The ConsensusPathDB interaction database: 2013 update

Gut microbiome and serum metabolome analyses identify molecular biomarkers and altered glutamate metabolism in fibromyalgia

BACKGROUND

Fibromyalgia is a complex, relatively unknown disease characterised by chronic, widespread musculoskeletal pain. The gut-brain axis connects the gut microbiome with the brain through the enteric nervous system (ENS); its disruption has been associated with psychiatric and gastrointestinal disorders. To gain an insight into the pathogenesis of fibromyalgia and identify diagnostic biomarkers, we combined different omics techniques to analyse microbiome and serum composition.

METHODS

We collected faeces and blood samples to study the microbiome, the serum metabolome and circulating cytokines and miRNAs from a cohort of 105 fibromyalgia patients and 54 age- and environment-matched healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from faeces samples. UPLC-MS metabolomics and custom multiplex cytokine and miRNA analysis (FirePlex™ technology) were used to examine sera samples. Finally, we combined the different data types to search for potential biomarkers.

RESULTS

We found that the diversity of bacteria is reduced in fibromyalgia patients. The abundance of the Bifidobacterium and Eubacterium genera (bacteria participating in the metabolism of neurotransmitters in the host) in these patients was significantly reduced. The serum metabolome analysis revealed altered levels of glutamate and serine, suggesting changes in neurotransmitter metabolism. The combined serum metabolomics and gut microbiome datasets showed a certain degree of correlation, reflecting the effect of the microbiome on metabolic activity. We also examined the microbiome and serum metabolites, cytokines and miRNAs as potential sources of molecular biomarkers of fibromyalgia.

CONCLUSIONS

Our results show that the microbiome analysis provides more significant biomarkers than the other techniques employed in the work. Gut microbiome analysis combined with serum metabolomics can shed new light onto the pathogenesis of fibromyalgia. We provide a list of bacteria whose abundance changes in this disease and propose several molecules as potential biomarkers that can be used to evaluate the current diagnostic criteria.

Defining Human Tyrosine Kinase Phosphorylation Networks Using Yeast as an In Vivo Model Substrate

Systematic assessment of tyrosine kinase-substrate relationships is fundamental to a better understanding of cellular signaling and its profound alterations in human diseases such as cancer. In human cells, such assessments are confounded by complex signaling networks, feedback loops, conditional activity, and intra-kinase redundancy. Here we address this challenge by exploiting the yeast proteome as an in vivo model substrate. We individually expressed 16 human non-receptor tyrosine kinases (NRTKs) in Saccharomyces cerevisiae and identified 3,279 kinase-substrate relationships involving 1,351 yeast phosphotyrosine (pY) sites. Based on the yeast data without prior information, we generated a set of linear kinase motifs and assigned ∼1,300 known human pY sites to specific NRTKs. Furthermore, experimentally defined pY sites for each individual kinase were shown to cluster within the yeast interactome network irrespective of linear motif information. We therefore applied a network inference approach to predict kinase-substrate relationships for more than 3,500 human proteins, providing a resource to advance our understanding of kinase biology.

Targeting the RNA m6A Reader YTHDF2 Selectively Compromises Cancer Stem Cells in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive clonal disorder of hematopoietic stem cells (HSCs) and primitive progenitors that blocks their myeloid differentiation, generating self-renewing leukemic stem cells (LSCs). Here, we show that the mRNA m6A reader YTHDF2 is overexpressed in a broad spectrum of human AML and is required for disease initiation as well as propagation in mouse and human AML. YTHDF2 decreases the half-life of diverse m6A transcripts that contribute to the overall integrity of LSC function, including the tumor necrosis factor receptor Tnfrsf2, whose upregulation in Ythdf2-deficient LSCs primes cells for apoptosis. Intriguingly, YTHDF2 is not essential for normal HSC function, with YTHDF2 deficiency actually enhancing HSC activity. Thus, we identify YTHDF2 as a unique therapeutic target whose inhibition selectively targets LSCs while promoting HSC expansion.

Systematic genetic analyses of GWAS data reveal an association between the immune system and insomnia

BACKGROUND

Previous studies have inferred a strong genetic component for insomnia. However, the etiology of insomnia is still unclear. The aim of the current study was to explore potential biological pathways, gene networks, and brain regions associated with insomnia.

METHODS

Using pathways (gene sets) from Reactome, we carried out a two-stage gene set enrichment analysis strategy. From a large genome-wide association studies (GWASs) of insomnia symptoms (32,155 cases/26,973 controls), significant gene sets were tested for replication in other large GWASs of insomnia complaints (32,384 cases/80,622 controls). After the network analysis of unique genes within the replicated pathways, a gene set analysis for genes in each cluster/module of the enhancing neuroimaging genetics through meta-analysis GWAS data was performed for the volumes of the intracranial and seven subcortical regions.

RESULTS

A total of 31 of 1,816 Reactome pathways were identified and showed associations with insomnia risk. In addition, seven functionally and topologically interconnected clusters (clusters 0-6) and six gene modules (named Yellow, Blue, Brown, Green, Red, and Turquoise) were associated with insomnia. Moreover, significant associations were detected between common variants of the genes in Cluster 2 with hippocampal volume (p = 0.035; family wise error [FWE] correction) and the red module with intracranial volume (p = 0.047; FWE correction). Functional enrichment for genes in the Cluster 2 and the Red module revealed the involvement of immune responses, nervous system development, NIK/NF-kappaB signaling, and I-kappaB kinase/NF-kappaB signaling. Core genes (UBC, UBB, and UBA52) in the interconnected functional network were found to be involved in regulating brain development.

CONCLUSIONS

The current study demonstrates that the immune system and the hippocampus may play central roles in neurodevelopment and insomnia risk.

Label propagation defines signaling networks associated with recurrently mutated cancer genes

Human tumors have distinct profiles of genomic alterations, and each of these alterations has the potential to cause unique changes to cellular homeostasis. Detailed analyses of these changes could reveal downstream effects of genomic alterations, contributing to our understanding of their roles in tumor development and progression. Across a range of tumor types, including bladder, lung, and endometrial carcinoma, we determined genes that are frequently altered in The Cancer Genome Atlas patient populations, then examined the effects of these alterations on signaling and regulatory pathways. To achieve this, we used a label propagation-based methodology to generate networks from gene expression signatures associated with defined mutations. Individual networks offered a large-scale view of signaling changes represented by gene signatures, which in turn reflected the scope of molecular events that are perturbed in the presence of a given genomic alteration. Comparing different networks to one another revealed common biological pathways impacted by distinct genomic alterations, highlighting the concept that tumors can dysregulate key pathways through multiple, seemingly unrelated mechanisms. Finally, altered genes inducing common changes to the signaling network were used to search for genomic markers of drug response, connecting shared perturbations to differential drug sensitivity.

R-spondin-3 induces secretory, antimicrobial Lgr5+ cells in the stomach

Wnt signalling stimulated by binding of R-spondin (Rspo) to Lgr-family members is crucial for gastrointestinal stem cell renewal. Infection of the stomach with Helicobacter pylori stimulates increased secretion of Rspo by myofibroblasts, leading to an increase in proliferation of Wnt-responsive Axin2⁺Lgr5^- stem cells in the isthmus of the gastric gland and finally gastric gland hyperplasia. Basal Lgr5⁺ cells are also exposed to Rspo3, but their response remains unclear. Here, we demonstrate that-in contrast to its known mitogenic activity-Rspo3 induces differentiation of basal Lgr5⁺ cells into secretory cells that express and secrete antimicrobial factors, such as intelectin-1, into the lumen. The depletion of Lgr5⁺ cells or the knockout of Rspo3 in myofibroblasts leads to hypercolonization of the gastric glands with H. pylori, including the stem cell compartment. By contrast, systemic administration or overexpression of Rspo3 in the stroma clears H. pylori from the gastric glands. Thus, the Rspo3-Lgr5 axis simultaneously regulates both antimicrobial defence and mucosal regeneration.

IL-33 drives group 2 innate lymphoid cell-mediated protection during Clostridium difficile infection

Clostridium difficile (C. difficile) incidence has tripled over the past 15 years and is attributed to the emergence of hypervirulent strains. While it is clear that C. difficile toxins cause damaging colonic inflammation, the immune mechanisms protecting from tissue damage require further investigation. Through a transcriptome analysis, we identify IL-33 as an immune target upregulated in response to hypervirulent C. difficile. We demonstrate that IL-33 prevents C. difficile-associated mortality and epithelial disruption independently of bacterial burden or toxin expression. IL-33 drives colonic group 2 innate lymphoid cell (ILC2) activation during infection and IL-33 activated ILC2s are sufficient to prevent disease. Furthermore, intestinal IL-33 expression is regulated by the microbiota as fecal microbiota transplantation (FMT) rescues antibiotic-associated depletion of IL-33. Lastly, dysregulated IL-33 signaling via the decoy receptor, sST2, predicts C. difficile-associated mortality in human patients. Thus, IL-33 signaling to ILC2s is an important mechanism of defense from C. difficile colitis.

Here, Frisbee et al. show that hypervirulent Clostridium difficile induces IL-33 expression in the gut and IL-33 reduces mortality and morbidity via group 2 innate lymphoid cells. Furthermore, serum levels of the soluble IL-33 decoy receptor, sST2, are associated with enhanced disease severity in human C. difficile patients.

A Comprehensive Study of Vesicular and Non-Vesicular miRNAs from a Volume of Cerebrospinal Fluid Compatible with Clinical Practice

Cerebrospinal fluid (CSF) microRNAs (miRNAs) have emerged as potential biomarkers for minimally invasive diagnosis of central nervous system malignancies. However, despite significant advances in recent years, this field still suffers from poor data reproducibility. This is especially true in cases of infants, considered a new subject group. Implementing efficient methods to study miRNAs from clinically realistic CSF volumes is necessary for the identification of new biomarkers.

Methods: We compared six protocols for characterizing miRNAs, using 200-µL CSF from infants (aged 0-7). Four of the methods employed extracellular vesicle (EV) enrichment step and the other two obtained the miRNAs directly from cleared CSF. The efficiency of each method was assessed using real-time PCR and small RNA sequencing. We also determined the distribution of miRNAs among different CSF shuttles, using size-exclusion chromatography.

Results: We identified 281 CSF miRNAs from infants. We demonstrated that the miRNAs could be efficiently detected using only 200 µL of biofluid in case of at least two of the six methods. In the exosomal fraction, we found 12 miRNAs that might be involved in neurodevelopment.

Conclusion: The Norgen and Invitrogen protocols appear suitable for the analysis of a large number of miRNAs using small CSF samples.

DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins

High-throughput sequencing methods are generating enormous amounts of genomic data, giving unprecedented insights into human genetic variation and its relation to disease. An individual human genome contains millions of Single Nucleotide Variants: to discriminate the deleterious from the benign ones, a variety of methods have been developed that predict whether a protein-coding variant likely affects the carrier individual's health. We present such a method, DEOGEN2, which incorporates heterogeneous information about the molecular effects of the variants, the domains involved, the relevance of the gene and the interactions in which it participates. This extensive contextual information is non-linearly mapped into one single deleteriousness score for each variant. Since for the non-expert user it is sometimes still difficult to assess what this score means, how it relates to the encoded protein, and where it originates from, we developed an interactive online framework (http://deogen2.mutaframe.com/) to better present the DEOGEN2 deleteriousness predictions of all possible variants in all human proteins. The prediction is visualized so both expert and non-expert users can gain insights into the meaning, protein context and origins of each prediction.

Cooperation between Constitutive and Inducible Chemokines Enables T Cell Engraftment and Immune Attack in Solid Tumors

We investigated the role of chemokines in regulating T cell accumulation in solid tumors. CCL5 and CXCL9 overexpression was associated with CD8⁺ T cell infiltration in solid tumors. T cell infiltration required tumor cell-derived CCL5 and was amplified by IFN-γ-inducible, myeloid cell-secreted CXCL9. CCL5 and CXCL9 coexpression revealed immunoreactive tumors with prolonged survival and response to checkpoint blockade. Loss of CCL5 expression in human tumors was associated with epigenetic silencing through DNA methylation. Reduction of CCL5 expression caused tumor-infiltrating lymphocyte (TIL) desertification, whereas forced CCL5 expression prevented Cxcl9 expression and TILs loss, and attenuated tumor growth in mice through IFN-γ. The cooperation between tumor-derived CCL5 and IFN-γ-inducible CXCR3 ligands secreted by myeloid cells is key for orchestrating T cell infiltration in immunoreactive and immunoresponsive tumors.

Co-expression Network Analysis Elucidated a Core Module in Association With Prognosis of Non-functioning Non-invasive Human Pituitary Adenoma

Non-functioning pituitary adenomas (NFPAs) are tumors with clinically challenging features since they have insidious progression. A complex network of gene interactions is thought to have roles in tumor formation and progression. Therefore, revealing the genetic network behind NFPA tumorigenesis is not only essential to attain further knowledge of tumor biology, but also plays a fundamental role in the development of efficacious treatment strategies. Differential co-expression network analysis is an outstanding approach for elucidation of groups of genes which show distinct co-expression patterns among phenotypes. In this study, we carried out a differential co-expression network analysis of NFPA-associated transcriptome dataset (n = 40) considering invasive (n = 22) and non-invasive (n = 18) phenotypes. Furthermore, we identified differentially co-expressed and co-regulated mRNA modules, which might be considered as potential systems biomarkers for NFPA prognosis and invasiveness. As a result, we have identified a novel 13-gene module, including CEACAM6, CYP4B1, EIF2S2, HID1, IFFO1, MYO18A, PDCD2, SGIP1, SWSAP1, and four unknown genes (A_24_P127621, A_24_P255786, A_24_P683553, and A_24_P916979), which was able to categorize the patients into two groups as invasive and non-invasive NFPA with distinct prognosis. The prognostic core module genes were associated with progression and prognosis of brain and glandular based cancers as well. Furthermore, these module genes were also expressed in blood, salivary gland, and spinal cord tissues. These results may provide the evidence on featured gene module which might play a prominent role in NFPA prognosis and sub-typing as effective biomarkers and therapeutic targets in the future.

Prospective Longitudinal ctDNA Workflow Reveals Clinically Actionable Alterations in Ovarian Cancer

PURPOSE

Circulating tumor DNA (ctDNA) detection is a minimally invasive technique that offers dynamic molecular snapshots of genomic alterations in cancer. Although ctDNA markers can be used for early detection of cancers or for monitoring treatment efficacy, the value of ctDNA in guiding treatment decisions in solid cancers is controversial. Here, we monitored ctDNA to detect clinically actionable alterations during treatment of high-grade serous ovarian cancer, the most common and aggressive form of epithelial ovarian cancer with a 5-year survival rate of 43%.

PATIENTS AND METHODS

We implemented a clinical ctDNA workflow to detect clinically actionable alterations in more than 500 cancer-related genes. We applied the workflow to a prospective cohort consisting of 78 ctDNA samples from 12 patients with high-grade serous ovarian cancer before, during, and after treatment. These longitudinal data sets were analyzed using our open-access ctDNA-tailored bioinformatics analysis pipeline and in-house Translational Oncology Knowledgebase to detect clinically actionable genomic alterations. The alterations were ranked according to the European Society for Medical Oncology scale for clinical actionability of molecular targets.

RESULTS

Our results show good concordance of mutations and copy number alterations in ctDNA and tumor samples, and alterations associated with clinically available drugs were detected in seven patients (58%). Treatment of one chemoresistant patient was changed on the basis of detection of ERBB2 amplification, and this ctDNA-guided decision was followed by significant tumor shrinkage and complete normalization of the cancer antigen 125 tumor marker.

CONCLUSION

Our results demonstrate a proof of concept for using ctDNA to guide clinical decisions. Furthermore, our results show that longitudinal ctDNA samples can be used to identify poor-responding patients after first cycles of chemotherapy. We provide what we believe to be the first comprehensive, open-source ctDNA workflow for detecting clinically actionable alterations in solid cancers.

Development of a PD-L1-Expressing Orthotopic Liver Cancer Model: Implications for Immunotherapy for Hepatocellular Carcinoma

BACKGROUND

Anti-programmed cell death-1(anti-PD1) treatment has shown promising antitumor efficacy in patients with advanced hepatocellular carcinoma (HCC). This study sought to explore the functional significance of programmed death ligand-1 (PD-L1) expression in tumor cells in the tumor microenvironment.

METHODS

The mouse liver cancer cell line BNL-MEA was transfected with PD-L1 plasmids and stable clones expressing PD-L1 were selected. An orthotopic HCC model was generated by implanting the cells into the subcapsular space of BALB/c mice. Cell growth features were measured by proliferation assay, colony formation, flow cytometry (in vitro), ultrasonography, and animal survival (in vivo). The changes in T-cell function were examined by cytokine assay, expression of T-cell related genes, and flow cytometry. The efficacy of anti-PD1 therapy was compared between the parental and PD-L1-expressing tumors.

RESULTS

PD-L1 expression did not affect growth characteristics of BNL-MEA cells but downregulated the expression of genes related to T-cell activation in the tumor microenvironment. Co-culture of PD-L1-expressing BNL-MEA cells with CD8+ T cells reduced T-cell proliferation and expression of cytokines IFNγ and TNFα. Tumors with PD-L1 expression showed better response to anti-PD1 therapy and depletion of CD8+ T cells abolished the antitumor effect. The difference in treatment response between parental and PD-L1-expressing tumors disappeared when a combination of anti-PD1 and sorafenib was given.

CONCLUSIONS

PD-L1 expression in HCC cells may inhibit T-cell function in the liver tumor microenvironment. Anti-PD1 therapy appeared more effective in PD-L1-expressing than nonexpressing tumors, but the difference was diminished by the addition of sorafenib.

Paracrine signalling during ZEB1-mediated epithelial-mesenchymal transition augments local myofibroblast differentiation in lung fibrosis

The contribution of epithelial-mesenchymal transition (EMT) to human lung fibrogenesis is controversial. Here we provide evidence that ZEB1-mediated EMT in human alveolar epithelial type II (ATII) cells contributes to the development of lung fibrosis by paracrine signalling to underlying fibroblasts. Activation of EGFR-RAS-ERK signalling in ATII cells induced EMT via ZEB1. ATII cells had extremely low extracellular matrix gene expression even after induction of EMT, however conditioned media from ATII cells undergoing RAS-induced EMT augmented TGFβ-induced profibrogenic responses in lung fibroblasts. This epithelial-mesenchymal crosstalk was controlled by ZEB1 via the expression of tissue plasminogen activator (tPA). In human fibrotic lung tissue, nuclear ZEB1 expression was detected in alveolar epithelium adjacent to sites of extracellular matrix (ECM) deposition, suggesting that ZEB1-mediated paracrine signalling has the potential to contribute to early fibrotic changes in the lung interstitium. Targeting this novel ZEB1 regulatory axis may be a viable strategy for the treatment of pulmonary fibrosis.

Prenatal Particulate Air Pollution and DNA Methylation in Newborns: An Epigenome-Wide Meta-Analysis

BACKGROUND

Prenatal exposure to air pollution has been associated with childhood respiratory disease and other adverse outcomes. Epigenetics is a suggested link between exposures and health outcomes.

OBJECTIVES

We aimed to investigate associations between prenatal exposure to particulate matter (PM) with diameter [Formula: see text] ([Formula: see text]) or [Formula: see text] ([Formula: see text]) and DNA methylation in newborns and children.

METHODS

We meta-analyzed associations between exposure to [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]) at maternal home addresses during pregnancy and newborn DNA methylation assessed by Illumina Infinium HumanMethylation450K BeadChip in nine European and American studies, with replication in 688 independent newborns and look-up analyses in 2,118 older children. We used two approaches, one focusing on single cytosine-phosphate-guanine (CpG) sites and another on differentially methylated regions (DMRs). We also related PM exposures to blood mRNA expression.

RESULTS

Six CpGs were significantly associated [false discovery rate (FDR) [Formula: see text]] with prenatal [Formula: see text] and 14 with [Formula: see text] exposure. Two of the [Formula: see text] CpGs mapped to FAM13A (cg00905156) and NOTCH4 (cg06849931) previously associated with lung function and asthma. Although these associations did not replicate in the smaller newborn sample, both CpGs were significant ([Formula: see text]) in 7- to 9-y-olds. For cg06849931, however, the direction of the association was inconsistent. Concurrent [Formula: see text] exposure was associated with a significantly higher NOTCH4 expression at age 16 y. We also identified several DMRs associated with either prenatal [Formula: see text] and or [Formula: see text] exposure, of which two [Formula: see text] DMRs, including H19 and MARCH11, replicated in newborns.

CONCLUSIONS

Several differentially methylated CpGs and DMRs associated with prenatal PM exposure were identified in newborns, with annotation to genes previously implicated in lung-related outcomes. https://doi.org/10.1289/EHP4522.

Biological Insights into Chemotherapy Resistance in Ovarian Cancer

The majority of patients with high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy; however, most will develop chemotherapy resistance. Gene signatures may change with the development of chemotherapy resistance in this population, which is important as it may lead to tailored therapies. The objective of this study was to compare tumor gene expression profiles in patients before and after treatment with neoadjuvant chemotherapy (NACT). Tumor samples were collected from six patients diagnosed with HGSOC before and after administration of NACT. RNA extraction and whole transcriptome sequencing was performed. Differential gene expression, hierarchical clustering, gene set enrichment analysis, and pathway analysis were examined in all of the samples. Tumor samples clustered based on exposure to chemotherapy as opposed to patient source. Pre-NACT samples were enriched for multiple pathways involving cell cycle growth. Post-NACT samples were enriched for drug transport and peroxisome pathways. Molecular subtypes based on the pre-NACT sample (differentiated, mesenchymal, proliferative and immunoreactive) changed in four patients after administration of NACT. Multiple changes in tumor gene expression profiles after exposure to NACT were identified from this pilot study and warrant further attention as they may indicate early changes in the development of chemotherapy resistance.

Novel Genomic Biomarker Candidates for Cervical Cancer As Identified by Differential Co-Expression Network Analysis

Cervical cancer is the second most common malignancy and the third reason for mortality among women in developing countries. Although infection by the oncogenic human papilloma viruses is a major cause, genomic contributors are still largely unknown. Network analyses, compared with candidate gene studies, offer greater promise to map the interactions among genomic loci contributing to cervical cancer risk. We report here a differential co-expression network analysis in five gene expression datasets (GSE7803, GSE9750, GSE39001, GSE52903, and GSE63514, from the Gene Expression Omnibus) in patients with cervical cancer and healthy controls. Kaplan-Meier Survival and principle component analyses were employed to evaluate prognostic and diagnostic performances of biomarker candidates, respectively. As a result, seven distinct co-expressed gene modules were identified. Among these, five modules (with sizes of 9-45 genes) presented high prognostic and diagnostic capabilities with hazard ratios of 2.28-11.3, and diagnostic odds ratios of 85.2-548.8. Moreover, these modules were associated with several key biological processes such as cell cycle regulation, keratinization, neutrophil degranulation, and the phospholipase D signaling pathway. In addition, transcription factors ETS1 and GATA2 were noted as common regulatory elements. These genomic biomarker candidates identified by differential co-expression network analysis offer new prospects for translational cancer research, not to mention personalized medicine to forecast cervical cancer susceptibility and prognosis. Looking into the future, we also suggest that the search for a molecular basis of common complex diseases should be complemented by differential co-expression analyses to obtain a systems-level understanding of disease phenotype variability.

Co-regulated gene expression of splicing factors as drivers of cancer progression

Splicing factors (SFs) act in dynamic macromolecular complexes to modulate RNA processing. To understand the complex role of SFs in cancer progression, we performed a systemic analysis of the co-regulation of SFs using primary tumor RNA sequencing data. Co-regulated SFs were associated with aggressive breast cancer phenotypes and enhanced metastasis formation, resulting in the classification of Enhancer- (21 genes) and Suppressor-SFs (64 genes). High Enhancer-SF levels were related to distinct splicing patterns and expression of known oncogenic pathways such as respiratory electron transport, DNA damage and cell cycle regulation. Importantly, largely identical SF co-regulation was observed in almost all major cancer types, including lung, pancreas and prostate cancer. In conclusion, we identified cancer-associated co-regulated expression of SFs that are associated with aggressive phenotypes. This study increases the global understanding of the role of the spliceosome in cancer progression and also contributes to the development of strategies to cure cancer patients.

Transcriptomic analysis of the Aquaporin (AQP) gene family interactome identifies a molecular panel of four prognostic markers in patients with pancreatic ductal adenocarcinoma

BACKGROUND

This study aimed to assess the differential gene expression of aquaporin (AQP) gene family interactome in pancreatic ductal adenocarcinoma (PDAC) using data mining techniques to identify novel candidate genes intervening in the pathogenicity of PDAC.

METHOD

Transcriptome data mining techniques were used in order to construct the interactome of the AQP gene family and to determine which genes members are differentially expressed in PDAC as compared to controls. The same techniques were used in order to evaluate the potential prognostic role of the differentially expressed genes.

RESULTS

Transcriptome microarray data of four GEO datasets were incorporated, including 142 primary tumor samples and 104 normal pancreatic tissue samples. Twenty differentially expressed genes were identified, of which nineteen were downregulated and one up-regulated. A molecular panel of four genes (Aquaporin 7 - AQP7; Archain 1 - ARCN1; Exocyst Complex Component 3 - EXOC3; Coatomer Protein Complex Subunit Epsilon - COPE) were identified as potential prognostic markers associated with overall survival.

