Integrative Multi-omics Approach Research Articles

Integrative Analysis of Glucometabolic Traits, Adipose Tissue DNA Methylation, and Gene Expression Identifies Epigenetic Regulatory Mechanisms of Insulin Resistance and Obesity in African Americans.

Decline in insulin sensitivity due to dysfunction of adipose tissue (AT) is one of the earliest pathogenic events in type 2 diabetes. We hypothesize that differential DNA methylation (DNAm) controls insulin sensitivity and obesity by modulating transcript expression in AT. Integrating AT DNAm profiles with transcript profile data measured in a cohort of 230 African Americans (AAs) from the African American Genetics of Metabolism and Expression cohort, we performed cis-expression quantitative trait methylation (cis-eQTM) analysis to identify epigenetic regulatory loci for glucometabolic trait-associated transcripts. We identified significantly associated cytosine-guanine dinucleotide regions for 82 transcripts (false discovery rate [FDR]-P < 0.05). The strongest eQTM locus was observed for the proopiomelanocortin (POMC; ρ = -0.632, P = 4.70 × 10-27) gene. Epigenome-wide association studies (EWAS) further identified 155, 46, and 168 cytosine-guanine dinucleotide regions associated (FDR-P < 0.05) with the Matsuda index, SI, and BMI, respectively. Intersection of EWAS, transcript level to trait association, and eQTM results, followed by causal inference test identified significant eQTM loci for 23 genes that were also associated with Matsuda index, SI, and/or BMI in EWAS. These associated genes include FERMT3, ITGAM, ITGAX, and POMC In summary, applying an integrative multiomics approach, our study provides evidence for DNAm-mediated regulation of gene expression at both previously identified and novel loci for many key AT transcripts influencing insulin resistance and obesity.

Integrative Analysis of Proteome and Transcriptome Dynamics during Bacillus subtilis Spore Revival.

Bacillus subtilis spores can reactivate their metabolism through germination upon contact with germinants and can develop into vegetative cells upon outgrowth. However, the mechanisms at the basis of the molecular machinery that triggers the spore germination and outgrowth processes are still largely unclear. To gain further insights into these processes, the transcriptome and proteome changes occurring during the conversion of spores to vegetative cells were analyzed in the present study. For each time point sampled, the changes in the spore proteome were quantitatively monitored relative to the proteome of metabolically 15N-labeled vegetative cells. Of the quantified proteins, 60% are shared by vegetative cells and spores, indicating that the spores have a minimal protein set, sufficient to resume metabolism upon completion of germination. These shared proteins thus represent the most basic "survival kit" for spore-based life. We observed no significant change in the proteome or the transcriptome until the spore's completion of germination. Our analysis identified 34 abundant mRNA transcripts in the dormant spores, 31 of which are rapidly degraded after germination. In outgrowing spores, we identified 3,152 differentially expressed genes and have demonstrated the differential expression of 322 proteins with our mass spectrometry analyses. Our data also showed that 173 proteins from dormant spores, including both proteins unique to spores and proteins shared with vegetative cells, were lost after completion of germination. The observed diverse timings of synthesis of different protein sets in spore outgrowth revealed a putative core strategy underlying the revival of 'life' from the B. subtilis spore.IMPORTANCE This study demonstrated the progress of macromolecular synthesis during Bacillus subtilis spore germination and outgrowth. The transcriptome analysis has additionally allowed us to trace gene expression during this transformation process. For the first time, the basic survival kit for spore-based life has been identified. In addition, in this analysis based on monitoring of protein levels in germinating and outgrowing spores, the transition from (ribo)nucleotide and amino acid biosynthesis to the restoration of all metabolic pathways can be clearly seen. The integrative multi-omics approach applied in this study thus has helped us to achieve a comprehensive overview of the molecular mechanisms at the basis of spore germination and outgrowth as well as to identify important knowledge gaps in need of further study.

Integrative Analysis of Multi-omics Data Identified EGFR and PTGS2 as Key Nodes in a Gene Regulatory Network Related to Immune Phenotypes in Head and Neck Cancer.

Malignant progression exhibits a tightly orchestrated balance between immune effector response and tolerance. However, underlying molecular principles that drive the establishment and maintenance of the tumor immune phenotype remain to be elucidated. We trained a novel molecular classifier based on immune cell subsets related to programmed death-ligand 1 (PD-L1) and interferon γ (IFNγ) expression, which revealed distinct subgroups with higher (cluster A) or lower (subcluster B3) cytotoxic immune phenotypes. Integrative analysis of multi-omics data was conducted to identify differences in genetic and epigenetic landscapes as well as their impact on differentially expressed genes (DEG) among immune phenotypes. A prognostic gene signature for immune checkpoint inhibition (ICI) was established by a least absolute shrinkage and selection operator (LASSO)-Cox regression model. Mutational landscape analyses unraveled a higher frequency of CASP8 somatic mutations in subcluster A1, while subcluster B3 exhibited a characteristic pattern of copy-number alterations affecting chemokine signaling and immune effector response. The integrative multi-omics approach identified EGFR and PTGS2 as key nodes in a gene regulatory network related to the immune phenotype, and several DEGs related to the immune phenotypes were affected by EGFR inhibition in tumor cell lines. Finally, we established a prognostic gene signature by a LASSO-Cox regression model based on DEGs between nonprogressive disease and progressive disease subgroups for ICI. Our data highlight a complex interplay between genetic and epigenetic events in the establishment of the tumor immune phenotype and provide compelling experimental evidence that a patient with squamous cell carcinoma of the head and neck at higher risk for ICI treatment failure might benefit from a combination with EGFR inhibition.

Abstract A094: Integrative multi-omics approach reveals complex interplay between HPV, host and microbiome during cervical carcinogenesis in Hispanic and non-Hispanic women

Abstract Persistent human papillomavirus (HPV) infection is the vital factor driving cervical carcinogenesis; however, other features of the local cervicovaginal microenvironment (CVM) may play a critical role in development of precancerous cervical dysplasia and progression to invasive cervical carcinoma (ICC). Here we investigated relationships between immunoproteomic and metabolic profiles and features of the cervicovaginal microenvironment, such as HPV status, vaginal microbiota (VMB), vaginal pH and genital inflammation, to better understand the complex interplay between host, virus and bacteria. In this multicenter study we enrolled 78 women with ICC, high- and low-grade squamous intraepithelial lesions, as well as HPV-positive and healthy HPV-negative controls. Vaginal swabs and cervicovaginal lavages (CVL) were collected for HPV genotyping, microbiome, metabolome and immunoproteome analyses. The VMB compositions were determined using 16S rRNA gene sequencing. Cervicovaginal metabolic fingerprints were profiled using liquid chromatography-mass spectrometry. Levels of immune mediators and other proteins in CVL samples were evaluated using multiplex cytometric bead arrays. Abnormal vaginal pH and dysbiotic non-Lactobacillus-dominated VMB were associated with Hispanic ethnicity and severity of cervical neoplasm. We also identified microbial signatures (e.g. Sneathia spp.) to be enriched in ICC and all precancerous groups. Notably, Sneathia abundance was also increased in patients with abnormal pH and those of Hispanic origin. Analyses of 62 protein targets in CVL samples revealed elevated levels of pro-inflammatory cytokines and chemokines, growth and angiogenic factors, apoptosis-related, immune checkpoint and other proteins in ICC patients. Levels of many of these proteins depended on the VMB structure and genital inflammation. These proteomic signatures positively correlated with dysbiotic non-Lactobacillus-dominated VMB and abnormal vaginal pH, both features associated with Hispanic ethnicity. Furthermore, metabolomic analysis also revealed that VMB, together with genital inflammation, are the major drivers of metabolic profiles in the local CVM. Finally, using hierarchical clustering analyses, we identified groups of patients who significantly varied in the levels of cancer-related proteins, genital inflammation, vaginal pH and VMB composition regardless of disease severity. These microenvironmental factors may impact the HPV persistence/progression and consequently increase the risk of cervical cancer. Our study demonstrated that the racial/ethnic differences in the VMB compositions may contribute to cervical cancer disparity in Hispanic women. In the future we are planning to expand our investigation of the VMB in Native American women, which will further illuminate the relationship between race/ethnicity, the VMB, and HPV. Citation Format: Pawel Laniewski, Zehra Esra Ilhan, Nicholas A. Bokulich, Haiyan Cui, Denise J. Roe, Dana M. Chase, J. Gregory Caporaso, Melissa M. Herbst-Kralovetz. Integrative multi-omics approach reveals complex interplay between HPV, host and microbiome during cervical carcinogenesis in Hispanic and non-Hispanic women [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr A094.

243-OR: Integrative Multiomics Analysis of Glucometabolic Traits, Adipose Tissue DNA Methylation, and Gene Expression Identifies Epigenetic Regulatory Mechanisms of Insulin Resistance and Obesity in African Americans

