Global Mapping of Small RNA-Target Interactions in Bacteria.

Small RNAs (sRNAs) associated with the RNA chaperon protein Hfq are key posttranscriptional regulators of gene expression in bacteria. Deciphering the sRNA-target interactome is an essential step toward understanding the roles of sRNAs in the cellular networks. We developed a broadly applicable methodology termed RIL-seq (RNA interaction by ligation and sequencing), which integrates experimental and computational tools for in vivo transcriptome-wide identification of interactions involving Hfq-associated sRNAs. By applying this methodology to Escherichia coli we discovered an extensive network of interactions involving RNA pairs showing sequence complementarity. We expand the ensemble of targets for known sRNAs, uncover additional Hfq-bound sRNAs encoded in various genomic regions along with their trans encoded targets, and provide insights into binding and possible cycling of RNAs on Hfq. Comparison of the sRNA interactome under various conditions has revealed changes in the sRNA repertoire as well as substantial re-wiring of the network between conditions.

A defense-offense multi-layered regulatory switch in a pathogenic bacterium.

Cells adapt to environmental changes by efficiently adjusting gene expression programs. Staphylococcus aureus, an opportunistic pathogenic bacterium, switches between defensive and offensive modes in response to quorum sensing signal. We identified and studied the structural characteristics and dynamic properties of the core regulatory circuit governing this switch by deterministic and stochastic computational methods, as well as experimentally. This module, termed here Double Selector Switch (DSS), comprises the RNA regulator RNAIII and the transcription factor Rot, defining a double-layered switch involving both transcriptional and post-transcriptional regulations. It coordinates the inverse expression of two sets of target genes, immuno-modulators and exotoxins, expressed during the defensive and offensive modes, respectively. Our computational and experimental analyses show that the DSS guarantees fine-tuned coordination of the inverse expression of its two gene sets, tight regulation, and filtering of noisy signals. We also identified variants of this circuit in other bacterial systems, suggesting it is used as a molecular switch in various cellular contexts and offering its use as a template for an effective switching device in synthetic biology studies.

Evolutionary patterns of Escherichia coli small RNAs and their regulatory interactions.

Most bacterial small RNAs (sRNAs) are post-transcriptional regulators of gene expression, exerting their regulatory function by base-pairing with their target mRNAs. While it has become evident that sRNAs play central regulatory roles in the cell, little is known about their evolution and the evolution of their regulatory interactions. Here we used the prokaryotic phylogenetic tree to reconstruct the evolutionary history of Escherichia coli sRNAs and their binding sites on target mRNAs. We discovered that sRNAs currently present in E. coli mainly accumulated inside the Enterobacteriales order, succeeding the appearance of other types of noncoding RNAs and concurrently with the evolution of a variant of the Hfq protein exhibiting a longer C-terminal region. Our analysis of the evolutionary ages of sRNA-mRNA interactions revealed that while all sRNAs were evolutionarily older than most of their known binding sites on mRNA targets, for quite a few sRNAs there was at least one binding site that coappeared with or preceded them. It is conceivable that the establishment of these first interactions forced selective pressure on the sRNAs, after which additional targets were acquired by fitting a binding site to the active region of the sRNA. This conjecture is supported by the appearance of many binding sites on target mRNAs only after the sRNA gain, despite the prior presence of the target gene in ancestral genomes. Our results suggest a selective mechanism that maintained the sRNAs across the phylogenetic tree, and shed light on the evolution of E. coli post-transcriptional regulatory network.

Stride-level analysis of mouse open field behavior using deep-learning-based pose estimation.

Gait and posture are often perturbed in many neurological, neuromuscular, and neuropsychiatric conditions. Rodents provide a tractable model for elucidating disease mechanisms and interventions. Here, we develop a neural-network-based assay that adopts the commonly used open field apparatus for mouse gait and posture analysis. We quantitate both with high precision across 62 strains of mice. We characterize four mutants with known gait deficits and demonstrate that multiple autism spectrum disorder (ASD) models show gait and posture deficits, implying this is a general feature of ASD. Mouse gait and posture measures are highly heritable and fall into three distinct classes. We conduct a genome-wide association study to define the genetic architecture of stride-level mouse movement in the open field. We provide a method for gait and posture extraction from the open field and one of the largest laboratory mouse gait and posture data resources for the research community.

Accessibility and evolutionary conservation mark bacterial small-rna target-binding regions.

Bacterial small noncoding RNAs have attracted much interest in recent years as posttranscriptional regulators of genes involved in diverse pathways. Small RNAs (sRNAs) are 50 to 400 nucleotides long and exert their regulatory function by directly base pairing with mRNA targets to alter their stability and/or affect their translation. This base pairing is achieved through a region of about 10 to 25 nucleotides, which may be located at various positions along different sRNAs. By compiling a data set of experimentally determined target-binding regions of sRNAs and systematically analyzing their properties, we reveal that they are both more evolutionarily conserved and more accessible than random regions. We demonstrate the use of these properties for computational identification of sRNA target-binding regions with high specificity and sensitivity. Our results show that these predicted regions are likely to base pair with known targets of an sRNA, suggesting that pointing out these regions in a specific sRNA can help in searching for its targets.

Functional analysis of the quorum-sensing streptococcal invasion locus (sil).

