From the beginning of the project we accomplished the following:
1) Our results suggest that competition between targets over binding to Hfq plays a major role in the regulatory outcomes that a sRNA exerts on different targets (Faigenbaum-Romm et al, 2020).
2) We developed a machine learning-based algorithm that predicts novel sRNAs from Hfq-mediated RIL-seq data. Applying this new algorithm to our original RIL-seq data of Escherichia coli K-12 (Melamed et al, 2016), we identified and verified experimentally novel Hfq-dependent sRNAs encoded in various genomic regions (Bar et al., 2021).
3) We completed the study of inference of the relative contribution of a sRNA to the response of cells to an environmental change (Barsheshet et al., 2022). Applying a special design of differential gene expression analysis by RNA-seq, we extract the contribution of a sRNA to the total change in the transcript level of each gene in response to an environmental change (applied to RyhB and GadF in Escherichia coli grown under iron limitation and acidic stress, respectively, and to MgrR in enteropathogenic E. coli (EPEC) grown on DMEM).
4) We published the sRNA-target network of the pathogenic bacterium EPEC, studied in collaboration with Prof. Ilan Rosenshine from our department (Pearl Mizrahi et al., 2021). We discovered novel sRNAs encoded in non-core genomic regions and found that sRNAs mediate substantial information flow between the core genome and genomic islands. We discovered a few sRNAs that act as “regulatory hubs” of virulence genes, one of which, MgrR, we studied in detail.
5) We completed deciphering the networks of additional two pathogenic bacteria, Salmonella enertica and Klebsiella pneumonia, in collaboration with Joerg Vogel (HIRI, Germany) and Yunn-Hwen Gan (NUS, Singapore), respectively. In the Salmonella study (Matera et al, 2022), we discovered a new sponging mechanism by a sRNA (OmpX) of another sRNA (MicF). In Klebsiella (Goh et al., 2024, under review), we found that the capsule, a major virulence component, is a hub target of sRNAs. Having the three sRNA-target networks of pathogenic bacteria and the network of E. coli K-12, we identified sRNA-target interactions that are conserved in all four bacteria, implying the importance of this regulation.
6) We applied ribo-seq to E. coli strains grown in normal conditions and under iron limitation, where the sRNA RyhB is induced. We analyze these data to study the effect of sRNAs on translation initiation and elongation. For the latter, we have been developing algorithms to identify ribosome pausing sites that are dependent on sRNA binding, suggesting possible regulation of translation elongation by sRNAs.
7) We completed the development of an original and accurate algorithm to determine transcript termini using only gene expression data (Bar et al., 2023). To this end, we exploit a specific read pattern that we identified at 3’ termini of transcripts in gene expression data generated by the RNAtag-seq protocol, a protocol that is widely used in microbiology research. We now apply our new algorithm to the wealth of RNAtag-seq data available for 32 bacteria grown under 12 conditions, identifying condition-dependent pre-mature transcaription termination sites that are conserved in evolution.
8) We completed the study of the interplay between a sRNA and an endoribonuclease, using GcvB and RNAse E as our model system (Barsheshet et al., 2024, in revision). To infer quantitatively the functional interaction between GcvB and RNase E, we appplied the approach of the double mutant cycle. This approach involves a special design of single and double mutants to which RNA-seq is applied. We can extract the separate contributions and mutual contributions of the endoribonuclease and sRNA to changes in gene expression by a special analysis of the data. We carried out these experiments for GcvB and RNase E, using RNA from E. coli wild type, ΔgcvB, rne ts conditional mutant and the double mutant strains. In addition, we applied TIER-seq to the four strains, to identify cleavage sites of RNase E that are affected by GcvB. By intersecting these results with data of GcvB binding sites, we attempted to delineate the mechanisms by which GcvB interacts with RNase E, either in a translation-dependent or independent manner.
