RECONSTRUCTING GENE REGULATORY NETWORKS FROM FUNGAL TRANSCRIPTOMIC DATA USING BAYESIAN NETWORK
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1 RECONSTRUCTING GENE REGULATORY NETWORKS FROM FUNGAL TRANSCRIPTOMIC DATA USING BAYESIAN NETWORK Li Guo Fungal Comparative Genomics Laboratory Department of Biochemistry and Molecular biology University of Massachusetts Amherst October 19, 2012
2 Outline! Introduction to gene expression and regulation! Fungal transcriptomics! Data collection and analysis! Building transcription regulation networks using Bayesian model learning
3 Why life forms are so diverse? What determines what they are?
4
5 Code of Life! Phenotype: biological characteristics or traits of an organism! Genotype: genetic background or makeup of an organism! Gene: a unity of heredity in a living organism, usually is a DNA fragment that codes for a type of protein or RNA! Genome: a collection of entire DNA in an organism! Transcription: DNA to RNA! Translation: RNA to protein
6
7 Whole genome shotgun sequencing
8 Gene expression regulation through transcription factors (TF)
9 What is a gene regulatory network?! Cells respond to internal and external cues by rapidly regulating expression of thousands of genes! Genes work together via a fine regulatory network to control life process! Two major kinds of regulatory molecules:! Transcription Factors (TF)! Signaling Proteins(SP):! SP and TF work in a combinatory way to determine the mrna levels in a living cell
10 An example of regulatory networks
11 Why study regulatory networks?! Understanding the mechanisms of cellular function! Applications in biomedical practices and agricultural production! Facilitating comparative analysis of regulatory networks in different organisms
12 Where to start?! How about studying one gene at a time?! Genome sequencing and annotation! Global transcriptional profiling transcriptomics! DNA microarray! RNA sequencing! Expression of thousands of genes (including SPs and TFs) can be simultaneously measured under various conditions and perturbations
13 DNA Microarray RNA sequencing
14 Pathogenic Fusarium Fungus Host Disease Mycotoxins Genome sequenced? Fusarium graminearum PH-1 Wheat, Barley, maize Head blight, ear rot and stalk rot deoxynivalenol, zearalenone Yes 36 Genome size (Mbps) Fusarium verticillioides 7600 Fusarium oxysporum 4287 Maize, sorghum Ear rot and stalk rot Fumonisins Yes 42 Tomato Vascular wilt Unknown Yes 60
15 Research focuses:! Functional characterization of Fusarium genes essential for fungal development and host infection! Comparative genomics! Transcriptomics! Fusarium comparative transcriptomics
16 Experimental procedure Minimal N Minimal C Mutations in key signaling pathways mrna cdna Hybridization image Data processing and normalization Statistical analysis
17 Data after normalization
18 Every gene has its expression levels measured at each condition in log2 scale
19 We have the expression data, now what?! Statistical analysis can reveal the genes differentially expressed in test samples versus reference sample! These genes are selected for downstream analysis such as gene ontology (GO) term enrichment and mutagenesis study! Used to construct transcription regulation networks
20 Fusarium gene expression changed responding to stress and mutation Guo et al. unpublished
21 QIAGEN