CONCLUSION

These outcomes should be further assessed in vitro in order to fully understand the role of these genes in the pathophysiological mechanism of PDAC.

Evaluation and Interpretation of Transcriptome Data Underlying Heterogeneous Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a type of progressive lung disease, featured by airflow obstruction. Recently, a comprehensive analysis of the transcriptome in lung tissue of COPD patients was performed, but the heterogeneity of the sample was not seriously considered in characterizing the mechanistic dysregulation of COPD. Here, we established a new transcriptome analysis pipeline using a deconvolution process to reduce the heterogeneity and clearly identified that these transcriptome data originated from the mild or moderate stage of COPD patients. Differentially expressed or co-expressed genes in the protein interaction subnetworks were linked with mitochondrial dysfunction and the immune response, as expected. Computational protein localization prediction revealed that 19 proteins showing changes in subcellular localization were mostly related to mitochondria, suggesting that mislocalization of mitochondria-targeting proteins plays an important role in COPD pathology. Our extensive evaluation of COPD transcriptome data could provide guidelines for analyzing heterogeneous gene expression profiles and classifying potential candidate genes that are responsible for the pathogenesis of COPD.

Topological scoring of protein interaction networks

It remains a significant challenge to define individual protein associations within networks where an individual protein can directly interact with other proteins and/or be part of large complexes, which contain functional modules. Here we demonstrate the topological scoring (TopS) algorithm for the analysis of quantitative proteomic datasets from affinity purifications. Data is analyzed in a parallel fashion where a prey protein is scored in an individual affinity purification by aggregating information from the entire dataset. Topological scores span a broad range of values indicating the enrichment of an individual protein in every bait protein purification. TopS is applied to interaction networks derived from human DNA repair proteins and yeast chromatin remodeling complexes. TopS highlights potential direct protein interactions and modules within complexes. TopS is a rapid method for the efficient and informative computational analysis of datasets, is complementary to existing analysis pipelines, and provides important insights into protein interaction networks.

Inferring direct protein−protein interactions (PPIs) and modules in PPI networks remains a challenge. Here, the authors introduce an algorithm to infer potential direct PPIs from quantitative proteomic AP-MS data by identifying enriched interactions of each bait relative to the other baits.

Cross-disease analysis of Alzheimer's disease and type-2 Diabetes highlights the role of autophagy in the pathophysiology of two highly comorbid diseases

Evidence is accumulating that the main chronic diseases of aging Alzheimer's disease (AD) and type-2 diabetes mellitus (T2DM) share common pathophysiological mechanisms. This study aimed at applying systems biology approaches to increase the knowledge of the shared molecular pathways underpinnings of AD and T2DM. We analysed transcriptomic data of post-mortem AD and T2DM human brains to obtain disease signatures of AD and T2DM and combined them with protein-protein interaction information to construct two disease-specific networks. The overlapping AD/T2DM network proteins were then used to extract the most representative Gene Ontology biological process terms. The expression of genes identified as relevant was studied in two AD models, 3xTg-AD and ApoE3/ApoE4 targeted replacement mice. The present transcriptomic data analysis revealed a principal role for autophagy in the molecular basis of both AD and T2DM. Our experimental validation in mouse AD models confirmed the role of autophagy-related genes. Among modulated genes, Cyclin-Dependent Kinase Inhibitor 1B, Autophagy Related 16-Like 2, and insulin were highlighted. In conclusion, the present investigation revealed autophagy as the central dys-regulated pathway in highly co-morbid diseases such as AD and T2DM allowing the identification of specific genes potentially involved in disease pathophysiology which could become novel targets for therapeutic intervention.

Identification of DNA methylation-driven genes in esophageal squamous cell carcinoma: a study based on The Cancer Genome Atlas

Background

Aberrant DNA methylations are significantly associated with esophageal squamous cell carcinoma (ESCC). In this study, we aimed to investigate the DNA methylation-driven genes in ESCC by integrative bioinformatics analysis.

Methods

Data of DNA methylation and transcriptome profiling were downloaded from TCGA database. DNA methylation-driven genes were obtained by methylmix R package. David database and ConsensusPathDB were used to perform gene ontology (GO) analysis and pathway analysis, respectively. Survival R package was used to analyze overall survival analysis of methylation-driven genes.

Results

Totally 26 DNA methylation-driven genes were identified by the methylmix, which were enriched in molecular function of DNA binding and transcription factor activity. Then, ABCD1, SLC5A10, SPIN3, ZNF69, and ZNF608 were recognized as significant independent prognostic biomarkers from 26 methylation-driven genes. Additionally, a further integrative survival analysis, which combined methylation and gene expression data, was identified that ABCD1, CCDC8, FBXO17 were significantly associated with patients’ survival. Also, multiple aberrant methylation sites were found to be correlated with gene expression.

Conclusion

In summary, we studied the DNA methylation-driven genes in ESCC by bioinformatics analysis, offering better understand of molecular mechanisms of ESCC and providing potential biomarkers precision treatment and prognosis detection.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0770-9) contains supplementary material, which is available to authorized users.

Treatment Avenues in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Split-gender Pharmacogenomic Study of Gene-expression Modules

PURPOSE

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating multisymptom illness impacting up to 1 million people in the United States. As the pathogenesis and etiology of this complex condition are unclear, prospective treatments are limited. Identifying US Food and Drug Administration-approved drugs that may be repositioned as treatments for ME/CFS may offer a rapid and cost-effective solution.

METHODS

Here we used gene-expression data from 33 patients with Fukuda-defined ME/CFS (23 females, 10 males) and 21 healthy demographically comparable controls (15 females, 6 males) to identify differential expression of predefined gene-module sets based on nonparametric statistics. Differentially expressed gene modules were then annotated via over-representation analysis using the Consensus Pathway database. Differentially expressed modules were then regressed onto measures of fatigue and cross-referenced with drug atlas and pharmacogenomics databases to identify putative treatment agents.

FINDINGS

The top 1% of modules identified in males indicated small effect sizes in modules associated with immune regulation and mitochondrial dysfunction. In females, modules identified included those related to immune factors and cardiac/blood factors, returning effect sizes ranging from very small to intermediate (0.147 < Cohen δ < 0.532). Regression analysis indicated that B-cell receptors, T-cell receptors, tumor necrosis factor α, transforming growth factor β, and metabolic and cardiac modules were strongly correlated with multiple composite measures of fatigue. Cross-referencing identified genes with pharmacogenomics data indicated immunosuppressants as potential treatments of ME/CFS symptoms.

IMPLICATIONS

The findings from our analysis suggest that ME/CFS symptoms are perpetuated by immune dysregulation that may be approached via immune modulation-based treatment strategies.

Genomic and molecular characterization of preterm birth

Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology.

New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

Reduced lung function predicts mortality and is key to the diagnosis of chronic obstructive pulmonary disease (COPD). In a genome-wide association study in 400,102 individuals of European ancestry, we define 279 lung function signals, 139 of which are new. In combination, these variants strongly predict COPD in independent populations. Furthermore, the combined effect of these variants showed generalizability across smokers and never smokers, and across ancestral groups. We highlight biological pathways, known and potential drug targets for COPD and, in phenome-wide association studies, autoimmune-related and other pleiotropic effects of lung function-associated variants. This new genetic evidence has potential to improve future preventive and therapeutic strategies for COPD.

Understanding the Early Biological Effects of Isoprene-Derived Particulate Matter Enhanced by Anthropogenic Pollutants

INTRODUCTION

Airborne fine particulate matter (PM2.5; particulate matter ≤ 2.5 μm in aerodynamic diameter) plays a key role in air quality, climate, and public health. Globally, the largest mass fraction of PM2.5 is organic, dominated by secondary organic aerosol (SOA) formed from atmospheric oxidation of volatile organic compounds (VOCs). Isoprene from vegetation is the most abundant nonmethane VOC emitted into Earth's atmosphere. Isoprene has been recently recognized as one of the major sources of global SOA production that is enhanced by the presence of anthropogenic pollutants, such as acidic sulfate derived from sulfur dioxide (SO2), through multiphase chemistry of its oxidation products. Considering the abundance of isoprene-derived SOA in the atmosphere, understanding mechanisms of adverse health effects through inhalation exposure is critical to mitigating its potential impact on public health. Although previous studies have examined the toxicological effects of certain isoprene-derived gas-phase oxidation products, to date, no systematic studies have examined the potential toxicological effects of isoprene-derived SOA, its constituents, or its SOA precursors on human lung cells.

SPECIFIC AIMS

The overall objective of this study was to investigate the early biological effects of isoprene-derived SOA and its subtypes on BEAS-2B cells (a human bronchial epithelial cell line), with a particular focus on the alteration of oxidative stress- and inflammation-related genes. To achieve this objective, there were two specific aims.

1. Examine toxicity and early biological effects of SOA derived from the photochemical oxidation of isoprene, considering both urban and downwind-urban types of chemistry.

2. Examine toxicity and early biological effects of SOA derived directly from downstream oxidation products of isoprene (i.e., epoxides and hydroperoxides).

METHODS

Isoprene-derived SOA was first generated by photooxidation of isoprene under natural sunlight in the presence of nitric oxide (NO) and acidified sulfate aerosols. Experiments were conducted in a 120-m3 outdoor Teflon-film chamber located on the roof of the Gillings School of Global Public Health, University of North Carolina at Chapel Hill (UNC-Chapel Hill). BEAS-2B cells were exposed to chamber- generated isoprene-derived SOA using the Electrostatic Aerosol in Vitro Exposure System (EAVES). This approach allowed us to generate atmospherically relevant compositions of isoprene-derived SOA and to examine its toxicity through in vitro exposures at an air-liquid interface, providing a more biologically relevant exposure model. Isoprene-derived SOA samples were also collected, concurrently with EAVES sampling, onto Teflon membrane filters for in vitro resuspension exposures and for analysis of aerosol chemical composition by gas chromatography/electron ionization-quadrupole mass spectrometry (GC/EI-MS) with prior trimethylsilylation and ultra-performance liquid-chromatography coupled to high-resolution quadrupole time-of-flight mass spectrometry equipped with electrospray ionization (UPLC/ESI-HR-QTOFMS). Isoprene-derived SOA samples were also analyzed by the dithiothreitol (DTT) assay in order to characterize their reactive oxygen species (ROS)-generation potential.

Organic synthesis of known isoprene-derived SOA precursors, which included isoprene epoxydiols (IEPOX), methacrylic acid epoxide (MAE), and isoprene-derived hydroxyhydroperoxides (ISOPOOH), was conducted in order to isolate major isoprene-derived SOA formation pathways from each other and to determine which of these pathways (or SOA types) is potentially more toxic. Since IEPOX and MAE produce SOA through multiphase chemistry onto acidic sulfate aerosol, dark reactive uptake experiments of IEPOX and MAE in the presence of acidic sulfate aerosol were performed in a 10-m3 flexible Teflon indoor chamber at UNC-Chapel Hill. Since the generation of SOA from ISOPOOH (through a non-IEPOX route) requires a hydroxyl radical (•OH)-initiated oxidation, ozonolysis of tetramethylethylene (TME) was used to form the needed •OH radicals in the indoor chamber. The resultant low-volatility multifunctional hydroperoxides condensed onto nonacidified sulfate aerosol, yielding the ISOPOOH-derived SOA needed for exposures. Similar to the outdoor chamber SOAs, IEPOX, MAE- and ISOPOOH-derived SOAs were collected onto Teflon membrane filters and were subsequently chemically characterized by GC/EI-MS and UPLC/ESI-HR-QTOFMS as well as for ROS-generation potential using the DTT assay. These filters were also used for resuspension in vitro exposures.

By conducting gene expression profiling, we provided mechanistic insights into the potential health effects of isoprene-derived SOA. First, gene expression profiling of 84 oxidative stress- and 249 inflammation-associated human genes was performed for cells exposed to isoprene-derived SOA generated in our outdoor chamber experiments in EAVES or by resuspension. Two pathway-focused panels were utilized for this purpose: (1) nCounter GX Human Inflammation Kit comprised of 249 human genes (NanoString), and (2) Human Oxidative Stress Plus RT2 Profiler PCR Array (Qiagen) comprised of 84 oxidative stress-associated genes. We compared the gene expression levels in cells exposed to SOA generated in an outdoor chamber from photochemical oxidation of isoprene in the presence of NO and acidified sulfate seed aerosol to cells exposed to a dark control mixture of isoprene, NO, and acidified sulfate seed aerosol to isolate the effects of the isoprene-derived SOA on the cells using the EAVES and resuspension exposure methods. Pathway-based analysis was performed for significantly altered genes using the ConsensusPathDB database, which is a database system for the integration of human gene functional interactions to provide biological pathway information for a gene set of interest. Pathway annotation was performed to provide biological pathway information for each gene set. The gene-gene interaction networks were constructed and visualized using the GeneMANIA Cytoscape app (version 3.4.1) to predict the putative function of altered genes. Lastly, isoprene-derived SOA collected onto filters was used in resuspension exposures to measure select inflammatory biomarkers, including interleukin 8 (IL-8) and prostaglandin-endoperoxide synthase 2 (PTGS2) genes, in BEAS-2B cells to ensure that effects observed from EAVES exposures were attributable to particle-phase organic products. Since EAVES and resuspension exposures compared well, gene expression profiling for IEPOX-, MAE- and ISOPOOH-derived SOA were conducted using only resuspension exposures.

RESULTS AND CONCLUSIONS

Chemical characterization coupled with biological analyses show that atmospherically relevant compositions of isoprene-derived SOA alter the levels of 41 oxidative stress-related genes. Of the different composition types of isoprene-derived SOA, MAE- and ISOPOOH-derived SOA altered the greatest number of genes, suggesting that carbonyl and hydroperoxide functional groups are oxidative stress promoters. Taken together, the different composition types accounted for 34 of the genes altered by the total isoprene-derived SOA mixture, while 7 remained unique to the total mixture exposures, indicating that there is either a synergistic effect of the different isoprene-derived SOA components or an unaccounted component in the mixture.

The high-oxides of nitrogen (NOx) regime, which yielded MAE- and methacrolein (MACR)-derived SOA, had a higher ROS-generation potential (as measured by the DTT assay) than the low- NOx regime, which included IEPOX- and isoprene-derived SOA. However, ISOPOOH-derived SOA, which also formed in the low- NOx regime, had the highest ROS-generation potential, similar to 1,4-naphthoquinone (1,4-NQ). This suggests that aerosol-phase organic peroxides contribute significantly to particulate matter (PM) oxidative potential. MAE- and MACR- derived SOA showed equal or greater ROS-generation potential than was reported in prior UNC-Chapel Hill studies on diesel exhaust PM, highlighting the importance of a comprehensive investigation of the toxicity of isoprene-derived SOA. Notably, ISOPOOH-derived SOA was one order of magnitude higher in ROS-generation potential than diesel exhaust particles previously examined at UNC-Chapel Hill. As an acellular assay, the DTT assay may not be predictive of oxidative stress; therefore, we also focused on the gene expression results from the cellular exposures.

We have demonstrated that the nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and the redox-sensitive activation protein-1 (AP-1) transcription factor networks have been significantly altered upon exposure to isoprene-derived SOA. The identification of Nrf2 pathway in cells exposed to isoprene-derived SOA is in accordance with our findings using the DTT assay, which measures the thiol reactivity of PM samples as a surrogate for their ROS-generation potential. Specifically, our results point to the cysteine-thiol modifications within cells that lead to activation of Nrf2-related gene expression.

However, based on our gene expression results showing no clear relationship between DTT activity and the number of altered oxidative stress-related genes, the DTT activity of isoprene-derived SOA may not be directly indicative of toxicity relative to other SOA types. While activation of Nrf2-associated genes has been identified with responses to oxidative stress and linked to traffic related air pollution exposure in both toxicological and epidemiological studies, their implicit involvement in this study suggests that activation of Nrf2-related gene expression may occur with exposures to all sorts of PM types.

By controlling the exposure time, method, and dose we demonstrated that among the SOA derived from previously identified individual precursors of isoprene-derived SOA, ISOPOOH-derived SOA alters more oxidative stress related genes than does IEPOX-derived SOA, but fewer than MAE-derived SOA. This suggests that the composition of MAE-derived SOA may be the greatest contributor to alterations of oxidative stress-related gene expression observed due to isoprene-derived SOA exposure. Further study on induced levels of protein expression and specific toxicological endpoints is necessary to determine if the observed gene expression changes lead to adverse health effects. In addition, such studies have implications for pollution-control strategies because NOx and SO2 are controllable pollutants that can alter the composition of SOA, and in turn alter its effects on gene expression. The mass fraction of different components of atmospheric isoprene derived SOA should be considered, but altering the fraction of high- NOx isoprene-derived SOA (e.g., MAE derived SOA) may yield greater changes in gene expression than altering the fraction of low- NOx isoprene derived SOA types (ISOPOOH- or IEPOX-derived SOA). Finally, this study confirms that total isoprene-derived SOA alters the expression of a greater number of genes than does SOA derived from the tested precursors. This warrants further work to determine the underlying explanation for this observation, which may be uncharacterized components of isoprene-derived SOA or the potential for synergism between the studied components.

