16 research outputs found

    A synchronized quorum of genetic clocks

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    The engineering of genetic circuits with predictive functionality in living cells represents a defining focus of the expanding field of synthetic biology. This focus was elegantly set in motion a decade ago with the design and construction of a genetic toggle switch and an oscillator, with subsequent highlights that have included circuits capable of pattern generation, noise shaping, edge detection, and event counting. Here, we describe an engineered gene network with global intercellular coupling that is capable of generating synchronized oscillations in a growing population of cells. Using microfluidic devices tailored for cellular populations at differing length scales, we investigate the collective synchronization properties along with spatiotemporal waves occurring on millimeter scales. We use computational modeling to quantitatively describe the observed dependence of the period and amplitude of the bulk oscillations on the flow rate. The synchronized genetic clock sets the stage for the use of microbes in the creation of a macroscopic biosensor with an oscillatory output. In addition, it provides a specific model system for the generation of a mechanistic description of emergent coordinated behavior at the colony level. Synchronized clocks are of fundamental importance in the coordination of rhythmi

    Entrainment of a Population of Synthetic Genetic Oscillators

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    Quantitative whole-tissue 3D imaging reveals bacteria in close association with mouse jejunum mucosa

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    Raw and processed data and imaging files [0.42 TB total] for the bioRxiv preprint "Quantitative whole-tissue 3D imaging reveals bacteria in close association with mouse jejunum mucosa"Related Publication: Quantitative whole-tissue 3D imaging reveals bacteria in close association with mouse jejunum mucosa bioRxiv engFiles available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/0_D1.20199/DataSummary.xlsx 0.0 GB Download Fig01-DataAnalysis-GrowthCurves.zip 0.0 GB Download Fig01-DataAnalysis-RTqPCR.zip 0.0 GB Download Fig02-DataAnalysis.zip 0.0 GB Download Fig03-DataAnalysis.zip 0.27 GB Download Fig04-Fig06-DataAnalysis.zip 0.07 GB Download FigS07-DataAnalysis.zip 0.29 GB Download FigS08-DataAnalysis.zip 0.64 GB Download FigS09-DataAnalysis.zip 0.23 GB Download FigS11-DataAnalysis.zip 0.0 GB Download R4P-p091-20xC-ImarisSurfaceFiles.zip 0.0 GB Download R4P-p091-D28C3M1-EJ-5x_Stitch.czi 2.69 GB Download R4P-p091-D29C2M1-EJ-5x_Stitch.czi 2.4 GB Download R4P-p091-D29C4M2-EJ-1.czi 6.81 GB Download R4P-p091-D29C4M2-EJ-1.ims 10.38 GB Download R4P-p091-D29C4M2-EJ-3.czi 4.25 GB Download R4P-p091-D29C4M2-EJ-3.ims 7.5 GB Download R4P-p091-D29C4M2-EJ-5x_Stitch.czi 1.82 GB Download R4P-p091-D31C1M1-EJ-5x_Stitch.czi 3.13 GB Download R4P-p121-Bfrag-v3EC-v3BAC-20x.czi 1.43 GB Download R4P-p121-Ecoli-v3EC-v3BAC-20x.czi 1.42 GB Download R4P-p124-20xC-ImarisSurfaceFiles.zip 0.0 GB Download R4P-p124-D28C1M1-EJ-5x_Stitch.czi 0.99 GB Download R4P-p124-D28C2M1-EJ-5x_Stitch.czi 1.31 GB Download R4P-p124-D28C3M1-EJ-1.czi 4.96 GB Download R4P-p124-D28C3M1-EJ-1.ims 6.88 GB Download R4P-p124-D28C3M1-EJ-2.czi 4.8 GB Download R4P-p124-D28C3M1-EJ-2.ims 7.07 GB Download R4P-p124-D28C3M1-EJ-3.czi 3.97 GB Download R4P-p124-D28C3M1-EJ-3.ims 5.96 GB Download R4P-p124-D28C3M1-EJ-5x_Stitch.czi 1.0 GB Download R4P-p124-D28C4M1-EJ-1.czi 4.51 GB Download R4P-p124-D28C4M1-EJ-1.ims 9.0 GB Download R4P-p124-D28C4M1-EJ-2.czi 4.5 GB Download R4P-p124-D28C4M1-EJ-2.ims 6.52 GB 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R4P-p124-D31C2M4-JE-5x_Stitch.czi 1.35 GB Download R4P-p124-D31C3M4-EJ-1.czi 4.89 GB Download R4P-p124-D31C3M4-EJ-1.ims 6.87 GB Download R4P-p124-D31C3M4-EJ-2.czi 5.07 GB Download R4P-p124-D31C3M4-EJ-2.ims 7.22 GB Download R4P-p124-D31C3M4-EJ-3.czi 4.57 GB Download R4P-p124-D31C3M4-EJ-3.ims 3.18 GB Download R4P-p124-D31C3M4-EJ-4.czi 4.93 GB Download R4P-p124-D31C3M4-EJ-5x_Stitch.czi 1.04 GB Download R4P-p124-D31C4M4-EJ-1.czi 4.97 GB Download R4P-p124-D31C4M4-EJ-1.ims 2.68 GB Download R4P-p124-D31C4M4-EJ-2.czi 4.47 GB Download R4P-p124-D31C4M4-EJ-2.ims 6.4 GB Download R4P-p124-D31C4M4-EJ-3.czi 5.22 GB Download R4P-p124-D31C4M4-EJ-4.czi 5.44 GB Download R4P-p124-D31C4M4-EJ-4.ims 7.52 GB Download R4P-p124-D31C4M4-EJ-5x_Stitch.czi 1.35 GB Download R4P-p125-D31C4M4-CD455-WGA2-Crop.ims 0.35 GB Download R4P-p125-D31C4M4-CD455-WGA2.czi 3.91 GB Download R4P-p125-D31C4M4-CD455-WGA2.ims 1.79 GB Download R4P-p125-D31C4M4-EpCAM5-WGA2-Crop.ims 0.76 GB Download R4P-p125-D31C4M4-EpCAM5-WGA2.czi 4.31 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    Functional characterization of helminth-associated Clostridiales reveals covariates of Treg differentiation

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    Abstract Background Parasitic helminths influence the composition of the gut microbiome. However, the microbiomes of individuals living in helminth-endemic regions are understudied. The Orang Asli, an indigenous population in Malaysia with high burdens of the helminth Trichuris trichiura, display microbiotas enriched in Clostridiales, an order of spore-forming obligate anaerobes with immunogenic properties. We previously isolated novel Clostridiales that were enriched in these individuals and found that a subset promoted the Trichuris life cycle. In this study, we aimed to further characterize the functional properties of these bacteria. Results Clostridiales isolates were profiled for their ability to perform 57 enzymatic reactions and produce short-chain fatty acids (SCFAs) and hydrogen sulfide, revealing that these bacteria were capable of a range of activities associated with metabolism and host response. Consistent with this finding, monocolonization of mice with individual isolates identified bacteria that were potent inducers of regulatory T-cell (Treg) differentiation in the colon. Comparisons between variables revealed by these studies identified enzymatic properties correlated with Treg induction and Trichuris egg hatching. Conclusion We identified Clostridiales species that are sufficient to induce high levels of Tregs. We also identified a set of metabolic activities linked with Treg differentiation and Trichuris egg hatching mediated by these newly isolated bacteria. Altogether, this study provides functional insights into the microbiotas of individuals residing in a helminth-endemic region. Video Abstrac

    Plasmid-mediated phenotypic noise leads to transient antibiotic resistance in bacteria

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    The rise of antibiotic resistance is a critical public health concern, requiring an understanding of mechanisms that enable bacteria to tolerate antimicrobial agents. Bacteria use diverse strategies, including the amplification of drug-resistance genes. In this paper, we showed that multicopy plasmids, often carrying antibiotic resistance genes in clinical bacteria, can rapidly amplify genes, leading to plasmid-mediated phenotypic noise and transient antibiotic resistance. By combining stochastic simulations of a computational model with high-throughput single-cell measurements of blaTEM-1 expression in Escherichia coli MG1655, we showed that plasmid copy number variability stably maintains populations composed of cells with both low and high plasmid copy numbers. This diversity in plasmid copy number enhances the probability of bacterial survival in the presence of antibiotics, while also rapidly reducing the burden of carrying multiple plasmids in drug-free environments. Our results further support the tenet that multicopy plasmids not only act as vehicles for the horizontal transfer of genetic information between cells but also as drivers of bacterial adaptation, enabling rapid modulation of gene copy numbers. Understanding the role of multicopy plasmids in antibiotic resistance is critical, and our study provides insights into how bacteria can transiently survive lethal concentrations of antibiotics.JCRHB was a doctoral student in Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México, and received fellowship 59691 from CONACYT. BAL is a student in Programa de Doctorado en Ciencias Bioquímicas, Universidad Nacional Autónoma de México and received fellowship 886346 from CONACYT. J.V.S. received a scholarship from PAPIIT-UNAM (grant IN209419). A.S.M. is funded by the European Union’s Horizon 2020 research and innovation program (ERC grant agreement no.757440-PLASREVOLUTION). J.R.-B. is supported by a Miguel Servet contract from Instituto de Salud Carlos III (ISCIII; grant no. CP20/00154), co-funded by ESB, ’Investing in your future’. R.P.M. and R.C.M. were supported by a Newton Advanced Fellowship awarded by the Royal Society (NA140196). A.F.H. and R.P.M. were supported by PAPIIT-UNAM (grants IA201418 and IN209419, respectively). This project was also funded by CONACYT Ciencia Básica (grant A1-S-32164) awarded to R.P.M.Peer reviewe

    Data from: Spatiotemporal-social association predicts immunological similarity in rewilded mice

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    <p>Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual's interaction with its environment. We therefore tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including associations measured from spatiotemporal cooccurrences, to immune phenotypes. We found extensive variation in individual and social behavior among and within mouse strains upon rewilding.  And we found that the more associated two individuals were, the more similar their immune phenotypes were. Spatiotemporal association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or shared infection status. These results highlight the importance of shared spatiotemporal activity patterns and/or social networks for immune phenotype and suggest potential immunological correlates of social life.<span><br></span></p><p>Funding provided by: National Science Foundation<br>Crossref Funder Registry ID: https://ror.org/021nxhr62<br>Award Number: DGE-2039656</p><p>Funding provided by: National Institute of Allergy and Infectious Diseases<br>Crossref Funder Registry ID: https://ror.org/043z4tv69<br>Award Number: </p><p>Funding provided by: New Jersey Alliance for Clinical and Translational Research*<br>Crossref Funder Registry ID: <br>Award Number: UL1TR003017</p><p>This dataset includes the data and analysis code for Downie et al. (2023 preprint, 202? publication). It is a mixture of immune cell phenotypes, serum cytokines, MLN cytokine production, microbiome (sequenced via 16S), and behavioral data from RFID check-ins. Please see the preprint (or eventual manuscript) for details about the methodology and degree of processing. The behavioral data is largely unprocessed, while the flow cytometry data and ELISA data are post-processing.</p&gt
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