bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transcriptome-wide association study of Alzheimer disease reveals many differentially expressed genes and multiple biological pathways in brain tissue from African American donors
Authors: Logue Mark W. , Labadorf Adam , O’Neill Nicholas K. , Dickson Dennis W. , Dugger Brittany N. , Flanagan Margaret E. , Frosch Matthew P. , Gearing Marla , Jin Lee-Way , Kofler Julia , Mayeux Richard , McKee Ann , Miller Carol A. , Murray Melissa E. , Nelson Peter T. , Perrin Richard J. , Schneider Julie A. , Stein Thor D. , Teich Andrew F. , Troncoso Juan C. , Wang Shih-Hsiu , Wolozin Benjamin , Mez Jesse , Farrer Lindsay A. 
Published: November 2024

AbstractBackgroundThe genetic basis of Alzheimer disease (AD) in African American (AA) individuals is much less well understood than in European-ancestry (EA) individuals. Furthermore, relatively few AA donors have been included in postmortem AD studies.MethodsWe generated transcriptome-wide bulk-level gene expression data derived from pre-frontal cortex (PFC) tissue obtained from 179 AA brain donors with a pathological diagnosis of AD or control. This sample was augmented by previously generated RNAseq data derived from PFC tissue from another group of 28 AA donors, yielding a total sample of 125 AD cases and 82 neuropathologically determined controls who were enrolled at 12 AD research centers across the United States. Expression of 33,611 genes was compared between AD cases and controls using LIMMA including covariates for age, sex, cell-type frequencies, and RNA integrity number. A false discovery rate corrected p-value (padj) was used to account for multiple testing. Gene-ontology (GO) term enrichment analysis was performed using GOseq.ResultsTranscriptome-wide significant differential expression was observed with 482 genes among which the most significant,ADAMTS2,showed 1.52 times higher expression in AD cases compared to controls (p=2.96×10−8, padj=0.0010). Many of the differentially expressed genes are involved in mitochondrial energy production. Examination of differentially expressed genes observed previously in brain tissue from EA AD cases and controls revealed that 385 (35%) were nominally significant, 65 (5.8%) were significant after multiple test correction, and most (89%) had the same effect direction in the AA sample. Several other significant associations were not observed in the EA cohort, and these genes may be especially relevant to AD risk in the AA population (e.g.,EFR3B,IRS4, andCA12). Examination of potential regulatory effects of AA GWAS-implicated AD risk variants identified several expression quantitative trait loci.ConclusionsIn this first large-scale transcriptome-wide gene expression study of AD in brain from AA donors, we identified many differentially expressed genes, includingADAMTS2which was recently reported to be differentially expressed in brain between pathologically confirmed EA AD cases with and without AD clinical symptoms. These results represent a substantial advance in knowledge about the genetic basis of AD in the AA population and suggest promising new targets for treatment.
Multimodal Hyperbolic Graph Learning for Alzheimer’s Disease Detection
Authors: Xie Chengyao , Zhou Wenhao , Peng Ciyuan , Hoshyar Azadeh Noori , Xu Chengpei , Naseem Usman , Xia Feng 
Published: November 2024

AbstractMultimodal graph learning techniques have demonstrated significant potential in modeling brain networks for Alzheimer’s disease (AD) detection. However, most existing methods rely on Euclidean space representations and overlook the scale-free and small-world properties of brain networks, which are characterized by power-law distributions and dense local clustering of nodes. This oversight results in distortions when representing these complex structures. To address this issue, we propose a novel multimodal Poincaré Fréchet mean graph convolutional network (MochaGCN) for AD detection. MochaGCN leverages the exponential growth characteristics of hyperbolic space to capture the scale-free and small-world properties of multimodal brain networks. Specifically, we combine hyperbolic graph convolution and Poincaré Fréchet mean to extract features from multimodal brain networks, enhancing their rep-resentations in hyperbolic space. Our approach constructs multimodal brain networks by integrating information from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data. Experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed method outperforms state-of-the-art techniques.
Chronic stress antagonizes formation of Stress Granules
Authors: Adachi Yuichiro , Williams Allison M. , Masuda Masashi , Taketani Yutaka , Anderson Paul J. , Ivanov Pavel 
Published: November 2024

AbstractChronic stress mediates cellular changes that can contribute to human disease. However, fluctuations in RNA metabolism caused by chronic stress have been largely neglected in the field. Stress granules (SGs) are cytoplasmic ribonucleoprotein condensates formed in response to stress-induced inhibition of mRNA translation and polysome disassembly. Despite the broad interest in SG assembly and disassembly in response to acute stress, SG assembly in response to chronic stress has not been extensively investigated. In this study, we show that cells pre-conditioned with low dose chronic (24-hour exposure) stresses such as oxidative stress, endoplasmic reticulum stress, mitochondrial stress, and starvation, fail to assemble SGs in response to acute stress. While translation is drastically decreased by acute stress in pre-conditioned cells, polysome profiling analysis reveals the partial preservation of polysomes resistant to puromycin-induced disassembly. We showed that chronic stress slows down the rate of mRNA translation at the elongation phase and triggers phosphorylation of translation elongation factor eEF2. Polysome profiling followed by RNase treatment confirmed that chronic stress induces ribosome stalling. Chronic stress-induced ribosome stalling is distinct from ribosome collisions that are known to trigger a specific stress response pathway. In summary, chronic stress triggers ribosome stalling, which blocks polysome disassembly and SG formation by subsequent acute stress.Significant statementsStress granules (SGs) are dynamic cytoplasmic biocondensates assembled in response to stress-induced inhibition of mRNA translation and polysome disassembly. SGs have been proposed to contribute to the survival of cells exposed to toxic conditions. Although the mechanisms of SG assembly and disassembly in the acute stress response are well understood, the role of SGs in modulating the response to chronic stress is unclear. Here, we show that human cells pre-conditioned with chronic stress fail to assemble SGs in response to acute stress despite inhibition of mRNA translation. Mechanistically, chronic stress induces ribosome stalling, which prevents polysome disassembly and subsequent SG formation. This finding suggests that chronically stressed or diseased human cells may have a dysfunctional SG response that could inhibit cell survival and promote disease.
TACSTD2expression marks the early transition to colon adenomas
Authors: Siskova Anna , Huebner Kerstin , Ferrero Giulio , Gehring Annemarie , Hampel Chuanpit , Naccarati Alessio , Pardini Barbara , Tarallo Sonia , Geppert Carol I. , Prochazka Jan , Tureckova Jolana , Jungwirth Jiri , Hucl Tomas , Kral Jan , Vodickova Ludmila , Erlenbach-Wuensch Katharina , Hartmann Arndt , Honkova Katerina , Matej Radoslav , Vodicka Pavel , Vymetalkova Veronika , Schneider-Stock Regine 
Published: November 2024

AbstractThis study aimed to address new molecular events occurring in precancerous stages of colorectal cancer (CRC) by integrated analysis of gene expression data and DNA methylation profiles.Whole-transcriptome sequencing analysis was performed on 16 fresh frozen colorectal adenoma and matched mucosa specimens along with validation of candidates in an independent cohort of 20 fresh frozen paired adenoma and adjacent mucosa tissues as well as eight independent public datasets (335 cases). Genome-wide methylation profiles were determined for 5 adenoma pairs and confirmed by pyrosequencing on 20 tissue pairs used for validation. Functional analysis was performedin vitroandin vivousing the inflammation-associated azoxymethane/dextran sodium sulfate (AOM/DSS) and the sporadic colorectal carcinogenesis (six AOM injections) mouse models as well asApcMin/+mice. Candidates were investigated by immunohistochemical staining of human adenomas and early-stage (pT1) CRC tumors.A total of 1,917 differentially expressed genes and 148,191 differentially methylated CpG sites were detected in adenomas compared with adjacent mucosa samples. Based on the transcriptome data and relevance to CRC,TACSTD2, MMP7, MMP1, CLDN2, CLDN1, and ETV4, were selected for further validation.TACSTD2promoter hypomethylation in adenoma tissues was validated in 20 additional tissue pairs and corresponded with increasedTACSTD2expression.TACSTD2was also overexpressed in anin vitrotransformation model of human colonic epithelial cells. TheTACSTD2protein TROP2 was elevated in human adenomas, pT1 tumors, and in murine adenomas, while it was absent in the unaffected adjacent mucosa. TROP2 overexpression might trigger the development of precancerous lesions and could help identify early transformation foci in colon biopsies.Graphical abstract
Kaizen: Decomposing cellular images with VQ-VAE
Authors: Majoral Daniel , Domnich Marharyta 
Published: November 2024

