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Contreras, L. M.

Publications and source records attributed to Contreras, L. M..

At least 19 recordsLinked to original sources

Modeling binding of the conserved Csr/Rsm protein family across species of the γ-proteobacteria reveals niche-specific adaptation of the post-transcriptional regulon

The {gamma}-proteobacteria are an exceptionally diverse bacterial class whose members thrive in environments from deep-sea vents to human intestinal tracts. Rapid gene expression responses mediated by global post-transcriptional regulatory networks like the Csr/Rsm system are critical for bacterial survival in dynamic niches. CsrA/RsmA functions as a global regulatory RNA-binding protein, directly controlling hundreds to thousands of mRNA targets simultaneously across the transcriptome to coordinate systems-level metabolic and behavioral responses. Despite conservation of the CsrA/RsmA regulatory protein across {gamma}-proteobacteria, the genes it regulates in different species remain poorly characterized. We extended a previously developed biophysical model of CsrA/RsmA-RNA binding from Escherichia coli and Pseudomonas aeruginosa to predict regulons across 16 diverse {gamma}-proteobacterial species. While CsrA/RsmA protein structure and RNA-binding motif recognition are highly conserved, predicted target regulons diverge dramatically across species. Pathway enrichment analysis demonstrated both conserved regulation of core metabolic processes and extensive species-specific regulation of niche-adapted functions including virulence, biocontrol, and environmental stress response. Only two gene groups were shared exclusively among non-pathogens, while pathogens showed no exclusively conserved targets, indicating extensive regulon rewiring. These findings demonstrate that post-transcriptional regulatory networks evolve primarily through mutations in RNA targets that create or eliminate regulatory binding sites, rapidly adapting target repertoires to ecological demands while the regulatory protein mechanism remains conserved. ImportanceThe CsrA/RsmA family represents one of the most influential global regulatory RNA-binding proteins in {gamma}-proteobacteria, directly binding and regulating hundreds of mRNA targets to orchestrate systems-scale control over metabolism, virulence, and environmental adaptation, yet how this conserved mechanism adapts across diverse niches remains unclear. By predicting CsrA/RsmA targets across 16 species, we demonstrate that regulatory evolution occurs primarily through changes in targeted genes rather than the regulatory protein itself. This conserved mechanism with flexible targets may represent an efficient evolutionary strategy for optimizing gene expression for specific lifestyles, highlighting the importance of studying regulation beyond model organisms.

molecular biology↗

Profiling of RNA 8-oxoG marks in Escherichia coli identifies critical intrinsic characteristics that contribute to 8-oxoG accumulation in bacteria

Reactive oxygen species (ROS) are environmentally ubiquitous and known to have pervasive impacts on cellular homeostasis. RNA is vulnerable to oxidative chemical alterations from a variety of endogenous and exogenous sources. The most common chemical modification resulting from ROS exposure to RNA is 8-oxo-7,8-dihydroguanine (8-oxoG)--an oxidized form of the canonical guanine (G) nucleobase. While 8-oxoG modifications are known to impact mRNA processing, understanding the broader biological impact of 8-oxoG requires knowledge of how these modifications accumulate. In this work, we assessed the disparate enrichment of 8-oxoG modifications within RNAs in the E. coli transcriptome using an RNA Immunoprecipitation Sequencing technique with a high-affinity 8-oxoG antibody (8-oxoG-RIP-Seq). Our investigation of the RNA 8-oxoG enrichment landscape uncovered several intrinsic RNA characteristics that correlate with 8-oxoG enrichment. These findings suggest intrinsic characteristics of RNA, most notably relative abundance, CDS length, and G nucleotide composition, significantly influence RNA 8-oxoG accumulation. We harnessed these intrinsic characteristics to construct a simple multiple linear regression model that predicts RNA 8-oxoG accumulation, which we validated in E. coli. This model was subsequently applied to predict 8-oxoG enriched RNA species in four other bacterial species spanning a wide range of oxidative stress tolerances; these predictions suggest that 8-oxoG accumulation is largely species dependent, with limited overlap in RNAs and functional pathways that are more susceptible to elevated levels of 8-oxoG accumulation. Overall, these findings better inform understanding of RNA 8-oxoG patterns in bacteria and have broader impacts towards advancing knowledge of the connection between RNA oxidation and cellular homeostasis.

