Onset of human preterm and term birth is related to unique inflammatory transcriptome profiles at the maternal fetal interface.

BackgroundPreterm birth is a main determinant of neonatal mortality and morbidity and a major contributor to the overall mortality and burden of disease. However, research of the preterm birth is hindered by the imprecise definition of the clinical phenotype and complexity of the molecular phenotype due to multiple pregnancy tissue types and molecular processes that may contribute to the preterm birth. Here we comprehensively evaluate the mRNA transcriptome that characterizes preterm and term labor in tissues comprising the pregnancy using precisely phenotyped samples. The four complementary phenotypes together provide comprehensive insight into preterm and term parturition.MethodsSamples of maternal blood, chorion, amnion, placenta, decidua, fetal blood, and myometrium from the uterine fundus and lower segment (n = 183) were obtained during cesarean delivery from women with four complementary phenotypes: delivering preterm with (PL) and without labor (PNL), term with (TL) and without labor (TNL). Enrolled were 35 pregnant women with four precisely and prospectively defined phenotypes: PL (n = 8), PNL (n = 10), TL (n = 7) and TNL (n = 10). Gene expression data were analyzed using shrunken centroid analysis to identify a minimal set of genes that uniquely characterizes each of the four phenotypes. Expression profiles of 73 genes and non-coding RNA sequences uniquely identified each of the four phenotypes. The shrunken centroid analysis and 10 times 10-fold cross-validation was also used to minimize false positive finings and overfitting. Identified were the pathways and molecular processes associated with and the cis-regulatory elements in gene's 5' promoter or 3'-UTR regions of the set of genes which expression uniquely characterized the four phenotypes.ResultsThe largest differences in gene expression among the four groups occurred at maternal fetal interface in decidua, chorion and amnion. The gene expression profiles showed suppression of chemokines expression in TNL, withdrawal of this suppression in TL, activation of multiple pathways of inflammation in PL, and an immune rejection profile in PNL. The genes constituting expression signatures showed over-representation of three putative regulatory elements in their 5'and 3' UTR regions.ConclusionsThe results suggest that pregnancy is maintained by downregulation of chemokines at the maternal-fetal interface. Withdrawal of this downregulation results in the term birth and its overriding by the activation of multiple pathways of the immune system in the preterm birth. Complications of the pregnancy associated with impairment of placental function, which necessitated premature delivery of the fetus in the absence of labor, show gene expression patterns associated with immune rejection

How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

Genome-wide discovery of modulators of transcriptional interactions in human B lymphocytes

Transcriptional interactions in a cell are modulated by a variety of mechanisms that prevent their representation as pure pairwise interactions between a transcription factor and its target(s). These include, among others, transcription factor activation by phosphorylation and acetylation, formation of active complexes with one or more co-factors, and mRNA/protein degradation and stabilization processes. This paper presents a first step towards the systematic, genome-wide computational inference of genes that modulate the interactions of specific transcription factors at the post-transcriptional level. The method uses a statistical test based on changes in the mutual information between a transcription factor and each of its candidate targets, conditional on the expression of a third gene. The approach was first validated on a synthetic network model, and then tested in the context of a mammalian cellular system. By analyzing 254 microarray expression profiles of normal and tumor related human B lymphocytes, we investigated the post transcriptional modulators of the MYC proto-oncogene, an important transcription factor involved in tumorigenesis. Our method discovered a set of 100 putative modulator genes, responsible for modulating 205 regulatory relationships between MYC and its targets. The set is significantly enriched in molecules with function consistent with their activities as modulators of cellular interactions, recapitulates established MYC regulation pathways, and provides a notable repertoire of novel regulators of MYC function. The approach has broad applicability and can be used to discover modulators of any other transcription factor, provided that adequate expression profile data are available.Comment: 15 pages, 3 figures, 2 tables; minor changes following referees' comments; accepted to RECOMB0

An integrated clinical program and crowdsourcing strategy for genomic sequencing and Mendelian disease gene discovery.

Despite major progress in defining the genetic basis of Mendelian disorders, the molecular etiology of many cases remains unknown. Patients with these undiagnosed disorders often have complex presentations and require treatment by multiple health care specialists. Here, we describe an integrated clinical diagnostic and research program using whole-exome and whole-genome sequencing (WES/WGS) for Mendelian disease gene discovery. This program employs specific case ascertainment parameters, a WES/WGS computational analysis pipeline that is optimized for Mendelian disease gene discovery with variant callers tuned to specific inheritance modes, an interdisciplinary crowdsourcing strategy for genomic sequence analysis, matchmaking for additional cases, and integration of the findings regarding gene causality with the clinical management plan. The interdisciplinary gene discovery team includes clinical, computational, and experimental biomedical specialists who interact to identify the genetic etiology of the disease, and when so warranted, to devise improved or novel treatments for affected patients. This program effectively integrates the clinical and research missions of an academic medical center and affords both diagnostic and therapeutic options for patients suffering from genetic disease. It may therefore be germane to other academic medical institutions engaged in implementing genomic medicine programs

Large-scale discovery of enhancers from human heart tissue.

Development and function of the human heart depend on the dynamic control of tissue-specific gene expression by distant-acting transcriptional enhancers. To generate an accurate genome-wide map of human heart enhancers, we used an epigenomic enhancer discovery approach and identified ∼6,200 candidate enhancer sequences directly from fetal and adult human heart tissue. Consistent with their predicted function, these elements were markedly enriched near genes implicated in heart development, function and disease. To further validate their in vivo enhancer activity, we tested 65 of these human sequences in a transgenic mouse enhancer assay and observed that 43 (66%) drove reproducible reporter gene expression in the heart. These results support the discovery of a genome-wide set of noncoding sequences highly enriched in human heart enhancers that is likely to facilitate downstream studies of the role of enhancers in development and pathological conditions of the heart

Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Motivation: Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Results: Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which has built on top of the work of Tarca et al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their deregulation degree, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. Availability: MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril

Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli.

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery