System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork

Inference of differential gene regulatory networks using boosted differential trees

Summary

Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses.

Availability and implementation

BoostDiff is available at https://github.com/scibiome/boostdiff_inference.

Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information

Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by severe lung fibrosis and a poor prognosis. Although the biomolecules related to IPF have been extensively studied, molecular mechanisms of the pathogenesis and their association with serum biomarkers and clinical findings have not been fully elucidated. We constructed a Bayesian network using multimodal data consisting of a proteome dataset from serum extracellular vesicles, laboratory examinations, and clinical findings from 206 patients with IPF and 36 controls. Differential protein expression analysis was also performed by edgeR and incorporated into the constructed network. We have successfully visualized the relationship between biomolecules and clinical findings with this approach. The IPF-specific network included modules associated with TGF-β signaling (TGFB1 and LRC32), fibrosis-related (A2MG and PZP), myofibroblast and inflammation (LRP1 and ITIH4), complement-related (SAA1 and SAA2), as well as serum markers, and clinical symptoms (KL-6, SP-D and fine crackles). Notably, it identified SAA2 associated with lymphocyte counts and PSPB connected with the serum markers KL-6 and SP-D, along with fine crackles as clinical manifestations. These results contribute to the elucidation of the pathogenesis of IPF and potential therapeutic targets.

Whole blood transcriptome profiling identifies gene expression subnetworks and a key gene characteristic of the rare type of osteomyelitis

Chronic non-bacterial osteomyelitis (CNO) is a rare and severe inflammatory bone disorder that can occur in the jaw. It is often associated with systemic conditions including autoimmune deficiency. Medical management of patients and establishment of a correct diagnosis are difficult as the etiology of the disease remains unknown. Therefore, little is known about the disease characteristics at the gene expression level. Here, we explored aspects of CNO based on whole blood RNA sequencing (>6 Gb per sample) of 11 patients and 9 healthy controls in Japan and on a recently developed method that is applicable to small datasets, can estimate a directed gene network, and extract a subnetwork of genes underlying patient characteristics. We identified nine subnetworks, comprising 26 differentially regulated edges and 36 genes, with the gene encoding glycophorin C (GYPC) presenting the highest discrimination ability. The expression of the gene was mostly lower in patients with CNO than in the healthy controls, suggesting an abnormal status of red cells in patients with CNO. This study enhances our understanding of CNO at the transcriptome level and further provides a framework for whole blood RNA sequencing and analysis of data obtained for a better diagnosis of the disease.

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We explored new aspects of chronic non-bacterial osteomyelitis (CNO) via whole-blood RNA-seq and gene network analysis.

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Nine subnetworks of genes were identified.

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A GYPC-encoding gene had the highest discrimination ability in these subnetworks.

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The expression level of the gene suggested an abnormal status of red cells in the patients.

Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study

An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.

Novel cancer subtyping method based on patient-specific gene regulatory network

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection

The current pandemic of SARS-CoV-2 has caused extensive damage to society. The characterization of SARS-CoV-2 profiles has been addressed by researchers globally with the aim of resolving this disruptive crisis. This investigation process is indispensable to understand how SARS-CoV-2 behaves in human host cells. However, little is known about the systematic molecular mechanisms involved in the effects of SARS-CoV-2 infection on human host cells. Here, we present gene-to-gene regulatory networks in response to SARS-CoV-2 using a Bayesian network. We examined the dynamic changes in the SARS-CoV-2-purturbated networks established by our proposed framework for gene network analysis, thus revealing that interferon signaling gradually switched to the subsequent inflammatory cytokine signaling cascades. Furthermore, we succeeded in capturing a COVID-19 patient-specific network in which transduction of these signals was concurrently induced. This enabled us to explore the local regulatory systems influenced by SARS-CoV-2 in host cells more precisely at an individual level. Our panel of network analyses has provided new insights into SARS-CoV-2 research from the perspective of cellular systems.