GeneWalk identifies relevant gene functions for a biological context using network representation learning

Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns

Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

SIMBA: single-cell embedding along with features

Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).

Early-stage lung cancer is driven by a transitional cell state dependent on a KRAS-ITGA3-SRC axis

Glycine-12 mutations in the GTPase KRAS (KRASG12) are an initiating event for development of lung adenocarcinoma (LUAD). KRASG12 mutations promote cell-intrinsic rewiring of alveolar type-II progenitor (AT2) cells, but to what extent such changes interplay with lung homeostasis and cell fate pathways is unclear. Here, we generated single-cell RNA-seq (scRNA-seq) profiles from AT2-mesenchyme organoid co-cultures, mice, and stage-IA LUAD patients, identifying conserved regulators of AT2 transcriptional dynamics and defining the impact of KRASG12D mutation with temporal resolution. In AT2WT organoids, we found a transient injury/plasticity state preceding AT2 self-renewal and AT1 differentiation. Early-stage AT2KRAS cells exhibited perturbed gene expression dynamics, most notably retention of the injury/plasticity state. The injury state in AT2KRAS cells of patients, mice, and organoids was distinguishable from AT2WT states via altered receptor expression, including co-expression of ITGA3 and SRC. The combination of clinically relevant KRASG12D and SRC inhibitors impaired AT2KRAS organoid growth. Together, our data show that an injury/plasticity state essential for lung repair is co-opted during AT2 self-renewal and LUAD initiation, suggesting that early-stage LUAD may be susceptible to interventions that target specifically the oncogenic nature of this cell state.

CDK9 inhibition activates innate immune response through viral mimicry

Cancer cells frequently exhibit hyperactivation of transcription, which can lead to increased sensitivity to compounds targeting the transcriptional kinases, in particular CDK9. However, mechanistic details of CDK9 inhibition-induced cancer cell-selective anti-proliferative effects remain largely unknown. Here, we discover that CDK9 inhibition activates the innate immune response through viral mimicry in cancer cells. In MYC over-expressing prostate cancer cells, CDK9 inhibition leads to the gross accumulation of mis-spliced RNA. Double-stranded RNA (dsRNA)-activated kinase can recognize these mis-spliced RNAs, and we show that the activity of this kinase is required for the CDK9 inhibitor-induced anti-proliferative effects. Using time-resolved transcriptional profiling (SLAM-seq), targeted proteomics, and ChIP-seq, we show that, similar to viral infection, CDK9 inhibition significantly suppresses transcription of most genes but allows selective transcription and translation of cytokines related to the innate immune response. In particular, CDK9 inhibition activates NFκB-driven cytokine signaling at the transcriptional and secretome levels. The transcriptional signature induced by CDK9 inhibition identifies prostate cancers with a high level of genome instability. We propose that it is possible to induce similar effects in patients using CDK9 inhibition, which, we show, causes DNA damage in vitro. In the future, it is important to establish whether CDK9 inhibitors can potentiate the effects of immunotherapy against late-stage prostate cancer, a currently lethal disease.

From tradition to innovation: conventional and deep learning frameworks in genome annotation

Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape.

Explainable Multilayer Graph Neural Network for cancer gene prediction

MOTIVATION

The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene-gene interactions or provide limited explanations for their predictions. These methods are restricted to a single biological network, which cannot capture the full complexity of tumorigenesis. Models trained on different biological networks often yield different and even opposite cancer gene predictions, hindering their trustworthy adaptation. Here, we introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes by leveraging multiple gene-gene interaction networks and pan-cancer multi-omics data. Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction.

RESULTS

Our method consistently outperforms all existing methods, with an average 7.15% improvement in area under the precision-recall curve over the current state-of-the-art method. Importantly, EMGNN integrated multiple graphs to prioritize newly predicted cancer genes with conflicting predictions from single biological networks. For each prediction, EMGNN provided valuable biological insights via both model-level feature importance explanations and molecular-level gene set enrichment analysis. Overall, EMGNN offers a powerful new paradigm of graph learning through modeling the multilayered topological gene relationships and provides a valuable tool for cancer genomics research.

AVAILABILITY AND IMPLEMENTATION

Our code is publicly available at https://github.com/zhanglab-aim/EMGNN.

