Ivan Erill: "Beyond the Regulon: reconstructing the SOS response of the human gut microbiome"

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Metagenomic projects provide a unique window into the genetic composition of microbial communities. To date, metagenomic analyses have focused primarily on studying the composition of microbial populations and inferring shared metabolic pathways. In this work we analyze how high-quality metagenomic data can be leveraged to infer the composition of transcriptional regulatory networks through a combination of in silico and in vitro methods. Using the SOS response as a case example, we analyze human gut microbiome data to determine the composition of the SOS meta-regulon in a natural context. Our analysis provides proof of concept that the existing knowledgebase on regulatory networks and reference genomes can be effectively leveraged to mine meta-genomic data and reconstruct multi-species regulatory networks. This approach allows us to identify de novo the core elements of the human gut SOS meta-regulon, highlighting the relevance of error-prone polymerases in this stress response, and identifies putative novel SOS protein clusters involved in cell wall biogenesis, chromosome partitioning and restriction modification. The methodology implemented in this work can be applied to other metagenomic datasets and transcriptional systems, potentially providing the means to compare regulatory networks across metagenomes. The use of metagenomic data to analyze transcriptional regulatory networks provides a realistic snapshot of these systems in their natural context and allows probing at their extended composition in non-culturable organisms, yielding insights into their interconnection and into the overall structure of transcriptional systems in microbiomes.

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Ivan Erill: "Beyond the Regulon: reconstructing the SOS response of the human gut microbiome"

  1. 1. CAATCCGAGGCATGGCATGGTCGTTAGATTGCTGATTTTGAATGATCGATCGATCGATGGGC010101001001000101010001 TGCCATCGATAGCTTGAGACTCGAAGGGAGATAGATGACGACAGCTATTCGAGCATC01011010100100100010100101011 CGACCTAGCTTGAGATCGAGCGAAGATAGATGACGACAGCTATTCGAGCATC0101101010100100110010100101011001 AGCCTCTGAGATCGAGGGAGATAAGATGACGACAGCTATTCGAGCATC01011010101001000101001010010110011110 ATCCGACTTCGATGCATCGATACAGTTGCTCTCTTCTCAGAGAGAG0101010100101010001000111111101001001010 ATTCGAATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG0101010100101010001000111111001001010101011010 GATGCCATCGATCAGTTGCTCTCTTCTCAGAGAGAG01010101001010100010001111110010010101010000101001 ATGCCATAAGCATGCATGGCCCCTTCGCTCGCTAAG10101010001010101000001011100010100010100101010111 ATGCCATGCATGGCCCCTTCGCTCGCTAAG10101010001010101000001011100010100010101010111101010110 ATGCCAATGGCCCCTTCGCTCGCTAAG10101010001010101000001011100010100010101010111101001011001 TATACTCACGGCTACGTTGCATGCAT010100010100010010010010010001111111100101010010101000100000 TACGCGCCTACGTTGCATGCAT0101000101000100100100100100011111111001010100101010001010101110 GCTACCCGTTGCATGCAT01010001010001001001001001000111111110010101001010100010101011011011 GGCTCGCATCCACATG0101010101010101010101001010101010000101001010010101010100001000011010 BIOLOGICAL SCIENCES Beyond the regulon reconstructing the SOS response of the human gut microbiome Ivan Erill
  2. 2. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 2 The researchsome Comparative genomics Molecular microbiology Computational biology Bioinformatics Transcription factors Stress responses Microbial metagenomics Codon usage indices Machine learning Evolutionary simulations Motif search & discovery High-throughput assays Clinical microbiology Molecular phylogeny 00000
  3. 3. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 3 The researchsome Comparative genomics Molecular microbiology Computational biology Bioinformatics Transcription factors Stress responses Microbial metagenomics Codon usage indices Machine learning Evolutionary simulations Motif search & discovery High-throughput assays Clinical microbiology Molecular phylogeny 00001
  4. 4. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 4 On regulons Regulons Sets of genes/operons (transcriptionally) regulated by a particular transcription factor (TF) Cellular response to specific internal or external stimuli Defined by specific binding of TF to promoter region of regulated genes  Regulon genes can be repressed or activated  TF recognizes a specific binding motif . Guzmán-Vargas and Santillán BMC Systems Biology 2:13 (2008) ATGTCGATCAGCTAGCC... RNA-polymerase Transcription Factor (TF) Open reading frame 00000 Schematic bacterial promoter TFi TG1 TG2 TG3 TG4 S TFx Gx TFyTFi TG1 TG2 TG3 TG4 S TFx Gx TFy Regulon CTGTAAAG CTGCACAG CTGATCAG TF-binding motif
  5. 5. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 5 On metagenomes Metagenome Multi-species, heterogeneous collection of high-throughput reads from a natural habitat The good “Unculturable” species Diversity sampling Natural population sampling The bad Low coverage High-levels of polymorphism Diversity of low complexity regions Contamination with eukaryotic DNA The ugly Lack of proper models for  Pre-filtering  Assembly  Gene calling  Analysis?. 00000 High-throughput sequencing Gest, H. Microbiology Today 35: 220 (2008) P. D. Schloss and J. Handelsman, Genome Biol. 6:229, (2005)
  6. 6. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 6 On metagenomes Metagenome Multi-species, heterogeneous collection of high-throughput reads from natural habitat Properties Lots of data! Noisy! Increasingly cheap and abundant! Post-processing typical format Assembled contigs/scaffolds with predicted, functionally annotated genes Problem  How do we extract useful information from metagenome data? (i.e. how do we evade Brenner’s “low input, high- throughput, no output” epithet?) . . 00001 Assembly, gene calling & functional annotation High-throughput sequencing Friedberg, E. C. Nat Rev Mol Cell Biol 9, 8-9 (2008)
  7. 7. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 7 Analysis of metagenomic data The metagenome & regulatory networks The metagenome Multi-species, heterogeneous collection of high- throughput reads from natural habitat Problem  How do we extract useful information from metagenome data? . Conventional workflow (e.g. metabolic networks)  Knowledge from references is used as terminal  Data is mapped onto existing, static knowledgebase  Inference on mapped data . 00000 Assembly, gene calling & functional annotation High-throughput sequencing Pathway mapping, clustering and enrichment x y z s w a m n Phylogeny Pathway Map to reference Low discovery potential
  8. 8. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 8 Analysis of metagenomic data The metagenome & regulatory networks The metagenome Multi-species, heterogeneous collection of high- throughput reads from natural habitat Problem  How do we extract useful information from metagenome data? . Conventional workflow (e.g. metabolic networks)  Knowledge from references is used as terminal  Data is mapped onto existing, static knowledgebase  Inference on mapped data  Interesting repertoire of new questions . 00001 Assembly, gene calling & functional annotation High-throughput sequencing Pathway mapping, clustering and enrichment x y z s w a m n Phylogeny Pathway Map to reference Low discovery potential Muegge, B. D. et al. Science, 332 (6032), 970-974 (2011)
  9. 9. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 9 Analysis of metagenomic data The metagenome & regulatory networks The metagenome Multi-species, heterogeneous collection of high- throughput reads from natural habitat Problem  How do we extract useful information on regulatory networks from metagenome data? . Alternative workflow  Knowledge from reference used as seed  Directed mining of metagenome data  Inference on mined data . 00010 Assembly, gene calling & functional annotation High-throughput sequencing Regulon analysis, clustering and enrichment x n w s m x n w s m x n w s m z x n w s m z Seed reference High discovery potential
  10. 10. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 10 Analysis of metagenomic data The metagenome & regulatory networks The metagenome Multi-species, heterogeneous collection of high- throughput reads from natural habitat Problem  How do we extract useful information on regulatory networks from metagenome data? . Alternative workflow (e.g. regulatory networks)  Knowledge from reference as seed  Directed mining of metagenome data  Inference on mined data  Promising questions and challenges . 00011 Assembly, gene calling & functional annotation High-throughput sequencing Regulon analysis, clustering and enrichment x n w s m x n w s m x n w s m z x n w s m z Is network composition governed by convergent evolution or by phylogeny? Can we effectively infer regulatory networks from metagenomics data?Seed reference High discovery potential
  11. 11. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 11 Analysis of metagenomic data Metagenomics and regulatory network analysis Advantages Real bacterial populations Unculturable organisms and mobile elements Variability at species and subspecies levels Challenges Noisy search process, huge dataset How to: data integration, enrichment and analysis Goals Proof of concept Analyze the potential of meta-genomic & regulatory sequence data to explore known regulatory systems Study a regulatory network in its natural setting . 00100
  12. 12. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 12 Analysis of metagenomic data Metagenomics and regulatory network analysis Requires A regulatory network to analyze The bacterial SOS response A metagenome on which to analyze it The human gut microbiome . 00101
  13. 13. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 13 The bacterial SOS response Transcriptional response against DNA damage . 00000 “Canonical” stress response Widespread in bacteria  Well-characterized in most bacterial phyla E. coli, B. subtilis, M. tuberculosis, V. parahaemolyticus, S. meliloti, B. bacteriovorus, X. campestris, G. sulfurreducens… Two-component system  RecA (sensor)  LexA (repressor) response to DNA damaging agents Well-characterized regulon  Target genes  ~40 in E. coli / ~30 B. subtilis  Functions  Recombination & DNA repair  Cell-division inhibition  Translesion synthesis . Erill, I. et al. FEMS Microbiol. Rev. 31 (6), 637 (2007)
  14. 14. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 14 The bacterial SOS response Transcriptional response against DNA damage . 00001 Erill, I. et al. FEMS Microbiol. Rev. 31 (6), 637 (2007) High clinical relevance Widespread in bacteria Two-component system  RecA (sensor)  LexA (repressor)  Response to  Broad range of antibiotics  Bacteriophage infection Extended regulon  Functions  Integron recombination  Bacteriophage induction  Toxin production  Dissemination of pathogenicity islands  Antibiotic-induced mutagenesis  Regulation of persistence . Guerin, E. et al., Science, 324 (5930), 1034 (2009)
  15. 15. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 15 The bacterial SOS response Transcriptional response against DNA damage . 00010 Erill, I. et al. FEMS Microbiol. Rev. 31 (6), 637 (2007) Interesting evolution Widespread in bacteria  Absent in some clades (Bacteroidetes/Chlorobi group)  Supplanted by competence regulon (S. pneumoniae) Extreme diversity of LexA-binding motifs  Clade-specific & monophyletic .Geobacteres Gram-positive Myxobacteriales Xanthomonadales Alpha Proteobacteria Beta/Gamma Proteobacteria Cyanobacteria Fibrobacteres
  16. 16. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 16 The human gut microbiome Metagenomics project Target metagenome Human microbiome Multiple datasets (locations: gut, armpit, etc.) Multiple initiatives (HMP & MetaHit) Available data & features: High-throughput sequencing + 16S RNA data ORF predictions & functional annotation . 00000 Qin, J. et al. Nature. 464, 59 (2010) Nelson, K.E, et al. Science. 328, 994 (2010) Segata, N. et al. Gen. Biol. 13, R42 (2012) MetaHit human gut microbiome Gammaproteobacteria Actinobacteria Other Bacteroides Firmicutes Gammaproteobacteria Actinobacteria Other Bacteroides Firmicutes 86 healthy subjects Large contigs, high-quality gene calling 7.1 Gbp total sequence – 4.5 M contigs (N50: 2.2 kbp) 9.3 M predicted ORF (3.7M complete), λ=660 bp 1 M COG annotations
  17. 17. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 17 Analysis workflow Workflow Data compilation  LexA-binding motif compilation  Gram-positive bacteria  CollecTF database  118 sites, 8 species  Reference genome panel  121 genomes from MetaHit and the Human Microbiome Jumpstart Reference Strains Consortium  Reference SOS response  18 described SOS responses  Acidobacteria  Alphaproteobacteria  Gammaproteobacteria  Deltaproteobacteria  Bacilli  Clostridia  Actinobacteria  Fibrobacteria  272 regulated genes . 00001 collectf.umbc.edu Kiliç, S. et al. Nuc. Acids Res. 42, D156-D160 (2013) Nelson, K.E, et al. Science. 328, 994 (2010) Cornish, J. P. et al. Evol Bioinform. 8: 449–461 (2012) Erill, I. et al. FEMS Microbiol. Rev. 31 (6), 637 (2007) Gram-positive reference motif
  18. 18. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 18 Analysis workflow Workflow Data compilation  LexA-binding motif compilation  Reference genome panel  Reference SOS response Metagenome mining  PSSM-based search  Reference motif, 2 strands  Operon prediction  Site-operon association  Distance-based  Taxonomic annotation  Through reference panel mapping  for phylogenetic filtering of results  Functional clustering  Through COG mapping  for functional analysis . 00010 GAACTACTGTTC GAACTACTGTTC GTACAACTGTTCGATCTATTGTTC GAACTCATGTTT GTTCAAAAGATC GAACTCCTGTCC PSSM-based search LexA-binding motif score histogram 0 0.05 0.1 0.15 0.2 0.25 0.3 1 5 9 13 17 21 25 29 Score Frequency
  19. 19. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 19 Analysis workflow Workflow Data compilation  LexA-binding motif compilation  Reference genome panel  Reference SOS response Metagenome mining  PSSM-based search  Reference motif, 2 strands  Operon prediction  Site-operon association  Distance-based  Taxonomic annotation  Through reference panel mapping  for phylogenetic filtering of results  Functional clustering  Through COG mapping  for functional analysis . 00011 GAACTACTGTTC GTACAACTGTTCGATCTATTGTTC GAACTCATGTTT GTTCAAAAGATC GAACTACTGTTC GAACTACTGTTC GAACTCATGTTT GAACTACTGTTCGAACTCCTGTCC Operon prediction
  20. 20. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 20 Analysis workflow Workflow Data compilation  LexA-binding motif compilation  Reference genome panel  Reference SOS response Metagenome mining  PSSM-based search  Reference motif, 2 strands  Operon prediction  Site-operon association  Distance-based  Taxonomic annotation  Through reference panel mapping  for phylogenetic filtering of results  Functional clustering  Through COG mapping  for functional analysis . 00100 GAACTACTGTTC GTACAACTGTTCGATCTATTGTTC GAACTCATGTTT GTTCAAAAGATC GAACTACTGTTC GAACTACTGTTC GAACTCATGTTT GAACTACTGTTCGAACTCCTGTCC Site-operon association
  21. 21. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 21 Analysis workflow Workflow Data compilation  LexA-binding motif compilation  Reference genome panel  Reference SOS response Metagenome mining  PSSM-based search  Reference motif, 2 strands  Operon prediction  Site-operon association  Distance-based  Taxonomic annotation  Through reference panel mapping  for phylogenetic filtering of results  Functional clustering  Through COG mapping  for functional analysis . 00101 GAACTCATGTTT GAACTACTGTTC GAACTCATGTTT GAACTACTGTTC Referencegenomelibrary Taxonomic annotation
  22. 22. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 22 Analysis workflow Workflow Data compilation  LexA-binding motif compilation  Reference genome panel  Reference SOS response Metagenome mining  PSSM-based search  Reference motif, 2 strands  Operon prediction  Site-operon association  Distance-based  Taxonomic annotation  Through reference panel mapping  for phylogenetic filtering of results  Functional clustering  Through COG mapping  for functional analysis . 00110 GAACTCATGTTT GAACTACTGTTC GAACTCATGTTT GAACTACTGTTC COGreferencelibrary Functional clustering COG123 COG345 COG567 COG789
  23. 23. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 23 The human gut microbiome Workflow Data compilation  Motif compilation  Reference genome panel  Reference SOS response Metagenome mining  PSSM-search  Operon prediction  Site-operon association  Phylogeny annotation  Functional clustering Analysis  Positional enrichment analysis  Data filtering  COG enrichment analysis  Gene-based functional analysis . 00111 GAACTCATGTTT GAACTACTGTTC GAACTCATGTTT GAACTACTGTTC Data for analysis
  24. 24. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 24 The human gut SOS response Initial search results Over 500,000 putative LexA-binding sites identified Positional enrichment analysis Promoter regions Site scores are significantly enriched in promoter regions High-scoring sites co-localize in promoter regions . 00000 Permutation analysis of site scores
  25. 25. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 25 The human gut SOS response Data filtering Two-pronged approach Distance-based Only sites located between -350 and +50 of predicted TLS Taxomomy-based Only sites associated with predicted protein-coding genes mapping to Gram- positive reference genomes Filtering results Dramatic reduction in the number of putative sites Over 43,000 sites meeting both criteria Taxonomy-based filtering provides enhanced resolution Law of large numbers: high-scoring sites can be identified in the promoter region of many Bacteroides genes . 00001
  26. 26. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 26 The human gut SOS response COG category analysis Inferred regulon maps experimentally characterized SOS responses Gradual enrichment of canonical SOS categories with score cutoff: repair/replication (L), signal transduction (T) and transcription (K) genes Cell cycle control (D) category not enriched  COGs are getting old! . 00010 0 0.1 0.2 0.3 0.4 0.5 J K L D V T M C G F R S COG category Relativefrequency MetaHit COG reference COGs with SOS site COGs with site >12 bits COGs with site >14 bits COGs with site >16 bits SOS ensemble reference
  27. 27. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 27 The human gut SOS response COG analysis Question How to identify “SOS COGs”? Score enrichment measure Goal  Identify bona-fide members of the regulon  Capture maximum number of known SOS genes Analysis of canonical SOS genes in 308 Gram-positive genomes LexA-binding site scores normally distributed (lexA: µ=16.2 bits, σ=2.3; recA: µ=16.3 bits, σ=2.5) Cumulative distribution approximately linear in central scoring range 12-20 bits Prototypical SOS COG High linear coefficient of determination (R2 >0.85, empirically set) At least:  one site above average score (16 bits)  10 sites in 12-20 bit range . 00011 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 9 11 13 15 17 19 21 23 Site score (bits) Cumulativedistribution lexA (Firmicutes) recA (Firmicutes) Quantile-quantile plot 9 11 13 15 17 19 21 23 9 11 13 15 17 19 21 23 Theoretical Empirical lexA (Firmicutes) recA (Firmicutes) Canonical SOS genes
  28. 28. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 28 The human gut SOS response COG analysis Results Detection of canonical SOS regulon  lexA, recA, excision repair, recombination SOS meta-regulon composition  Four major functions  Transcriptional repression (lexA)  Translesion synthesis (dinB, uvrX, imuB, umuD)  Sensing of DNA-damage & stabilization (recA)  Excision repair (uvrA, uvrB, uvrD, pcrA)  Translesion synthesis as primary SOS component Interesting new putative SOS regulon COGs  COG0732  HsdS – restriction endonuclease  COG2001  MraZ – cell wall biogenesis  COG4974  CodV – chromosome partitioning . 00100 0.86recNCOG0497 0.87ruvACOG0632 0.87codVCOG4974 0.88parECOG0187 0.91uvrACOG0178 0.91hsdSCOG0732 0.91MraZCOG2001 0.92uvrD, pcrACOG0210 0.96lexA,umuDCOG1974 0.97uvrBCOG0556 0.98recA,imuACOG0468 0.98dinB, imuB, uvrXCOG0389 r2 Associated genesCOG 0.86recNCOG0497 0.87ruvACOG0632 0.87codVCOG4974 0.88parECOG0187 0.91uvrACOG0178 0.91hsdSCOG0732 0.91MraZCOG2001 0.92uvrD, pcrACOG0210 0.96lexA,umuDCOG1974 0.97uvrBCOG0556 0.98recA,imuACOG0468 0.98dinB, imuB, uvrXCOG0389 r2 Associated genesCOG COG1974-lexA,umuD COG0389-dinB,uvrX,imuB COG0468-recA,imuA COG5056-uvrB COG0210-uvrD,pcrA COG0178-uvrA COG2001-mraZ COG0497-recN COG0187-parE COG0732-hsdS COG4974-codV COG0632-ruvA
  29. 29. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 29 The human gut SOS response Targeted gene analysis Assessment of non-canonical functions in genes with high-scoring sites Toxin-antitoxin / virulence systems (higB / rhuM)  Linked to persistence phenotypes Phage integrases (intP )  In line with integron integrase regulation and phage control by SOS response Validation of enriched COGs Cell wall biogenesis (mraZ)  Possible role in cell division control  Evidence of convergent regulation  YneA (B. subtilis), DivS (C. glutamicum) Experimental validation EMSA with purified B. subtilis protein . 00101 recA - + - + - + - + mraZ intPrhuM
