Dataset Information

SRNA-Effector: A tool to expedite discovery of small RNA regulators.

ABSTRACT: microRNAs (miRNAs) are small regulatory RNAs that repress target mRNA transcripts through base pairing. Although the mechanisms of miRNA production and function are clearly established, new insights into miRNA regulation or miRNA-mediated gene silencing are still emerging. In order to facilitate the discovery of miRNA regulators or effectors, we have developed sRNA-Effector, a machine learning algorithm trained on enhanced crosslinking and immunoprecipitation sequencing and RNA sequencing data following knockdown of specific genes. sRNA-Effector can accurately identify known miRNA biogenesis and effector proteins and identifies 9 putative regulators of miRNA function, including serine/threonine kinase STK33, splicing factor SFPQ, and proto-oncogene BMI1. We validated the role of STK33, SFPQ, and BMI1 in miRNA regulation, showing that sRNA-Effector is useful for identifying new players in small RNA biology. sRNA-Effector will be a web tool available for all researchers to identify potential miRNA regulators in any cell line of interest.

SUBMITTER: Wilson B

PROVIDER: S-EPMC10926228 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

sRNA-Effector: A tool to expedite discovery of small RNA regulators.

Wilson Briana B Esmaeili Fatemeh F Parsons Matthew M Salah Wafa W Su Zhangli Z Dutta Anindya A

iScience 20240220 3

microRNAs (miRNAs) are small regulatory RNAs that repress target mRNA transcripts through base pairing. Although the mechanisms of miRNA production and function are clearly established, new insights into miRNA regulation or miRNA-mediated gene silencing are still emerging. In order to facilitate the discovery of miRNA regulators or effectors, we have developed sRNA-Effector, a machine learning algorithm trained on enhanced crosslinking and immunoprecipitation sequencing and RNA sequencing data f ...[more]

PMID: 38469560

Similar Datasets

Project description:Viroids are circular, highly structured, single-stranded, non-coding RNA pathogens known to infect and cause disease in several plant species. They are known to trigger the host plant's RNA silencing machinery. The detection of viroid-derived small RNAs (vd-sRNA) in viroid-infected host plants opened a new avenue of study in host-viroid pathogenicity. Since then, several viroid research groups have studied the vd-sRNA retrieved from different host-viroid combinations. Such studies require the segregation of 21- to 24-nucleotide long small RNAs (sRNA) from a deep-sequencing databank, followed by separating the vd-sRNA from any sRNA within this group that showed sequence similarity with either the genomic or the antigenomic strands of the viroid. Such mapped vd-sRNAs are then profiled on both the viroid's genomic and antigenomic strands for visualization. Although several commercial interfaces are currently available for this purpose, they are all programmed for linear RNA molecules. Hence, viroid researchers must develop a computer program that accommodates the sRNAs derived from the circular viroid genome. This is a laborious process, and consequently, it often creates a bottleneck for biologists. In order to overcome this constraint, and to help the research community in general, in this study, a python-based pattern matching interface was developed so as to be able to both profile and map sRNAs on a circular genome. A "matching tolerance" feature has been included in the program, thus permitting the mapping of the sRNAs derived from the quasi-species. Additionally, the "topology" feature allows the researcher to profile sRNA derived from both linear and circular RNA molecules. The efficiency of the program was tested using previously reported deep-sequencing data obtained from two independent studies. Clearly, this novel software should be a key tool with which to both evaluate the production of sRNA and to profile them on their target RNA species, irrespective of the topology of the target RNA molecule.

Project description:Acid tolerance of microorganisms is a desirable phenotype for many industrial fermentation applications. In Escherichia coli, the stress response sigma factor RpoS is a promising target for engineering acid-tolerant phenotypes. However, the simple overexpression of RpoS alone is insufficient to confer these phenotypes. In this study, we show that the simultaneous overexpression of the noncoding small RNA (sRNA) DsrA and the sRNA chaperone Hfq, which act as RpoS activators, significantly increased acid tolerance in terms of cell growth under modest acidic pH, as well as cell survival upon extreme acid shock. Directed evolution of the DsrA-Hfq module further improved the acid tolerance, with the best mutants showing a 51 to 72% increase in growth performance at pH 4.5 compared with the starting strain, MG1655. Further analyses found that the improved acid tolerance of these DsrA-Hfq strains coincided with activation of genes associated with proton-consuming acid resistance system 2 (AR2), protein chaperone HdeB, and reactive oxygen species (ROS) removal in the exponential phase. This study illustrated that the fine-tuning of sRNAs and their chaperones can be a novel strategy for improving the acid tolerance of E. coliIMPORTANCE Many of the traditional studies on bacterial acid tolerance generally focused on improving cell survival under extreme-pH conditions, but cell growth under less harsh acidic conditions is more relevant to industrial applications. Under normal conditions, the general stress response sigma factor RpoS is maintained at low levels in the growth phase through a number of mechanisms. This study showed that RpoS can be activated prior to the stationary phase via engineering its activators, the sRNA DsrA and the sRNA chaperone Hfq, resulting in significantly improved cell growth at modest acidic pH. This work suggests that the sigma factors and likely other transcription factors can be retuned or retimed by manipulating the respective regulatory sRNAs along with the sufficient supply of the respective sRNA chaperones (i.e., Hfq). This provides a novel avenue for strain engineering of microbes.

Dataset Information

SRNA-Effector: A tool to expedite discovery of small RNA regulators.

Publications

sRNA-Effector: A tool to expedite discovery of small RNA regulators.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets