Transcription Factor Binding Site Mapping Using ChIP-Seq

Suma Jaini; Anna Lyubetskaya; Antonio Gomes; Matthew Peterson; Sang Tae Park; Sahadevan Raman; Gary Schoolnik; James Galagan

doi:10.1128/microbiolspec.MGM2-0035-2013

Chapter 8 : Transcription Factor Binding Site Mapping Using ChIP-Seq

Authors: Suma Jaini¹, Anna Lyubetskaya³, Antonio Gomes⁴, Matthew Peterson⁵, Sang Tae Park⁶, Sahadevan Raman⁷, Gary Schoolnik⁸, James Galagan⁹

VIEW AFFILIATIONS HIDE AFFILIATIONS

Affiliations: 1: Department of Biomedical Engineering, Boston University, Boston, MA 02215; 2: National Emerging Infectious Diseases Laboratory, Boston University, Boston, MA 02118; 3: Bioinformatics Program, Boston University, Boston, MA 02215; 4: Bioinformatics Program, Boston University, Boston, MA 02215; 5: Department of Biomedical Engineering, Boston University, Boston, MA 02215; 6: Macrogen Clinical Laboratory, Macrogen Corp, Rockville, MD 20850; 7: National Emerging Infectious Diseases Laboratory, Boston University, Boston, MA 02118; 8: Department of Medicine and Department of Microbiology and Immunology, Stanford Medical School, Stanford, CA 94305; 9: Department of Biomedical Engineering, Boston University, Boston, MA 02215; 10: Bioinformatics Program, Boston University, Boston, MA 02215; 11: National Emerging Infectious Diseases Laboratory, Boston University, Boston, MA 02118; 12: Broad Institute of MIT and Harvard, Cambridge, MA 02142

Content Type: Monograph

Publication Year: 2014

Category: Microbial Genetics and Molecular Biology

Book DOI: 10.1128/9781555818845

Chapter DOI: 10.1128/microbiolspec.MGM2-0035-2013

MyBook is a cheap paperback edition of the original book and will be sold at uniform, low price.

Preview this chapter:

Transcription Factor Binding Site Mapping Using ChIP-Seq, Page 1 of 2

< Previous page | Next page > /docserver/preview/fulltext/10.1128/9781555818845/9781555818838_Chap08-1.gif /docserver/preview/fulltext/10.1128/9781555818845/9781555818838_Chap08-2.gif

Abstract:

This chapter describes a chromatin immunoprecipitation followed by sequencing (ChIP-Seq) method tailored for the study of Mycobacterium tuberculosis transcription factors (TFs) but amenable for the study of other prokaryotes. Noteworthy features of this method include the following: (i) it is conducted using a standard and readily reproducible growth condition that is the same for each TF studied; (ii) the binding behavior of each TF is studied using a tagged variant of the TF whose production is driven by an inducible promoter; (iii) each TF is studied using the same concentration of an exogenously added chemical inducer where the strength of TF gene induction is varied to systematically measure the effect of TF concentration on binding site strength and location; (iv) the resulting binding site data is spatially well resolved and highly reproducible, and binding strength is correlated with the degree of binding site motif conservation; and (v), because this method does not require knowledge of the physiological conditions that normally cause TF gene expression or of antibodies specific to each TF, it is applicable for the high-throughput study of multiple TFs; thus far it has generated binding site data for over 119 annotated M. tuberculosis TFs. Despite the use of a standard growth condition for all TFs and an inducible promoter system, the binding site data was found to agree well with data from a subset of TFs expressed under their own promoter by physiologically relevant conditions and was immunoprecipitated using specific antibodies to the native TF. The development and use of this method has been combined with the development of a data analysis pipeline; features of this pipeline are described below. Taken together, these binding data provide additional evidence that the long-accepted spatial relationship between TF binding site, promoter motif, and the corresponding regulated gene may be too simple a paradigm, failing to adequately capture the variety of TF binding sites found in prokaryotes.

Citation: Jaini S, Lyubetskaya A, Gomes A, Peterson M, Park S, Raman S, Schoolnik G, Galagan J. 2014. Transcription Factor Binding Site Mapping Using ChIP-Seq, p 161-181. In Hatfull G, Jacobs W (ed), Molecular Genetics of Mycobacteria, Second Edition. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.MGM2-0035-2013

Highlighted Text: Show | Hide

Full text loading...

Figures

Transcription Factor Binding Site Mapping Using ChIP-Seq

[com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/author/10.1128/9781555818845.chap8-1,webId=/content/author/10.1128/9781555818845.chap8-1,properties={foaf_givenname=Suma, foaf_name=Suma Jaini, foaf_surname=Jaini, pub_isAffiliatedWith=[com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/affiliation/10.1128/9781555818845.chap8-aff1]]}], com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/author/10.1128/9781555818845.chap8-2,webId=/content/author/10.1128/9781555818845.chap8-2,properties={foaf_givenname=Anna, foaf_name=Anna Lyubetskaya, foaf_surname=Lyubetskaya, pub_isAffiliatedWith=[com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/affiliation/10.1128/9781555818845.chap8-aff3]]}], com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/author/10.1128/9781555818845.chap8-3,webId=/content/author/10.1128/9781555818845.chap8-3,properties={foaf_givenname=Antonio, foaf_name=Antonio Gomes, foaf_surname=Gomes, pub_isAffiliatedWith=[com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/affiliation/10.1128/9781555818845.chap8-aff4]]}], com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/author/10.1128/9781555818845.chap8-4,webId=/content/author/10.1128/9781555818845.chap8-4,properties={foaf_givenname=Matthew, foaf_name=Matthew Peterson, foaf_surname=Peterson, pub_isAffiliatedWith=[com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/affiliation/10.1128/9781555818845.chap8-aff5]]}], com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/author/10.1128/9781555818845.chap8-5,webId=/content/author/10.1128/9781555818845.chap8-5,properties={foaf_givenname=Sang Tae, foaf_name=Sang Tae Park, foaf_surname=Park, pub_isAffiliatedWith=[com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/affiliation/10.1128/9781555818845.chap8-aff6]]}], com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/author/10.1128/9781555818845.chap8-6,webId=/content/author/10.1128/9781555818845.chap8-6,properties={foaf_givenname=Sahadevan, foaf_name=Sahadevan Raman, foaf_surname=Raman, pub_isAffiliatedWith=[com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/affiliation/10.1128/9781555818845.chap8-aff7]]}], com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/author/10.1128/9781555818845.chap8-7,webId=/content/author/10.1128/9781555818845.chap8-7,properties={foaf_givenname=Gary, foaf_name=Gary Schoolnik, foaf_surname=Schoolnik, pub_isAffiliatedWith=[com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/affiliation/10.1128/9781555818845.chap8-aff8]]}], com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/author/10.1128/9781555818845.chap8-8,webId=/content/author/10.1128/9781555818845.chap8-8,properties={foaf_givenname=James, foaf_name=James Galagan, foaf_surname=Galagan, pub_isAffiliatedWith=[com.pub2web.rdf.cci.facet.ContentItem[id=http://asm.metastore.ingenta.com/content/affiliation/10.1128/9781555818845.chap8-aff9]]}]]

/content/10.1128/9781555818845.chap8.fig8-1

Figure 1

Diagram of the ChIP-Seq method. ChIP-Seq is performed on log-phase M. tuberculosis cells. The cross-linked cells are first lysed using BeadBeater. The cells are further lysed, and DNA is sheared using Covaris. Anti-FLAG antibody is used to immunoprecipitate the protein of interest, and the protein-DNA complexes are further captured using protein-G agarose beads. The cross-links are reversed using proteinase K, and the DNA is purified using a PCR purification kit. The standard Illumina protocol is used to prepare the library, which is then sent for next generation sequencing.

10.1128/9781555818845/fig8-1_thmb.gif

10.1128/9781555818845/fig8-1.gif

Figure 1

/content/10.1128/9781555818845.chap8.fig8-2

Figure 2

Overview of analysis pipeline. First, sequencing reads are aligned to the genome of the organism, and a profile of coverage is calculated by counting the number of reads that overlap a given position along the chromosome. This profile is used to fit a log normal background model of coverage. This model is used to identify regions of the genome that are statistically enriched relative to background. A cross-correlation filter is applied to identify regions consistent with the bimodal profile associated with ChIP-Seq binding, and comparison to control experiments is used to identify regions specific to the transcription factor of interest. Finally, a blind deconvolution approach combined with motif identification is used to identify binding sites at high resolution.

10.1128/9781555818845/fig8-2_thmb.gif

10.1128/9781555818845/fig8-2.gif

Figure 2

/content/10.1128/9781555818845.chap8.fig8-3

Figure 3

Blind deconvolution analysis of ChIP-Seq data. This schematic representation illustrates the steps of BRACIL ( 12 ) in the analysis of ChIP-Seq data. BRACIL uses a blind deconvolution algorithm that takes advantage of ChIP-Seq coverage and genome sequence to refine the resolution of ChIP-Seq binding regions into single-nucleotide resolution. The top panel illustrates the steps of the blind-deconvolution algorithm. The algorithm starts with a guess about the shape of the impulse response as well as the location of binding sites. Both the shape of the impulse response and the binding site locations are updated iteratively until convergence. The predicted binding site locations are used in motif discovery to predict a binding motif. This motif is used to constrain the search space for deconvolution and refine prediction of binding site locations. A set of high-resolution binding site locations is obtained as a final prediction.

10.1128/9781555818845/fig8-3_thmb.gif

10.1128/9781555818845/fig8-3.gif

Figure 3

/content/10.1128/9781555818845.chap8.fig8-4

Figure 4

Example ChIP-Seq results from M. tuberculosis. (Upper) The top panel displays the fold read coverage for a single binding region with two known binding sites for the TF KstR. ChIP-Seq coverage visually resolves both binding sites and confirms the experimental observation that the site closest to Rv3571 is a weaker affinity. Total coverage is shown in blue, and the forward and reverse coverage is shown in red and green, respectively. The binding event also displays the expected shift in position between the forward and reverse reads. The bottom panel displays the genome-wide fold coverage for the same experiment. Peaks above a coverage threshold are shown in blue. The peak shown in the top panel is marked with a star in the bottom panel. The horizontal gray lines are multiples of the standard deviation of background coverage. (Lower) Binding site identification is highly reproducible. Bar plots show the distance between corresponding sites in two replicates for two TFs. The blue line indicates the length of known motifs. Insets show relationship of peak height between corresponding peaks in two replicates (R2 > 0.83 for all TFs). Figures from reference 1 .

10.1128/9781555818845/fig8-4_thmb.gif

10.1128/9781555818845/fig8-4.gif

Figure 4

/content/10.1128/9781555818845.chap8.fig8-5a

Figure 5a

Binding sites replicate between normoxia and hypoxia. Each panel compares the results of ChIP-Seq under both normoxic (x axis, top traces) and hypoxic (y axis, bottom traces). Strong concordance was seen in both peak heights (scatter plots) and coverage profiles (sequencing traces) in experiments performed under both conditions. While no binding sites were identified in normoxia that were not identified in hypoxia, a small number of sites exhibited greatly increased binding under hypoxic conditions. The three sites showing the largest increases are shown in red on the scatter plots.

10.1128/9781555818845/fig8-5a_thmb.gif

10.1128/9781555818845/fig8-5a.gif

Figure 5a

/content/10.1128/9781555818845.chap8.fig8-5b

Figure 5b

10.1128/9781555818845/fig8-5b_thmb.gif

10.1128/9781555818845/fig8-5b.gif

Figure 5b

/content/10.1128/9781555818845.chap8.fig8-6

Figure 6

Independent replication of EspR binding sites. The ChIP-Seq experiment performed with the native EspR antibody ( 31 ) compares well to the ChIP-Seq with the inducible promoter. (A) Binding sites are categorized by their locations relative to target genes. Motifs and binding site categories detected by independent protocols are very similar. (B) Coverage tracks between two experiments are in concordance.

10.1128/9781555818845/fig8-6_thmb.gif

10.1128/9781555818845/fig8-6.gif

Figure 6

/content/10.1128/9781555818845.chap8.fig8-7

Figure 7

Binding site affinity corresponds to sequence and occupancy at different levels of expression. For transcription factor KstR, the heat map shows experimentally detected binding sites at the bottom (bound sites) and binding sites found by sequence similarity but not experimentally at the top (unbound strong motifs). Each row is a binding site, and each column is a particular position of the binding motif. The KstR binding motif describing all sites in the heat map is shown at the bottom of the figure (full motif). Binding site coverage is shown in four bar plots, corresponding to four induction levels as indicated by Atc concentration. As shown by the arrows, high-coverage binding sites correspond to a wide high-affinity motif, while low-coverage sites correspond to a degraded version of the same motif.

10.1128/9781555818845/fig8-7_thmb.gif

10.1128/9781555818845/fig8-7.gif

Figure 7

/content/10.1128/9781555818845.chap8.fig8-8

Figure 8

Diversity of binding site locations. Binding sites are assigned to five categories depending on their location relative to the target. The top figure shows a few binding sites located in divergent and covergent areas of the genome. The bottom figure shows the percentage of binding sites in each category for 49 TFs.

10.1128/9781555818845/fig8-8_thmb.gif

10.1128/9781555818845/fig8-8.gif

Figure 8

/content/10.1128/9781555818845.chap8.fig8-9

Figure 9

M. tuberculosis TF binding data are available at TBDatabase (TBDB). Binding data for 50 TFs generated by the NIAID-funded TB Systems Biology Project have been integrated with the genome sequence and annotation of M. tuberculosis and released at TBDB.org. Selected screen shots show online tools available for searching, browsing, and downloading these data.

10.1128/9781555818845/fig8-9_thmb.gif

10.1128/9781555818845/fig8-9.gif

Figure 9

References

/content/book/10.1128/9781555818845.chap8

1. Galagan JE,, Minch K,, Peterson M,, Lyubetskaya A,, Azizi E,, Sweet L,, Gomes A,, Rustad T,, Dolganov G,, Glotova I,, Abeel T,, Mahwinney C,, Kennedy AD,, Allard R,, Brabant W,, Krueger A,, Jaini S,, Honda B,, Yu WH,, Hickey MJ,, Zucker J,, Garay C,, Weiner B,, Sisk P,, Stolte C,, Winkler JK,, Van de Peer Y,, Iazzetti P,, Camacho D,, Dreyfuss J,, Liu Y,, Dorhoi A,, Mollenkopf HJ,, Drogaris P,, Lamontagne J,, Zhou Y,, Piquenot J,, Park ST,, Raman S,, Kaufmann SH,, Mohney RP,, Chelsky D,, Moody DB,, Sherman DR,, Schoolnik GK . 2013. The Mycobacterium tuberculosis regulatory network and hypoxia. Nature 499 : 178– 183.[PubMed][CrossRef]

2. Robertson G,, Hirst M,, Bainbridge M,, Bilenky M,, Zhao Y,, Zeng T,, Euskirchen G,, Bernier B,, Varhol R,, Delaney A,, Thiessen N,, Griffith OL,, He A,, Marra M,, Snyder M,, Jones S . 2007. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 4 : 651– 657.[PubMed][CrossRef]

3. Mikkelsen TS,, Ku M,, Jaffe DB,, Issac B,, Lieberman E,, Giannoukos G,, Alvarez P,, Brockman W,, Kim TK,, Koche RP,, Lee W,, Mendenhall E,, O’Donovan A,, Presser A,, Russ C,, Xie X,, Meissner A,, Wernig M,, Jaenisch R,, Nusbaum C,, Lander ES,, Bernstein BE . 2007. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448 : 553– 560.[PubMed][CrossRef]

4. Johnson DS,, Mortazavi A,, Myers RM,, Wold B . 2007. Genome-wide mapping of in vivo protein-DNA interactions. Science 316 : 1497– 1502.[PubMed][CrossRef]

5. Valouev A,, Johnson DS,, Sundquist A,, Medina C,, Anton E,, Batzoglou S,, Myers RM,, Sidow A . 2008. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat Methods 5 : 829– 834.[PubMed][CrossRef]

6. Li H,, Ruan J,, Durbin R . 2008. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 18 : 1851– 1858.[PubMed][CrossRef]

7. Li H,, Handsaker B,, Wysoker A,, Fennell T,, Ruan J,, Homer N,, Marth G,, Abecasis G,, Durbin R, 1000 Genome Project Data Processing Subgroup . 2009. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25 : 2078– 2079.[PubMed][CrossRef]

8. Gordon BR,, Li Y,, Wang L,, Sintsova A,, van Bakel H,, Tian S,, Navarre WW,, Xia B,, Liu J . 2010. Lsr2 is a nucleoid-associated protein that targets AT-rich sequences and virulence genes in Mycobacterium tuberculosis. Proc Natl Acad Sci USA 107 : 5154– 5159.[PubMed][CrossRef]

9. Oppenheim AV,, Willsky AS,, Nawab SH . 1997. Signals & Systems, 2nd ed. Prentice Hall, Upper Saddle River, NJ.

10. Levin A,, Weiss Y,, Durand F,, Freeman WT . 2011. Understanding blind deconvolution algorithms. IEEE Trans Pattern Anal 33 : 2354– 2367.[PubMed][CrossRef]

11. Lun DS,, Sherrid A,, Weiner B,, Sherman DR,, Galagan JE . 2009. A blind deconvolution approach to high-resolution mapping of transcription factor binding sites from ChIP-seq data. Genome Biol 10 : R142. [PubMed][CrossRef]

12. (Reference deleted.)

13. Chauhan S,, Tyagi JS . 2008. Cooperative binding of phosphorylated DevR to upstream sites is necessary and sufficient for activation of the Rv3134c-devRS operon in Mycobacterium tuberculosis: implication in the induction of DevR target genes. J Bacteriol 190 : 4301– 4312.[PubMed][CrossRef]

14. Mikkelsen TS,, Ku M,, Jaffe DB,, Issac B,, Lieberman E,, Giannoukos G,, Alvarez P,, Brockman W,, Kim T-K,, Koche RP,, Lee W,, Mendenhall E,, O’Donovan A,, Presser A,, Russ C,, Xie X,, Meissner A,, Wernig M,, Jaenisch R,, Nusbaum C,, Lander ES,, Bernstein BE . 2007. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448 : 553– 560.[PubMed][CrossRef]

