Gene2DisCo : gene to disease using disease commonalities

OBJECTIVE: Finding the human genes co-causing complex diseases, also known as "disease-genes", is one of the emerging and challenging tasks in biomedicine. This process, termed gene prioritization (GP), is characterized by a scarcity of known disease-genes for most diseases, and by a vast amount of heterogeneous data, usually encoded into networks describing different types of functional relationships between genes. In addition, different diseases may share common profiles (e.g. genetic or therapeutic profiles), and exploiting disease commonalities may significantly enhance the performance of GP methods. This work aims to provide a systematic comparison of several disease similarity measures, and to embed disease similarities and heterogeneous data into a flexible framework for gene prioritization which specifically handles the lack of known disease-genes. METHODS: We present a novel network-based method, Gene2DisCo, based on generalized linear models (GLMs) to effectively prioritize genes by exploiting data regarding disease-genes, gene interaction networks and disease similarities. The scarcity of disease-genes is addressed by applying an efficient negative selection procedure, together with imbalance-aware GLMs. Gene2DisCo is a flexible framework, in the sense it is not dependent upon specific types of data, and/or upon specific disease ontologies. RESULTS: On a benchmark dataset composed of nine human networks and 708 medical subject headings (MeSH) diseases, Gene2DisCo largely outperformed the best benchmark algorithm, kernelized score functions, in terms of both area under the ROC curve (0.94 against 0.86) and precision at given recall levels (for recall levels from 0.1 to 1 with steps 0.1). Furthermore, we enriched and extended the benchmark data to the whole human genome and provided the top-ranked unannotated candidate genes even for MeSH disease terms without known annotations

Clostridium difficile Colonization and Infection in the Elderly and Associations with the Aging Intestinal Microbiome

The widespread use of antibiotics has led to dramatic increases in the incidence and severity of Clostridium difficile infection (CDI). No group of patients suffers more from CDI than the elderly. Nursing homes (NH) represent the perfect storm of a vulnerable group of frail elders living in confined communities. Nursing home residents suffer from increased morbidity and mortality from CDI and corresponding high rates of C. difficile colonization. Upwards of 40 to 50% of CDI current cases originate from NHs and the prevalence of colonization rates remain high within these facilities, with as many as half of the residents being colonized with C. difficile at any given time. One factor that has become of increasing interest and a target of preventive strategies is the human intestinal microbiome. A healthy, diverse microbiome interacts with the host immune system and contributes to pathogen resistance. In this investigation, we first examine elder specific variables to determine if the associated risks of CDI differ by home living environment (nursing home versus community-dwelling). We then go on explore the relationships of NH environment, frailty, nutritional status, and residents’ age with microbiome composition and potential metabolic function. Finally, we describe the C. difficile colonization patterns among elderly NH residents and the associated risk of colonization based on clinical variables and microbiome determinants. A better understanding of the microbiome’s contribution to C. difficile colonization will provide the basis for informing rational interventions and public health policies to better combat CDI in the nursing home

PREDICT: a method for inferring novel drug indications with application to personalized medicine

The authors present a new method, PREDICT, for the large-scale prediction of drug indications, and demonstrate its use on both approved drugs and novel molecules. They also provide a proof-of-concept for its potential utility in predicting patient-specific medications

Band-based similarity indices for gene expression classification and clustering

The concept of depth induces an ordering from centre outwards in multivariate data. Most depth definitions are unfeasible for dimensions larger than three or four, but the Modified Band Depth (MBD) is a notable exception that has proven to be a valuable tool in the analysis of high-dimensional gene expression data. This depth definition relates the centrality of each individual to its (partial) inclusion in all possible bands formed by elements of the data set. We assess (dis)similarity between pairs of observations by accounting for such bands and constructing binary matrices associated to each pair. From these, contingency tables are calculated and used to derive standard similarity indices. Our approach is computationally efficient and can be applied to bands formed by any number of observations from the data set. We have evaluated the performance of several band-based similarity indices with respect to that of other classical distances in standard classification and clustering tasks in a variety of simulated and real data sets. However, the use of the method is not restricted to these, the extension to other similarity coefficients being straightforward. Our experiments show the benefits of our technique, with some of the selected indices outperforming, among others, the Euclidean distance.This work has been financially supported by the FEDER/ Ministerio de Ciencia, Innovación y Universidades- Agencia Estatal de Investigación, Grant Numbers FIS2017-84440-C2-2-P and MTM2017-84446-C2-2-R, and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M23), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).Publicad

The interplay of global chromosomal organisation, promoter-enhancer interactions and transcription

All somatic cells within an organism contain the same genetic material, yet they display pronounced differences in function and morphology. Precise control of gene expression is of fundamental importance to allow cells to properly develop, maintain homeostasis, and respond to external stimuli. The first step in gene expression is transcription, which starts at the core promoter region. While core promoters are crucial for transcriptional initiation, they are insufficient for establishing complex tissue- and condition-specific gene expression patterns in multicellular organisms. Additional transcriptional control elements, such as gene enhancers, are required for this, with many such elements localising considerable distances away from their target promoters. Enhancers commonly convey their regulatory signals to target promoters by forming physical contacts with them through three-dimensional DNA looping, underpinning the importance of chromosomal organisation in transcriptional control. In recent years, the emergence of chromosome conformation capture and related methodologies has dramatically increased our understanding of chromosomal organisation. In particular, high-throughput Hi-C analyses across cell types have led to the identification of spatial genomic structures, including Topologically Associating Domains (TADs). In parallel, high-resolution versions of these technologies (such as 5C, CHiA-PET, HiChIP and Capture Hi-C) have detected multitudes of novel looping interactions, including connections between promoters and enhancers. The interplay between precise regulatory interactions, the higher-order chromosomal organisation, and their joint contribution to transcriptional control is incompletely understood and is the focus of this work. In the first part of this work, I take advantage of high-resolution Promoter Capture Hi-C (PCHi-C) data to investigate the localisation of promoter interactions with respect to TAD boundaries in human primary blood cells and cell-cycle synchronised HeLa cells. I show that the majority of promoter interactions originate at, and are constrained by TAD boundaries. However, a minority of promoter interactions appear to cross TAD boundaries in all analysed cell types. Furthermore, I identify genes with multiple TAD-boundary crossing interactions per promoter and present evidence that these interactions may be supported by transcriptional machinery. These results suggest a role for transcriptional machinery in shaping promoter interactions in a TAD independent manner. In the second part of this work, I investigate promoter interaction rewiring upon perturbations of architectural proteins. For this analysis, I use PCHi-C data from HeLa cells, in which cohesin or CTCF are rapidly depleted using Auxin-induced degradation. I show that promoter interactions that are lost, maintained, or gained upon cohesin depletion possess distinct distance profiles and relate to TAD organisation in markedly different ways. I demonstrate that promoter-interacting regions that are lost upon cohesin depletion associate with architectural proteins, while those that are maintained or gained show characteristics of enhancers. Finally, I show evidence for a functional role of cohesin-mediated interactions in transcriptional regulation. Collectively, this work reveals the interplay between TADs, promoter interactions and transcription, while suggesting that promoter interactions may be supported by TAD independent mechanisms.MRC DTP studentship BBS/E/B/000M0816 and164259

Bioinformatics for comparative cell biology

For hundreds of years biologists have studied the naturally occurring diversity in plant and animal species. The invention of the electron microscope in the rst half of the 1900's reveled that cells also can be incredible complex (and often stunningly beautiful). However, despite the fact that the eld of cell biology has existed for over 100 years we still lack a formal understanding of how cells evolve: It is unclear what the extents are in cell and organelle morphology, if and how diversity might be constrained, and how organelles change morphologically over time.(...

Discovering topic structures of a temporally evolving document corpus

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes: emergence and disappearance, evolution, splitting, and merging. The power of the proposed framework is demonstrated on two medical literature corpora concerned with the autism spectrum disorder (ASD) and the metabolic syndrome (MetS)—both increasingly important research subjects with significant social and healthcare consequences. In addition to the collected ASD and metabolic syndrome literature corpora which we made freely available, our contribution also includes an extensive empirical analysis of the proposed framework. We describe a detailed and careful examination of the effects that our algorithms’s free parameters have on its output and discuss the significance of the findings both in the context of the practical application of our algorithm as well as in the context of the existing body of work on temporal topic analysis. Our quantitative analysis is followed by several qualitative case studies highly relevant to the current research on ASD and MetS, on which our algorithm is shown to capture well the actual developments in these fields.Publisher PDFPeer reviewe

Human Gut Microbiota and Obesity: How Is Gut Microbiota Associated with Obesity Improvement Induced by Bariatric Surgeries or Low-Calorie Diet Treatment?

An increasing number of studies suggest that the complex microbial community of the human gut may have an important role in human health and disease conditions such as obesity and diabetes. However, the causal relationship among gut microbiota, obesity, weight loss, and the influence of bariatric surgery remains largely unknown. This study investigated the correlation between human gut microbiota and obesity via determining how gut microbiota is associated with obesity improvement induced by bariatric surgeries (BRS) and low-calorie diet (LCD) treatment. Specifically, we investigated the effects of various bariatric surgery (BRS) procedures and a low-calorie diet (LCD) treatment on the diversity, composition, and metabolism of the gut microbiota of obese patients using a combination of robust, high-throughput metagenomic technologies, (Illumina sequencing and HuMiChip microarray), quantitative polymerase chain reaction (qPCR), and gas chromatography. We discovered that following BRS procedures or LCD treatment the gut microbial community structure significantly altered, along with efficient, persistent obesity improvement after bariatric surgeries and, to a lesser degree, LCD treatment. First, microbial richness and diversity were significantly increased after both treatments. Second, the distribution and composition of microbial community were switched toward a healthier profile. For instance, phylum Actinobacteria was significantly reduced, while phylum Verrucomicrobia was significantly increased at week 52 after BRS. Phylum Firmicutes was significantly reduced, while phylum Bacteroidetes was significantly increased after LCD treatment. Microbial community structure at different taxonomic levels and their connectivity were significantly correlated with obesity-related physiological variables, and high-molecular-weight adiponectin (ADPHMW) seemed to be an important factor that links to the correlation. Third, microbial functional gene profile was significantly altered at week 7 post treatments, with the BRS group showing significantly higher gene richness and diversity than the LCD group. Over half of the gene categories showed significant correlations with obesity-related physiological variables, and the entire gene community significantly correlated with obesity-related hormones (adiponectin [ADP], APHMW and active Ghrelin [GHRA]), with the gene richness and diversity significantly negatively correlated with the active hunger hormone GHRA. In addition, we found that genus Akkermansia was significantly correlated with better health condition. It significantly increased after BRS treatment, and its change showed a significantly negative correlation with a change of obesity conditions following the treatments. Moreover, the concentrations of gut microbial metabolites (short-chain fatty acids [SCFAs]) were significantly altered. Total SCFAs and acetate were significantly reduced after both treatments. Following BRS, the proportion of acetate was significantly reduced, while proportions of propionate and butyrate were significantly increased. Correspondingly, the relative abundance of most butyrate-producing genera was significantly increased after BRS, and the gene communities relevant to the metabolism of SCFAs significantly correlated with obesity-related hormones (ADP, ADPHW, peptide tyrosine tyrosine [PYY] and GHRA). Using a combination of multiple approaches, this research revealed that both taxonomical and functional gut microbial communities significantly correlate with obesity-related variables. The study indicates that weight-loss treatments might induce the alteration of gut microbial community structure toward a healthier profile, which then further fosters the weight loss and better health condition and thus form a positive loop. Obesity-related hormones, including ADPHMW, ADP, glucagon-like peptide-1 (GLP-1), PYY, GHR, and GHRA, in particular, ADPHMW, might play important roles in linking gut microbiota and obesity. A pathway of gut microbiota-ADPHMW-obesity could be one of the important mechanisms that underlie the substantial and persistent weight loss following bariatric surgeries. The study suggests several potential therapeutic methods that could be developed for treatment and/or prevention of obesity and obesity-related health conditions, including (1) supplying obese people with prebiotics (materials that boost beneficial microbes) and/or probiotics (beneficial microbes, such as Akkermansia and butyrate-producing bacteria) that can improve the healthiness of gut microbial community structure, (2) supplying human subjects with beneficial gut microbial products, such as butyrate and propionate that can increase the level of GLP-1 and PYY which have been proven to signal satiety, and (3) direct use of hormones such as ADPHMW, GLP-1, and PYY with caution of other possible health effects