Biology subjects

Schroeder, J. W.

Publications and source records attributed to Schroeder, J. W..

8 recordsLinked to original sources

bioRxiv2025

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↗

bioRxiv2025

ShapeME: A tool and web front-end for de novo discovery of structural motifs underpinning protein-DNA interactions

Determining where transcriptional regulators bind within a genome is paramount to understanding how gene expression is regulated. Historically, position weight matrices (PWMs) have been used to define the binding preferences of DNA binding proteins1. However, PWMs treat the identity of each base in a sequence as an independent and additive measure of binding preference, which can limit their utility2. Models that consider higher order interactions between nearby bases yield greater success in predicting proteins binding to DNA, but for many proteins there is still substantial room for improvement in predicting and understanding the determinants of proteins binding to DNA3. In addition to DNA sequence motifs, structural motifs (e.g., a narrow minor groove width) are important determinants of binding for some DNA-binding proteins4. Despite the initial success of algorithms using structural features of DNA to predict binding properties of proteins from either ChIP-seq or SELEX data5-8, there remains a need for a de novo structural motif discovery framework which can be applied to data from a variety of experimental designs. Here, we present a unified workflow, capable of utilizing virtually any type of data representing sequence coverage or enrichment (e.g. ChIP-seq, RNA-seq, SELEX, etc.), to discover short structural motifs with explanatory power for a proteins DNA binding preference. We couple the DNAshapeR algorithm9 with our own information-theoretic approach to de novo motif discovery, and wrap shape and sequence motif inference and model selection into a single tool called ShapeME. Application of our structural motif discovery algorithm to proteins with ChIP-seq data in ENCODE datasets reveals a subset of proteins where short structural motifs outperform the best PWM for that protein as determined from the JASPAR database, or as identified by the sequence motif elicitation tool STREME. Our approach offers a powerful and versatile framework for inferring structural DNA binding motifs, and will complement current sequence-based motif elicitation tools in discovery of protein-DNA interaction principles. A web-based interface to ShapeME is available at https://seq2fun.dcmb.med.umich.edu/shapeme, with full source code available at https://github.com/freddolino-lab/ShapeME.

bioinformatics↗

bioRxiv2024

Negative feedback of cyclic di-GMP levels optimizes switching between sessile and motile lifestyles in Vibrio cholerae

The signaling molecule cyclic di-GMP (c-di-GMP) controls the switch between bacterial motility and biofilm production, and fluctuations in cellular levels of c-di-GMP have been implicated in Vibrio cholerae pathogenesis. Intracellular concentrations of c-di-GMP are controlled by the interplay of diguanylate cyclase (DGC) enzymes, which synthesize c-di-GMP to promote biofilms, and phosphodiesterase (PDE) enzymes, which hydrolyze c-di-GMP to drive motility. To track the complete regulatory logic of how V. cholerae responds to changing c-di-GMP levels, we followed a time course of overexpression of either the V. campbellii diguanylate cyclase QrgB or a variant of QrgB lacking catalytic activity (QrgB*). We find that QrgB increases c-di-GMP levels relative to QrgB* for 30 minutes after overexpression, but the effect of QrgB on c-di-GMP levels plateaus at 30 minutes, indicating tight adaptive control of c-di-GMP levels. In contrast, loss of VpsR, a master regulator activating biofilm formation upon binding to c-di-GMP, leads to higher baseline levels of c-di-GMP and continuously increasing c-di-GMP through 60 minutes after QrgB induction, revealing the existence of a negative feedback loop on c-di-GMP levels operating through VpsR. Through a combination of RNA polymerase ChIP-seq, RNA-seq, and genetic approaches, we show that transcription of a gene encoding a PDE, cdgC, is activated by VpsR at high c-di-GMP concentrations, mediating this negative feedback on c-di-GMP levels. Further, although cells lacking cdgC exhibit enhanced biofilm formation, these mutants are outcompeted by wild type V. cholerae in colonization assays that reward a combination of attachment, dispersal, and motility behaviors. These results underscore the importance of negative feedback regulation of c-di-GMP to maintain appropriate homeostatic levels for efficient transitioning between biofilm formation and motility, both of which are necessary over the course of the V. cholerae infection cycle.