Decline in insulin sensitivity due to dysfunction of adipose tissue (AT) is one of the earliest pathogenic events in type 2 diabetes. We hypothesize that differential DNA methylation (DNAm) in regulatory regions of a subset of genes controls insulin sensitivity and obesity by modulating transcript expression in AT. Integrating reduced representation bisulfite sequencing-derived AT DNAm profiles for 1,054,719 CpG sites and 200,800 CpG regions with AT transcript profile data in 230 African Americans, we performed cis-expression quantitative trait methylation (cis-eQTM) analysis focused on 6,774 glucometabolic trait-associated transcripts to identify local epigenetic regulatory loci for these transcripts. We identified significantly associated CpG regions for 140 transcripts (FDR-P&amp;lt;0.1). The strongest eQTM locus was observed for the proopiomelanocortin (POMC; ρ= -0.632, P = 4.70X10-27) gene at the chr2:25,384,001-25,385,000 region covering a CpG island in the gene. Epigenome-wide association studies (EWAS) further examined the association of DNAm levels of CpG sites/regions with insulin sensitivity (Matsuda index and SI) and BMI. EWAS identified 520, 154, and 482 CpG regions associated (FDR-P &amp;lt;0.1) with Matsuda index, SI and BMI, respectively. We jointly analyzed the EWAS, transcript level to trait association, and eQTM data and identified significant eQTM loci for 26, 15 and 25 genes that were also associated with Matsuda index, SI and BMI, respectively in EWAS. These associated genes include fructose-2,6-biphosphatase 3 (PFKFB3), integrin alpha M (ITGAM), integrin alpha X (ITGAX), and POMC. In summary, applying an integrative multi-omics approach, our study provides evidence for DNAm-mediated regulation of gene expression at both previously identified and novel loci for many key AT transcripts influencing insulin resistance and obesity. Disclosure N.K. Sharma: None. M. Comeau: None. D.J. Montoya: None. M. Pellegrini: None. T. Howard: None. C.D. Langefeld: None. S.K. Das: None. Funding American Diabetes Association (1-18-ICTS-113 to S.K.D.); National Institutes of Health (R01DK090111)

An integrative multi-omics approach uncovers the regulatory role of CDK7 and CDK4 in autophagy activation induced by silica nanoparticles

ABSTRACT Dysfunction of macroautophagy/autophagy has been postulated as a major cellular toxicological response to nanomaterials. It has been reported that excessive autophagy activation, induced by silica nanoparticles (SiNPs), contributes to autophagy dysfunction, whereas little is known how SiNPs trigger autophagy activation. Here, we treated normal rat kidney (NRK) cells using 3 different sizes of SiNPs (16, 29, and 51 nm) and observed that 16-nm SiNPs, with a final concentration of 60 μg/mL, dramatically induce autophagy activation without reducing cell viability. We further conducted a transcriptomic, proteomic, and phosphoproteomic profiling, and detected 23 autophagy-related (Atg) genes and 35 autophagy regulators regulated on at least one omic layer. To identify key regulators from the multi-omics data, we developed a new algorithm of computational prediction of master autophagy-regulating kinases (cMAK) to detect 21 candidates and revealed the CDK7-CDK4 cascade to be functional. The silence or inhibition of Cdk7 or Cdk4 significantly attenuated autophagic activation but not influenced autophagic flux blockage induced by 16-nm SiNPs. Further computational modeling indicated that the CDK7-CDK4 signaling axis potentially triggers autophagy activation by phosphorylating RB1 (RB transcriptional corepressor 1), activating two critical transcription factors, E2F1 (E2F transcription factor 1) and FOXO3 (forkhead box O3), and enhancing the transcriptional levels of at least 8 Atg genes and autophagy regulators in response to SiNPs. Our studies not only established a powerful method for predicting regulatory kinases from the multi-omics data but also revealed a potential mechanism of SiNP-triggered autophagy activation through modulating the CDK7-CDK4 cascade. Abbreviations: 3-MA: 3-methyladenine; Atg: autophagy-related; BECN1: beclin 1; CCK-8: cell counting kit-8; CDK4: cyclin dependent kinase 4; CDK7: cyclin dependent kinase 7; cMAK: computational prediction of master autophagy-regulating kinases; CQ: chloroquine; DMEM: Dulbecco’s modified Eagle’s medium; DMSO: dimethyl sulfoxide; E-ratio: enrichment ratio; E2F1: E2F transcription factor 1; EBSS: Earle’s balanced salt solution; ER: endoplasmic reticulum; FOXO3: forkhead box O3; FPKM: fragments per kilobase of exon per million fragments mapped; GO: gene ontology; H2O2: hydrogen peroxide; iGPS: in vivo GPS; KEGG: Kyoto Encyclopedia of Genes and Genomes; LC-MS/MS: liquid chromatography–tandem mass spectrometry; LDH: lactate dehydrogenase; MAP1LC3B/LC3: microtubule associated protein 1 light chain 3 beta; NRK: normal rat kidney; p-site: phosphorylation site; PBS: phosphate-buffered saline; PDI: polydispersity index; PTM: post-translational modification; QKS: quantitative kinase state; RB1: RB transcriptional corepressor 1; RBHs: reciprocal best hits; RNA-Seq: RNA sequencing; ROS: reactive oxygen species; rSiNPs: SiNPs fluorescently labeled with rhodamine B; SEM: scanning electronic microscopy; SiNPs: silica nanoparticles; siRNA: small interfering RNA; SQSTM1/p62: sequestosome 1; ssKSR: site-specific kinase-substrate relation; TEM: transmission electron microscopy; tfLC3: mRFP-GFP tandem fluorescent-tagged LC3.

Integration of whole-body [18F]FDG PET/MRI with non-targeted metabolomics can provide new insights on tissue-specific insulin resistance in type 2 diabetes

Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific phenotypes requires a multi-omics approach. In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body mass index, we calculated associations between parameters of whole-body positron emission tomography (PET)/magnetic resonance imaging (MRI) during hyperinsulinemic euglycemic clamp and non-targeted metabolomics profiling for subcutaneous adipose tissue (SAT) and plasma. Plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Visceral adipose tissue (VAT) and SAT insulin sensitivity (Ki), were positively associated with several lysophospholipids, while the opposite applied to branched-chain amino acids. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver Ki. Bile acids and carnitines in adipose tissue were inversely associated with VAT Ki. Furthermore, we detected several metabolites that were significantly higher in T2D than normal/prediabetes. In this study we present novel associations between several metabolites from SAT and plasma with the fat fraction, volume and insulin sensitivity of various tissues throughout the body, demonstrating the benefit of an integrative multi-omics approach.

Novel Insights into Rheumatoid Arthritis Through Characterisation of Concordant Changes in DNA Methylation and Gene Expression in Synovial Biopsies of Patients with Differing Numbers of Swollen Joints

In this study, we sought to characterize synovial tissue obtained from individuals with arthralgia and disease-specific auto-antibodies, and patients with established rheumatoid arthritis (RA) by applying an integrative multi-omics approach where we investigated changes at the level of DNA methylation and gene expression and its relation to disease pathogenesis. We performed genome-wide DNA methylation and gene expression profiling of synovial tissue obtained from the knee and ankle from 4 auto-antibody positive arthralgia patients and thirteen RA patients. Through multi-omics factor analysis we observed that the latent factor explaining the variance in gene expression and DNA methylation was associated with Swollen Joint Count 66 (SJC66) where patients with SJC66 of 9 or more displayed reasonable separation from the rest. Specifically, combined DNA methylation and gene expression patterns of patients with SJC66 ≥ 9 displayed activation of the immune response as well as dysregulation of cell adhesion pathways. Through an integrative analysis framework, we reveal differences in both methylation and expression at 59 genes associated with the difference between SJC66 ≥ 9 and 0 < SJC66 ≤ 8, for which we identify specific transcripts correlated with differential methylation. Our results highlight the utility of genome-wide multi-omics profiling of synovial samples for improved understanding of the changes associated with an increasing number of swollen joints in RA patients, and point to novel candidate targets for the treatment of the disease. Funding Statement: AYFLY was funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. ITN-2014-EID-641665. Declaration of Interests: AYFLY, KM, GR, EC, HDL, DFT, CL, DG, and RB are employees of GSK. EF and PPT are employees of Novartis and Kintai Therapeutics, respectively. WJJ is financially supported by GSK and Mead Johnson. All declarations are correct for the time of writing and/or conducting this study. Ethics Approval Statement: All subjects provided written informed consent and the collection and use of the samples received Institutional Review Board review and approval. Characteristics of patients included in this study are listed in Table S1.

An integrative multi-omics network-based approach identifies key regulators for breast cancer

Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.

Correlation between renin-angiotensin system (RAS) related genes, type 2 diabetes, and cancer: Insights from metanalysis of transcriptomics data

Although studies have provided significant evidence about the role of RAS in mediating cancer risk in type 2 diabetes mellitus (DM), conclusions about the central molecular mechanisms underlying this disease remain to be reached, because this type of information requires an integrative multi-omics approach. In the current study, meta-analysis was performed on type 2 diabetes and breast, bladder, liver, pancreas, colon and rectum cancer-associated transcriptome data, and reporter biomolecules were identified at RNA, protein, and metabolite levels using the integration of gene expression profiles with genome-scale biomolecular networks in diabetes samples. This approach revealed that RAS biomarkers could be associated with cancer initiation and progression, which include metabolites (particularly, aminoacyl-tRNA biosynthesis and ABC transporters) as novel biomarker candidates and potential therapeutic targets. We detected downregulation and upregulation of differentially expressed genes (DEGs) in blood, pancreatic islets, liver and skeletal muscle from normal and diabetic patients. DEGs were combined with 211 renin-angiotensin-system related genes. Upregulated genes were enriched using Pathway analysis of cancer in pancreatic islets, blood and skeletal muscle samples. It seems that the changes in mRNA are contributing to the phenotypic changes in carcinogenesis, or that they are as a result of the phenotypic changes associated with the malignant transformation. Our analyses showed that Ctsg and Ednrb are downregulated in cancer samples. However, by immunohistochemistry experiments we observed that EDNRB protein showed increased expression in tumor samples. It is true that alterations in mRNA expression do not always reflect alterations in protein expression, since post-translational changes can occur in proteins. In this study, we report valuable data for further experimental and clinical analysis, because the proposed biomolecules have significant potential as systems biomarkers for screening or for therapeutic purposes in type 2 diabetes and cancer-associated pathways.

NECTIN4 (PVRL4) as Putative Therapeutic Target for a Specific Subtype of High Grade Serous Ovarian Cancer—An Integrative Multi-Omics Approach

In high grade serous ovarian cancer patients with peritoneal involvement and unfavorable outcome would benefit from targeted therapies. The aim of this study was to find a druggable target against peritoneal metastasis. We constructed a planar—scale free small world—co-association gene expression network and searched for clusters with hub-genes associated to peritoneal spread. Protein expression and impact was validated via immunohistochemistry and correlations of deregulated pathways with comprehensive omics data were used for biological interpretation. A cluster up-regulated in miliary tumors with NECTIN4 as hub-gene was identified and impact on survival validated. High Nectin 4 protein expression was associated with unfavorable survival and (i) reduced expression of HLA genes (mainly MHC I); (ii) with reduced expression of genes from chromosome 22q11/12; (iii) higher BCAM in ascites and in a high-scoring expression cluster; (iv) higher Kallikrein gene and protein expressions; and (v) substantial immunologic differences; locally and systemically; e.g., reduced CD14 positive cells and reduction of different natural killer cell populations. Each three cell lines with high (miliary) or low NECTIN4 expression (non-miliary) were identified. An anti-Nectin 4 antibody with a linked antineoplastic drug–already under clinical investigation–could be a candidate for a targeted therapy in patients with extensive peritoneal involvement.

Temporal covariation of epibacterial community and surface metabolome in the Mediterranean seaweed holobiont Taonia atomaria.

An integrative multi-omics approach allowed monthly variations for a year of the surface metabolome and the epibacterial community of the Mediterranean Phaeophyceae Taonia atomaria to be investigated. The LC-MS-based metabolomics and 16S rDNA metabarcoding data sets were integrated in a multivariate meta-omics analysis (multi-block PLS-DA from the MixOmic DIABLO analysis) showing a strong seasonal covariation (Mantel test: p < 0.01). A network based on positive and negative correlations between the two data sets revealed two clusters of variables, one relative to the 'spring period' and a second to the 'summer period'. The 'spring period' cluster was mainly characterized by dipeptides positively correlated with a single bacterial taxon of the Alteromonadaceae family (BD1-7 clade). Moreover, 'summer' dominant epibacterial taxa from the second cluster (including Erythrobacteraceae, Rhodospirillaceae, Oceanospirillaceae and Flammeovirgaceae) showed positive correlations with few metabolites known as macroalgal antifouling defences [e.g. dimethylsulphoniopropionate (DMSP) and proline] which exhibited a key role within the correlation network. Despite a core community that represents a significant part of the total epibacteria, changes in the microbiota structure associated with surface metabolome variations suggested that both environment and algal host shape the bacterial surface microbiota.

A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification.

BackgroundAn integrative multi-omics analysis approach that combines multiple types of omics data including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics has become increasing popular for understanding the pathophysiology of complex diseases. Although many multi-omics analysis methods have been developed for complex disease studies, only a few simulation tools that simulate multiple types of omics data and model their relationships with disease status are available, and these tools have their limitations in simulating the multi-omics data.ResultsWe developed the multi-omics data simulator OmicsSIMLA, which simulates genomics (i.e., single-nucleotide polymorphisms [SNPs] and copy number variations), epigenomics (i.e., bisulphite sequencing), transcriptomics (i.e., RNA sequencing), and proteomics (i.e., normalized reverse phase protein array) data at the whole-genome level. Furthermore, the relationships between different types of omics data, such as methylation quantitative trait loci (SNPs influencing methylation), expression quantitative trait loci (SNPs influencing gene expression), and expression quantitative trait methylations (methylations influencing gene expression), were modeled. More importantly, the relationships between these multi-omics data and the disease status were modeled as well. We used OmicsSIMLA to simulate a multi-omics dataset for breast cancer under a hypothetical disease model and used the data to compare the performance among existing multi-omics analysis methods in terms of disease classification accuracy and runtime. We also used OmicsSIMLA to simulate a multi-omics dataset with a scale similar to an ovarian cancer multi-omics dataset. The neural network–based multi-omics analysis method ATHENA was applied to both the real and simulated data and the results were compared. Our results demonstrated that complex disease mechanisms can be simulated by OmicsSIMLA, and ATHENA showed the highest prediction accuracy when the effects of multi-omics features (e.g., SNPs, copy number variations, and gene expression levels) on the disease were strong. Furthermore, similar results can be obtained from ATHENA when analyzing the simulated and real ovarian multi-omics data.ConclusionsOmicsSIMLA will be useful to evaluate the performace of different multi-omics analysis methods. Sample sizes and power can also be calculated by OmicsSIMLA when planning a new multi-omics disease study.

Histaminergic transmission slows progression of amyotrophic lateral sclerosis.

BackgroundHistamine is an immune modulator, neuroprotective, and remyelinating agent, beneficially acting on skeletal muscles and promoting anti‐inflammatory features in amyotrophic lateral sclerosis (ALS) microglia. Drugs potentiating the endogenous release of histamine are in trial for neurological diseases, with a role not systematically investigated in ALS. Here, we examine histamine pathway associations in ALS patients and the efficacy of a histamine‐mediated therapeutic strategy in ALS mice.MethodsWe adopted an integrative multi‐omics approach combining gene expression profiles, copy number variants, and single nucleotide polymorphisms of ALS patients. We treated superoxide dismutase 1 (SOD1)‐G93A mice that recapitulate key ALS features, with the brain‐permeable histamine precursor histidine in the symptomatic phase of the disease and analysed the rescue from disease pathological signs. We examined the action of histamine in cultured SOD1‐G93A motor neuron‐like cells.ResultsWe identified 13 histamine‐related genes deregulated in the spinal cord of two ALS patient subgroups, among which genes involved in histamine metabolism, receptors, transport, and secretion. Some histamine‐related genes overlapped with genomic regions disrupted by DNA copy number and with ALS‐linked pathogenic variants. Histidine treatment in SOD1‐G93A mice proved broad efficacy in ameliorating ALS features, among which most importantly lifespan, motor performance, microgliosis, muscle atrophy, and motor neurons survival in vivo and in vitro.ConclusionsOur gene set/pathway enrichment analyses and preclinical studies started at the onset of symptoms establish that histamine‐related genes are modifiers in ALS, supporting their role as candidate biomarkers and therapeutic targets. We disclose a novel important role for histamine in the characterization of the multi‐gene network responsible for ALS and, furthermore, in the drug development process.

Abstract B45: Amplification of ADNP and CEP250 promotes poor prognosis in high-grade serous ovarian cancer

Abstract High-grade serous ovarian cancer (HGSOC) is the most commonly diagnosed and most lethal subtype of gynecologic cancer. The poor prognosis associated with this disease stems from two major problems: diagnosis at later stages when ovarian cancer cells have metastasized to distant organs and intrinsic or developed resistance to primary platinum-taxane based therapies. Therefore, understanding the driver mechanisms of HGSOC is required to discover more effective therapeutic agents and identify potential drug targets. In this study, we applied an integrative multi-omics approach based on the use of a poor-prognostic gene expression signature as a conceptual framework to analyze orthogonal genomic and proteomic data from human ovarian tumors derived from the TCGA (n=489) and CPTAC (n=169) projects, including RNA expression, DNA copy number alterations, protein and phosphoprotein expression. In combination with data from a genome-wide RNA-mediated interference screen in human ovarian cancer cell lines, we also identified essential genetic drivers of poor prognosis. We identified genes in amplified regions that were positively correlated with poor-prognostic signature and showed an increased amplification frequency (q &amp;lt; 0.05). Then, we applied the poor-prognostic gene expression signatures to a panel of 29 ovarian cancer cell lines that had mRNA expression data and were also part of an RNAi proliferation screen in which a genome-wide shRNA library (~9,000 genes) had been used to identify essential genes. We used a negative Spearman rank correlation to identify essential genes in context of poor-prognosis signature. To prioritize these candidate genes, we next compared the results of these analyses and identified 128 genes that were uniquely essential for cell viability in ovarian cancer cell lines and that were amplified in the context of the poor-prognosis signature. In order to uncover underlying mechanisms in HGSOC, we examined relationships between the poor-prognostic gene expression signature score and proteins and phosphoprotein expression. We used a Spearman correlation comparing poor-prognostic signature score to protein expression level. The proteomic data obtained from different platforms that are analyses of reverse phase protein array (RPPA) (n=412) data including 141 proteins and 31 phosphoproteins and mass-spectrometry (MS)-based (n=169) data including 3330 proteins (n=169) and 2533 (n=69) phosphoproteins. Significant proteins derived from TCGA data enriched with biologic processes including cell cycle control (p=6.8E-11), cell proliferation and differentiation (P=1.9E-7) and inhibition of apoptosis (P=1.4E-5) and signaling pathways including ERBB signaling (P=3.0E-10), mTOR signaling (P=5.9E-9) and insulin signaling (P=2.9E-7) pathways. Significant proteins derived from CPTAC data played role in splicesome (P=2.4E-24), chromatin packaging and remodeling (P=3.7E-4), and cell cycle (P=1.2E-3). Next, we integrated all these analyses and determined that amplified essential genes including ADNP (Activity-Dependent Neuroprotective Protein), CEP250 (Centrosomal Nek2-Associated Protein 1), and MED1 (Mediator Complex Subunit 1) are also involved in significant phosphorylated proteins (P&amp;lt;0.05) correlated with poor-prognostic signature. We determined that amplification of ADNP (P = 0.0228; hazard ratio (HR), 0.84), CEP250 (P = 0.0346; HR, 0.82) predicted a significantly worse outcome in HGSOC patients, whereas MED1 amplification had no effect on patient survival (P = 0.0803). In conclusion, these analyses suggest that ADNP and CEP250 amplification are novel modulators of poor prognosis in ovarian cancer, and that these alterations are not only potential drivers of oncogenesis but also their associated pathways may represent novel therapeutic targets in HGSOC. Citation Format: Kubra Karagoz, Christen Khella, Michael L. Gatza. Amplification of ADNP and CEP250 promotes poor prognosis in high-grade serous ovarian cancer. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr B45.