Group A streptococcus (GAS) causes a wide variety of human diseases, and at the same time, GAS can also circulate without producing symptoms, similar to its close commensal relative, group G streptococcus (GGS). We previously identified, by transposon-tagged mutagenesis, the streptococcal invasion locus (sil). sil is a quorum-sensing regulated locus which is activated by the autoinducer peptide SilCR through the two-component system SilA-SilB. Here we characterize the DNA promoter region necessary for SilA-mediated activation. This site is composed of two direct repeats of 10 bp, separated by a spacer of 11 bp. Fusion of this site to gfp allowed us to systematically introduce single-base substitutions in the repeats region and to assess the relative contribution of various positions to promoter strength. We then developed an algorithm giving different weights to these positions, and performed a chromosome-wide bioinformatics search which was validated by transcriptome analysis. We identified 13 genes, mostly bacteriocin related, that are directly under the control of SilA. Having developed the ability to quantify SilCR signaling via GFP accumulation prompted us to search for GAS and GGS strains that sense and produce SilCR. While the majority of GAS strains lost sil, all GGS strains examined still possess the locus and approximately 63% are able to respond to exogenously added SilCR. By triggering the autoinduction circle using a minute concentration of synthetic SilCR, we identified GAS and GGS strains that are capable of sensing and naturally producing SilCR, and showed that SilCR can be sensed across these streptococci species. These findings suggest that sil may be involved in colonization and establishment of commensal host-bacterial relationships.

Action detection using a neural network elucidates the genetics of mouse grooming behavior.

Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endophenotype in psychiatric genetics. Here, we used mouse grooming behavior as an example and developed a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operating across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We surveyed grooming behavior in the open field in 2457 mice across 62 strains, determined its heritable components, conducted GWAS to outline its genetic architecture, and performed PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of behavioral mechanisms.

Behavior is one of the ultimate and most complex outputs of the body's central nervous system, which controls movement, emotion and mood. It is also influenced by a person's genetics. Scientists studying the link between behavior and genetics often conduct experiments using animals, whose actions can be more easily characterized than humans. However, this involves recording hours of video footage, typically of mice or flies. Researchers must then add labels to this footage, identifying certain behaviors before further analysis. This task of annotating video clips ­ similar to image captioning ­ is very time-consuming for investigators. But it could be automated by applying machine learning algorithms, trained with sufficient data. Some computer programs are already in use to detect patterns of behavior, however, there are some limitations. These programs could detect animal behavior (of flies and mice) in trimmed video clips, but not raw footage, and could not always accommodate different lighting conditions or experimental setups. Here, Geuther et al. set out to improve on these previous efforts to automate video annotation. To do so, they used over 1,250 video clips annotated by experienced researchers to develop a general-purpose neural network for detecting mouse behaviors. After sufficient training, the computer model could detect mouse grooming behaviors in raw, untrimmed video clips just as well as human observers could. It also worked with mice of different coat colors, body shapes and sizes in open field animal tests. Using the new computer model, Geuther et al. also studied the genetics underpinning behavior ­ far more thoroughly than previously possible ­ to explain why mice display different grooming behaviors. The algorithm analyzed 2,250 hours of video featuring over 60 kinds of mice and thousands of other animals. It found that mice bred in the laboratory groom less than mice recently collected from the wild do. Further analyses also identified genes linked to grooming traits in mice and found related genes in humans associated with behavioral disorders. Automating video annotation using machine learning models could alleviate the costs of running lengthy behavioral experiments and enhance the reproducibility of study results. The latter is vital for translating behavioral research findings in mice to humans. This study has also provided insights into the amount of human-annotated training data needed to develop high-performing computer models, along with new understandings of how genetics shapes behavior.

Mapping the small RNA interactome in bacteria using RIL-seq.

Small RNAs (sRNAs) are major post-transcriptional regulators of gene expression in bacteria. To enable transcriptome-wide mapping of bacterial sRNA-target pairs, we developed RIL-seq (RNA interaction by ligation and sequencing). RIL-seq is an experimental-computational methodology for capturing sRNA-target interactions in vivo that takes advantage of the mutual binding of the sRNA and target RNA molecules to the RNA chaperone protein Hfq. The experimental part of the protocol involves co-immunoprecipitation of Hfq and bound RNAs, ligation of RNAs, library preparation and sequencing. The computational pipeline maps the sequenced fragments to the genome, reveals chimeric fragments (fragments comprising two ligated independent fragments) and determines statistically significant overrepresented chimeric fragments as interacting RNAs. The statistical filter is aimed at reducing the number of spurious interactions resulting from ligation of random neighboring RNA fragments, thus increasing the reliability of the determined sRNA-target pairs. A major advantage of RIL-seq is that it does not require overexpression of sRNAs; instead, it simultaneously captures the in vivo targets of all sRNAs in the native state of the cell. Application of RIL-seq to bacteria grown under different conditions provides distinctive snapshots of the sRNA interactome and sheds light on the dynamics and rewiring of the post-transcriptional regulatory network. As RIL-seq needs no prior information about the sRNA and target sequences, it can identify novel sRNAs, along with their targets. It can be adapted to detect protein-mediated RNA-RNA interactions in any bacterium with a sequenced genome. The experimental part of the RIL-seq protocol takes 7-9 d and the computational analysis takes ∼2 d.