A gene ontology inferred from molecular networks

Global siRNA Screen Reveals Critical Human Host Factors of SARS-CoV-2 Multicycle Replication

Defining the subset of cellular factors governing SARS-CoV-2 replication can provide critical insights into viral pathogenesis and identify targets for host-directed antiviral therapies. While a number of genetic screens have previously reported SARS-CoV-2 host dependency factors, these approaches relied on utilizing pooled genome-scale CRISPR libraries, which are biased towards the discovery of host proteins impacting early stages of viral replication. To identify host factors involved throughout the SARS-CoV-2 infectious cycle, we conducted an arrayed genome-scale siRNA screen. Resulting data were integrated with published datasets to reveal pathways supported by orthogonal datasets, including transcriptional regulation, epigenetic modifications, and MAPK signalling. The identified proviral host factors were mapped into the SARS-CoV-2 infectious cycle, including 27 proteins that were determined to impact assembly and release. Additionally, a subset of proteins were tested across other coronaviruses revealing 17 potential pan-coronavirus targets. Further studies illuminated a role for the heparan sulfate proteoglycan perlecan in SARS-CoV-2 viral entry, and found that inhibition of the non-canonical NF-kB pathway through targeting of BIRC2 restricts SARS-CoV-2 replication both in vitro and in vivo. These studies provide critical insight into the landscape of virus-host interactions driving SARS-CoV-2 replication as well as valuable targets for host-directed antivirals.

A foundational atlas of autism protein interactions reveals molecular convergence

Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation in Xenopus tropicalis and human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.

A multi-scale map of protein assemblies in the DNA damage response

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Genomics, convergent neuroscience and progress in understanding autism spectrum disorder

More than a hundred genes have been identified that, when disrupted, impart large risk for autism spectrum disorder (ASD). Current knowledge about the encoded proteins - although incomplete - points to a very wide range of developmentally dynamic and diverse biological processes. Moreover, the core symptoms of ASD involve distinctly human characteristics, presenting challenges to interpreting evolutionarily distant model systems. Indeed, despite a decade of striking progress in gene discovery, an actionable understanding of pathobiology remains elusive. Increasingly, convergent neuroscience approaches have been recognized as an important complement to traditional uses of genetics to illuminate the biology of human disorders. These methods seek to identify intersection among molecular-level, cellular-level and circuit-level functions across multiple risk genes and have highlighted developing excitatory neurons in the human mid-gestational prefrontal cortex as an important pathobiological nexus in ASD. In addition, neurogenesis, chromatin modification and synaptic function have emerged as key potential mediators of genetic vulnerability. The continued expansion of foundational 'omics' data sets, the application of higher-throughput model systems and incorporating developmental trajectories and sex differences into future analyses will refine and extend these results. Ultimately, a systems-level understanding of ASD genetic risk holds promise for clarifying pathobiology and advancing therapeutics.

An evidence-based lexical pattern approach for quality assurance of Gene Ontology relations

Gene Ontology (GO) is widely used in the biological domain. It is the most comprehensive ontology providing formal representation of gene functions (GO concepts) and relations between them. However, unintentional quality defects (e.g. missing or erroneous relations) in GO may exist due to the large size of GO concepts and complexity of GO structures. Such quality defects would impact the results of GO-based analyses and applications. In this work, we introduce a novel evidence-based lexical pattern approach for quality assurance of GO relations. We leverage two layers of evidence to suggest potentially missing relations in GO as follows. We first utilize related concept pairs (i.e. existing relations) in GO to extract relationship-specific lexical patterns, which serve as the first layer evidence to automatically suggest potentially missing relations between unrelated concept pairs. For each suggested missing relation, we further identify two other existing relations as the second layer of evidence that resemble the difference between the missing relation and the existing relation based on which the missing relation is suggested. Applied to the 15 December 2021 release of GO, this approach suggested a total of 866 potentially missing relations. Local domain experts evaluated the entire set of potentially missing relations, and identified 821 as missing relations and 45 indicate erroneous existing relations. We submitted these findings to the GO consortium for further validation and received encouraging feedback. These indicate that our evidence-based approach can be utilized to uncover missing relations and erroneous existing relations in GO.

Identification of key miRNAs and targeted genes involved in the progression of oral squamous cell carcinoma

Background/purpose

Oral squamous cell carcinoma (OSCC) is one of the most common types of head and neck squamous cell carcinoma. Accurate biomarkers are needed for early diagnosis and prognosis of OSCC. MicroRNAs (miRNAs) have shown great values in different types of cancers including OSCC. However, most of the miRNAs involved in the development of OSCC remain uncovered. This study aimed to identify hub miRNAs and mRNAs in OSCC.

Materials and methods

We explored the roles of key miRNAs, target genes and their relationships in OSCC using an integrated bioinformatics approach. Initially, Two OSCC microarray datasets from the Gene Expression Omnibus database were obtained to analyze miRNA expression. MiRNA-targeted mRNAs were acquired, and gene ontology/kyoto encyclopedia of genes and genomes analyses were performed. Thereafter, we constructed a protein–protein interaction (PPI) network to identify hub genes and a miRNA-mRNA interaction network was used to identify key miRNAs. Furthermore, differential gene expression and Kaplan–Meier Plotter survival analysis was performed to evaluate their potential clinical application values.

Results

Four upregulated, two downregulated miRNAs and 608 target genes of the differentially expressed miRNAs were identified. The PPI and miRNA-mRNA interaction networks highlighted 10 hub genes and two key miRNAs, and pathway analyses showed their correlative involvement in tumorigenesis-related processes. Of these miRNAs and genes, miR-125b, β-actin, vinculin and histone deacetylase 1 were correlated with overall survival (P < 0.05).

Conclusion

These findings indicate that miR-21 and miR-125b, associated with the 10 hub genes, jointly participate in OSCC tumorigenesis, offering insight into the molecular mechanisms underlying OSCC as potential targets for early diagnosis, treatment and prognosis.

Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment

Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.

Artificial intelligence, molecular subtyping, biomarkers, and precision oncology

A targeted cancer therapy is only useful if there is a way to accurately identify the tumors that are susceptible to that therapy. Thus rapid expansion in the number of available targeted cancer treatments has been accompanied by a robust effort to subdivide the traditional histological and anatomical tumor classifications into molecularly defined subtypes. This review highlights the history of the paired evolution of targeted therapies and biomarkers, reviews currently used methods for subtype identification, and discusses challenges to the implementation of precision oncology as well as possible solutions.

Genetic dissection of complex traits using hierarchical biological knowledge

Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use an ontology-guided ML system to map single nucleotide variants (SNVs) focusing on 6 classic phenotypic traits in natural yeast populations. The 29 identified loci are largely novel and account for ~17% of the phenotypic variance, versus <3% for standard genetic analysis. Representative results show that sensitivity to hydroxyurea is linked to SNVs in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. This work demonstrates a knowledge-based approach to amplifying and interpreting signals in population genetic studies.

Genome-wide association studies (GWAS) have identified many important loci for common diseases and other traits. However, the loci identified by these studies are almost always many steps away from an understanding of underlying biological mechanisms. Here we develop an approach using hierarchical biological knowledge to identify genes and pathways responsible for phenotypic traits. Variants identified by the new method could explain a substantially greater fraction of heritability than previously reported. Moreover, we identified mechanistic pathways by which each causal variant affects cellular function. For example, we find that sensitivity to hydroxyurea is tied to genetic variants in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. The new approach is a potentially transformative concept for understanding the genetic drivers of phenotypic variance, with potential applications in understanding traits in biomedicine and agriculture.

Identification of Potential Genomic Alterations and the circRNA-miRNA-mRNA Regulatory Network in Primary and Recurrent Synovial Sarcomas

Introduction: Synovial sarcoma (SS) is one of the most invasive soft tissue sarcomas, prone to recurrence and metastasis, and the efficacy of surgical treatment and chemotherapy for SS remains poor. Therefore, the diagnosis and treatment of SS remain a significant challenge. This study aimed to analyze the mutated genes of primary SS (PSS) and recurrent SS (RSS), discover whether these sarcomas exhibit some potential mutated genes, and then predict associated microRNAs (miRNA) and circular RNAs (circRNA) by analyzing the mutated genes. We focused on the regulation mechanism of the circRNA-miRNA-mutated hub gene in PSS and RSS.

Methods: We performed a comprehensive genomic analysis of four pairs of formalin-fixed paraffin-embedded samples of PSS and RSS, using Illumina human exon microarrays. The gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) function, and pathway enrichment of the mutated genes were analyzed, and the protein-protein interaction (PPI) network was forecast using String software 11.0. The hub genes were then obtained using the Molecular Complex Detection (MCODE) plug-in for Cytoscape 3.7.2 and were used to analyze overall survival (OS) using the Gene Expression Profiling Interactive Analysis (GEPIA) database. The corresponding miRNAs were obtained from the miRDB 5.0 and TargetScan 7.2 databases. The corresponding circRNAs of the hub genes were found through the miRNAs from these databases: Circbank, CircInteractome, and StarBase v2.0. Thereafter we set up a competing endogenous RNA (ceRNA) network with circRNA-miRNA and miRNA-messenger RNA (mRNA) pairs.

Results: Using the chi-squared test, 391 mutated genes were screened using a significance level of p-values < 0.01 from the four pairs of PSS and RSS samples. A GO pathway analysis of 391 mutated genes demonstrated that differential expression mRNAs (DEmRNAs) might be bound up with the “positive regulation of neurogenesis,” “cell growth,” “axon part,” “cell−substrate junction,” or “protein phosphatase binding” of SS. The PPI network was constructed using 391 mutated genes, and 53 hub genes were identified (p < 0.05). Eight variant hub genes were discovered to be statistically significant using the OS analysis (p < 0.05). The circRNA-miRNA-mRNA (ceRNA) network was constructed, and it identified two circRNAs (hsa_circ_0070557 and hsa_circ_0070558), 10 miRNAs (hsa-let-7a-3p, hsa-let-7b-3p, hsa-let-7f-1-3p, hsa-let-7f-2-3p, hsa-mir-1244, hsa-mir-1197, hsa-mir-124-3p, hsa-mir-1249-5p, hsa-mir-1253, and hsa-mir-1271-5p) and five hub genes (CENPE, ENPP3, GPR18, MDC1, and PLOD2).

Conclusion: This study screened novel biological markers and investigated the differentiated circRNA-miRNA-mutated hub gene axis, which may play a pivotal role in the nosogenesis of PSS and RSS. Some circRNAs may be deemed new diagnostic or therapeutic targets that could be conducive to the future clinical treatment of SS.

Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

BACKGROUND

Recent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand.

RESULTS

In this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1, and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression.

CONCLUSIONS

We established a predictive five-gene signature, which could be helpful for a personalized management in breast cancer patients.

Computational biology approaches for mapping transcriptional regulatory networks

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

Investigation of RNA metabolism through large-scale genetic interaction profiling in yeast

Gene deletion and gene expression alteration can lead to growth defects that are amplified or reduced when a second mutation is present in the same cells. We performed 154 genetic interaction mapping (GIM) screens with query mutants related with RNA metabolism and estimated the growth rates of about 700 000 double mutant Saccharomyces cerevisiae strains. The tested targets included the gene deletion collection and 900 strains in which essential genes were affected by mRNA destabilization (DAmP). To analyze the results, we developed RECAP, a strategy that validates genetic interaction profiles by comparison with gene co-citation frequency, and identified links between 1471 genes and 117 biological processes. In addition to these large-scale results, we validated both enhancement and suppression of slow growth measured for specific RNA-related pathways. Thus, negative genetic interactions identified a role for the OCA inositol polyphosphate hydrolase complex in mRNA translation initiation. By analysis of suppressors, we found that Puf4, a Pumilio family RNA binding protein, inhibits ribosomal protein Rpl9 function, by acting on a conserved UGUAcauUA motif located downstream the stop codon of the RPL9B mRNA. Altogether, the results and their analysis should represent a useful resource for discovery of gene function in yeast.