Microbiome metabolomics reveals new drivers of human liver steatosis.

An integrative multiomics approach in nondiabetic obese women identifies phenylacetate as a microbial metabolite contributing to the accumulation of lipids in the liver and hence to nonalcoholic steatohepatitis.

Insights into the Mutational Burden of Human Induced Pluripotent Stem Cells from an Integrative Multi-Omics Approach

To understand the mutational burden of human induced pluripotent stem cells (iPSCs), we sequenced genomes of 18 fibroblast-derived iPSC lines and identified different classes of somatic mutations based on structure, origin, and frequency. Copy-number alterations affected 295 kb in each sample and strongly impacted gene expression. UV-damage mutations were present in ∼45% of the iPSCs and accounted for most of the observed heterogeneity in mutation rates across lines. Subclonal mutations (not present in all iPSCs within a line) composed 10% of point mutations and, compared with clonal variants, showed an enrichment in active promoters and increased association with altered gene expression. Our study shows that, by combining WGS, transcriptome, and epigenome data, we can understand the mutational burden of each iPSC line on an individual basis and suggests that this information could be used to prioritize iPSC lines for models of specific human diseases and/or transplantation therapy.

Clostridium difficile Alters the Structure and Metabolism of Distinct Cecal Microbiomes during Initial Infection To Promote Sustained Colonization.

Susceptibility to Clostridium difficile infection (CDI) is primarily associated with previous exposure to antibiotics, which compromise the structure and function of the gut bacterial community. Specific antibiotic classes correlate more strongly with recurrent or persistent C.difficile infection. As such, we utilized a mouse model of infection to explore the effect of distinct antibiotic classes on the impact that infection has on community-level transcription and metabolic signatures shortly following pathogen colonization and how those changes may associate with persistence of C.difficile Untargeted metabolomic analysis revealed that C.difficile infection had significantly larger impacts on the metabolic environment across cefoperazone- and streptomycin-pretreated mice, which became persistently colonized compared to clindamycin-pretreated mice, where infection quickly became undetectable. Through metagenome-enabled metatranscriptomics, we observed that transcripts for genes associated with carbon and energy acquisition were greatly reduced in infected animals, suggesting that those niches were instead occupied by C.difficile Furthermore, the largest changes in transcription were seen in the least abundant species, indicating that C.difficile may "attack the loser" in gut environments where sustained infection occurs more readily. Overall, our results suggest that C.difficile is able to restructure the nutrient-niche landscape in the gut to promote persistent infection.IMPORTANCEClostridium difficile has become the most common single cause of hospital-acquired infection over the last decade in the United States. Colonization resistance to the nosocomial pathogen is primarily provided by the gut microbiota, which is also involved in clearing the infection as the community recovers from perturbation. As distinct antibiotics are associated with different risk levels for CDI, we utilized a mouse model of infection with 3 separate antibiotic pretreatment regimens to generate alternative gut microbiomes that each allowed for C.difficile colonization but varied in clearance rate. To assess community-level dynamics, we implemented an integrative multi-omics approach that revealed that infection significantly changed many aspects of the gut community. The degree to which the community changed was inversely correlated with clearance during the first 6days of infection, suggesting that C.difficile differentially modifies the gut environment to promote persistence. This is the first time that metagenome-enabled metatranscriptomics have been employed to study the behavior of a host-associated microbiota in response to an infection. Our results allow for a previously unseen understanding of the ecology associated with C.difficile infection and provide the groundwork for identification of context-specific probiotic therapies.

