Systematic identification of functional plant modules through the integration of complementary data sources

A major challenge is to unravel how genes interact and are regulated to exert specific biological functions. The integration of genome-wide functional genomics data, followed by the construction of gene networks, provides a powerful approach to identify functional gene modules. Large-scale expression data, functional gene annotations, experimental protein-protein interactions, and transcription factor-target interactions were integrated to delineate modules in Arabidopsis (Arabidopsis thaliana). The different experimental input data sets showed little overlap, demonstrating the advantage of combining multiple data types to study gene function and regulation. In the set of 1,563 modules covering 13,142 genes, most modules displayed strong coexpression, but functional and cis-regulatory coherence was less prevalent. Highly connected hub genes showed a significant enrichment toward embryo lethality and evidence for cross talk between different biological processes. Comparative analysis revealed that 58% of the modules showed conserved coexpression across multiple plants. Using module-based functional predictions, 5,562 genes were annotated, and an evaluation experiment disclosed that, based on 197 recently experimentally characterized genes, 38.1% of these functions could be inferred through the module context. Examples of confirmed genes of unknown function related to cell wall biogenesis, xylem and phloem pattern formation, cell cycle, hormone stimulus, and circadian rhythm highlight the potential to identify new gene functions. The module-based predictions offer new biological hypotheses for functionally unknown genes in Arabidopsis (1,701 genes) and six other plant species (43,621 genes). Furthermore, the inferred modules provide new insights into the conservation of coexpression and coregulation as well as a starting point for comparative functional annotation

Inference of the genetic network regulating lateral root initiation in Arabidopsis thaliana

Regulation of gene expression is crucial for organism growth, and it is one of the challenges in Systems Biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyse two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants to infer their regulatory network. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale-free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation

Systems biology in inflammatory bowel diseases

Purpose of review: Ulcerative colitis (UC) and Crohn’s Disease (CD) are the two predominant types of inflammatory bowel disease (IBD), affecting over 1.4 million individuals in the US. IBD results from complex interactions between pathogenic components, including genetic and epigenetic factors, the immune response and the microbiome through an unknown sequence of events. The purpose of this review is to describe a system biology approach to IBD as a novel and exciting methodology aiming at developing novel IBD therapeutics based on the integration of molecular and cellular "omics" data. Recent Findings: Recent evidence suggested the presence of genetic, epigenetic, transcriptomic, proteomic and metabolomic alterations in IBD patients. Furthermore, several studies have shown that different cell types, including fibroblasts, epithelial, immune and endothelial cells together with the intestinal microbiota are involved in IBD pathogenesis. Novel computational methodologies have been developed aiming to integrate high - throughput molecular data. Summary: A systems biology approach could potentially identify the central regulators (hubs) in the IBD interactome and improve our understanding of the molecular mechanisms involved in IBD pathogenesis. The future IBD therapeutics should be developed on the basis of targeting the central hubs in the IBD network

Reverse Engineering of Gene Regulatory Networks for Discovery of Novel Interactions in Pathways Using Gene Expression Data

A variety of chemicals in the environment have the potential to adversely affect the biological systems. We examined the responses of Rat (Rattus norvegicus) to the RDX exposure and female fathead minnows (FHM, Pimephales promelas) to a model aromatase inhibitor, fadrozole, using a transcriptional network inference approach. Rats were exposed to RDX and fish were exposed to 0 or 30mg/L fadrozole for 8 days. We analyzed gene expression changes using 8000 probes microarrays for rat experiment and 15,000 probe microarrays for fish. We used these changes to infer a transcriptional network. The central nervous system is remarkably plastic in its ability to recover from trauma. We examined recovery from chemicals in rats and fish through changes in transcriptional networks. Transcriptional networks from time series experiments provide a good basis for organizing and studying the dynamic behavior of biological processes. The goal of this work was to identify networks affected by chemical exposure and track changes in these networks as animals recover. The top 1254 significantly changed genes based upon 1.5-fold change and P\u3c 0.05 across all the time points from the fish data and 937 significantly changed genes from rat data were chosen for network modeling using either a Mutual Information network (MIN) or a Graphical Gaussian Model (GGM) or a Dynamic Bayesian Network (DBN) approach. The top interacting genes were queried to find sub-networks, possible biological networks, biochemical pathways, and network topologies impacted after exposure to fadrozole. The methods were able to reconstruct transcriptional networks with few hub structures, some of which were found to be involved in major biological process and molecular function. The resulting network from rat experiment exhibited a clear hub (central in terms of connections and direction) connectivity structure. Genes such as Ania-7, Hnrpdl, Alad, Gapdh, etc. (all CNS related), GAT-2, Gabra6, Gabbrl, Gabbr2 (GABA, neurotransmitter transporters and receptors), SLC2A1 (glucose transporter), NCX3 (Na-Ca exchanger), Gnal (Olfactory related), skn-la were showed up in our network as the \u27hub\u27 genes while some of the known transcription factors Msx3, Cacngl, Brs3, NGF1 etc. were also matched with our network model. Aromatase in the fish experiment was a highly connected gene in a sub-network along with other genes involved in steroidogenesis. Many of the sub-networks were involved in fatty acid metabolism, gamma-hexachlorocyclohexane degradation, and phospholipase activating pathways. Aromatase was a highly connected gene in a sub-network along with the genes LDLR, StAR, KRT18, HER1, CEBPB, ESR2A, and ACVRL1. Many of the subnetworks were involved in fatty acid metabolism, gamma-hexachlorocyclohexane degradation, and phospholipase activating pathways. A credible transcriptional network was recovered from both the time series data and the static data. The network included transcription factors and genes with roles in brain function, neurotransmission and sex hormone synthesis. Examination of the dynamic changes in expression within this network over time provided insight into recovery from traumas and chemical exposures

Network and biosignature analysis for the integration of transcriptomic and metabolomic data to characterize leaf senescence process in sunflower

In recent years, high throughput technologies have led to an increase of datasets from omics disciplines allowing the understanding of the complex regulatory networks associated with biological processes. Leaf senescence is a complex mechanism controlled by multiple genetic and environmental variables, which has a strong impact on crop yield. Transcription factors (TFs) are key proteins in the regulation of gene expression, regulating different signaling pathways; their function is crucial for triggering and/or regulating different aspects of the leaf senescence process. The study of TF interactions and their integration with metabolic profiles under different developmental conditions, especially for a non-model organism such as sunflower, will open new insights into the details of gene regulation of leaf senescence.Fil: Moschen, Sebastián Nicolás. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Higgins, Janet. The Genome Analysis Centre; Reino UnidoFil: Di Rienzo, Julio Alejandro. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; ArgentinaFil: Heinz, Ruth Amelia. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernández, Paula del Carmen. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice.

To gain insight into how mutant huntingtin (mHtt) CAG repeat length modifies Huntington's disease (HD) pathogenesis, we profiled mRNA in over 600 brain and peripheral tissue samples from HD knock-in mice with increasing CAG repeat lengths. We found repeat length-dependent transcriptional signatures to be prominent in the striatum, less so in cortex, and minimal in the liver. Coexpression network analyses revealed 13 striatal and 5 cortical modules that correlated highly with CAG length and age, and that were preserved in HD models and sometimes in patients. Top striatal modules implicated mHtt CAG length and age in graded impairment in the expression of identity genes for striatal medium spiny neurons and in dysregulation of cyclic AMP signaling, cell death and protocadherin genes. We used proteomics to confirm 790 genes and 5 striatal modules with CAG length-dependent dysregulation at the protein level, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo

Comparative Microbial Modules Resource: Generation and Visualization of Multi-species Biclusters

The increasing abundance of large-scale, high-throughput datasets for many closely related organisms provides opportunities for comparative analysis via the simultaneous biclustering of datasets from multiple species. These analyses require a reformulation of how to organize multi-species datasets and visualize comparative genomics data analyses results. Recently, we developed a method, multi-species cMonkey, which integrates heterogeneous high-throughput datatypes from multiple species to identify conserved regulatory modules. Here we present an integrated data visualization system, built upon the Gaggle, enabling exploration of our method's results (available at http://meatwad.bio.nyu.edu/cmmr.html). The system can also be used to explore other comparative genomics datasets and outputs from other data analysis procedures – results from other multiple-species clustering programs or from independent clustering of different single-species datasets. We provide an example use of our system for two bacteria, Escherichia coli and Salmonella Typhimurium. We illustrate the use of our system by exploring conserved biclusters involved in nitrogen metabolism, uncovering a putative function for yjjI, a currently uncharacterized gene that we predict to be involved in nitrogen assimilation

Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

Comparative genomics and transcriptomics elucidate virulence mechanisms and host responses in infectious diseases

The main thematic area of the present thesis is the development and application of bioinformatics pipelines, namely whole-genome sequence (WGS) analysis and transcriptome profile analysis. These pipelines were applied to study the fungal pathogen Aspergillus fumigatus (Manuscripts I, III, and IV) and the early human immune mechanisms activated in response to different types of pathogens (bacteria, fungi, and co-infections) in sepsis patients (Manuscript II). The comparative genomic and transcriptomic analyses applied in my thesis have significantly improved our understanding of fungal pathogenicity as well as the pathogen-specific immune response mechanisms of the human host. Next to a number of novel insights, my work included in this thesis has generated a large number of new hypotheses based on big-data analysis, offering the scientific community the possibility to design exciting new research to confirm them in future experimental studies and bring us closer to actual precision medicine for infectious diseases