Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach.

Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank.

Network-medicine framework for studying disease trajectories in U.S. veterans.

A better understanding of the sequential and temporal aspects in which diseases occur in patient's lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.

The Hippo pathway modulates resistance to BET proteins inhibitors in lung cancer cells.

Inhibitors of BET proteins (BETi) are anti-cancer drugs that have shown efficacy in pre-clinical settings and are currently in clinical trials for different types of cancer, including non-small cell lung cancer (NSCLC). Currently, no predictive biomarker is available to identify patients that may benefit from this treatment. To uncover the mechanisms of resistance to BETi, we performed a genome-scale CRISPR/Cas9 screening in lung cancer cells. We identified three Hippo pathway genes, LATS2, TAOK1, and NF2, as key determinants for sensitivity to BETi. The knockout of these genes induces resistance to BETi, by promoting TAZ nuclear localization and transcriptional activity. Conversely, TAZ expression promotes resistance to these drugs. We also showed that TAZ, YAP, and their partner TEAD are direct targets of BRD4 and that treatment with BETi downregulates their expression. Noticeably, molecular alterations in one or more of these genes are present in a large fraction of NSCLC patients and TAZ amplification or overexpression correlates with a worse outcome in lung adenocarcinoma. Our data define the central role of Hippo pathway in mediating resistance to BETi and provide a rationale for using BETi to counter-act YAP/TAZ-mediated pro-oncogenic activity.

Aneuploid acute myeloid leukemia exhibits a signature of genomic alterations in the cell cycle and protein degradation machinery.

BACKGROUND: Aneuploidy occurs in more than 20% of acute myeloid leukemia (AML) cases and correlates with an adverse prognosis. METHODS: To understand the molecular bases of aneuploid acute myeloid leukemia (A-AML), this study examined the genomic profile in 42 A-AML cases and 35 euploid acute myeloid leukemia (E-AML) cases. RESULTS: A-AML was characterized by increased genomic complexity based on exonic variants (an average of 26 somatic mutations per sample vs 15 for E-AML). The integration of exome, copy number, and gene expression data revealed alterations in genes involved in DNA repair (eg, SLX4IP, RINT1, HINT1, and ATR) and the cell cycle (eg, MCM2, MCM4, MCM5, MCM7, MCM8, MCM10, UBE2C, USP37, CK2, CK3, CK4, BUB1B, NUSAP1, and E2F) in A-AML, which was associated with a 3-gene signature defined by PLK1 and CDC20 upregulation and RAD50 downregulation and with structural or functional silencing of the p53 transcriptional program. Moreover, A-AML was enriched for alterations in the protein ubiquitination and degradation pathway (eg, increased levels of UHRF1 and UBE2C and decreased UBA3 expression), response to reactive oxygen species, energy metabolism, and biosynthetic processes, which may help in facing the unbalanced protein load. E-AML was associated with BCOR/BCORL1 mutations and HOX gene overexpression. CONCLUSIONS: These findings indicate that aneuploidy-related and leukemia-specific alterations cooperate to tolerate an abnormal chromosome number in AML, and they point to the mitotic and protein degradation machineries as potential therapeutic targets.

Network integration of multi-tumour omics data suggests novel targeting strategies.

We characterize different tumour types in search for multi-tumour drug targets, in particular aiming for drug repurposing and novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allows us to define three cluster-specific signatures, with genes belonging to NF-κB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we define new pharmacological strategies validated by in vitro experiments that show inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways that could possibly be used, singularly or in combination, for the design of novel treatment strategies.

Acceleration of leukocytes' epigenetic age as an early tumor and sex-specific marker of breast and colorectal cancer.