Abstract P4-04-03: Hyperactive FOXA1 activates super-enhancer-engaged HIF2α/EPAS1 to promote endocrine-resistant metastatic ER-positive breast cancer

Abstract Background: We have recently reported that acquired endocrine resistance (Endo-R) in multiple ER+ breast cancer (BC) Endo-R cell models is driven by high levels of FOXA1 (High-FOXA1), via gene amplification and/or overexpression (OE), leading to coordinated reprogramming of the FOXA1 genomic binding (cistrome) and transcriptome. Forced FOXA1 OE in parental (P) cells induced similar transcriptional reprogramming leading to Endo-R and metastasis. Recent clinical data showing enrichment of FOXA1 amplification in ER+ metastases further support the clinical importance of our findings. However, the molecular components and the mechanism of High-FOXA1-induced transcriptional reprogramming in Endo-R and metastasis are unknown. Methods: High-FOXA1-containing MCF7 tamoxifen-resistant (TamR) and P/FOXA1-OE cells were used in this study. An integrative multi-OMICS approach was employed to analyze transcriptome (RNA-seq), FOXA1 cistrome, and histone H3K27 acetylation (ac) (ChIP-seq). Intersection of High-FOXA1-induced transcriptome and distinct FOXA1 cistrome-predicted genes defined a High-FOXA1 core gene signature (CGS). Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) were used for functional annotation. Cell growth and migration/invasion were measured by a bright-field automated cell counter and Transwell insert system. Altered gene expression was measured by RT-qPCR. High-FOXA1 signaling inhibition included gene knockdown (siRNA) or pharmacologic blockade (the EPAS1 inhibitor PT2385). The predictive role of EPAS1 and the associated gene signature were analyzed using publicly available BC datasets. Results: FOXA1 OE reprogrammed the FOXA1 cistrome in P cells to resemble that of the TamR cells. The FOXA1 cistrome was significantly correlated with the deposition of H3K27ac in TamR vs. P cells (P&amp;lt;2.2e-16). Similarly, the differentially expressed genes in TamR vs. P cells were enriched for FOXA1 binding at enhancers demarcated by H3K27ac (P=8e-125). The FOXA1-CGS was linked to multiple metastasis-related GO terms including “hypoxia response”, enriched for the cancer secretome gene set (P=4.1e-16), and highly represented in the Endo-R transcriptome across our multiple cell models (MCF7, 600MPE, and CAMA1) (P&amp;lt;0.01). Integrative analysis of H3K27ac-defined super-enhancers (SEs) and altered cistrome/transcriptome upon High-FOXA1 nominated EPAS1, a hypoxia-inducible transcription factor (TF), as a top candidate of SE-activated TFs amplifying High-FOXA1 signaling. EPAS1 blockade markedly repressed the secretome genes (e.g., IL8 and S100P) and cell migration and invasion in TamR cells. Primary ER+ tumors (TCGA) with high EPAS1 are enriched for a cancer secretome gene set (P=3e-4). High EPAS1 predicts poor distant metastasis-free survival in ER+ BC treated with endocrine therapy (P=.034). Conclusions: High-FOXA1 induces transcriptional reprogramming by coordinating histone enhancer marks to activate EPAS1 via an SE mechanism, which in turn mediates transcriptional reprogramming, partly via inducing a pro-metastatic secretome, to promote Endo-R and metastasis. Targeting the High-FOXA1/EPAS1 axis to block transcriptional reprogramming may offer a new therapeutic strategy to prevent and treat Endo-R metastatic ER+ BC. Citation Format: Fu X, Pereira R, De Angelis C, Veeraraghavan J, Shea MJ, Nanda S, Feng Q, Jeselsohn R, O'Malley BW, Brown M, Osborne CK, Schiff R. Hyperactive FOXA1 activates super-enhancer-engaged HIF2α/EPAS1 to promote endocrine-resistant metastatic ER-positive breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-04-03.

Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain.

Detailed characterization of the cell types in the human brain requires scalable experimental approaches to examine multiple aspects of the molecular state of individual cells, and computational integration of the data to produce unified cell-state annotations. Here we report improved high-throughput methods for single-nucleus Droplet-based sequencing (snDrop-seq) and single-cell transposome hypersensitive-site sequencing (scTHS-seq). We used each method to acquire nuclear transcriptomic and DNA accessibility maps for >60,000 single cells from the human adult visual cortex, frontal cortex, and cerebellum. Integration of these data revealed regulatory elements and transcription factors that underlie cell-type distinctions, providing a basis for studying complex processes in the brain, such as genetic programs coordinating adult remyelination. We also mapped disease-associated risk variants to specific cellular populations, providing insights into normal and pathogenic cellular processes in the human brain. This integrative multi-omics approach permits more detailed single-cell interrogation of complex organs and tissues.