Systematic Review of the Potential of MicroRNAs in Diffuse Large B Cell Lymphoma

Diffuse large B cell lymphoma (DLBCL) is the most common subtype of invasive non-Hodgkin’s lymphoma (NHL). DLBCL presents with variable backgrounds, which results in heterogeneous outcomes among patients. Although new tools have been developed for the classification and management of patients, 40% of them still have primary refractory disease or relapse. In addition, multiple factors regarding the pathogenesis of this disease remain unclear and identification of novel biomarkers is needed. In this context, recent investigations point to microRNAs as useful biomarkers in cancer. The aim of this systematic review was to provide new insight into the role of miRNAs in the diagnosis, classification, treatment response and prognosis of DLBCL patients. We used the following terms in PubMed” ((‘Non-coding RNA’) OR (‘microRNA’ OR ‘miRNA’ OR ‘miR’) OR (‘exosome’) OR (‘extracellular vesicle’) OR (‘secretome’)) AND (‘Diffuse large B cell lymphoma’ OR ‘DLBCL’)” to search for studies evaluating miRNAs as a diagnosis, subtype, treatment response or prognosis biomarkers in primary DLBCL in human patient populations. As a result, the analysis was restricted to the role of miRNAs in tumor tissue and we did not consider circulating miRNAs. A total of thirty-six studies met the inclusion criteria. Among them, twenty-one were classified in the diagnosis category, twenty in classification, five in treatment response and nineteen in prognosis. In this review, we have identified miR-155-5p and miR-21-5p as miRNAs of potential utility for diagnosis, while miR-155-5p and miR-221-3p could be useful for classification. Further studies are needed to exploit the potential of this field.

Identification of Sex-Specific Transcriptome Responses to Polychlorinated Biphenyls (PCBs)

PCBs are classified as xenoestrogens and carcinogens and their health risks may be sex-specific. To identify potential sex-specific responses to PCB-exposure we established gene expression profiles in a population study subdivided into females and males. Gene expression profiles were determined in a study population consisting of 512 subjects from the EnviroGenomarkers project, 217 subjects who developed lymphoma and 295 controls were selected in later life. We ran linear mixed models in order to find associations between gene expression and exposure to PCBs, while correcting for confounders, in particular distribution of white blood cells (WBC), as well as random effects. The analysis was subdivided according to sex and development of lymphoma in later life. The changes in gene expression as a result of exposure to the six studied PCB congeners were sex- and WBC type specific. The relatively large number of genes that are significantly associated with PCB-exposure in the female subpopulation already indicates different biological response mechanisms to PCBs between the two sexes. The interaction analysis between different PCBs and WBCs provides only a small overlap between sexes. In males, cancer-related pathways and in females immune system-related pathways are identified in association with PCBs and WBCs. Future lymphoma cases and controls for both sexes show different responses to the interaction of PCBs with WBCs, suggesting a role of the immune system in PCB-related cancer development.

Molecular features of premenopausal breast cancers in Latin American women: Pilot results from the PRECAMA study

BACKGROUND

In Latin America (LA), there is a high incidence rate of breast cancer (BC) in premenopausal women, and the genomic features of these BC remain unknown. Here, we aim to characterize the molecular features of BC in young LA women within the framework of the PRECAMA study, a multicenter population-based case-control study of BC in premenopausal women.

METHODS

Pathological tumor tissues were collected from incident cases from four LA countries. Immunohistochemistry (IHC) was performed centrally for ER, PR, HER2, Ki67, EGFR, CK5/6, and p53 protein markers. Targeted deep sequencing was done on genomic DNA extracted from formalin-fixed, paraffin-embedded tumor tissues and their paired blood samples to screen for somatic mutations in eight genes frequently mutated in BC. A subset of samples was analyzed by exome sequencing to identify somatic mutational signatures.

RESULTS

The majority of cases were positive for ER or PR (168/233; 72%), and 21% were triple-negative (TN), mainly of basal type. Most tumors were positive for Ki67 (189/233; 81%). In 126 sequenced cases, TP53 and PIK3CA were the most frequently mutated genes (32.5% and 21.4%, respectively), followed by AKT1 (9.5%). TP53 mutations were more frequent in HER2-enriched and TN IHC subtypes, whereas PIK3CA/AKT1 mutations were more frequent in ER-positive tumors, as expected. Interestingly, a higher proportion of G:C>T:A mutations was observed in TP53 in PRECAMA cases compared with TCGA and METABRIC BC series (27% vs 14%). Exome-wide mutational patterns in 10 TN cases revealed alterations in signal transduction pathways and major contributions of mutational signatures caused by altered DNA repair pathways.

CONCLUSIONS

These pilot results on PRECAMA tumors give a preview of the molecular features of premenopausal BC in LA. Although the overall mutation burden was as expected from data in other populations, mutational patterns observed in TP53 and exome-wide suggested possible differences in mutagenic processes giving rise to these tumors compared with other populations. Further -omics analyses of a larger number of cases in the near future will enable the investigation of relationships between these molecular features and risk factors.

PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data

Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70–88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data.

Untargeted metabolomics can systematically profile thousands of metabolite signals in biological samples and is an increasingly popular approach for discovering biomarkers and predictors for human traits and diseases. However, currently, a significant portion of the measured signals cannot be identified as known metabolites or easily compared across datasets, and thus are usually excluded from downstream analyses. Here, we present PAIRUP-MS, a suite of computational methods designed to analyze unknown, unidentified signals across multiple mass spectrometry-based profiling datasets. Specifically, PAIRUP-MS contains a flexible imputation-based approach for pairing up unknown signals across datasets, allowing for meta-analysis of matched signals across studies that would otherwise be incompatible. PAIRUP-MS also offers a pathway annotation and enrichment analysis framework that links metabolite signals to plausible biological functions without using their chemical identities. Importantly, we validated both components of PAIRUP-MS using genetic data and applied them to study an example trait, body mass index. Overall, our results demonstrate that PAIRUP-MS enables previously infeasible analyses of unknown, unidentified signals across multiple datasets, thereby greatly improving power for discovery and biological inference.

Personalized Prediction of Acquired Resistance to EGFR-Targeted Inhibitors Using a Pathway-Based Machine Learning Approach

Epidermal growth factor receptor (EGFR) inhibitors have benefitted cancer patients worldwide, but resistance inevitably develops over time, resulting in treatment failures. An accurate prediction model for acquired resistance (AR) to EGFR inhibitors is critical for early diagnosis and according intervention, but is not yet available due to personal variations and the complex mechanisms of AR. Here, we have developed a novel pipeline to build a meta-analysis-based, multivariate model for personalized pathways in AR to EGFR inhibitors, using sophisticated machine learning algorithms. Surprisingly, the model achieved excellent predictive performance, with a cross-study validation area under curve (AUC) of over 0.9, and generalization performance on independent cohorts of samples, with a perfect AUC score of 1. Furthermore, the model showed excellent transferability across different cancer cell lines and EGFR inhibitors, including gefitinib, erlotinib, afatinib, and cetuximab. In conclusion, our model achieved high predictive accuracy through robust cross study validation, and enabled individualized prediction on newly introduced data. We also discovered common pathway alteration signatures for AR to EGFR inhibitors, which can provide directions for other follow-up studies.

GWAS and network analysis of co-occurring nicotine and alcohol dependence identifies significantly associated alleles and network

Alcohol dependence (AD) and nicotine dependence (ND) co-occur frequently (AD+ND). We integrated SNP-based, gene-based, and protein-protein interaction network analyses to identify shared risk genes or gene subnetworks for AD+ND in African Americans (AAs, N = 2,094) and European Americans (EAs, N = 1,207). The DSM-IV criterion counts for AD and ND were modeled as two dependent variables in a multivariate linear mixed model, and analyzed separately for the two populations. The most significant SNP was rs6579845 in EAs (p < 1.29 × 10^-8 ) in GM2A, which encodes GM2 ganglioside activator, and is a cis-expression quantitative locus that affects GM2A expression in blood and brain tissues. However, this SNP was not replicated in our another small sample (N = 678). We identified a subnetwork of 24 genes that contributed to the AD+ND criterion counts. In the gene-set analysis for the subnetwork in an independent sample, the Study of Addiction: Genetics and Environment project (predominately EAs), these 24 genes as a set differed in AD+ND versus control subjects in EAs (p = .041). Functional enrichment analysis for this subnetwork revealed that the gene enrichment involved primarily nerve growth factor pathways, and cocaine and amphetamine addiction. In conclusion, we identified a genome-wide significant variant at GM2A and a gene subnetwork underlying the genetic trait of shared AD+ND. These results increase our understanding of the shared (pleiotropic) genetic risk that underlies AD+ND.

