DNA variants affecting the expression of numerous genes in trans have diverse mechanisms of action and evolutionary histories

Molecular mechanisms underlying gene regulatory variation of maize metabolic traits

Variation in gene expression levels is pervasive among individuals and races or varieties, and has substantial agronomic consequences, for example, by contributing to hybrid vigor. Gene expression level variation results from mutations in regulatory sequences (cis) and/or transcription factor (TF) activity (trans), but the mechanisms underlying cis- and/or trans-regulatory variation of complex phenotypes remain largely unknown. Here, we investigated gene expression variation mechanisms underlying the differential accumulation of the insecticidal compounds maysin and chlorogenic acid in silks of widely used maize (Zea mays) inbreds, B73 and A632. By combining transcriptomics and cistromics, we identified 1,338 silk direct targets of the maize R2R3-MYB TF Pericarp color1 (P1), consistent with it being a regulator of maysin and chlorogenic acid biosynthesis. Among these P1 targets, 464 showed allele-specific expression (ASE) between B73 and A632 silks. Allelic DNA-affinity purification sequencing identified 34 examples in which P1 allelic specific binding (ASB) correlated with cis-expression variation. From previous yeast one-hybrid studies, we identified 9 TFs potentially implicated in the control of P1 targets, with ASB to 83 out of 464 ASE genes (cis) and differential expression of 4 out of 9 TFs between B73 and A632 silks (trans). These results provide a molecular framework for understanding universal mechanisms underlying natural variation of gene expression levels, and how the regulation of metabolic diversity is established.

Marker-free genomic editing in Saccharomyces cerevisiae using universal donor templates and multiplexing CRISPR-CAS9

The budding yeast Saccharomyces cerevisiae is an excellent model organism for studying a variety of critical cellular processes. Traditional methods to knock in or -out at specific yeast loci utilize polymerase chain reaction-based techniques, in which marker cassettes with gene-specific homologies are integrated into the genome via homologous recombination. While simple and cost-effective, these methods are limited by marker availability when multiple edits are desired. More recently, CRISPR-Cas9 technology has introduced methods to edit the yeast genome without the need for selectable markers. Although efficient, this method is hindered by additional reagents and lengthy protocols to design and test unique guide RNAs and donor templates for each desired edit. In this study, we have combined these two approaches and have developed a highly efficient economical method to edit the yeast genome marker-free. We have designed two universal donor templates that efficiently repair commonly used selectable markers when targeted by a novel guideRNA-Cas9 designed to promoter regions in Ashbya gossypii found in most integration modules. Furthermore, we find our newly designed guideRNA-Cas9 successfully multiplexes when multiple markers are present. Using these new tools, we have significantly improved the cost and efficiency to generate single or multiple marker-free genetic modifications. In this study, we demonstrate the effectiveness of these new tools by marker-free ablating PRC1, PEP4, and PRB1 vacuolar proteases typically inactivated before many biochemical and membrane-trafficking studies using budding yeast.

Trans-eQTL hotspots shape complex traits by modulating cellular states

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.

Genome-wide expression QTL mapping reveals the highly dynamic regulatory landscape of a major wheat pathogen

BACKGROUND

In agricultural ecosystems, outbreaks of diseases are frequent and pose a significant threat to food security. A successful pathogen undergoes a complex and well-timed sequence of regulatory changes to avoid detection by the host immune system; hence, well-tuned gene regulation is essential for survival. However, the extent to which the regulatory polymorphisms in a pathogen population provide an adaptive advantage is poorly understood.