AbstractA fundamental problem in cell and tissue biology is finding cells in microscopy images. Traditionally, this detection has been performed by segmenting the pixel intensities. However, these methods struggle to delineate cells in more densely packed micrographs, where local decisions about boundaries are not trivial. Here, we develop a new methodology to decompose microscopy images into individual cells by making object-level decisions. We formulate the segmentation problem as training a flexible factorized representation of the image. To this end, we introduce Kaizen, an approach inspired by predictive coding in the brain that maintains an internal representation of an image while generating object hypotheses over the external image, and keeping the ones that improve the consistency of internal and external representations. We achieve this by training a Vector Quantised-Variational AutoEncoder (VQ-VAE). During inference, the VQ-VAE is iteratively applied on locations where the internal representation differs from the external image, making new guesses, and keeping only the ones that improve the overall image prediction until the internal representation matches the input. We demonstrate Kaizen’s merits on two fluorescence microscopy datasets, improving the separation of nuclei and neuronal cells in cell culture images.
Predicting Mouse Lifespan-Extending Chemical Compounds with Machine Learning
Authors: Belikov Aleksey V. , Ribeiro Caio , Farmer Christopher K. , de Magalhães João Pedro , Freitas Alex A. 
Published: November 2024

AbstractPharmacological interventions targeting the biological processes of ageing hold significant potential to extend healthspan and promote longevity. In this study, we employed machine learning to predict how likely it is for a given chemical compound to extend lifespan. We used murine lifespan data from the DrugAge database for training the models. Our most successful Random Forest classifiers were trained on the annotations of direct protein targets of compounds, such as Gene Ontology, UniProt Keywords, pathways (KEGG, Reactome, Wiki) and protein domains (InterPro), whereas models trained on gene expression (LINCS) and chemical substructures (PubChem) underperformed. Models trained on male datasets performed better than those trained on mixed-sex and female datasets, with the latter suffering from severe class imbalance due to much fewer positive-class instances. Notably, features related to G-protein coupled receptors, especially receptors for neurotransmitters, metabolic hormones and sex hormones, were identified as strong predictors of lifespan extension. We used ensemble classifiers comprised of top models to screen compounds from DrugBank, highlighting novel candidates for longevity studies. Major clusters of compounds with the highest predicted longevity-promoting effects appear to target IGF1 and insulin receptors, beta adrenergic receptors, carbonic anhydrases, dopamine and serotonin receptors, voltage-gated potassium and calcium channels, sodium-dependent dopamine, serotonin and noradrenalin transporters, muscarinic acetylcholine receptors and adenosine receptors. Our study provides an important contribution not only to the longevity pharmacology field but also informs research on the fundamental mechanisms of ageing.
De novo clustering of large long-read transcriptome datasets with isONclust3
Authors: Petri Alexander J. , Sahlin Kristoffer 
Published: November 2024

AbstractLong-read sequencing techniques can sequence transcripts from end to end, greatly improving our ability to study the transcription process. Although there are several well-established tools for long-read transcriptome analysis, most are reference-based. This limits the analysis of organisms without high-quality reference genomes and samples or genes with high variability (e.g., cancer samples or some gene families). In such settings, analysis using a reference-free method is favorable. The computational problem of clustering long reads by region of common origin is well-established for reference-free transcriptome analysis pipelines. Such clustering enables large datasets to be split up roughly by gene family and, therefore, an independent analysis of each cluster. There exist tools for this. However, none of those tools can efficiently process the large amount of reads that are now generated by long-read sequencing technologies.We present isONclust3, an improved algorithm over isONclust and isONclust2, to cluster massive long-read transcriptome datasets into gene families. Like isONclust, IsONclust3 represents each cluster with a set of minimizers. However, unlike other approaches, isONclust3 dynamically updates the cluster representation during clustering by adding high-confidence minimizers from new reads assigned to the cluster and employs an iterative cluster-merging step. We show that isONclust3 yields results with higher or comparable quality to state-of-the-art algorithms but is 10-100 times faster on large datasets. Also, using a 256Gb computing node, isONclust3 was the only tool that could cluster 37 million PacBio reads, which is a typical throughput of the recent PacBio Revio sequencing machine.Availabilityhttps://github.com/aljpetri/isONclust3Contactkristoffer.sahlin@math.su.se
Genomic divergence of sympatric lineages withinStichopuscf.horrens(Echinodermata: Stichopodidae): Insights on reproductive isolation inferred from SNP markers
Authors: Kim Kenneth M. , Lizano Apollo Marco D. , Toonen Robert J. , Ravago-Gotanco Rachel 
Published: November 2024

AbstractHow reproductive barriers arise in early stages of divergence among broadcast spawning organisms that exist in sympatry remains poorly understood. Reproductively isolated lineages (Clade A and B) ofStichopuscf.horrenswere previously reported across the western Pacific, with an additional putative cryptic species detected within Clade B lineage warranting further examination. The present study further examines the hypothesis that the two mitochondrial lineages (Clade A and Clade B) ofStichopuscf. horrensrepresent putative cryptic species and whether another cryptic species within the Clade B lineage exists using a reduced representation genomic approach. Using double-digest RAD (ddRAD) sequencing, a total of 9,788 single nucleotide polymorphism (SNP) markers were used to examine divergence amongStichopuscf. horrenslineages (n = 82). Individuals grouped into three SNP genotype clusters, broadly concordant with their mitochondrial lineages and microsatellite genotype clusters, with limited gene flow inferred among clusters. Outlier analysis recovered highly divergent SNP loci with significant homology to proteins related to rhodopsin and tachykinin receptor signaling, sperm motility, transmembrane transport and hormone response. This study confirms the existence of three reproductively isolated genotype clusters withinStichopuscf.horrensand highlights gene regions related to reproduction that may contribute to establishing reproductive barriers between broadcast spawning species at an early stage of divergence.
Identifying Prognostic Cell State Interactions in the Tumor Microenvironment of IDH-Mutant Gliomas Using CSI-TME
Authors: Singh Arashdeep , Mehani Bharati , Gopalan Vishaka , Aldape Kenneth , Hannenhalli Sridhar 
Published: November 2024

AbstractIntercellular communication between distinct transcriptional states of various cell types in the tumor microenvironment (TME) influence the progression and clinical outcome of the tumors. Details of such clinically relevant cellular state interactions (CSIs) in IDH-mutant gliomas remain obscure. Here we developed CSI-TME, a computational pipeline that deconvolves cell type-specific gene expression from bulk transcriptomic data, identifies distinct cell states, and uncovers prognostic cell state interactions by modelling the clinical data based on the joint activity of cell state pairs. We found a significantly reproducible cell state interaction network (CSIN) that is (i) predominantly pro-tumor, (ii) likely mediated through the interaction between cell surface molecules, (iii) differentially activated in IDH-mut astrocytoma and oligodendroglioma, and (iv) significantly associated with response to immune checkpoint blockade therapy. The distinct malignant cell states involved in CSIs resembled neuronal lineages such as astrocyte-like and oligodendrocyte progenitor cells-like states and captured key interactions between glioma stem cells and immune cells. Integration of CSIN with somatic mutation data suggests the anti-tumor role of CSIs in the early stages that transitions towards pro-tumor role as glioma progresses. In summary, CSI-TME provides valuable insights into the physiology of the TME in IDH-mutant glioma and provides a framework to prioritize ligand-receptor interactions or patients stratification for therapeutic interventions.
A polyketide-based biosynthetic platform for diols, amino alcohols and hydroxyacids
Authors: Dan Qingyun , Chiu Yan , Lee Namil , Pereira Jose Henrique , Zhao Xixi , Zhan Chunjun , Rong Yiou , Chen Yan , Cheong Seokjung , Li Chenyi , Gin Jennifer W. , Rodrigues Andria , Backman Tyler W. H. , Baidoo Edward E. K. , Petzold Christopher J. , Adams Paul D. , Keasling Jay D. 
Published: November 2024

AbstractMedium- and branched-chain diols and amino alcohols are important industrial solvents, polymer building blocks, cosmetics and pharmaceutical ingredients, yet biosynthetically challenging to produce. Here, we present a novel approach utilising a modular polyketide synthase (PKS) platform for the efficient production of these compounds. This platform takes advantage of a versatile loading module from the rimocidin PKS and NADPH-dependent terminal thioreductases (TRs), previously untapped in engineered PKSs. Reduction of the terminal aldehyde with specific alcohol dehydrogenases enables production of diols, oxidation enables production of hydroxy acids, and transamination with specific transaminases enables production of various amino alcohols. Furthermore, replacement of the malonyl-coenzyme A (CoA)–specific acyltransferase (AT) in the extension module with methyl- or ethylmalonyl- CoA–specific ATs enables production of branched-chain diols and amino alcohols. In total, we demonstrated production of nine 1,3-diols (including the difficult-to-produce insect repellent and cosmetic ingredient 2-ethyl-1,3-hexanediol), six amino alcohols, and two carboxylic acids using our PKS platform inStreptomyces albus. Finally, tuning production of the PKS acyl-CoA substrates enabled production of high titers of specific diols and amino alcohols (1 g/L diol titer in shake flasks), demonstrating high tunability and efficiency of the platform.
Discovery and isolation of novel capsaicinoids and their TRPV1-related activity
Authors: Smith Joshua David , Stillerová Vendula Tvrdoňová , Dračinský Martin , Gaustad Hannah Lovinda Angermeier , Lorenzi Quentin , Smrčková Helena , Reinhardt Jakob K. , Liénard Marjorie Anne , Bednárová Lucie , Šácha Pavel , Pluskal Tomáš 
Published: November 2024

AbstractChilis contain capsaicin, an acclaimed molecule in both science and culinary ventures. Capsaicin’s target protein, the transient receptor potential cation channel subfamily V member 1 (TRPV1), has been linked to many post-activation effects, including changes in metabolism and pain sensation. Beneficial effects of TRPV1 activation by capsaicin have been explored through clinical trials. Capsaicinoids other than capsaicin also bind to TRPV1, but current studies often disregard non-capsaicin interactions. To fill in these gaps, we screened 40 different chili varieties derived from fourCapsicumspecies by means of untargeted metabolomics and a rat TRPV1 (rTRPV1) calcium influx activation assay. The resulting capsaicinoid profiles were specific to each variety but only partially corresponded with species delimitations. Based on rTRPV1 activation elicited by crude chili extracts, capsaicinoids act in a synergistic manner and a capsaicinoid profile can serve as a gauge of this activation. In addition, we isolated eighteen capsaicinoids, including five previously unreported ones, and confirmed their structure by NMR and MS/MS. We then tested rTRPV1 activation by 23 capsaicinoids and three related compounds. This testing revealed that even slight deviations from the structure of capsaicin reduce the ability to activate the target, with a mere single hydroxylation on the acyl tail reducing potency towards rTRPV1 by more than 100-fold. In addition, we tested how rTRPV1 activity changes in the presence of capsaicin together with non-activating capsaicin analogs and weakly activating capsaicinoids and found both classes of molecules to positively modulate the effects of capsaicin. This demonstrates that even such compounds have measurable pharmacological effects, making a case for the use and study of natural chili extracts.
Robust fine-mapping in the presence of linkage disequilibrium mismatch
Authors: Zhang Wenmin , Lu Tianyuan , Sladek Robert , Dupuis Josée , Lettre Guillaume 
Published: November 2024

AbstractFine-mapping methods based on summary statistics from genome-wide association studies (GWAS) and linkage disequilibrium (LD) information are widely used to identify potential causal variants. However, LD mismatch between the external LD reference panel and the GWAS population is common and can lead to compromised accuracy of fine-mapping. We developed RSparsePro, a probabilistic graphical model with an efficient variational inference algorithm, to perform robust fine-mapping in the presence of LD mismatch. In simulation studies with a varying degree of LD mismatch, RSparsePro identified credible sets with a consistently higher power and coverage than SuSiE. In fine-mapping cis-protein quantitative trait loci, RSparsePro identified credible sets with a consistently higher enrichment of variants with functional impacts and cross-study replication rates. In fine-mapping risk loci for low-density lipoprotein cholesterol in ancestry-specific GWAS, RSparsePro identified biologically relevant variants in drug target genes and implicated potential regulatory mechanisms. RSparsePro is openly available athttps://github.com/zhwm/RSparsePro_LD.