Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

BACKGROUND: It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. RESULTS: In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. CONCLUSION: We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs

Dissecting interferon-induced transcriptional programs in human peripheral blood cells

Interferons are key modulators of the immune system, and are central to the control of many diseases. The response of immune cells to stimuli in complex populations is the product of direct and indirect effects, and of homotypic and heterotypic cell interactions. Dissecting the global transcriptional profiles of immune cell populations may provide insights into this regulatory interplay. The host transcriptional response may also be useful in discriminating between disease states, and in understanding pathophysiology. The transcriptional programs of cell populations in health therefore provide a paradigm for deconvoluting disease-associated gene expression profiles.We used human cDNA microarrays to (1) compare the gene expression programs in human peripheral blood mononuclear cells (PBMCs) elicited by 6 major mediators of the immune response: interferons alpha, beta, omega and gamma, IL12 and TNFalpha; and (2) characterize the transcriptional responses of purified immune cell populations (CD4+ and CD8+ T cells, B cells, NK cells and monocytes) to IFNgamma stimulation. We defined a highly stereotyped response to type I interferons, while responses to IFNgamma and IL12 were largely restricted to a subset of type I interferon-inducible genes. TNFalpha stimulation resulted in a distinct pattern of gene expression. Cell type-specific transcriptional programs were identified, highlighting the pronounced response of monocytes to IFNgamma, and emergent properties associated with IFN-mediated activation of mixed cell populations. This information provides a detailed view of cellular activation by immune mediators, and contributes an interpretive framework for the definition of host immune responses in a variety of disease settings

Inflammatory Gene Regulatory Networks in Amnion Cells Following Cytokine Stimulation: Translational Systems Approach to Modeling Human Parturition

A majority of the studies examining the molecular regulation of human labor have been conducted using single gene approaches. While the technology to produce multi-dimensional datasets is readily available, the means for facile analysis of such data are limited. The objective of this study was to develop a systems approach to infer regulatory mechanisms governing global gene expression in cytokine-challenged cells in vitro, and to apply these methods to predict gene regulatory networks (GRNs) in intrauterine tissues during term parturition. To this end, microarray analysis was applied to human amnion mesenchymal cells (AMCs) stimulated with interleukin-1β, and differentially expressed transcripts were subjected to hierarchical clustering, temporal expression profiling, and motif enrichment analysis, from which a GRN was constructed. These methods were then applied to fetal membrane specimens collected in the absence or presence of spontaneous term labor. Analysis of cytokine-responsive genes in AMCs revealed a sterile immune response signature, with promoters enriched in response elements for several inflammation-associated transcription factors. In comparison to the fetal membrane dataset, there were 34 genes commonly upregulated, many of which were part of an acute inflammation gene expression signature. Binding motifs for nuclear factor-κB were prominent in the gene interaction and regulatory networks for both datasets; however, we found little evidence to support the utilization of pathogen-associated molecular pattern (PAMP) signaling. The tissue specimens were also enriched for transcripts governed by hypoxia-inducible factor. The approach presented here provides an uncomplicated means to infer global relationships among gene clusters involved in cellular responses to labor-associated signals

Genetic Networks Modulating Retinal Injury

Many advances have and are being made with the advent of modern sciences. One area that remains resistant to significant advances is the recovery of the mammalian central nervous system (CNS) after injury. Considerable efforts were placed on understanding the cellular and biochemical changes occurring after injury and during wound healing. Aside from cataloging the changes that occur, little progress is being made in understanding the molecular cascades associated with the control of the healing process. The present series of studies define the global changes that occur after injury. They also define CNS repair mechanisms by identifying genetic networks that underlie the temporal changes of retinal wound healing. Gene expression changes in the injured rat retina were analyzed with microarray technology, genetic linkage analysis of gene expression, and higher-level bioinformatic analyses. This work yielded three complementary insights. First, groups of functionally related genes underlie the early, delayed, and sustained responses of wound healing. For example, transcriptional factors such as Fos and Egr1 define the early response, whereas, glial reactive markers such as Gfap and Cd81 define the sustained response. Second, three specific genomic loci modulate coordinated changes in gene expression in mouse brains: regulatory loci on chromosomes 6, 12, and 14. Of the three only the regulatory locus on chromosome 12 specifically modulates the expression of a group of genes involved in early wound-healing events (mainly, the regulation of transcription, differentiation, apoptosis, and proliferation). Third, candidate genes Id2 and Lpin1 are current modulators for the network controlled by the chromosome 12 locus. Higher levels of Id2 and Lpin1 correlated with higher levels of the survival gene Crygd, and with lower levels of acute phase genes Fos and Stat3, reactive gliosis genes Gfap and Cd81, and apoptotic gene Casp3. In this body of work I have moved beyond cataloging changes in the transcriptome to identifying candidate genes modulating the retinal response to injury. During this process I developed an integrated approach of gene expression profiling and higher-level bioinformatic analyses to define genetic networks. This work not only advances our understanding of the molecular networks controlling the CNS response to injury, but may also form the basis for interventions that can rescue injured neurons and re-establish lost CNS connections

