Habenular TCF7L2 links nicotine addiction to diabetes.

Diabetes is far more prevalent in smokers than non-smokers, but the underlying mechanisms of vulnerability are unknown. Here we show that the diabetes-associated gene Tcf7l2 is densely expressed in the medial habenula (mHb) region of the rodent brain, where it regulates the function of nicotinic acetylcholine receptors. Inhibition of TCF7L2 signalling in the mHb increases nicotine intake in mice and rats. Nicotine increases levels of blood glucose by TCF7L2-dependent stimulation of the mHb. Virus-tracing experiments identify a polysynaptic connection from the mHb to the pancreas, and wild-type rats with a history of nicotine consumption show increased circulating levels of glucagon and insulin, and diabetes-like dysregulation of blood glucose homeostasis. By contrast, mutant Tcf7l2 rats are resistant to these actions of nicotine. Our findings suggest that TCF7L2 regulates the stimulatory actions of nicotine on a habenula-pancreas axis that links the addictive properties of nicotine to its diabetes-promoting actions.

Enrichr-KG: bridging enrichment analysis across multiple libraries.

Gene and protein set enrichment analysis is a critical step in the analysis of data collected from omics experiments. Enrichr is a popular gene set enrichment analysis web-server search engine that contains hundreds of thousands of annotated gene sets. While Enrichr has been useful in providing enrichment analysis with many gene set libraries from different categories, integrating enrichment results across libraries and domains of knowledge can further hypothesis generation. To this end, Enrichr-KG is a knowledge graph database and a web-server application that combines selected gene set libraries from Enrichr for integrative enrichment analysis and visualization. The enrichment results are presented as subgraphs made of nodes and links that connect genes to their enriched terms. In addition, users of Enrichr-KG can add gene-gene links, as well as predicted genes to the subgraphs. This graphical representation of cross-library results with enriched and predicted genes can illuminate hidden associations between genes and annotated enriched terms from across datasets and resources. Enrichr-KG currently serves 26 gene set libraries from different categories that include transcription, pathways, ontologies, diseases/drugs, and cell types. To demonstrate the utility of Enrichr-KG we provide several case studies. Enrichr-KG is freely available at: https://maayanlab.cloud/enrichr-kg.

GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery.

Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.

Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages.

We assessed gene expression in tissue macrophages from various mouse organs. The diversity in gene expression among different populations of macrophages was considerable. Only a few hundred mRNA transcripts were selectively expressed by macrophages rather than dendritic cells, and many of these were not present in all macrophages. Nonetheless, well-characterized surface markers, including MerTK and FcγR1 (CD64), along with a cluster of previously unidentified transcripts, were distinctly and universally associated with mature tissue macrophages. TCEF3, C/EBP-α, Bach1 and CREG-1 were among the transcriptional regulators predicted to regulate these core macrophage-associated genes. The mRNA encoding other transcription factors, such as Gata6, was associated with single macrophage populations. We further identified how these transcripts and the proteins they encode facilitated distinguishing macrophages from dendritic cells.

SigCom LINCS: data and metadata search engine for a million gene expression signatures.

Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.

Krüppel-like factor 6-mediated loss of BCAA catabolism contributes to kidney injury in mice and humans.

Altered cellular metabolism in kidney proximal tubule (PT) cells plays a critical role in acute kidney injury (AKI). The transcription factor Krüppel-like factor 6 (KLF6) is rapidly and robustly induced early in the PT after AKI. We found that PT-specific Klf6 knockdown (Klf6PTKD) is protective against AKI and kidney fibrosis in mice. Combined RNA and chromatin immunoprecipitation sequencing analysis demonstrated that expression of genes encoding branched-chain amino acid (BCAA) catabolic enzymes was preserved in Klf6PTKD mice, with KLF6 occupying the promoter region of these genes. Conversely, inducible KLF6 overexpression suppressed expression of BCAA genes and exacerbated kidney injury and fibrosis in mice. In vitro, injured cells overexpressing KLF6 had similar decreases in BCAA catabolic gene expression and were less able to utilize BCAA. Furthermore, knockdown of BCKDHB, which encodes one subunit of the rate-limiting enzyme in BCAA catabolism, resulted in reduced ATP production, while treatment with BCAA catabolism enhancer BT2 increased metabolism. Analysis of kidney function, KLF6, and BCAA gene expression in human chronic kidney disease patients showed significant inverse correlations between KLF6 and both kidney function and BCAA expression. Thus, targeting KLF6-mediated suppression of BCAA catabolism may serve as a key therapeutic target in AKI and kidney fibrosis.

blitzGSEA: efficient computation of gene set enrichment analysis through gamma distribution approximation.

