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RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels. For developing ARCHS4, available FASTQ files from RNA-seq experiments from the Gene Expression Omnibus (GEO) were aligned using a cloud-based infrastructure. In total 187,946 samples are accessible through ARCHS4 with 103,083 mouse and 84,863 human. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene pages that provide average expression across cell lines and tissues, top co-expressed genes for each gene, and predicted biological functions and protein–protein interactions for each gene based on prior knowledge combined with co-expression. Publicly available RNA-seq data is provided mostly in raw form, resulting in a barrier for integrative analyses. Here, Lachmann et al. develop a high-throughput processing infrastructure and search database (ARCHS4) that provides processed RNA-seq data for 187,946 publicly available mouse and human samples to support exploration and reuse.

Andrea Califano, Mariano Alvarez and colleagues present an approach, VIPER, for inferring protein activity in single cancer samples based on expression of a protein's downstream targets. The authors use VIPER to predict aberrant protein activity independent of mutational status and validate drug sensitivity predictions using in vitro assays. Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, virtual inference of protein activity by enriched regulon analysis (VIPER), for accurate assessment of protein activity from gene expression data. We used VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all samples in The Cancer Genome Atlas (TCGA). In addition to accurately infer aberrant protein activity induced by established mutations, we also identified a fraction of tumors with aberrant activity of druggable oncoproteins despite a lack of mutations, and vice versa. In vitro assays confirmed that VIPER-inferred protein activity outperformed mutational analysis in predicting sensitivity to targeted inhibitors.

Antiretroviral therapy (ART) has revolutionized the clinical management of people with human immunodeficiency virus (HIV), transforming HIV infection into a chronic condition. Yet, the mechanisms of action and off-target effects of modern combination ART regimens versus individual ART medications are not fully understood. Using the L1000 assay, we profiled transcriptional responses to 11 single ART drugs and 6 ART combination regimens across three human cell lines, HepPG2 (liver), HK2 (kidney), and THP-1 (monocyte). Differentially expressed genes were analyzed against host-HIV protein-protein interactions (PPIs) and genes implicated in ART-associated side effects. Across all cell types, ART combination regimens induced distinct transcriptional profiles compared with their component drugs. Combinations more strongly perturbed genes encoding proteins involved in HIV-host PPIs, consistent with their enhanced antiviral efficacy. Transcriptional responses also recapitulated known ART-induced adverse effects related to dyslipidemia, altered body composition, and renal impairment. Combination regimens were less coupled to these gene signatures, suggesting mechanisms that may underlie their improved safety profiles. Several genes and pathways were consistently modulated across treatments: , or actin gamma 1 - a gene that encodes gamma actin, a protein crucial for the localization of the HIV reverse transcription complex, was downregulated in four combination regimens, while , Orosomucoid 2, upregulation emerged as a common response to individual drugs. was previously found to be downregulated in ART naïve people living with HIV who naturally control HIV replication, suggesting its role as a candidate host mediator. To facilitate data exploration, we developed , an interactive portal enabling visualization of gene expression changes before and after ART exposure across all three cell lines. ART regimens affected transcriptional signatures of genes involved in HIV-host PPIs and were less tied to common ART-related side effects. Our findings support the use of high-throughput transcriptomics to detect specific mechanisms of ART on- and off-target effects to help prioritize new drug targets and compounds in future development and optimization of safer and more efficient ART.

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 .

β-Arrestin–mediated signaling downstream of seven transmembrane receptors (7TMRs) is a relatively new paradigm for signaling by these receptors. We examined changes in protein phosphorylation occurring when HEK293 cells expressing the angiotensin II type 1A receptor (AT1aR) were stimulated with the β-arrestin–biased ligand Sar 1 , Ile 4 , Ile 8 -angiotensin (SII), a ligand previously found to signal through β-arrestin–dependent, G protein-independent mechanisms. Using a phospho-antibody array containing 46 antibodies against signaling molecules, we found that phosphorylation of 35 proteins increased upon SII stimulation. These SII-mediated phosphorylation events were abrogated after depletion of β-arrestin 2 through siRNA-mediated knockdown. We also performed an MS-based quantitative phosphoproteome analysis after SII stimulation using a strategy of stable isotope labeling of amino acids in cell culture (SILAC). We identified 1,555 phosphoproteins (4,552 unique phosphopeptides), of which 171 proteins (222 phosphopeptides) showed increased phosphorylation, and 53 (66 phosphopeptides) showed decreased phosphorylation upon SII stimulation of the AT1aR. This study identified 38 protein kinases and three phosphatases whose phosphorylation status changed upon SII treatment. Using computational approaches, we performed system-based analyses examining the β-arrestin–mediated phosphoproteome including construction of a kinase-substrate network for β-arrestin–mediated AT1aR signaling. Our analysis demonstrates that β-arrestin–dependent signaling processes are more diverse than previously appreciated. Notably, our analysis identifies an AT1aR-mediated cytoskeletal reorganization network whereby β-arrestin regulates phosphorylation of several key proteins, including cofilin and slingshot. This study provides a system-based view of β-arrestin–mediated phosphorylation events downstream of a 7TMR and opens avenues for research in a rapidly evolving area of 7TMR signaling.

