Exodus: sequencing-based pipeline for quantification of pooled variants.

SUMMARY: Next-Generation Sequencing is widely used as a tool for identifying and quantifying microorganisms pooled together in either natural or designed samples. However, a prominent obstacle is achieving correct quantification when the pooled microbes are genetically related. In such cases, the outcome mostly depends on the method used for assigning reads to the individual targets. To address this challenge, we have developed Exodus-a reference-based Python algorithm for quantification of genomes, including those that are highly similar, when they are sequenced together in a single mix. To test Exodus' performance, we generated both empirical and in silico next-generation sequencing data of mixed genomes. When applying Exodus to these data, we observed median error rates varying between 0% and 0.21% as a function of the complexity of the mix. Importantly, no false negatives were recorded, demonstrating that Exodus' likelihood of missing an existing genome is very low, even if the genome's relative abundance is low and similar genomes are present in the same mix. Taken together, these data position Exodus as a reliable tool for identifying and quantifying genomes in mixed samples. Exodus is open source and free to use at: https://github.com/ilyavs/exodus. AVAILABILITY AND IMPLEMENTATION: Exodus is implemented in Python within a Snakemake framework. It is available on GitHub alongside a docker containing the required dependencies: https://github.com/ilyavs/exodus. The data underlying this article will be shared on reasonable request to the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sequence determinants of polyadenylation-mediated regulation.

The cleavage and polyadenylation reaction is a crucial step in transcription termination and pre-mRNA maturation in human cells. Despite extensive research, the encoding of polyadenylation-mediated regulation of gene expression within the DNA sequence is not well understood. Here, we utilized a massively parallel reporter assay to inspect the effect of over 12,000 rationally designed polyadenylation sequences (PASs) on reporter gene expression and cleavage efficiency. We find that the PAS sequence can modulate gene expression by over five orders of magnitude. By using a uniquely designed scanning mutagenesis data set, we gain mechanistic insight into various modes of action by which the cleavage efficiency affects the sensitivity or robustness of the PAS to mutation. Furthermore, we employ motif discovery to identify both known and novel sequence motifs associated with PAS-mediated regulation. By leveraging the large scale of our data, we train a deep learning model for the highly accurate prediction of RNA levels from DNA sequence alone (R = 0.83). Moreover, we devise unique approaches for predicting exact cleavage sites for our reporter constructs and for endogenous transcripts. Taken together, our results expand our understanding of PAS-mediated regulation, and provide an unprecedented resource for analyzing and predicting PAS for regulatory genomics applications.

Unraveling the determinants of microRNA mediated regulation using a massively parallel reporter assay.

Despite extensive research, the sequence features affecting microRNA-mediated regulation are not well understood, limiting our ability to predict gene expression levels in both native and synthetic sequences. Here we employed a massively parallel reporter assay to investigate the effect of over 14,000 rationally designed 3' UTR sequences on reporter construct repression. We found that multiple factors, including microRNA identity, hybridization energy, target accessibility, and target multiplicity, can be manipulated to achieve a predictable, up to 57-fold, change in protein repression. Moreover, we predict protein repression and RNA levels with high accuracy (R = 0.84 and R = 0.80, respectively) using only 3' UTR sequence, as well as the effect of mutation in native 3' UTRs on protein repression (R = 0.63). Taken together, our results elucidate the effect of different sequence features on miRNA-mediated regulation and demonstrate the predictability of their effect on gene expression with applications in regulatory genomics and synthetic biology.

TRUB1 is the predominant pseudouridine synthase acting on mammalian mRNA via a predictable and conserved code.

Following synthesis, RNA can be modified with over 100 chemically distinct modifications, which can potentially regulate RNA expression post-transcriptionally. Pseudouridine (Ψ) was recently established to be widespread and dynamically regulated on yeast mRNA, but less is known about Ψ presence, regulation, and biogenesis in mammalian mRNA. Here, we sought to characterize the Ψ landscape on mammalian mRNA, to identify the main Ψ-synthases (PUSs) catalyzing Ψ formation, and to understand the factors governing their specificity toward selected targets. We first developed a framework allowing analysis, evaluation, and integration of Ψ mappings, which we applied to >2.5 billion reads from 30 human samples. These maps, complemented with genetic perturbations, allowed us to uncover TRUB1 and PUS7 as the two key PUSs acting on mammalian mRNA and to computationally model the sequence and structural elements governing the specificity of TRUB1, achieving near-perfect prediction of its substrates (AUC = 0.974). We then validated and extended these maps and the inferred specificity of TRUB1 using massively parallel reporter assays in which we monitored Ψ levels at thousands of synthetically designed sequence variants comprising either the sequences surrounding pseudouridylation targets or systematically designed mutants perturbing RNA sequence and structure. Our findings provide an extensive and high-quality characterization of the transcriptome-wide distribution of pseudouridine in human and the factors governing it and provide an important resource for the community, paving the path toward functional and mechanistic dissection of this emerging layer of post-transcriptional regulation.

Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness.

Data of gene expression levels across individuals, cell types, and disease states is expanding, yet our understanding of how expression levels impact phenotype is limited. Here, we present a massively parallel system for assaying the effect of gene expression levels on fitness in Saccharomyces cerevisiae by systematically altering the expression level of ∼100 genes at ∼100 distinct levels spanning a 500-fold range at high resolution. We show that the relationship between expression levels and growth is gene and environment specific and provides information on the function, stoichiometry, and interactions of genes. Wild-type expression levels in some conditions are not optimal for growth, and genes whose fitness is greatly affected by small changes in expression level tend to exhibit lower cell-to-cell variability in expression. Our study addresses a fundamental gap in understanding the functional significance of gene expression regulation and offers a framework for evaluating the phenotypic effects of expression variation.

Pyruvate Formate-Lyase Enables Efficient Growth of Escherichia coli on Acetate and Formate.

Pyruvate formate-lyase (PFL) is a ubiquitous enzyme that supports increased ATP yield during sugar fermentation. While the PFL reaction is known to be reversible in vitro, the ability of PFL to support microbial growth by condensing acetyl-CoA and formate in vivo has never been directly tested. Here, we employ Escherichia coli mutant strains that cannot assimilate acetate via the glyoxylate shunt and use carbon labeling experiments to unequivocally demonstrate PFL-dependent co-assimilation of acetate and formate. Moreover, PFL-dependent growth is faster than growth on acetate using the glyoxylate shunt. Hence, growth via the reverse activity of PFL could have substantial ecological and biotechnological significance.