molecular biology↗

Cytoplasmic localization of PUS7 facilitates a pseudouridine-dependent enhancement of cellular stress tolerance

Pseudouridine ({Psi}) is an abundant post-transcriptional modification found across all classes of RNA. It has been widely speculated that {Psi} inclusion in mRNAs might provide an avenue for cells to control gene expression post-transcriptionally. Here we demonstrate that one of the principal mRNA pseudouridylating enzymes, pseudouridine synthase 7 (PUS7), exhibits a stress-induced accumulation in the cytoplasm of yeast and human epithelial lung cells. Stress-induced and cytoplasmic localization of PUS7 promote {Psi}-incorporation into hundreds of mRNA targets. Furthermore, engineered PUS7 cytoplasmic localization increases cellular fitness under ROS and divalent metal ion stress. Consistent with this, transcripts modified upon PUS7 cytoplasmic localization are enriched within mRNAs encoding proteins involved in divalent metal metabolism and ROS stress pathways. In contrast, tRNA sites modified by PUS7 ({Psi}13 and {Psi}35 are unperturbed). Quantitative proteomics reveal a reshaping of the proteome upon PUS7 relocalization under stress, with proteins involved in metal and ROS homeostasis being particularly sensitive to PUS7 localization. Collectively, our data demonstrate that PUS7 localization alters mRNA pseudouridylation patterns to modulate protein production and enhance cellular fitness.

biochemistry↗

RNA Binding Proteins KhpA and KhpB Interact with Small Regulatory RNAs and Affect Global Gene Expression in Deinococcus radiodurans

Small regulatory RNAs (sRNAs) in bacteria often associate with RNA-binding proteins to gain intracellular stability and/or to enable regulatory efficiency. While much of the current knowledge about those sRNA binding proteins is derived from studies in Gram-negative organisms, the characterization of such proteins in Gram-positive species is still lagging behind. Here, we identified and characterized two sRNA binding proteins (KhpA and KhpB) in Deinococcus radiodurans, a Gram-positive bacterium that exhibits extreme resistance to radiation and other oxidative stressors. We demonstrate that KhpA and KhpB interact with key sRNAs in D. radiodurans and influence their stabilities. Although KhpA and KhpB interact with each other, they do not bind the sRNAs as a complex. Insightfully, KhpA and KhpB facilitate the interactions of the representative sRNAs PprS and Dsr9 in D. radiodurans with their respective mRNA targets, pprM and DR_1968. Through RNA-seq analysis, we further revealed that KhpA and KhpB have both overlapping and specific roles in a global gene regulation in D. radiodurans. Overall, this study expands our knowledge of posttranscriptional regulation in D. radiodurans and supports the growing consensus that KhpA and KhpB homologs constitute a new family of sRNA binding proteins in Gram-positive bacteria. IMPORTANCEThe bacterium Deinococcus radiodurans is the most radiation-resistant organism identified to date. Understanding the mechanisms of resistance of D. radiodurans is essential for leveraging this bacterium in biomedical and biomanufacturing applications. It was previously revealed that small regulatory RNAs (sRNAs) play crucial roles in the gene regulation of D. radiodurans. However, how these sRNAs are influenced by RNA binding proteins is poorly understood. Here we identified two conserved RNA binding proteins, KhpA and KhpB, as sRNA binding partners in D. radiodurans. These proteins affect the sRNA stability, sRNA-target interaction, and global gene regulation. Characterization of KhpA and KhpB will help us advance the understanding of how post-transcriptional network regulates the physiology and radioresistance of D. radiodurans.

molecular biology↗

Conserved Heterochromatin-like Structures with Local Regulators Mediate the Iron Stress Response in Mycobacteria

Recent studies have demonstrated the importance of dynamic heterochromatin-like regions in bacterial gene regulation, particularly for adaptation to changing environments. Here, we have measured the dynamic regulatory protein-DNA landscape of the tuberculosis vaccine strain, M. bovis BCG Pasteur, under the pathogenically-relevant condition of iron starvation. Our results capture for the first time the overall protein occupancy landscape of the genome of M. bovis BCG, identifying extended protein occupancy domains likely composed of diverse sets of nucleoid-associated proteins and transcription factors. Importantly, we find chromatin-directed regulation of stress-responsive genes like siderophores. Furthermore, through comparison with the free-living M. smegmatis, we identified a specific class of extended protein occupancy domains that are associated with conserved genomic regions across the two organisms, whereas regions with low protein occupancy often lack conservation. Our findings thus comprehensively reveal the contributions of both local regulators and chromatin structure to gene regulation and evolution in mycobacteria.

microbiology↗

Establishing a standardized genetic toolkit for the radiation-resistant extremophile Deinococcus radiodurans

Deinococcus radiodurans is a highly radiation-resistant extremophile with potential for biomanufacturing and bioremediation in harsh environments, including extraterrestrial settings. However, genetic engineering in this organism has been constrained by limited genetic tools. Here, we establish a comprehensive genetic toolkit for D. radiodurans that enables tunable gene regulation with genome-engineering tools. We have standardized a library of 36 constitutive promoter sequences sourced from the native D. radiodurans genome and from synthetic sources, spanning 45-fold range gene expression in the context of plasmids. We have also identified 136 variants of ribosome binding sites (RBS), variants using a high-throughput screen for precise translational control across a 963-fold range of expression when used in our plasmid-based system. Additionally, we have developed a codon-optimizer script that we leverage to improve the function of four fluorescent proteins in D. radiodurans. Next, we characterized 9 small molecule-inducible promoter systems, and identified four key inducible promoter systems that achieve between 3-fold and 12-fold signal amplification, as well as titratability across induction concentrations in D. radiodurans. To engineer the D. radiodurans genome, we present a novel method for gene integrations, compatible with de novo sequences, up to 3kB in length, doing so with 87% efficiency. Lastly, we repurpose the RNA-directed nuclease, TnpB, as a novel post-transcriptional tool for programmable gene repression analogous to CRISPRi-based systems, and this tool can achieve up to 70% repression of its target. Collectively, this toolkit provides modular, standardized components for both plasmid engineering and chromosomal engineering in D. radiodurans to improve its genetic tractability and to facilitate its deployment. TOC Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/658681v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@c961deorg.highwire.dtl.DTLVardef@1386776org.highwire.dtl.DTLVardef@10977aorg.highwire.dtl.DTLVardef@1490931_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

Predicting characterization of microbiome taxonomy from imaging using machine learning approaches

AO_SCPLOWBSTRACTC_SCPLOWFor this study, a total of 47 mock human skin microbiome communities were created using microorganisms collected from human donors and grown in vitro for between eight and 32 days. Each mock community sample was split. Ten mL of each sample was used to determine the taxonomy of the community, using metatranscriptomics and Kraken2 to provide population-level taxonomic information; five mL of each sample was used for imaging. The resulting micrographs served as the basis for establishing a new analysis pipeline that sequentially used two different methods for machine learning and one statistical technique: (1) confocal microscopy images were segmented into individual cells using the generalist, deep learning, publicly available machine learning model Cellpose; (2) continuous probability density functions describing the joint distribution of the cell area and eccentricity were found using algorithms expressing the statistical technique of kernel density estimation; (3) these probability density functions were used as input for convolutional neural networks, that were trained to predict both the taxonomic diversity and the most common bacterial class, independently of metatranscriptomics. Specifically, models were made to predict the Shannon index (a quantitative measure of taxonomic diversity) and to predict the most common bacterial class, for each micrograph. Measured Shannon indices (based on metatranscriptomics) ranged from nearly 0 to 1.4. The model predictions of Shannon indices had a mean squared error of 0.0321 +/- 0.0035. The model predictions of the most common taxonomic class of bacteria had an accuracy of 94.0% +/- 0.7%. IO_SCPLOWMPORTANCEC_SCPLOWTaxonomic diversity is a useful metric for describing microbial communities and can be used as a measure of ecosystems health, resilience, and biological interactions. Characterization of microbial community diversity also has diagnostic applications. For the human skin microbiome in particular, microbial diversity directly impacts skin health, including resilience against pathogens and regulation of immune responses. Currently, microbial diversity can be determined either using traditional staining methods that are limited to pure cultures or using sequencing methods that require high investment in cost, time, and expertise. In this study, we demonstrate an innovative method that employs microscopy images of bacterial communities and machine learning to predict taxonomic diversity and the dominant bacterial classes of bacterial communities. The underlying framework of the pipeline for taxonomy prediction has the potential to be adapted and extended to other organisms and microbiomes and to make taxonomic analyses less expensive and more feasible in low-resource settings.

microbiology↗

Concentration dependent CsrA regulation of the uxuB transcript leads to development of post-transcriptional BANDPASS Filter

Post-transcriptional control systems offer new avenues to design synthetic circuits that offer reduced burden and less synthetic regulatory components compared to transcriptionally based tools. Herein, we repurpose a newly identified post-transcriptional interaction between the uxuB leader sequence and the E. coli CsrA regulatory protein to design a biological post-transcriptional BANDPASS filter. In this work, we characterize the uxuB mRNA as heterogenous target of the Carbon Storage Regulatory A (CsrA) protein, where the protein can both activate and repress uxuB activity depending on its intracellular concentration. We leverage this interaction to implement a novel strategy of regulation within the 5UTR of an mRNA. Specifically, we report a hierarchical binding strategy that may be leveraged by CsrA within uxuB to result in a dose- dependent response in regulatory outcomes. In our semi-synthetic circuit, the uxuB mRNA leader sequence is used as a scaffold that is fused to a gene of interest, which allows the circuit to transition between ON/OFF states based on a range of free native concentration of the CsrA regulatory protein. Notably, this system exerts regulation comparable to previously developed transcriptional BANDPASS filters while reducing the number of circuit components and can be used in concert with additional controlled circuits to achieve complex multi-signal control. We anticipate that future characterization of native regulatory RNA-protein systems will allow for development of more complex RNP-based circuits for synthetic biology applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/615595v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@107348dorg.highwire.dtl.DTLVardef@b4be0org.highwire.dtl.DTLVardef@1a272a3org.highwire.dtl.DTLVardef@1443c3_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

Thermodynamic modeling of Csr/Rsm- RNA interactions capture novel, direct binding interactions across the Pseudomonas aeruginosa transcriptome

BackgroundPseudomonas aeruginosa (PA) is a ubiquitous, Gram-negative, bacteria that can attribute its survivability to numerous sensing and signaling pathways; conferring fitness due to speed of response. Post-transcriptional regulation is an energy efficient approach to quickly shift gene expression in response to the environment. The conserved post-transcriptional regulator RsmA is involved in regulating translation of genes involved in pathways that contribute to virulence, metabolism, and antibiotic resistance. Prior high-throughput approaches to map the full regulatory landscape of RsmA have estimated a target pool of approximately 500 genes; however, these approaches have been limited to a narrow range of growth phase, strain, and media conditions. Computational modeling presents a condition-independent approach to generating predictions for binding between the RsmA protein and highest affinity mRNAs. In this study, we draft a two-state thermodynamic model to predict the likelihood of RsmA binding to the 5 UTR sequence of genes present in the PA genome. ResultsOur modeling approach predicts 1043 direct RsmA-mRNA binding interactions, including 457 novel mRNA targets. We then perform GO term enrichment tests on our predictions that reveal significant enrichment for DNA binding transcriptional regulators. In addition, quorum sensing, biofilm formation, and two-component signaling pathways were represented in KEGG enrichment analysis. We confirm binding predictions using in vitro binding assays, and regulatory effects using in vivo translational reporters. These reveal RsmA binding and regulation of a broader number of genes not previously reported. An important new observation of this work is the direct regulation of several novel mRNA targets encoding for factors involved in Quorum Sensing and the Type IV Secretion system, such as rsaL and mvaT. ConclusionsOur study demonstrates the utility of thermodynamic modeling for predicting interactions independent of complex and environmentally-sensitive systems, specifically for profiling the post-transcriptional regulator RsmA. Our experimental validation of RsmA binding to novel targets both supports our model and expands upon the pool of characterized target genes in PA. Overall, our findings demonstrate that a modeling approach can differentiate direct from indirect binding interactions and predict specific sites of binding for this global regulatory protein, thus broadening our understanding of the role of RsmA regulation in this relevant pathogen.

