A combinatorial approach for improved target prediction, validation, and characterization of small RNAs in Escherichia coli

King, Alisa Margaret

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https://hdl.handle.net/2142/106343 Copy

Description

Title

A combinatorial approach for improved target prediction, validation, and characterization of small RNAs in Escherichia coli

Author(s)

King, Alisa Margaret

Issue Date

2019-11-15

Director of Research (if dissertation) or Advisor (if thesis)

Vanderpool, Carin K

Doctoral Committee Chair(s)

Vanderpool, Carin K

Committee Member(s)

• Metcalf, William W
• Slauch, James M
• Cronan, John E

Department of Study

Microbiology

Discipline

Microbiology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Date of Ingest

2020-03-02T22:12:17Z

Keyword(s)

• gene regulation
• small rna
• genetics
• algorithms

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.

Graduation Semester

2019-12

Type of Resource

text

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http://hdl.handle.net/2142/106343

Copyright and License Information

Copyright 2019 Alisa King

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