Changes in blood epigenetic age have been associated with several pathological conditions and have recently been described to anticipate cancer development. In this work, we analyze a publicly available leukocytes methylation dataset to evaluate the relation between DNA methylation age and the prospective development of specific types of cancer. We calculated DNA methylation age acceleration using five state-of-the-art estimators (three multi-site: Horvath, Hannum, Weidner; and two CpG specific: ELOV2 and FHL2) in a cohort including 845 subjects from the EPIC-Italy project and we compared 424 samples that remained cancer-free over the approximately ten years of follow-up with 235 and 166 subjects who developed breast and colorectal cancer, respectively. We show that the epigenetic age estimated from blood DNA methylation data is statistically significantly associated to future breast and male colorectal cancer development. These results are corroborated by survival analysis that shows significant association between age acceleration and cancer incidence suggesting that the chance of developing age-related diseases may be predicted by circulating epigenetic markers, with a dependence upon tumor type, sex and age estimator. These are encouraging results towards the non-invasive and perspective usage of epigenetic biomarkers.

Aberrant methylation patterns in colorectal cancer: a meta-analysis.

Colorectal cancer is among the leading causes of cancer death worldwide. Despite numerous molecular characterizations of the phenomenon, the exact dynamics of its onset and progression remain elusive. Colorectal cancer onset has been characterized by changes in DNA methylation profiles, that, owing to the stability of their patterns, are promising candidates to shed light on the molecular events laying at the base of this phenomenon.To exploit this stability and reinforce it, we conducted a meta-analysis on publicly available DNA methylation datasets generated on: normal colorectal, adenoma (ADE) and adenocarcinoma (CRC) samples using the Illumina 450k array, in the systems medicine frame, searching for tumor gene episignatures, to produce a carefully selected list of potential drivers, markers and targets of the disease. The analysis proceeds from a differential meta-analysis of the methylation profiles using an analytical pipeline recently developed by our group [1], through network reconstruction, topological and functional analyses, to finally highlight relevant epigenomic features. Our results show that genes already highlighted for their genetic or transcriptional alteration in colorectal cancer are also differentially methylated, reinforcing -regardless of the level of cellular control- their role in the complex of alterations involved in tumorigenesis.These findings were finally validated in an independent cohort from The Cancer Genome Atlas (TCGA).

Stochastic neutral modelling of the Gut Microbiota's relative species abundance from next generation sequencing data.

BACKGROUND: Interest in understanding the mechanisms that lead to a particular composition of the Gut Microbiota is highly increasing, due to the relationship between this ecosystem and the host health state. Particularly relevant is the study of the Relative Species Abundance (RSA) distribution, that is a component of biodiversity and measures the number of species having a given number of individuals. It is the universal behaviour of RSA that induced many ecologists to look for theoretical explanations. In particular, a simple stochastic neutral model was proposed by Volkov et al. relying on population dynamics and was proved to fit the coral-reefs and rain forests RSA. Our aim is to ascertain if this model also describes the Microbiota RSA and if it can help in explaining the Microbiota plasticity. RESULTS: We analyzed 16S rRNA sequencing data sampled from the Microbiota of three different animal species by Jeraldo et al. Through a clustering procedure (UCLUST), we built the Operational Taxonomic Units. These correspond to bacterial species considered at a given phylogenetic level defined by the similarity threshold used in the clustering procedure. The RSAs, plotted in the form of Preston plot, were fitted with Volkov's model. The model fits well the Microbiota RSA, except in the tail region, that shows a deviation from the neutrality assumption. Looking at the model parameters we were able to discriminate between different animal species, giving also a biological explanation. Moreover, the biodiversity estimator obtained by Volkov's model also differentiates the animal species and is in good agreement with the first and second order Hill's numbers, that are common evenness indexes simply based on the fraction of individuals per species. CONCLUSIONS: We conclude that the neutrality assumption is a good approximation for the Microbiota dynamics and the observation that Volkov's model works for this ecosystem is a further proof of the RSA universality. Moreover, the ability to separate different animals with the model parameters and biodiversity number are promising results if we think about future applications on human data, in which the Microbiota composition and biodiversity are in close relationships with a variety of diseases and life-styles.

Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data.

