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ode-of-action discovery approaches based on target-specific reference compounds, as they highlight the intraclass variability of cellular compound effects.When determining human microbiota composition, shotgun sequencing is a powerful tool that can generate high-resolution taxonomic and functional information at once. However, the technique is limited by missing information about host-to-microbe ratios observed in different body compartments. This limitation makes it difficult to plan shotgun sequencing assays, especially in the context of high sample multiplexing and limited sequencing output and is of particular importance for studies employing the recently described shallow shotgun sequencing technique. In this study, we evaluated the use of a quantitative PCR (qPCR)-based assay to predict host-to-microbe ratio prior to sequencing. Combining a two-target assay involving the bacterial 16S rRNA gene and the human beta-actin gene, we derived a model to predict human-to-microbe ratios from two sample types, including stool samples and oropharyngeal swabs. We then validated it on two independently collected sample types, including rectal swabs and vaginal secreti gauging shallow sequencing viability of samples.Much of our knowledge of bacterial transcription initiation has been derived from studying the promoters of Escherichia coli and Bacillus subtilis. Given the expansive diversity across the bacterial phylogeny, it is unclear how much of this knowledge can be applied to other organisms. Here, we report on bioinformatic analyses of promoter sequences of the primary σ factor (σ70) by leveraging publicly available transcription start site (TSS) sequencing data sets for nine bacterial species spanning five phyla. This analysis identifies previously unreported differences in the -35 and -10 elements of σ70-dependent promoters in several groups of bacteria. We found that Actinobacteria and Betaproteobacteria σ70-dependent promoters lack the TTG triad in their -35 element, which is predicted to be conserved across the bacterial phyla. In addition, the majority of the Alphaproteobacteria σ70-dependent promoters analyzed lacked the thymine at position -7 that is highly conserved in other phyla. Bioinformatic examinationunit of RNA polymerase. Selleck MEK inhibitor We used bioinformatic analyses to reveal previously unreported differences in promoter DNA sequences across the bacterial phylogeny. We found that many Actinobacteria and Betaproteobacteria promoters lack a sequence in their -35 DNA recognition element that was previously assumed to be conserved and that Alphaproteobacteria lack a thymine residue at position -7, also previously assumed to be conserved. Our work reports important new information about bacterial transcription, illustrates the benefits of studying bacteria across the phylogenetic tree, and proposes new lines of future investigation.The analysis of microbial growth is one of the central methods in the field of microbiology. Microbial growth dynamics can be characterized by meaningful parameters, including carrying capacity, exponential growth rate, and growth lag. However, microbial assays with clinical isolates, fastidious organisms, or microbes under stress often produce atypical growth shapes that do not follow the classical microbial growth pattern. Here, we introduce the analysis of microbial growth assays (AMiGA) software, which streamlines the analysis of growth curves without any assumptions about their shapes. AMiGA can pool replicates of growth curves and infer summary statistics for biologically meaningful growth parameters. In addition, AMiGA can quantify death phases and characterize diauxic shifts. It can also statistically test for differential growth under distinct experimental conditions. Altogether, AMiGA streamlines the organization, analysis, and visualization of microbial growth assays. IMPORTANCE Our current understanding of microbial physiology relies on the simple method of measuring microbial populations' sizes over time and under different conditions. Many advances have increased the throughput of those assays and enabled the study of nonlab-adapted microbes under diverse conditions that widely affect their growth dynamics. Our software provides an all-in-one tool for estimating the growth parameters of microbial cultures and testing for differential growth in a high-throughput and user-friendly fashion without any underlying assumptions about how microbes respond to their growth conditions.In many low- and middle-income countries, antibiotic-resistant bacteria spread in the environment due to inadequate treatment of wastewater and the poorly regulated use of antibiotics in agri- and aquaculture. Here, we characterized the abundance and diversity of antibiotic-resistant bacteria and antibiotic resistance genes in surface waters and sediments in Bangladesh through quantitative culture of extended-spectrum beta-lactamase (ESBL)-producing coliforms and shotgun metagenomics. Samples were collected from highly urbanized settings (n = 7), rural ponds with a history of aquaculture-related antibiotic use (n = 11), and rural ponds with no history of antibiotic use (n = 6). ESBL-producing coliforms were found to be more prevalent in urban samples than in rural samples. Shotgun sequencing showed that sediment samples were dominated by the phylum Proteobacteria (on average, 73.8% of assigned reads), while in the water samples, Cyanobacteria were the predominant phylum (on average, 60.9% of assigned reads). iotic resistance genes, with a higher number of them associated with plasmids, indicating that they are more likely to spread horizontally. The abundance of antibiotic resistance genes was strongly correlated with the abundance of bacteria that originate from the human gut, suggesting that uncontrolled release of human waste is a major driver for the spread of antibiotic resistance in the urban environment. Improvements in sanitation in LMICs may thus be a key intervention to reduce the dissemination of antibiotic-resistant bacteria.Microbiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here, we propose as a solution a search-based microbiome transition network. By traversing a composition-similarity-based network of 177,022 microbiomes, we show that although the compositions are distinct by habitat, each microbiome is on-average only seven neighbors from any other microbiome on Earth, indicating the inherent homology of microbiomes at the global scale. This network is scale-free, suggesting a high degree of stability and robustness in microbiome transition. By tracking the minimum spanning tree in this network, a global roadmap of microbiome dispersal was derived that tracks the potential paths of formulating and propagating microbiome diversity. Such search-based global microbiome networks, reconstructed within hours on just one computing node, provide a readily expanded reference for tracing the origin and evolution of existing or new microbiomes. IMPORTANCE It remains unclear whether and how compositional changes at the "community to community" level among microbiomes are linked to the origin and evolution of global microbiome diversity.