Pathway Commons 2019 Update: integration, analysis and exploration of pathway data

Role of SYT11 in human pan-cancer using comprehensive approaches

BACKGROUND

Synaptotagmin 11 (SYT11) plays a pivotal role in neuronal vesicular trafficking and exocytosis. However, no independent prognostic studies have focused on various cancers. In this study, we aimed to summarize the clinical significance and molecular landscape of SYT11 in various tumor types.

METHODS

Using several available public databases, we investigated abnormal SYT11 expression in different tumor types and its potential clinical association with prognosis, methylation profiling, immune infiltration, gene enrichment analysis, and protein-protein interaction analysis, and identified common pathways.

RESULTS

TCGA and Genotype-Tissue Expression (GTEx) showed that SYT11 was widely expressed across tumor and corresponding normal tissues. Survival analysis showed that SYT11 expression correlated with the prognosis of seven cancer types. Additionally, SYT11 mRNA expression was not affected by promoter methylation, but regulated by certain miRNAs and associated with cancer patient prognosis. In vitro experiments further verified a negative correlation between the expression of SYT11 and miR-19a-3p in human colorectal, lung, and renal cancer cell lines. Moreover, aberrant SYT11 expression was significantly associated with immune infiltration. Pathway enrichment analysis revealed that the biological and molecular processes of SYT11 were related to clathrin-mediated endocytosis, Rho GTPase signaling, and cell motility-related functions.

CONCLUSIONS

Our results provide a clear understanding of the role of SYT11 in various cancer types and suggest that SYT11 may be of prognostic and clinical significance.

Boolean Network Models of Human Preimplantation Development

Single-cell transcriptomic studies of differentiating systems allow meaningful understanding, especially in human embryonic development and cell fate determination. We present an innovative method aimed at modeling these intricate processes by leveraging scRNAseq data from various human developmental stages. Our implemented method identifies pseudo-perturbations, since actual perturbations are unavailable due to ethical and technical constraints. By integrating these pseudo-perturbations with prior knowledge of gene interactions, our framework generates stage-specific Boolean networks (BNs). We apply our method to medium and late trophectoderm developmental stages and identify 20 pseudo-perturbations required to infer BNs. The resulting BN families delineate distinct regulatory mechanisms, enabling the differentiation between these developmental stages. We show that our program outperforms existing pseudo-perturbation identification tool. Our framework contributes to comprehending human developmental processes and holds potential applicability to diverse developmental stages and other research scenarios.

drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network

Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is important in biomedicine, where models need to be understandable in comparison with established knowledge of drug interactions with proteins. drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs. drGAT is designed with two objectives: DR prediction as a binary sensitivity prediction and elucidation of drug mechanism from attention coefficients. drGAT has demonstrated superior performance over existing models, achieving 78% accuracy (and precision), and 76% F1 score for 269 DNA-damaging compounds of the NCI60 drug response dataset. To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug. We also examined whether known relationships were retained in the model by inspecting the neighborhoods of topoisomerase-related drugs. For example, our model retained TOP1 as a highly weighted predictive feature for irinotecan and topotecan, in addition to other genes that could potentially be regulators of the drugs. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.

State of the Interactomes: an evaluation of molecular networks for generating biological insights

Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

Meta-analysis of differential gene expression in lower motor neurons isolated by laser capture microdissection from post-mortem ALS spinal cords

Introduction

ALS is a fatal neurodegenerative disease for which underlying mechanisms are incompletely understood. The motor neuron is a central player in ALS pathogenesis but different transcriptome signatures have been derived from bulk analysis of post-mortem tissue and iPSC-derived motor neurons (iPSC-MNs).

Methods

This study performed a meta-analysis of six gene expression studies (microarray and RNA-seq) in which laser capture microdissection (LCM) was used to isolate lower motor neurons from post-mortem spinal cords of ALS and control (CTL) subjects. Differentially expressed genes (DEGs) with consistent ALS versus CTL expression differences across studies were identified.

Results

The analysis identified 222 ALS-increased DEGs (FDR <0.10, SMD >0.80) and 278 ALS-decreased DEGs (FDR <0.10, SMD < -0.80). ALS-increased DEGs were linked to PI3K-AKT signaling, innate immunity, inflammation, motor neuron differentiation and extracellular matrix. ALS-decreased DEGs were associated with the ubiquitin-proteosome system, microtubules, axon growth, RNA-binding proteins and synaptic membrane. ALS-decreased DEG mRNAs frequently interacted with RNA-binding proteins (e.g., FUS, HuR). The complete set of DEGs (increased and decreased) overlapped significantly with genes near ALS-associated SNP loci (p < 0.01). Transcription factor target motifs with increased proximity to ALS-increased DEGs were identified, most notably DNA elements predicted to interact with forkhead transcription factors (e.g., FOXP1) and motor neuron and pancreas homeobox 1 (MNX1). Some of these DNA elements overlie ALS-associated SNPs within known enhancers and are predicted to have genotype-dependent MNX1 interactions. DEGs were compared to those identified from SOD1-G93A mice and bulk spinal cord segments or iPSC-MNs from ALS patients. There was good correspondence with transcriptome changes from SOD1-G93A mice (r ≤ 0.408) but most DEGs were not differentially expressed in bulk spinal cords or iPSC-MNs and transcriptome-wide effect size correlations were weak (bulk tissue: r ≤ 0.207, iPSC-MN: r ≤ 0.037).

Conclusion

This study defines a robust transcriptome signature from LCM-based motor neuron studies of post-mortem tissue from ALS and CTL subjects. This signature differs from those obtained from analysis of bulk spinal cord segments and iPSC-MNs. Results provide insight into mechanisms underlying gene dysregulation in ALS and highlight connections between these mechanisms, ALS genetics, and motor neuron biology.

Predicting transcription factor activity using prior biological information

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

Priority index for critical Covid-19 identifies clinically actionable targets and drugs

While genome-wide studies have identified genomic loci in hosts associated with life-threatening Covid-19 (critical Covid-19), the challenge of resolving these loci hinders further identification of clinically actionable targets and drugs. Building upon our previous success, we here present a priority index solution designed to address this challenge, generating the target and drug resource that consists of two indexes: the target index and the drug index. The primary purpose of the target index is to identify clinically actionable targets by prioritising genes associated with Covid-19. We illustrate the validity of the target index by demonstrating its ability to identify pre-existing Covid-19 phase-III drug targets, with the majority of these targets being found at the leading prioritisation (leading targets). These leading targets have their evolutionary origins in Amniota ('four-leg vertebrates') and are predominantly involved in cytokine-cytokine receptor interactions and JAK-STAT signaling. The drug index highlights opportunities for repurposing clinically approved JAK-STAT inhibitors, either individually or in combination. This proposed strategic focus on the JAK-STAT pathway is supported by the active pursuit of therapeutic agents targeting this pathway in ongoing phase-II/III clinical trials for Covid-19.

Analysis of SIGLEC12 expression, immunomodulation and prognostic value in renal cancer using multiomic databases

Siglecs belong to a family of immune regulatory receptors predominantly found on hematopoietic cells. They interact with Sia, resulting in the activation or inhibition of the immune response. Previous reports have suggested that the SIGLEC12 gene, which encodes the Siglec-XII protein, is expressed in the epithelial tissues and upregulated in carcinomas. However, studies deciphering the role of Siglec-XII in renal cancer (RC) are still unavailable, and here we provide insights on this question. We conducted expression analysis using the Human Protein Atlas and UALCAN databases. The impact of SIGLEC12 on RC prognosis was determined using the KM plotter, and an assessment of immune infiltration with SIGLEC12 was performed using the TIMER database. GSEA was conducted to identify the pathways affected by SIGLEC12. Finally, using GeneMania, we identified Siglec-XII interacting proteins. Our findings indicated that macrophages express SIGLEC12 in the kidney. Furthermore, we hypothesize that Siglec-XII expression might be involved in the increase of primary RC, but this effect may not be dependent on the age of the patient. In the tumour microenvironment, oncogenic pathways appeared to be upregulated by SIGLEC12. Similarly, our analysis suggested that SIGLEC12-related kidney renal papillary cell carcinomas may be more suitable for targeted immunotherapy, such as CTLA-4 and PD-1/PD-L1 inhibitors. These preliminary results suggested that high expression of SIGLEC12 is associated with poor prognosis for RC. Future studies to assess its clinical utility are necessitated.

GINv2.0: a comprehensive topological network integrating molecular interactions from multiple knowledge bases

Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a "meta-pathway" structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0 .

Slow cycling and durable Flt3+ progenitors contribute to hematopoiesis under native conditions

The dynamics of the hematopoietic flux responsible for blood cell production in native conditions remains a matter of debate. Using CITE-seq analyses, we uncovered a distinct progenitor population that displays a cell cycle gene signature similar to the one found in quiescent hematopoietic stem cells. We further determined that the CD62L marker can be used to phenotypically enrich this population in the Flt3+ multipotent progenitor (MPP4) compartment. Functional in vitro and in vivo analyses validated the heterogeneity of the MPP4 compartment and established the quiescent/slow-cycling properties of the CD62L- MPP4 cells. Furthermore, studies under native conditions revealed a novel hierarchical organization of the MPP compartments in which quiescent/slow-cycling MPP4 cells sustain a prolonged hematopoietic activity at steady-state while giving rise to other lineage-biased MPP populations. Altogether, our data characterize a durable and productive quiescent/slow-cycling hematopoietic intermediary within the MPP4 compartment and highlight early paths of progenitor differentiation during unperturbed hematopoiesis.

TidyGEO: preparing analysis-ready datasets from Gene Expression Omnibus

TidyGEO is a Web-based tool for downloading, tidying, and reformatting data series from Gene Expression Omnibus (GEO). As a freely accessible repository with data from over 6 million biological samples across more than 4000 organisms, GEO provides diverse opportunities for secondary research. Although scientists may find assay data relevant to a given research question, most analyses require sample-level annotations. In GEO, such annotations are stored alongside assay data in delimited, text-based files. However, the structure and semantics of the annotations vary widely from one series to another, and many annotations are not useful for analysis purposes. Thus, every GEO series must be tidied before it is analyzed. Manual approaches may be used, but these are error prone and take time away from other research tasks. Custom computer scripts can be written, but many scientists lack the computational expertise to create such scripts. To address these challenges, we created TidyGEO, which supports essential data-cleaning tasks for sample-level annotations, such as selecting informative columns, renaming columns, splitting or merging columns, standardizing data values, and filtering samples. Additionally, users can integrate annotations with assay data, restructure assay data, and generate code that enables others to reproduce these steps.

