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The transcriptomic landscape of mice with primary auditory neurons degeneration (PAND) indicates key pathways in its pathogenesis, including complement cascades, immune responses, tumor necrosis factor (TNF) signaling pathway, and cytokine-cytokine receptor interaction. Toll-like receptors (TLRs) are important immune and inflammatory molecules that have been shown to disrupt the disease network of PAND. In a PAND model involving administration of kanamycin combined with furosemide to destroy cochlear hair cells, Tlr 2/4 double knockout (DKO) mice had auditory preservation advantages, which were mainly manifested at 4–16 kHz. DKO mice and wild type (WT) mice had completely damaged cochlear hair cells on the 30th day, but the density of spiral ganglion neurons (SGN) in the Rosenthal canal was significantly higher in the DKO group than in the WT group. The results of immunohistochemistry for p38 and p65 showed that the attenuation of SGN degeneration in DKO mice may not be mediated by canonical Tlr signaling pathways. The SGN transcriptome of DKO and WT mice indicated that there was an inverted gene set enrichment relationship between their different transcriptomes and the SGN degeneration transcriptome, which is consistent with the morphology results. Core module analysis suggested that DKO mice may modulate SGN degeneration by activating two clusters, and the involved molecules include EGF, STAT3, CALB2, LOX, SNAP25, CAV2, SDC4, MYL1, NCS1, PVALB, TPM4, and TMOD4.

Abstract 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.

Salmonella enterica serovar Typhimurium (S. Typhimurium) is one of the leading causes of food-borne illnesses worldwide. To colonize the gastrointestinal tract, S. Typhimurium produces multiple virulence factors that facilitate cellular invasion. Chitinases have been recently emerging as virulence factors for various pathogenic bacterial species, and the S. Typhimurium genome contains two annotated chitinases: STM0018 (chiA) and STM0233. However, the role of these chitinases during S. Typhimurium pathogenesis is unknown. The putative chitinase STM0233 has not been studied previously, and only limited data exists on ChiA. Chitinases typically hydrolyze chitin polymers, which are absent in vertebrates. However, chiA expression was detected in infection models and purified ChiA cleaved carbohydrate subunits present on mammalian surface glycoproteins, indicating a role during pathogenesis. Here, we demonstrate that expression of chiA and STM0233 is upregulated in the mouse gut and that both chitinases facilitate epithelial cell adhesion and invasion. S. Typhimurium lacking both chitinases showed a 70% reduction in invasion of small intestinal epithelial cells in vitro. In a gastroenteritis mouse model, chitinase-deficient S. Typhimurium strains were also significantly attenuated in the invasion of small intestinal tissue. This reduced invasion resulted in significantly delayed S. Typhimurium dissemination to the spleen and the liver, but chitinases were not required for systemic survival. The invasion defect of the chitinase-deficient strain was rescued by the presence of wild-type S. Typhimurium, suggesting that chitinases are secreted. By analyzing N-linked glycans of small intestinal cells, we identified specific N-acetylglucosamine-containing glycans as potential extracellular targets of S. Typhimurium chitinases. This analysis also revealed a differential abundance of Lewis X/A-containing glycans that is likely a result of host cell modulation due to the detection of S. Typhimurium chitinases. Similar glycomic changes elicited by chitinase deficient strains indicate functional redundancy of the chitinases. Overall, our results demonstrate that S. Typhimurium chitinases contribute to intestinal adhesion and invasion through modulation of the host glycome.