microbiology↗

bioRxiv2023

RNase H genes cause distinct impacts on RNA:DNA hybrid formation and mutagenesis genome-wide

RNA:DNA hybrids such as R-loops affect genome integrity and DNA replication fork progression. The overall impacts of naturally occurring RNA:DNA hybrids on genome integrity, and the relative contributions of ribonucleases H to mitigating the negative effects of hybrids, remain unknown. Here, we investigate the contributions of RNases HII (RnhB) and HIII (RnhC) to hybrid removal, DNA replication, and mutagenesis genome-wide. Deletion of either rnhB or rnhC triggers RNA:DNA hybrid accumulation, but with distinct patterns of mutagenesis and hybrid accumulation. Across all cells, hybrids accumulate most strongly in non-coding RNAs and 5'-UTRs of coding sequences. For {Delta}rnhB, hybrids accumulate preferentially in untranslated regions and early in coding sequences. Hybrid accumulation is particularly sensitive to gene expression in {Delta}rnhC; in cells lacking RnhC, DNA replication is disrupted leading to transversions and structural variation. Our results resolve the outstanding question of how hybrids in native genomic contexts interact with replication to cause mutagenesis and shape genome organization.

genetics↗

bioRxiv2023

Tools and methods for high-throughput single-cell imaging with the mother machine

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) - i.e., 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.

systems biology↗

bioRxiv2020

A shared alarmone-GTP switch underlies triggered and spontaneous persistence

Phenotypically-switched, antibiotic-refractory persisters may prevent pathogen eradication. Although how triggered persistence via starvation-induced (p)ppGpp is well characterized, generation of persisters without starvation are poorly understood. Here we visualized the formation of spontaneous persisters in a small fraction of cells from growing wild type bacteria, revealing a striking single cell rapid switch from growth to dormancy. This switch-like entrance is triggered by GTP dropping beneath a threshold due to stochastic production and self-amplification of (p)ppGpp via allosteric enzyme activation. In addition, persisters are induced by lethal and sublethal concentrations of cell wall antibiotics by inducing (p)ppGpp via cell wall stress response. Thus spontaneous, triggered and antibiotic-induced persisters can all stem from a common metabolic switch: GTP depletion by (p)ppGpp induction, and each pathway of persister formation is activated by different (p)ppGpp synthetases. These persistence pathways are likely conserved in pathogens which may be exploited to potentiate antibiotic efficacy.

microbiology↗

bioRxiv2019

Mother machine image analysis with MM3

The mother machine is a microfluidic device for high-throughput time-lapse imaging of microbes. Here, we present MM3, a complete and modular image analysis pipeline. MM3 turns raw mother machine images, both phase contrast and fluorescence, into a data structure containing cells with their measured features. MM3 employs machine learning and non-learning algorithms, and is implemented in Python. MM3 is easy to run as a command line tool with the occasional graphical user interface on a PC or Mac. A typical mother machine experiment can be analyzed within one day. It has been extensively tested, is well documented and publicly available via Github.

systems biology↗

bioRxiv2018

Discovery of a two protease DNA damage checkpoint recovery mechanism

The DNA damage response is a signaling pathway found throughout biology. In many bacteria the DNA damage checkpoint is enforced by inducing expression of a small, membrane bound inhibitor that delays cell division providing time to repair damaged chromosomes. How cells sense successful DNA repair and promote checkpoint recovery is unknown. By using a high-throughput, forward genetic screen, we identified two unrelated proteases, YlbL and CtpA, that promote DNA damage checkpoint recovery in Bacillus subtilis. Deletion of both proteases leads to accumulation of the checkpoint protein YneA. DNA damage sensitivity and increased cell elongation in protease mutants depends on yneA. Further, expression of YneA in protease mutants was sufficient to inhibit cell proliferation. Finally, we show that one of the two proteases, CtpA, directly cleaves YneA in vitro. With these results, we report the mechanism for DNA damage checkpoint recovery in bacteria that use membrane bound cell division inhibitors.

molecular biology↗