SAMD13 serves as a useful prognostic biomarker for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common form of liver cancer and the 5-year relative overall survival (OS) rate is less than 20%. Since there are no specific symptoms, most patients with HCC are diagnosed in an advanced stage with poor prognosis. Therefore, identifying novel prognostic biomarkers to improve the survival of patients with HCC is urgently needed. In the present study, we attempted to identify SAMD13 (Sterile Alpha Motif Domain-Containing Protein 13) as a novel biomarker associated with the prognosis of HCC using various bioinformatics tools. SAMD13 was found to be highly expressed pan-cancer; however, the SAMD13 expression was significantly correlated with the worst prognosis in HCC. Clinicopathological analysis revealed that SAMD13 upregulation was significantly associated with advanced HCC stage and high-grade tumor type. Simultaneously, high SAMD13 expression resulted in association with various immune markers in the immune cell subsets by TIMER databases and efficacy of immunotherapy. Methylation analysis showed SAMD13 was remarkably associated with prognosis. Furthermore, a six-hub gene signature associated with poor prognosis was correlated with the cell cycle, transcription, and epigenetic regulation and this analysis may support the connection between SAMD13 expression and drug-resistance. Our study illustrated the characteristics of SAMD13 role in patients with HCC using various bioinformatics tools and highlights its potential role as a therapeutic target and promising biomarker for prognosis in HCC.

Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network

BACKGROUND

In cancer genomic medicine, finding driver mutations involved in cancer development and tumor growth is crucial. Machine-learning methods to predict driver missense mutations have been developed because variants are frequently detected by genomic sequencing. However, even though the abnormalities in molecular networks are associated with cancer, many of these methods focus on individual variants and do not consider molecular networks. Here we propose a new network-based method, Net-DMPred, to predict driver missense mutations considering molecular networks. Net-DMPred consists of the graph part and the prediction part. In the graph part, molecular networks are learned by a graph neural network (GNN). The prediction part learns whether variants are driver variants using features of individual variants combined with the graph features learned in the graph part.

RESULTS

Net-DMPred, which considers molecular networks, performed better than conventional methods. Furthermore, the prediction performance differed by the molecular network structure used in learning, suggesting that it is important to consider not only the local network related to cancer but also the large-scale network in living organisms.

CONCLUSIONS

We propose a network-based machine learning method, Net-DMPred, for predicting cancer driver missense mutations. Our method enables us to consider the entire graph architecture representing the molecular network because it uses GNN. Net-DMPred is expected to detect driver mutations from a lot of missense mutations that are not known to be associated with cancer.

Nucleosome repositioning in chronic lymphocytic leukemia

The location of nucleosomes in the human genome determines the primary chromatin structure and regulates access to regulatory regions. However, genome-wide information on deregulated nucleosome occupancy and its implications in primary cancer cells is scarce. Here, we conducted a genome-wide comparison of high-resolution nucleosome maps in peripheral blood B cells from patients with chronic lymphocytic leukemia (CLL) and healthy individuals at single-base-pair resolution. Our investigation uncovered significant changes of nucleosome positioning in CLL. Globally, the spacing between nucleosomes-the nucleosome repeat length (NRL)-is shortened in CLL. This effect is stronger in the more aggressive IGHV-unmutated CLL subtype than in the IGHV-mutated CLL subtype. Changes in nucleosome occupancy at specific sites are linked to active chromatin remodeling and reduced DNA methylation. Nucleosomes lost or gained in CLL marks differential binding of 3D chromatin organizers such as CTCF as well as immune response-related transcription factors and delineated mechanisms of epigenetic deregulation. The principal component analysis of nucleosome occupancy in cancer-specific regions allowed the classification of samples between cancer subtypes and normal controls. Furthermore, patients could be better assigned to CLL subtypes according to differential nucleosome occupancy than based on DNA methylation or gene expression. Thus, nucleosome positioning constitutes a novel readout to dissect molecular mechanisms of disease progression and to stratify patients. Furthermore, we anticipate that the global nucleosome repositioning detected in our study, such as changes in the NRL, can be exploited for liquid biopsy applications based on cell-free DNA to stratify patients and monitor disease progression.

COVIDpro: Database for Mining Protein Dysregulation in Patients with COVID-19

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.

Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathways

Introduction: Intercellular adhesion molecule 1 (ICAM-1) is a critical molecule responsible for interactions between cells. Previous studies have suggested that ICAM-1 triggers cell-to-cell transmission of HIV-1 or HTLV-1, that SARS-CoV-2 shares several features with these viruses via interactions between cells, and that SARS-CoV-2 cell-to-cell transmission is associated with COVID-19 severity. From these previous arguments, it is assumed that ICAM-1 can be related to SARS-CoV-2 cell-to-cell transmission in COVID-19 patients. Indeed, the time-dependent change of the ICAM-1 expression level has been detected in COVID-19 patients. However, signaling pathways that consist of ICAM-1 and other molecules interacting with ICAM-1 are not identified in COVID-19. For example, the current COVID-19 Disease Map has no entry for those pathways. Therefore, discovering unknown ICAM1-associated pathways will be indispensable for clarifying the mechanism of COVID-19. Materials and methods: This study builds ICAM1-associated pathways by gene network inference from single-cell omics data and multiple knowledge bases. First, single-cell omics data analysis extracts coexpressed genes with significant differences in expression levels with spurious correlations removed. Second, knowledge bases validate the models. Finally, mapping the models onto existing pathways identifies new ICAM1-associated pathways. Results: Comparison of the obtained pathways between different cell types and time points reproduces the known pathways and indicates the following two unknown pathways: (1) upstream pathway that includes proteins in the non-canonical NF-κB pathway and (2) downstream pathway that contains integrins and cytoskeleton or motor proteins for cell transformation. Discussion: In this way, data-driven and knowledge-based approaches are integrated into gene network inference for ICAM1-associated pathway construction. The results can contribute to repairing and completing the COVID-19 Disease Map, thereby improving our understanding of the mechanism of COVID-19.

