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Biology subjects

Rosenthal, A. Z.

Publications and source records attributed to Rosenthal, A. Z..

5 recordsLinked to original sources

(p)ppGpp-dependent activation of gene expression during nutrient limitation

As rapidly growing bacteria begin to exhaust nutrients, their growth rate slows, ultimately leading to stasis or quiescence. Adaptation to nutrient limitation requires widespread metabolic remodeling that leads to lower cellular energy consumption. Examples of such changes include attenuated transcription of genes encoding ribosome components, in part mediated by the phosphorylated nucleotides guanosine tetra- and penta-phosphate, collectively (p)ppGpp. In addition, genes encoding proteins that facilitate survival nutrient limitation exhibit increased expression. An example is the hpf gene, encoding a broadly conserved protein responsible for protecting the ribosome from degradation under conditions limiting for ribosome synthesis. Here we show that (p)ppGpp plays a key role in the transcriptional activation of hpf as B. subtilis cells exit rapid growth. Specifically, we demonstrate that hpf transcription during nutrient limitation requires an RNA polymerase holoenzyme containing the alternative sigma factor {sigma}H, encoded by sigH, whose expression is normally inhibited by the AbrB repressor. However, when global protein synthesis decreases, in part dependent on (p)ppGpp, AbrB levels fall, leading to increased sigH transcription and, consequently, hpf activation. This mechanism couples a key physiological consequence of nutrient limitation - reduced protein synthesis - with specific gene activation, thereby linking transcriptional and translational regulation. Finally, we demonstrate that (p)ppGpp is necessary for the gene expression underlying the elaboration of developmental fates including sporulation and genetic competence. ImportanceBacteria often experience nutrient limitation and, in response, they attenuate energetically costly metabolic processes like protein synthesis. At the same time, however, they stimulate the expression of a subset of proteins that facilitate survival under this condition. This study identifies a new molecular mechanism in the model Gram-positive bacterium Bacillus subtilis responsible for gene expression in response to nutrient limitation that couples reduced global protein synthesis with increased transcription of specific genes. This mechanism mediates the elaboration of developmental fates including sporulation and genetic competence that are known responses to nutrient limitation in this organism.

microbiology↗

BacSC: A general workflow for bacterial single-cell RNA sequencing data analysis

Bacterial single-cell RNA sequencing has the potential to elucidate within-population heterogeneity of prokaryotes, as well as their interaction with host systems. Despite conceptual similarities, the statistical properties of bacterial single-cell datasets are highly dependent on the protocol, making proper processing essential to tap their full potential. We present BacSC, a fully data-driven computational pipeline that processes bacterial single-cell data without requiring manual intervention. BacSC performs data-adaptive quality control and variance stabilization, selects suitable parameters for dimension reduction, neighborhood embedding, and clustering, and provides false discovery rate control in differential gene expression testing. We validated BacSC on a broad selection of bacterial single-cell datasets spanning multiple protocols and species. Here, BacSC detected subpopulations in Klebsiella pneumoniae, found matching structures of Pseudomonas aeruginosa under regular and low-iron conditions, and better represented subpopulation dynamics of Bacillus subtilis. BacSC thus simplifies statistical processing of bacterial single-cell data and reduces the danger of incorrect processing.

bioinformatics↗

Droplet-based single cell RNA sequencing of bacteria identifies known and previously unseen cellular states

Clonal bacterial populations rely on transcriptional variation to differentiate into specialized cell states that increase the communitys fitness. Such heterogeneous gene expression is implicated in many fundamental microbial processes including sporulation, cell communication, detoxification, substrate utilization, competence, biofilm formation, motility, pathogenicity, and antibiotic resistance1. To identify these specialized cell states and determine the processes by which they develop, we need to study isogenic bacterial populations at the single cell level2,3. Here, we develop a method that uses DNA probes and leverages an existing commercial microfluidic platform (10X Chromium) to conduct bacterial single cell RNA sequencing. We sequenced the transcriptome of over 15,000 individual bacterial cells, detecting on average 365 transcripts mapping to 265 genes per cell in B. subtilis and 329 transcripts mapping to 149 genes per cell in E. coli. Our findings correctly identify known cell states and uncover previously unreported cell states. Interestingly, we find that some metabolic pathways segregate into distinct subpopulations across different bacteria and growth conditions, suggesting that some cellular processes may be more prone to differentiation than others. Our high throughput, highly resolved single cell transcriptomic platform can be broadly used for understanding heterogeneity in microbial populations.

microbiology↗

A murine aging cell atlas reveals cell identity and tissue-specific trajectories of aging

BackgroundAging is a pleiotropic process affecting many aspects of organismal and cellular physiology. Mammalian organisms are composed of a constellation of distinct cell type and state identities residing within different tissue environments. Due to technological limitations, the study of aging has traditionally focused on changes within individual cell types, or the aggregate changes across cell types within a tissue. The influence of cell identity and tissue environment on the trajectory of aging therefore remains unclear.\n\nResultsHere, we perform single cell RNA-seq on >50,000 individual cells across three tissues in young and aged mice. These molecular profiles allow for comparison of aging phenotypes across cell types and tissue environments. We find transcriptional features of aging common across many cell types, as well as features of aging unique to each type. Leveraging matrix factorization and optimal transport methods, we compute a trajectory and magnitude of aging for each cell type. We find that cell type exerts a larger influence on these measures than tissue environment.\n\nConclusionIn this study, we use single cell RNA-seq to dissect the influence of cell identity and tissue environment on the aging process. Single cell analysis reveals that cell identities age in unique ways, with some common features of aging shared across identities. We find that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating. These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.

bioinformatics↗

Metabolic Interactions Between Dynamic Bacterial Subpopulations

Within multi-species microbial communities, individual species are known to occupy distinct metabolic niches. By contrast, it has remained largely unclear whether and how metabolic specialization occurs within clonal bacterial populations. The possibility of such metabolic specialization in clonal populations raises several questions: Does specialization occur, and if it does, which metabolic processes are involved? How is specialization coordinated? How rapidly do cells switch between states? And finally, what functions might metabolic specialization provide? One potential function of metabolic specialization could be to manage overflow metabolites such as acetate, which presents a toxic challenge due to low pH, and protective pH-neutral overflow metabolites. Here we show that exponentially dividing Bacillus subtilis cultures divide into distinct interacting metabolic subpopulations including one population that produces acetate, and another population that differentially expresses metabolic genes for the production of acetoin, a pH-neutral storage molecule. These subpopulations grew at distinct rates, and cells switched dynamically between states, with acetate influencing the relative sizes of the different subpopulations. These results show that clonal populations can use metabolic specialization to control the environment through a process of dynamic, environmentally-sensitive state-switching.

microbiology↗