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The genus Clostridium is a large and diverse group within the Bacillota (formerly Firmicutes), whose members can encode useful complex traits such as solvent production, gas-fermentation, and lignocellulose breakdown. We describe 270 genome sequences of solventogenic clostridia from a comprehensive industrial strain collection assembled by Professor David Jones that includes 194 C. beijerinckii, 57 C. saccharobutylicum, 4 C. saccharoperbutylacetonicum, 5 C. butyricum, 7 C. acetobutylicum, and 3 C. tetanomorphum genomes. We report methods, analyses and characterization for phylogeny, key attributes, core biosynthetic genes, secondary metabolites, plasmids, prophage/CRISPR diversity, cellulosomes and quorum sensing for the 6 species. The expanded genomic data described here will facilitate engineering of solvent-producing clostridia as well as non-model microorganisms with innately desirable traits. Sequences could be applied in conventional platform biocatalysts such as yeast or Escherichia coli for enhanced chemical production. Recently, gene sequences from this collection were used to engineer Clostridium autoethanogenum, a gas-fermenting autotrophic acetogen, for continuous acetone or isopropanol production, as well as butanol, butanoic acid, hexanol and hexanoic acid production.

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, what you put is what you get (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

The review provides an overview of the current status of the solvent-producing clostridia. The origin and development of industrial clostridial species, as well as the history of the industrial Acetone Butanol Ethanol fermentation process, is reexamined, and the recent resurgence of interest in the production of biobutanol is reviewed. Over 300 fully sequenced genomes for solvent-producing and closely related clostridial species are currently available in public databases. These include 270 genomes sourced from the David Jones culture collection. These genomes were allocated arbitrary DJ codes, and a conversion table to identify the species and strains has now been provided. The expanded genomic database facilitated new comparative genomic and phylogenetic analysis. A synopsis of the common features, molecular taxonomy, and phylogeny of solvent-producing clostridia and the application of comparative phylogenomics are evaluated. A survey and analysis of resident prophages in solvent-producing clostridia are discussed, and the discovery, occurrence, and role of novel R-type tailocins are reported. Prophage genomes with R-type tailocin-like features were detected in all 12 species investigated. The widespread occurrence of tailocins in Gram-negative species is well documented; this survey has indicated that they may also be widespread in clostridia.

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. In addition, the rapid adoption and widespread popularity of deep-learning methods by the scientific community raises an important question: to what extent can users trust the results generated by such "black box" methods? We explicitly demonstrate "What You Put Is What You Get" (WYPIWYG); i.e., the image analysis results can reflect the user bias encoded in the training dataset. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over a decade in our lab, we also provide useful information for those who want to implement mother-machine-based high-throughput imaging and image analysis methods in their research. This includes our guiding principles and best practices to ensure transparency and reproducible results.

The authors examine the perceptions of undergraduate students with business majors to determine the leadership competencies they most value. Prior research examined business major rankings in a smaller private school. This research examined the generalizability of those findings by surveying 667 students from a regional public college in the Southeast of the USA. The study also examined student empathy and age, as a representation of life experience, as indicators of preferred leadership trait rankings. Students ranked 10 leadership competencies and both student empathy and age were examined in relation to those 10 competencies through ordinal regression. The results indicate that both empathy and age influence leadership competency preference. The specifics of those findings as well as the theoretical and practical implications are discussed, as are potential directions for future research.

Live bacterial therapeutics (LBTs) could reverse diseases by engrafting in the gut and providing persistent beneficial functions in the host. However, attempts to functionally manipulate the gut microbiome of conventionally raised (CR) hosts have been unsuccessful because engineered microbial organisms (i.e., chassis) have difficulty in colonizing the hostile luminal environment. In this proof-of-concept study, we use native bacteria as chassis for transgene delivery to impact CR host physiology. Native Escherichia coli bacteria isolated from the stool cultures of CR mice were modified to express functional genes. The reintroduction of these strains induces perpetual engraftment in the intestine. In addition, engineered native E. coli can induce functional changes that affect physiology of and reverse pathology in CR hosts months after administration. Thus, using native bacteria as chassis to "knock in" specific functions allows mechanistic studies of specific microbial activities in the microbiome of CR hosts and enables LBT with curative intent.

Compositional oscillations of the gut microbiome are essential for normal peripheral circadian rhythms, both of which are disrupted in diet-induced obesity (DIO). Although time-restricted feeding (TRF) maintains circadian synchrony and protects against DIO, its impact on the dynamics of the cecal gut microbiome is modest. Thus, other regions of the gut, particularly the ileum, the nexus for incretin and bile acid signaling, may play an important role in entraining peripheral circadian rhythms. We demonstrate the effect of diet and feeding rhythms on the ileal microbiome composition and transcriptome in mice. The dynamic rhythms of ileal microbiome composition and transcriptome are dampened in DIO. TRF partially restores diurnal rhythms of the ileal microbiome and transcriptome, increases GLP-1 release, and alters the ileal bile acid pool and farnesoid X receptor (FXR) signaling, which could explain how TRF exerts its metabolic benefits. Finally, we provide a web resource for exploration of ileal microbiome and transcriptome circadian data.