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1.
Nucleic Acids Res ; 43(3): e20, 2015 Feb 18.
Article in English | MEDLINE | ID: mdl-25428368

ABSTRACT

Identifying conserved and divergent response patterns in gene networks is becoming increasingly important. A common approach is integrating expression information with gene association networks in order to find groups of connected genes that are activated or repressed. In many cases, researchers are also interested in comparisons across species (or conditions). Finding an active sub-network is a hard problem and applying it across species requires further considerations (e.g. orthology information, expression data and networks from different sources). To address these challenges we devised ModuleBlast, which uses both expression and network topology to search for highly relevant sub-networks. We have applied ModuleBlast to expression and interaction data from mouse, macaque and human to study immune response and aging. The immune response analysis identified several relevant modules, consistent with recent findings on apoptosis and NFκB activation following infection. Temporal analysis of these data revealed cascades of modules that are dynamically activated within and across species. We have experimentally validated some of the novel hypotheses resulting from the analysis of the ModuleBlast results leading to new insights into the mechanisms used by a key mammalian aging protein.


Subject(s)
Gene Regulatory Networks , Aging/genetics , Animals , Apoptosis , Humans , Macaca , Mice , Species Specificity
3.
Nature ; 483(7388): 218-21, 2012 Feb 22.
Article in English | MEDLINE | ID: mdl-22367546

ABSTRACT

The significant increase in human lifespan during the past century confronts us with great medical challenges. To meet these challenges, the mechanisms that determine healthy ageing must be understood and controlled. Sirtuins are highly conserved deacetylases that have been shown to regulate lifespan in yeast, nematodes and fruitflies. However, the role of sirtuins in regulating worm and fly lifespan has recently become controversial. Moreover, the role of the seven mammalian sirtuins, SIRT1 to SIRT7 (homologues of the yeast sirtuin Sir2), in regulating lifespan is unclear. Here we show that male, but not female, transgenic mice overexpressing Sirt6 (ref. 4) have a significantly longer lifespan than wild-type mice. Gene expression analysis revealed significant differences between male Sirt6-transgenic mice and male wild-type mice: transgenic males displayed lower serum levels of insulin-like growth factor 1 (IGF1), higher levels of IGF-binding protein 1 and altered phosphorylation levels of major components of IGF1 signalling, a key pathway in the regulation of lifespan. This study shows the regulation of mammalian lifespan by a sirtuin family member and has important therapeutic implications for age-related diseases.


Subject(s)
Longevity/physiology , Sex Characteristics , Sirtuins/metabolism , Animals , Female , Gene Expression , Gene Expression Profiling , Insulin-Like Growth Factor I/analysis , Kaplan-Meier Estimate , Longevity/genetics , Male , Mice , Mice, Inbred BALB C , Mice, Inbred C57BL , Mice, Transgenic , Oligonucleotide Array Sequence Analysis , Sirtuins/genetics
4.
BMC Syst Biol ; 5: 134, 2011 Aug 23.
Article in English | MEDLINE | ID: mdl-21861884

ABSTRACT

BACKGROUND: Orthologous genes are highly conserved between closely related species and biological systems often utilize the same genes across different organisms. However, while sequence similarity often implies functional similarity, interaction data is not well conserved even for proteins with high sequence similarity. Several recent studies comparing high throughput data including expression, protein-protein, protein-DNA, and genetic interactions between close species show conservation at a much lower rate than expected. RESULTS: In this work we collected comprehensive high-throughput interaction datasets for four model organisms (S. cerevisiae, S. pombe, C. elegans, and D. melanogaster) and carried out systematic analyses in order to explain the apparent lower conservation of interaction data when compared to the conservation of sequence data. We first showed that several previously proposed hypotheses only provide a limited explanation for such lower conservation rates. We combined all interaction evidences into an integrated network for each species and identified functional modules from these integrated networks. We then demonstrate that interactions that are part of functional modules are conserved at much higher rates than previous reports in the literature, while interactions that connect between distinct functional modules are conserved at lower rates. CONCLUSIONS: We show that conservation is maintained between species, but mainly at the module level. Our results indicate that interactions within modules are much more likely to be conserved than interactions between proteins in different modules. This provides a network based explanation to the observed conservation rates that can also help explain why so many biological processes are well conserved despite the lower levels of conservation for the interactions of proteins participating in these processes.Accompanying website: http://www.sb.cs.cmu.edu/CrossSP.


Subject(s)
Conserved Sequence/genetics , Gene Regulatory Networks/genetics , Multiprotein Complexes/metabolism , Protein Interaction Maps/genetics , Animals , Caenorhabditis elegans , Drosophila melanogaster , Saccharomyces cerevisiae , Schizosaccharomyces , Species Specificity , Systems Biology
5.
PLoS One ; 6(7): e22401, 2011.
Article in English | MEDLINE | ID: mdl-21789257

ABSTRACT

Viral and bacterial infections of the lower respiratory tract are major causes of morbidity and mortality worldwide. Alveolar macrophages line the alveolar spaces and are the first cells of the immune system to respond to invading pathogens. To determine the similarities and differences between the responses of mice and macaques to invading pathogens we profiled alveolar macrophages from these species following infection with two viral (PR8 and Fuj/02 influenza A) and two bacterial (Mycobacterium tuberculosis and Francisella tularensis Schu S4) pathogens. Cells were collected at 6 time points following each infection and expression profiles were compared across and between species. Our analyses identified a core set of genes, activated in both species and across all pathogens that were predominantly part of the interferon response pathway. In addition, we identified similarities across species in the way innate immune cells respond to lethal versus non-lethal pathogens. On the other hand we also found several species and pathogen specific response patterns. These results provide new insights into mechanisms by which the innate immune system responds to, and interacts with, invading pathogens.


Subject(s)
Bacteria/immunology , Host-Pathogen Interactions/immunology , Immunity, Innate/genetics , Immunity, Innate/immunology , Macaca/microbiology , Macaca/virology , Viruses/immunology , Animals , Francisella tularensis/immunology , Gene Expression Profiling , Gene Expression Regulation , Host-Pathogen Interactions/genetics , Influenza A virus/immunology , Interferon Regulatory Factor-7/genetics , Interferon Regulatory Factor-7/metabolism , Macrophages, Alveolar/metabolism , Macrophages, Alveolar/microbiology , Macrophages, Alveolar/virology , Mice , Mycobacterium tuberculosis/immunology , Oligonucleotide Array Sequence Analysis , Orthomyxoviridae Infections/genetics , Orthomyxoviridae Infections/virology , Signal Transduction/genetics , Species Specificity , Tuberculosis/genetics , Tuberculosis/microbiology , Tularemia/genetics , Tularemia/microbiology , Up-Regulation
6.
BMC Genomics ; 11: 478, 2010 Aug 17.
Article in English | MEDLINE | ID: mdl-20716365

ABSTRACT

BACKGROUND: Regulation of meiosis and sporulation in Saccharomyces cerevisiae is a model for a highly regulated developmental process. Meiosis middle phase transcriptional regulation is governed by two transcription factors: the activator Ndt80 and the repressor Sum1. It has been suggested that the competition between Ndt80 and Sum1 determines the temporal expression of their targets during middle meiosis. RESULTS: Using a combination of ChIP-on-chip and expression profiling, we characterized a middle phase transcriptional network and studied the relationship between Ndt80 and Sum1 during middle and late meiosis. While finding a group of genes regulated by both factors in a feed forward loop regulatory motif, our data also revealed a large group of genes regulated solely by Ndt80. Measuring the expression of all Ndt80 target genes in various genetic backgrounds (WT, sum1Delta and MK-ER-Ndt80 strains), allowed us to dissect the exact transcriptional network regulating each gene, which was frequently different than the one inferred from the binding data alone. CONCLUSION: These results highlight the need to perform detailed genetic experiments to determine the relative contribution of interactions in transcriptional regulatory networks.


Subject(s)
Gene Expression Regulation, Fungal , Genes, Fungal/genetics , Genomics/methods , Meiosis/genetics , Repressor Proteins/genetics , Saccharomyces cerevisiae/cytology , Saccharomyces cerevisiae/genetics , Chromatin Immunoprecipitation , Cluster Analysis , DNA-Binding Proteins/genetics , DNA-Binding Proteins/metabolism , Gene Expression Profiling , Genotype , Kinetics , Models, Genetic , Nuclear Proteins/genetics , Nuclear Proteins/metabolism , Oligonucleotide Array Sequence Analysis , Phenotype , Promoter Regions, Genetic/genetics , Protein Binding , Repressor Proteins/metabolism , Reproducibility of Results , Reverse Transcriptase Polymerase Chain Reaction , Saccharomyces cerevisiae Proteins/genetics , Saccharomyces cerevisiae Proteins/metabolism , Time Factors , Transcription Factors/genetics , Transcription Factors/metabolism
7.
Genome Biol ; 11(7): R77, 2010.
Article in English | MEDLINE | ID: mdl-20653936

ABSTRACT

BACKGROUND: Fungal infections are an emerging health risk, especially those involving yeast that are resistant to antifungal agents. To understand the range of mechanisms by which yeasts can respond to anti-fungals, we compared gene expression patterns across three evolutionarily distant species - Saccharomyces cerevisiae, Candida glabrata and Kluyveromyces lactis - over time following fluconazole exposure. RESULTS: Conserved and diverged expression patterns were identified using a novel soft clustering algorithm that concurrently clusters data from all species while incorporating sequence orthology. The analysis suggests complementary strategies for coping with ergosterol depletion by azoles - Saccharomyces imports exogenous ergosterol, Candida exports fluconazole, while Kluyveromyces does neither, leading to extreme sensitivity. In support of this hypothesis we find that only Saccharomyces becomes more azole resistant in ergosterol-supplemented media; that this depends on sterol importers Aus1 and Pdr11; and that transgenic expression of sterol importers in Kluyveromyces alleviates its drug sensitivity. CONCLUSIONS: We have compared the dynamic transcriptional responses of three diverse yeast species to fluconazole treatment using a novel clustering algorithm. This approach revealed significant divergence among regulatory programs associated with fluconazole sensitivity. In future, such approaches might be used to survey a wider range of species, drug concentrations and stimuli to reveal conserved and divergent molecular response pathways.


Subject(s)
Algorithms , Evolution, Molecular , Fluconazole/pharmacology , Fungi/drug effects , Fungi/genetics , Genetic Variation , Biological Transport/drug effects , Candida/drug effects , Candida/genetics , Cluster Analysis , Conserved Sequence/genetics , Drug Resistance/drug effects , Drug Resistance/genetics , Gene Expression Profiling , Gene Expression Regulation, Fungal/drug effects , Kluyveromyces/drug effects , Kluyveromyces/genetics , Membrane Transport Proteins/genetics , RNA, Messenger/genetics , RNA, Messenger/metabolism , Regulatory Sequences, Nucleic Acid/genetics , Saccharomyces cerevisiae/drug effects , Saccharomyces cerevisiae/genetics , Species Specificity , Sterols/metabolism
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