MOTIVATION: The identification of pathways and biological processes from differential gene expression is central for interpretation of data collected by transcriptomics assays. Gene set enrichment analysis (GSEA) is the most commonly used algorithm to calculate the significance of the relevancy of an annotated gene set with a differential expression signature. To compute significance, GSEA implements permutation tests which are slow and inaccurate for comparing many differential expression signatures to thousands of annotated gene sets. RESULTS: Here, we present blitzGSEA, an algorithm that is based on the same running sum statistic as GSEA, but instead of performing permutations, blitzGSEA approximates the enrichment score probabilities based on Gamma distributions. blitzGSEA achieves significant improvement in performance compared with prior GSEA implementations, while approximating small P-values more accurately. AVAILABILITY AND IMPLEMENTATION: The data, a python package, together with all source code, and a detailed user guide are available from GitHub at: https://github.com/MaayanLab/blitzgsea. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

KEA3: improved kinase enrichment analysis via data integration.

Phosphoproteomics and proteomics experiments capture a global snapshot of the cellular signaling network, but these methods do not directly measure kinase state. Kinase Enrichment Analysis 3 (KEA3) is a webserver application that infers overrepresentation of upstream kinases whose putative substrates are in a user-inputted list of proteins. KEA3 can be applied to analyze data from phosphoproteomics and proteomics studies to predict the upstream kinases responsible for observed differential phosphorylations. The KEA3 background database contains measured and predicted kinase-substrate interactions (KSI), kinase-protein interactions (KPI), and interactions supported by co-expression and co-occurrence data. To benchmark the performance of KEA3, we examined whether KEA3 can predict the perturbed kinase from single-kinase perturbation followed by gene expression experiments, and phosphoproteomics data collected from kinase-targeting small molecules. We show that integrating KSIs and KPIs across data sources to produce a composite ranking improves the recovery of the expected kinase. The KEA3 webserver is available at https://maayanlab.cloud/kea3.

Transforming L1000 profiles to RNA-seq-like profiles with deep learning.

The L1000 technology, a cost-effective high-throughput transcriptomics technology, has been applied to profile a collection of human cell lines for their gene expression response to > 30,000 chemical and genetic perturbations. In total, there are currently over 3 million available L1000 profiles. Such a dataset is invaluable for the discovery of drug and target candidates and for inferring mechanisms of action for small molecules. The L1000 assay only measures the mRNA expression of 978 landmark genes while 11,350 additional genes are computationally reliably inferred. The lack of full genome coverage limits knowledge discovery for half of the human protein coding genes, and the potential for integration with other transcriptomics profiling data. Here we present a Deep Learning two-step model that transforms L1000 profiles to RNA-seq-like profiles. The input to the model are the measured 978 landmark genes while the output is a vector of 23,614 RNA-seq-like gene expression profiles. The model first transforms the landmark genes into RNA-seq-like 978 gene profiles using a modified CycleGAN model applied to unpaired data. The transformed 978 RNA-seq-like landmark genes are then extrapolated into the full genome space with a fully connected neural network model. The two-step model achieves 0.914 Pearson's correlation coefficients and 1.167 root mean square errors when tested on a published paired L1000/RNA-seq dataset produced by the LINCS and GTEx programs. The processed RNA-seq-like profiles are made available for download, signature search, and gene centric reverse search with unique case studies.

DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules.

BACKGROUND: PubMed contains millions of abstracts that co-mention terms that describe drugs with other biomedical terms such as genes or diseases. Unique opportunities exist for leveraging these co-mentions by integrating them with other drug-drug similarity resources such as the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 signatures to develop novel hypotheses. RESULTS: DrugShot is a web-based server application and an Appyter that enables users to enter any biomedical search term into a simple input form to receive ranked lists of drugs and other small molecules based on their relevance to the search term. To produce ranked lists of small molecules, DrugShot cross-references returned PubMed identifiers (PMIDs) with DrugRIF or AutoRIF, which are curated resources of drug-PMID associations, to produce an associated small molecule list where each small molecule is ranked according to total co-mentions with the search term from shared PubMed IDs. Additionally, using two types of drug-drug similarity matrices, lists of small molecules are predicted to be associated with the search term. Such predictions are based on literature co-mentions and signature similarity from LINCS L1000 drug-induced gene expression profiles. CONCLUSIONS: DrugShot prioritizes drugs and small molecules associated with biomedical search terms. In addition to listing known associations, DrugShot predicts additional drugs and small molecules related to any search term. Hence, DrugShot can be used to prioritize drugs and preclinical compounds for drug repurposing and suggest indications and adverse events for preclinical compounds. DrugShot is freely and openly available at: https://maayanlab.cloud/drugshot and https://appyters.maayanlab.cloud/#/DrugShot .

Transcriptomic profiling of recessive dystrophic epidermolysis bullosa wounded skin highlights drug repurposing opportunities to improve wound healing.