15. Ehrt S,, Schnappinger D . 2006. Controlling gene expression in mycobacteria. Future Microbiol 1 : 177– 184.[PubMed][CrossRef]

16. Ehrt S,, Guo XV,, Hickey CM,, Ryou M,, Monteleone M,, Riley LW,, Schnappinger D . 2005. Controlling gene expression in mycobacteria with anhydrotetracycline and Tet repressor. Nucleic Acids Res 33 : e21. [PubMed][CrossRef]

17. Klotzsche M,, Ehrt S,, Schnappinger D . 2009. Improved tetracycline repressors for gene silencing in mycobacteria. Nucleic Acids Res 37 : 1778. [PubMed][CrossRef]

18. Guo XV,, Monteleone M,, Klotzsche M,, Kamionka A,, Hillen W,, Braunstein M,, Ehrt S,, Schnappinger D . 2007. Silencing Mycobacterium smegmatis by using tetracycline repressors. J Bacteriol 189 : 4614– 4623.[PubMed][CrossRef]

19. Park HD,, Guinn KM,, Harrell MI,, Liao R,, Voskuil MI,, Tompa M,, Schoolnik GK,, Sherman DR . 2003. Rv3133c/dosR is a transcription factor that mediates the hypoxic response of Mycobacterium tuberculosis. Mol Microbiol 48 : 833– 843.[PubMed]

20. Saini DK,, Malhotra V,, Dey D,, Pant N,, Das TK,, Tyagi JS . 2004. DevR-DevS is a bona fide two-component system of Mycobacterium tuberculosis that is hypoxia-responsive in the absence of the DNA-binding domain of DevR. Microbiology 150 : 865– 875.[PubMed]

21. Flores Valdez MA,, Schoolnik GK . 2010. DosR-regulon genes induction in Mycobacterium bovis BCG under aerobic conditions. Tuberculosis (Edinb) 90 : 197– 200.[PubMed][CrossRef]

22. Chauhan S,, Sharma D,, Singh A,, Surolia A,, Tyagi JS . 2011. Comprehensive insights into Mycobacterium tuberculosis DevR (DosR) regulon activation switch. Nucleic Acids Res 39 : 7400– 7414.[PubMed][CrossRef]

23. Sherman DR,, Voskuil M,, Schnappinger D,, Liao R,, Harrell MI,, Schoolnik GK . 2001. Regulation of the Mycobacterium tuberculosis hypoxic response gene encoding alpha -crystallin. Proc Natl Acad Sci USA 98 : 7534– 7539.[PubMed][CrossRef]

24. Vasudeva-Rao HM,, McDonough KA . 2008. Expression of the Mycobacterium tuberculosis acr-coregulated genes from the DevR (DosR) regulon is controlled by multiple levels of regulation. Infect Immun 76 : 2478– 2489.[PubMed][CrossRef]

25. Kendall SL,, Burgess P,, Balhana R,, Withers M,, Ten Bokum A,, Lott JS,, Gao C,, Uhia-Castro I,, Stoker NG . 2010. Cholesterol utilization in mycobacteria is controlled by two TetR-type transcriptional regulators: kstR and kstR2. Microbiology 156 : 1362– 1371.[PubMed][CrossRef]

26. Kendall SL,, Withers M,, Soffair CN,, Moreland NJ,, Gurcha S,, Sidders B,, Frita R,, Ten Bokum A,, Besra GS,, Lott JS,, Stoker NG . 2007. A highly conserved transcriptional repressor controls a large regulon involved in lipid degradation in Mycobacterium smegmatis and Mycobacterium tuberculosis. Mol Microbiol 65 : 684– 699.[PubMed][CrossRef]

27. Nesbitt NM,, Yang X,, Fontan P,, Kolesnikova I,, Smith I,, Sampson NS,, Dubnau E . 2010. A thiolase of Mycobacterium tuberculosis is required for virulence and production of androstenedione and androstadienedione from cholesterol. Infect Immun 78 : 275– 282.[PubMed][CrossRef]

28. Uhia I,, Galan B,, Medrano FJ,, Garcia JL . 2011. Characterization of the KstR-dependent promoter of the first step of cholesterol degradative pathway in Mycobacterium smegmatis. Microbiology 157 : 2670– 2680.[PubMed][CrossRef]

29. Gao CH,, Yang M,, He ZG . 2012. Characterization of a novel ArsR-like regulator encoded by Rv2034 in Mycobacterium tuberculosis. PLoS One 7 : e36255. [PubMed][CrossRef]

30. Gao CH,, Yang M,, He ZG . 2011. An ArsR-like transcriptional factor recognizes a conserved sequence motif and positively regulates the expression of phoP in mycobacteria. Biochem Biophys Res Commun 411 : 726– 731.[PubMed][CrossRef]

31. Blasco B,, Chen JM,, Hartkoorn R,, Sala C,, Uplekar S,, Rougemont J,, Pojer F,, Cole ST . 2012. Virulence regulator EspR of Mycobacterium tuberculosis is a nucleoid-associated protein. PLoS Pathog 8 : e1002621. [PubMed][CrossRef]

32. Fordyce PM,, Gerber D,, Tran D,, Zheng J,, Li H,, DeRisi JL,, Quake SR . 2010. De novo identification and biophysical characterization of transcription-factor binding sites with microfluidic affinity analysis. Nat Biotechnol 28 : 970– 975.[PubMed][CrossRef]

33. Fernandez PC,, Frank SR,, Wang L,, Schroeder M,, Liu S,, Greene J,, Cocito A,, Amati B . 2003. Genomic targets of the human c-Myc protein. Genes Dev 17 : 1115– 1129.[PubMed][CrossRef]

34. Farnham PJ . 2009. Insights from genomic profiling of transcription factors. Nat RevGenet 10 : 605– 616.[PubMed][CrossRef]

35. Wade JT,, Reppas NB,, Church GM,, Struhl K . 2005. Genomic analysis of LexA binding reveals the permissive nature of the Escherichia coli genome and identifies unconventional target sites. Genes Dev 19 : 2619– 2630.[PubMed][CrossRef]

36. Galagan J,, Lyubetskaya A,, Gomes A . 2013. ChIP-Seq and the complexity of bacterial transcriptional regulation. Curr Top Microbiol Immunol 363 : 43– 68.[PubMed][CrossRef]

37. Huerta AM,, Francino MP,, Morett E,, Collado-Vides J . 2006. Selection for unequal densities of sigma70 promoter-like signals in different regions of large bacterial genomes. PLoS Genet 2 : e185. [PubMed][CrossRef]

38. Froula JL,, Francino MP . 2007. Selection against spurious promoter motifs correlates with translational efficiency across bacteria. PLoS One 2 : e745. [PubMed][CrossRef]

39. Koide T,, Reiss DJ,, Bare JC,, Pang WL,, Facciotti MT,, Schmid AK,, Pan M,, Marzolf B,, Van PT,, Lo FY,, Pratap A,, Deutsch EW,, Peterson A,, Martin D,, Baliga NS . 2009. Prevalence of transcription promoters within archaeal operons and coding sequences. Mol Syst Biol 5 : 285. [PubMed][CrossRef]

40. Czaplewski LG,, North AK,, Smith MC,, Baumberg S,, Stockley PG . 1992. Purification and initial characterization of AhrC: the regulator of arginine metabolism genes in Bacillus subtilis. Mol Microbiol 6 : 267– 275.[PubMed]

41. Mullin DA,, Newton A . 1993. A sigma 54 promoter and downstream sequence elements ftr2 and ftr3 are required for regulated expression of divergent transcription units flaN and flbG in Caulobacter crescentus. J Bacteriol 175 : 2067– 2076.[PubMed]

42. Madan Babu M,, Teichmann SA . 2003. Functional determinants of transcription factors in Escherichia coli: protein families and binding sites. Trends Genet 19 : 75– 79.[PubMed][CrossRef]

43. Collado-Vides J,, Magasanik B,, Gralla JD . 1991. Control site location and transcriptional regulation in Escherichia coli. Microbiol Rev 55 : 371– 394.[PubMed]

44. Dunn TM,, Hahn S,, Ogden S,, Schleif RF . 1984. An operator at –280 base pairs that is required for repression of araBAD operon promoter: addition of DNA helical turns between the operator and promoter cyclically hinders repression. Proc Natl Acad Sci USA 81 : 5017– 5020.[PubMed]

45. Wedel A,, Weiss DS,, Popham D,, Droge P,, Kustu S . 1990. A bacterial enhancer functions to tether a transcriptional activator near a promoter. Science 248 : 486– 490.[PubMed]

46. Dandanell G,, Valentin-Hansen P,, Larsen JE,, Hammer K . 1987. Long-range cooperativity between gene regulatory sequences in a prokaryote. Nature 325 : 823– 826.[PubMed][CrossRef]

47. Belitsky BR,, Sonenshein AL . 1999. An enhancer element located downstream of the major glutamate dehydrogenase gene of Bacillus subtilis. Proc Natl Acad Sci USA 96 : 10290– 10295.[PubMed]

48. Oehler S,, Eismann ER,, Kramer H,, Muller-Hill B . 1990. The three operators of the lac operon cooperate in repression. EMBO J 9 : 973– 979.[PubMed]

49. Narang A . 2007. Effect of DNA looping on the induction kinetics of the lac operon. J Theor Biol 247 : 695– 712.[PubMed][CrossRef]

50. Flashner Y,, Gralla JD . 1988. Dual mechanism of repression at a distance in the lac operon. Proc Natl Acad Sci USA 85 : 8968– 8972.[PubMed]

51. Ninfa AJ,, Reitzer LJ,, Magasanik B . 1987. Initiation of transcription at the bacterial glnAp2 promoter by purified E. coli components is facilitated by enhancers. Cell 50 : 1039– 1046.[PubMed]

52. Reitzer LJ,, Magasanik B . 1986. Transcription of glnA in E. coli is stimulated by activator bound to sites far from the promoter. Cell 45 : 785– 792.[PubMed]

53. Ueno-Nishio S,, Mango S,, Reitzer LJ,, Magasanik B . 1984. Identification and regulation of the glnL operator-promoter of the complex glnALG operon of Escherichia coli. J Bacteriol 160 : 379– 384.[PubMed]

54. Ueno-Nishio S,, Backman KC,, Magasanik B . 1983. Regulation at the glnL-operator-promoter of the complex glnALG operon of Escherichia coli. J Bacteriol 153 : 1247– 1251.[PubMed]

55. Hunt DM,, Sweeney NP,, Mori L,, Whalan RH,, Comas I,, Norman L,, Cortes T,, Arnvig KB,, Davis EO,, Stapleton MR,, Green J,, Buxton RS . 2012. Long-range transcriptional control of an operon necessary for virulence-critical ESX-1 secretion in Mycobacterium tuberculosis. J Bacteriol 194 : 2307– 2320.[PubMed][CrossRef]

56. Cao Y,, Yao Z,, Sarkar D,, Lawrence M,, Sanchez GJ,, Parker MH,, MacQuarrie KL,, Davison J,, Morgan MT,, Ruzzo WL,, Gentleman RC,, Tapscott SJ . 2010. Genome-wide MyoD binding in skeletal muscle cells: a potential for broad cellular reprogramming. Dev Cell 18 : 662– 674.[PubMed][CrossRef]

57. Dillon SC,, Dorman CJ . 2010. Bacterial nucleoid-associated proteins, nucleoid structure and gene expression. Nat Rev Microbiol 8 : 185– 195.[PubMed][CrossRef]

58. Browning DF,, Grainger DC,, Busby SJ . 2010. Effects of nucleoid-associated proteins on bacterial chromosome structure and gene expression. Curr Opin Microbiol 13 : 773– 780.[PubMed]

59. Rimsky S,, Travers A . 2011. Pervasive regulation of nucleoid structure and function by nucleoid-associated proteins. Curr Opin Microbiol 14 : 136– 141.[PubMed][CrossRef]

60. Wang W,, Li GW,, Chen C,, Xie XS,, Zhuang X . 2011. Chromosome organization by a nucleoid-associated protein in live bacteria. Science 333 : 1445– 1449.[PubMed][CrossRef]

61. Colangeli R,, Haq A,, Arcus VL,, Summers E,, Magliozzo RS,, McBride A,, Mitra AK,, Radjainia M,, Khajo A,, Jacobs WR Jr,, Salgame P,, Alland D . 2009. The multifunctional histone-like protein Lsr2 protects mycobacteria against reactive oxygen intermediates. Proc Natl Acad Sci USA 106 : 4414– 4418.[PubMed][CrossRef]

62. Colangeli R,, Helb D,, Vilcheze C,, Hazbon MH,, Lee CG,, Safi H,, Sayers B,, Sardone I,, Jones MB,, Fleischmann RD,, Peterson SN,, Jacobs WR Jr,, Alland D . 2007. Transcriptional regulation of multi-drug tolerance and antibiotic-induced responses by the histone-like protein Lsr2 in M. tuberculosis. PLoS Pathog 3 : e87. [PubMed][CrossRef]

63. Rosenberg OS,, Dovey C,, Tempesta M,, Robbins RA,, Finer-Moore JS,, Stroud RM,, Cox JS . 2011. EspR, a key regulator of Mycobacterium tuberculosis virulence, adopts a unique dimeric structure among helix-turn-helix proteins. Proc Natl Acad Sci USA 108 : 13450– 13455.[PubMed][CrossRef]

64. Blasco B,, Stenta M,, Alonso-Sarduy L,, Dietler G,, Peraro MD,, Cole ST,, Pojer F . 2011. Atypical DNA recognition mechanism used by the EspR virulence regulator of Mycobacterium tuberculosis. Mol Microbiol 82 : 251– 264.[PubMed][CrossRef]

65. Rosenfeld N,, Elowitz MB,, Alon U . 2002. Negative autoregulation speeds the response times of transcription networks. J Mol Biol 323 : 785– 793.[PubMed]

66. Robertson G,, Hirst M,, Bainbridge M,, Bilenky M,, Zhao Y,, Zeng T,, Euskirchen G,, Bernier B,, Varhol R,, Delaney A,, Thiessen N,, Griffith OL,, He A,, Marra M,, Snyder M,, Jones S . 2007. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 4 : 651– 657.[PubMed][CrossRef]

67. MacQuarrie KL,, Fong AP,, Morse RH,, Tapscott SJ . 2011. Genome-wide transcription factor binding: beyond direct target regulation. Trends Genet 27 : 141– 148.

68. Tanay A . 2006. Extensive low-affinity transcriptional interactions in the yeast genome. Genome Res 16 : 962– 972.[PubMed][CrossRef]

69. Rhee HS,, Pugh BF . 2011. Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution. Cell 147 : 1408– 1419.[PubMed][CrossRef]

70. Zhong M,, Niu W,, Lu ZJ,, Sarov M,, Murray JI,, Janette J,, Raha D,, Sheaffer KL,, Lam HY,, Preston E,, Slightham C,, Hillier LW,, Brock T,, Agarwal A,, Auerbach R,, Hyman AA,, Gerstein M,, Mango SE,, Kim SK,, Waterston RH,, Reinke V,, Snyder M . 2010. Genome-wide identification of binding sites defines distinct functions for Caenorhabditis elegans PHA-4/FOXA in development and environmental response. PLoS Genet 6 : e1000848. [PubMed]

71. Zeitlinger J,, Zinzen RP,, Stark A,, Kellis M,, Zhang H,, Young RA,, Levine M . 2007. Whole-genome ChIP-chip analysis of Dorsal, Twist, and Snail suggests integration of diverse patterning processes in the Drosophila embryo. Genes Dev 21 : 385– 390.[PubMed][CrossRef]

72. Li XY,, MacArthur S,, Bourgon R,, Nix D,, Pollard DA,, Iyer VN,, Hechmer A,, Simirenko L,, Stapleton M,, Luengo Hendriks CL,, Chu HC,, Ogawa N,, Inwood W,, Sementchenko V,, Beaton A,, Weiszmann R,, Celniker SE,, Knowles DW,, Gingeras T,, Speed TP,, Eisen MB,, Biggin MD . 2008. Transcription factors bind thousands of active and inactive regions in the Drosophila blastoderm. PLoS Biol 6 : e27. [PubMed][CrossRef]

73. Gertz J,, Siggia ED,, Cohen BA . 2009. Analysis of combinatorial cis-regulation in synthetic and genomic promoters. Nature 457 : 215– 218.[PubMed][CrossRef]

74. Kalir S,, McClure J,, Pabbaraju K,, Southward C,, Ronen M,, Leibler S,, Surette MG,, Alon U . 2001. Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science 292 : 2080– 2083.[PubMed][CrossRef]

75. Zaslaver A,, Mayo AE,, Rosenberg R,, Bashkin P,, Sberro H,, Tsalyuk M,, Surette MG,, Alon U . 2004. Just-in-time transcription program in metabolic pathways. Nat Genet 36 : 486– 491.[PubMed][CrossRef]

76. Galagan JE,, Sisk P,, Stolte C,, Weiner B,, Koehrsen M,, Wymore F,, Reddy TB,, Zucker JD,, Engels R,, Gellesch M,, Hubble J,, Jin H,, Larson L,, Mao M,, Nitzberg M,, White J,, Zachariah ZK,, Sherlock G,, Ball CA,, Schoolnik GK . 2010. TB database 2010: overview and update. Tuberculosis (Edinb) 90 : 225– 235.[PubMed][CrossRef]

77. Abeel T,, Van Parys T,, Saeys Y,, Galagan J,, Van de Peer Y . 2012. GenomeView: a next-generation genome browser. Nucleic Acids Res 40 : e12. [PubMed][CrossRef]

Tables

Transcription Factor Binding Site Mapping Using ChIP-Seq

/content/10.1128/9781555818845.chap8.tab8-1

Untitled

Buffer 1 and buffer 1 + PI a

Untitled

Buffer 1 and buffer 1 + PI a

/content/10.1128/9781555818845.chap8.tab8-2

Untitled

IPP150 buffer

Untitled

IPP150 buffer

/content/10.1128/9781555818845.chap8.tab8-3

Untitled

“Elution from beads” buffer

Untitled

“Elution from beads” buffer