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Functional Characterization of Bacterial Srnas Using a Network Biology Approach

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Functional Characterization of Bacterial Srnas Using a Network Biology Approach Functional characterization of bacterial sRNAs using a network biology approach Sheetal R. Modia,1, Diogo M. Camachoa,1, Michael A. Kohanskia,b, Graham C. Walkerc, and James J. Collinsa,b,d,2 aThe Howard Hughes Medical Institute, Department of Biomedical Engineering, and Center for BioDynamics, Boston University, Boston, MA 02215; bBoston University School of Medicine, Boston, MA 02118; cDepartment of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139; and dThe Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115 Edited* by Susan Gottesman, National Cancer Institute, Bethesda, MD, and approved August 3, 2011 (received for review March 19, 2011) Small RNAs (sRNAs) are important components of posttranscriptional sRNAs in bacteria (see Fig. S1 and SI Materials and Methods for regulation. These molecules are prevalent in bacterial and eukaryotic a more complete overview of this method). As a first step, we organisms, and involved in a variety of responses to environmental used the Context Likelihood of Relatedness (CLR) algorithm stresses. The functional characterization of sRNAs is challenging and (16) to infer the sRNA regulatory network in E. coli. The CLR requires highly focused and extensive experimental procedures. algorithm is an inference approach based on mutual information fi Here, using a network biology approach and a compendium of gene and allows for the identi cation of regulatory relationships be- expression profiles, we predict functional roles and regulatory inter- tween biomolecular entities. This algorithm previously has been actions for sRNAs in Escherichia coli. We experimentally validate pre- used to infer transcriptional regulatory networks (17) by exam- dictions for three sRNAs in our inferred network: IsrA, GlmZ, and ining the functional relationships between transcription factors GcvB. Specifically, we validate a predicted role for IsrA and GlmZ in and target genes. We applied the CLR algorithm to an existing compendium of E. coli microarrays collected under different the SOS response, and we expand on current knowledge of the GcvB experimental conditions (Table S1A) to reverse engineer and sRNA, demonstrating its broad role in the regulation of amino acid analyze the regulatory subnetworks for Hfq-dependent sRNAs. metabolism and transport. We also show, using the inferred network This process allowed us to infer potential targets of each of these coupled with experiments, that GcvB and Lrp, a transcription factor, sRNAs with a highly significant false-discovery rate (FDR)-cor- repress each other in a mutually inhibitory network. This work shows rected P value (q < 0.005) (18). The inferred network (Fig. 1 and that a network-based approach can be used to identify the cellular Table S2A) consists of 459 putative direct and indirect targets SYSTEMS BIOLOGY function of sRNAs and characterize the relationship between sRNAs for the Hfq-dependent sRNAs, including sRNA–sRNA inter- and transcription factors. actions as well as a number of genes predicted to be coregulated by two sRNAs. microbiology | network inference | sRNA regulation A cellular regulatory scheme in which each transcription factor regulates at least one sRNA (4) has been hypothesized. It is mall noncoding RNAs (sRNAs) are ubiquitous in all king- therefore interesting to note that 10 of the sRNAs in the network Sdoms of life. These molecules range in length from a few are predicted to interact with at least one transcription factor, al- nucleotides to a few hundred nucleotides, and are involved in the though directionality of regulation is not implied. Transcriptional regulation of a wide range of physiological processes (1–3). regulators in the network include LexA (SOS response), FlhD Bacterial sRNAs, some of which have been studied extensively (chemotaxis), and GadE, GadW, and GadX (acid stress response). (4), have been implicated in the regulation of bacterial stress These network results indicate that regulation of the associated responses, iron uptake, quorum sensing, virulence, and biofilm cellular processes may involve a complex interplay between sRNAs, formation (5–8). transcription factors, and their respective targets. The most widely studied class of bacterial sRNAs act as post- We subsequently performed pathway enrichment for each of transcriptional regulators by base-pairing to target mRNAs. This the inferred sRNA subnetworks, either by Gene Ontology (GO) interaction is facilitated by the Hfq protein, a bacterial Sm-like term enrichment analysis or by using gene function information protein with chaperone function, which acts as a general cofactor obtained from EcoCyc (19). These analyses allowed us to classify subnetworks according to function, and thereby implicate the in RNA interactions (9). Binding of an sRNA to its mRNA target fi can result in changes in translational efficiency as well as tran- sRNAs as regulators of speci c cellular processes (Fig. 1). We script instability, a process dependent on the RNase-E–including were able to identify functional enrichment for seven of the degradosome complex (10). To date, w80 sRNAs have been inferred sRNA subnetworks: iron homeostasis (under ryhB reg- identified in Escherichia coli, 30 of which are Hfq-dependent. A ulation), amino acid metabolism (gcvB), motility and chemotaxis number of these sRNAs have been well characterized, such as (micF), pH adaptation (gadY), DNA repair (glmZ and isrA), RyhB, which is known to regulate iron homeostasis (8). protection and adaptation to stress (cyaR), and extracellular Small RNAs and their targets are being discovered and transport (dicF). The involvement of RyhB in iron homeostasis identified with greater efficiency (11–15); however, the functions and GadY in the regulation of acid response has been reported of many sRNAs remain unknown. In the present study, we were previously (7, 20). Our network analyses correctly characterized interested in exploring the possibility of developing and using a network biology approach to elucidate sRNA functional roles. We applied a network inference algorithm to a compendium of Author contributions: S.R.M., D.M.C., M.A.K., G.C.W., and J.J.C. designed research; S.R.M., E. coli microarray expression profiles to reconstruct an sRNA D.M.C., and M.A.K. performed research; S.R.M. and D.M.C. analyzed data; and S.R.M., regulatory network. Functional enrichment of the resulting D.M.C., M.A.K., G.C.W., and J.J.C. wrote the paper. sRNA subnetworks confirmed known functions for some sRNAs The authors declare no conflict of interest. and identified putative functions for others. We experimentally *This Direct Submission article had a prearranged editor. validated predicted functional roles for three sRNAs, and an in- Freely available online through the PNAS open access option. depth analysis of the inferred network led to the discovery of Data deposition: The microarray data reported in this paper have been deposited at a unique sRNA transcription-factor mutual inhibitory network. www.bu.edu/abl. 1S.R.M. and D.M.C. contributed equally to this work. Results and Discussion 2To whom correspondence should be addressed. E-mail: [email protected]. Small RNA Regulatory Network Inference. We developed a compu- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. tational biology approach to characterize functional roles for 1073/pnas.1104318108/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1104318108 PNAS Early Edition | 1of6 Downloaded by guest on September 29, 2021 tolC xylB yjbE talB secM aaeB yfeN yggR yeaN omrA ybbY wza rprA ydiM yfdN ydeJ nupX eutM ybbW pinE uidB pscK Extracellular yjbG hokE wcaB rpe phnL yebQ ybgQ maoC tauC valW ychS pdxA ydfA tktA yagA fryB yagS yebB transport ydiB yiaB hokD arnE yeiW ygcQ ybiW ygcS proK ychE ydbL ygbN yiaA ampG ydeE mdtQ dicF rseX ytcA basS glxK ycaM fsaA dcd intA A gM yfaX yjeE ogrK aaeR ybcD yecR Chemotaxis dB motB slyX insD btuD narQ Z gK gB aceA mokC C A pepT N rrlA sgrS recX yceO gD and motility uvrD ileY serU cheR tsr yibV yceF leuU C cheZ ihfA oxyS crcB ydcZ ileX rnlA ygjP yibT yigG ymfE micF yiiG ompF ybhT ygiW mokA ydiI yjbJ abgT ulaB polA feaB yhcN dsrA yneJ ygdT emrY xapR dinF lpp micC yeiI pgi yciY pepP omrB mliC araE D cspG yoaH ydjG yjgM chiX ais ydhU dinD galT pabB yneG A ynaJ intR yedA yddW araB yhjX clcB ccmE araA ynjA ydiK galP ccmB ydaL ygeA yeaE ydgJ cmoB ydfT cedA torZ ydcO yfbS adiC ynfK yhiD abgA gadX lexA hdeA lysU leuL fdoG fdoI abgR hdeD gadE ydiP yoaE yddG ydaN nudG ydhX gadA gadW fnr ynbC mdtF anmK tqsA pps serT gadB uvrA isrA galE dbpA pck gadY ydaU narJ hdeB ydhB uspE hisI recG thiC iL ycjG araJ aroA zapA yecC yddE yebG ydhP btuR ydhV yhiM deoC thiS argB dmsC ydjZ ttcA trmJ ydaM ydcF ydfZ argW dctR pphA cbl slp yqjK lhr yebT arnB mdtE ydhT ompW cysN gntX ynjF ompT thiM dcp ynbD metF yncJ araD marC potH mog yobB abgB srlA ogt uidA argA yicG csgF yneI fdnG treC gcvT aroH ynfG yciK livJ yecH ydhW srlD ilvH gcvH ymfT yeaW cysW leuB kbl yhjE hisC purM cysC ymfJ potG thiG pH hisJ bioB cysH purF ucpA ilvL ilvI argF hisQ metI hemD adaptation ndh lysA gcvB rydC metA art J aroG trpE recN ymfM trpD ybiC potF ybdL hisG pnuC serC argD leuC metC argI fdhE thiF nlpA nadB nadA tnaA livM livF trpT aroP cysP trpL uxuA arnC cysK fadB gdhA yjcB metY yagM purD speC metQ gltD argG lrp spf ytfQ lysC ubiD dinQ pheA fdoH cysD wbbK modF hisM livH leuA thiH micA psiE sdaC metE recA thrL pntA dinI tdh leuD bioF leuP livG thiE livK hisD glmZ rseA yeeD pheL slyD purC thrA hisR proM aldA purK yigP treB bioD argC rmf yhcB tisA aroF cvpA potI thrB yiaF thrV trpC tnaC serA gsiD trxA thiD eco argH rraB yheM ymfN amiB xisE lpxC intE ppiC tisB hisL ymfL yjfO Amino acid agp mtfA DNA repair mglB zupT yfdY yaiA metabolism arcZ Small RNA ivbL rybA ybeD ytfJ ybaQ dinJ mokB Transcriptional regulator ydcH hokB agaX fumA cyaR mntH fes Iron yaiZ bfd ftnA yghZ entH entB Gene associated with pspE cstA exbD metabolism glcC yfeD ryhB mscS exbB enriched functional process rbsB ybhQ nrdH yjjZ fhuF nrdF nrdI sodB ybdZ fecR nrdE Gene not associated with Protection fecI entF enriched functional process and adaptation Fig.
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