RESULTS

We used Zymoseptoria tritici, one of the most important pathogens of wheat, to generate a genome-wide map of regulatory polymorphism governing gene expression. We investigated genome-wide transcription levels of 146 strains grown under nutrient starvation and performed expression quantitative trait loci (eQTL) mapping. We identified cis-eQTLs for 65.3% of all genes and the majority of all eQTL loci are within 2kb upstream and downstream of the transcription start site (TSS). We also show that polymorphism in different gene elements contributes disproportionally to gene expression variation. Investigating regulatory polymorphism in gene categories, we found an enrichment of regulatory variants for genes predicted to be important for fungal pathogenesis but with comparatively low effect size, suggesting a separate layer of gene regulation involving epigenetics. We also show that previously reported trait-associated SNPs in pathogen populations are frequently cis-regulatory variants of neighboring genes with implications for the trait architecture.

CONCLUSIONS

Overall, our study provides extensive evidence that single populations segregate large-scale regulatory variation and are likely to fuel rapid adaptation to resistant hosts and environmental change.

Genome-wide mapping of regulatory variants for temperature- and salinity-adaptive genes reveals genetic basis of genotype-by-environment interaction in Crassostrea ariakensis

Regulatory variants in gene expression serve as bridges linking genetic variation and phenotypic plasticity. Environmental conditions typically influence the effects of regulatory variants on phenotypic plasticity; however, such genotype-by-environment interactions (G × E) are poorly understood. This study aimed to investigate the genetic basis of G × E in estuarine oyster (Crassostrea ariakensis), which is an important model animal for studying environmental adaption owing to its high plasticity and large intraspecific divergence. Genome-wide mapping of expression quantitative trait loci (eQTLs) for 23 environmental adaptive genes was performed for 256 estuarine oysters. We identified 1194 eQTL single nucleotide polymorphisms (eSNPs), including 433 cis-eSNPs in four genes and 722 trans-eSNPs in eight genes. The expression variation explanation of cis-eSNPs (9.95%) was significantly higher than that of trans-eSNPs (9.15%). We specifically showed cis- and trans-eSNPs with high linkage disequilibrium (LD) for Traf7, Slc6a5, Ggt, and Dap3. For example, we identified a cis-regulatory LD block containing 68 cis-eSNP and a trans-regulatory LD block, including 20 trans-eSNPs in Traf7. A high proportion (85%) of 40 vital eSNPs exhibited significant G × E effects. We identified crossing and nonparallel interactions of G × E, with the tag cis-eSNPs of Baat and Slc6a5 as representatives. Our results indicated that cis-eQTLs are highly conserved. This study provides insights into the understanding of adaptive evolutionary mechanisms and phenotypic response prediction to variable environments, as well as the genetic improvement for superior adaptive traits for genetic resource conservation and aquaculture.

The association study between changes in HbA1C with rs2250486 and rs67238751 genetic variants for SLC47A1 in newly diagnosed Iranian patients with type 2 diabetes mellitus: 6 months follow-up study

OBJECTIVES

One of the most well-known oral medications for the treatment of T2DM is metformin. Variants have been found in studies to be useful in detecting new genes connected to T2DM aetiology and affecting metformin's mechanism of action. In this research, we aimed to study two variations of the SLC47A1 gene; rs2250486 and rs67238751, in T2DM patients who had been taking metformin for the first 6 months after the diagnosis in the Iranian population for the first time.

DESIGN AND METHODS

A total of 200 individuals were recruited for the study. According to their glycosylated haemoglobin (HbA1c) levels, the patients were divided into two groups: responders (HbA1c levels were reduced by at least 1% after 6 months of metformin treatment.) and non-responders. DNA was extracted from whole blood and genotyped by Tetra ARMS PCR. High-performance liquid chromatography (HPLC) was used to measure HbA1c levels at the start of the treatment and again 6 months later.

RESULTS

rs2250486 variant in the dominant model reduces the HbA1C levels after 6 months of metformin treatment. In fact, when compared to the T/C + C/C genotypes, the T/T genotype improves HbA1C levels (p-value = .014). Furthermore, in the allelic model, the T allele improves HbA1C levels in comparison to the C allele (p-value = .008). After 6 months of metformin treatment, serum levels of HbA1C in responders were reduced significantly in both groups (T/T and T/C + C/C), (p-value = <.0001). However, the rs67238751 variant did not reveal a meaningful relationship with lower HbA1C levels in any of the models.