Temporal Coordination of Gene Networks by Zelda in the Early Drosophila Embryo

In past years, much attention has focused on the gene networks that regulate early developmental processes, but less attention has been paid to how multiple networks and processes are temporally coordinated. Recently the discovery of the transcriptional activator Zelda (Zld), which binds to CAGGTAG and related sequences present in the enhancers of many early-activated genes in Drosophila, hinted at a mechanism for how batteries of genes could be simultaneously activated. Here we use genome-wide binding and expression assays to identify Zld target genes in the early embryo with the goal of unraveling the gene circuitry regulated by Zld. We found that Zld binds to genes involved in early developmental processes such as cellularization, sex determination, neurogenesis, and pattern formation. In the absence of Zld, many target genes failed to be activated, while others, particularly the patterning genes, exhibited delayed transcriptional activation, some of which also showed weak and/or sporadic expression. These effects disrupted the normal sequence of patterning-gene interactions and resulted in highly altered spatial expression patterns, demonstrating the significance of a timing mechanism in early development. In addition, we observed prevalent overlap between Zld-bound regions and genomic “hotspot” regions, which are bound by many developmental transcription factors, especially the patterning factors. This, along with the finding that the most over-represented motif in hotspots, CAGGTA, is the Zld binding site, implicates Zld in promoting hotspot formation. We propose that Zld promotes timely and robust transcriptional activation of early-gene networks so that developmental events are coordinated and cell fates are established properly in the cellular blastoderm embryo

High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation

Leaf senescence is an essential developmental process that impacts dramatically on crop yields and involves altered regulation of thousands of genes and many metabolic and signaling pathways, resulting in major changes in the leaf. The regulation of senescence is complex, and although senescence regulatory genes have been characterized, there is little information on how these function in the global control of the process. We used microarray analysis to obtain a highresolution time-course profile of gene expression during development of a single leaf over a 3-week period to senescence. A complex experimental design approach and a combination of methods were used to extract high-quality replicated data and to identify differentially expressed genes. The multiple time points enable the use of highly informative clustering to reveal distinct time points at which signaling and metabolic pathways change. Analysis of motif enrichment, as well as comparison of transcription factor (TF) families showing altered expression over the time course, identify clear groups of TFs active at different stages of leaf development and senescence. These data enable connection of metabolic processes, signaling pathways, and specific TF activity, which will underpin the development of network models to elucidate the process of senescence

Extreme learning machines for reverse engineering of gene regulatory networks from expression time series

The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause-effect of regulation among a group of genes and its reconstruction is today a challenging computational problem. Several methods were proposed, but most of them require different input sources to provide an acceptable prediction. Thus, it is a great challenge to reconstruct a GRN only from temporal gene-expression data. Results: Extreme Learning Machine (ELM) is a new supervised neural model that has gained interest in the last years because of its higher learning rate and better performance than existing supervised models in terms of predictive power. This work proposes a novel approach for GRNs reconstruction in which ELMs are used for modeling the relationships between gene expression time series. Artificial datasets generated with the well-known benchmark tool used in DREAM competitions were used. Real datasets were used for validation of this novel proposal with well-known GRNs underlying the time series. The impact of increasing the size of GRNs was analyzed in detail for the compared methods. The results obtained confirm the superiority of the ELM approach against very recent state-of-the-art methods in the same experimental conditions.Fil: Rubiolo, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin