ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information

Phosphoproteomics data-driven signalling network inference: Does it work?

The advent of global phosphoproteome profiling has led to wide phosphosite coverage and therefore the opportunity to predict kinase-substrate associations from these datasets. However, the regulatory kinase is unknown for most substrates, due to biased and incomplete database annotations. In this study we compare the performance of six pairwise measures to predict kinase-substrate associations using a data driven approach on publicly available time resolved and perturbation mass spectrometry-based phosphoproteome data. First, we validated the performance of these measures using as a reference both a literature-based phosphosite-specific protein interaction network and a predicted kinase-substrate (KS) interactions set. The overall performance in predicting kinase-substrate associations using pairwise measures across both these reference sets was poor. To expand into the wider interactome space, we applied the approach on a network comprising pairs of substrates regulated by the same kinase (substrate-substrate associations) but found the performance to be equally poor. However, the addition of a sequence similarity filter for substrate-substrate associations led to a significant boost in performance. Our findings imply that the use of a filter to reduce the search space, such as a sequence similarity filter, can be used prior to the application of network inference methods to reduce noise and boost the signal. We also find that the current gold standard for reference sets is not adequate for evaluation as it is limited and context-agnostic. Therefore, there is a need for additional evaluation methods that have increased coverage and take into consideration the context-specific nature of kinase-substrate associations.

An approach of gene regulatory network construction using mixed entropy optimizing context-related likelihood mutual information

MOTIVATION

The question of how to construct gene regulatory networks has long been a focus of biological research. Mutual information can be used to measure nonlinear relationships, and it has been widely used in the construction of gene regulatory networks. However, this method cannot measure indirect regulatory relationships under the influence of multiple genes, which reduces the accuracy of inferring gene regulatory networks.

APPROACH

This work proposes a method for constructing gene regulatory networks based on mixed entropy optimizing context-related likelihood mutual information (MEOMI). First, two entropy estimators were combined to calculate the mutual information between genes. Then, distribution optimization was performed using a context-related likelihood algorithm to eliminate some indirect regulatory relationships and obtain the initial gene regulatory network. To obtain the complex interaction between genes and eliminate redundant edges in the network, the initial gene regulatory network was further optimized by calculating the conditional mutual inclusive information (CMI2) between gene pairs under the influence of multiple genes. The network was iteratively updated to reduce the impact of mutual information on the overestimation of the direct regulatory intensity.

RESULTS

The experimental results show that the MEOMI method performed better than several other kinds of gene network construction methods on DREAM challenge simulated datasets (DREAM3 and DREAM5), three real Escherichia coli datasets (E.coli SOS pathway network, E.coli SOS DNA repair network and E.coli community network) and two human datasets.

AVAILABILITY AND IMPLEMENTATION

Source code and dataset are available at https://github.com/Dalei-Dalei/MEOMI/ and http://122.205.95.139/MEOMI/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ER stress transforms random olfactory receptor choice into axon targeting precision

Olfactory sensory neurons (OSNs) convert the stochastic choice of one of >1,000 olfactory receptor (OR) genes into precise and stereotyped axon targeting of OR-specific glomeruli in the olfactory bulb. Here, we show that the PERK arm of the unfolded protein response (UPR) regulates both the glomerular coalescence of like axons and the specificity of their projections. Subtle differences in OR protein sequences lead to distinct patterns of endoplasmic reticulum (ER) stress during OSN development, converting OR identity into distinct gene expression signatures. We identify the transcription factor Ddit3 as a key effector of PERK signaling that maps OR-dependent ER stress patterns to the transcriptional regulation of axon guidance and cell-adhesion genes, instructing targeting precision. Our results extend the known functions of the UPR from a quality-control pathway that protects cells from misfolded proteins to a sensor of cellular identity that interprets physiological states to direct axon wiring.

Gene Regulatory Network Characterization of Gastric Cancer's Histological Subtypes: Distinctive Biological and Clinically Relevant Master Regulators

Gastric cancer (GC) molecular heterogeneity represents a major determinant for clinical outcomes, and although new molecular classifications have been introduced, they are not easy to translate from bench to bedside. We explored the data from GC public databases by performing differential gene expression analysis (DEGs) and gene network reconstruction to identify master regulators (MRs), as well as a gene set analysis (GSA) to reveal their biological features. Moreover, we evaluated the association of MRs with clinicopathological parameters. According to the GSA, the Diffuse group was characterized by an epithelial-mesenchymal transition (EMT) and inflammatory response, while the Intestinal group was associated with a cell cycle and drug resistance pathways. In particular, the regulons of Diffuse MRs, such as Vgll3 and Ciita, overlapped with the EMT and interferon-gamma response, while the regulons Top2a and Foxm1 were shared with the cell cycle pathways in the Intestinal group. We also found a strict association between MR activity and several clinicopathological features, such as survival. Our approach led to the identification of genes and pathways differentially regulated in the Intestinal and Diffuse GC histotypes, highlighting biologically interesting MRs and subnetworks associated with clinical features and prognosis, suggesting putative actionable candidates.

Immune repertoire and evolutionary trajectory analysis in the development of diabetic nephropathy

Diabetic nephropathy (DN) is the leading cause of death and the greatest risk to the lives of people with advanced diabetes. Yet, the molecular mechanisms underlying its development and progression remain unknown. In this research, we studied the primary pathways driving DN using transcriptome sequencing and immune repertoire analysis. Firstly, we found that the diversity and abundance of the immune repertoire in late DN were significantly increased, while there was no significant change in early DN. Furthermore, B cell-mediated antibody responses may be the leading cause of DN progression. By analyzing master regulators, we found the key DN-driving transcription factors. In the late stage of DN, immune cells, fibroblasts, and epithelial cells were abundant, but other stromal cells were few. Early DN kidneys had a higher tissue stemness score than normal and advanced DN kidneys. We showed that DN progression involves proximal tubular metabolic reprogramming and stemness restoration using Monocle3. Through WGCNA, we found that co-expression modules that regulate DN progression and immune repertoire diversity mainly regulate immune-related signaling pathways. In addition, we also found that early DN had apparent activation of immune-related signaling pathways mainly enriched in immune cells. Finally, we found that activation of fibroblasts is typical of early DN. These results provide a research basis for further exploring the molecular biology and cellular mechanisms of the occurrence and development of DN and provide a theoretical basis for the prevention and treatment of DN.

Detection of pan-cancer surface protein biomarkers via a network-based approach on transcriptomics data

Cell surface proteins have been used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. Many of these proteins lie at the top of signaling cascades regulating cell responses and gene expression, therefore acting as ‘signaling hubs’. It has been previously demonstrated that the integrated network analysis on transcriptomic data is able to infer cell surface protein activity in breast cancer. Such an approach has been implemented in a publicly available method called ‘SURFACER’. SURFACER implements a network-based analysis of transcriptomic data focusing on the overall activity of curated surface proteins, with the final aim to identify those proteins driving major phenotypic changes at a network level, named surface signaling hubs. Here, we show the ability of SURFACER to discover relevant knowledge within and across cancer datasets. We also show how different cancers can be stratified in surface-activity-specific groups. Our strategy may identify cancer-wide markers to design targeted therapies and biomarker-based diagnostic approaches.

A Patient-to-Model-to-Patient (PMP) Cancer Drug Discovery Protocol for Identifying and Validating Therapeutic Agents Targeting Tumor Regulatory Architecture

The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six-step patient-to-model-to-patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context-specific drug mechanism of action (MOA) and permit precise, biomarker-based preclinical validations of drug efficacy. The combination of determination of a drug's context-specific MOA and precise identification of patients' tumor checkpoints provides a personalized, mechanism-based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Integrated molecular and pharmacological characterization of patient-derived xenografts from bladder and ureteral cancers identifies new potential therapies

Background

Muscle-invasive bladder cancer (MIBC) and upper urinary tract urothelial carcinoma (UTUC) are molecularly heterogeneous. Despite chemotherapies, immunotherapies, or anti-fibroblast growth factor receptor (FGFR) treatments, these tumors are still of a poor outcome. Our objective was to develop a bank of patient-derived xenografts (PDXs) recapitulating the molecular heterogeneity of MIBC and UTUC, to facilitate the preclinical identification of therapies.

Methods

Fresh tumors were obtained from patients and subcutaneously engrafted into immune-compromised mice. Patient tumors and matched PDXs were compared regarding histopathology, transcriptomic (microarrays), and genomic profiles [targeted Next-Generation Sequencing (NGS)]. Several PDXs were treated with chemotherapy (cisplatin/gemcitabine) or targeted therapies [FGFR and epidermal growth factor (EGFR) inhibitors].

Results

A total of 31 PDXs were established from 1 non-MIBC, 25 MIBC, and 5 upper urinary tract tumors, including 28 urothelial (UC) and 3 squamous cell carcinomas (SCCs). Integrated genomic and transcriptomic profiling identified the PDXs of three different consensus molecular subtypes [basal/squamous (Ba/Sq), luminal papillary, and luminal unstable] and included FGFR3-mutated PDXs. High histological and genomic concordance was found between matched patient tumor/PDX. Discordance in molecular subtypes, such as a Ba/Sq patient tumor giving rise to a luminal papillary PDX, was observed (n=5) at molecular and histological levels. Ten models were treated with cisplatin-based chemotherapy, and we did not observe any association between subtypes and the response. Of the three Ba/Sq models treated with anti-EGFR therapy, two models were sensitive, and one model, of the sarcomatoid variant, was resistant. The treatment of three FGFR3-mutant PDXs with combined FGFR/EGFR inhibitors was more efficient than anti-FGFR3 treatment alone.

Conclusions

We developed preclinical PDX models that recapitulate the molecular heterogeneity of MIBCs and UTUC, including actionable mutations, which will represent an essential tool in therapy development. The pharmacological characterization of the PDXs suggested that the upper urinary tract and MIBCs, not only UC but also SCC, with similar molecular characteristics could benefit from the same treatments including anti-FGFR for FGFR3-mutated tumors and anti-EGFR for basal ones and showed a benefit for combined FGFR/EGFR inhibition in FGFR3-mutant PDXs, compared to FGFR inhibition alone.

A model for network-based identification and pharmacological targeting of aberrant, replication-permissive transcriptional programs induced by viral infection

SARS-CoV-2 hijacks the host cell transcriptional machinery to induce a phenotypic state amenable to its replication. Here we show that analysis of Master Regulator proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2 in infected cells revealed coordinated inactivation of Master Regulators enriched in physical interactions with SARS-CoV-2 proteins, suggesting their mechanistic role in maintaining a host cell state refractory to virus replication. To test their functional relevance, we measured SARS-CoV-2 replication in epithelial cells treated with drugs predicted to activate the entire repertoire of repressed Master Regulators, based on their experimentally elucidated, context-specific mechanism of action. Overall, 15 of the 18 drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 replication, without affecting cell viability. This model for host-directed pharmacological therapy is fully generalizable and can be deployed to identify drugs targeting host cell-based Master Regulator signatures induced by virtually any pathogen.

Dissecting the treatment-naive ecosystem of human melanoma brain metastasis

Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma; yet, our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naive MBMs and 10 extracranial melanoma metastases (ECMs) and matched spatial single-cell transcriptomics and T cell receptor (TCR)-seq. Cancer cells from MBM were more chromosomally unstable, adopted a neuronal-like cell state, and enriched for spatially variably expressed metabolic pathways. Key observations were validated in independent patient cohorts, patient-derived MBM/ECM xenograft models, RNA/ATAC-seq, proteomics, and multiplexed imaging. Integrated spatial analyses revealed distinct geography of putative cancer immune evasion and evidence for more abundant intra-tumoral B to plasma cell differentiation in lymphoid aggregates in MBM. MBM harbored larger fractions of monocyte-derived macrophages and dysfunctional TOX+CD8+ T cells with distinct expression of immune checkpoints. This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration.

Fast and accurate inference of gene regulatory networks through robust precision matrix estimation

MOTIVATION

Transcriptional regulation mechanisms allow cells to adapt and respond to external stimuli by altering gene expression. The possible cell transcriptional states are determined by the underlying gene regulatory network (GRN), and reliably inferring such network would be invaluable to understand biological processes and disease progression.

RESULTS

In this article, we present a novel method for the inference of GRNs, called PORTIA, which is based on robust precision matrix estimation, and we show that it positively compares with state-of-the-art methods while being orders of magnitude faster. We extensively validated PORTIA using the DREAM and MERLIN+P datasets as benchmarks. In addition, we propose a novel scoring metric that builds on graph-theoretical concepts.

AVAILABILITY AND IMPLEMENTATION

The code and instructions for data acquisition and full reproduction of our results are available at https://github.com/AntoinePassemiers/PORTIA-Manuscript. PORTIA is available on PyPI as a Python package (portia-grn).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Role of Copy Number Variants in Gene Co-Expression Patterns for Luminal B Breast Tumors

Gene co-expression networks have become a usual approach to integrate the vast amounts of information coming from gene expression studies in cancer cohorts. The reprogramming of the gene regulatory control and the molecular pathways depending on such control are central to the characterization of the disease, aiming to unveil the consequences for cancer prognosis and therapeutics. There is, however, a multitude of factors which have been associated with anomalous control of gene expression in cancer. In the particular case of co-expression patterns, we have previously documented a phenomenon of loss of long distance co-expression in several cancer types, including breast cancer. Of the many potential factors that may contribute to this phenomenology, copy number variants (CNVs) have been often discussed. However, no systematic assessment of the role that CNVs may play in shaping gene co-expression patterns in breast cancer has been performed to date. For this reason we have decided to develop such analysis. In this study, we focus on using probabilistic modeling techniques to evaluate to what extent CNVs affect the phenomenon of long/short range co-expression in Luminal B breast tumors. We analyzed the co-expression patterns in chromosome 8, since it is known to be affected by amplifications/deletions during cancer development. We found that the CNVs pattern in chromosome 8 of Luminal B network does not alter the co-expression patterns significantly, which means that the co-expression program in this cancer phenotype is not determined by CNV structure. Additionally, we found that region 8q24.3 is highly dense in interactions, as well as region p21.3. The most connected genes in this network belong to those cytobands and are associated with several manifestations of cancer in different tissues. Interestingly, among the most connected genes, we found MAF1 and POLR3D, which may constitute an axis of regulation of gene transcription, in particular for non-coding RNA species. We believe that by advancing on our knowledge of the molecular mechanisms behind gene regulation in cancer, we will be better equipped, not only to understand tumor biology, but also to broaden the scope of diagnostic, prognostic and therapeutic interventions to ultimately benefit oncologic patients.

MicroRNA regulatory networks associated with abnormal muscle repair in survivors of critical illness

BACKGROUND

Intensive care unit (ICU)-acquired weakness is characterized by muscle atrophy and impaired contractility that may persist after ICU discharge. Dysregulated muscle repair and regeneration gene co-expression networks are present in critical illness survivors with persistent muscle wasting and weakness. We aimed to identify microRNAs (miRs) regulating the gene networks and determine their role in the self-renewal of muscle in ICU survivors.

METHODS

Muscle whole-transcriptome expression was assessed with microarrays in banked quadriceps biopsies obtained at 7 days and 6 months post-ICU discharge from critically ill patients (n = 15) in the RECOVER programme and healthy individuals (n = 8). We conducted an integrated miR-messenger RNA analysis to identify miR/gene pairs associated with muscle recovery post-critical illness and evaluated their impact on myoblast proliferation and differentiation in human AB1167 and murine C2C12 cell lines in vitro. Select target genes were validated with quantitative PCR.

RESULTS

Twenty-two miRs were predicted to regulate the Day 7 post-ICU muscle transcriptome vs. controls. Thirty per cent of all differentially expressed genes shared a 3'UTR regulatory sequence for miR-424-3p/5p, which was 10-fold down-regulated in patients (P < 0.001) and correlated with quadriceps size (R = 0.86, P < 0.001), strength (R = 0.75, P = 0.007), and physical function (Functional Independence Measures motor subscore, R = 0.92, P < 0.001) suggesting its potential role as a master regulator of early recovery of muscle mass and strength following ICU discharge. Network analysis demonstrated enrichment for cellular respiration and muscle fate commitment/development related genes. At 6 months post-ICU discharge, a 14-miR expression signature, including miRs-490-3p and -744-5p, identified patients with muscle mass recovery vs. those with sustained atrophy. Constitutive overexpression of the novel miR-490-3p significantly inhibited AB1167 and C2C12 myoblast proliferation (cell count AB1167 miR-490-3p mimic or scrambled-miR transfected myoblasts 7926 ± 4060 vs. 14 159 ± 3515 respectively, P = 0.006; proportion Ki67-positive nuclei AB1167 miR-490-3p mimic or scrambled-miR transfected myoblasts 0.38 ± 0.07 vs. 0.54 ± 0.06 respectively, P < 0.001; proliferating cell nuclear antigen expression AB1167 miR-490-3p mimic or scrambled-miR transfected myoblasts 11.48 ± 1.97 vs. 16.75 ± 1.19 respectively, P = 0.040). Constitutive overexpression of miR-744-5p, a known regulator of myogenesis, significantly inhibited AB1167 and C2C12 myoblast differentiation (fusion index AB1167 miR-744-5p mimic or scrambled-miR transfected myoblasts 8.31 ± 7.00% vs. 40.29 ± 9.37% respectively, P < 0.001; myosin heavy chain expression miR-744-5p mimic or scrambled-miR transfected myoblasts 0.92 ± 0.39 vs. 13.53 ± 5.5 respectively, P = 0.01).

CONCLUSIONS

Combined functional transcriptomics identified 36 miRs including miRs-424-3p/5p, -490-3p, and -744-5p as potential regulators of gene networks associated with recovery of muscle mass and strength following critical illness. MiR-490-3p is identified as a novel regulator of myogenesis.

Dissecting chicken germ cell dynamics by combining a germ cell tracing transgenic chicken model with single-cell RNA sequencing

Avian germ cells can be distinguished by certain characteristics during development. On the basis of these characteristics, germ cells can be used for germline transmission. However, the dynamic transcriptional landscape of avian germ cells during development is unknown. Here, we used a novel germ-cell-tracing method to monitor and isolate chicken germ cells at different stages of development. We targeted the deleted in azoospermia like (DAZL) gene, a germ-cell-specific marker, to integrate a green fluorescent protein (GFP) reporter gene without affecting endogenous DAZL expression. The resulting transgenic chickens (DAZL::GFP) were used to uncover the dynamic transcriptional landscape of avian germ cells. Single-cell RNA sequencing of 4,752 male and 13,028 female DAZL::GFP germ cells isolated from embryonic day E2.5 to 1 week post-hatch identified sex-specific developmental stages (4 stages in male and 5 stages in female) and trajectories (apoptosis and meiosis paths in female) of chicken germ cells. The male and female trajectories were characterized by a gradual acquisition of stage-specific transcription factor activities. We also identified evolutionary conserved and species-specific gene expression programs during both chicken and human germ-cell development. Collectively, these novel analyses provide mechanistic insights into chicken germ-cell development.

Ageing induces tissue‐specific transcriptomic changes in Caenorhabditis elegans

Ageing is a complex process with common and distinct features across tissues. Unveiling the underlying processes driving ageing in individual tissues is indispensable to decipher the mechanisms of organismal longevity. Caenorhabditis elegans is a well‐established model organism that has spearheaded ageing research with the discovery of numerous genetic pathways controlling its lifespan. However, it remains challenging to dissect the ageing of worm tissues due to the limited description of tissue pathology and access to tissue‐specific molecular changes during ageing. In this study, we isolated cells from five major tissues in young and old worms and profiled the age‐induced transcriptomic changes within these tissues. We observed a striking diversity of ageing across tissues and identified different sets of longevity regulators therein. In addition, we found novel tissue‐specific factors, including irx‐1 and myrf‐2, which control the integrity of the intestinal barrier and sarcomere structure during ageing respectively. This study demonstrates the complexity of ageing across worm tissues and highlights the power of tissue‐specific transcriptomic profiling during ageing, which can serve as a resource to the field.