CONCLUSIONS

This study found that the rs2250486 variant could be associated with reducing HbA1C levels while the rs67238751 variant, had no relationship.

Mechanisms of regulatory evolution in yeast

Studies of regulatory variation in yeast - at the level of new mutations, polymorphisms within a species, and divergence between species - have provided great insight into the molecular and evolutionary processes responsible for the evolution of gene expression in eukaryotes. The increasing ease with which yeast genomes can be manipulated and expression quantified in a high-throughput manner has recently accelerated mechanistic studies of cis- and trans-regulatory variation at multiple evolutionary timescales. These studies have, for example, identified differences in the properties of cis- and trans-acting mutations that affect their evolutionary fate, experimentally characterized the molecular mechanisms through which cis- and trans-regulatory variants act, and illustrated how regulatory networks can diverge between species with or without changes in gene expression.

Genome-wide base editor screen identifies regulators of protein abundance in yeast

Proteins are key molecular players in a cell, and their abundance is extensively regulated not just at the level of gene expression but also post-transcriptionally. Here, we describe a genetic screen in yeast that enables systematic characterization of how protein abundance regulation is encoded in the genome. The screen combines a CRISPR/Cas9 base editor to introduce point mutations with fluorescent tagging of endogenous proteins to facilitate a flow-cytometric readout. We first benchmarked base editor performance in yeast with individual gRNAs as well as in positive and negative selection screens. We then examined the effects of 16,452 genetic perturbations on the abundance of eleven proteins representing a variety of cellular functions. We uncovered hundreds of regulatory relationships, including a novel link between the GAPDH isoenzymes Tdh1/2/3 and the Ras/PKA pathway. Many of the identified regulators are specific to one of the eleven proteins, but we also found genes that, upon perturbation, affected the abundance of most of the tested proteins. While the more specific regulators usually act transcriptionally, broad regulators often have roles in protein translation. Overall, our novel screening approach provides unprecedented insights into the components, scale and connectedness of the protein regulatory network.

The relationship of mRNA with protein expression in CD8+ T cells associates with gene class and gene characteristics

T cells are key players in our defence against infections and malignancies. When T cells differentiate or become activated, they undergo substantial alterations in gene expression. Even though RNA expression levels are now well documented throughout different stages of T cells, it is not well understood how mRNA expression translates into the protein landscape. By combining paired RNA sequencing and mass spectrometry data of primary human CD8+ T cells, we report that mRNA expression is a poor proxy for the overall protein output, irrespective of the differentiation or activation status. Yet, gene class stratification revealed a function-specific correlation of mRNA with protein expression. This gene class-specific expression pattern associated with differences in gene characteristics such as sequence conservation and untranslated region (UTR) lengths. In addition, the presence of AU-rich elements in the 3'UTR associated with alterations in mRNA and protein abundance T cell activation dependent, gene class-specific manner. In conclusion, our study highlights the role of gene characteristics as a determinant for gene expression in T cells.

High-throughput approaches to functional characterization of genetic variation in yeast

Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.

Response and regulatory mechanisms of heat resistance in pathogenic fungi

Abstract

Both the increasing environmental temperature in nature and the defensive body temperature response to pathogenic fungi during mammalian infection cause heat stress during the fungal existence, reproduction, and pathogenic infection. To adapt and respond to the changing environment, fungi initiate a series of actions through a perfect thermal response system, conservative signaling pathways, corresponding transcriptional regulatory system, corresponding physiological and biochemical processes, and phenotypic changes. However, until now, accurate response and regulatory mechanisms have remained a challenge. Additionally, at present, the latest research progress on the heat resistance mechanism of pathogenic fungi has not been summarized. In this review, recent research investigating temperature sensing, transcriptional regulation, and physiological, biochemical, and morphological responses of fungi in response to heat stress is discussed. Moreover, the specificity thermal adaptation mechanism of pathogenic fungi in vivo is highlighted. These data will provide valuable knowledge to further understand the fungal heat adaptation and response mechanism, especially in pathogenic heat-resistant fungi.