  30. 30. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 30 Beyond the regulon Proof of concept: the human gut SOS meta-regulon Methodology Provides the means to expand our knowledge on described regulatory systems COG enrichment as a method for functional assessment of the meta-regulon Analysis allows visualizing a regulatory response in a wild-population Inference of novel knowledge on regulon function and components Consistent with known SOS responses; primary focus on mutagenesis Contains several elements linking it to other cellular processes of clinical relevance Future directions Analyze and compare regulatory networks in metagenomes Is network evolution dictated by phylogeny or habitat? How do changes in habitat affect meta-regulons? How does the overlap between meta-regulons vary among populations? 00000
  31. 31. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 31 Beyond the regulon Automating meta-regulon inference A transcription factor Exists in a subset of species Binding sites for the TF are enriched in a subset of functional clusters How can we automatically determine the set of species & COGs? 00001 0 0.05 0.1 0.15 0.2 0.25 0.3 5 10 15 20 25 30 Averagescorecountingeneupstreamregions Score (bits) LexA-binding site score distribution Firmicutes (SOS COGs) Firmicutes (random COGs) All taxa, all COGs 0 2 4 6 8 10 12 14 16 18 -60 -40 -20 0 20 40 Averagescorecountingeneupstreamregions Score (bits) LexA-binding site score distribution Firmicutes (SOS COGs) Firmicutes (random COGs) All taxa, all COGs
  32. 32. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 32 Beyond the regulon EM algorithm for isolation of enriched COGs/taxa Define likelihood function Statistical test for mixture model in observed distribution Assign weights to COGs (Ci) and taxa (Tj) For given COG weights, compute likelihood of each taxon, update weight with likelihood For given taxon weights, compute likelihood of each COG, update weight with likelihood 00010 C6 0.1 C5 0.8 C4 0.7 C3 0.3 C2 0.2 C1 0.3 T6T5T4T3T2T1 0.50.40.20.90.60.5 C6 0.1 C5 0.8 C4 0.7 C3 0.3 C2 0.2 C1 0.3 T6T5T4T3T2T1 0.50.40.20.90.60.5 C6 0.1 C5 0.8 C4 0.7 C3 0.3 C2 0.2 C1 0.3 T6T5T4T3T2T1 0.50.40.20.80.60.5 C6 0.1 C5 0.8 C4 0.7 C3 0.3 C2 0.2 C1 0.3 T6T5T4T3T2T1 0.50.40.20.80.60.5
  33. 33. ACACGGATCGATCGAGGCATGGCATGGTCGTTGATTGCTGATTTTGAATGATCGATCGATCGATGGGC01010100110010000 1 ACCATCGATTCGATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG010101010010101000100011111101010111101 0 CGGATGCATGCATGCATGGCCCCTTCGCTCGCTAAG1010101000101010100000101110001010001010110100111 0 GGCTGATCCACATG01010101010101010101010010101010100001010010100101010101000010001001101 1 ACAACGCCTERILLGTATAGCAGTGTGTCATTGCTTTAGCTAGTACACAGACACGCBIOLOGICALATUMBC0101010101110 0 01010100010LAB010010101001000011110001010001010001001011100SCIENCESCCAGGACATGAGCTAAAA 33 Conclusions & Acknowledgements Acknowledgements Erill Lab Joe Cornish Neus Sanchez-Alberola Pat O’Neill Jameel Gheba Ron O’Keefe Talmo Pereira David Nicholson Wolf Lab Richard Wolf Lanyn Perez Barbé Lab Susana Campoy Jordi Barbé Funding UMBC Office of Research – Special Research Assistantship/Initiative Support NSF grant MCB-1158056 .
  34. 34. CAATCCGAGGCATGGCATGGTCGTTAGATTGCTGATTTTGAATGATCGATCGATCGATGGGC010101001001000101010001 TGCCATCGATAGCTTGAGACTCGAAGGGAGATAGATGACGACAGCTATTCGAGCATC01011010100100100010100101011 CGACCTAGCTTGAGATCGAGCGAAGATAGATGACGACAGCTATTCGAGCATC0101101010100100110010100101011001 AGCCTCTGAGATCGAGGGAGATAAGATGACGACAGCTATTCGAGCATC01011010101001000101001010010110011110 ATCCGACTTCGATGCATCGATACAGTTGCTCTCTTCTCAGAGAGAG0101010100101010001000111111101001001010 ATTCGAATGCATCGATCAGTTGCTCTCTTCTCAGAGAGAG0101010100101010001000111111001001010101011010 GATGCCATCGATCAGTTGCTCTCTTCTCAGAGAGAG01010101001010100010001111110010010101010000101001 ATGCCATAAGCATGCATGGCCCCTTCGCTCGCTAAG10101010001010101000001011100010100010100101010111 ATGCCATGCATGGCCCCTTCGCTCGCTAAG10101010001010101000001011100010100010101010111101010110 ATGCCAATGGCCCCTTCGCTCGCTAAG10101010001010101000001011100010100010101010111101001011001 TATACTCACGGCTACGTTGCATGCAT010100010100010010010010010001111111100101010010101000100000 TACGCGCCTACGTTGCATGCAT0101000101000100100100100100011111111001010100101010001010101110 GCTACCCGTTGCATGCAT01010001010001001001001001000111111110010101001010100010101011011011 GGCTCGCATCCACATG0101010101010101010101001010101010000101001010010101010100001000011010 BIOLOGICAL SCIENCES Beyond the regulon reconstructing the SOS response of the human gut microbiome Ivan Erill

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