{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106343"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106343","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A combinatorial approach for improved target prediction, validation, and characterization of small RNAs in Escherichia coli","abstract":"Bacterial small RNAs (sRNAs) are involved in regulating targets involved in various processes in the cell, including stress responses and metabolic processes. As RNA regulators that are degraded rapidly, they are ideal for fine-tuning gene expression in response to rapid environmental changes. Even though hundreds of sRNAs have been discovered in multiple bacterial species, the challenge in sRNA characterization lies in mRNA target identification. Here, we have developed a pipeline called sRNA-target Prediction Organizing Tool (SPOT) that streamlines the process of finding putative mRNA targets for the sRNA of interest. We were able to train the pipeline with well-characterized sRNAs SgrS and RyhB, with SPOT achieving > 75% sensitivity (Correctly Predicted Targets/Total Known targets). We employed a combinatorial approach combining both SPOT and experimental evidence to identify potentially overlooked targets for the well-characterized sRNAs Spot42, SgrS, GcvB, and RyhB. We constructed a tiered ranking system with strict criteria to produce an experimentally tractable list of putative novel targets for further verification. After testing 28 potential targets across all 4 sRNAs, we identified four new targets for RyhB (iron homeostasis) and eight new targets for GcvB (amino acid transport/metabolism). For RyhB, the new targets cybB, yciS, and hypB were activated, whereas narG was repressed when RyhB was overexpressed. GcvB repressed genes cysB, sucC, ebgR, cstA, yhjE, acs, and trpE and activated dtpB. All in all, we were able to expand the target regulon for the sRNAs GcvB and RyhB using SPOT and existing experimental evidence. Using SPOT, we were able to identify two additional targets for the poorly characterized sRNA RydC, which was previously shown to inhibit the yejA and csgD mRNAs, which encodes a putative ABC transporter and the master regulator of curli biosynthesis, respectively, and activate cfa, which is involved in membrane reconstruction. We found that RydC represses pheA, which encodes a chorismate mutase and is involved in phenylalanine biosynthesis, and activates trpE, encoding an anthranilate synthase involved in tryptophan biosynthesis. In addition to the minor RydC role in biofilm formation and sugar transport, we were able to identify the RydC involvement in amino acid biosynthesis, which had not previously been shown. Overall, we have shown that this combinatorial approach can be used to increase the predictability of true sRNA targets.","abstract_html":"Bacterial small RNAs (sRNAs) are involved in regulating targets involved in various processes in the cell, including stress responses and metabolic processes. As RNA regulators that are degraded rapidly, they are ideal for fine-tuning gene expression in response to rapid environmental changes. Even though hundreds of sRNAs have been discovered in multiple bacterial species, the challenge in sRNA characterization lies in mRNA target identification. Here, we have developed a pipeline called sRNA-target Prediction Organizing Tool (SPOT) that streamlines the process of finding putative mRNA targets for the sRNA of interest. We were able to train the pipeline with well-characterized sRNAs SgrS and RyhB, with SPOT achieving > 75% sensitivity (Correctly Predicted Targets/Total Known targets). We employed a combinatorial approach combining both SPOT and experimental evidence to identify potentially overlooked targets for the well-characterized sRNAs Spot42, SgrS, GcvB, and RyhB. We constructed a tiered ranking system with strict criteria to produce an experimentally tractable list of putative novel targets for further verification. After testing 28 potential targets across all 4 sRNAs, we identified four new targets for RyhB (iron homeostasis) and eight new targets for GcvB (amino acid transport/metabolism). For RyhB, the new targets cybB, yciS, and hypB were activated, whereas narG was repressed when RyhB was overexpressed. GcvB repressed genes cysB, sucC, ebgR, cstA, yhjE, acs, and trpE and activated dtpB. All in all, we were able to expand the target regulon for the sRNAs GcvB and RyhB using SPOT and existing experimental evidence. Using SPOT, we were able to identify two additional targets for the poorly characterized sRNA RydC, which was previously shown to inhibit the yejA and csgD mRNAs, which encodes a putative ABC transporter and the master regulator of curli biosynthesis, respectively, and activate cfa, which is involved in membrane reconstruction. We found that RydC represses pheA, which encodes a chorismate mutase and is involved in phenylalanine biosynthesis, and activates trpE, encoding an anthranilate synthase involved in tryptophan biosynthesis. In addition to the minor RydC role in biofilm formation and sugar transport, we were able to identify the RydC involvement in amino acid biosynthesis, which had not previously been shown. Overall, we have shown that this combinatorial approach can be used to increase the predictability of true sRNA targets.","abstract_has_math":false,"creators":["King, Alisa Margaret"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Microbiology","degree_department":null,"school":null,"contributors":["Vanderpool, Carin K","Metcalf, William W","Slauch, James M","Cronan, John E"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T22:12:17Z","date_published":"2020-03-02T22:12:17Z","updated_at":"2026-07-22T22:24:45Z","subjects":["gene regulation","small rna","genetics","algorithms"],"languages":["en"],"rights":["Copyright 2019 Alisa King"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106343","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Vanderpool, Carin K","Metcalf, William W","Slauch, James M","Cronan, John E"]},{"key":"dc:creator","label":"Author","values":["King, Alisa