22 !"#"$%&' ()* +,-./' -0#12)#34'5"617892)#'!"#"$%&' ()* +,-./' -0#12)#34'5"617892)#' FGSG_03969! 8.74! conserved hypothetical protein! FGSG_08624! 5.55! conserved hypothetical protein! FGSG_07894! 7.67! "#$%&#'()!(*'+%&$*(!&*$(),+! FGSG_00793! 5.41! conserved hypothetical protein! FGSG_03737! 7.47! related to major facilitator MirA! FGSG_02880! 5.4! nitrate reductase! probable manganese superoxide FGSG_03738! 7.42! conserved hypothetical protein FGSG_02051! 5.28! dismutase precursor (sod-2)! FGSG_00773! 7.02! copper transport protein! FGSG_04089! 5.21! conserved hypothetical protein! FGSG_02050! 6.9! conserved hypothetical protein! FGSG_03692! 5.19! related to Fre1p and Fre2p! related to integral membrane FGSG_02324! 6.86! "-./0! FGSG_07792! 5.18! protein! FGSG_02327! 6.85! '1*2! FGSG_10608! 5.15! conserved hypothetical protein! FGSG_02329! 6.72! conserved hypothetical protein! FGSG_02589! 4.94! probable ZRT2 - Zinc transporter II putative multidrug transporter FGSG_03593! 6.66! 6-hydroxy-D-nicotine oxidase! FGSG_07802! 4.87! Mfs1.1! FGSG_03586! 6.53! conserved hypothetical protein FGSG_07375! 4.8! alkaline phosphatase! FGSG_04780! 6.52! ferric reductase Fre2p! FGSG_11205! 4.79! "*$3'34)!.+$5"*$(/!&)*61%$*! FGSG_03735! 6.45! ABC1 transport protein! FGSG_08375! 4.72! dicarboxylate carrier protein! FGSG_02325! 6.43! conserved hypothetical protein! FGSG_03592! 4.66! conserved hypothetical protein! related to Staphylococcus multidrug FGSG_02326! 6.32! '1*7! FGSG_07567! 4.65! resistance protein! FGSG_07804! 6.18! cytochrome p450! FGSG_07596! 4.58! 8,91+6:$+'4!";<=>! FGSG_06564! 6.15! conserved hypothetical protein! FGSG_13223! 4.54! related to elongation factor 1- gamma! FGSG_04512! 6.1! PMR1 - Ca++-transporting P-type ATPase located in Golgi! FGSG_00260! 4.47! conserved hypothetical protein! FGSG_02328! 6.09! GIP1! FGSG_11379! 4.47! conserved hypothetical protein! FGSG_03736! 6.05! transferase family protein! FGSG_11310! 4.39! conserved hypothetical protein! FGSG_08697! 5.92! conserved hypothetical protein! FGSG_10506! 4.32! monocarboxylate transporter 2! FGSG_07798! 5.9! PKS10! FGSG_07678! 4.29! acid phosphatase Pho610! FGSG_07801! 5.7! conserved hypothetical protein! FGSG_07805! 4.21! related to S-adenosylmethionine FGSG_03046! 5.69! conserved hypothetical protein! FGSG_00742! 4.19! related to S-adenosylmethionine! FGSG_10655! 5.69! ferric reductase FRE2 precursor! FGSG_09727! 4.19! conserved hypothetical protein FGSG_08151! 5.56! heme peroxidase! FGSG_03372! 4.18! conserved hypothetical protein related to myo-inositol transport FGSG_13222! 5.56! conserved hypothetical protein! FGSG_03957! 4.18! protein ITR1! FGSG_12214! 4.07! conserved hypothetical protein! Frequency % Top up-regulated genes in F. graminearum mac-1 mutant Potassium binding Cellular transport Cell rescue, Interaction defense and with the virulence environment GO Description Genome FgMac1 up P<0.05 Guo et al. unpublished
23 !"#"$%&' ()*+,-./' -0#12)#34'5"617892)#'!"#"$%&' ()*+,-./' -0#12)#34''5"617892)#' related to 3-oxoacyl-[acylcarrier-protein] FGSG_02034! -7.1! alcohol dehydrogenase I - ADH1! FGSG_03838! -4.27! reductase! FGSG_03162! -7.01! formate transport protein! FGSG_02949! -4.25! conserved hypothetical protein FGSG_04468! -6.49! neutral amino acid permease! FGSG_10647! -4.21! methylase involved in ubiquinone menaquinone biosynthesis! FGSG_07522! -6! conserved hypothetical protein! FGSG_01234! -4.15! MAC1! related to neutral amino acid FGSG_13979! -6! conserved hypothetical protein FGSG_08055! -4.15! permease! FGSG_08415! -5.52! extracellular invertase! FGSG_03990! -4.14! conserved hypothetical protein FGSG_07411! -5.47! sulphite efflux pump protein! FGSG_11073! -4.09! related to aminopeptidase! Related to integral membrane FGSG_01947! -5.37! nitrate reductase! FGSG_11385! -4.09! protein PTH11! FGSG_12890! -5.17!?)4'()5!($!3*(/!