Chronic wounds present a major disease burden in people with recessive dystrophic epidermolysis bullosa (RDEB), an inherited blistering skin disorder caused by mutations in COL7A1 encoding type VII collagen, the major component of anchoring fibrils at the dermal-epidermal junction. Treatment of RDEB wounds is mostly symptomatic, and there is considerable unmet need in trying to improve and accelerate wound healing. In this study, we defined transcriptomic profiles and gene pathways in RDEB wounds and compared these to intact skin in RDEB and healthy control subjects. We then used a reverse transcriptomics approach to discover drugs or compounds, which might restore RDEB wound profiles towards intact skin. Differential expression analysis identified >2000 differences between RDEB wounds and intact skin, with RDEB wounds displaying aberrant cytokine-cytokine interactions, Toll-like receptor signalling, and JAK-STAT signalling pathways. In-silico prediction for compounds that reverse gene expression signatures highlighted methotrexate as a leading candidate. Overall, this study provides insight into the molecular profiles of RDEB wounds and underscores the possible clinical value of reverse transcriptomics data analysis in RDEB, and the potential of this approach in discovering or repurposing drugs for other diseases.

Minimal differentiation of classical monocytes as they survey steady-state tissues and transport antigen to lymph nodes.

It is thought that monocytes rapidly differentiate to macrophages or dendritic cells (DCs) upon leaving blood. Here we have shown that Ly-6C⁺ monocytes constitutively trafficked into skin, lung, and lymph nodes (LNs). Entry was unaffected in gnotobiotic mice. Monocytes in resting lung and LN had similar gene expression profiles to blood monocytes but elevated transcripts of a limited number of genes including cyclo-oxygenase-2 (COX-2) and major histocompatibility complex class II (MHCII), induced by monocyte interaction with endothelium. Parabiosis, bromodoxyuridine (BrdU) pulse-chase analysis, and intranasal instillation of tracers indicated that instead of contributing to resident macrophages in the lung, recruited endogenous monocytes acquired antigen for carriage to draining LNs, a function redundant with DCs though differentiation to DCs did not occur. Thus, monocytes can enter steady-state nonlymphoid organs and recirculate to LNs without differentiation to macrophages or DCs, revising a long-held view that monocytes become tissue-resident macrophages by default.

Chemokine Signatures of Pathogen-Specific T Cells II: Memory T Cells in Acute and Chronic Infection.

Pathogen-specific memory T cells (TM) contribute to enhanced immune protection under conditions of reinfection, and their effective recruitment into a recall response relies, in part, on cues imparted by chemokines that coordinate their spatiotemporal positioning. An integrated perspective, however, needs to consider TM as a potentially relevant chemokine source themselves. In this study, we employed a comprehensive transcriptional/translational profiling strategy to delineate the identities, expression patterns, and dynamic regulation of chemokines produced by murine pathogen-specific TM CD8+TM, and to a lesser extent CD4+TM, are a prodigious source for six select chemokines (CCL1/3/4/5, CCL9/10, and XCL1) that collectively constitute a prominent and largely invariant signature across acute and chronic infections. Notably, constitutive CCL5 expression by CD8+TM serves as a unique functional imprint of prior antigenic experience; induced CCL1 production identifies highly polyfunctional CD8+ and CD4+TM subsets; long-term CD8+TM maintenance is associated with a pronounced increase of XCL1 production capacity; chemokines dominate the earliest stages of the CD8+TM recall response because of expeditious synthesis/secretion kinetics (CCL3/4/5) and low activation thresholds (CCL1/3/4/5/XCL1); and TM chemokine profiles modulated by persisting viral Ags exhibit both discrete functional deficits and a notable surplus. Nevertheless, recall responses and partial virus control in chronic infection appear little affected by the absence of major TM chemokines. Although specific contributions of TM-derived chemokines to enhanced immune protection therefore remain to be elucidated in other experimental scenarios, the ready visualization of TM chemokine-expression patterns permits a detailed stratification of TM functionalities that may be correlated with differentiation status, protective capacities, and potential fates.

Chemokine Signatures of Pathogen-Specific T Cells I: Effector T Cells.

The choreography of complex immune responses, including the priming, differentiation, and modulation of specific effector T cell populations generated in the immediate wake of an acute pathogen challenge, is in part controlled by chemokines, a large family of mostly secreted molecules involved in chemotaxis and other patho/physiological processes. T cells are both responsive to various chemokine cues and a relevant source for certain chemokines themselves; yet, the actual range, regulation, and role of effector T cell-derived chemokines remains incompletely understood. In this study, using different in vivo mouse models of viral and bacterial infection as well as protective vaccination, we have defined the entire spectrum of chemokines produced by pathogen-specific CD8+ and CD4+T effector cells and delineated several unique properties pertaining to the temporospatial organization of chemokine expression patterns, synthesis and secretion kinetics, and cooperative regulation. Collectively, our results position the "T cell chemokine response" as a notably prominent, largely invariant, yet distinctive force at the forefront of pathogen-specific effector T cell activities and establish novel practical and conceptual approaches that may serve as a foundation for future investigations into the role of T cell-produced chemokines in infectious and other diseases.