Margaret"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-03-02T22:12:17Z","2022-03-03T10:15:19Z","2019-11-15","2019-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Microbiology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["gene regulation","small rna","genetics","algorithms"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Alisa King"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/106343"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Bacterial small RNAs (sRNAs) are involved in regulating targets involved in various processes in the cell, including stress responses and metabolic processes. As RNA regulators that are degraded rapidly, they are ideal for fine-tuning gene expression in response to rapid environmental changes. Even though hundreds of sRNAs have been discovered in multiple bacterial species, the challenge in sRNA characterization lies in mRNA target identification. Here, we have developed a pipeline called sRNA-target Prediction Organizing Tool (SPOT) that streamlines the process of finding putative mRNA targets for the sRNA of interest. We were able to train the pipeline with well-characterized sRNAs SgrS and RyhB, with SPOT achieving > 75% sensitivity (Correctly Predicted Targets/Total Known targets). We employed a combinatorial approach combining both SPOT and experimental evidence to identify potentially overlooked targets for the well-characterized sRNAs Spot42, SgrS, GcvB, and RyhB. We constructed a tiered ranking system with strict criteria to produce an experimentally tractable list of putative novel targets for further verification. After testing 28 potential targets across all 4 sRNAs, we identified four new targets for RyhB (iron homeostasis) and eight new targets for GcvB (amino acid transport/metabolism). For RyhB, the new targets cybB, yciS, and hypB were activated, whereas narG was repressed when RyhB was overexpressed. GcvB repressed genes cysB, sucC, ebgR, cstA, yhjE, acs, and trpE and activated dtpB. All in all, we were able to expand the target regulon for the sRNAs GcvB and RyhB using SPOT and existing experimental evidence. Using SPOT, we were able to identify two additional targets for the poorly characterized sRNA RydC, which was previously shown to inhibit the yejA and csgD mRNAs, which encodes a putative ABC transporter and the master regulator of curli biosynthesis, respectively, and activate cfa, which is involved in membrane reconstruction. We found that RydC represses pheA, which encodes a chorismate mutase and is involved in phenylalanine biosynthesis, and activates trpE, encoding an anthranilate synthase involved in tryptophan biosynthesis. In addition to the minor RydC role in biofilm formation and sugar transport, we were able to identify the RydC involvement in amino acid biosynthesis, which had not previously been shown. Overall, we have shown that this combinatorial approach can be used to increase the predictability of true sRNA targets.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-12-01","The student, Alisa King, accepted the attached license on 2019-11-14 at 13:49.","The student, Alisa King, submitted this Dissertation for approval on 2019-11-14 at 13:59.","This Dissertation was approved for publication on 2019-11-15 at 15:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14552 on 2020-02-28 at 17:22:05","Made available in DSpace on 2020-03-02T22:12:17Z (GMT). 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As RNA regulators that are degraded rapidly, they are ideal for fine-tuning gene expression in response to rapid environmental changes. Even though hundreds of sRNAs have been discovered in multiple bacterial species, the challenge in sRNA characterization lies in mRNA target identification. Here, we have developed a pipeline called sRNA-target Prediction Organizing Tool (SPOT) that streamlines the process of finding putative mRNA targets for the sRNA of interest. We were able to train the pipeline with well-characterized sRNAs SgrS and RyhB, with SPOT achieving > 75% sensitivity (Correctly Predicted Targets/Total Known targets). We employed a combinatorial approach combining both SPOT and experimental evidence to identify potentially overlooked targets for the well-characterized sRNAs Spot42, SgrS, GcvB, and RyhB. We constructed a tiered ranking system with strict criteria to produce an experimentally tractable list of putative novel targets for further verification. After testing 28 potential targets across all 4 sRNAs, we identified four new targets for RyhB (iron homeostasis) and eight new targets for GcvB (amino acid transport/metabolism). For RyhB, the new targets cybB, yciS, and hypB were activated, whereas narG was repressed when RyhB was overexpressed. GcvB repressed genes cysB, sucC, ebgR, cstA, yhjE, acs, and trpE and activated dtpB. All in all, we were able to expand the target regulon for the sRNAs GcvB and RyhB using SPOT and existing experimental evidence. Using SPOT, we were able to identify two additional targets for the poorly characterized sRNA RydC, which was previously shown to inhibit the yejA and csgD mRNAs, which encodes a putative ABC transporter and the master regulator of curli biosynthesis, respectively, and activate cfa, which is involved in membrane reconstruction. We found that RydC represses pheA, which encodes a chorismate mutase and is involved in phenylalanine biosynthesis, and activates trpE, encoding an anthranilate synthase involved in tryptophan biosynthesis. In addition to the minor RydC role in biofilm formation and sugar transport, we were able to identify the RydC involvement in amino acid biosynthesis, which had not previously been shown. Overall, we have shown that this combinatorial approach can be used to increase the predictability of true sRNA targets.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-12-01","The student, Alisa King, accepted the attached license on 2019-11-14 at 13:49.","The student, Alisa King, submitted this Dissertation for approval on 2019-11-14 at 13:59.","This Dissertation was approved for publication on 2019-11-15 at 15:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14552 on 2020-02-28 at 17:22:05","Made available in DSpace on 2020-03-02T22:12:17Z (GMT). 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