Comparative serum microRNA array analysis of the spleen-stomach dampness-heat syndrome in different diseases: Chronic hepatitis B and chronic gastritis

Spleen-stomach dampness-heat syndrome (SSDHS) is the common Traditional Chinese Medicine (TCM) syndrome observed in both chronic hepatitis B (CHB) and chronic gastritis (CG). The specialized TCM prescription for CHB and CG patients with SSDHS is same, but there is limited information about the biological characteristics of this TCM syndrome. This study aimed to identify the serum miRNAs profile for the SSDHS in two different diseases in order to evaluate the miRNA-mediated biological characteristics of this TCM syndrome. We performed comparative microarray analysis of serum miRNA expression profiles in 10 CHB patients with SSDHS (SSDHS-CHB), 10 CG patients with SSDHS (SSDHS-CG), and 10 healthy controls (HC). The selected miRNAs were further validated by qRT-PCR in 13 SSDHS-CHB patients, 13 SSDHS-CG patients, and 13 HC. Moreover, bioinformatics analysis (GO and KEGG pathway enrichment analyses) was applied to identify the involved target genes and pathways for these selected miRNAs. Nine significantly differentially expressed (SDE)-miRNAs in the SSDHS-CHB group and 24 SDE-miRNAs in the SSDHS-CG group were identified, compared with the HC group (fold change >2.0 and p < .05). Among these, upregulated hsa-miR-483-3p and downregulated hsa-miR-223-3p were identified as the common SDE-miRNAs for both SSDHS-CHB and SSDHS-CG groups. Bioinformatics analysis of the common SDE-miRNA's target genes showed their involvement in the regulation of inflammation, immune response, and tumorigenesis. SSDHS-specific hsa-miR-483-3p and hsa-miR-223-3p identified in this study indicated a relevance to the underlying biological basis of SSDHS, and may provide scientific basis for the application of same TCM prescription in CHB and CG.

Functional landscape of SARS-CoV-2 cellular restriction

A deficient interferon (IFN) response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been implicated as a determinant of severe coronavirus disease 2019 (COVID-19). To identify the molecular effectors that govern IFN control of SARS-CoV-2 infection, we conducted a large-scale gain-of-function analysis that evaluated the impact of human IFN-stimulated genes (ISGs) on viral replication. A limited subset of ISGs were found to control viral infection, including endosomal factors inhibiting viral entry, RNA binding proteins suppressing viral RNA synthesis, and a highly enriched cluster of endoplasmic reticulum (ER)/Golgi-resident ISGs inhibiting viral assembly/egress. These included broad-acting antiviral ISGs and eight ISGs that specifically inhibited SARS-CoV-2 and SARS-CoV-1 replication. Among the broad-acting ISGs was BST2/tetherin, which impeded viral release and is antagonized by SARS-CoV-2 Orf7a protein. Overall, these data illuminate a set of ISGs that underlie innate immune control of SARS-CoV-2/SARS-CoV-1 infection, which will facilitate the understanding of host determinants that impact disease severity and offer potential therapeutic strategies for COVID-19.

Mapping the multiscale structure of biological systems

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.

Biological differentiation of traditional Chinese medicine from excessive to deficient syndromes in AIDS: Comparative microRNA microarray profiling and syndrome-specific biomarker identification

Traditional Chinese medicine (TCM) has been widely applied as a supplementary therapy of human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS) in China. TCM has a positive effect on improving the quality of life, prolonging life, and ameliorating the symptoms of HIV/AIDS patients. Yang deficiency of spleen and kidney (YDSK) syndrome is a typical deficient TCM syndrome in AIDS patients, and accumulation of heat-toxicity (AHT) syndrome is a common excessive syndrome in the earlier stage of AIDS. Thus, accurate diagnosis of these two syndromes can improve the targeted treatment effect, and predict the prognosis of the disease. However, the scientific basis of TCM syndromes remains lacking, greatly hindering the accuracy of diagnosis and effectiveness of treatment. In this research, microRNA (miRNA) microarray and quantitative real-time polymerase chain reaction combined with bioinformatics were used for comparative analysis between YDSK and AHT patients. Significantly differential expressed miRNAs (SDE-miRNAs) of each TCM syndrome were identified, including hsa-miR-766-3p and hsa-miR-1260a and so on, as well hsa-miR-6124, hsa-let-7g-5p and so on, for YDSK and AHT, respectively. Biological differences were found between their SDE-miRNAs based on bioinformatics analyses, for example, ErbB signaling pathway mainly linked to AHT, while focal adhesion dominated in YDSK. Syndrome-specific SDE-miRNAs were further identified as potential biomarkers, including hsa-miR-30e-5p, hsa-miR-144-5p for YDSK and hsa-let-7g-5p, hsa-miR-126-3p for AHT, respectively. All of them have laid biological and clinical bases for TCM diagnosis and treatment of AIDS syndrome at the miRNA level, offering potential diagnostic indicators of immune reconstitution.

Comparative Genomic and Transcriptomic Analysis Reveals Specific Features of Gene Regulation in Kluyveromyces marxianus

Kluyveromyces marxianus is a promising host for producing bioethanol and heterologous proteins. It displays many superior traits to a conventional industrial yeast species, Saccharomyces cerevisiae, including fast growth, thermotolerance and the capacity to assimilate a wider variety of sugars. However, little is known about the mechanisms underlying the fast-growing feature of K. marxianus. In this study, we performed a comparative genomic analysis between K. marxianus and other Saccharomycetaceae species. Genes involved in flocculation, iron transport, and biotin biosynthesis have particularly high copies in K. marxianus. In addition, 60 K. marxianus specific genes were identified, 45% of which were upregulated during cultivation in rich medium and these genes may participate in glucose transport and mitochondrion related functions. Furthermore, the transcriptomic analysis revealed that under aerobic condition, normalized levels of genes participating in TCA cycles, respiration chain and ATP biosynthesis in the lag phase were higher in K. marxianus than those in S. cerevisiae. Levels of highly copied genes, genes involved in the respiratory chain and mitochondrion assembly, were upregulated in K. marxianus, but not in S. cerevisiae, in later time points during cultivation compared with those in the lag phase. Notably, during the fast-growing phase, genes involved in the respiratory chain, ATP synthesis and glucose transport were co-upregulated in K. marxianus. A few shared motifs in upstream sequences of relevant genes might result in the co-upregulation. Specific features in the co-regulations of gene expressions might contribute to the fast-growing phenotype of K. marxianus. Our study underscores the importance of genome-wide rewiring of the transcriptional network during evolution.

Disentangling the Molecular Pathways of Parkinson's Disease using Multiscale Network Modeling

Parkinson's disease (PD) is a complex neurodegenerative disorder. The identification of genetic variants has shed light on the molecular pathways for inherited PD, while the disease mechanism for idiopathic PD remains elusive, partly due to a lack of robust tools. The complexity of PD arises from the heterogeneity of clinical symptoms, pathologies, environmental insults contributing to the disease, and disease comorbidities. Molecular networks have been increasingly used to identify molecular pathways and drug targets in complex human diseases. Here, we review recent advances in molecular network approaches and their application to PD. We discuss how network modeling can predict functions of PD genetic risk factors through network context and assist in the discovery of network-based therapeutics for neurodegenerative diseases.

HiDeF: identifying persistent structures in multiscale 'omics data

In any 'omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from protein networks can vary widely in sizes depending on the method. Here, we use the concept of persistent homology, drawn from mathematical topology, to identify robust structures in data at all scales simultaneously. Application to mouse single-cell transcriptomes significantly expands the catalog of identified cell types, while analysis of SARS-COV-2 protein interactions suggests hijacking of WNT. The method, HiDeF, is available via Python and Cytoscape.

Multiscale community detection in Cytoscape

Detection of community structure has become a fundamental step in the analysis of biological networks with application to protein function annotation, disease gene prediction, and drug discovery. This recent impact creates a need to make these techniques and their accompanying visualization schemes available to a broad range of biologists. Here we present a service-oriented, end-to-end software framework, CDAPS (Community Detection APplication and Service), that integrates the identification, annotation, visualization, and interrogation of multiscale network communities, accessible within the popular Cytoscape network analysis platform. With novel design principles, CDAPS addresses unmet new challenges, such as identifying hierarchical community structures, comparison of outputs generated from diverse network resources, and easy deployment of new algorithms, to facilitate community-sourced science. We demonstrate that the CDAPS framework can be applied to high-throughput protein-protein interaction networks to gain novel insights, such as the identification of putative new members of known protein complexes.

Identifying persistent structures in multiscale 'omics data

In any 'omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from protein networks can vary widely in sizes depending on the method. Here we use the concept of "persistent homology", drawn from mathematical topology, to identify robust structures in data at all scales simultaneously. Application to mouse single-cell transcriptomes significantly expands the catalog of identified cell types, while analysis of SARS-COV-2 protein interactions suggests hijacking of WNT. The method, HiDeF, is available via Python and Cytoscape.

Identification of MMP1 as a potential gene conferring erlotinib resistance in non-small cell lung cancer based on bioinformatics analyses

BACKGROUND

Non-small cell lung cancer (NSCLC) is the major type of lung cancer with high morbidity and poor prognosis. Erlotinib, an inhibitor of epidermal growth factor receptor (EGFR), has been clinically applied for NSCLC treatment. Nevertheless, the erlotinib acquired resistance of NSCLC occurs inevitably in recent years.

METHODS

Through analyzing two microarray datasets, erlotinib resistant NSCLC cells microarray (GSE80344) and NSCLC tissue microarray (GSE19188), the differentially expressed genes (DEGs) were screened via R language. DEGs were then functionally annotated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, which up-regulated more than 2-folds in both datasets were further functionally analyzed by Oncomine, GeneMANIA, R2, Coremine, and FunRich.

RESULTS

We found that matrix metalloproteinase 1 (MMP1) may confer the erlotinib therapeutic resistance in NSCLC. MMP1 highly expressed in erlotinib-resistant cells and NSCLC tissues, and it associated with poor overall survival. In addition, MMP1 may be associated with COPS5 and be involve in an increasing transcription factors HOXA9 and PBX1 in erlotinib resistance.

CONCLUSIONS

Generally, these results demonstrated that MMP1 may play a crucial role in erlotinib resistance in NSCLC, and MMP1 could be a prognostic biomarker for erlotinib treatment.

SSIF: Subsumption-based Sub-term Inference Framework to audit Gene Ontology

MOTIVATION

The Gene Ontology (GO) is the unifying biological vocabulary for codifying, managing and sharing biological knowledge. Quality issues in GO, if not addressed, can cause misleading results or missed biological discoveries. Manual identification of potential quality issues in GO is a challenging and arduous task, given its growing size. We introduce an automated auditing approach for suggesting potentially missing is-a relations, which may further reveal erroneous is-a relations.

RESULTS

We developed a Subsumption-based Sub-term Inference Framework (SSIF) by leveraging a novel term-algebra on top of a sequence-based representation of GO concepts along with three conditional rules (monotonicity, intersection and sub-concept rules). Applying SSIF to the October 3, 2018 release of GO suggested 1938 unique potentially missing is-a relations. Domain experts evaluated a random sample of 210 potentially missing is-a relations. The results showed SSIF achieved a precision of 60.61, 60.49 and 46.03% for the monotonicity, intersection and sub-concept rules, respectively.