LINCS Data Portal 2.0: next generation access point for perturbation-response signatures.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program with the goal of generating a large-scale and comprehensive catalogue of perturbation-response signatures by utilizing a diverse collection of perturbations across many model systems and assay types. The LINCS Data Portal (LDP) has been the primary access point for the compendium of LINCS data and has been widely utilized. Here, we report the first major update of LDP (http://lincsportal.ccs.miami.edu/signatures) with substantial changes in the data architecture and APIs, a completely redesigned user interface, and enhanced curated metadata annotations to support more advanced, intuitive and deeper querying, exploration and analysis capabilities. The cornerstone of this update has been the decision to reprocess all high-level LINCS datasets and make them accessible at the data point level enabling users to directly access and download any subset of signatures across the entire library independent from the originating source, project or assay. Access to the individual signatures also enables the newly implemented signature search functionality, which utilizes the iLINCS platform to identify conditions that mimic or reverse gene set queries. A newly designed query interface enables global metadata search with autosuggest across all annotations associated with perturbations, model systems, and signatures.

piNET: a versatile web platform for downstream analysis and visualization of proteomics data.

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

Prioritizing Pain-Associated Targets with Machine Learning.

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.

Loss of proximal tubular transcription factor Krüppel-like factor 15 exacerbates kidney injury through loss of fatty acid oxidation.

Loss of fatty acid ß-oxidation (FAO) in the proximal tubule is a critical mediator of acute kidney injury and eventual fibrosis. However, transcriptional mediators of FAO in proximal tubule injury remain understudied. Krüppel-like factor 15 (KLF15), a highly enriched zinc-finger transcription factor in the proximal tubule, was significantly reduced in proximal tubule cells after aristolochic acid I (AAI) treatment, a proximal tubule-specific injury model. Proximal tubule specific knockout of Klf15 exacerbated proximal tubule injury and kidney function decline compared to control mice during the active phase of AAI treatment, and after ischemia-reperfusion injury. Furthermore, along with worsening proximal tubule injury and kidney function decline, knockout mice exhibited increased kidney fibrosis as compared to control mice during the remodeling phase after AAI treatment. RNA-sequencing of kidney cortex demonstrated increased transcripts involved in immune system and integrin signaling pathways and decreased transcripts encompassing metabolic pathways, specifically FAO, and PPARα signaling, in knockout versus control mice after AAI treatment. In silico and experimental chromatin immunoprecipitation studies collectively demonstrated that KLF15 occupied the promoter region of key FAO genes, CPT1A and ACAA2, in close proximity to transcription factor PPARα binding sites. While the loss of Klf15 reduced the expression of Cpt1a and Acaa2 and led to compromised FAO, induction of KLF15 partially rescued loss of FAO in AAI-treated cells. Klf15, Ppara, Cpt1a, and Acaa2 expression was also decreased in other mouse kidney injury models. Tubulointerstitial KLF15 independently correlated with eGFR, PPARA and CPT1A appearance in expression arrays from human kidney biopsies. Thus, proximal tubule-specific loss of Klf15 exacerbates acute kidney injury and fibrosis, likely due to loss of interaction with PPARα leading to loss of FAO gene transcription.

EnrichrBot: Twitter bot tracking tweets about human genes.

MOTIVATION: Micro-blogging with Twitter to communicate new results, discuss ideas and share techniques is becoming central. While most Twitter users are real people, the Twitter API provides the opportunity to develop Twitter bots and to analyze global trends in tweets. RESULTS: EnrichrBot is a bot that tracks and tweets information about human genes implementing six principal functions: (i) tweeting information about under-studied genes including non-coding lncRNAs, (ii) replying to requests for information about genes, (iii) responding to GWASbot, another bot that tweets Manhattan plots from genome-wide association study analysis of the UK Biobank, (iv) tweeting randomly selected gene sets from the Enrichr database for analysis with Enrichr, (v) responding to mentions of human genes in tweets with additional information about these genes and (vi) tweeting a weekly report about the most trending genes on Twitter. AVAILABILITY AND IMPLEMENTATION: https://twitter.com/botenrichr; source code: https://github.com/MaayanLab/EnrichrBot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Far away from the lamppost.

This Formal Comment responds to a recent Meta-Research Article by identifying initiatives that are already in place for funding risky exploratory research that illuminate mysteries of the dark genome.