Understanding Tissue-Specific Gene Regulation

The acetyltransferase GCN5 contributes to neuroinflammation in mice by acetylating and activating the NF-κB subunit p65 in microglia

Neuroinflammation promotes the progression of various neurological and neurodegenerative diseases. Disrupted homeostasis of protein acetylation is implicated in neurodegeneration, and the lysine acetyltransferase GCN5 (also known as KAT2A) is implicated in peripheral inflammation. Here, we investigated whether GCN5 plays a role in neuroinflammation in the brain. Systemic administration of the bacterial molecule LPS in mice to induce peripheral inflammation increased the abundance of GCN5 in various organs, including in the brain and specifically in microglia. In response to LPS, GCN5 mediated the induction of the proinflammatory cytokines TNF-α and IL-6 and the inflammatory mediators COX-2 and iNOS in microglia. Further investigation in cultured microglial cells revealed that GCN5 was activated downstream of the innate immune receptor TLR4 to acetylate Lys310 in the NF-κB subunit p65, thereby enabling the nuclear translocation and transcriptional activity of NF-κB and the resulting inflammatory response. Thus, targeting GCN5 might be explored further as a strategy to reduce neuroinflammation in the treatment of associated diseases.

Expression quantitative trait loci (eQTL): From population genetics to precision medicine

Evidence has shown that differential transcriptomic profiles among human populations from diverse ancestries, supporting the role of genetic architecture in regulating gene expression alongside environmental stimuli. Genetic variants that regulate gene expression, known as expression quantitative trait loci (eQTL), are primarily shaped by human migration history and evolutionary forces, likewise, regulation of gene expression in principle could have been influenced by these events. Therefore, a comprehensive understanding of how human evolution impacts eQTL offers important insights into how phenotypic diversity is shaped. Recent studies, however, suggest that eQTL is enriched in genes that are selectively constrained. Whether eQTL is minimally affected by selective pressures remains an open question and requires comprehensive investigations. In addition, such studies are primarily dominated by the major populations of European ancestry, leaving many marginalized populations underrepresented. These observations indicate there exists a fundamental knowledge gap in the role of genomics variation on phenotypic diversity, which potentially hinders precision medicine. This article aims to revisit the abundance of eQTL across diverse populations and provide an overview of their impact from the population and evolutionary genetics perspective, subsequently discuss their influence on phenomics, as well as challenges and opportunities in the applications to precision medicine.

Characterization of transcriptomic changes across Coccidioides morphologies using RiboMarker®-enhanced RNA sequencing

Coccidioides is a dimorphic, pathogenic fungus responsible for transmission of the mammalian disease colloquially known as "Valley fever". To better understand the molecular basis of Coccidioides pathogenesis, previous studies have characterized transcriptomes that define transitions between the saprobic and pathogenic life stages of the two species that cause Valley fever - Coccidioides immitis and Coccidioides posadasii . However, none of these studies have focused on small RNA profiles, which have been shown in several pathogenic fungi to play crucial roles in host-pathogen communication, affecting virulence and infectivity. In this study, we analyzed changes in small RNA expression across three major morphologies of C. posadasii : arthroconidia, mycelia, and spherules, from both intracellular and extracellular fractions. Utilizing RiboMarker® small RNA and RNA fragment library preparation, we show enhanced coverage across the transcriptome by increasing incorporation of normally incompatible RNAs into the sequencing pool. Using these data, we observed transcriptomic shifts during the transition of arthroconidia to either mycelia or spherules, marked largely by changes in both protein-coding, tRNA, and unannotated loci. As little is known regarding the mechanisms governing these life stage transitions, these data provide better insight into those small RNA- and fragment-producing genes and loci that may be required for progression between Coccidioides saprobic and parasitic life cycles. Additionally, analysis of fragmentation patterns across all morphologies suggests unique patterns of RNA fragmentation across a cohort of RNA species that correlate with a given ecotype. Finally, we noted evidence of RNA export to the extracellular space, particularly regarding snRNA and tRNA-derived fragments as well as mRNA-derived transcripts, during the transition to either mycelia or spherules, which may play roles in cell-cell, and/or host-pathogen communication. Going forward, this newly established intra- and extracellular Coccidioides sRNA atlas will provide a foundation for potential biomarker discovery and contribute to our understanding of the molecular basis for virulence in Valley fever.

CellCircLoc: Deep Neural Network for Predicting and Explaining Cell Line-Specific CircRNA Subcellular Localization

The subcellular localization of circular RNAs (circRNAs) is crucial for understanding their functional relevance and regulatory mechanisms. CircRNA subcellular localization exhibits variations across different cell lines, demonstrating the diversity and complexity of circRNA regulation within distinct cellular contexts. However, existing computational methods for predicting circRNA subcellular localization often ignore the importance of cell line specificity and instead train a general model on aggregated data from all cell lines. Considering the diversity and context-dependent behavior of circRNAs across different cell lines, it is imperative to develop cell line-specific models to accurately predict circRNA subcellular localization. In the study, we proposed CellCircLoc, a sequence-based deep learning model for circRNA subcellular localization prediction, which is trained for different cell lines. CellCircLoc utilizes a combination of convolutional neural networks, Transformer blocks, and bidirectional long short-term memory to capture both sequence local features and long-range dependencies within the sequences. In the Transformer blocks, CellCircLoc uses an attentive convolution mechanism to capture the importance of individual nucleotides. Extensive experiments demonstrate the effectiveness of CellCircLoc in accurately predicting circRNA subcellular localization across different cell lines, outperforming other computational models that do not consider cell line specificity. Moreover, the interpretability of CellCircLoc facilitates the discovery of important motifs associated with circRNA subcellular localization.

The Utility of Ultra-Deep RNA sequencing in Mendelian Disorder Diagnostics

Clinical RNA-seq has become an essential tool for resolving variants of uncertain significance (VUS), particularly those affecting gene expression and splicing. However, most reference data and diagnostic protocols employ relatively modest sequencing depths (∼50-150 million reads), which may fail to capture low-abundance transcripts and rare splicing events critical for accurate diagnoses. We evaluated the diagnostic and translational utility of ultra-high-depth (up to ∼one billion unique reads) RNA-seq in four clinically accessible tissues (blood, fibroblast, LCL, and iPSC) using Ultima sequencing platform. After validating the performance of Ultima RNA-seq, we investigated how increasing depth affects gene and isoform detection, splicing variant discovery, and clinical interpretation of VUS. Deep RNA-seq substantially improved sensitivity for detecting lowly-expressed genes and isoforms. At ∼1 billion reads, near-saturation was achieved for gene-level detection, although isoform-level coverage continued to benefit from even deeper sequencing. In two clinical cases with VUS, pathogenic splicing abnormalities were undetected at ∼50 million reads but emerged at 200 million reads, becoming even more pronounced at ∼one billion reads. Using deep RNA-seq data, we constructed a novel resource, MRSD-deep, to estimate the minimum required sequencing depth to achieve desired coverage thresholds. MRSD-deep provided gene- and junction-level guidelines, aiding labs in selecting suitable coverage targets for specific applications. Leveraging deep RNA-seq data on fibroblast, we also built an expanded splicing-variation reference that successfully identified rare splicing events missed by standard-depth data. Our findings underscore the diagnostic and research benefits of deep RNA-seq for Mendelian disease investigations. By capturing rare transcripts and splicing events, ultra-high-depth RNA-seq can facilitate more definitive variant interpretations and enrich splicing-reference databases. We anticipate that cost-effective deep sequencing technologies and robust reference cohorts will further advance RNA-based diagnostics in precision medicine.

Machine learning-driven identification of critical gene programs and key transcription factors in migraine

BACKGROUND

Migraine is a complex neurological disorder characterized by recurrent episodes of severe headaches. Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associated brain regions, remain unclear. This study applies machine learning techniques to explore region-specific gene expression profiles and identify critical gene programs and transcription factors linked to migraine pathogenesis.

METHODS

We utilized single-nucleus RNA sequencing (snRNA-seq) data from 43 brain regions, along with genome-wide association study (GWAS) data, to investigate susceptibility to migraine. The cell-type-specific expression (CELLEX) algorithm was employed to calculate specific expression profiles for each region, while non-negative matrix factorization (NMF) was applied to decompose gene programs within the single-cell data from these regions. Following the annotation of brain region expression profiles and gene programs to the genome, we employed stratified linkage disequilibrium score regression (S-LDSC) to assess the associations between brain regions, gene programs, and migraine-related SNPs. Key transcription factors regulating critical gene programs were identified using a random forest model based on regulatory networks derived from the GTEx consortium.

RESULTS

Our analysis revealed significant enrichment of migraine-associated single nucleotide polymorphisms (SNPs) in the posterior nuclear complex-medial geniculate nuclei (PoN_MG) of the thalamus, highlighting this region's crucial role in migraine pathogenesis. Gene program 1, identified through NMF, was enriched in the calcium signaling pathway, a known contributor to migraine pathophysiology. Random forest analysis predicted ARID3A as the top transcription factor regulating gene program 1, suggesting its potential role in modulating calcium-related genes involved in migraine.

CONCLUSION

This study provides new insights into the molecular mechanisms underlying migraine, emphasizing the importance of the PoN_MG thalamic region, calcium signaling pathways, and key transcription factors like ARID3A. These findings offer potential avenues for developing targeted therapeutic strategies for migraine treatment.

Prediction of transcript isoforms and identification of tissue-specific genes in cucumber

BACKGROUND

Identification of global transcriptional events is crucial for genome annotation, as accurate annotation enhances the efficiency and comparability of genomic information across species. However, the annotation of transcripts in the cucumber genome remains to be improved, and many transcriptional events have not been well studied.

RESULTS

We collected 1,904 high-quality public cucumber transcriptome samples from the National Center for Biotechnology Information (NCBI) to identify and annotate transcript isoforms in the cucumber genome. Over 44.26 billion Q30 clean reads were mapped to the cucumber genome with an average mapping rate of 92.75%. Transcriptome assembly identified 151,453 transcripts spanning 20,442 loci. Among these, 12.7% of transcripts exactly matched annotated genes in the cucumber reference genome. More than 80% of the transcripts were classified as novel isoforms. Approximately 96.6% of these isoforms originated from known gene loci, while around 3.3% were derived from novel gene loci. Coding potential prediction identified 4,543 long non-coding RNAs (lncRNAs) across 3,376 loci. Building on these results, we identified tissue-specific transcripts in 10 tissues. Among that, 1,655 annotated genes and 4,214 predicted transcripts were considered as tissue-specific. The root exhibited the highest number of tissue-specific transcripts, followed by shoot apex. Subsequent selective pressure analysis revealed that tissue-specific regions experienced stronger directional selection compared to non-specific regions.

CONCLUSIONS

By analyzing thousands of published transcriptome data, we identified abundant transcriptional events and tissue-specific transcripts in cucumbers. This study presented here adds the great value to the public data and offers insights for further exploration of a more comprehensive tissue regulatory network in cucumber.

Chromatin enables precise and scalable gene regulation with factors of limited specificity

Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs). Under what conditions and by how much can chromatin reduce regulatory errors on a global scale? We use a theoretical approach to compare two scenarios for gene regulation: one that relies on TF binding to free DNA alone and one that uses a combination of TFs and chromatin-regulating PFs to achieve desired gene expression patterns. We find, first, that chromatin effectively silences groups of genes that should be simultaneously OFF, thereby allowing more accurate graded control of expression for the remaining ON genes. Second, chromatin buffers the deleterious consequences of nontarget binding as the number of OFF genes grows, permitting a substantial expansion in regulatory complexity. Third, chromatin-based regulation productively co-opts nontarget TF binding for ON genes in order to establish a "leaky" baseline expression level, which targeted activator or repressor binding subsequently up- or down-modulates. Thus, on a global scale, using chromatin simultaneously alleviates pressure for high specificity of regulatory interactions and enables an increase in genome size with minimal impact on global expression error.

BACH1 as a key driver in rheumatoid arthritis fibroblast-like synoviocytes identified through gene network analysis

RNA-sequencing and differential gene expression studies have significantly advanced our understanding of pathogenic pathways underlying rheumatoid arthritis (RA). Yet, little is known about cell-specific regulatory networks and their contributions to disease. In this study, we focused on fibroblast-like synoviocytes (FLS), a cell type central to disease pathogenesis and joint damage in RA. We used a strategy that computed sample-specific gene regulatory networks to compare network properties between RA and osteoarthritis FLS. We identified 28 transcription factors (TFs) as key regulators central to the signatures of RA FLS. Six of these TFs are new and have not been previously implicated in RA through ex vivo or in vivo studies, and included BACH1, HLX, and TGIF1. Several of these TFs were found to be co-regulated, and BACH1 emerged as the most significant TF and regulator. The main BACH1 targets included those implicated in fatty acid metabolism and ferroptosis. The discovery of BACH1 was validated in experiments with RA FLS. Knockdown of BACH1 in RA FLS significantly affected the gene expression signatures, reduced cell adhesion and mobility, interfered with the formation of thick actin fibers, and prevented the polarized formation of lamellipodia, all required for the RA destructive behavior of FLS. This study establishes BACH1 as a central regulator of RA FLS phenotypes and suggests its potential as a therapeutic target to selectively modulate RA FLS.

Mitigating off-target effects of small RNAs: conventional approaches, network theory and artificial intelligence

Three types of highly promising small RNA therapeutics, namely, small interfering RNAs (siRNAs), microRNAs (miRNAs) and the RNA subtype of antisense oligonucleotides (ASOs), offer advantages over small-molecule drugs. These small RNAs can target any gene product, opening up new avenues of effective and safe therapeutic approaches for a wide range of diseases. In preclinical research, synthetic small RNAs play an essential role in the investigation of physiological and pathological pathways as silencers of specific genes, facilitating discovery and validation of drug targets in different conditions. Off-target effects of small RNAs, however, could make it difficult to interpret experimental results in the preclinical phase and may contribute to adverse events of small RNA therapeutics. Out of the two major types of off-target effects we focused on the hybridization-dependent, especially on the miRNA-like off-target effects. Our main aim was to discuss several approaches, including sequence design, chemical modifications and target prediction, to reduce hybridization-dependent off-target effects that should be considered even at the early development phase of small RNA therapy. Because there is no standard way of predicting hybridization-dependent off-target effects, this review provides an overview of all major state-of-the-art computational methods and proposes new approaches, such as the possible inclusion of network theory and artificial intelligence (AI) in the prediction workflows. Case studies and a concise survey of experimental methods for validating in silico predictions are also presented. These methods could contribute to interpret experimental results, to minimize off-target effects and hopefully to avoid off-target-related adverse events of small RNA therapeutics. LINKED ARTICLES: This article is part of a themed issue Non-coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc.