&*$(),+! FGSG_10598! -3.93! conserved hypothetical protein related to D-arabinitol 2- FGSG_11413! -5.14! alpha methylacyl- racemase! FGSG_03430! -3.87! dehydrogenase! FGSG_03026! -5.07! FGSG_07672! -3.8! conserved hypothetical protein FGSG_03881! -4.96! related to TRI15 - putative transcription factor! FGSG_03882! -3.79! probable ABC1 transport protein! FGSG_06537! -4.85! neutral amino acid permease! FGSG_11741! -3.77! conserved hypothetical protein FGSG_08402! -4.82! nitrite reductase! FGSG_02901! -3.72! related to sulfatase! FGSG_11992! -4.82! related to UDPglucose 4-epimerase!FGSG_07837! -3.7! carbon-nitrogen hydrolase! related to trna 2`phosphotransferase! FGSG_08357! -4.65! conserved hypothetical protein FGSG_06974! -3.69! FGSG_00817! -4.63! conserved hypothetical protein FGSG_01980! -3.64! conserved hypothetical protein FGSG_02821! -4.56! conserved hypothetical protein FGSG_09354! -3.62! probable neutral amino acid permease! FGSG_11412! -4.54! conserved hypothetical protein FGSG_09706! -3.6! related to positive effector protein GCN20! FGSG_04458! -4.52! probable flavohemoglobin! FGSG_03422! -3.57! conserved hypothetical protein FGSG_00285! -4.48! conserved hypothetical protein FGSG_07705! -3.53! conserved hypothetical protein related to neutral amino acid FGSG_02950! -4.45! permease! FGSG_04892! -3.52! retinol dehydrogenase 8! FGSG_05683! -4.45! related to monooxygenase! FGSG_04251! -3.51! short chain oxidoreductase! FGSG_11500! -4.34! related to pyridoxamine 5`phosphate oxidase! FGSG_07832! -3.5! Related to CCC1 protein (involved in calcium homeostasis)! FGSG_08721! -4.32! probable superoxide dismutase [Cu-Zn]! FGSG_13962! -3.5! related to 3-hydroxybutyryl- CoA dehydrogenase! FGSG_03368! -4.27! conserved hypothetical protein Frequency % Top down-regulated genes in F. graminearum mac-1 mutant Go Description Genome Guo et al. unpublished FgMac1down
24 F. verticillioides mac1xcpka 4 TFs CELLULAR TRANSPORT Lipid metabolism and transport rrna synthesis 6 TFs
25 How to use transcriptomic data to elucidate the regulatory networks?! Regulatory interactions between genes give statistical dependencies between random variables representing their expression levels! Bayesian networks model! Why Bayesian networks?! Unknown network structures! Microarray typically noisy, needing probabilistic model! Hidden variables
26 Bayesian network! Representation: graph! Reasoning: probability theory Earthquake Burglary Alarm NeighborCalls
27 Score!based Learning Define scoring function that evaluates how well a structure matches the data E, B, A <Y,N,N> <Y,Y,Y> <N,N,Y> <N,Y,Y>.. <N,Y,Y> E A B E A B B E A Search for a structure that maximizes the score
28 Bayesian Score! The score evaluates the posterior probability of the graph given the data: Likelihood Prior P(D G)P(G) P(G D) = (1) P(D) Posterior Probability of data (2) (3)
29 Using Prior Biological Knowledge Pe er et al. 2006
30 Problem Definition! Input! 10 samples of Gene Expression level under different condition Genes! List of candidate Regulator as TF and SP! Output! List of most activated Regulator (k = 30)! Bayesian Network for Genes
31 Learning Bayesian Networks! Parameter Estimation! Structure Learning
32 Structure Learning! Using the typical heuristic greedy hill-climbing search, we pick a candidate regulator which can give us the high score as the regulator amount all the candidates, add one to the output regulator each iteration. Candidate Regulator 294 Output Regulator Empty Candidate Regulator 293 Output Regulator Gat1
33 X Y Z A B C D Predicted networks will be validated based on existed biological knowledge.
34 Summary! Regulation of gene expression determines the cell state and response to the environment! Transcriptomics studies the gene expression of given times and cell types in a global scale! Gene expression (transcript) levels are measured by microarray and RNA sequencing technology! Connection between genes and their expression regulators such as transcription factors and signaling molecules can be revealed by probabilistic and graphic model such as Bayesian networks
35 ! UMass Amherst! Dr. Li-Jun Ma! Dr. Lixin Gao! Guoyi Zhao! Andy Berg! Jiangtao Yin Acknowledgment! Collaborators:! Corby Kistler (University of Minnesota)! Jin-Rong Xu (Purdue University)
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