EnGRaiN: a supervised ensemble learning method for recovery of large-scale gene regulatory networks

MOTIVATION

Reconstruction of genome-scale networks from gene expression data is an actively studied problem. A wide range of methods that differ between the types of interactions they uncover with varying trade-offs between sensitivity and specificity have been proposed. To leverage benefits of multiple such methods, ensemble network methods that combine predictions from resulting networks have been developed, promising results better than or as good as the individual networks. Perhaps owing to the difficulty in obtaining accurate training examples, these ensemble methods hitherto are unsupervised.

RESULTS

In this article, we introduce EnGRaiN, the first supervised ensemble learning method to construct gene networks. The supervision for training is provided by small training datasets of true edge connections (positives) and edges known to be absent (negatives) among gene pairs. We demonstrate the effectiveness of EnGRaiN using simulated datasets as well as a curated collection of Arabidopsis thaliana datasets we created from microarray datasets available from public repositories. EnGRaiN shows better results not only in terms of receiver operating characteristic and PR characteristics for both real and simulated datasets compared with unsupervised methods for ensemble network construction, but also generates networks that can be mined for elucidating complex biological interactions.

AVAILABILITY AND IMPLEMENTATION

EnGRaiN software and the datasets used in the study are publicly available at the github repository: https://github.com/AluruLab/EnGRaiN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma

Backgrounds: Lung cancer is the leading cause of cancer related death worldwide. Current treatment strategies primarily involve surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, determined by TNM stages, histologic types, and genetic profiles. Plenty of studies have been trying to identify robust prognostic gene expression signatures. Even for high performance signatures, they usually have few shared genes. This is not totally unexpected, since a prognostic signature is associated with patient survival and may contain no upstream regulators. Identification of master regulators driving disease progression is a vital step to understand underlying molecular mechanisms and develop new treatments.

Methods: In this study, we have utilized a robust workflow to identify potential master regulators that drive poor prognosis in patients with lung adenocarcinoma. This workflow takes gene expression signatures that are associated with poor survival of early-stage lung adenocarcinoma, EGFR-TKI resistance, and responses to immune checkpoint inhibitors, respectively, and identifies recurrent master regulators from seven public gene expression datasets by a regulatory network-based approach.

Results: We have found that majority of the master regulators driving poor prognosis in early stage LUAD are cell-cycle related according to Gene Ontology annotation. However, they were demonstrated experimentally to promote a spectrum of processes such as tumor cell proliferation, invasion, metastasis, and drug resistance. Master regulators predicted from EGFR-TKI resistance signature and the EMT pathway signature are largely shared, which suggests that EMT pathway functions as a hub and interact with other pathways such as hypoxia, angiogenesis, TNF-α signaling, inflammation, TNF-β signaling, Wnt, and Notch signaling pathways. Master regulators that repress immunotherapy are enriched with MYC targets, E2F targets, oxidative phosphorylation, and mTOR signaling.

Conclusion: Our study uncovered possible mechanisms underlying recurrence, resistance to targeted therapy, and immunotherapy. The predicted master regulators may serve as potential therapeutic targets in patients with lung adenocarcinoma.

RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling

Changes in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the gene/protein regulatory interactions. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.

TcoFBase: a comprehensive database for decoding the regulatory transcription co-factors in human and mouse

Transcription co-factors (TcoFs) play crucial roles in gene expression regulation by communicating regulatory cues from enhancers to promoters. With the rapid accumulation of TcoF associated chromatin immunoprecipitation sequencing (ChIP-seq) data, the comprehensive collection and integrative analyses of these data are urgently required. Here, we developed the TcoFBase database (http://tcof.liclab.net/TcoFbase), which aimed to document a large number of available resources for mammalian TcoFs and provided annotations and enrichment analyses of TcoFs. TcoFBase curated 2322 TcoFs and 6759 TcoFs associated ChIP-seq data from over 500 tissues/cell types in human and mouse. Importantly, TcoFBase provided detailed and abundant (epi) genetic annotations of ChIP-seq based TcoF binding regions. Furthermore, TcoFBase supported regulatory annotation information and various functional annotations for TcoFs. Meanwhile, TcoFBase embedded five types of TcoF regulatory analyses for users, including TcoF gene set enrichment, TcoF binding genomic region annotation, TcoF regulatory network analysis, TcoF-TF co-occupancy analysis and TcoF regulatory axis analysis. TcoFBase was designed to be a useful resource that will help reveal the potential biological effects of TcoFs and elucidate TcoF-related regulatory mechanisms.

gene2gauss: A multi-view gaussian gene embedding learner for analyzing transcriptomic networks

Analyzing gene co-expression networks can help in the discovery of biological processes and regulatory mechanisms underlying normal or perturbed states. Unlike standard differential analysis, network-based approaches consider the interactions between the genes involved leading to biologically relevant results. Applying such network-based methods to jointly analyze multiple transcriptomic networks representing independent disease cohorts or studies could lead to the identification of more robust gene modules or gene regulatory networks. We present gene2gauss, a novel feature learning framework that is capable of embedding genes as multivariate gaussian distributions by taking into account their long-range interaction neighborhoods across multiple transcriptomic studies. Using multiple gene co-expression networks from idiopathic pulmonary fibrosis, we demonstrate that these multi-dimensional gaussian features are suitable for identifying regulons of known transcription factors (TF). Using standard TF-target libraries, we demonstrate that the features from our method are highly relevant in comparison with other feature learning approaches on transcriptomic data.

Transcriptomic Regulations Underlying Pair-bond Formation and Maintenance in the Socially Monogamous Male and Female Prairie Vole

BACKGROUND

The ability to form enduring social bonds is characteristic of human nature, and impairments in social affiliation are central features of severe neuropsychiatric disorders including autism spectrum disorder and schizophrenia. Owing to its ability to form long-term pair-bonds, the socially monogamous prairie vole has emerged as an excellent model to study the neurobiology of social attachment. Despite the enduring nature of the bond, however, surprisingly few genes have been implicated in the pair-bonding process in either sex.

METHODS

Male and female prairie voles (Microtus ochrogaster) were cohabitated with an opposite-sex partner for 24 hours or 3 weeks, and transcriptomic regulations in the nucleus accumbens were measured by RNA sequencing.