Epigenomic and 3D genomic mapping reveals developmental dynamics and subgenomic asymmetry of transcriptional regulatory architecture in allotetraploid cotton

Although epigenetic modification has long been recognized as a vital force influencing gene regulation in plants, the dynamics of chromatin structure implicated in the intertwined transcriptional regulation of duplicated genes in polyploids have yet to be understood. Here, we document the dynamic organization of chromatin structure in two subgenomes of allotetraploid cotton (Gossypium hirsutum) by generating 3D genomic, epigenomic and transcriptomic datasets from 12 major tissues/developmental stages covering the life cycle. We systematically identify a subset of genes that are closely associated with specific tissue functions. Interestingly, these genes exhibit not only higher tissue specificity but also a more pronounced homoeologous bias. We comprehensively elucidate the intricate process of subgenomic collaboration and divergence across various tissues. A comparison among subgenomes in the 12 tissues reveals widespread differences in the reorganization of 3D genome structures, with the Dt subgenome exhibiting a higher extent of dynamic chromatin status than the At subgenome. Moreover, we construct a comprehensive atlas of putative functional genome elements and discover that 37 cis-regulatory elements (CREs) have selection signals acquired during domestication and improvement. These data and analyses are publicly available to the research community through a web portal. In summary, this study provides abundant resources and depicts the regulatory architecture of the genome, which thereby facilitates the understanding of biological processes and guides cotton breeding.

Emerging Signatures of Hematological Malignancies from Gene Expression and Transcription Factor-Gene Regulations

Hematological malignancies are a diverse group of cancers developing in the peripheral blood, the bone marrow or the lymphatic system. Due to their heterogeneity, the identification of novel and advanced molecular signatures is essential for enhancing their characterization and facilitate its translation to new pharmaceutical solutions and eventually to clinical applications. In this study, we collected publicly available microarray data for more than five thousand subjects, across thirteen hematological malignancies. Using PANDA to estimate gene regulatory networks (GRNs), we performed hierarchical clustering and network analysis to explore transcription factor (TF) interactions and their implications on biological pathways. Our findings reveal distinct clustering patterns among leukemias and lymphomas, with notable differences in gene and TF expression profiles. Gene Set Enrichment Analysis (GSEA) identified 57 significantly enriched KEGG pathways, highlighting both common and unique biological processes across HMs. We also identified potential drug targets within these pathways, emphasizing the role of TFs such as CEBPB and NFE2L1 in disease pathophysiology. Our comprehensive analysis enhances the understanding of the molecular landscape of HMs and suggests new avenues for targeted therapeutic strategies. These findings also motivate the adoption of regulatory networks, combined with modern biotechnological possibilities, for insightful pan-cancer exploratory studies.

Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives

BACKGROUND

Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.

AIM OF REVIEW

This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.

Integrating Gene Expression Data into Single-Step Method (ssBLUP) Improves Genomic Prediction Accuracy for Complex Traits of Duroc × Erhualian F2 Pig Population

The development of multi-omics has increased the likelihood of further improving genomic prediction (GP) of complex traits. Gene expression data can directly reflect the genotype effect, and thus, they are widely used for GP. Generally, the gene expression data are integrated into multiple random effect models as independent data layers or used to replace genotype data for genomic prediction. In this study, we integrated pedigree, genotype, and gene expression data into the single-step method and investigated the effects of this integration on prediction accuracy. The integrated single-step method improved the genomic prediction accuracy of more than 90% of the 54 traits in the Duroc × Erhualian F2 pig population dataset. On average, the prediction accuracy of the single-step method integrating gene expression data was 20.6% and 11.8% higher than that of the pedigree-based best linear unbiased prediction (ABLUP) and genome-based best linear unbiased prediction (GBLUP) when the weighting factor (w) was set as 0, and it was 5.3% higher than that of the single-step best linear unbiased prediction (ssBLUP) under different w values. Overall, the analyses confirmed that the integration of gene expression data into a single-step method could effectively improve genomic prediction accuracy. Our findings enrich the application of multi-omics data to genomic prediction and provide a valuable reference for integrating multi-omics data into the genomic prediction model.

Reproducible processing of TCGA regulatory networks

Background

Technological advances in sequencing and computation have allowed deep exploration of the molecular basis of diseases. Biological networks have proven to be a useful framework for interrogating omics data and modeling regulatory gene and protein interactions. Large collaborative projects, such as The Cancer Genome Atlas (TCGA), have provided a rich resource for building and validating new computational methods resulting in a plethora of open-source software for downloading, pre-processing, and analyzing those data. However, for an end-to-end analysis of regulatory networks a coherent and reusable workflow is essential to integrate all relevant packages into a robust pipeline.

Findings

We developed tcga-data-nf, a Nextflow workflow that allows users to reproducibly infer regulatory networks from the thousands of samples in TCGA using a single command. The workflow can be divided into three main steps: multi-omics data, such as RNA-seq and methylation, are downloaded, preprocessed, and lastly used to infer regulatory network models with the netZoo software tools. The workflow is powered by the NetworkDataCompanion R package, a standalone collection of functions for managing, mapping, and filtering TCGA data. Here we show how the pipeline can be used to study the differences between colon cancer subtypes that could be explained by epigenetic mechanisms. Lastly, we provide pre-generated networks for the 10 most common cancer types that can be readily accessed.

Conclusions

tcga-data-nf is a complete yet flexible and extensible framework that enables the reproducible inference and analysis of cancer regulatory networks, bridging a gap in the current universe of software tools.

Leveraging Single-Cell RNA-Seq to Generate Robust Microglia Aging Clocks

'Biological aging clocks' - composite molecular markers thought to capture an individual's biological age - have been traditionally developed through bulk-level analyses of mixed cells and tissues. However, recent evidence highlights the importance of gaining single-cell-level insights into the aging process. Microglia are key immune cells in the brain shown to adapt functionally in aging and disease. Recent studies have generated single-cell RNA sequencing (scRNA-seq) datasets that transcriptionally profile microglia during aging and development. Leveraging such datasets, we develop and compare computational approaches for generating transcriptome-wide summaries to establish robust microglia aging clocks. Our results reveal that unsupervised, frequency-based featurization approaches strike a balance in accuracy, interpretability, and computational efficiency. We further extrapolate and demonstrate applicability of such microglia clocks to readily available bulk RNA-seq data with environmental inputs. Single-cell-derived clocks can yield insights into the determinants of brain aging, ultimately promoting interventions that beneficially modulate health and disease trajectories.

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases

Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-learning framework, denoted "Tissue Risk Assessment of Causality by Expression for variants" (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/ ), that offers two advancements. First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific tissues. This was achieved by creating 14 tissue-specific models that were trained on over 14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant effect prediction tools. Second, the resulting models are interpretable, thereby illuminating variants' mode of action. Application of TRACEvar to variants of 52 rare-disease patients highlighted pathogenicity mechanisms and relevant disease processes. Lastly, the interpretation of all tissue models revealed that top-ranking determinants of pathogenicity included attributes of disease-affected tissues, particularly cellular process activities. Collectively, these results show that tissue contexts and interpretable machine-learning models can greatly enhance the etiology of rare diseases.

Genetic insights into the connection between pulmonary TB and non-communicable diseases: An integrated analysis of shared genes and potential treatment targets

BACKGROUND

Pulmonary Tuberculosis (PTB) is a significant global health issue due to its high incidence, drug resistance, contagious nature, and impact on people with compromised immune systems. As mentioned by the World Health Organization (WHO), TB is responsible for more global fatalities than any other infectious illness. On the other side, WHO also claims that noncommunicable diseases (NCDs) kill 41 million people yearly worldwide. In this regard, several studies suggest that PTB and NCDs are linked in various ways and that people with PTB are more likely to acquire NCDs. At the same time, NCDs can increase susceptibility to active TB infection. Furthermore, because of potential drug interactions and therapeutic challenges, treating individuals with both PTB and NCDs can be difficult. This study focuses on seven NCDs (lung cancer (LC), diabetes mellitus (DM), Parkinson's disease (PD), silicosis (SI), chronic kidney disease (CKD), cardiovascular disease (CVD), and rheumatoid arthritis (RA)) and rigorously presents the genetic relationship with PTB regarding shared genes and outlines possible treatment plans.

OBJECTIVES

BlueThis study aims to identify the drug components that can regulate abnormal gene expression in NCDs. The study will reveal hub genes, potential biomarkers, and drug components associated with hub genes through statistical measures. This will contribute to targeted therapeutic interventions.

METHODS

Numerous investigations, including protein-protein interaction (PPI), gene regulatory network (GRN), enrichment analysis, physical interaction, and protein-chemical interaction, have been carried out to demonstrate the genetic correlation between PTB and NCDs. During the study, nine shared genes such as TNF, IL10, NLRP3, IL18, IFNG, HMGB1, CXCL8, IL17A, and NFKB1 were discovered between TB and the above-mentioned NCDs, and five hub genes (NFKB1, TNF, CXCL8, NLRP3, and IL10) were selected based on degree values.

RESULTS AND CONCLUSION

In this study, we found that all of the hub genes are linked with the 10 drug components, and it was observed that aspirin CTD 00005447 was mostly associated with all the other hub genes. This bio-informatics study may help researchers better understand the cause of PTB and its relationship with NCDs, and eventually, this can lead to exploring effective treatment plans.

An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions

Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications like phosphorylation, are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites across eight mouse tissues and various brain regions, using advanced proteomics and stable isotope labeling. We revealed tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discovered that peroxisomes are regulated by protein turnover across tissues, and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides new fundamental insights into protein stability, tissue dynamic proteotypes, and the role of protein phosphorylation, and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.

ELiAH: the atlas of E3 ligases in human tissues for targeted protein degradation with reduced off-target effect

The development of therapeutic agents has mainly focused on designing small molecules to modulate target proteins or genes which are conventionally druggable. Therefore, targeted protein degradation (TPD) for undruggable cases has emerged as promising pharmaceutical approach. TPD, often referred PROTACs (PROteolysis TArgeting Chimeras), uses a linker to degrade target proteins by hijacking the ubiquitination system. Therefore, unravel the relationship including reversal and co-expression between E3 ligands and other possible target genes in various human tissues is essential to mitigate off-target effects of TPD. Here, we developed the atlas of E3 ligases in human tissues (ELiAH), to prioritize E3 ligase-target gene pairs for TPD. Leveraging over 2900 of RNA-seq profiles consisting of 11 human tissues from the GTEx (genotype-tissue expression) consortium, users of ELiAH can identify tissue-specific genes and E3 ligases (FDR P-value of Mann-Whitney test < .05). ELiAH unravels 933 830 relationships consisting of 614 E3 ligases and 20 924 of expressed genes considering degree of tissue specificity, which are indispensable for ubiquitination based TPD development. In addition, docking properties of those relationships are also modeled using RosettaDock. Therefore, ELiAH presents comprehensive repertoire of E3 ligases for ubiquitination-based TPD drug development avoiding off-target effects. Database URL: https://eliahdb.org.

An integrative systems-biology approach defines mechanisms of Alzheimer's disease neurodegeneration

Despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer's disease, the most common neurodegenerative disorder, remain incompletely understood. To define relevant pathways, we integrated the results of an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in Drosophila with human and Drosophila Alzheimer's disease-associated multi-omics. We measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer's disease and identified Alzheimer's disease human genetic variants that modify expression in disease-vulnerable neurons. We used a network optimization approach to integrate these data with previously published Alzheimer's disease multi-omic data. We computationally predicted and experimentally demonstrated how HNRNPA2B1 and MEPCE enhance tau-mediated neurotoxicity. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate DNA damage in Drosophila and human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer's disease.

Expression patterns of housekeeping genes and tissue-specific genes in black goats across multiple tissues

Black goats are a significant meat breed in southern China. To investigate the expression patterns and biological functions of genes in various tissues of black goats, we analyzed housekeeping genes (HKGs), tissue-specific genes (TSGs), and hub genes (HUBGs) across 23 tissues. Additionally, we analyzed HKGs in 13 tissues under different feeding conditions. We identified 2968 HKGs, including six important ones. Interestingly, HKGs in grazing black goats demonstrated higher and more stable expression levels. We discovered a total of 9912 TSGs, including 134 newly identified ones. The number of TSGs for mRNA and lncRNA were nearly equal, with 127 mRNA TSGs expressed solely in one tissue. Additionally, the predicted functions of tissue-specific long non-coding RNAs (lncRNAs) targeting mRNAs corresponded with the physiological functions of the tissues.Weighted gene co-expression network analysis (WGCNA) constructed 30 modules, where the dark grey module consists almost entirely of HKGs, and TSGs are located in modules most correlated with their respective tissues. Additionally, we identified 289 HUBGs, which are involved in regulating the physiological functions of their respective tissues. Overall, these identified HKGs, TSGs, and HUBGs provide a foundation for studying the molecular mechanisms affecting the genetic and biological processes of complex traits in black goats.

Unraveling the key role of chromatin structure in cancer development through epigenetic landscape characterization of oral cancer

Epigenetic alterations, such as those in chromatin structure and DNA methylation, have been extensively studied in a number of tumor types. But oral cancer, particularly oral adenocarcinoma, has received far less attention. Here, we combined laser-capture microdissection and muti-omics mini-bulk sequencing to systematically characterize the epigenetic landscape of oral cancer, including chromatin architecture, DNA methylation, H3K27me3 modification, and gene expression. In carcinogenesis, tumor cells exhibit reorganized chromatin spatial structures, including compromised compartment structures and altered gene-gene interaction networks. Notably, some structural alterations are observed in phenotypically non-malignant paracancerous but not in normal cells. We developed transformer models to identify the cancer propensity of individual genome loci, thereby determining the carcinogenic status of each sample. Insights into cancer epigenetic landscapes provide evidence that chromatin reorganization is an important hallmark of oral cancer progression, which is also linked with genomic alterations and DNA methylation reprogramming. In particular, regions of frequent copy number alternations in cancer cells are associated with strong spatial insulation in both cancer and normal samples. Aberrant methylation reprogramming in oral squamous cell carcinomas is closely related to chromatin structure and H3K27me3 signals, which are further influenced by intrinsic sequence properties. Our findings indicate that structural changes are both significant and conserved in two distinct types of oral cancer, closely linked to transcriptomic alterations and cancer development. Notably, the structural changes remain markedly evident in oral adenocarcinoma despite the considerably lower incidence of genomic copy number alterations and lesser extent of methylation alterations compared to squamous cell carcinoma. We expect that the comprehensive analysis of epigenetic reprogramming of different types and subtypes of primary oral tumors can provide additional guidance to the design of novel detection and therapy for oral cancer.

Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning

BACKGROUND

Alternative splicing is a pivotal mechanism of post-transcriptional modification that contributes to the transcriptome plasticity and proteome diversity in metazoan cells. Although many splicing regulations around the exon/intron regions are known, the relationship between promoter-bound transcription factors and the downstream alternative splicing largely remains unexplored.

RESULTS

In this study, we present computational approaches to unravel the regulatory relationship between promoter-bound transcription factor binding sites (TFBSs) and the splicing patterns. We curated a fine dataset that includes DNase I hypersensitive site sequencing and transcriptomes across fifteen human tissues from ENCODE. Specifically, we proposed different representations of TF binding context and splicing patterns to examine the associations between the promoter and downstream splicing events. While machine learning models demonstrated potential in predicting splicing patterns based on TFBS occupancies, the limitations in the generalization of predicting the splicing forms of singleton genes across diverse tissues was observed with carefully examination using different cross-validation methods. We further investigated the association between alterations in individual TFBS at promoters and shifts in exon splicing efficiency. Our results demonstrate that the convolutional neural network (CNN) models, trained on TF binding changes in the promoters, can predict the changes in splicing patterns. Furthermore, a systemic in silico substitutions analysis on the CNN models highlighted several potential splicing regulators. Notably, using empirical validation using K562 CTCFL shRNA knock-down data, we showed the significant role of CTCFL in splicing regulation.

CONCLUSION

In conclusion, our finding highlights the potential role of promoter-bound TFBSs in influencing the regulation of downstream splicing patterns and provides insights for discovering alternative splicing regulations.

Impact of putatively beneficial genomic loci on gene expression in little brown bats (Myotis lucifugus, Le Conte, 1831) affected by white-nose syndrome

Genome-wide scans for selection have become a popular tool for investigating evolutionary responses in wildlife to emerging diseases. However, genome scans are susceptible to false positives and do little to demonstrate specific mechanisms by which loci impact survival. Linking putatively resistant genotypes to observable phenotypes increases confidence in genome scan results and provides evidence of survival mechanisms that can guide conservation and management efforts. Here we used an expression quantitative trait loci (eQTL) analysis to uncover relationships between gene expression and alleles associated with the survival of little brown bats (Myotis lucifugus) despite infection with the causative agent of white-nose syndrome. We found that 25 of the 63 single-nucleotide polymorphisms (SNPs) associated with survival were related to gene expression in wing tissue. The differentially expressed genes have functional annotations associated with the innate immune system, metabolism, circadian rhythms, and the cellular response to stress. In addition, we observed differential expression of multiple genes with survival implications related to loci in linkage disequilibrium with focal SNPs. Together, these findings support the selective function of these loci and suggest that part of the mechanism driving survival may be the alteration of immune and other responses in epithelial tissue.

Pathological Mechanisms Involved in Epidermolysis Bullosa Simplex: Current Knowledge and Therapeutic Perspectives

Epidermolysis bullosa (EB) is a clinically and genetically heterogeneous group of mechanobullous diseases characterized by non-scarring blisters and erosions on the skin and mucous membranes upon mechanical trauma. The simplex form (EBS) is characterized by recurrent blister formation within the basal layer of the epidermis. It most often results from dominant mutations in the genes coding for keratin (K) 5 or 14 proteins (KRT5 and KRT14). A disruptive mutation in KRT5 or KRT14 will not only structurally impair the cytoskeleton, but it will also activate a cascade of biochemical mechanisms contributing to EBS. Skin lesions are painful and disfiguring and have a significant impact on life quality. Several gene expression studies were accomplished on mouse model and human keratinocytes to define the gene expression signature of EBS. Several key genes associated with EBS were identified as specific immunological mediators, keratins, and cell junction components. These data deepened the understanding of the EBS pathophysiology and revealed important functional biological processes, particularly inflammation. This review emphasizes the three EBS subtypes caused by dominant mutations on either KRT5 or KRT14 (localized, intermediate, and severe). It aims to summarize current knowledge about the EBS expression profiling pattern and predicted molecular mechanisms involved and to outline progress in therapy.

Machine Learning Uncovers Vascular Endothelial Cell Identity Genes by Expression Regulation Features in Single Cells

Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and many diseases involving cell identity dysregulation. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In alignment with recent reports that cell identity genes are regulated with unique epigenetic signatures, we found cell identity genes exhibit distinctive genetic sequence signatures, e.g., unique enrichment patterns of cis-regulatory elements. Using these genetic sequence signatures, along with gene expression information from single-cell RNA-seq data, enables SCIG to uncover the identity genes of a cell without a need for comparison to other cells. Cell identity gene score defined by SCIG surpassed expression value in network analysis to uncover master transcription factors regulating cell identity. Applying SCIG to the human endothelial cell atlas revealed that the tissue microenvironment is a critical supplement to master transcription factors for cell identity refinement. SCIG is publicly available at https://github.com/kaifuchenlab/SCIG , offering a valuable tool for advancing cell differentiation, development, and regenerative medicine research.

SexAnnoDB, a knowledgebase of sex-specific regulations from multi-omics data of human cancers

BACKGROUND

Sexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules.

METHODS

In this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, as well as sex-biased expression quantitative trait loci, sex-biased expression quantitative trait methylation, sex-biased splicing quantitative trait loci, and the identification of sex-biased cancer therapeutic drug target genes. All findings from these analyses are accessible on SexAnnoDB ( https://ccsm.uth.edu/SexAnnoDB/ ).

RESULTS

From these analyses, we defined 126 cancer therapeutic target sex-associated genes. Among them, 9 genes showed sex-biased at both the mRNA and protein levels. Specifically, S100A9 was the target of five drugs, of which calcium has been approved by the FDA for the treatment of colon and rectal cancers. Transcription factor (TF)-gene regulatory network analysis suggested that four TFs in the SARC male group targeted S100A9 and upregulated the expression of S100A9 in these patients. Promoter region methylation status was only associated with S100A9 expression in KIRP female patients. Hypermethylation inhibited S100A9 expression and was responsible for the downregulation of S100A9 in these female patients.

CONCLUSIONS

Comprehensive network and association analyses indicated that the sex differences at the transcriptome level were partially the result of corresponding sex-biased epigenetic and genetic molecules. Overall, SexAnnoDB offers a discipline-specific search platform that could potentially assist basic experimental researchers or physicians in developing personalized treatment plans.

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multi-Omics Research-A Review on the Role of Super-Enhancers

Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

An in vitro model of acute horizontal basal cell activation reveals gene regulatory networks underlying the nascent activation phase

While horizontal basal cells (HBCs) make minor contributions to olfactory epithelium (OE) regeneration during homeostatic conditions, they possess a potent, latent capacity to activate and subsequently regenerate the OE following severe injury. Activation requires, and is mediated by, the downregulation of the transcription factor (TF) TP63. In this paper, we describe the cellular processes that drive the nascent stages of HBC activation. The compound phorbol 12-myristate 13-acetate (PMA) induces a rapid loss in TP63 protein and rapid enrichment of HOPX and the nuclear translocation of RELA, previously identified as components of HBC activation. Using bulk RNA sequencing (RNA-seq), we find that PMA-treated HBCs pass through various stages of activation identifiable by transcriptional regulatory signatures that mimic stages identified in vivo. These temporal stages are associated with varying degrees of engraftment and differentiation potential in transplantation assays. Together, these data show that our in vitro HBC activation system models physiologically relevant features of in vivo HBC activation and identifies new candidates for mechanistic testing.

Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

TransTEx: novel tissue-specificity scoring method for grouping human transcriptome into different expression groups

MOTIVATION

Although human tissues carry out common molecular processes, gene expression patterns can distinguish different tissues. Traditional informatics methods, primarily at the gene level, overlook the complexity of alternative transcript variants and protein isoforms produced by most genes, changes in which are linked to disease prognosis and drug resistance.

RESULTS

We developed TransTEx (Transcript-level Tissue Expression), a novel tissue-specificity scoring method, for grouping transcripts into four expression groups. TransTEx applies sequential cut-offs to tissue-wise transcript probability estimates, subsampling-based P-values and fold-change estimates. Application of TransTEx on GTEx mRNA-seq data divided 199 166 human transcripts into different groups as 17 999 tissue-specific (TSp), 7436 tissue-enhanced, 36 783 widely expressed (Wide), 79 191 lowly expressed (Low), and 57 757 no expression (Null) transcripts. Testis has the most (13 466) TSp isoforms followed by liver (890), brain (701), pituitary (435), and muscle (420). We found that the tissue specificity of alternative transcripts of a gene is predominantly influenced by alternate promoter usage. By overlapping brain-specific transcripts with the cell-type gene-markers in scBrainMap database, we found that 63% of the brain-specific transcripts were enriched in nonneuronal cell types, predominantly astrocytes followed by endothelial cells and oligodendrocytes. In addition, we found 61 brain cell-type marker genes encoding a total of 176 alternative transcripts as brain-specific and 22 alternative transcripts as testis-specific, highlighting the complex TSp and cell-type specific gene regulation and expression at isoform-level. TransTEx can be adopted to the analysis of bulk RNA-seq or scRNA-seq datasets to find tissue- and/or cell-type specific isoform-level gene markers.

AVAILABILITY AND IMPLEMENTATION

TransTEx database: https://bmi.cewit.stonybrook.edu/transtexdb/ and the R package is available via GitHub: https://github.com/pallavisurana1/TransTEx.

The transcription factor PAX5 activates human LINE1 retrotransposons to induce cellular senescence

As a hallmark of senescent cells, the derepression of Long Interspersed Elements 1 (LINE1) transcription results in accumulated LINE1 cDNA, which triggers the secretion of the senescence-associated secretory phenotype (SASP) and paracrine senescence in a cGAS-STING pathway-dependent manner. However, transcription factors that govern senescence-associated LINE1 reactivation remain ill-defined. Here, we predict several transcription factors that bind to human LINE1 elements to regulate their transcription by analyzing the conserved binding motifs in the 5'-untranslated regions (UTR) of the commonly upregulated LINE1 elements in different types of senescent cells. Further analysis reveals that PAX5 directly binds to LINE1 5'-UTR and the binding is enhanced in senescent cells. The enrichment of PAX5 at the 5'-UTR promotes cellular senescence and SASP by activating LINE1. We also demonstrate that the longevity gene SIRT6 suppresses PAX5 transcription by directly binding to the PAX5 promoter, and overexpressing PAX5 abrogates the suppressive effect of SIRT6 on stress-dependent cellular senescence. Our work suggests that PAX5 could serve as a potential target for drug development aiming to suppress LINE1 activation and treat senescence-associated diseases.

Expression-environment associations in transcriptomic heat stress responses for a global plant lineage

The increasing frequency and severity of heatwaves will intensify stress on plants. Given regional variation in heatwave exposure and expected differences in thermal tolerance between species it is unlikely that all plant species will be affected equally by climate change. However, little is currently known about variation in the responses of plants to heat stress, or how those responses differ among closely related species adapted to different environments. Here we quantify the response of 17 Acacia species (175 RNA-seq libraries), from across Australia's diverse biomes, to a multi-day experimental heatwave treatment to identify variation in transcriptomic and physiological responses to heat stress. Genes with known heat response functions showed consistent responses across Acacia species. Up to 10% of all genes and over 100 gene families showed significant clinal variation in the magnitude of their expression plasticity across species. Specifically, gene families linked to the temperature stress response were overrepresented among significant relationships with home range temperature conditions. Gene expression responses seen on the first day of the heatwave were more frequently associated with home range climates, while expression responses by day four were more commonly related to photosystem II acclimation. Comparative transcriptomics on non-model species has the potential to provide key information on stress response plasticity, especially when linked with our understanding of model species. Our study indicates that the pressing challenge to identifying potentially vulnerable species to climate change could be benefited by the further exploration of clinal variation in transcriptome plasticity.

Prioritizing Maize Metabolic Gene Regulators through Multi-Omic Network Integration

Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed ∼4.6M interactions to generate four distinct types of TF-target networks: co-expression, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

GRAPHICAL ABSTRACT

Primary osteoarthritis chondrocyte map of chromatin conformation reveals novel candidate effector genes

OBJECTIVES

Osteoarthritis is a complex disease with a huge public health burden. Genome-wide association studies (GWAS) have identified hundreds of osteoarthritis-associated sequence variants, but the effector genes underpinning these signals remain largely elusive. Understanding chromosome organisation in three-dimensional (3D) space is essential for identifying long-range contacts between distant genomic features (e.g., between genes and regulatory elements), in a tissue-specific manner. Here, we generate the first whole genome chromosome conformation analysis (Hi-C) map of primary osteoarthritis chondrocytes and identify novel candidate effector genes for the disease.

METHODS

Primary chondrocytes collected from 8 patients with knee osteoarthritis underwent Hi-C analysis to link chromosomal structure to genomic sequence. The identified loops were then combined with osteoarthritis GWAS results and epigenomic data from primary knee osteoarthritis chondrocytes to identify variants involved in gene regulation via enhancer-promoter interactions.

RESULTS

We identified 345 genetic variants residing within chromatin loop anchors that are associated with 77 osteoarthritis GWAS signals. Ten of these variants reside directly in enhancer regions of 10 newly described active enhancer-promoter loops, identified with multiomics analysis of publicly available chromatin immunoprecipitation sequencing (ChIP-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data from primary knee chondrocyte cells, pointing to two new candidate effector genes SPRY4 and PAPPA (pregnancy-associated plasma protein A) as well as further support for the gene SLC44A2 known to be involved in osteoarthritis. For example, PAPPA is directly associated with the turnover of insulin-like growth factor 1 (IGF-1) proteins, and IGF-1 is an important factor in the repair of damaged chondrocytes.

CONCLUSIONS

We have constructed the first Hi-C map of primary human chondrocytes and have made it available as a resource for the scientific community. By integrating 3D genomics with large-scale genetic association and epigenetic data, we identify novel candidate effector genes for osteoarthritis, which enhance our understanding of disease and can serve as putative high-value novel drug targets.

Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing

Technology for spatial multi-omics aids the discovery of new insights into cellular functions and disease mechanisms. Here we report the development and applicability of multi-omics in situ pairwise sequencing (MiP-seq), a method for the simultaneous detection of DNAs, RNAs, proteins and biomolecules at subcellular resolution. Compared with other in situ sequencing methods, MiP-seq enhances decoding capacity and reduces sequencing and imaging costs while maintaining the efficacy of detection of gene mutations, allele-specific expression and RNA modifications. MiP-seq can be integrated with in vivo calcium imaging and Raman imaging, which enabled us to generate a spatial multi-omics atlas of mouse brain tissues and to correlate gene expression with neuronal activity and cellular biochemical fingerprints. We also report a sequential dilution strategy for resolving optically crowded signals during in situ sequencing. High-throughput in situ pairwise sequencing may facilitate the multidimensional analysis of molecular and functional maps of tissues.