Key points

• Mechanisms of fungal perception of heat pressure are reviewed.

• The regulatory mechanism of fungal resistance to heat stress is discussed.

• The thermal adaptation mechanism of pathogenic fungi in the human body is highlighted.

The influence of SLC22A3 rs543159 and rs1317652 genetic variants on metformin therapeutic efficacy in newly diagnosed patients with type 2 diabetes mellitus: 25 weeks follow-up study

BACKGROUND AND AIMS

Among anti-diabetic medications, metformin has been proven to be the preferred initial pharmacologic agent for type 2 diabetes mellitus (T2DM) treatment. Despite its safety and efficacy, the response to metformin varies between individuals. Genetic variations, especially within genes involved in pharmacokinetics and pharmacodynamics of metformin (e.g SLC22A3), have been suggested to be responsible for the observed inter-individual differences. By considering the undeniable role of organic cation transporter 3 in hepatic uptake of metformin, this study was aimed to investigate the association of rs543159 and rs1317652 variants in SLC22A3 gene with response to metformin monotherapy in newly diagnosed patients with T2DM.

METHODS

The study included 200 T2DM patients who received metformin monotherapy for 25 weeks. The patients were classified into 2 groups according to their HbA1c values: the responders (reduction in HbA1c levels by at least 1% after 25 weeks treatment with metformin) and non-responders (less than 1% reduction in HbA1c levels after 25 weeks treatment with metformin). We used tetra ARMS-PCR method to determine genotypes of the target variants.

RESULTS

For the rs543159, CA and AA genotypes were more frequent in responders as compared to non-responders (OR = 2.48; 95% CI = 1.28-4.78, P-value = 0.0057) under the dominant model. In case of rs1317652 CC and CT genotypes were more frequent in metformin responders as compared to non-responder group (OR = 2.49; 95% CI = 1.32-4.70, P-value = 0.0043) under the dominant model. Parameters such as fasting blood sugar (FBS), HbA1c, and total cholesterol (TC) levels were significantly lower in the responder group after 25 weeks of metformin monotherapy. Moreover, according to the result of multiple linear regression rs543159 and base line HbA1c values are significantly associated with response to metformin monotherapy.

CONCLUSION

Our results suggested that rs543159 and rs1317652 in SLC22A3 gene might be associated with variability in response to metformin therapy in T2DM patients.

Multiple epistatic DNA variants in a single gene affect gene expression in trans

DNA variants that alter gene expression in trans are important sources of phenotypic variation. Nevertheless, the identity of trans-acting variants remains poorly understood. Single causal variants in several genes have been reported to affect the expression of numerous distant genes in trans. Whether these simple molecular architectures are representative of trans-acting variation is unknown. Here, we studied the large RAS signaling regulator gene IRA2, which contains variants with extensive trans-acting effects on gene expression in the yeast Saccharomyces cerevisiae. We used systematic CRISPR-based genome engineering and a sensitive phenotyping strategy to dissect causal variants to the nucleotide level. In contrast to the simple molecular architectures known so far, IRA2 contained at least seven causal nonsynonymous variants. The effects of these variants were modulated by nonadditive, epistatic interactions. Two variants at the 5'-end affected gene expression and growth only when combined with a third variant that also had no effect in isolation. Our findings indicate that the molecular basis of trans-acting genetic variation may be considerably more complex than previously appreciated.