A ligand-receptor interactome atlas of the zebrafish

Studies in zebrafish can unravel the functions of cellular communication and thus identify novel bench-to-bedside drugs targeting cellular communication signaling molecules. Due to the incomplete annotation of zebrafish proteome, the knowledge of zebrafish receptors, ligands, and tools to explore their interactome is limited. To address this gap, we de novo predicted the cellular localization of zebrafish reference proteome using deep learning algorithm. We combined the predicted and existing annotations on cellular localization of zebrafish proteins and created repositories of zebrafish ligands, membrane receptome, and interactome as well as associated diseases and targeting drugs. Unlike other tools, our interactome atlas is based on both the physical interaction data of zebrafish proteome and existing human ligand-receptor pair databases. The resources are available as R and Python scripts. DanioTalk provides a novel resource for researchers interested in targeting cellular communication in zebrafish, as we demonstrate in applications studying synapse and axo-glial interactome. DanioTalk methodology can be applied to build and explore the ligand-receptor atlas of other non-mammalian model organisms.

Visualization of automatically combined disease maps and pathway diagrams for rare diseases

Introduction: Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. Methods: In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. Results: We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. Discussion: In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.

IndepthPathway: an integrated tool for in-depth pathway enrichment analysis based on single-cell sequencing data

MOTIVATION

Single-cell sequencing enables exploring the pathways and processes of cells, and cell populations. However, there is a paucity of pathway enrichment methods designed to tolerate the high noise and low gene coverage of this technology. When gene expression data are noisy and signals are sparse, testing pathway enrichment based on the genes expression may not yield statistically significant results, which is particularly problematic when detecting the pathways enriched in less abundant cells that are vulnerable to disturbances.

RESULTS

In this project, we developed a Weighted Concept Signature Enrichment Analysis specialized for pathway enrichment analysis from single-cell transcriptomics (scRNA-seq). Weighted Concept Signature Enrichment Analysis took a broader approach for assessing the functional relations of pathway gene sets to differentially expressed genes, and leverage the cumulative signature of molecular concepts characteristic of the highly differentially expressed genes, which we termed as the universal concept signature, to tolerate the high noise and low coverage of this technology. We then incorporated Weighted Concept Signature Enrichment Analysis into an R package called "IndepthPathway" for biologists to broadly leverage this method for pathway analysis based on bulk and single-cell sequencing data. Through simulating technical variability and dropouts in gene expression characteristic of scRNA-seq as well as benchmarking on a real dataset of matched single-cell and bulk RNAseq data, we demonstrate that IndepthPathway presents outstanding stability and depth in pathway enrichment results under stochasticity of the data, thus will substantially improve the scientific rigor of the pathway analysis for single-cell sequencing data.

AVAILABILITY AND IMPLEMENTATION

The IndepthPathway R package is available through: https://github.com/wangxlab/IndepthPathway.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations

BACKGROUND

Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs.

METHODS

Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis.

RESULTS

The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis.

CONCLUSION

The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.

Clustering rare diseases within an ontology-enriched knowledge graph

Objective

Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing and/or platform based therapeutic development. Toward that aim, we utilized an integrative knowledge graph-based approach to constructing clusters of rare diseases.

Materials and Methods

Data on 3,242 rare diseases were extracted from the National Center for Advancing Translational Science (NCATS) Genetic and Rare Diseases Information center (GARD) internal data resources. The rare disease data was enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were used to convert nodes into vectors upon which k-means clustering was applied. We validated the disease clusters through semantic similarity and feature enrichment analysis.

Results

A node embedding model was trained on the ontology enriched rare disease KG and k-means clustering was applied to the embedding vectors resulting in 37 disease clusters with a mean size of 87 diseases. We validate the disease clusters quantitatively by looking at semantic similarity of clustered diseases, using the Orphanet Rare Disease Ontology. In addition, the clusters were analyzed for enrichment of associated genes, revealing that the enriched genes within clusters were shown to be highly related.

Discussion

We demonstrate that node embeddings are an effective method for clustering diseases within a heterogenous KG. Semantically similar diseases and relevant enriched genes have been uncovered within the clusters. Connections between disease clusters and approved or investigational drugs are enumerated for follow-up efforts.

Conclusion

Our study lays out a method for clustering rare diseases using the graph node embeddings. We develop an easy to maintain pipeline that can be updated when new data on rare diseases emerges. The embeddings themselves can be paired with other representation learning methods for other data types, such as drugs, to address other predictive modeling problems. Detailed subnetwork analysis and in-depth review of individual clusters may lead to translatable findings. Future work will focus on incorporation of additional data sources, with a particular focus on common disease data.

Nociceptor neuroimmune interactomes reveal cell type- and injury-specific inflammatory pain pathways

Inflammatory pain associated with tissue injury and infections, results from the heightened sensitivity of the peripheral terminals of nociceptor sensory neurons in response to exposure to inflammatory mediators. Targeting immune-derived inflammatory ligands, like prostaglandin E2, has been effective in alleviating inflammatory pain. However, the diversity of immune cells and the vast array of ligands they produce make it challenging to systematically map all neuroimmune pathways that contribute to inflammatory pain. Here, we constructed a comprehensive and updatable database of receptor-ligand pairs and complemented it with single-cell transcriptomics of immune cells and sensory neurons in three distinct inflammatory pain conditions, to generate injury-specific neuroimmune interactomes. We identified cell-type-specific neuroimmune axes that are common, as well as unique, to different injury types. This approach successfully predicts neuroimmune pathways with established roles in inflammatory pain as well as ones not previously described. We found that thrombospondin-1 produced by myeloid cells in all three conditions, is a negative regulator of nociceptor sensitization, revealing a non-canonical role of immune ligands as an endogenous reducer of peripheral sensitization. This computational platform lays the groundwork to identify novel mechanisms of immune-mediated peripheral sensitization and the specific disease contexts in which they act.

Organism-Wide Analysis of Sepsis Reveals Mechanisms of Systemic Inflammation

Sepsis is a systemic response to infection with life-threatening consequences. Our understanding of the impact of sepsis across organs of the body is rudimentary. Here, using mouse models of sepsis, we generate a dynamic, organism-wide map of the pathogenesis of the disease, revealing the spatiotemporal patterns of the effects of sepsis across tissues. These data revealed two interorgan mechanisms key in sepsis. First, we discover a simplifying principle in the systemic behavior of the cytokine network during sepsis, whereby a hierarchical cytokine circuit arising from the pairwise effects of TNF plus IL-18, IFN-γ, or IL-1β explains half of all the cellular effects of sepsis on 195 cell types across 9 organs. Second, we find that the secreted phospholipase PLA2G5 mediates hemolysis in blood, contributing to organ failure during sepsis. These results provide fundamental insights to help build a unifying mechanistic framework for the pathophysiological effects of sepsis on the body.

Investigating the Complexity of Gene Co-expression Estimation for Single-cell Data

With the rapid advance of single-cell RNA sequencing (scRNA-seq) technology, understanding biological processes at a more refined single-cell level is becoming possible. Gene co-expression estimation is an essential step in this direction. It can annotate functionalities of unknown genes or construct the basis of gene regulatory network inference. This study thoroughly tests the existing gene co-expression estimation methods on simulation datasets with known ground truth co-expression networks. We generate these novel datasets using two simulation processes that use the parameters learned from the experimental data. We demonstrate that these simulations better capture the underlying properties of the real-world single-cell datasets than previously tested simulations for the task. Our performance results on tens of simulated and eight experimental datasets show that all methods produce estimations with a high false discovery rate potentially caused by high-sparsity levels in the data. Finally, we find that commonly used pre-processing approaches, such as normalization and imputation, do not improve the co-expression estimation. Overall, our benchmark setup contributes to the co-expression estimator development, and our study provides valuable insights for the community of single-cell data analyses.

Specific gene module pair-based target identification and drug discovery

Identification of the biological targets of a compound is of paramount importance for the exploration of the mechanism of action of drugs and for the development of novel drugs. A concept of the Connectivity Map (CMap) was previously proposed to connect genes, drugs, and disease states based on the common gene-expression signatures. For a new query compound, the CMap-based method can infer its potential targets by searching similar drugs with known targets (reference drugs) and measuring the similarities into their specific transcriptional responses between the query compound and those reference drugs. However, the available methods are often inefficient due to the requirement of the reference drugs as a medium to link the query agent and targets. Here, we developed a general procedure to extract target-induced consensus gene modules from the transcriptional profiles induced by the treatment of perturbagens of a target. A specific transcriptional gene module pair (GMP) was automatically identified for each target and could be used as a direct target signature. Based on the GMPs, we built the target network and identified some target gene clusters with similar biological mechanisms. Moreover, a gene module pair-based target identification (GMPTI) approach was proposed to predict novel compound-target interactions. Using this method, we have discovered novel inhibitors for three PI3K pathway proteins PI3Kα/β/δ, including PU-H71, alvespimycin, reversine, astemizole, raloxifene HCl, and tamoxifen.

Graph algorithms for predicting subcellular localization at the pathway level

Protein subcellular localization is an important factor in normal cellular processes and disease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data. We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We compare a variety of models including graph neural networks, probabilistic graphical models, and discriminative classifiers for predicting localization annotations from curated pathway databases. We also perform a case study where we construct biological pathways and predict localizations of human fibroblasts undergoing viral infection. Pathway localization prediction is a promising approach for integrating publicly available localization data into the analysis of large-scale biological data.

DUSP1 Signaling Pathway Regulates Cytarabine Sensitivity in Acute Myeloid Leukemia

Objectives: Dual specificity phosphatase 1 (DUSP1) is high-expressed in various cancers and plays an important role in the cellular response to agents that damage DNA. We aimed to investigate the expressions and mechanisms of DUSP1 signaling pathway regulating cytarabine (Ara-C) resistance in acute myeloid leukemia (AML). Methods: Immunohistochemistry was performed on bone marrow biopsy specimens from AML and controls to explore the expression of DUSP1. Western blot and Q-PCR were used to detect the protein and mRNA expression levels. MTT assay was used to detect the proliferation of cells. Cell apoptosis was detected by flow cytometry. The immune protein-protein interaction (PPI) network of DUSP1 was analyzed in the platform of Pathway Commons, and immune infiltration analysis was used to study the immune microenvironment of AML. Results: We found that the expression levels of DUSP1 in AML patients exceeded that in controls. Survival analysis in public datasets showed that AML patients with higher levels of DUSP1 had poor clinical outcomes. Further public data analysis indicated that DUSP1 was overexpressed in NRAS mutated AML. DUSP1 knockdown by siRNA could sensitize AML cells to Ara-C treatments. The phosphorylation level of mitogen-activated protein kinase (MAPK) pathway was significantly elevated in DUSP1 down-regulated NRAS G13D mutated AML cells. The PPI analysis showed DUSP1 correlated with immune gene CREB1 and CXCL8 in NRAS mutated AML. We also revealed a correlation between tumor-infiltrating immune cells in RAS mutated AML microenvironment. Conclusion: Our findings suggest that DUSP1 signaling pathways may regulate Ara-C sensitivity in AML.

Cell annotation using scRNA-seq data: A protein-protein interaction network approach

Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.•We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes.•The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.

Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study

A popular approach to reduce the high dimensionality resulting from genome-wide association studies is to analyze a whole pathway in a single test for association with a phenotype. Kernel machine regression (KMR) is a highly flexible pathway analysis approach. Initially, KMR was developed to analyze a simple phenotype with just one measurement per individual. Recently, however, the investigation into the influence of genomic factors in the development of disease-related phenotypes across time (trajectories) has gained in importance. Thus, novel statistical approaches for KMR analyzing longitudinal data, i.e. several measurements at specific time points per individual are required. For longitudinal pathway analysis, we extend KMR to long-KMR using the estimation equivalence of KMR and linear mixed models. We include additional random effects to correct for the dependence structure. Moreover, within long-KMR we created a topology-based pathway analysis by combining this approach with a kernel including network information of the pathway. Most importantly, long-KMR not only allows for the investigation of the main genetic effect adjusting for time dependencies within an individual, but it also allows to test for the association of the pathway with the longitudinal course of the phenotype in the form of testing the genetic time-interaction effect. The approach is implemented as an R package, kalpra. Our simulation study demonstrates that the power of long-KMR exceeded that of another KMR method previously developed to analyze longitudinal data, while maintaining (slightly conservatively) the type I error. The network kernel improved the performance of long-KMR compared to the linear kernel. Considering different pathway densities, the power of the network kernel decreased with increasing pathway density. We applied long-KMR to cognitive data on executive function (Trail Making Test, part B) from the PsyCourse Study and 17 candidate pathways selected from Reactome. We identified seven nominally significant pathways.

Looking for Tumor Specific Promoters In Silico

Abstract—

Previously we demonstrated the tumor-specific activity of several human native and chimeric promoters. Here we have analyzed the DNA sequences of experimentally tested tumor-specific promoters for the presence of recognition matrices of transcription factors and for de novo motif discovery. CiiiDER and MEME Suite software tools were used for this purpose. A number of transcription factor matrices have been identified, which are present more often in tumor-specific promoters than in the promoters of housekeeping genes. New promoter–TF regulatory relationships have been predicted by pathway analysis. A motif of 44 bp characteristic of tumor-specific promoters but not of housekeeping gene promoters has been discovered. The search through 29 598 human promoters from the EPDnew promoter database has revealed a series of promoters with this motif, their genes being associated with unfavorable prognoses in cancer. We suppose that some of these promoters may possess a tumor specific activity. In addition, a close similarity in nucleotide motifs between the promoters of the BIRC5 and MCM2 genes has been shown. The results of the study may contribute to understanding the peculiarities of gene transcription in tumors, as well as to searching for native tumor-specific promoters or creating artificial ones for cancer gene therapy, as well as in the development of anticancer vaccines.

Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Survey for Computer-Aided Tools and Databases in Metabolomics

Metabolomics has advanced from innovation and functional genomics tools and is currently a basis in the big data-led precision medicine era. Metabolomics is promising in the pharmaceutical field and clinical research. However, due to the complexity and high throughput data generated from such experiments, data mining and analysis are significant challenges for researchers in the field. Therefore, several efforts were made to develop a complete workflow that helps researchers analyze data. This paper introduces a review of the state-of-the-art computer-aided tools and databases in metabolomics established in recent years. The paper provides computational tools and resources based on functionality and accessibility and provides hyperlinks to web pages to download or use. This review aims to present the latest computer-aided tools, databases, and resources to the metabolomics community in one place.

Is IIIG9 a New Protein with Exclusive Ciliary Function? Analysis of Its Potential Role in Cancer and Other Pathologies

The identification of new proteins that regulate the function of one of the main cellular phosphatases, protein phosphatase 1 (PP1), is essential to find possible pharmacological targets to alter phosphatase function in various cellular processes, including the initiation and development of multiple diseases. IIIG9 is a regulatory subunit of PP1 initially identified in highly polarized ciliated cells. In addition to its ciliary location in ependymal cells, we recently showed that IIIG9 has extraciliary functions that regulate the integrity of adherens junctions. In this review, we perform a detailed analysis of the expression, localization, and function of IIIG9 in adult and developing normal brains. In addition, we provide a 3D model of IIIG9 protein structure for the first time, verifying that the classic structural and conformational characteristics of the PP1 regulatory subunits are maintained. Our review is especially focused on finding evidence linking IIIG9 dysfunction with the course of some pathologies, such as ciliopathies, drug dependence, diseases based on neurological development, and the development of specific high-malignancy and -frequency brain tumors in the pediatric population. Finally, we propose that IIIG9 is a relevant regulator of PP1 function in physiological and pathological processes in the CNS.

scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies

BACKGROUND

The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes.

RESULTS

scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants.

CONCLUSIONS

We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.

CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research

BACKGROUND

Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated.

METHODS

To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes.

RESULTS

Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy.

CONCLUSIONS

Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/ . The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway .

EZCancerTarget: an open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers

The expanding body of potential therapeutic targets requires easily accessible, structured, and transparent real-time interpretation of molecular data. Open-access genomic, proteomic and drug-repurposing databases transformed the landscape of cancer research, but most of them are difficult and time-consuming for casual users. Furthermore, to conduct systematic searches and data retrieval on multiple targets, researchers need the help of an expert bioinformatician, who is not always readily available for smaller research teams. We invite research teams to join and aim to enhance the cooperative work of more experienced groups to harmonize international efforts to overcome devastating malignancies. Here, we integrate available fundamental data and present a novel, open access, data-aggregating, drug repurposing platform, deriving our searches from the entries of Clue.io. We show how we integrated our previous expertise in small-cell lung cancer (SCLC) to initiate a new platform to overcome highly progressive cancers such as triple-negative breast and pancreatic cancer with data-aggregating approaches. Through the front end, the current content of the platform can be further expanded or replaced and users can create their drug-target list to select the clinically most relevant targets for further functional validation assays or drug trials. EZCancerTarget integrates searches from publicly available databases, such as PubChem, DrugBank, PubMed, and EMA, citing up-to-date and relevant literature of every target. Moreover, information on compounds is complemented with biological background information on eligible targets using entities like UniProt, String, and GeneCards, presenting relevant pathways, molecular- and biological function and subcellular localizations of these molecules. Cancer drug discovery requires a convergence of complex, often disparate fields. We present a simple, transparent, and user-friendly drug repurposing software to facilitate the efforts of research groups in the field of cancer research.

A genetic map of the chromatin regulators to drug response in cancer cells

BACKGROUND

Diverse drug vulnerabilities owing to the Chromatin regulators (CRs) genetic interaction across various cancers, but the identification of CRs genetic interaction remains challenging.

METHODS

In order to provide a global view of the CRs genetic interaction in cancer cells, we developed a method to identify potential drug response-related CRs genetic interactions for specific cancer types by integrating the screen of CRISPR-Cas9 and pharmacogenomic response datasets.

RESULTS

Totally, 625 drug response-related CRs synthetic lethality (CSL) interactions and 288 CRs synthetic viability (CSV) interactions were detected. Systematically network analysis presented CRs genetic interactions have biological function relationship. Furthermore, we validated CRs genetic interactions induce multiple omics deregulation in The Cancer Genome Atlas. We revealed the colon adenocarcinoma patients (COAD) with mutations of a CRs set (EP300, MSH6, NSD2 and TRRAP) mediate a better survival with low expression of MAP2 and could benefit from taxnes. While the COAD patients carrying at least one of the CSV interactions in Vorinostat CSV module confer a poor prognosis and may be resistant to Vorinostat treatment.

CONCLUSIONS

The CRs genetic interaction map provides a rich resource to investigate cancer-associated CRs genetic interaction and proposes a powerful strategy of biomarker discovery to guide the rational use of agents in cancer therapy.

COVIDpro: Database for mining protein dysregulation in patients with COVID-19

Background

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers.

Methods

We built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers.

Findings

We built and maintain a web server COVIDpro ( https://www.guomics.com/covidPro/ ) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80.

Interpretation

COVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients.

Funding

National Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504).

Research in context

Evidence before this study: Although an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the disease’s mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets.Added value of this study: We integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples.Implications of all the available evidence: COVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.

The heterogeneous pharmacological medical biochemical network PharMeBINet

Heterogeneous biomedical pharmacological databases are important for multiple fields in bioinformatics. Hetionet is a freely available database combining diverse entities and relationships from 29 public resources. Therefore, it is used as the basis for this project. 19 additional pharmacological medical and biological databases such as CTD, DrugBank, and ClinVar are parsed and integrated into Neo4j. Afterwards, the information is merged into the Hetionet structure. Different mapping methods are used such as external identification systems or name mapping. The resulting open-source Neo4j database PharMeBINet has 2,869,407 different nodes with 66 labels and 15,883,653 relationships with 208 edge types. It is a heterogeneous database containing interconnected information on ADRs, diseases, drugs, genes, gene variations, proteins, and more. Relationships between these entities represent drug-drug interactions or drug-causes-ADR relations, to name a few. It has much potential for developing further data analyses including machine learning applications. A web application for accessing the database is free to use for everyone and available at https://pharmebi.net. Additionally, the database is deposited on Zenodo at 10.5281/zenodo.6578218.

Measurement(s)	data integration objective
Technology Type(s)	database creation objective

A hexa-species transcriptome atlas of mammalian embryogenesis delineates metabolic regulation across three different implantation modes

Mammalian embryogenesis relies on glycolysis and oxidative phosphorylation to balance the generation of biomass with energy production. However, the dynamics of metabolic regulation in the postimplantation embryo in vivo have remained elusive due to the inaccessibility of the implanted conceptus for biochemical studies. To address this issue, we compiled single-cell embryo profiling data in six mammalian species and determined their metabolic dynamics through glycolysis and oxidative phosphorylation associated gene expression. Strikingly, we identify a conserved switch from bivalent respiration in the late blastocyst towards a glycolytic metabolism in early gastrulation stages across species, which is independent of embryo implantation. Extraembryonic lineages followed the dynamics of the embryonic lineage, except visceral endoderm. Finally, we demonstrate that in vitro primate embryo culture substantially impacts metabolic gene regulation by comparison to in vivo samples. Our work reveals a conserved metabolic programme despite different implantation modes and highlights the need to optimise postimplantation embryo culture protocols.

Individualized discovery of rare cancer drivers in global network context

Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become ‘hard currency’ of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations’ co-occurrence in each tumour genome – as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.

Downregulated ADAMTS1 Incorporating A2M Contributes to Tumorigenesis and Alters Tumor Immune Microenvironment in Lung Adenocarcinoma

Simple Summary

Lung cancer is the most dreadful cancer type and has the worst cancer-related clinical outcomes. This study used specimens from the in-house lung cancer cohort and public cohort to verify the roles of downregulated ADAMTS1, a protease remodeling extracellular matrix, to facilitate cancer promotion and progress. Based on the clinical specimens, cell and animal study with the aid of the public databases, we concluded that downregulated expression of ADAMTS1 might promote tumor progression and metastasis and modify the tumor microenvironment in lung cancer. Further investigation would be required for its application in treating lung cancer.

Abstract

Lung adenocarcinoma (LUAD) still holds the most dreadful clinical outcomes worldwide. Despite advanced treatment strategies, there are still some unmet needs. Next-generation sequencing of large-scale cancer genomics discovery projects combined with bioinformatics provides the opportunity to take a step forward in meeting clinical conditions. Based on in-house and The Cancer Genome Atlas (TCGA) cohorts, the results showed decreased levels of ADAMTS1 conferred poor survival compared with normal parts. Gene set enrichment analyses (GSEA) indicated the negative correlation between ADAMTS1 and the potential roles of epithelial–mesenchymal transition (EMT), metastasis, and poor prognosis in LUAD patients. With the knockdown of ADAMTS1, A549 lung cancer cells exhibited more aggressive behaviors such as EMT and increased migration, resulting in cancer metastasis in a mouse model. The pathway interaction network disclosed the linkage of downregulated α2-macroglobulin (A2M), which regulates EMT and metastasis. Furthermore, immune components analysis indicated a positive relationship between ADAMTS1 and the infiltrating levels of multiple immune cells, especially anticancer CD4⁺ T cells in LUAD. Notably, ADAMTS1 expression was also inversely correlated with the accumulation of immunosuppressive myeloid-derived suppressor cells and regulatory T cells, implying the downregulated ADAMTS1 mediated immune adjustment to fit the tumor survival disadvantages in LUAD patients. In conclusion, our study indicates that ADAMTS1 interacts with A2M in regulating EMT and metastasis in LUAD. Additionally, ADAMTS1 contributes to poor prognosis and immune infiltration in LUAD patients

A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism

The high number of failed pre-clinical and clinical studies for compounds targeting Alzheimer disease (AD) has demonstrated that there is a need to reassess existing strategies. Here, we pursue a holistic, mechanism-centric drug repurposing approach combining computational analytics and experimental screening data. Based on this integrative workflow, we identified 77 druggable modifiers of tau phosphorylation (pTau). One of the upstream modulators of pTau, HDAC6, was screened with 5,632 drugs in a tau-specific assay, resulting in the identification of 20 repurposing candidates. Four compounds and their known targets were found to have a link to AD-specific genes. Our approach can be applied to a variety of AD-associated pathophysiological mechanisms to identify more repurposing candidates.

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Drug-repurposing approach that combines in silico analyses and in vitro screenings

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A drug- and mechanism-oriented model, the Human Brain Pharmacome (HBP) was created

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The HBP was used to mine data related to drugs and targets to generate a hypothesis

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Experimental evidence validated predicted drug-target combinations

Owing to current setbacks in the discovery and development of novel treatments tackling Alzheimer disease (AD), a re-evaluation of research and development (R&D) strategies is underway. Here, we present a holistic pharmacological approach that combines drug-target information with knowledge graphs that represent essential pathophysiology mechanisms. The resulting Human Brain Pharmacome (HBP) embeds hundreds of relevant drug-target interactions in the context of disease mechanisms governing AD. We demonstrate how such a tool can be used to aid AD research by identifying already-approved drugs that have the potential to treat the disease, thereby bypassing the expensive and time-consuming task of researching and developing a new drug. In our study, we identified new drug-target combinations and provided mechanistic explanations that help to improve our understanding of AD pathology and support future development of effective therapeutic strategies.

Application of Transcriptomics for Predicting Protein Interaction Networks, Drug Targets and Drug Candidates

Protein interaction pathways and networks are critically-required for a vast range of biological processes. Improved discovery of candidate druggable proteins within specific cell, tissue and disease contexts will aid development of new treatments. Predicting protein interaction networks from gene expression data can provide valuable insights into normal and disease biology. For example, the resulting protein networks can be used to identify potentially druggable targets and drug candidates for testing in cell and animal disease models. The advent of whole-transcriptome expression profiling techniques—that catalogue protein-coding genes expressed within cells and tissues—has enabled development of individual algorithms for particular tasks. For example,: (i) gene ontology algorithms that predict gene/protein subsets involved in related cell processes; (ii) algorithms that predict intracellular protein interaction pathways; and (iii) algorithms that correlate druggable protein targets with known drugs and/or drug candidates. This review examines approaches, advantages and disadvantages of existing gene expression, gene ontology, and protein network prediction algorithms. Using this framework, we examine current efforts to combine these algorithms into pipelines to enable identification of druggable targets, and associated known drugs, using gene expression datasets. In doing so, new opportunities are identified for development of powerful algorithm pipelines, suitable for wide use by non-bioinformaticians, that can predict protein interaction networks, druggable proteins, and related drugs from user gene expression datasets.

Novel epigenetic network biomarkers for early detection of esophageal cancer

BACKGROUND

Early detection of esophageal cancer is critical to improve survival. Whilst studies have identified biomarkers, their interpretation and validity is often confounded by cell-type heterogeneity.

RESULTS

Here we applied systems-epigenomic and cell-type deconvolution algorithms to a discovery set encompassing RNA-Seq and DNA methylation data from esophageal adenocarcinoma (EAC) patients and matched normal-adjacent tissue, in order to identify robust biomarkers, free from the confounding effect posed by cell-type heterogeneity. We identify 12 gene-modules that are epigenetically deregulated in EAC, and are able to validate all 12 modules in 4 independent EAC cohorts. We demonstrate that the epigenetic deregulation is present in the epithelial compartment of EAC-tissue. Using single-cell RNA-Seq data we show that one of these modules, a proto-cadherin module centered around CTNND2, is inactivated in Barrett's Esophagus, a precursor lesion to EAC. By measuring DNA methylation in saliva from EAC cases and controls, we identify a chemokine module centered around CCL20, whose methylation patterns in saliva correlate with EAC status.

CONCLUSIONS

Given our observations that a CCL20 chemokine network is overactivated in EAC tissue and saliva from EAC patients, and that in independent studies CCL20 has been found to be overactivated in EAC tissue infected with the bacterium F. nucleatum, a bacterium that normally inhabits the oral cavity, our results highlight the possibility of using DNAm measurements in saliva as a proxy for changes occurring in the esophageal epithelium. Both the CTNND2/CCL20 modules represent novel promising network biomarkers for EAC that merit further investigation.