptors (GPCRs). However, the functional consequences of voltage sensitivity in GPCRs are not well understood.In this study, we investigated the voltage sensitivity of the post-synaptic metabotropic glutamate receptor mGlu5 and its impact on synaptic transmission. Using biosensors and electrophysiological recordings in non-excitable HEK293T cells or neurons, we found that mGlu5 receptor function is optimal at resting membrane potentials. We observed that membrane depolarization significantly reduced mGlu5 receptor activation, Gq-PLC/PKC stimulation, Ca2+release, and mGlu5 receptor-gated currents through TRPC6 channels or NMDA receptors. Notably, we report a previously unknown activity of the NMDA receptor at the resting potential of neurons, enabled by mGlu5.Our findings suggest that mGlu5 receptor activity is directly regulated by membrane voltage which may have a significant impact on synaptic processes and pathophysiological functions.