Introduction to transcriptional networks Regulation of the expression of the Lac operon Finding Biclusters in Bipar

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Systems Biology. Today’s lecture will cover the following three topics. Introduction to transcriptional networks Regulation of the expression of the Lac operon Finding Biclusters in Bipartite Graphs. transcriptional networks.
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Systems BiologyToday’s lecture will cover the following three topicsIntroduction to transcriptional networksRegulation of the expression of the Lac operonFinding Biclusters in Bipartite Graphstranscriptional networksBy the term transcriptional networks we generally mean gene regulatory networksUnlike protein-protein interaction networks the transcriptional networks are directed networkstranscriptional networks: Basic mechanism of gene regulation transcriptional networkstranscriptional networksMost genes are regulated at transcription level and it is assumed that 5-10% of protein coding genes encode regulatory proteins.Some regulatory proteins play targeted role i.e. they take part in regulation of a few genes.Some regulatory proteins play more general role in initiating transcription (for example the eukaryotic transcription factors of type II or the RNA polymerase itself that is essential for the transcription of all genes).It is considered that dedicated regulatory proteins are those that affect up to 5% genes of a genome.However the boundary between the generalist and dedicated regulatory proteins is blurred.transcriptional networks
  • Experiments and methods used to determine regulatory relations
  • Complementary DNA microarrays
  • Oligonucleotide chips
  • Reverse transcription polymerase chain reaction
  • Serial analysis of gene expression
  • Chromatin Immunoprecipitation
  • Bioinformatics—e.g. by way of identifying binding sites
  • Transcriptional Networks: Case study 1
  • An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs
  • Hong-Wu Ma, Bharani Kumar, Uta Ditges2, Florian Gunzer2, Jan Buer1,2 and An-Ping Zeng*
  • Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649
  • This work combined data sets from 3 different sources:
  • RegulonDB (version 4.0, http://www.cifn.unam.mx/Computational_Genomics/regulondb/)
  • Ecocyc (version 8.0, www.ecocyc.org)
  • Shen-Orr,S.S., Milo,R., Mangan,S. and Alon,U. (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genet., 31, 64–68.
  • Transcriptional Network: Case study 1Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649Comparison of the TRN of E.coli from three different data sources (A) Based on number of genes (B) Based on number regulatory interactionsTranscriptional Network: Case study 1Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649A combined network that includes all the 2624 interactions from the three data sets has been produced.In addition, this work extended this network by adding 23 additional genes and around 100 regulatory relationships through literature survey.The final TRN altogether includes 1278 genes and 2724 interactions.Transcriptional Network: Case study 1Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649
  • This work discovered a hierarchical structure in the TRN.
  • The hierachical structure was identified according to the following way:
  • genes which do not code for transcription factors (TFs) or code for a TF which only regulates its own expression (auto-regulatory loop) were assigned to layer 1 (the lowest layer);
  • then we removed all the genes in layer 1 and from the remaining network identified TFs which do not regulate other genes and assigned the corresponding genes in layer 2;
  • we repeated step 2 to remove nodes which have been assigned to a layer and identified a new layer until all the genes were assigned to different layers. As a result, a nine layer hierarchical structure was uncovered.
  • From BMC Bioinformatics 2004, 5:199 of the related authorsTranscriptional Network: Case study 1Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649Transcriptional Network: Case study 1Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649The hierarchical structure implies absence of cycles in the network i.e. feedback loops (though auto regulatory and inter-regulatory loops exist)As the network is not complete, we cannot say that feedback loop could not be found in future however it seems they would not be too many.A possible biological explanation for the existence of this hierarchical structure is that the interactions in this particular TRN are between proteins and genes without involving metabolites. Only after a regulating gene has been transcribed, translated and eventually further modified by cofactors or other proteins, it canregulate the target gene. A feedback from the regulated gene at transcriptional level may delay the process for the target gene to access a desired expression level in a new environment.Transcriptional Network: Case study 1Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649Feedback control may be mainly through other interactions (e.g. metabolite and protein interaction) at post-transcriptional level rather than through transcriptional interactions between proteins and genes.For example, a gene at the bottom layer may code for a metabolic enzyme, the product of which can bind to a regulator which in turn regulates its expression. In this case, the feedback is through metabolite–protein interaction to change the activity of the transcription factor and then to affect the expression of the regulated gene.Therefore, to fully understand the gene expression regulation, an integrated network that includes different interactions is needed.Transcriptional Network: Case study 1Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649To calculate network motifs in the E.coli TRN, this work removed all the loops in the network (including the autoregulatory loops and the two-gene regulatory loops). Then they used the program Mfinder developed by Kashtan et al. to generate the motif profiles. The first four types are the so-called coherent FFLs in which the direct effect of the up regulator is consistent with its indirect effect through the mid regulator. In contrast, the last four types of FFLs are incoherent because the direct effect of the up regulator is contradictive with its indirect effectTranscriptional Network: Case study 1Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649(A) Gene gadA is regulated by six FFLs (B)Gene lpd is regulated by five FFLs (C) Gene slp is regulated by 17 regulators Transcriptional Network: Case study 1Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649Transcriptional Network: Case study 2Topological and causal structure of the yeast transcriptional regulatory networkNabil Guelzim1,2, Samuele Bottani3, Paul Bourgine2 & François Képès1nature genetics • volume 31 • may 2002In this work the yeast transcriptional network was constructed by manual inspection of the websites of MIPS, SwissProt, Yeast Protein Database, S. cerevisiae Promoter Database and the Saccharomyces Genome DatabaseThe network consists of 491 genes and 909 regulatory relationsTranscriptional Network: Case study 2nature genetics • volume 31 • may 2002The network consists of 491 genes and 909 regulatory relationsBold type indicates self-activation, bold italics indicates self-inhibition and borders indicate essential genes. Thick lines represent activation, thin lines represent inhibition and the dashed gray line represents dual regulation. Transcriptional Network: Case study 2nature genetics • volume 31 • may 2002Indegree distribution of this yeast transcriptional network is exponentialTypical exponential distribution on normal scaleTranscriptional Network: Case study 2nature genetics • volume 31 • may 2002Indegree distribution of this yeast transcriptional network is exponentialopen squares, full line --for all 402 regulated genes (367 nonregulatory and 35 interregulatory genes), 909 connections, p(k)=157e–0.45k; R=0.99)filled circles, broken line ---for the subset of 35 interregulatory genes, 72 connections; p(k)=15e–0.43k; R=0.94Indegree distribution of the transcriptional network on semi-log scaleTranscriptional Network: Case study 2nature genetics • volume 31 • may 2002Outdegree distribution of this yeast transcriptional network follows power lawTypical power law distribution on normal scaleTranscriptional Network: Case study 2nature genetics • volume 31 • may 2002Outdegree distribution of this yeast transcriptional network follows power lawOpen squares, full line --for all 124 regulating proteins (909 connections; P(k)=23k−0.87; R=0.95)filled circles, broken line – for 37 regulating proteins that control regulatory genes (72 connections; P(k)=19k−1.14; R=0.99) Outdegree distribution of the transcriptional network on log-log scaleThe operonan operon is a functioning unit of genomic material containing a cluster of genes under the control of a single regulatory signal or promoter. The genes are transcribed together into an mRNA strand and either translated together in the cytoplasm, or undergo trans-splicing to create monocistronic mRNAs that are translated separately.The result of this is that the genes contained in the operon are either expressed together or not at all. Originally operons were thought to exist solely in prokaryotes but since the discovery of the first operons in eukaryotes in the early 1990s, more evidence has arisen to suggest they are more common than previously assumed.The Lac operonThe lacoperon of e.coli consists of three genes LacZ, LacY and LacAThey are the codes of enzymes needed for processing lactoseLacI is an adjacent gene which is a regulator ( transcriptional repressor) of the Lac operonBesides the promoter operator region there is a region where a complex called CAP binds which affect the transcription positivelyLacZ codes for the enzyme B-galactosidase and LacY codes for lactose permease, an enzyme that facilitates the flux of lactose through the cell membraneLacA is not directly involved in processing LactoseSource: Models of cellular regulation by Baltazar D. Aguda and Avner FriedmanThe Lac operonThe LacI tetramer binds at the promoter region and stops the transcriptionThe CAP complex binds the cap region and enhance the binding of RNA polymeraseStatic model of the regulation of the expression of the Lac operonSource: Models of cellular regulation by Baltazar D. Aguda and Avner FriedmancAMP binds and LacI is suppressed by AllolactosecAMP cannot bind and repressor protein LacI bindscAMP binds and repressor protein LacI bindscAMP cannot bind and LacI is suppressed by AllolactoseSummary in TableIntroduction to transcriptional networksRegulation of the expression of the Lac operonFinding Biclusters in Bipartite GraphsThe technique of finding biclusters can be used to determine co-expressed gene groupsDefinition of a biclusterGiven a nxp data matrix X, where n is the number of objects (e.g. genes) and p is the number of conditions (e.g. array), a bicluster is defined as a submatrix XIJ of X within which a subset of objects I express similar behavior across the subset of conditions J.A nxp data matrix X can be easily converted to a bipartite graph by considering a threshold or so.Finding bicluster (densely connected regions) in a bipartite graph is a similar problem.V1V2 A Graph G=(V,E) is bipartite if its vertex set V can be partitioned into two subsets V1, V2 such that each edge of E has one end vertex in V1 and another in V2.Biclusters are densely connected regions in a bipartite graphGene expression data can be represented as bipartite graphsBefore transforming, the data can be normalizedBiclusters in gene expression data represents transcription modules/co-expressed gene groupsBy transforming highest 5% values to 1Tanay,A. et al. (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics, 18 (Suppl. 1), S136–S144.
  • Ihmels,J. et al. (2002) Revealing modular organization in the yeast transcriptional network. Nat. Genet., 31, 370–377.
  • Ben-Dor,A., Chor,B., Karp,R. and Yakhini,Z. (2002) Discovering local structure in gene expression data: the order-preserving sub-matrix problem. In Proceedings of the 6th Annual International Conference on Computational Biology, ACM Press, New York, NY, USA, pp. 49–57.
  • Cheng,Y. and Church,G. (2000) Biclustering of expression data. Proc. Int. Conf. Intell. Syst. Mol. Biol. pp. 93–103.
  • Murali,T.M. and Kasif,S. (2003) Extracting conserved gene expression motifs from gene expression data. Pac. Symp. Biocomput., 8, 77–88.
  • We propose a biclustering method incorporating DPClusAn example bipartite graph and its corresponding matrix(for ik) BiClus:Biclustering method incorporating DPClusConcerning each row i (i=0 to |G|-1) of MCN, we calculate thresholdi=avgi+(maxi- avgi)  Gmarginand set (MSG)ik =(MSG)ki=1if (MCN)ikthresholdi and thresholdi is not an indeterminate number (for k=0 to |G|-1).Here, avgi = SUMi/niwhere ni is the number of non-zero entries in row i of MCNand maxiis the maximum value of the entries in row i of MCNGmargin is a user defined value 1.Common neighbor matrix of the bipartite graphBiClus:Biclustering method incorporating DPClusThis matrix represents a simple graphBiClus:Biclustering method incorporating DPClusSimple graph derived from the common neighbor matrix.We can use DPClus to find clusters in the simple graph.BiClus:Biclustering method incorporating DPClusClustering by DPClusBiClus:Biclustering method incorporating DPClusClustering by DPClusFinally determined biclustersBiClus:Biclustering method incorporating DPClusEvaluation of BiClus-Using Synthetic data-Using real dataEvaluation of BiClusSynthetic dataArtificially embedded biclusters with noiseEvaluation of BiClusSynthetic dataArtificially embedded biclusters with overlapEvaluation of BiClusLet M1, M2 be two sets of biclusters. The gene match score of M1 with respect to M2 is given by the functionA systematic comparison and evaluation of biclustering methodsfor gene expression dataAmela Prelic´, Stefan Bleuler, Philip Zimmermann, Anja Wille, Peter Bu¨ hlmann, Wilhelm Gruissem, Lars Hennig, Lothar Thiele and Eckart ZitzleBIOINFORMATICS, Vol. 22 no. 9 2006, pages 1122–1129Evaluation of BiClusSynthetic dataArtificially embedded biclusters with noiseEvaluation of BiClusSynthetic dataArtificially embedded biclusters with overlapGasch,A.P. et al. (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell, 11, 4241–4257. Gene expression data collected from the above workGene expression data can be represented as bipartite graphsBefore transforming, the data can be normalizedBiclusters in gene expression data represents transcription modulesBy transforming highest 5% values to 10.0010.0020.0030.01Evaluation of BiClusReal gene expression data of yeastP-values represents statistical significance of functional richness of the modulesP-Values calculated using FuncAssociate: The Gene Set Functionator from http://llama.med.harvard.edu/cgi/func/funcassociate
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