PLEX.I: a tool to discover features in multiplex networks that reflect clinical variation

Molecular profiling technologies, such as RNA sequencing, offer new opportunities to better discover and understand the molecular networks involved in complex biological processes. Clinically important variations of diseases, or responses to treatment, are often reflected, or even caused, by the dysregulation of molecular interaction networks specific to particular network regions. In this work, we propose the R package PLEX.I, that allows quantifying and testing variation in the direct neighborhood of a given node between networks corresponding to different conditions or states. We illustrate PLEX.I in two applications in which we discover variation that is associated with different responses to tamoxifen treatment and to sex-specific responses to bacterial stimuli. In the first case, PLEX.I analysis identifies two known pathways i) that have already been implicated in the same context as the tamoxifen mechanism of action, and ii) that would have not have been identified using classical differential gene expression analysis.

Capturing the dynamics of microbial interactions through individual-specific networks

Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.

Automated assembly of molecular mechanisms at scale from text mining and curated databases

The analysis of omic data depends on machine-readable information about protein interactions, modifications, and activities as found in protein interaction networks, databases of post-translational modifications, and curated models of gene and protein function. These resources typically depend heavily on human curation. Natural language processing systems that read the primary literature have the potential to substantially extend knowledge resources while reducing the burden on human curators. However, machine-reading systems are limited by high error rates and commonly generate fragmentary and redundant information. Here, we describe an approach to precisely assemble molecular mechanisms at scale using multiple natural language processing systems and the Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA identifies full and partial overlaps in information extracted from published papers and pathway databases, uses predictive models to improve the reliability of machine reading, and thereby assembles individual pieces of information into non-redundant and broadly usable mechanistic knowledge. Using INDRA to create high-quality corpora of causal knowledge we show it is possible to extend protein-protein interaction databases and explain co-dependencies in the Cancer Dependency Map.

Revealing the History and Mystery of RNA-Seq

Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data.

Interaction between Microbes and Host in Sow Vaginas in Early Pregnancy

Extensive research has explored the causes of embryo losses during early pregnancy by analyzing interaction mechanisms in sows' uterus, ignoring the importance of the lower reproductive tract in pregnancy development regulation. Despite recent progress in understanding the diversity of vaginal microbes under different physiological states, the dynamic of sows' vaginal microbiotas during pregnancy and the interaction between vaginal microbes and the host are poorly understood. Here, we performed a comprehensive analysis of sows' vaginal microbial communities in early pregnancy coupled with overall patterns of vaginal mucosal epithelium gene expression. The vaginal microbiota was analyzed by 16s rRNA or metagenome sequencing, and the vaginal mucosal epithelium transcriptome was analyzed by RNA sequencing, followed by integration of the data layers. We found that the sows' vaginal microbiotas in early pregnancy develop dynamically, and there is a homeostasis balance of Firmicutes and Proteobacteria. Subsequently, we identified two pregnancy-specific communities, which play diverse roles. The microbes in the vagina stimulate the epithelial cells, while vaginal epithelium changes its structure and functions in response to stimulation. These changes produce specific inflammation responses to promote pregnancy development. Our findings demonstrate the interaction between microbes and host in the sow vagina in early pregnancy to promote pregnancy development, meanwhile providing a reference data set for the study of targeted therapies of microbial homeostasis dysregulation in the female reproductive tract. IMPORTANCE This work sheds light on the dynamics of the sow vaginal microbiotas in early pregnancy and its roles in pregnancy development. Furthermore, this study provides insight into the functional mechanisms of reproductive tract microbes by outlining vaginal microbe-host interactions, which might identify new research and intervention targets for improving pregnancy development by modulating lower reproductive tract microbiota.

Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19

MOTIVATION

Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. However, explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods.

RESULTS

We propose an extension of gene set enrichment analysis to a latent embedding space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to a version of traditional gene set enrichment analysis through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for severe COVID-19.

