Impact Of Genomic Variation Research Articles

DOP64 Utilising genomics to predict outcomes and delineate novel subgroups in inflammatory bowel disease

Abstract Background The rise of ‘big data’ in inflammatory bowel disease (IBD) presents an opportunity to improve understanding of pathogenesis and unpick the molecular complexity of this heterogenous condition. Personalisation of IBD management relies on predicting outcomes, response to therapy and prevention of complications. Here, we present results outlining subgrouping of patients and outcome prediction using multiomic/clinical data. Methods Using whole exome sequencing from 1100 patients in the Southampton IBD cohort, including 650 paediatric cases, we have performed iterative studies focused on 1) Impact of genomic variation across the NOD-signaling pathway measured by perturbation of transcription across multiple genes, 2) Development of NOD2 as a genomic biomarker of stricturing Crohn’s disease (CD), 3) Utilising machine learning and genomic data to develop disease classification models. These data utilise GenePy, a tool developed in house that summarises genomic variation to give a per individual, per gene deleteriousness metric. Results Within the NOD-signaling pathway patients harbouring deleterious variation in NOD2 had reduced NOD2 expression and increased NFKBIA expression, reflecting reduced NFKB signaling, figure 1A. We report deleterious variation in several key complexes including NOD2-RIPK2 and TAK1-TAB, resulted in reduced transcription of NFKB activators and alternative inflammatory pathway activation, figure 1C-D. Utilising genomic data we constructed a NOD2 prediction model for stricturing disease in Crohn’s disease; 56.7% of patients in the ‘high-risk group’ had stricturing behaviour, whilst in the low-risk group only 21.4% had strictures. Addition of terminal ileal (TI) disease to the NOD2 risk groups significantly improved prediction, figure 2A. Using survival modelling, high-risk group paediatric patients presenting with TI disease had a HR of 4.89 (P = 2.3×10-5) compared with the low-risk group patients without TI disease, figure 2B. Finally, we used supervised machine learning of genomic data to classify patients with CD or ulcerative colitis. We employed different gene lists and assessed how accurately we could assign patients to their diagnosis. An autoimmune gene panel produced the best model (AUROC 0.68), compared to an IBD panel (AUCROC 0.61). NOD2 was the most discriminating gene in all the gene panels. Conclusion These iterative projects demonstrate the utility of integrating genomic and clinical data to improve the subtyping of patients with IBD and provide disease prediction models. Future work will include analyses of additional inflammatory pathways and targeting different clinical outcomes. We hope clinical translation of these findings will be a step-change in precision medicine for IBD.

The impact of genomic variation on protein phosphorylation states and regulatory networks

Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high‐quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well‐defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.

Spatial covariance analysis reveals the residue-by-residue thermodynamic contribution of variation to the CFTR fold

Although the impact of genome variation on the thermodynamic properties of function on the protein fold has been studied in vitro, it remains a challenge to assign these relationships across the entire polypeptide sequence in vivo. Using the Gaussian process regression based principle of Spatial CoVariance, we globally assign on a residue-by-residue basis the biological thermodynamic properties that contribute to the functional fold of CFTR in the cell. We demonstrate the existence of a thermodynamically sensitive region of the CFTR fold involving the interface between NBD1 and ICL4 that contributes to its export from endoplasmic reticulum. At the cell surface a new set of residues contribute uniquely to the management of channel function. These results support a general ‘quality assurance’ view of global protein fold management as an SCV principle describing the differential pre- and post-ER residue interactions contributing to compartmentalization of the energetics of the protein fold for function. Our results set the stage for future analyses of the quality systems managing protein sequence-to-function-to-structure broadly encompassing genome design leading to protein function in complex cellular relationships responsible for diversity and fitness in biology in response to the environment.

Spatial Covariant Management of Thermodynamics Contributes to Protein Fold Function Through Quality Assurance

Abstract Although the impact of genome variation on the thermodynamic properties of the protein fold has been studied in vitro, it remains a challenge to assign these relationships across the entire polypeptide sequence in vivo. Using the Gaussian process regression-based principle of Spatial CoVariance (SCV), we globally assign on a residue-by-residue the biological thermodynamic properties contributing to the functional fold in the cell using CFTR as an example. We demonstrate the existence of a thermodynamically sensitive region of the CFTR fold involving the interface between NBD1 and ICL4 that contributes to the endoplasmic reticulum (ER) export. At the cell surface a new set of residues contribute uniquely to the management of channel function. These results support a general 'quality assurance' (QA) view of global protein fold management as an SCV principle describing the differential pre- and post-ER residue interactions contributing to compartmentalization of the energetics of the protein fold for function.

Genomic and transcriptomic analysis of pituitary adenomas reveals the impacts of copy number variations on gene expression and clinical prognosis among prolactin-secreting subtype.

Pituitary adenomas (PAs) are slow growing and benign primary intracranial tumors that often cause occupying effects or endocrine symptoms. PAs can be classified into various subtypes according to hormone secretion. Although widespread transcriptional alterations that cause aberrant hormone secretion have been characterized, the impact of genomic variations on transcriptional alterations is unclear due to the rare occurrence of single-nucleotide variations in PA. In this study, we performed whole-genome sequencing (WGS) on 76 PA samples across three clinical subtypes (PRL-PAs; GH-PAs, and NFPAs); transcriptome sequencing (RNA-seq) of 54 samples across these subtypes was also conducted. Nine normal pituitary tissues were used as controls. Common and subtype-specific transcriptional alterations in PAs were identified. Strikingly, widespread genomic copy number amplifications were discovered for PRL-PAs, which are causally involved in transcriptomic changes in this subtype. Moreover, we found that the high copy number variations (CNVs) in PRL-PA cause increased prolactin production, drug resistance and proliferative capacity, potentially through key genes with copy number amplification and transcriptional activation, such as BCAT1. This study provides insight into how genomic CNVs affect the transcriptome and clinical outcomes of PRL-PA and sheds light on the development of potential therapeutics for aberrantly activated targets.

Intersexuality in female moles

Developmental Biology Female moles are intersexual and develop masculinizing ovotestes, a distinctive trait among mammals. Real et al. investigated the origin of this trait by sequencing the Iberian mole genome and applying comparative strategies that integrate transcriptomic, epigenetic, and chromatin interaction data. They identified mole-specific genomic rearrangements that alter the three-dimensional regulatory landscape of the androgen-converting gene CYP17A1 and the pro-testicular factor gene FGF9 , both of which show distinct expression patterns in mole gonads. The use of transgenic mice confirms the capability of these factors to increase circulating testosterone levels and to induce gonadal masculinization. This study highlights how integrative approaches can reveal the phenotypic impact of genomic variation. Science , this issue p. [208][1] [1]: /lookup/doi/10.1126/science.aaz2582

A Perfect Storm: Impact of Genomic Variation and Serial Vaccination on Low Influenza Vaccine Effectiveness During the 2014-2015 Season.

The 2014-2015 influenza season was distinguished by an epidemic of antigenically-drifted A(H3N2) viruses and vaccine components identical to 2013-2014. We report 2014-2015 vaccine effectiveness (VE) from Canada and explore contributing agent-host factors. VE against laboratory-confirmed influenza was derived using a test-negative design among outpatients with influenza-like illness. Sequencing identified amino acid mutations at key antigenic sites of the viral hemagglutinin protein. Overall, 815/1930 (42%) patients tested influenza-positive: 590 (72%) influenza A and 226 (28%) influenza B. Most influenza A viruses with known subtype were A(H3N2) (570/577; 99%); 409/460 (89%) sequenced viruses belonged to genetic clade 3C.2a and 39/460 (8%) to clade 3C.3b. Dominant clade 3C.2a viruses bore the pivotal mutations F159Y (a cluster-transition position) and K160T (a predicted gain of glycosylation) compared to the mismatched clade 3C.1 vaccine. VE against A(H3N2) was -17% (95% confidence interval [CI], -50% to 9%) overall with clade-specific VE of -13% (95% CI, -51% to 15%) for clade 3C.2a but 52% (95% CI, -17% to 80%) for clade 3C.3b. VE against A(H3N2) was 53% (95% CI, 10% to 75%) for patients vaccinated in 2014-2015 only, significantly lower at -32% (95% CI, -75% to 0%) if also vaccinated in 2013-2014 and -54% (95% CI, -108% to -14%) if vaccinated each year since 2012-2013. VE against clade-mismatched B(Yamagata) viruses was 42% (95% CI, 10% to 62%) with less-pronounced reduction from prior vaccination compared to A(H3N2). Variation in the viral genome and negative effects of serial vaccination likely contributed to poor influenza vaccine performance in 2014-2015.

Using EHR-Linked Biobank Data to Study Metformin Pharmacogenomics.

Metformin is a commonly prescribed diabetes medication whose mechanism of action is poorly understood. In this study we utilized EHR-linked biobank data to elucidate the impact of genomic variation on glycemic response to metformin. Our study found significant gene- and SNP-level associations within the beta-2 subunit of the heterotrimeric adenosine monophosphate-activated protein kinase complex. Using EHR phenotypes where were able to add additional clarity to ongoing metformin pharmacogenomic dialogue.

Abstract 3887: Genomic analysis identifies drug targetable pathways and predicts immune infiltration in inflammatory breast cancer tumors

Abstract Background Inflammatory breast Cancer (IBC) is a rare aggressive breast cancer in which cancer cells block the lymph vessels in the skin of the breast. IBC tumors have a high rate of ERBB2 positivity and response to ERBB2 targeted therapies. Due to the rarity of this cancer, the molecular etiology of this disease is poorly understood. Materials & Methods A 208 gene next-generation sequencing (NGS) panel was used to analyze 20 IBC patient tumor and matched normal samples. Tissues were stained for cell signaling proteins and immune-markers. Cell line studies were performed to understand the impact of genomic variants on therapeutic selection. Results Common pathway alterations reoccur among IBC samples that influence genome stability, PI3K signaling, and ERBB signaling. NGS revealed alterations in both the ERBB/PI3K pathways, including: ERBB2 amplification, ERBB3 mutations, and activating PI3K mutations. Immunohistochemistry (IHC) staining for pS6 and pERBB3 identified ERBB/PI3K pathway activity in IBC samples. Cell line studies using siRNA and neutralizing antibodies demonstrate that mutant ERBB3 signaling contributes to IBC proliferation. IBC tumors with high levels of CD8+ immune infiltrate have a significantly higher somatic mutation rate than other IBC tumors. A proposed score (iScore) based on the somatic mutation rate and the average mutant allele frequency, showed greater correlation with the level of CD8+ immune infiltration. Furthermore, tumors with high CD8+ infiltrating lymphocytes were associated with a higher frequency of alterations in DNA mismatch repair (MMR) genes. IHC revealed high levels of the immune checkpoint signaling molecule PD-L1 in the inflammatory infiltrate of IBC tumors. Conclusions This study identifies a higher level of ERBB3 mutations than reported in other cancers and an important role for ERBB3 mutation in IBC. ERBB3 targeted therapies in combination with ERBB/PI3K drugs may be important for IBC treatment. Furthermore, high somatic mutations rates in a subset of IBC tumors harboring MMR mutations lead to greater levels of PD-L1+ immune infiltrates, which suggests a possible benefit from immunotherapies such as anti-PD-L1 antibodies. iScore, a more predictive value of immune infiltration in tumors, may be indicative of the level of neoantigen exposure to the immune system. The utility of the newly proposed iScore is currently being investigated as a method to predict immune cell infiltrates and immunotherapy response in other tumor types. Citation Format: Diarmuid Moran, Christopher A. Hamm, Kakuturu Rao, Patricia Bacon-Trusk, Karen Pry, Victor Velculescu, Massimo Cristofanilli, Sarah S. Bacus. Genomic analysis identifies drug targetable pathways and predicts immune infiltration in inflammatory breast cancer tumors. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3887. doi:10.1158/1538-7445.AM2015-3887

Bioinformatics challenges for personalized medicine

Motivation: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.Results: This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.Contact: russ.altman@stanford.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Behavioral neuroendocrinology in nontraditional species of mammals: Things the ‘knockout’ mouse CAN'T tell us

The exploration of many of the fundamental features of mammalian behavioral neuroendocrinology has benefited greatly throughout the short history of the discipline from the study of highly inbred, genetically characterized rodents and several other “traditional” exemplars. More recently, the impact of genomic variation in the determination of complex neuroendocrine and behavioral systems has advanced through the use of single and multiple gene knockouts or knockins. In our essay, we argue that the study of nontraditional mammals is an essential approach that complements these methodologies by taking advantage of allelic variation produced by natural selection. Current and future research will continue to exploit these systems to great advantage and will bring new techniques developed in more traditional laboratory animals to bear on problems that can only be addressed with nontraditional species. We highlight our points by discussing advances in our understanding of neuroendocrine and behavioral systems in phenomena of widely differing time scales. These examples include neuroendocrine variation in the regulation of reproduction across seasons in Peromyscus, variation in parental care by biparental male rodents and primates within a single infant rearing attempt, and circadian variation in the regulation of the substrates underlying mating in diurnal vs. nocturnal rodents. Our essay reveals both important divergences in neuroendocrine systems in our nontraditional model species, and important commonalities in these systems.

Pharmacogenetic aspects in treatment of colorectal cancer--an update.

Pharmacogenomic analyses will become crucial to predict patients' toxicity to treatment and will also help to predict the tumor response. Processes of drug metabolism, drug efflux, DNA-repair and characteristics of drug targets are critical checkpoints of drug efficacy. These crucial pathways for drug action have been part of pharmacogenomic studies evaluating the impact of genomic variants on transcription, translation, protein structure and substrate binding of the genes involved. Promising candidates have been identified with predictive value for response and toxicity to chemotherapy in colorectal cancers. These candidates need to be incorporated into large, prospective clinical trials to confirm their impact for response and survival to chemotherapy that has been reported in retrospective analyses. Confirmed predictive markers, together with additional yet to be identified pharmacogenomic key players, will provide the basis for tailoring chemotherapy in the future.