Predicting herb-disease associations using network-based measures in human protein interactome

BACKGROUND

Natural herbs are frequently used to treat diseases or to relieve symptoms in many countries. Moreover, as their safety has been proven for a long time, they are considered as main sources of new drug development. However, in many cases, the herbs are still prescribed relying on ancient records and/or traditional practices without scientific evidences. More importantly, the medicinal efficacy of the herbs has to be evaluated in the perspective of MCMT (multi-compound multi-target) effects, but most efforts focus on identifying and analyzing a single compound experimentally. To overcome these hurdles, computational approaches which are based on the scientific evidences and are able to handle the MCMT effects are needed to predict the herb-disease associations.

RESULTS

In this study, we proposed a network-based in silico method to predict the herb-disease associations. To this end, we devised a new network-based measure, WACP (weighted average closest path length), which not only quantifies proximity between herb-related genes and disease-related genes but also considers compound compositions of each herb. As a result, we confirmed that our method successfully predicts the herb-disease associations in the human protein interactome (AUROC = 0.777). In addition, we observed that our method is superior than the other simple network-based proximity measures (e.g. average shortest and closest path length). Additionally, we analyzed the associations between Brassica oleracea var. italica and its known associated diseases more specifically as case studies. Finally, based on the prediction results of the WACP, we suggested novel herb-disease pairs which are expected to have potential relations and their literature evidences.

CONCLUSIONS

This method could be a promising solution to modernize the use of the natural herbs by providing the scientific evidences about the molecular associations between the herb-related genes targeted by multiple compounds and the disease-related genes in the human protein interactome.

THAP3 recruits SMYD3 to OXPHOS genes and epigenetically promotes mitochondrial respiration in hepatocellular carcinoma

Mitochondria harbor the oxidative phosphorylation (OXPHOS) system to sustain cellular respiration. However, the transcriptional regulation of OXPHOS remains largely unexplored. Through the cancer genome atlas (TCGA) transcriptome analysis, transcription factor THAP domain-containing 3 (THAP3) was found to be strongly associated with OXPHOS gene expression. Mechanistically, THAP3 recruited the histone methyltransferase SET and MYND domain-containing protein 3 (SMYD3) to upregulate H3K4me3 and promote OXPHOS gene expression. The levels of THAP3 and SMYD3 were altered by metabolic cues. They collaboratively supported liver cancer cell proliferation and colony formation. In clinical human liver cancer, both of them were overexpressed. THAP3 positively correlated with OXPHOS gene expression. Together, THAP3 cooperates with SMYD3 to epigenetically upregulate cellular respiration and liver cancer cell proliferation.

Prime Editing and DNA Repair System: Balancing Efficiency with Safety

Prime editing (PE), a recent progression in CRISPR-based technologies, holds promise for precise genome editing without the risks associated with double-strand breaks. It can introduce a wide range of changes, including single-nucleotide variants, insertions, and small deletions. Despite these advancements, there is a need for further optimization to overcome certain limitations to increase efficiency. One such approach to enhance PE efficiency involves the inhibition of the DNA mismatch repair (MMR) system, specifically MLH1. The rationale behind this approach lies in the MMR system's role in correcting mismatched nucleotides during DNA replication. Inhibiting this repair pathway creates a window of opportunity for the PE machinery to incorporate the desired edits before permanent DNA repair actions. However, as the MMR system plays a crucial role in various cellular processes, it is important to consider the potential risks associated with manipulating this system. The new versions of PE with enhanced efficiency while blocking MLH1 are called PE4 and PE5. Here, we explore the potential risks associated with manipulating the MMR system. We pay special attention to the possible implications for human health, particularly the development of cancer.

Transcriptome analysis revealed potential mechanisms of channel catfish growth advantage over blue catfish in a tank culture environment

Channel catfish (Ictalurus punctatus) and blue catfish (Ictalurus furcatus) are two economically important freshwater aquaculture species in the United States, with channel catfish contributing to nearly half of the country's aquaculture production. While differences in economic traits such as growth rate and disease resistance have been noted, the extent of transcriptomic variance across various tissues between these species remains largely unexplored. The hybridization of female channel catfish with male blue catfish has led to the development of superior hybrid catfish breeds that exhibit enhanced growth rates and improved disease resistance, which dominate more than half of the total US catfish production. While hybrid catfish have significant growth advantages in earthen ponds, channel catfish were reported to grow faster in tank culture environments. In this study, we confirmed channel fish's superiority in growth over blue catfish in 60-L tanks at 10.8 months of age (30.3 g and 11.6 g in this study, respectively; p < 0.001). In addition, we conducted RNA sequencing experiments and established transcriptomic resources for the heart, liver, intestine, mucus, and muscle of both species. The number of expressed genes varied across tissues, ranging from 5,036 in the muscle to over 20,000 in the mucus. Gene Ontology analysis has revealed the functional specificity of differentially expressed genes within their respective tissues, with significant pathway enrichment in metabolic pathways, immune activity, and stress responses. Noteworthy tissue-specific marker genes, including lrrc10, fabp2, myog, pth1a, hspa9, cyp21a2, agt, and ngtb, have been identified. This transcriptome resource is poised to support future investigations into the molecular mechanisms underlying environment-dependent heterosis and advance genetic breeding efforts of hybrid catfish.

Identification of bone mineral density associated genes with shared genetic architectures across multiple tissues: Functional insights for EPDR1, PKDCC, and SPTBN1

Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone using the most up-to-date genome-wide association study (GWAS) summary statistics from bone mineral density (BMD) and fracture-related genetic variants. We employed an advanced statistical functional mapping method to investigate shared genetic architecture between muscle and bone, focusing on genes highly expressed in muscle tissue. Our analysis identified three genes, EPDR1, PKDCC, and SPTBN1, which are highly expressed in muscle tissue and previously unlinked to bone metabolism. About 90% and 85% of filtered Single-Nucleotide Polymorphisms were in the intronic and intergenic regions for the threshold at P≤5×10-8 and P≤5×10-100, respectively. EPDR1 was highly expressed in multiple tissues, including muscles, adrenal glands, blood vessels, and the thyroid. SPTBN1 was highly expressed in all 30 tissue types except blood, while PKDCC was highly expressed in all 30 tissue types except the brain, pancreas, and skin. Our study provides a framework for using GWAS findings to highlight functional evidence of crosstalk between multiple tissues based on shared genetic architecture between muscle and bone. Further research should focus on functional validation, multi-omics data integration, gene-environment interactions, and clinical relevance in musculoskeletal disorders.

New role for cardiomyocyte Bmal1 in the regulation of sex-specific heart transcriptomes

It has been well established that cardiovascular diseases exhibit significant differences between sexes in both preclinical models and humans. In addition, there is growing recognition that disrupted circadian rhythms can contribute to the onset and progression of cardiovascular diseases. However little is known about sex differences between the cardiac circadian clock and circadian transcriptomes in mice. Here, we show that the the core clock genes are expressed in common in both sexes but the circadian transcriptome of the mouse heart is very sex-specific. Hearts from female mice expressed significantly more rhythmically expressed genes (REGs) than male hearts and the temporal pattern of REGs was distinctly different between sexes. We next used a cardiomyocyte-specific knock out of the core clock gene, Bmal1, to investigate its role in sex-specific gene expression in the heart. All sex differences in the circadian transcriptomes were significantly diminished with cardiomyocyte-specific loss of Bmal1. Surprisingly, loss of cardiomyocyte Bmal1 also resulted in a roughly 8-fold reduction in the number of all the differentially expressed genes between male and female hearts. We conclude that cardiomyocyte-specific Bmal1, and potentially the core clock mechanism, is vital in conferring sex-specific gene expression in the adult mouse heart.

Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns

BACKGROUND

Deciphering gene regulation is essential for understanding the underlying mechanisms of healthy and disease states. While the regulatory networks formed by transcription factors (TFs) and their target genes has been mostly studied with relation to cis effects such as in TF binding sites, we focused on trans effects of TFs on the expression of their transcribed genes and their potential mechanisms.

RESULTS

We provide a comprehensive tissue-specific atlas, spanning 49 tissues of TF variations affecting gene expression through computational models considering two potential mechanisms, including combinatorial regulation by the expression of the TFs, and by genetic variants within the TF. We demonstrate that similarity between tissues based on our discovered genes corresponds to other types of tissue similarity. The genes affected by complex TF regulation, and their modelled TFs, were highly enriched for pharmacogenomic functions, while the TFs themselves were also enriched in several cancer and metabolic pathways. Additionally, genes that appear in multiple clusters are enriched for regulation of immune system while tissue clusters include cluster-specific genes that are enriched for biological functions and diseases previously associated with the tissues forming the cluster. Finally, our atlas exposes multilevel regulation across multiple tissues, where TFs regulate other TFs through the two tested mechanisms.

CONCLUSIONS

Our tissue-specific atlas provides hierarchical tissue-specific trans genetic regulations that can be further studied for association with human phenotypes.

Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

BACKGROUND

Aneuploidy, an abnormal number of chromosomes within a cell, is a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression.

RESULTS

Here, we apply interpretable machine learning methods to study tissue-selective aneuploidy patterns. We define 20 types of features corresponding to genomic attributes of chromosome-arms, normal tissues, primary tumors, and cancer cell lines (CCLs), and use them to model gains and losses of chromosome arms in 24 cancer types. To reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpret the machine learning models by estimating the relative contribution of each feature to the models. While confirming known drivers of positive selection, our quantitative analysis highlights the importance of negative selection for shaping aneuploidy landscapes. This is exemplified by tumor suppressor gene density being a better predictor of gain patterns than oncogene density, and vice versa for loss patterns. We also identify the importance of tissue-selective features and demonstrate them experimentally, revealing KLF5 as an important driver for chr13q gain in colon cancer. Further supporting an important role for negative selection in shaping the aneuploidy landscapes, we find compensation by paralogs to be among the top predictors of chromosome arm loss prevalence and demonstrate this relationship for one paralog interaction. Similar factors shape aneuploidy patterns in human CCLs, demonstrating their relevance for aneuploidy research.

CONCLUSIONS

Our quantitative, interpretable machine learning models improve the understanding of the genomic properties that shape cancer aneuploidy landscapes.

The broad impact of cell death genes on the human disease phenome

Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.

Genome-wide identification, subcellular localization, and expression analysis of the phosphatidyl ethanolamine-binding protein family reveals the candidates involved in flowering and yield regulation of Tartary buckwheat (Fagopyrum tataricum)

Background

PEBP (phosphatidyl ethanolamine-binding protein) is widely found in eukaryotes including plants, animals and microorganisms. In plants, the PEBP family plays vital roles in regulating flowering time and morphogenesis and is highly associated to agronomic traits and yields of crops, which has been identified and characterized in many plant species but not well studied in Tartary buckwheat (Fagopyrum tataricum Gaertn.), an important coarse food grain with medicinal value.

Methods

Genome-wide analysis of FtPEBP gene family members in Tartary buckwheat was performed using bioinformatic tools. Subcellular localization analysis was performed by confocal microscopy. The expression levels of these genes in leaf and inflorescence samples were analyzed using qRT-PCR.

Results

Fourteen Fagopyrum tataricum PEBP (FtPEBP) genes were identified and divided into three sub-clades according to their phylogenetic relationships. Subcellular localization analysis of the FtPEBP proteins in tobacco leaves indicated that FT- and TFL-GFP fusion proteins were localized in both the nucleus and cytoplasm. Gene structure analysis showed that most FtPEBP genes contain four exons and three introns. FtPEBP genes are unevenly distributed in Tartary buckwheat chromosomes. Three tandem repeats were found among FtFT5/FtFT6, FtMFT1/FtMFT2 and FtTFL4/FtTFL5. Five orthologous gene pairs were detected between F. tataricum and F. esculentum. Seven light-responsive, nine hormone-related and four stress-responsive elements were detected in FtPEBPs promoters. We used real-time PCR to investigate the expression levels of FtPEBPs among two flowering-type cultivars at floral transition time. We found FtFT1/FtFT3 were highly expressed in leaf and young inflorescence of early-flowering type, whereas they were expressed at very low levels in late-flowering type cultivars. Thus, we deduced that FtFT1/FtFT3 may be positive regulators for flowering and yield of Tartary buckwheat. These results lay an important foundation for further studies on the functions of FtPEBP genes which may be utilized for yield improvement.

Signature construction and molecular subtype identification based on liver-specific genes for prediction of prognosis, immune activity, and anti-cancer drug sensitivity in hepatocellular carcinoma

BACKGROUND

Liver specific genes (LSGs) are crucial for hepatocyte differentiation and maintaining normal liver function. A deep understanding of LSGs and their heterogeneity in hepatocellular carcinoma (HCC) is necessary to provide clues for HCC diagnosis, prognosis, and treatment.

METHODS

The bulk and single-cell RNA-seq data of HCC were downloaded from TCGA, ICGC, and GEO databases. Through unsupervised cluster analysis, LSGs-based HCC subtypes were identified in TCGA-HCC samples. The prognostic effects of the subtypes were investigated with survival analyses. With GSVA and Wilcoxon test, the LSGs score, stemness score, aging score, immune score and stromal score of the samples were estimated and compared. The HCC subtype-specific genes were identified. The subtypes and their differences were validated in ICGC-HCC samples. LASSO regression analysis was used for key gene selection and risk model construction for HCC overall survival. The model performance was estimated and validated. The key genes were validated for their heterogeneities in HCC cell lines with quantitative real-time PCR and at single-cell level. Their dysregulations were investigated at protein level. Their correlations with HCC response to anti-cancer drugs were estimated in HCC cell lines.

RESULTS

We identified three LSGs-based HCC subtypes with different prognosis, tumor stemness, and aging level. The C1 subtype with low LSGs score and high immune score presented a poor survival, while the C2 subtype with high LSGs score and immune score indicated an enduring survival. Although no significant survival difference between C2 and C3 HCCs was shown, the C2 HCCs presented higher immune score and stroma score. The HCC subtypes and their differences were confirmed in ICGC-HCC dataset. A five-gene prognostic signature for HCC survival was constructed. Its good performance was shown in both the training and validation datasets. The five genes presented significant heterogeneities in different HCC cell lines and hepatocyte subclusters. Their dysregulations were confirmed at protein level. Furthermore, their significant associations with HCC sensitivities to anti-cancer drugs were shown.

CONCLUSIONS

LSGs-based HCC subtype classification and the five-gene risk model might provide useful clues not only for HCC stratification and risk prediction, but also for the development of more personalized therapies for effective HCC treatment.