bioinformatics↗

Multiscale regulation of nutrient stress responses in Escherichia coli from chromatin structure to small regulatory RNAs

Recent research has indicated the presence of heterochromatin-like regions of extended protein occupancy and transcriptional silencing of bacterial genomes. We utilized an integrative approach to track chromatin structure and transcription in E. coli K-12 across a wide range of nutrient conditions. In the process, we identified multiple loci which act similarly to facultative heterochromatin in eukaryotes, normally silenced but permitting expression of genes under specific conditions. We also found a strong enrichment of small regulatory RNAs (sRNAs) among the set of differentially expressed transcripts during nutrient stress. Using a newly developed bioinformatic pipeline, the transcription factors regulating sRNA expression were bioinformatically predicted, with experimental follow-up revealing novel relationships for 36 sRNA-transcription factors candidates. Direct regulation of sRNA expression was confirmed by mutational analysis for five sRNAs of metabolic interest: IsrB, CsrB and CsrC, GcvB, and GadY. Our integrative analysis thus reveals additional layers of complexity in the nutrient stress response in E. coli and provides a framework for revealing similar poorly understood regulatory logic in other organisms.

systems biology↗

Optimized chemical labeling method for isolation of 8-oxoG-modified RNA, ChLoRox-Seq, identifies mRNAs enriched in oxidation and transcriptome-wide distribution biases of oxidation events post environmental stress

Bulk increases in nucleobase oxidation, most commonly manifesting as the guanine (G) nucleobase modification 8-oxo-7,8-dihydroguanine (8-oxoG), have been linked to several disease pathologies. Elucidating the effects of RNA oxidation on cellular homeostasis is limited by a lack of effective tools for detecting specific regions modified with 8-oxoG. Building on a previously published method for studying 8-oxoG in DNA, we developed ChLoRox-Seq, which works by covalently functionalizing 8-oxoG sites in RNA with biotin. Importantly, this method enables antibody-free enrichment of 8-oxoG-containing RNA fragments for Next Generation Sequencing-based detection of modified regions transcriptome-wide. We demonstrate the high specificity of ChLoRox-Seq for functionalizing 8-oxoG over unmodified nucleobases in RNA and benchmark this specificity to a commonly used antibody-based approach. Key advantages of ChLoRox-Seq include: (1) heightened resolution of RNA oxidation regions (e.g. exon-level) and (2) lower experimental costs. By applying ChLoRox-Seq to mRNA extracted from human lung epithelial cells (BEAS-2B) after exposure to environmentally relevant stress, we observe that 8-oxoG modifications tend to cluster in regions that are G-rich and within mRNA transcripts possessing longer 5 UTR and CDS regions. These findings provide new insight into the complex mechanisms that bias the accumulation of RNA oxidation across the transcriptome. Notably, our analysis suggests the possibility that most mRNA oxidation events are probabilistically driven and that mRNAs that possess more favorable intrinsic properties are prone to incur oxidation events at elevated rates. ChLoRox-Seq can be readily applied in future studies to identify regions of elevated RNA oxidation in any cellular model of interest.

molecular biology↗

A high-throughput search for intracellular factors that affect RNA folding identifies E. coli proteins PepA and YagL as RNA chaperones that promote RNA remodeling

General RNA chaperones are RNA-binding proteins (RBPs) that interact transiently and non-specifically with RNA substrates and assist in their folding into native state. In bacteria, these chaperones impact both coding and non-coding RNAs and are particularly important for large, structured RNAs which are prone to becoming kinetically trapped in misfolded states. Currently, due to the limited number of well-characterized examples and the lack of a consensus structural or sequence motif, it is difficult to identify general RNA chaperones in bacteria. Here, we adapted a previously published in vivo RNA regional accessibility probing assay to screen genome wide for intracellular factors in E. coli affecting RNA folding, among which we aimed to uncover novel RNA chaperones. Through this method, we identified and validated eight proteins whose deletion gives changes in regional accessibility within the exogenously expressed Tetrahymena group I intron ribozyme. Further, we purified and measured in vitro properties of two of these proteins, YagL and PepA, which were especially attractive as general chaperone candidates. We showed that both proteins bind RNA and that YagL accelerates native refolding of the ribozyme from a long-lived misfolded state. Further dissection of YagL showed that a putative helix-turn-helix (HTH) domain is responsible for most of its RNA binding activity but only the full protein shows chaperone activity. Altogether, this work expands the current repertoire of known general RNA chaperones in bacteria.

molecular biology↗

Geometric changes in the nucleoids of Deinococcus radiodurans reveal involvement of new proteins in recovery from ionizing radiation.

The extremophile Deinococcus radiodurans maintains a highly-organized and condensed nucleoid as its default state, possibly contributing to high tolerance of ionizing radiation (IR). Previous studies of the D. radiodurans nucleoid were limited by reliance on manual image annotation and qualitative metrics. Here, we introduce a high-throughput approach to quantify the geometric properties of cells and nucleoids, using confocal microscopy, digital reconstructions of cells, and computational modeling. We utilize this novel approach to investigate the dynamic process of nucleoid condensation in response to IR stress. Our quantitative analysis reveals that at the population level, exposure to IR induced nucleoid compaction and decreased size of D. radiodurans cells. Morphological analysis and clustering identified six distinct sub-populations across all tested experimental conditions. Results indicate that exposure to IR induces fractional redistributions of cells across sub-populations to exhibit morphologies that associate with greater nucleoid condensation, and decreased abundance of sub-populations associated with cell division. Nucleoid associated proteins (NAPs) may link nucleoid compaction and stress tolerance, but their roles in regulating compaction in D. radiodurans is unknown. Imaging of genomic mutants of known and suspected NAPs that contribute to nucleoid condensation found that deletion of nucleic acid binding proteins, not previously described as NAPs, can remodel the nucleoid by driving condensation or decondensation in the absence of stress and that IR increases the abundance of these morphological states. Thus, our integrated analysis introduces a new methodology for studying environmental influences on bacterial nucleoids and provides an opportunity to further investigate potential regulators of nucleoid condensation. ImportanceD. radiodurans, an extremophile known for its stress tolerance, constitutively maintains a highly-condensed nucleoid. Qualitative studies have described nucleoid behavior under a variety of conditions. However, a lack of quantitative data regarding nucleoid organization and dynamics have limited our understanding of regulatory mechanisms controlling nucleoid organization in D. radiodurans. Here, we introduce a quantitative approach that enables high-throughput quantitative measurements of subcellular spatial characteristics in bacterial cells. Applying this to wild-type or single-protein-deficient populations of D. radiodurans subjected to ionizing radiation, we identified significant stress-responsive changes in cell shape, nucleoid organization, and morphology. These findings highlight this methodologys adaptability and capacity for quantitatively analyzing the cellular response to stressors for screening cellular proteins involved in bacterial nucleoid organization.

molecular biology↗

TolRad: A model for predicting radiation tolerance using Pfam annotations identifies novel radiosensitive bacterial species from reference genomes and MAGs

The trait of ionizing radiation (IR) tolerance is variable between bacterial species, with radiosensitive bacteria succumbing to acute doses around 100Gy and extremophiles able to survive doses exceeding 10,000Gy. While survival screens have identified multiple highly radioresistant bacteria, such systemic searches have not been conducted for radiosensitive bacteria. The taxonomy-level diversity of IR of intolerance across bacteria is poorly understood, as are genetic elements that influence IR sensitivity. Using the protein domain frequencies from 61 bacterial species with experimentally determined IR D10 values (the dose at which only 10% of the population survives) we trained TolRad, a random forest binary classifier, to distinguish between radiosensitive bacteria (D10 < 200Gy) and radiation tolerant bacteria (D10 > 200Gy). On the hidden species, TolRad had an accuracy of 0.900. We applied TolRad to 152 UniProt-hosted bacterial proteomes, including 37 strains from the ATCC Human Microbiome Collection, and classified 34 species as radiosensitive. Whereas IR intolerance (D10 < 200Gy) in the training dataset had been confined to the phylum Proteobacterium, this initial TolRad screen identified radiosensitive bacteria in 2 additional phyla. We experimentally validated the predicted radiosensitivity of a key species of the human microbiome from the Bacteroidota phyla. To demonstrate that TolRad can be applied to Metagenome-Assembled Genome (MAGs), we tested the accuracy of TolRad on Egg-NOG assembled proteomes (0.965) and partial proteomes. Finally, three collections of MAGs were screened using TolRad, identifying further phylum with radiosensitive species and suggesting that environmental conditions influence the abundance of radiosensitive bacteria. ImportanceBacterial species have vast genetic diversity, allowing for life in extreme environments and the conduction of complex chemistry. The ability to harness the full potential of bacterial diversity is hampered by the lack of high-throughput experimental or bioinformatic methods for characterizing bacterial traits. Here, we present a computational model that uses de novo generated genome annotations to classify a bacterium as tolerant of ionizing radiation (IR) or as radiosensitive. This model allows for rapid screening of bacterial communities for low-tolerance species that are of interest for both mechanistic studies into bacterial sensitivity to IR and biomarkers of IR exposure.

bioinformatics↗

Rewiring native post-transcriptional carbon regulators to build multi-layered genetic circuits and optimize engineered microbes for bioproduction

As the bioprocessing and synthetic biology spaces rapidly expand, post-transcriptional regulation is emerging as driver for maximizing signal response rate and for minimizing cost per protein within cells. Designing robust post-transcriptional control systems that have precise tunability and can achieve diverse regulatory outcomes are paramount to advance the field. Herein, we develop a new approach for engineered post-transcriptional control in bacteria by rewiring a native regulatory system in Escherichia coli, the Carbon Storage Regulatory (Csr) Network, to create tunable, complex genetic circuits. First, by co-opting native components of the Csr Network to regulate translation of a target mRNA transcript, we establish a Csr-regulated Buffer Gate. Next, by rationally engineering the interactions between our synthetic construct and the native components of the Csr Network, we expand our original design into a genetic toolbox of 12 Buffer Gates that achieve precise tunability across a 10-fold range of target gene expression. Subsequently, to further regulatory capabilities using this approach, we develop a Csr-regulated NOT Gate through engineering a Csr-activated sequence into our synthetic constructs. We then build upon the Csr Buffer and Not Gates to create post-transcriptional dual input Boolean OR, NOR, AND and NAND Logic Gates, as well as a genetic pulse circuit. As a third step, we demonstrate portability of our Csr-regulated Buffer Gates into three industrially relevant bacteria by recapitulating Buffer Gate activity simply by leveraging the conserved homologous Csr Network in each species. Lastly, as a demonstration of downstream application, we apply our system to a proof-of-concept synthetic mevalonate pathway. Using our engineered constructs, we optimize mevalonate production in E. coli resulting in a three-fold increase in production relative to a transcriptionally controlled mevalonate pathway. As a whole, we establish a novel approach to rewire post-transcriptional regulatory networks for complex bacterial computation that can be utilized for efficient bioproduction in engineered microbes.

bioengineering↗

Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells

Particulate matter (PM) is a ubiquitous component of indoor and outdoor air pollution that is epidemiologically linked to many human pulmonary diseases. PM has many emission sources, making it challenging to understand the biological effects of exposure due to the high variance in chemical composition. However, the effects of compositionally unique particulate matter mixtures on cells have not been analyzed using both biophysical and biomolecular approaches. Here, we show that in a human bronchial epithelial cell model (BEAS-2B), exposure to three chemically distinct PM mixtures drives unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability and DNA damage responses and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization and structure, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Lastly, we observed that particulate matter mixtures with high contents of heavy metals, such as cadmium and lead, induced larger drops in viability, increased DNA damage, and drove a redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of cellular morphology provides a robust approach to gauge the effects of environmental stressors on biological systems and determine cellular susceptibilities to pollution.

systems biology↗

CsrA Shows Selective Regulation of sRNA-mRNA Networks

Post-transcriptional regulation, by small RNAs (sRNAs) as well as the global Carbon Storage Regulator A (CsrA) protein, play critical roles in bacterial metabolic control and stress responses. The CsrA protein affects selective sRNA-mRNA networks, in addition to regulating transcription factors and sigma factors, providing additional avenues of cross talk between other stress-response regulators. Here, we expand the known set of sRNA-CsrA interactions and study their regulatory effects. In vitro binding assays confirm novel CsrA interactions with ten sRNAs, many of which are previously recognized as key regulatory nodes. Of those 10 sRNA, we identify that McaS, FnrS, SgrS, MicL, and Spot42 interact with CsrA in vivo. We find that the presence of CsrA impacts the downstream regulation of mRNA targets of the respective sRNA. In vivo evidence supports enhanced CsrA McaS-csgD mRNA repression and showcase CsrA-dependent repression of the fucP mRNA via the Spot42 sRNA. We additionally identify SgrS and FnrS as potential new sRNA sponges of CsrA. Overall, our results further support the expanding impact of the Csr system on cellular physiology via CsrA impact on the regulatory roles of these sRNAs.

molecular biology↗

A Strategy to Quantify Myofibroblast Activation on a Continuous Spectrum

Myofibroblasts are a highly secretory and contractile phenotype most commonly identified by the de novo expression and assembly of alpha-smooth muscle actin stress fibers. Traditionally, this activation process has been thought of as a binary process, with cells being labeled as "activated" or "quiescent (non-activated)". More recently, this view has been expanded to consider activation on a continuous spectrum. However, there is no established method to quantify a cells position on this spectrum, and as a result, the binary labeling system is still widely used. While transcriptomic analyses provide a continuous measure of myofibroblast markers, a faster and more facile screening method is needed. To this end, we utilized optical microscopy and machine learning methods to quantify myofibroblast activation on a spectrum. We first measured size and shape features of over 1,000 individual cardiac fibroblasts and found that these features provide enough information to predict activation state, on the binary scale, with 94% accuracy as compared to manual classification. We next performed dimensionality reduction techniques on these features to create a continuous scale of activation. Importantly, this new classification system captures a range of fibroblast activation states, but still possesses inherent bias due to choice of morphological features. Thus, we next used self-supervised machine learning to create a second continuous labeling system free from biases associated with the manually measured features. Lastly, we compared our findings for mechanically activated cardiac fibroblasts to a distribution of cell phenotypes generated from transcriptomic data using single-cell RNA sequencing. Altogether, these results demonstrate a continuous spectrum of activation from fibroblast to myofibroblast and provide a strategy to quantify a cells position on that spectrum.

bioengineering↗