Recently, accumulating evidence has supported that circular RNA (circRNA) plays important roles in tumorigenesis by regulating gene expression at transcriptional and post-transcriptional levels. Expression of circRNAs can be epigenetically silenced by DNA methylation; however, the underlying regulatory mechanisms of circRNAs by DNA methylation remains largely unknown. We explored this regulation in hepatocellular carcinoma (HCC) using genome-wide DNA methylation and RNA sequencing data of the primary tumor and matched adjacent normal tissues from 20 HCC patients. Our pipeline identified 1012 upregulated and 747 downregulated circRNAs (collectively referred to as differentially expressed circRNAs, or DE circRNAs) from HCC RNA-seq data. Among them, 329 DE circRNAs covered differentially methylated sites (adjusted p-value < 0.05, |ΔM| > 0.5) in circRNAs’ interior and/or flanking regions. Interestingly, the corresponding parental genes of 46 upregulated and 31 downregulated circRNAs did not show significant expression change in the HCC tumor versus normal samples. Importantly, 34 of the 77 DE circRNAs (44.2%) had significant correlation with DNA methylation change in HCC (Spearman’s rank-order correlation, p-value < 0.05), suggesting that aberrant DNA methylation might regulate circular RNA expression in HCC. Our study revealed genome-wide differential circRNA expression in HCC. The significant correlation with DNA methylation change suggested that epigenetic regulation might act on both mRNA and circRNA expression. The specific regulation in HCC and general view in other cancer or disease requires further investigation.

Summary: We systematically investigated the landscape of tumor-suppressor gene (TSG) inactivation events in 33 cancer types by quantitatively measuring their global and local genomic features and their transcriptional and signaling footprints. Using The Cancer Genome Atlas data, we identified with high confidence 337 TSG × cancer events in 30 cancer types, of which 277 were unique events. The majority (91.0%) of these events had a significant downstream impact measured by reduced expression of the TSG itself (cis-effect), disturbance of the transcriptome (trans-effect), or combinatorial effects. Importantly, the transcriptomic changes associated with TSG inactivation events were stronger than the cancer lineage difference, and the same TSGs inactivated in different cancer types tended to cluster together. Several TSGs (e.g., RB1, TP53, and CDKN2A) involved in the regulation of the cell-cycle-formed clusters. Finally, we constructed subnetworks of the TSG × cancer inactivation events, including the local genes frequently disturbed upon the inactivation events. : Jia and Zhao present a pan-cancer analysis of tumor-suppressor gene (TSG) inactivation events. They examine the genetic mutations that lead to TSG inactivation and the functional impact of TSG inactivation events. The results suggest that TSG × cancer inactivation events tend to cluster by TSG function, rather than by cancer lineage. Keywords: tumor suppressor gene, two-hit model, The Cancer Genome Atlas, transcriptome impact, cell cycle pathway, RB1, CDKN2A

Summary: DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ∼20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy. : Knijnenburg et al. present The Cancer Genome Atlas (TCGA) Pan-Cancer analysis of DNA damage repair (DDR) deficiency in cancer. They use integrative genomic and molecular analyses to identify frequent DDR alterations across 33 cancer types, correlate gene- and pathway-level alterations with genome-wide measures of genome instability and impaired function, and demonstrate the prognostic utility of DDR deficiency scores. Keywords: The Cancer Genome Atlas PanCanAtlas project, DNA damage repair, somatic mutations, somatic copy-number alterations, epigenetic silencing, DNA damage footprints, mutational signatures, integrative statistical analysis, protein structure analysis

Patients with EGFR-mutant lung adenocarcinomas (LUADs) who initially respond to first-generation tyrosine kinase inhibitors (TKIs) develop resistance to these drugs. A combination of the irreversible TKI afatinib and the EGFR antibody cetuximab can be used to overcome resistance to first-generation TKIs; however, resistance to this drug combination eventually emerges. We identified activation of the mTORC1 signaling pathway as a mechanism of resistance to dual inhibition of EGFR in mouse models. The addition of rapamycin reversed resistance in vivo. Analysis of afatinib-plus-cetuximab-resistant biopsy specimens revealed the presence of genomic alterations in genes that modulate mTORC1 signaling, including NF2 and TSC1. These findings pinpoint enhanced mTORC1 activation as a mechanism of resistance to afatinib plus cetuximab and identify genomic mechanisms that lead to activation of this pathway, revealing a potential therapeutic strategy for treating patients with resistance to these drugs.

Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics.

A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.

A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P(meta)