AVAILABILITY AND IMPLEMENTATION

SSIF is implemented in Java. The source code is available at https://github.com/rashmie/SSIF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mining Relationships among Multiple Entities in Biological Networks

Identifying topological relationships among multiple entities in biological networks is critical towards the understanding of the organizational principles of network functionality. Theoretically, this problem can be solved using minimum Steiner tree (MSTT) algorithms. However, due to large network size, it remains to be computationally challenging, and the predictive value of multi-entity topological relationships is still unclear. We present a novel solution called Cluster-based Steiner Tree Miner (CST-Miner) to instantly identify multi-entity topological relationships in biological networks. Given a list of user-specific entities, CST-Miner decomposes a biological network into nested cluster-based subgraphs, on which multiple minimum Steiner trees are identified. By merging all of them into a minimum cost tree, the optimal topological relationships among all the user-specific entities are revealed. Experimental results showed that CST-Miner can finish in nearly log-linear time and the tree constructed by CST-Miner is close to the global minimum.

Identifying Candida albicans Gene Networks Involved in Pathogenicity

Candida albicans is a normal member of the human microbiome. It is also an opportunistic pathogen, which can cause life-threatening systemic infections in severely immunocompromized individuals. Despite the availability of antifungal drugs, mortality rates of systemic infections are high and new drugs are needed to overcome therapeutic challenges including the emergence of drug resistance. Targeting known disease pathways has been suggested as a promising avenue for the development of new antifungals. However, <30% of C. albicans genes are verified with experimental evidence of a gene product, and the full complement of genes involved in important disease processes is currently unknown. Tools to predict the function of partially or uncharacterized genes and generate testable hypotheses will, therefore, help to identify potential targets for new antifungal development. Here, we employ a network-extracted ontology to leverage publicly available transcriptomics data and identify potential candidate genes involved in disease processes. A subset of these genes has been phenotypically screened using available deletion strains and we present preliminary data that one candidate, PEP8, is involved in hyphal development and immune evasion. This work demonstrates the utility of network-extracted ontologies in predicting gene function to generate testable hypotheses that can be applied to pathogenic systems. This could represent a novel first step to identifying targets for new antifungal therapies.

Circular RNA CDR1as promotes adipogenic and suppresses osteogenic differentiation of BMSCs in steroid-induced osteonecrosis of the femoral head

Steroid-induced osteonecrosis of the femoral head (SONFH) is a common debilitating orthopedic disease. The bone marrow mesenchymal stem cells (BMSCs) are a type of mesenchymal stem cells which play crucial roles in bone repair. The adipogenic/osteogenic differentiation disorder of BMSCs has been widely perceived contributing to SONFH. However, the regulatory mechanism of BMSCs differentiation disorder still remains unclear. Circular RNA (circRNA), a kind of stable ncRNA, plays important roles in regulating gene expression via various ways. To date, there are no studies to uncover the circRNA expression profile and screen out the key circRNAs playing crucial roles in adipogenic/osteogenic differentiation disorder of SONFH-BMSCs. In present study, we detected the circRNA expression profiles in SONFH-BMSCs for the first time. A total of 820 circRNAs were differentially expressed in SONFH-BMSCs, including 460 up- and 360 down-regulated circRNAs. Bioinformatics analysis indicates circRNA CDR1as, one up-regulated circRNA, may play crucial role in adipogenic/osteogenic differentiation disorder of SONFH-BMSCs via CDR1as-miR-7-5p-WNT5B axis. Knocking-down CDR1as resulted in increasing of osteogenic differentiation and decreasing of adipogenic differentiation of BMSCs, while over-expressing CDR1as resulted in decreasing of osteogenic differentiation and increasing of adipogenic differentiation of BMSCs. The miR-7-5p binding sites of CDR1as and WNT5B were verified by luciferase reporter gene assay. Our study may provide new insights into the molecular mechanisms of osteogenic/adipogenic differentiation disorder of SONFH-BMSCs and new biomarkers for the diagnosis and treatment of SONFH.

Biological Differentiation of Dampness-Heat Syndromes in Chronic Hepatitis B: From Comparative MicroRNA Microarray Profiling to Biomarker Identification

Increasing interest is aroused by traditional Chinese medicine (TCM) treatment of chronic hepatitis B (CHB) based on specific TCM syndrome. As the most common CHB syndromes, spleen-stomach dampness-heat (SSDH) syndrome and liver-gallbladder dampness-heat (LGDH) syndrome are still apt to be confused in TCM diagnosis, greatly hindering the stable exertion of TCM effectiveness. It is urgently needed to provide objective and biological evidences for differentiation and identification of the two significant syndromes. In this study, microRNA (miRNA) microarray analyses coupled with bioinformatics were employed for comparative miRNA profiling of SSDH and LGDH patients. It was found that the two syndromes had both the same and different significantly differentially expressed miRNAs (SDE-miRNAs). Commonness and specificity were also both found between their SDE-miRNA-based bioinformatics analyses, including Hierarchical Clustering, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and miRNA-GO/pathway networks. Furthermore, syndrome-specific SDE-miRNAs were identified as the potential biomarkers, including hsa-miR-1273g-3p and hsa-miR-4419b for SSDH as well as hsa-miR-129-1-3p and hsa-miR-129-2-3p for LGDH. All these laid biological and clinical bases for classification and diagnosis of the two significant CHB dampness-heat syndromes including SSDH and LGDH, providing more opportunities for better application of TCM efficacy and superiority in CHB treatment.

Integrated Multichip Analysis Identifies Potential Key Genes in the Pathogenesis of Nonalcoholic Steatohepatitis

BACKGROUND

Nonalcoholic steatohepatitis (NASH) is rapidly becoming a major chronic liver disease worldwide. However, little is known concerning the pathogenesis and progression mechanism of NASH. Our aim here is to identify key genes and elucidate their biological function in the progression from hepatic steatosis to NASH.

METHODS

Gene expression datasets containing NASH patients, hepatic steatosis patients, and healthy subjects were downloaded from the Gene Expression Omnibus database, using the R packages biobase and GEOquery. Differentially expressed genes (DEGs) were identified using the R limma package. Functional annotation and enrichment analysis of DEGs were undertaken using the R package ClusterProfile. Protein-protein interaction (PPI) networks were constructed using the STRING database.

RESULTS

Three microarray datasets GSE48452, GSE63067 and GSE89632 were selected. They included 45 NASH patients, 31 hepatic steatosis patients, and 43 healthy subjects. Two up-regulated and 24 down-regulated DEGs were found in both NASH patients vs. healthy controls and in steatosis subjects vs. healthy controls. The most significantly differentially expressed genes were FOSB (P = 3.43×10^-15), followed by CYP7A1 (P = 2.87×10^-11), and FOS (P = 6.26×10^-11). Proximal promoter DNA-binding transcription activator activity, RNA polymerase II-specific (P = 1.30×10^-5) was the most significantly enriched functional term in the gene ontology analysis. KEGG pathway enrichment analysis indicated that the MAPK signaling pathway (P = 3.11×10^-4) was significantly enriched.

CONCLUSION

This study characterized hub genes of the liver transcriptome, which may contribute functionally to NASH progression from hepatic steatosis.

Distinct Genetic Signatures of Cortical and Subcortical Regions Associated with Human Memory

Despite the discovery of gene variants linked to memory performance, understanding the genetic basis of adult human memory remains a challenge. Here, we devised an unsupervised framework that relies on spatial correlations between human transcriptome data and functional neuroimaging maps to uncover the genetic signatures of memory in functionally-defined cortical and subcortical memory regions.

Despite the discovery of gene variants linked to memory performance, understanding the genetic basis of adult human memory remains a challenge. Here, we devised an unsupervised framework that relies on spatial correlations between human transcriptome data and functional neuroimaging maps to uncover the genetic signatures of memory in functionally-defined cortical and subcortical memory regions. Results were validated with animal literature and showed that our framework is highly effective in identifying memory-related processes and genes compared to a control cognitive function. Genes preferentially expressed in cortical memory regions are linked to memory-related processes such as immune and epigenetic regulation. Genes expressed in subcortical memory regions are associated with neurogenesis and glial cell differentiation. Genes expressed in both cortical and subcortical memory areas are involved in the regulation of transcription, synaptic plasticity, and glutamate receptor signaling. Furthermore, distinct memory-associated genes such as PRKCD and CDK5 are linked to cortical and subcortical regions, respectively. Thus, cortical and subcortical memory regions exhibit distinct genetic signatures that potentially reflect functional differences in health and disease, and nominates gene candidates for future experimental investigations.

Genome sequence of the model rice variety KitaakeX

BACKGROUND

The availability of thousands of complete rice genome sequences from diverse varieties and accessions has laid the foundation for in-depth exploration of the rice genome. One drawback to these collections is that most of these rice varieties have long life cycles, and/or low transformation efficiencies, which limits their usefulness as model organisms for functional genomics studies. In contrast, the rice variety Kitaake has a rapid life cycle (9 weeks seed to seed) and is easy to transform and propagate. For these reasons, Kitaake has emerged as a model for studies of diverse monocotyledonous species.

RESULTS

Here, we report the de novo genome sequencing and analysis of Oryza sativa ssp. japonica variety KitaakeX, a Kitaake plant carrying the rice XA21 immune receptor. Our KitaakeX sequence assembly contains 377.6 Mb, consisting of 33 scaffolds (476 contigs) with a contig N50 of 1.4 Mb. Complementing the assembly are detailed gene annotations of 35,594 protein coding genes. We identified 331,335 genomic variations between KitaakeX and Nipponbare (ssp. japonica), and 2,785,991 variations between KitaakeX and Zhenshan97 (ssp. indica). We also compared Kitaake resequencing reads to the KitaakeX assembly and identified 219 small variations. The high-quality genome of the model rice plant KitaakeX will accelerate rice functional genomics.

CONCLUSIONS

The high quality, de novo assembly of the KitaakeX genome will serve as a useful reference genome for rice and will accelerate functional genomics studies of rice and other species.

Handling Noise in Protein Interaction Networks

Protein-protein interactions (PPIs) can be conveniently represented as networks, allowing the use of graph theory for their study. Network topology studies may reveal patterns associated with specific organisms. Here, we propose a new methodology to denoise PPI networks and predict missing links solely based on the network topology, the organization measurement (OM) method. The OM methodology was applied in the denoising of the PPI networks of two Saccharomyces cerevisiae datasets (Yeast and CS2007) and one Homo sapiens dataset (Human). To evaluate the denoising capabilities of the OM methodology, two strategies were applied. The first strategy compared its application in random networks and in the reference set networks, while the second strategy perturbed the networks with the gradual random addition and removal of edges. The application of the OM methodology to the Yeast and Human reference sets achieved an AUC of 0.95 and 0.87, in Yeast and Human networks, respectively. The random removal of 80% of the Yeast and Human reference set interactions resulted in an AUC of 0.71 and 0.62, whereas the random addition of 80% interactions resulted in an AUC of 0.75 and 0.72, respectively. Applying the OM methodology to the CS2007 dataset yields an AUC of 0.99. We also perturbed the network of the CS2007 dataset by randomly inserting and removing edges in the same proportions previously described. The false positives identified and removed from the network varied from 97%, when inserting 20% more edges, to 89%, when 80% more edges were inserted. The true positives identified and inserted in the network varied from 95%, when removing 20% of the edges, to 40%, after the random deletion of 80% edges. The OM methodology is sensitive to the topological structure of the biological networks. The obtained results suggest that the present approach can efficiently be used to denoise PPI networks.

Aconitine induces cardiomyocyte damage by mitigating BNIP3-dependent mitophagy and the TNFα-NLRP3 signalling axis

Objectives

Aconitine, the natural product extracted from Aconitum species, is widely used for the treatment of various diseases, including rheumatism, arthritis, bruises, fractures and pains. However, many studies have reported cardiotoxicity and neurotoxicity caused by aconitine, but the detailed mechanism underlying aconitine's effect on these processes remains unclear.

Materials and methods

The effects of aconitine on the inflammation, apoptosis and viability of H9c2 rat cardiomyocytes were evaluated by flow cytometry, Western blot, RNA sequencing and bioinformatics analysis.

Results

Aconitine suppressed cardiomyocyte proliferation and induced inflammation and apoptosis in a dose‐ and time‐dependent manner. These inflammatory damages could be reversed by a TNFα inhibitor and BNIP3‐mediated mitophagy. Consistent with the in vitro results, overexpression of BNIP3 in heart tissue partially suppressed the cardiotoxicity of aconitine by inhibiting apoptosis and the NLRP3 inflammasome.

Conclusions

Our findings lay a foundation for the application of a TNFα inhibitor and BNIP3 to aconitine‐induced cardiac toxicity prevention and therapy, thereby demonstrating potential for further investigation.

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

Pan-cancer analysis of the metabolic reaction network

Metabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other or from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumor samples across 13 types based on the probability of expression for 3765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. These findings were subsequently replicated in an independent validation set of 3388 genome-scale metabolic models. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.

Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations

Motivation

Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications.

Results

We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the gene ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec-generated feature vectors can significantly improve prediction of protein-protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data-driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state-of-the-art in several predictive applications in which ontologies are involved.

Availability and implementation

https://github.com/bio-ontology-research-group/onto2vec.

Supplementary information

Supplementary data are available at Bioinformatics online.

Updating genome annotation for the microbial cell factory Aspergillus niger using gene co-expression networks

A significant challenge in our understanding of biological systems is the high number of genes with unknown function in many genomes. The fungal genus Aspergillus contains important pathogens of humans, model organisms, and microbial cell factories. Aspergillus niger is used to produce organic acids, proteins, and is a promising source of new bioactive secondary metabolites. Out of the 14,165 open reading frames predicted in the A. niger genome only 2% have been experimentally verified and over 6,000 are hypothetical. Here, we show that gene co-expression network analysis can be used to overcome this limitation. A meta-analysis of 155 transcriptomics experiments generated co-expression networks for 9,579 genes (∼65%) of the A. niger genome. By populating this dataset with over 1,200 gene functional experiments from the genus Aspergillus and performing gene ontology enrichment, we could infer biological processes for 9,263 of A. niger genes, including 2,970 hypothetical genes. Experimental validation of selected co-expression sub-networks uncovered four transcription factors involved in secondary metabolite synthesis, which were used to activate production of multiple natural products. This study constitutes a significant step towards systems-level understanding of A. niger, and the datasets can be used to fuel discoveries of model systems, fungal pathogens, and biotechnology.

The number of titrated microRNA species dictates ceRNA regulation

microRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts-our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were co-regulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor- and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent.

DDOT: A Swiss Army Knife for Investigating Data-Driven Biological Ontologies

Systems biology requires not only genome-scale data but also methods to integrate these data into interpretable models. Previously, we developed approaches that organize omics data into a structured hierarchy of cellular components and pathways, called a "data-driven ontology." Such hierarchies recapitulate known cellular subsystems and discover new ones. To broadly facilitate this type of modeling, we report the development of a software library called the Data-Driven Ontology Toolkit (DDOT), consisting of a Python package (https://github.com/idekerlab/ddot) to assemble and analyze ontologies and a web application (http://hiview.ucsd.edu) to visualize them. Using DDOT, we programmatically assemble a compendium of ontologies for 652 diseases by integrating gene-disease mappings with a gene similarity network derived from omics data. For example, the ontology for Fanconi anemia describes known and novel disease mechanisms in its hierarchy of 194 genes and 74 subsystems. DDOT provides an easy interface to share ontologies online at the Network Data Exchange.

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Embedding of Genes Using Cancer Gene Expression Data: Biological Relevance and Potential Application on Biomarker Discovery

Artificial neural networks (ANNs) have been utilized for classification and prediction task with remarkable accuracy. However, its implications for unsupervised data mining using molecular data is under-explored. We found that embedding can extract biologically relevant information from The Cancer Genome Atlas (TCGA) gene expression dataset by learning a vector representation through gene co-occurrence. Ground truth relationship, such as cancer types of the input sample and semantic meaning of genes, were showed to retain in the resulting entity matrices. We also demonstrated the interpretability and usage of these matrices in shortlisting candidates from a long gene list as in the case of immunotherapy response. 73 related genes are singled out while the relatedness of 55 genes with immune checkpoint proteins (PD-1, PD-L1, and CTLA-4) are supported by literature. 16 novel genes (ACAP1, C11orf45, CD79B, CFP, CLIC2, CMPK2, CXCR2P1, CYTIP, FER, MCTO1, MMP25, RASGEF1B, SLFN12, TBC1D10C, TRAF3IP3, TTC39B) related to immune checkpoint proteins were identified. Thus, this method is feasible to mine big volume of biological data, and embedding would be a valuable tool to discover novel knowledge from omics data. The resulting embedding matrices mined from TCGA gene expression data are interactively explorable online (http://bit.ly/tcga-embedding-cancer) and could serve as an informative reference for gene relatedness in the context of cancer and is readily applicable to biomarker discovery of any molecular targeted therapy.

Integrating Multiple Interaction Networks for Gene Function Inference

In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.

Development and Validation of a Novel Signature to Predict Overall Survival in "Driver Gene-negative" Lung Adenocarcinoma (LUAD): Results of a Multicenter Study

PURPOSE

Examining the role of developmental signaling pathways in "driver gene-negative" lung adenocarcinoma (patients with lung adenocarcinoma negative for EGFR, KRAS, BRAF, HER2, MET, ALK, RET, and ROS1 were identified as "driver gene-negative") may shed light on the clinical research and treatment for this lung adenocarcinoma subgroup. We aimed to investigate whether developmental signaling pathways activation can stratify the risk of "driver gene-negative" lung adenocarcinoma.

EXPERIMENTAL DESIGN

In the discovery phase, we profiled the mRNA expression of each candidate gene using genome-wide microarrays in 52 paired lung adenocarcinoma and adjacent normal tissues. In the training phase, tissue microarrays and LASSO Cox regression analysis were applied to further screen candidate molecules in 189 patients, and we developed a predictive signature. In the validation phase, one internal cohort and two external cohorts were used to validate our novel prognostic signature.