RESULTS

We found sex-specific response patterns despite similar behavioral indicators of pair-bond establishment. Indeed, 24 hours of cohabitation with an opposite-sex partner induced widespread transcriptomic changes that remained sustained to some extent in females after 3 weeks but returned to baseline before a second set of regulations in males. This led to a highly sexually biased nucleus accumbens transcriptome at 3 weeks related to processes such as neurotransmission, protein turnover, and DNA transcription. In particular, we found sex-specific alterations of mitochondrial dynamics following cohabitation, with a shift toward fission in males.

CONCLUSIONS

In addition to identifying the genes, networks, and pathways involved in the pair-bonding process in the nucleus accumbens, our work illustrates the vast extent of sex differences in the molecular mechanisms underlying pair-bonding in prairie voles and paves the way to further our understanding of the complex social bonding process.

Predictive modeling of gene expression regulation

Background

In-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted.

Results

We developed a quantitative analysis approach to investigate the main biological relationships among different regulatory elements and target genes; we applied it to Ovarian Serous Cystadenocarcinoma and 177 target genes belonging to three main pathways (DNA REPAIR, STEM CELLS and GLUCOSE METABOLISM) relevant for this tumor. Combining data from ENCODE and TCGA datasets, we built a predictive linear model for the regulation of each target gene, assessing the relationships between its expression, promoter methylation, expression of genes in the same or in the other pathways and of putative transcription factors. We proved the reliability and significance of our approach in a similar tumor type (basal-like Breast cancer) and using a different existing algorithm (ARACNe), and we obtained experimental confirmations on potentially interesting results.

Conclusions

The analysis of the proposed models allowed disclosing the relations between a gene and its related biological processes, the interconnections between the different gene sets, and the evaluation of the relevant regulatory elements at single gene level. This led to the identification of already known regulators and/or gene correlations and to unveil a set of still unknown and potentially interesting biological relationships for their pharmacological and clinical use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04481-1.

Hsa-miR-21-3p associates with breast cancer patient survival and targets genes in tumor suppressive pathways

Breast cancer is the cancer most often diagnosed in women. MicroRNAs (MIRs) are short RNA molecules that bind mRNA resulting in their downregulation. MIR21 has been shown to be an oncomiR in most cancer types, including breast cancer. Most of the effects of miR-21 have been attributed to hsa-miR-21-5p that is transcribed from the leading strand of MIR21, but hsa-miR-21-3p (miR-21-3p), transcribed from the lagging strand, is much less studied. The aim of the study is to analyze whether expression of miR-21-3p is prognostic for breast cancer. MiR-21-3p association with survival, clinical and pathological characteristics was analyzed in a large breast cancer cohort and validated in three separate cohorts, including TCGA and METABRIC. Analytical tools were also used to infer miR-21-3p function and to identify potential target genes and functional pathways. The results showed that in the exploration cohort, high miR-21-3p levels associated with shorter survival and lymph node positivity. In the three validation cohorts, high miR-21-3p levels associated with pathological characteristics that predict worse prognosis. Specifically, in the largest validation cohort, METABRIC (n = 1174), high miR-21-3p levels associated with large tumors, a high grade, lymph node and HER2 positivity, and shorter breast-cancer-specific survival (HR = 1.38, CI 1.13–1.68). This association remained significant after adjusting for confounding factors. The genes with expression levels that correlated with miR-21-3p were enriched in particular pathways, including the epithelial-to-mesenchymal transition and proliferation. Among the most significantly downregulated targets were MAT2A and the tumor suppressive genes STARD13 and ZNF132. The results from this study emphasize that both 3p- and 5p-arms from a MIR warrant independent study. The data show that miR-21-3p overexpression in breast tumors is a marker of worse breast cancer progression and it affects genes in pathways that drive breast cancer by down-regulating tumor suppressor genes. The results suggest miR-21-3p as a potential biomarker.

Inferred expression regulator activities suggest genes mediating cardiometabolic genetic signals

Expression QTL (eQTL) analyses have suggested many genes mediating genome-wide association study (GWAS) signals but most GWAS signals still lack compelling explanatory genes. We have leveraged an adipose-specific gene regulatory network to infer expression regulator activities and phenotypic master regulators (MRs), which were used to detect activity QTLs (aQTLs) at cardiometabolic trait GWAS loci. Regulator activities were inferred with the VIPER algorithm that integrates enrichment of expected expression changes among a regulator's target genes with confidence in their regulator-target network interactions and target overlap between different regulators (i.e., pleiotropy). Phenotypic MRs were identified as those regulators whose activities were most important in predicting their respective phenotypes using random forest modeling. While eQTLs were typically more significant than aQTLs in cis, the opposite was true among candidate MRs in trans. Several GWAS loci colocalized with MR trans-eQTLs/aQTLs in the absence of colocalized cis-QTLs. Intriguingly, at the 1p36.1 BMI GWAS locus the EPHB2 cis-aQTL was stronger than its cis-eQTL and colocalized with the GWAS signal and 35 BMI MR trans-aQTLs, suggesting the GWAS signal may be mediated by effects on EPHB2 activity and its downstream effects on a network of BMI MRs. These MR and aQTL analyses represent systems genetic methods that may be broadly applied to supplement standard eQTL analyses for suggesting molecular effects mediating GWAS signals.

Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

Resolving the intertwining of inflammation and fibrosis in human heart failure at single-cell level

Inflammation and fibrosis are intertwined mechanisms fundamentally involved in heart failure. Detailed deciphering gene expression perturbations and cell-cell interactions of leukocytes and non-myocytes is required to understand cell-type-specific pathology in the failing human myocardium. To this end, we performed single-cell RNA sequencing and single T cell receptor sequencing of 200,615 cells in both human dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) hearts. We sampled both lesion and mild-lesion tissues from each heart to sequentially capture cellular and molecular alterations to different extents of cardiac fibrosis. By which, left (lesion) and right ventricle (mild-lesion) for DCM hearts were harvest while infarcted (lesion) and non-infarcted area (mild-lesion) were dissected from ICM hearts. A novel transcription factor AEBP1 was identified as a crucial cardiac fibrosis regulator in ACTA2⁺ myofibroblasts. Within fibrotic myocardium, an infiltration of a considerable number of leukocytes was witnessed, especially cytotoxic and exhausted CD8⁺ T cells and pro-inflammatory CD4⁺ T cells. Furthermore, a subset of tissue-resident macrophage, CXCL8^hiCCR2⁺HLA-DR^hi macrophage was particularly identified in severely fibrotic area, which interacted with activated endothelial cell via DARC, that potentially facilitate leukocyte recruitment and infiltration in human heart failure.

VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics

Deep learning architectures such as variational autoencoders have revolutionized the analysis of transcriptomics data. However, the latent space of these variational autoencoders offers little to no interpretability. To provide further biological insights, we introduce a novel sparse Variational Autoencoder architecture, VEGA (VAE Enhanced by Gene Annotations), whose decoder wiring mirrors user-provided gene modules, providing direct interpretability to the latent variables. We demonstrate the performance of VEGA in diverse biological contexts using pathways, gene regulatory networks and cell type identities as the gene modules that define its latent space. VEGA successfully recapitulates the mechanism of cellular-specific response to treatments, the status of master regulators as well as jointly revealing the cell type and cellular state identity in developing cells. We envision the approach could serve as an explanatory biological model for development and drug treatment experiments.

Systems pharmacology: a combination strategy for improving efficacy of PD-1/PD-L1 blockade

Targeting tumor microenvironment (TME), such as immune checkpoint blockade (ICB), has achieved increased overall response rates in many advanced cancers, such as non-small cell lung cancer (NSCLC), however, only in a fraction of patients. To improve the overall and durable response rates, combining other therapeutics, such as natural products, with ICB therapy is under investigation. Unfortunately, due to the lack of systematic methods to characterize the relationship between TME and ICB, development of rational immune-combination therapy is a critical challenge. Here, we proposed a systems pharmacology strategy to identify resistance regulators of PD-1/PD-L1 blockade and develop its combinatorial drug by integrating multidimensional omics and pharmacological methods. First, a high-resolution TME cell atlas was inferred from bulk sequencing data by referring to a high-resolution single-cell data and was used to predict potential resistance regulators of PD-1/PD-L1 blockade through TME stratification analysis. Second, to explore the drug targeting the resistance regulator, we carried out the large-scale target fishing and the network analysis between multi-target drug and the resistance regulator. Finally, we predicted and verified that oxymatrine significantly enhances the infiltration of CD8+ T cells into TME and is a powerful combination agent to enhance the therapeutic effect of anti-PD-L1 in a mouse model of lung adenocarcinoma. Overall, the systems pharmacology strategy offers a paradigm to identify combinatorial drugs for ICB therapy with a systems biology perspective of drug-target-pathway-TME phenotype-ICB combination.

Computational biology approaches for mapping transcriptional regulatory networks

Transcriptional Regulatory Networks (TRNs) are mainly responsible for the cell-type- or cell-state-specific expression of gene sets from the same DNA sequence. However, so far there are no precise maps of TRNs available for each cell-type or cell-state, and no ideal tool to map those networks clearly and in full from biological samples. In this review, major approaches and tools to map TRNs from high-throughput data are presented, depending on the type of methods or data used to infer them, and their advantages and limitations are discussed. After summarizing the main principles defining the topology and structure–function relationships in TRNs, an overview of the extensive work done to map TRNs from bulk transcriptomic data will be presented by type of methodological approach. Most recent modellings of TRNs using other types of molecular data or integrating different data types, including single-cell RNA-sequencing and chromatin information, will then be discussed, before briefly concluding with improvements expected to come in the field.

The trans-omics landscape of COVID-19

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.

A molecular single-cell lung atlas of lethal COVID-19

Respiratory failure is the leading cause of death in patients with severe SARS-CoV-2 infection^1,2, but the host response at the lung tissue level is poorly understood. Here we performed single-nucleus RNA sequencing of about 116,000 nuclei from the lungs of nineteen individuals who died of COVID-19 and underwent rapid autopsy and seven control individuals. Integrated analyses identified substantial alterations in cellular composition, transcriptional cell states, and cell-to-cell interactions, thereby providing insight into the biology of lethal COVID-19. The lungs from individuals with COVID-19 were highly inflamed, with dense infiltration of aberrantly activated monocyte-derived macrophages and alveolar macrophages, but had impaired T cell responses. Monocyte/macrophage-derived interleukin-1β and epithelial cell-derived interleukin-6 were unique features of SARS-CoV-2 infection compared to other viral and bacterial causes of pneumonia. Alveolar type 2 cells adopted an inflammation-associated transient progenitor cell state and failed to undergo full transition into alveolar type 1 cells, resulting in impaired lung regeneration. Furthermore, we identified expansion of recently described CTHRC1⁺ pathological fibroblasts³ contributing to rapidly ensuing pulmonary fibrosis in COVID-19. Inference of protein activity and ligand-receptor interactions identified putative drug targets to disrupt deleterious circuits. This atlas enables the dissection of lethal COVID-19, may inform our understanding of long-term complications of COVID-19 survivors, and provides an important resource for therapeutic development.

Single-cell RNA-seq reveals fibroblast heterogeneity and increased mesenchymal fibroblasts in human fibrotic skin diseases

Fibrotic skin disease represents a major global healthcare burden, characterized by fibroblast hyperproliferation and excessive accumulation of extracellular matrix. Fibroblasts are found to be heterogeneous in multiple fibrotic diseases, but fibroblast heterogeneity in fibrotic skin diseases is not well characterized. In this study, we explore fibroblast heterogeneity in keloid, a paradigm of fibrotic skin diseases, by using single-cell RNA-seq. Our results indicate that keloid fibroblasts can be divided into 4 subpopulations: secretory-papillary, secretory-reticular, mesenchymal and pro-inflammatory. Interestingly, the percentage of mesenchymal fibroblast subpopulation is significantly increased in keloid compared to normal scar. Functional studies indicate that mesenchymal fibroblasts are crucial for collagen overexpression in keloid. Increased mesenchymal fibroblast subpopulation is also found in another fibrotic skin disease, scleroderma, suggesting this is a broad mechanism for skin fibrosis. These findings will help us better understand skin fibrotic pathogenesis, and provide potential targets for fibrotic disease therapies.

Melanoma dedifferentiation induced by IFN-γ epigenetic remodeling in response to anti-PD-1 therapy

Melanoma dedifferentiation has been reported to be a state of cellular resistance to targeted therapies and immunotherapies as cancer cells revert to a more primitive cellular phenotype. Here, we show that, counterintuitively, the biopsies of patient tumors that responded to anti-programmed cell death 1 (anti-PD-1) therapy had decreased expression of melanocytic markers and increased neural crest markers, suggesting treatment-induced dedifferentiation. When modeling the effects in vitro, we documented that melanoma cell lines that were originally differentiated underwent a process of neural crest dedifferentiation when continuously exposed to IFN-γ, through global chromatin landscape changes that led to enrichment in specific hyperaccessible chromatin regions. The IFN-γ-induced dedifferentiation signature corresponded with improved outcomes in patients with melanoma, challenging the notion that neural crest dedifferentiation is entirely an adverse phenotype.

Impairment of the ER/mitochondria compartment in human cardiomyocytes with PLN p.Arg14del mutation

The phospholamban (PLN) p.Arg14del mutation causes dilated cardiomyopathy, with the molecular disease mechanisms incompletely understood. Patient dermal fibroblasts were reprogrammed to hiPSC, isogenic controls were established by CRISPR/Cas9, and cardiomyocytes were differentiated. Mutant cardiomyocytes revealed significantly prolonged Ca2+ transient decay time, Ca2+ -load dependent irregular beating pattern, and lower force. Proteomic analysis revealed less endoplasmic reticulum (ER) and ribosomal and mitochondrial proteins. Electron microscopy showed dilation of the ER and large lipid droplets in close association with mitochondria. Follow-up experiments confirmed impairment of the ER/mitochondria compartment. PLN p.Arg14del end-stage heart failure samples revealed perinuclear aggregates positive for ER marker proteins and oxidative stress in comparison with ischemic heart failure and non-failing donor heart samples. Transduction of PLN p.Arg14del EHTs with the Ca2+ -binding proteins GCaMP6f or parvalbumin improved the disease phenotype. This study identified impairment of the ER/mitochondria compartment without SR dysfunction as a novel disease mechanism underlying PLN p.Arg14del cardiomyopathy. The pathology was improved by Ca2+ -scavenging, suggesting impaired local Ca2+ cycling as an important disease culprit.

Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages

Clear cell renal carcinoma (ccRCC) is a heterogeneous disease with a variable post-surgical course. To assemble a comprehensive ccRCC tumor microenvironment (TME) atlas, we performed single-cell RNA sequencing (scRNA-seq) of hematopoietic and non-hematopoietic subpopulations from tumor and tumor-adjacent tissue of treatment-naive ccRCC resections. We leveraged the VIPER algorithm to quantitate single-cell protein activity and validated this approach by comparison to flow cytometry. The analysis identified key TME subpopulations, as well as their master regulators and candidate cell-cell interactions, revealing clinically relevant populations, undetectable by gene-expression analysis. Specifically, we uncovered a tumor-specific macrophage subpopulation characterized by upregulation of TREM2/APOE/C1Q, validated by spatially resolved, quantitative multispectral immunofluorescence. In a large clinical validation cohort, these markers were significantly enriched in tumors from patients who recurred following surgery. The study thus identifies TREM2/APOE/C1Q-positive macrophage infiltration as a potential prognostic biomarker for ccRCC recurrence, as well as a candidate therapeutic target.

Pan-cancer characterization of metabolism-related biomarkers identifies potential therapeutic targets

Background

Generally, cancer cells undergo metabolic reprogramming to adapt to energetic and biosynthetic requirements that support their uncontrolled proliferation. However, the mutual relationship between two critical metabolic pathways, glycolysis and oxidative phosphorylation (OXPHOS), remains poorly defined.

Methods

We developed a “double-score” system to quantify glycolysis and OXPHOS in 9668 patients across 33 tumor types from The Cancer Genome Atlas and classified them into four metabolic subtypes. Multi-omics bioinformatical analyses was conducted to detect metabolism-related molecular features.

Results

Compared with patients with low glycolysis and high OXPHOS (LGHO), those with high glycolysis and low OXPHOS (HGLO) were consistently associated with worse prognosis. We identified common dysregulated molecular features between different metabolic subgroups across multiple cancers, including gene, miRNA, transcription factor, methylation, and somatic alteration, as well as investigated their mutual interfering relationships.

Conclusion

Overall, this work provides a comprehensive atlas of metabolic heterogeneity on a pan-cancer scale and identified several potential drivers of metabolic rewiring, suggesting corresponding prognostic and therapeutic utility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02889-0.

Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF-TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF-TG relations where a module of TF is regulating a module of TGs upon specific stress.

Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories

Single-cell RNA sequencing (scRNA-seq) is a widely used method for identifying cell types and trajectories in biologically heterogeneous samples, but it is limited in its detection and quantification of lowly expressed genes. This results in missing important biological signals, such as the expression of key transcription factors (TFs) driving cellular differentiation. We show that targeted sequencing of ∼1000 TFs (scCapture-seq) in iPSC-derived neuronal cultures greatly improves the biological information garnered from scRNA-seq. Increased TF resolution enhanced cell type identification, developmental trajectories, and gene regulatory networks. This allowed us to resolve differences among neuronal populations, which were generated in two different laboratories using the same differentiation protocol. ScCapture-seq improved TF-gene regulatory network inference and thus identified divergent patterns of neurogenesis into either excitatory cortical neurons or inhibitory interneurons. Furthermore, scCapture-seq revealed a role for of retinoic acid signaling in the developmental divergence between these different neuronal populations. Our results show that TF targeting improves the characterization of human cellular models and allows identification of the essential differences between cellular populations, which would otherwise be missed in traditional scRNA-seq. scCapture-seq TF targeting represents a cost-effective enhancement of scRNA-seq, which could be broadly applied to improve scRNA-seq resolution.

Network-based identification of key master regulators associated with an immune-silent cancer phenotype

A cancer immune phenotype characterized by an active T-helper 1 (Th1)/cytotoxic response is associated with responsiveness to immunotherapy and favorable prognosis across different tumors. However, in some cancers, such an intratumoral immune activation does not confer protection from progression or relapse. Defining mechanisms associated with immune evasion is imperative to refine stratification algorithms, to guide treatment decisions and to identify candidates for immune-targeted therapy. Molecular alterations governing mechanisms for immune exclusion are still largely unknown. The availability of large genomic datasets offers an opportunity to ascertain key determinants of differential intratumoral immune response. We follow a network-based protocol to identify transcription regulators (TRs) associated with poor immunologic antitumor activity. We use a consensus of four different pipelines consisting of two state-of-the-art gene regulatory network inference techniques, regularized gradient boosting machines and ARACNE to determine TR regulons, and three separate enrichment techniques, including fast gene set enrichment analysis, gene set variation analysis and virtual inference of protein activity by enriched regulon analysis to identify the most important TRs affecting immunologic antitumor activity. These TRs, referred to as master regulators (MRs), are unique to immune-silent and immune-active tumors, respectively. We validated the MRs coherently associated with the immune-silent phenotype across cancers in The Cancer Genome Atlas and a series of additional datasets in the Prediction of Clinical Outcomes from Genomic Profiles repository. A downstream analysis of MRs specific to the immune-silent phenotype resulted in the identification of several enriched candidate pathways, including NOTCH1, TGF-$\beta $, Interleukin-1 and TNF-$\alpha $ signaling pathways. TGFB1I1 emerged as one of the main negative immune modulators preventing the favorable effects of a Th1/cytotoxic response.

The tepary bean genome provides insight into evolution and domestication under heat stress

Tepary bean (Phaseolus acutifolis A. Gray), native to the Sonoran Desert, is highly adapted to heat and drought. It is a sister species of common bean (Phaseolus vulgaris L.), the most important legume protein source for direct human consumption, and whose production is threatened by climate change. Here, we report on the tepary genome including exploration of possible mechanisms for resilience to moderate heat stress and a reduced disease resistance gene repertoire, consistent with adaptation to arid and hot environments. Extensive collinearity and shared gene content among these Phaseolus species will facilitate engineering climate adaptation in common bean, a key food security crop, and accelerate tepary bean improvement.

Neuronal metabotropic glutamate receptor 8 protects against neurodegeneration in CNS inflammation

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system with continuous neuronal loss. Treatment of clinical progression remains challenging due to lack of insights into inflammation-induced neurodegenerative pathways. Here, we show that an imbalance in the neuronal receptor interactome is driving glutamate excitotoxicity in neurons of MS patients and identify the MS risk-associated metabotropic glutamate receptor 8 (GRM8) as a decisive modulator. Mechanistically, GRM8 activation counteracted neuronal cAMP accumulation, thereby directly desensitizing the inositol 1,4,5-trisphosphate receptor (IP3R). This profoundly limited glutamate-induced calcium release from the endoplasmic reticulum and subsequent cell death. Notably, we found Grm8-deficient neurons to be more prone to glutamate excitotoxicity, whereas pharmacological activation of GRM8 augmented neuroprotection in mouse and human neurons as well as in a preclinical mouse model of MS. Thus, we demonstrate that GRM8 conveys neuronal resilience to CNS inflammation and is a promising neuroprotective target with broad therapeutic implications.

Progression signature underlies clonal evolution and dissemination of multiple myeloma

Clonal evolution drives tumor progression, dissemination, and relapse in multiple myeloma (MM), with most patients dying of relapsed disease. This multistage process requires tumor cells to enter the circulation, extravasate, and colonize distant bone marrow (BM) sites. Here, we developed a fluorescent or DNA-barcode clone-tracking system on MM PrEDiCT (progression through evolution and dissemination of clonal tumor cells) xenograft mouse model to study clonal behavior within the BM microenvironment. We showed that only the few clones that successfully adapt to the BM microenvironment can enter the circulation and colonize distant BM sites. RNA sequencing of primary and distant-site MM tumor cells revealed a progression signature sequentially activated along human MM progression and significantly associated with overall survival when evaluated against patient data sets. A total of 28 genes were then computationally predicted to be master regulators (MRs) of MM progression. HMGA1 and PA2G4 were validated in vivo using CRISPR-Cas9 in the PrEDiCT model and were shown to be significantly depleted in distant BM sites, indicating their role in MM progression and dissemination. Loss of HMGA1 and PA2G4 also compromised the proliferation, migration, and adhesion abilities of MM cells in vitro. Overall, our model successfully recapitulates key characteristics of human MM disease progression and identified potential new therapeutic targets for MM.

Human plasmacytoid dendritic cells mount a distinct antiviral response to virus-infected cells

Plasmacytoid dendritic cells (pDCs) can rapidly produce interferons and other soluble factors in response to extracellular viruses or virus mimics such as CpG-containing DNA. pDCs can also recognize live cells infected with certain RNA viruses, but the relevance and functional consequences of such recognition remain unclear. We studied the response of primary DCs to the prototypical persistent DNA virus, human cytomegalovirus (CMV). Human pDCs produced high amounts of type I interferon (IFN-I) when incubated with live CMV-infected fibroblasts but not with free CMV; the response involved integrin-mediated adhesion, transfer of DNA-containing virions to pDCs, and the recognition of DNA through TLR9. Compared with transient polyfunctional responses to CpG or free influenza virus, pDC response to CMV-infected cells was long-lasting, dominated by the production of IFN-I and IFN-III, and lacked diversification into functionally distinct populations. Similarly, pDC activation by influenza-infected lung epithelial cells was highly efficient, prolonged, and dominated by interferon production. Prolonged pDC activation by CMV-infected cells facilitated the activation of natural killer cells critical for CMV control. Last, patients with CMV viremia harbored phenotypically activated pDCs and increased circulating IFN-I and IFN-III. Thus, recognition of live infected cells is a mechanism of virus detection by pDCs that elicits a unique antiviral immune response.

Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

A survey of multiscale modeling: Foundations, historical milestones, current status, and future prospects

Research problems in the domains of physical, engineering, biological sciences often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.

Single-cell analyses reveal YAP/TAZ as regulators of stemness and cell plasticity in Glioblastoma

Glioblastoma (GBM) is a devastating human malignancy. GBM stem-like cells (GSCs) drive tumor initiation and progression. Yet, the molecular determinants defining GSCs in their native state in patients remain poorly understood. Here we used single cell datasets and identified GSCs at the apex of the differentiation hierarchy of GBM. By reconstructing the GSCs' regulatory network, we identified the YAP/TAZ coactivators as master regulators of this cell state, irrespectively of GBM subtypes. YAP/TAZ are required to install GSC properties in primary cells downstream of multiple oncogenic lesions, and required for tumor initiation and maintenance in vivo in different mouse and human GBM models. YAP/TAZ act as main roadblock of GSC differentiation and their inhibition irreversibly lock differentiated GBM cells into a non-tumorigenic state, preventing plasticity and regeneration of GSC-like cells. Thus, GSC identity is linked to a key molecular hub integrating genetics and microenvironmental inputs within the multifaceted biology of GBM.

Gene regulatory network inference as relaxed graph matching

Bipartite network inference is a ubiquitous problem across disciplines. One important example in the field molecular biology is gene regulatory network inference. Gene regulatory networks are an instrumental tool aiding in the discovery of the molecular mechanisms driving diverse diseases, including cancer. However, only noisy observations of the projections of these regulatory networks are typically assayed. In an effort to better estimate regulatory networks from their noisy projections, we formulate a non-convex but analytically tractable optimization problem called OTTER. This problem can be interpreted as relaxed graph matching between the two projections of the bipartite network. OTTER's solutions can be derived explicitly and inspire a spectral algorithm, for which we provide network recovery guarantees. We also provide an alternative approach based on gradient descent that is more robust to noise compared to the spectral algorithm. Interestingly, this gradient descent approach resembles the message passing equations of an established gene regulatory network inference method, PANDA. Using three cancer-related data sets, we show that OTTER outperforms state-of-the-art inference methods in predicting transcription factor binding to gene regulatory regions. To encourage new graph matching applications to this problem, we have made all networks and validation data publicly available.

A modular master regulator landscape controls cancer transcriptional identity

Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.