Trans-acting genetic variation affects the expression of adjacent genes

Gene expression differences among individuals are shaped by trans-acting expression quantitative trait loci (eQTLs). Most trans-eQTLs map to hotspot locations that influence many genes. The molecular mechanisms perturbed by hotspots are often assumed to involve "vertical" cascades of effects in pathways that can ultimately affect the expression of thousands of genes. Here, we report that trans-eQTLs can affect the expression of adjacent genes via "horizontal" mechanisms that extend along a chromosome. Genes affected by trans-eQTL hotspots in the yeast Saccharomyces cerevisiae were more likely to be located next to each other than expected by chance. These paired hotspot effects tended to occur at adjacent genes that also show coexpression in response to genetic and environmental perturbations, suggesting shared mechanisms. Physical proximity and shared chromatin state, in addition to regulation of adjacent genes by similar transcription factors, were independently associated with paired hotspot effects among adjacent genes. Paired effects of trans-eQTLs can occur at neighboring genes even when these genes do not share a common function. This phenomenon could result in unexpected connections between regulatory genetic variation and phenotypes.

Mutational sources of trans-regulatory variation affecting gene expression in Saccharomyces cerevisiae

Heritable variation in a gene’s expression arises from mutations impacting cis- and trans-acting components of its regulatory network. Here, we investigate how trans-regulatory mutations are distributed within the genome and within a gene regulatory network by identifying and characterizing 69 mutations with trans-regulatory effects on expression of the same focal gene in Saccharomyces cerevisiae. Relative to 1766 mutations without effects on expression of this focal gene, we found that these trans-regulatory mutations were enriched in coding sequences of transcription factors previously predicted to regulate expression of the focal gene. However, over 90% of the trans-regulatory mutations identified mapped to other types of genes involved in diverse biological processes including chromatin state, metabolism, and signal transduction. These data show how genetic changes in diverse types of genes can impact a gene’s expression in trans, revealing properties of trans-regulatory mutations that provide the raw material for trans-regulatory variation segregating within natural populations.

A genome-scale yeast library with inducible expression of individual genes

The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which > 5,600 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains SGA screening markers and a unique barcode, enabling high-throughput genetics. We characterized YETI using growth phenotyping and BAR-seq screens, and we used a YETI allele to identify the regulon of Rof1, showing that it acts to repress transcription. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from eukaryotic and prokaryotic systems shows that native expression is a variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3 EB42, that gives lower expression than Z3 EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.

Whole-organism eQTL mapping at cellular resolution with single-cell sequencing

Genetic regulation of gene expression underlies variation in disease risk and other complex traits. The effect of expression quantitative trait loci (eQTLs) varies across cell types; however, the complexity of mammalian tissues makes studying cell-type eQTLs highly challenging. We developed a novel approach in the model nematode Caenorhabditis elegans that uses single-cell RNA sequencing to map eQTLs at cellular resolution in a single one-pot experiment. We mapped eQTLs across cell types in an extremely large population of genetically distinct C. elegans individuals. We found cell-type-specific trans eQTL hotspots that affect the expression of core pathways in the relevant cell types. Finally, we found single-cell-specific eQTL effects in the nervous system, including an eQTL with opposite effects in two individual neurons. Our results show that eQTL effects can be specific down to the level of single cells.

Learning causal networks using inducible transcription factors and transcriptome-wide time series

We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors (TFs) and measuring timecourses of the resulting gene expression responses in budding yeast. Each experiment captures a regulatory cascade connecting a single induced regulator to the genes it causally regulates. We discuss the regulatory cascade of a single TF, Aft1, in detail; however, IDEA contains > 200 TF induction experiments with 20 million individual observations and 100,000 signal‐containing dynamic responses. As an application of IDEA, we integrate all timecourses into a whole‐cell transcriptional model, which is used to predict and validate multiple new and underappreciated transcriptional regulators. We also find that the magnitudes of coefficients in this model are predictive of genetic interaction profile similarities. In addition to being a resource for exploring regulatory connectivity between TFs and their target genes, our modeling approach shows that combining rapid perturbations of individual genes with genome‐scale time‐series measurements is an effective strategy for elucidating gene regulatory networks.