RESULTS

Kyoto Encyclopedia of Genes and Genomes pathway analysis based on whole-genome microarrays indicated that the Wnt/β-catenin pathway was activated in "driver gene-negative" lung adenocarcinoma. Furthermore, the Wnt/β-catenin pathway-based gene expression profiles revealed 39 transcripts differentially expressed. Finally, a Wnt/β-catenin pathway-based CSDW signature comprising 4 genes (CTNNB1 or β-catenin, SOX9, DVL3, and Wnt2b) was developed to classify patients into high-risk and low-risk groups in the training cohort. Patients with high-risk scores in the training cohort had shorter overall survival [HR, 10.42; 6.46-16.79; P < 0.001) than patients with low-risk scores.

CONCLUSIONS

The CSDW signature is a reliable prognostic tool and may represent genes that are potential drug targets for "driver gene-negative" lung adenocarcinoma.

Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes

Protein interactomes are epitomes of incomplete and noisy networks. Methods for assessing link-reliability using exclusively topology are valuable in network biology, and their investigation facilitates the general understanding of topological mechanisms and models to draw and correct complex network connectivity. Here, I revise and extend the local-community-paradigm (LCP). Initially detected in brain-network topological self-organization and afterward generalized to any complex network, the LCP is a theory to model local-topology-dependent link-growth in complex networks using network automata. Four novel LCP-models are compared versus baseline local-topology-models. It emerges that the reliability of an interaction between two proteins is higher: (i) if their common neighbours are isolated in a complex (local-community) that has low tendency to interact with other external proteins; (ii) if they have a low propensity to link with other proteins external to the local-community. These two rules are mathematically combined in C1*: a proposed mechanistic model that, in fact, outperforms the others. This theoretical study elucidates basic topological rules behind self-organization principia of protein interactomes and offers the conceptual basis to extend this theory to any class of complex networks. The link-reliability improvement, based on the mere topology, can impact many applied domains such as systems biology and network medicine.

The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders

Although gene discovery in neuropsychiatric disorders, including autism spectrum disorder, intellectual disability, epilepsy, schizophrenia, and Tourette disorder, has accelerated, resulting in a large number of molecular clues, it has proven difficult to generate specific hypotheses without the corresponding datasets at the protein complex and functional pathway level. Here, we describe one path forward-an initiative aimed at mapping the physical and genetic interaction networks of these conditions and then using these maps to connect the genomic data to neurobiology and, ultimately, the clinic. These efforts will include a team of geneticists, structural biologists, neurobiologists, systems biologists, and clinicians, leveraging a wide array of experimental approaches and creating a collaborative infrastructure necessary for long-term investigation. This initiative will ultimately intersect with parallel studies that focus on other diseases, as there is a significant overlap with genes implicated in cancer, infectious disease, and congenital heart defects.

A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies

Although some methods are proposed for automatic ontology generation, none of them address the issue of integrating large-scale heterogeneous biomedical ontologies. We propose a novel approach for integrating various types of ontologies efficiently and apply it to integrate International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9CM), and Gene Ontologies. This approach is one of the early attempts to quantify the associations among clinical terms (e.g., ICD9 codes) based on their corresponding genomic relationships. We reconstructed a merged tree for a partial set of GO and ICD9 codes and measured the performance of this tree in terms of associations' relevance by comparing them with two well-known disease-gene datasets (i.e., MalaCards and Disease Ontology). Furthermore, we compared the genomic-based ICD9 associations to temporal relationships between them from electronic health records. Our analysis shows promising associations supported by both comparisons suggesting a high reliability. We also manually analyzed several significant associations and found promising support from literature.

Differential Integration of Transcriptome and Proteome Identifies Pan-Cancer Prognostic Biomarkers

High-throughput analysis of the transcriptome and proteome individually are used to interrogate complex oncogenic processes in cancer. However, an outstanding challenge is how to combine these complementary, yet partially disparate data sources to accurately identify tumor-specific gene products and clinical biomarkers. Here, we introduce inteGREAT for robust and scalable differential integration of high-throughput measurements. With inteGREAT, each data source is represented as a co-expression network, which is analyzed to characterize the local and global structure of each node across networks. inteGREAT scores the degree by which the topology of each gene in both transcriptome and proteome networks are conserved within a tumor type, yet different from other normal or malignant cells. We demonstrated the high performance of inteGREAT based on several analyses: deconvolving synthetic networks, rediscovering known diagnostic biomarkers, establishing relationships between tumor lineages, and elucidating putative prognostic biomarkers which we experimentally validated. Furthermore, we introduce the application of a clumpiness measure to quantitatively describe tumor lineage similarity. Together, inteGREAT not only infers functional and clinical insights from the integration of transcriptomic and proteomic data sources in cancer, but also can be readily applied to other heterogeneous high-throughput data sources. inteGREAT is open source and available to download from https://github.com/faryabib/inteGREAT.

Archetypal transcriptional blocks underpin yeast gene regulation in response to changes in growth conditions

The transcriptional responses of yeast cells to diverse stresses typically include gene activation and repression. Specific stress defense, citric acid cycle and oxidative phosphorylation genes are activated, whereas protein synthesis genes are coordinately repressed. This view was achieved from comparative transcriptomic experiments delineating sets of genes whose expression greatly changed with specific stresses. Less attention has been paid to the biological significance of 1) consistent, albeit modest, changes in RNA levels across multiple conditions, and 2) the global gene expression correlations observed when comparing numerous genome-wide studies. To address this, we performed a meta-analysis of 1379 microarray-based experiments in yeast, and identified 1388 blocks of RNAs whose expression changes correlate across multiple and diverse conditions. Many of these blocks represent sets of functionally-related RNAs that act in a coordinated fashion under normal and stress conditions, and map to global cell defense and growth responses. Subsequently, we used the blocks to analyze novel RNA-seq experiments, demonstrating their utility and confirming the conclusions drawn from the meta-analysis. Our results provide a new framework for understanding the biological significance of changes in gene expression: 'archetypal' transcriptional blocks that are regulated in a concerted fashion in response to external stimuli.