Rahul V. Kulkarni

CsrA and three redundant small RNAs regulate quorum sensing in Vibrio cholerae

Bacteria communicate using a process called quorum sensing which involves production, secretion and detection of signalling molecules called autoinducers. Quorum sensing allows populations of bacteria to simultaneously regulate gene expression in response to changes in cell density. The human pathogen, Vibrio cholerae, uses a quorum‐sensing circuit composed of parallel systems that transduce information through four redundant regulatory small RNAs (sRNAs) called quorum regulatory RNAs (Qrr) to control the expression of numerous genes, most notably those required for virulence. We show that the VarS/VarA two‐component sensory system comprises an additional regulatory input controlling quorum‐sensing‐dependent gene expression in V. cholerae. VarS/VarA controls transcription of three previously unidentified small regulatory RNAs (sRNAs) that are similar to the sRNAs CsrB and CsrC of Escherichia coli. The three V. cholerae sRNAs, which we name CsrB, CsrC and CsrD, act redundantly to control the activity of the global regulatory protein, CsrA. The VarS/VarA‐CsrA/BCD system converges with the V. cholerae quorum‐sensing systems to regulate the expression of the Qrr sRNAs, and thus, the entire quorum‐sensing regulon.

Regulatory targets of quorum sensing in Vibrio cholerae: evidence for two distinct HapR-binding motifs

The quorum-sensing pathway in Vibrio cholerae controls the expression of the master regulator HapR, which in turn regulates several important processes such as virulence factor production and biofilm formation. While HapR is known to control several important phenotypes, there are only a few target genes known to be transcriptionally regulated by HapR. In this work, we combine bioinformatic analysis with experimental validation to discover a set of novel direct targets of HapR. Our results provide evidence for two distinct binding motifs for HapR-regulated genes in V. cholerae. The first binding motif is similar to the motifs recently discovered for orthologs of HapR in V. harveyi and V. vulnificus. However, our results demonstrate that this binding motif can be of variable length in V. cholerae. The second binding motif shares common elements with the first motif, but is of fixed length and lacks dyad symmetry at the ends. The contributions of different bases to HapR binding for this second motif were demonstrated using systematic mutagenesis experiments. The current analysis presents an approach for systematically expanding our knowledge of the quorum-sensing regulon in V. cholerae and other related bacteria.

Intrinsic noise in stochastic models of gene expression with molecular memory and bursting

Regulation of intrinsic noise in gene expression is essential for many cellular functions. Correspondingly, there is considerable interest in understanding how different molecular mechanisms of gene expression impact variations in protein levels across a population of cells. In this work, we analyze a stochastic model of bursty gene expression which considers general waiting-time distributions governing arrival and decay of proteins. By mapping the system to models analyzed in queueing theory, we derive analytical expressions for the noise in steady-state protein distributions. The derived results extend previous work by including the effects of arbitrary probability distributions representing the effects of molecular memory and bursting. The analytical expressions obtained provide insight into the role of transcriptional, post-transcriptional, and post-translational mechanisms in controlling the noise in gene expression.

Exact distributions for stochastic gene expression models with bursting and feedback.

Stochasticity in gene expression can give rise to fluctuations in protein levels and lead to phenotypic variation across a population of genetically identical cells. Recent experiments indicate that bursting and feedback mechanisms play important roles in controlling noise in gene expression and phenotypic variation. A quantitative understanding of the impact of these factors requires analysis of the corresponding stochastic models. However, for stochastic models of gene expression with feedback and bursting, exact analytical results for protein distributions have not been obtained so far. Here, we analyze a model of gene expression with bursting and feedback regulation and obtain exact results for the corresponding protein steady-state distribution. The results obtained provide new insights into the role of bursting and feedback in noise regulation and optimization. Furthermore, for a specific choice of parameters, the system studied maps on to a two-state biochemical switch driven by a bursty input noise source. The analytical results derived provide quantitative insights into diverse cellular processes involving noise in gene expression and biochemical switching.

Structural Rearrangement in an RsmA/CsrA Ortholog of Pseudomonas aeruginosa Creates a Dimeric RNA-Binding Protein, RsmN.

Summary In bacteria, the highly conserved RsmA/CsrA family of RNA-binding proteins functions as global posttranscriptional regulators acting on mRNA translation and stability. Through phenotypic complementation of an rsmA mutant in Pseudomonas aeruginosa, we discovered a family member, termed RsmN. Elucidation of the RsmN crystal structure and that of the complex with a hairpin from the sRNA, RsmZ, reveals a uniquely inserted α helix, which redirects the polypeptide chain to form a distinctly different protein fold to the domain-swapped dimeric structure of RsmA homologs. The overall β sheet structure required for RNA recognition is, however, preserved with compensatory sequence and structure differences, allowing the RsmN dimer to target binding motifs in both structured hairpin loops and flexible disordered RNAs. Phylogenetic analysis indicates that, although RsmN appears unique to P. aeruginosa, homologous proteins with the inserted α helix are more widespread and arose as a consequence of a gene duplication event.

Connecting protein and mRNA burst distributions for stochastic models of gene expression.

The intrinsic stochasticity of gene expression can lead to large variability in protein levels for genetically identical cells. Such variability in protein levels can arise from infrequent synthesis of mRNAs which in turn give rise to bursts of protein expression. Protein expression occurring in bursts has indeed been observed experimentally and recent studies have also found evidence for transcriptional bursting, i.e. production of mRNAs in bursts. Given that there are distinct experimental techniques for quantifying the noise at different stages of gene expression, it is of interest to derive analytical results connecting experimental observations at different levels. In this work, we consider stochastic models of gene expression for which mRNA and protein production occurs in independent bursts. For such models, we derive analytical expressions connecting protein and mRNA burst distributions which show how the functional form of the mRNA burst distribution can be inferred from the protein burst distribution. Additionally, if gene expression is repressed such that observed protein bursts arise only from single mRNAs, we show how observations of protein burst distributions (repressed and unrepressed) can be used to completely determine the mRNA burst distribution. Assuming independent contributions from individual bursts, we derive analytical expressions connecting means and variances for burst and steady-state protein distributions. Finally, we validate our general analytical results by considering a specific reaction scheme involving regulation of protein bursts by small RNAs. For a range of parameters, we derive analytical expressions for regulated protein distributions that are validated using stochastic simulations. The analytical results obtained in this work can thus serve as useful inputs for a broad range of studies focusing on stochasticity in gene expression.

A sequence-based approach for prediction of CsrA/RsmA targets in bacteria with experimental validation in Pseudomonas aeruginosa

CsrA/RsmA homologs are an extensive family of ribonucleic acid (RNA)-binding proteins that function as global post-transcriptional regulators controlling important cellular processes such as secondary metabolism, motility, biofilm formation and the production and secretion of virulence factors in diverse bacterial species. While direct messenger RNA binding by CsrA/RsmA has been studied in detail for some genes, it is anticipated that there are numerous additional, as yet undiscovered, direct targets that mediate its global regulation. To assist in the discovery of these targets, we propose a sequence-based approach to predict genes directly regulated by these regulators. In this work, we develop a computer code (CSRA_TARGET) implementing this approach, which leads to predictions for several novel targets in Escherichia coli and Pseudomonas aeruginosa. The predicted targets in other bacteria, specifically Salmonella enterica serovar Typhimurium, Pectobacterium carotovorum and Legionella pneumophila, also include global regulators that control virulence in these pathogens, unraveling intricate indirect regulatory roles for CsrA/RsmA. We have experimentally validated four predicted RsmA targets in P. aeruginosa. The sequence-based approach developed in this work can thus lead to several testable predictions for direct targets of CsrA homologs, thereby complementing and accelerating efforts to unravel global regulation by this important family of proteins.

Transcriptional Bursting in Gene Expression: Analytical Results for General Stochastic Models.

Gene expression in individual cells is highly variable and sporadic, often resulting in the synthesis of mRNAs and proteins in bursts. Such bursting has important consequences for cell-fate decisions in diverse processes ranging from HIV-1 viral infections to stem-cell differentiation. It is generally assumed that bursts are geometrically distributed and that they arrive according to a Poisson process. On the other hand, recent single-cell experiments provide evidence for complex burst arrival processes, highlighting the need for analysis of more general stochastic models. To address this issue, we invoke a mapping between general stochastic models of gene expression and systems studied in queueing theory to derive exact analytical expressions for the moments associated with mRNA/protein steady-state distributions. These results are then used to derive noise signatures, i.e. explicit conditions based entirely on experimentally measurable quantities, that determine if the burst distributions deviate from the geometric distribution or if burst arrival deviates from a Poisson process. For non-Poisson arrivals, we develop approaches for accurate estimation of burst parameters. The proposed approaches can lead to new insights into transcriptional bursting based on measurements of steady-state mRNA/protein distributions.

Pattern formation within Escherichia coli: diffusion, membrane attachment, and self-interaction of MinD molecules.

In E. coli, accurate cell division depends upon the oscillation of Min proteins from pole to pole. We provide a model for the polar localization of MinD based only on diffusion, a delay for nucleotide exchange, and different rates of attachment to the bare membrane and the occupied membrane. We derive analytically the probability density, and correspondingly the length scale, for MinD attachment zones. Our simple analytical model illustrates the processes giving rise to the observed localization of cellular MinD zones.

Post-transcriptional regulation of noise in protein distributions during gene expression.

The intrinsic stochasticity of gene expression can lead to a large variability of protein levels across a population of cells. Variability (or noise) in protein distributions can be modulated by cellular mechanisms of gene regulation; in particular, there is considerable interest in understanding the role of post-transcriptional regulation. To address this issue, we propose and analyze a stochastic model for post-transcriptional regulation of gene expression. The analytical solution of the model provides insight into the effects of different mechanisms of post-transcriptional regulation on the noise in protein distributions. The results obtained also demonstrate how different sources of intrinsic noise in gene expression can be discriminated based on observations of regulated protein distributions.