Molecular and evolutionary processes generating variation in gene expression

Heritable variation in gene expression is common within and between species. This variation arises from mutations that alter the form or function of molecular gene regulatory networks that are then filtered by natural selection. High-throughput methods for introducing mutations and characterizing their cis- and trans-regulatory effects on gene expression (particularly, transcription) are revealing how different molecular mechanisms generate regulatory variation, and studies comparing these mutational effects with variation seen in the wild are teasing apart the role of neutral and non-neutral evolutionary processes. This integration of molecular and evolutionary biology allows us to understand how the variation in gene expression we see today came to be and to predict how it is most likely to evolve in the future.

Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation

Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.

Systematic identification of cis-regulatory variants that cause gene expression differences in a yeast cross

Sequence variation in regulatory DNA alters gene expression and shapes genetically complex traits. However, the identification of individual, causal regulatory variants is challenging. Here, we used a massively parallel reporter assay to measure the cis-regulatory consequences of 5832 natural DNA variants in the promoters of 2503 genes in the yeast Saccharomyces cerevisiae. We identified 451 causal variants, which underlie genetic loci known to affect gene expression. Several promoters harbored multiple causal variants. In five promoters, pairs of variants showed non-additive, epistatic interactions. Causal variants were enriched at conserved nucleotides, tended to have low derived allele frequency, and were depleted from promoters of essential genes, which is consistent with the action of negative selection. Causal variants were also enriched for alterations in transcription factor binding sites. Models integrating these features provided modest, but statistically significant, ability to predict causal variants. This work revealed a complex molecular basis for cis-acting regulatory variation.

What Has a Century of Quantitative Genetics Taught Us About Nature's Genetic Tool Kit?

The complexity of heredity has been appreciated for decades: Many traits are controlled not by a single genetic locus but instead by polymorphisms throughout the genome. The importance of complex traits in biology and medicine has motivated diverse approaches to understanding their detailed genetic bases. Here, we focus on recent systematic studies, many in budding yeast, which have revealed that large numbers of all kinds of molecular variation, from noncoding to synonymous variants, can make significant contributions to phenotype. Variants can affect different traits in opposing directions, and their contributions can be modified by both the environment and the epigenetic state of the cell. The integration of prospective (synthesizing and analyzing variants) and retrospective (examining standing variation) approaches promises to reveal how natural selection shapes quantitative traits. Only by comprehensively understanding nature's genetic tool kit can we predict how phenotypes arise from the complex ensembles of genetic variants in living organisms.

Applying a New REFINE Approach in Zymomonas mobilis Identifies Novel sRNAs That Confer Improved Stress Tolerance Phenotypes

As global controllers of gene expression, small RNAs represent powerful tools for engineering complex phenotypes. However, a general challenge prevents the more widespread use of sRNA engineering strategies: mechanistic analysis of these regulators in bacteria lags far behind their high-throughput search and discovery. This makes it difficult to understand how to efficiently identify useful sRNAs to engineer a phenotype of interest. To help address this, we developed a forward systems approach to identify naturally occurring sRNAs relevant to a desired phenotype: RNA-seq Examiner for Phenotype-Informed Network Engineering (REFINE). This pipeline uses existing RNA-seq datasets under different growth conditions. It filters the total transcriptome to locate and rank regulatory-RNA-containing regions that can influence a metabolic phenotype of interest, without the need for previous mechanistic characterization. Application of this approach led to the uncovering of six novel sRNAs related to ethanol tolerance in non-model ethanol-producing bacterium Zymomonas mobilis. Furthermore, upon overexpressing multiple sRNA candidates predicted by REFINE, we demonstrate improved ethanol tolerance reflected by up to an approximately twofold increase in relative growth rate compared to controls not expressing these sRNAs in 7% ethanol (v/v) RMG-supplemented media. In this way, the REFINE approach informs strain-engineering strategies that we expect are applicable for general strain engineering.