Genetic Predisposition for Immune System, Hormone, and Metabolic Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Pilot Study

Introduction: Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS) is a multifactorial illness of unknown etiology with considerable social and economic impact. To investigate a putative genetic predisposition to ME/CFS we conducted genome-wide single-nucleotide polymorphism (SNP) analysis to identify possible variants. Methods: 383 ME/CFS participants underwent DNA testing using the commercial company 23andMe. The deidentified genetic data was then filtered to include only non-synonymous and nonsense SNPs from exons and microRNAs, and SNPs close to splice sites. The frequencies of each SNP were calculated within our cohort and compared to frequencies from the Kaviar reference database. Functional annotation of pathway sets containing SNP genes with high frequency in ME/CFS was performed using over-representation analysis via ConsensusPathDB. Furthermore, these SNPs were also scored using the Combined Annotation Dependent Depletion (CADD) algorithm to gauge their deleteriousness. Results: 5693 SNPs were found to have at least 10% frequency in at least one cohort (ME/CFS or reference) and at least two-fold absolute difference for ME/CFS. Functional analysis identified the majority of SNPs as related to immune system, hormone, metabolic, and extracellular matrix organization. CADD scoring identified 517 SNPs in these pathways that are among the 10% most deleteriousness substitutions to the human genome.

Re-curation and rational enrichment of knowledge graphs in Biological Expression Language

The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.

Involvement of miRNA polymorphism in mucositis development in childhood acute lymphoblastic leukemia treatment

Aim: Mucositis, linked to methotrexate, daunorubicin or cyclophosphamide, is a frequent childhood acute lymphoblastic leukemia (ALL) therapy side effect. miRNAs regulate the expression of pharmacokinetic/pharmacodynamic pathway genes. SNPs in miRNAs could affect their levels or function, and affect their pharmacokinetic/pharmacodynamic pathway target genes. Our aim was to determine the association between miRNA genetic variants targeting mucositis-related genes and mucositis-developing risk. Patients & methods: We analyzed 160 SNPs in 179 Spanish children with B-cell precursor ALL homogeneously treated with LAL/SHOP protocols. Results: We identified three SNPs in miR-4268, miR-4751 and miR-3117 associated with mucositis, diarrhea and vomiting, respectively. Conclusion: The effect of these SNPs on genes related to drug pharmacokinetics/pharmacodynamics could explain mucositis, diarrhea and vomiting development during ALL therapy.

Exploration of methylation-driven genes for monitoring and prognosis of patients with lung adenocarcinoma

Background

As one of the most common malignant tumors in humans, lung cancer has experienced a gradual increase in morbidity and mortality. This study examined prognosis-related methylation-driven genes specific to lung adenocarcinoma (LUAD) to provide a basis for prognosis prediction and personalized targeted therapy for LUAD patients.

Methods

The methylation and survival time data from LUAD patients in the TCGA database were downloaded. The MethylMix algorithm was used to identify the differential methylation status of LUAD and adjacent tissues based on the β-mixture model to obtain disease-related methylation-driven genes. A COX regression model was then used to screen for LUAD prognosis-related methylation-driven genes, and a linear risk model based on five methylation-driven gene expression profiles was constructed. A methylation and gene expression combined survival analysis was performed to further explore the prognostic value of 5 genes independently.

Results

There were 118 differentially expressed methylation-driven genes in the LUAD tissues and adjacent tissues. Five of the genes, CCDC181, PLAU, S1PR1, ELF3, and KLHDC9, were used to construct a prognostic risk model. Overall, the survival time was significantly lower in the high-risk group compared with that in the low-risk group (P < 0.05). In addition, the methylation and gene expression combined survival analysis found that the combined expression levels of the genes CCDC181, PLAU, and S1PR1 as well as KLHDC9 alone can be used as independent prognostic markers or drug targets.

Conclusion

Our findings provide an important bioinformatic basis and relevant theoretical basis for guiding subsequent LUAD early diagnosis and prognosis assessments.

Electronic supplementary material

The online version of this article (10.1186/s12935-018-0691-z) contains supplementary material, which is available to authorized users.

Imprinted and ancient gene: a potential mediator of cancer cell survival during tryptophan deprivation

BACKGROUND

Depletion of tryptophan and the accumulation of tryptophan metabolites mediated by the immunosuppressive enzyme indoleamine 2,3-dioxygenase 1 (IDO1), trigger immune cells to undergo apoptosis. However, cancer cells in the same microenvironment appear not to be affected. Mechanisms whereby cancer cells resist accelerated tryptophan degradation are not completely understood. We hypothesize that cancer cells co-opt IMPACT (the product of IMPrinted and AnCienT gene), to withstand periods of tryptophan deficiency.

METHODS

A range of bioinformatic techniques including correlation and gene set variation analyses was applied to genomic datasets of cancer (The Cancer Genome Atlas) and normal (Genotype Tissue Expression Project) tissues to investigate IMPACT's role in cancer. Survival of IMPACT-overexpressing GL261 glioma cells and their wild type counterparts cultured in low tryptophan media was assessed using fluorescence microscopy and MTT bio-reduction assay. Expression of the Integrated Stress Response proteins was measured using Western blotting.

RESULTS

We found IMPACT to be upregulated and frequently amplified in a broad range of clinical cancers relative to their non-malignant tissue counterparts. In a subset of clinical cancers, high IMPACT expression associated with decreased activity of pathways and genes involved in stress response and with increased activity of translational regulation such as the mTOR pathway. Experimental studies using the GL261 glioma line showed that cells engineered to overexpress IMPACT, gained a survival advantage over wild-type lines when cultured under limiting tryptophan concentrations. No significant difference in the expression of proteins in the Integrated Stress Response pathway was detected in tryptophan-deprived GL261 IMPACT-overexpressors compared to that in wild-type cells. IMPACT-overexpressing GL261 cells but not their wild-type counterparts, showed marked enlargement of their nuclei and cytoplasmic area when stressed by tryptophan deprivation.

CONCLUSIONS

The bioinformatics data together with our laboratory studies, support the hypothesis that IMPACT mediates a protective mechanism allowing cancer cells to overcome microenvironmental stresses such as tryptophan deficiency.

Gene Expression Signatures Point to a Male Sex-Specific Lung Mesenchymal Cell PDGF Receptor Signaling Defect in Infants Developing Bronchopulmonary Dysplasia

Male sex is a risk factor for development of bronchopulmonary dysplasia (BPD), a common chronic lung disease following preterm birth. We previously found that tracheal aspirate mesenchymal stromal cells (MSCs) from premature infants developing BPD show reduced expression of PDGFRα, which is required for normal lung development. We hypothesized that MSCs from male infants developing BPD exhibit a pathologic gene expression profile deficient in PDGFR and its downstream effectors, thereby favoring delayed lung development. In a discovery cohort of 6 male and 7 female premature infants, we analyzed the tracheal aspirate MSCs transcriptome. A unique gene signature distinguished MSCs from male infants developing BPD from all other MSCs. Genes involved in lung development, PDGF signaling and extracellular matrix remodeling were differentially expressed. We sought to confirm these findings in a second cohort of 13 male and 12 female premature infants. mRNA expression of PDGFRA, FGF7, WNT2, SPRY1, MMP3 and FOXF2 were significantly lower in MSCs from male infants developing BPD. In female infants developing BPD, tracheal aspirate levels of proinflammatory CCL2 and profibrotic Galectin-1 were higher compared to male infants developing BPD and female not developing BPD. Our findings support a notion for sex-specific differences in the mechanisms of BPD development.

Genetic variation in the Estonian population: pharmacogenomics study of adverse drug effects using electronic health records

Pharmacogenomics aims to tailor pharmacological treatment to each individual by considering associations between genetic polymorphisms and adverse drug effects (ADEs). With technological advances, pharmacogenomic research has evolved from candidate gene analyses to genome-wide association studies. Here, we integrate deep whole-genome sequencing (WGS) information with drug prescription and ADE data from Estonian electronic health record (EHR) databases to evaluate genome- and pharmacome-wide associations on an unprecedented scale. We leveraged WGS data of 2240 Estonian Biobank participants and imputed all single-nucleotide variants (SNVs) with allele counts over 2 for 13,986 genotyped participants. Overall, we identified 41 (10 novel) loss-of-function and 567 (134 novel) missense variants in 64 very important pharmacogenes. The majority of the detected variants were very rare with frequencies below 0.05%, and 6 of the novel loss-of-function and 99 of the missense variants were only detected as single alleles (allele count = 1). We also validated documented pharmacogenetic associations and detected new independent variants in known gene-drug pairs. Specifically, we found that CTNNA3 was associated with myositis and myopathies among individuals taking nonsteroidal anti-inflammatory oxicams and replicated this finding in an extended cohort of 706 individuals. These findings illustrate that population-based WGS-coupled EHRs are a useful tool for biomarker discovery.

Genomic and transcriptional Profiling of tumor infiltrated CD8+ T cells revealed functional heterogeneity of antitumor immunity in hepatocellular carcinoma

As key players in HCC antitumor response, the functions of tumor infiltrated CD8⁺ T cells are significantly affected by surrounding microenvironment. A detailed profiling of their genomic and transcriptional changes could provide valuable insights for both future immunotherapy development and prognosis evaluation. We performed whole exome and transcriptome sequencing on tumor infiltrated CD8⁺ T cells and CD8⁺ T cells isolated from other tissue origins (peritumor tissues and corresponding PBMCs) in eight treatment-naive HCC patients. The results demonstrated that transcriptional changes, rather than genomic alterations were the main contributors to the functional alterations of CD8⁺ T cells in the process of tumor progression. The origins of CD8⁺ T cells defined their transcriptional landscape, while the tumor infiltrated CD8⁺ T cells shared more similarity with peritumor-derived CD8⁺ T cells compared with those CD8⁺ T cells in blood. In addition, tumor infiltrated CD8⁺ T cells also showed larger transcriptional heterogeneity among individuals, which was modulated by clinical features such as HBV levels, preoperative anti-viral treatment and the degree of T cell infiltration. We also identified multiple inter-connected pathways involved in the activation and exhaustion of tumor infiltrated CD8⁺ T cells, among which IL-12 mediated pathway could dynamically reflect the functional status of CD8⁺ TILs and activation of this pathway indicated a better prognosis. Our results presented an overview picture of CD8⁺ TILs' genomic and transcriptional landscape and features, as well as how the functional status of CD8⁺ TILs correlated with patients' clinical course.

Evaluation and prediction of hepatocellular carcinoma prognosis based on molecular classification

Purpose

Prediction of hepatocellular carcinoma (HCC) prognosis faced great difficulty due to tumor heterogeneity. We aimed to identify the prognosis-associated molecular subtypes existing in HCC patients and construct an evaluation model based on identified molecular classification.

Materials and methods

Non-negative matrix factorization consensus clustering was performed using 371 HCC patients from The Cancer Genome Atlas (TCGA) to identify molecular subtypes, based on the expression profile of the survival-associated genes. Signature genes for different subtypes were identified by Significance Analysis of Microarray and Prediction Analysis for Microarrays. Model for subtype discrimination and prognosis evaluation was established using binary logistic regression. The model and its clinical implications were further validated in GSE5436 cohort and Fujian cohort.

Results

Based on TCGA data, we observed two molecular subtypes with distinct clinical outcomes including significantly different overall survival, tumor differentiation, TNM stage, and vascular invasion (all P<0.05). The existence of these two molecular subtypes was further validated in five other Gene Expression Omnibus datasets. Furthermore, we constructed an evaluation model based on six subtype signature genes, which can discriminate different subtypes with the cutoff of 0.385. Meanwhile, both Cox regression analysis and stratification analysis showed that the calculated continuous prognostic value could also effectively indicate HCC prognosis, regardless of patients’ clinical conditions. The prognostic evaluation model was successfully validated in GSE54236 cohort and Fujian cohort.

Conclusion

Two prognostic molecular subtypes existed among HCC patients, which provided promising strategies for overcoming HCC heterogeneity and could be utilized in future clinical application for predicting HCC prognosis.

Leveraging genome-wide association and clinical data in revealing schizophrenia subgroups

Schizophrenia (SCZ) has long been recognized as a highly heterogeneous disorder. Patients differed in their clinical manifestations, prognosis, and underlying pathophysiologies. Here we presented and applied a framework for finding subtypes of SCZ utilizing genome-wide association study (GWAS) and clinical data. We postulated that genetic information may help stratify patient into useful subgroups, and incorporation of other clinical information and cognitive profiles will further improve patient subtyping. We conducted cluster analysis in 387 Hong Kong Chinese with SCZ. First we performed 'single-view' clustering using genetic or clinical data alone, then proceeded to 'multi-view' clustering (MVC) accounting for both types of information. We validated clustering results by assessing subgroup differences in various outcomes. We found significant differences in outcomes including treatment response, disease course and symptom severity (Simes overall p-value using MVC = 1.64E-9). Overall speaking, we identified three subgroups with good, intermediate and poor prognosis respectively. MVC generally out-performed single-view methods. The analysis was repeated for different sets of input SNPs, and stratified analysis of male and female patients, and the results remained largely robust. We also found significant enrichment for SCZ loci among the SNPs selected by the cluster algorithm. Numerous selected genes (e.g. NRG1, ERBB4, NRXN1, ANK3) and pathways (e.g. neuregulin-ErbB4 and calcium signaling) were implicated in SCZ or related pathophysiological processes. This is first study to combine both genetic and clinical data for subtyping SCZ, and to employ genome-wide SNP data in cluster analysis of a complex disease. This work points to a new way of GWAS analysis of translational potential.

Short-term transcriptome and microRNAs responses to exposure to different air pollutants in two population studies

Diesel vehicle emissions are the major source of genotoxic compounds in ambient air from urban areas. These pollutants are linked to risks of cardiovascular diseases, lung cancer, respiratory infections and adverse neurological effects. Biological events associated with exposure to some air pollutants are widely unknown but applying omics techniques may help to identify the molecular processes that link exposure to disease risk. Most data on health risks are related to long-term exposure, so the aim of this study is to investigate the impact of short-term exposure (two hours) to air pollutants on the blood transcriptome and microRNA expression levels. We analyzed transcriptomics and microRNA expression using microarray technology on blood samples from volunteers participating in studies in London, the Oxford Street cohort, and, in Barcelona, the TAPAS cohort. Personal exposure levels measurements of particulate matter (PM₁₀, PM_2.5), ultrafine particles (UFPC), nitrogen oxides (NO₂, NO and NOx), black carbon (BC) and carbon oxides (CO and CO₂) were registered for each volunteer. Associations between air pollutant levels and gene/microRNA expression were evaluated using multivariate normal models (MVN). MVN-models identified compound-specific expression of blood cell genes and microRNAs associated with air pollution despite the low exposure levels, the short exposure periods and the relatively small-sized cohorts. Hsa-miR-197-3p, hsa-miR-29a-3p, hsa-miR-15a-5p, hsa-miR-16-5p and hsa-miR-92a-3p are found significantly expressed in association with exposures. These microRNAs target also relevant transcripts, indicating their potential relevance in the research of omics-biomarkers responding to air pollution. Furthermore, these microRNAs are also known to be associated with diseases previously linked to air pollution exposure including several cancers such lung cancer and Alzheimer's disease. In conclusion, we identified in this study promising compound-specific mRNA and microRNA biomarkers after two hours of exposure to low levels of air pollutants during two hours that suggest increased cancer risks.

Comparative Gene Expression Analysis in WM164 Melanoma Cells Revealed That β-β-Dimethylacrylshikonin Leads to ROS Generation, Loss of Mitochondrial Membrane Potential, and Autophagy Induction

Skin cancer is currently diagnosed as one in every three cancers. Melanoma, the most aggressive form of skin cancer, is responsible for 79% of skin cancer deaths and the incidence is rising faster than in any other solid tumor type. Previously, we have demonstrated that dimethylacrylshikonin (DMAS), isolated from the roots of Onosma paniculata (Boraginaceae), exhibited the lowest IC₅₀ values against different tumor types out of several isolated shikonin derivatives. DMAS was especially cytotoxic towards melanoma cells and led to apoptosis and cell cycle arrest. In this study, we performed a comprehensive gene expression study to investigate the mechanism of action in more detail. Gene expression signature was compared to vehicle-treated WM164 control cells after 24 h of DMAS treatment; where 1192 distinct mRNAs could be identified as expressed in all replicates and 89 were at least 2-fold differentially expressed. DMAS favored catabolic processes and led in particular to p62 increase which is involved in cell growth, survival, and autophagy. More in-depth experiments revealed that DMAS led to autophagy, ROS generation, and loss of mitochondrial membrane potential in different melanoma cells. It has been reported that the induction of an autophagic cell death represents a highly effective approach in melanoma therapy.

Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression

Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.