Background Systems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses. Results We present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system. Conclusions Lists2Networks is a user friendly web-based software system expected to significantly ease the computational analysis process for experimental systems biologists employing high-throughput experiments at multiple layers of regulation. The system is freely available at http://www.lists2networks.org .

A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.

Gene-gene co-expression correlations measured by mRNA-sequencing (RNA-seq) can be used to predict gene annotations based on the co-variance structure within these data. In our prior work, we showed that uniformly aligned RNA-seq co-expression data from thousands of diverse studies is highly predictive of both gene annotations and protein-protein interactions. However, the performance of the predictions varies depending on whether the gene annotations and interactions are cell type and tissue specific or agnostic. Tissue and cell type-specific gene-gene co-expression data can be useful for making more accurate predictions because many genes perform their functions in unique ways in different cellular contexts. However, identifying the optimal tissues and cell types to partition the global gene-gene co-expression matrix is challenging. Here we introduce and validate an approach called PRediction of gene Insights from Stratified Mammalian gene co-EXPression (PrismEXP) for improved gene annotation predictions based on RNA-seq gene-gene co-expression data. Using uniformly aligned data from ARCHS4, we apply PrismEXP to predict a wide variety of gene annotations including pathway membership, Gene Ontology terms, as well as human and mouse phenotypes. Predictions made with PrismEXP outperform predictions made with the global cross-tissue co-expression correlation matrix approach on all tested domains, and training using one annotation domain can be used to predict annotations in other domains. By demonstrating the utility of PrismEXP predictions in multiple use cases we show how PrismEXP can be used to enhance unsupervised machine learning methods to better understand the roles of understudied genes and proteins. To make PrismEXP accessible, it is provided a user-friendly web interface, a Python package, and an Appyter. AVAILABILITY. The PrismEXP web-based application, with pre-computed PrismEXP predictions, is available from: https://maayanlab.cloud/prismexp; PrismEXP is also available as an Appyter: https://appyters.maayanlab.cloud/PrismEXP/; and as Python package: https://github.com/maayanlab/prismexp.

A fundamental question in G protein coupled receptor biology is how a single ligand acting at a specific receptor is able to induce a range of signaling that results in a variety of physiological responses. We focused on Type 1 cannabinoid receptor (CB₁R) as a model GPCR involved in a variety of processes spanning from analgesia and euphoria to neuronal development, survival and differentiation. We examined receptor dimerization as a possible mechanism underlying expanded signaling responses by a single ligand and focused on interactions between CB₁R and delta opioid receptor (DOR). Using co-immunoprecipitation assays as well as analysis of changes in receptor subcellular localization upon co-expression, we show that CB₁R and DOR form receptor heteromers. We find that heteromerization affects receptor signaling since the potency of the CB₁R ligand to stimulate G-protein activity is increased in the absence of DOR, suggesting that the decrease in CB₁R activity in the presence of DOR could, at least in part, be due to heteromerization. We also find that the decrease in activity is associated with enhanced PLC-dependent recruitment of arrestin3 to the CB₁R-DOR complex, suggesting that interaction with DOR enhances arrestin-mediated CB₁R desensitization. Additionally, presence of DOR facilitates signaling via a new CB₁R-mediated anti-apoptotic pathway leading to enhanced neuronal survival. Taken together, these results support a role for CB₁R-DOR heteromerization in diversification of endocannabinoid signaling and highlight the importance of heteromer-directed signal trafficking in enhancing the repertoire of GPCR signaling.

Polycomb repressive complex 2 (PRC2) is a key mammalian epigenetic regulator that supports neuron specification during development. In this paper, the authors find that PRC2 plays a role in the survival of adult neurons. The loss of PRC2 activity in adult striatum led to the de-repression of multiple genes with bivalent histone methylation marks and to a fatal neurodegeneration phenotype. Normal brain function depends on the interaction between highly specialized neurons that operate within anatomically and functionally distinct brain regions. Neuronal specification is driven by transcriptional programs that are established during early neuronal development and remain in place in the adult brain. The fidelity of neuronal specification depends on the robustness of the transcriptional program that supports the neuron type-specific gene expression patterns. Here we show that polycomb repressive complex 2 (PRC2), which supports neuron specification during differentiation, contributes to the suppression of a transcriptional program that is detrimental to adult neuron function and survival. We show that PRC2 deficiency in striatal neurons leads to the de-repression of selected, predominantly bivalent PRC2 target genes that are dominated by self-regulating transcription factors normally suppressed in these neurons. The transcriptional changes in PRC2-deficient neurons lead to progressive and fatal neurodegeneration in mice. Our results point to a key role of PRC2 in protecting neurons against degeneration.