AVAILABILITY AND IMPLEMENTATION

GSPA is available for download as a command-line Python package at https://github.com/henrycousins/gspa.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparative RNA-Sequencing Analysis Reveals High Complexity and Heterogeneity of Transcriptomic and Immune Profiles in Hepatocellular Carcinoma Tumors of Viral (HBV, HCV) and Non-Viral Etiology

Background and Objectives: Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, is the leading cause of cancer-related mortality. It arises and progresses against fibrotic or cirrhotic backgrounds mainly due to infection with hepatitis viruses B (HBV) or C (HCV) or non-viral causes that lead to chronic inflammation and genomic changes. A better understanding of molecular and immune mechanisms in HCC subtypes is needed. Materials and Methods: To identify transcriptional changes in primary HCC tumors with or without hepatitis viral etiology, we analyzed the transcriptomes of 24 patients by next-generation sequencing. Results: We identified common and unique differentially expressed genes for each etiological tumor group and analyzed the expression of SLC, ATP binding cassette, cytochrome 450, cancer testis, and heat shock protein genes. Metascape functional enrichment analysis showed mainly upregulated cell-cycle pathways in HBV and HCV and upregulated cell response to stress in non-viral infection. GeneWalk analysis identified regulator, hub, and moonlighting genes and highlighted CCNB1, ACTN2, BRCA1, IGF1, CDK1, AURKA, AURKB, and TOP2A in the HCV group and HSF1, HSPA1A, HSP90AA1, HSPB1, HSPA5, PTK2, and AURKB in the group without viral infection as hub genes. Immune infiltrate analysis showed that T cell, cytotoxic, and natural killer cell markers were significantly more highly expressed in HCV than in non-viral tumors. Genes associated with monocyte activation had the highest expression levels in HBV, while high expression of genes involved in primary adaptive immune response and complement receptor activity characterized tumors without viral infection. Conclusions: Our comprehensive study underlines the high degree of complexity of immune profiles in the analyzed groups, which adds to the heterogeneous HCC genomic landscape. The biomarkers identified in each HCC group might serve as therapeutic targets.

Rencofilstat, a cyclophilin inhibitor: A phase 2a, multicenter, single-blind, placebo-controlled study in F2/F3 NASH

Rencofilstat (RCF) demonstrated antifibrotic effects in preclinical models and was safe and well tolerated in Phase 1 studies. The aim of this Phase 2a study was safety, tolerability, pharmacokinetics, and exploration of efficacy biomarkers in subjects with nonalcoholic steatohepatitis (NASH). This Phase 2a, multicenter, single-blind, placebo-controlled study randomized 49 presumed F2/F3 subjects to RCF 75 mg once daily (QD), RCF 225 mg QD, or placebo for 28 days. Primary safety and tolerability endpoints were explored using descriptive statistics with post hoc analyses comparing active to placebo groups. Pharmacokinetics were evaluated using population pharmacokinetics methods. Efficacy was explored using biomarkers, transcriptomics, and lipidomics. RCF was safe and well tolerated, with no safety signals identified. The most frequently reported treatment-emergent adverse events were constipation, diarrhea, back pain, dizziness, and headache. No clinically significant changes in laboratory parameters were observed, and RCF pharmacokinetics were unchanged in subjects with NASH. Alanine transaminase (ALT) reduction was greater in active subjects than in placebo groups. Nonparametric analysis suggested that ALT reductions were statistically different in the 225-mg cohort compared with matching placebo: -16.3 ± 25.5% versus -0.7 ± 13.4%, respectively. ProC3 and C6M reduction was statistically significant in groups having baseline ProC3 > 15.0 ng/ml. RCF was safe and well tolerated after 28 days in subjects with presumed F2/F3 NASH. Presence of NASH did not alter its pharmacokinetics. Reductions in ALT, ProC3, and C6M suggest direct antifibrotic effects with longer treatment duration. Reductions in key collagen genes support a mechanism of action via suppression and/or regression of collagen deposition. Conclusion: These results support advancement of rencofilstat into a larger and longer Phase 2b study.

BRANEnet: embedding multilayer networks for omics data integration

Background

Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and efficiently integrated. Here, we propose BRANEnet, a novel multi-omics integration framework for multilayer heterogeneous networks. BRANEnet is an expressive, scalable, and versatile method to learn node embeddings, leveraging random walk information within a matrix factorization framework. Our goal is to efficiently integrate multi-omics data to study different regulatory aspects of multilayered processes that occur in organisms. We evaluate our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied yeast model organism.

Results

We test BRANEnet on transcriptomics (RNA-seq) and targeted metabolomics (NMR) data for wild-type yeast strain during a heat-shock time course of 0, 20, and 120 min. Our framework learns features for differentially expressed bio-molecules showing heat stress response. We demonstrate the applicability of the learned features for targeted omics inference tasks: transcription factor (TF)-target prediction, integrated omics network (ION) inference, and module identification. The performance of BRANEnet is compared to existing network integration methods. Our model outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04955-w.

Identification of a transcriptional signature found in multiple models of ASD and related disorders

Epigenetic regulation plays a critical role in many neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD). In particular, many such disorders are the result of mutations in genes that encode chromatin-modifying proteins. However, although these disorders share many features, it is unclear whether they also share gene expression disruptions resulting from the aberrant regulation of chromatin. We examined five chromatin modifiers that are all linked to ASD despite their different roles in regulating chromatin. Specifically, we depleted ASH1L, CHD8, CREBBP, EHMT1, and NSD1 in parallel in a highly controlled neuronal culture system. We then identified sets of shared genes, or transcriptional signatures, that are differentially expressed following loss of multiple ASD-linked chromatin modifiers. We examined the functions of genes within the transcriptional signatures and found an enrichment in many neurotransmitter transport genes and activity-dependent genes. In addition, these genes are enriched for specific chromatin features such as bivalent domains that allow for highly dynamic regulation of gene expression. The down-regulated transcriptional signature is also observed within multiple mouse models of NDDs that result in ASD, but not those only associated with intellectual disability. Finally, the down-regulated transcriptional signature can distinguish between control and idiopathic ASD patient iPSC-derived neurons as well as postmortem tissue, demonstrating that this gene set is relevant to the human disorder. This work identifies a transcriptional signature that is found within many neurodevelopmental syndromes, helping to elucidate the link between epigenetic regulation and the underlying cellular mechanisms that result in ASD.

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

Distinct immune and transcriptomic profiles in dominant versus subordinate males in mouse social hierarchies

Social status is a critical factor determining health outcomes in human and nonhuman social species. In social hierarchies with reproductive skew, individuals compete to monopolize resources and increase mating opportunities. This can come at a significant energetic cost leading to trade-offs between different physiological systems. In particular, changes in energetic investment in the immune system can have significant short and long-term effects on fitness and health. We have previously found that dominant alpha male mice living in social hierarchies have increased metabolic demands related to territorial defense. In this study, we tested the hypothesis that high-ranking male mice favor adaptive immunity, while subordinate mice show higher investment in innate immunity. We housed 12 groups of 10 outbred CD-1 male mice in a social housing system. All formed linear social hierarchies and subordinate mice had higher concentrations of plasma corticosterone (CORT) than alpha males. This difference was heightened in highly despotic hierarchies. Using flow cytometry, we found that dominant status was associated with a significant shift in immunophenotypes towards favoring adaptive versus innate immunity. Using Tag-Seq to profile hepatic and splenic transcriptomes of alpha and subordinate males, we identified genes that regulate metabolic and immune defense pathways that are associated with status and/or CORT concentration. In the liver, dominant animals showed a relatively higher expression of specific genes involved in major urinary production and catabolic processes, whereas subordinate animals showed relatively higher expression of genes promoting biosynthetic processes, wound healing, and proinflammatory responses. In spleen, subordinate mice showed relatively higher expression of genes facilitating oxidative phosphorylation and DNA repair and CORT was negatively associated with genes involved in lymphocyte proliferation and activation. Together, our findings suggest that dominant and subordinate animals adaptively shift immune profiles and peripheral gene expression to match their contextual needs.

Focal adhesion kinase plays a dual role in TRAIL resistance and metastatic outgrowth of malignant melanoma

Despite remarkable advances in therapeutic interventions, malignant melanoma (MM) remains a life-threating disease. Following high initial response rates to targeted kinase-inhibition metastases quickly acquire resistance and present with enhanced tumor progression and invasion, demanding alternative treatment options. We show 2nd generation hexameric TRAIL-receptor-agonist IZI1551 (IZI) to effectively induce apoptosis in MM cells irrespective of the intrinsic BRAF/NRAS mutation status. Conditioning to the EC50 dose of IZI converted the phenotype of IZI-sensitive parental MM cells into a fast proliferating and invasive, IZI-resistant metastasis. Mechanistically, we identified focal adhesion kinase (FAK) to play a dual role in phenotype-switching. In the cytosol, activated FAK triggers survival pathways in a PI3K- and MAPK-dependent manner. In the nucleus, the FERM domain of FAK prevents activation of wtp53, as being expressed in the majority of MM, and consequently intrinsic apoptosis. Caspase-8-mediated cleavage of FAK as well as FAK knockdown, and pharmacological inhibition, respectively, reverted the metastatic phenotype-switch and restored IZI responsiveness. FAK inhibition also re-sensitized MM cells isolated from patient metastasis that had relapsed from targeted kinase inhibition to cell death, irrespective of the intrinsic BRAF/NRAS mutation status. Hence, FAK-inhibition alone or in combination with 2nd generation TRAIL-receptor agonists may be recommended for treatment of initially resistant and relapsed MM, respectively.

Clinico-histopathologic and single nuclei RNA sequencing insights into cardiac injury and microthrombi in critical COVID-19

Acute cardiac injury is prevalent in critical COVID-19 and associated with increased mortality. Its etiology remains debated, as initially presumed causes--- myocarditis and cardiac necrosis--- have proven uncommon. To elucidate the pathophysiology of COVID-19-associated cardiac injury, we conducted a prospective study of the first 69 consecutive COVID-19 decedents at Columbia University Irving Medical Center in New York City. Of six acute cardiac histopathologic features, microthrombi was the most commonly detected amongst our cohort (n=48, 70%). We tested associations of cardiac microthrombi with biomarkers of inflammation, cardiac injury, and fibrinolysis and with in-hospital antiplatelet therapy, therapeutic anticoagulation, and corticosteroid treatment, while adjusting for multiple clinical factors, including COVID-19 therapies. Higher peak erythrocyte sedimentation rate and c-reactive protein were independently associated with increased odds of microthrombi, supporting an immunothrombotic etiology. Using single nuclei RNA-sequencing analysis on 3 patients with and 4 patients without cardiac microthrombi, we discovered an enrichment of pro-thrombotic/anti-fibrinolytic, extracellular matrix remodeling, and immune-potentiating signaling amongst cardiac fibroblasts in microthrombi-positive, relative to microthrombi-negative, COVID-19 hearts. Non-COVID-19 non-failing hearts were used as reference controls. Our study identifies a specific transcriptomic signature in cardiac fibroblasts as a salient feature of microthrombi-positive COVID-19 hearts. Our findings warrant further mechanistic study as cardiac fibroblasts may represent a potential therapeutic target for COVID-19-associated cardiac microthrombi.

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.

High content genome-wide siRNA screen to investigate the coordination of cell size and RNA production

Coordination of RNA abundance and production rate with cell size has been observed in diverse organisms and cell populations. However, how cells achieve such ‘scaling’ of transcription with size is unknown. Here we describe a genome-wide siRNA screen to identify regulators of global RNA production rates in HeLa cells. We quantify the single-cell RNA production rate using metabolic pulse-labelling of RNA and subsequent high-content imaging. Our quantitative, single-cell measurements of DNA, nascent RNA, proliferating cell nuclear antigen (PCNA), and total protein, as well as cell morphology and population-context, capture a detailed cellular phenotype. This allows us to account for changes in cell size and cell-cycle distribution (G1/S/G2) in perturbation conditions, which indirectly affect global RNA production. We also take advantage of the subcellular information to distinguish between nascent RNA localised in the nucleolus and nucleoplasm, to approximate ribosomal and non-ribosomal RNA contributions to perturbation phenotypes. Perturbations uncovered through this screen provide a resource for exploring the mechanisms of regulation of global RNA metabolism and its coordination with cellular states.

Measurement(s)	nascent RNA • Image • S phase • nucleolus organization • Cellular Morphology • Cell Cycle Phase
Technology Type(s)	metabolic labelling: 5-ethynyl uridine • spinning-disk confocal microscope • supervised machine learning • Image Processing
Factor Type(s)	gene expression
Sample Characteristic - Organism	HeLa cell
Sample Characteristic - Environment	cell culture

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14332916

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.