BACKGROUND: Detecting somatic mutations in whole exome sequencing data of cancer samples has become a popular approach for profiling cancer development, progression and chemotherapy resistance. Several studies have proposed software packages, filters and parametrizations. However, many research groups reported low concordance among different methods. We aimed to develop a pipeline which detects a wide range of single nucleotide mutations with high validation rates. We combined two standard tools - Genome Analysis Toolkit (GATK) and MuTect - to create the GATK-LODN method. As proof of principle, we applied our pipeline to exome sequencing data of hematological (Acute Myeloid and Acute Lymphoblastic Leukemias) and solid (Gastrointestinal Stromal Tumor and Lung Adenocarcinoma) tumors. We performed experiments on simulated data to test the sensitivity and specificity of our pipeline. RESULTS: The software MuTect presented the highest validation rate (90 %) for mutation detection, but limited number of somatic mutations detected. The GATK detected a high number of mutations but with low specificity. The GATK-LODN increased the performance of the GATK variant detection (from 5 of 14 to 3 of 4 confirmed variants), while preserving mutations not detected by MuTect. However, GATK-LODN filtered more variants in the hematological samples than in the solid tumors. Experiments in simulated data demonstrated that GATK-LODN increased both specificity and sensitivity of GATK results. CONCLUSION: We presented a pipeline that detects a wide range of somatic single nucleotide variants, with good validation rates, from exome sequencing data of cancer samples. We also showed the advantage of combining standard algorithms to create the GATK-LODN method, that increased specificity and sensitivity of GATK results. This pipeline can be helpful in discovery studies aimed to profile the somatic mutational landscape of cancer genomes.

Comparative modeling of proteins: a method for engaging students' interest in bioinformatics tools.

The huge increase in data being produced in the genomic era has produced a need to incorporate computers into the research process. Sequence generation, its subsequent storage, interpretation, and analysis are now entirely computer-dependent tasks. Universities from all over the world have been challenged to seek a way of encouraging students to incorporate computational and bioinformatics skills since undergraduation in order to understand biological processes. The aim of this article is to report the experience of awakening students' interest in bioinformatics tools during a course focused on comparative modeling of proteins. The authors start by giving a full description of the course environmental context and students' backgrounds. Then they detail each class and present a general overview of the protein modeling protocol. The positive and negative aspects of the course are also reported, and some of the results generated in class and in projects outside the classroom are discussed. In the last section of the article, general perspectives about the course from students' point of view are given. This work can serve as a guide for professors who teach subjects for which bioinformatics tools are useful and for universities that plan to incorporate bioinformatics into the curriculum.

Evidence of substantial recombination among Trypanosoma cruzi II strains from Minas Gerais.

Due to the scarcity of evidence of sexuality in Trypanosoma cruzi, the causative agent of Chagas disease, it has been general accepted that the parasite reproduction is essentially clonal with infrequent genetic recombination. This assumption is mainly supported by indirect evidence, such as Hardy-Weinberg imbalances, linkage disequilibrium and a strong correlation between independent sets of genetic markers of T. cruzi populations. However, because the analyzed populations are usually isolated from different geographic regions, the possibility of population substructuring as generating these genetic marker imbalances cannot be eliminated. To investigate this possibility, we firstly compared the allele frequencies and haplotype networks using seven different polymorphic loci (two from mitochondrial and five from different nuclear chromosomes) in two groups of TcII strains: one including isolates obtained from different regions in Latin America and the other including isolates obtained only from patients of the Minas Gerais State in Brazil. Our hypothesis was that if the population structure is essentially clonal, Hardy-Weinberg disequilibrium and a sharp association between the clusters generated by analyzing independent markers should be observed in both strain groups, independent of the geographic origin of the samples. The results demonstrated that the number of microsatellite loci in linkage disequilibrium decreased from 4 to 1 when only strains from Minas Gerais were analyzed. Moreover, we did not observed any correlation between the clusters when analyzing the nuclear and mitochondrial loci, suggesting independent inheritance of these markers among the Minas Gerais strains. Besides, using a second subset of five physically linked microsatellite loci and the Minas Gerais strains, we could also demonstrate evidence of homologous recombination roughly proportional to the relative distance among them. Taken together, our results do not support a clonal population structure for T. cruzi, particularly in TcII, which coexists in the same geographical area, suggesting that genetic exchanges among these strains may occur more frequently than initially expected.