Biology subjects

Wiggins, P. A.

Publications and source records attributed to Wiggins, P. A..

10 recordsLinked to original sources

bioRxiv2024

OmniSegger: A time-lapse image analysis pipeline for bacterial cells

Time-lapse microscopy is a powerful tool for studying the cell biology of bacterial cells. The development of pipelines that facilitate the automated analysis of these datasets is a long-standing goal of the field. In this paper, we describe OmniSegger, an updated version of our SuperSegger pipeline, developed as an open-source, modular, and holistic suite of algorithms whose input is raw microscopy images and whose output is a wide range of quantitative cellular analyses, including dynamical cell cytometry data and cellular visualizations. The updated version described in this paper introduces two principal refinements: (i) robustness to cell morphologies and (ii) support for a range of common imaging modalities. To demonstrate robustness to cell morphology, we present an analysis of the proliferation dynamics of Escherichia coli treated with a drug that induces filamentation. To demonstrate extended support for new image modalities, we analyze cells imaged by five distinct modalities: phase-contrast, two brightfield modalities, and cytoplasmic and membrane fluorescence. Together, this pipeline should greatly increase the scope of tractable analyses for bacterial microscopists.

biophysics↗

bioRxiv2024

Protein overabundance is driven by growth robustness

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naively predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium Acinetobacter baylyi by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale. In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of the fitness landscape with respect to protein abundance. We find that most essential proteins are subject to a threshold-like fitness landscape: growth is independent of protein abundance above a critical threshold and arrests below that threshold. We have recently analyzed the implications of this landscape for growth robustness. Confirming signature predictions of this model, we find that (i) roughly 70% of essential proteins are overabundant, (ii) overabundance increases as the expression level decreases and (iii) the lowest abundance proteins are in vast excess (>10x) of what is required for growth in the typical cell. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.

systems biology↗

bioRxiv2023

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function. One-sentence summaryFitness maximization predicts protein overabundance, a transcriptional floor, and the balancing of transcription and translation.

systems biology↗

bioRxiv2023

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise. In yeast, we demonstrate that this quantity predicts the non-canonical scaling of noise with protein abundance, as well as quantitatively predicting its magnitude. We then hypothesize that growth robustness requires an upper ceiling on noise for the expression of essential genes, corresponding to a lower floor on the transcription level. We show that just such a floor exists: a minimum transcription level of one message per cell cycle is conserved between three model organisms: Escherichia coli, yeast, and human. Furthermore, all three organisms transcribe the same number of messages per gene, per cell cycle. This common transcriptional program reveals that robustness to noise plays a central role in determining the expression level of a large fraction of essential genes, and that this fundamental optimal strategy is conserved from E. coli to human cells.

biophysics↗

bioRxiv2022

The high-resolution in vivo measurement of replication fork velocity and pausing

An important step towards understanding the mechanistic basis of the central dogma is the quantitative characterization of the dynamics of nucleic-acid-bound molecular motors in the context of the living cell, where a crowded cytoplasm as well as competing and potentially antagonistic processes may significantly affect their rapidity and reliability. To capture these dynamics, we develop a novel method, lag-time analysis, for measuring in vivo dynamics. The approach uses exponential growth as the stopwatch to resolve dynamics in an asynchronous culture and therefore circumvents the difficulties and potential artifacts associated with synchronization or fluorescent labeling. Although lag-time analysis has the potential to be widely applicable to the quantitative analysis of in vivo dynamics, we focus on an important application: characterizing replication dynamics. To benchmark the approach, we analyze replication dynamics in three different species and a collection of mutants. We provide the first quantitative locus-specific measurements of fork velocity, in units of kb per second, as well as replisome-pause durations, some with the precision of seconds. The measured fork velocity is observed to be both locus and time dependent, even in wild-type cells. In addition to quantitatively characterizing known phenomena, we detect brief, locus-specific pauses at rDNA in wild-type cells for the first time. We also observe temporal fork velocity oscillations in three highly-divergent bacterial species. Lag-time analysis not only has great potential to offer new insights into replication, as demonstrated in the paper, but also has potential to provide quantitative insights into other important processes.

biophysics↗

bioRxiv2021

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism and on the segmentation of non-bacterial objects. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

microbiology↗

bioRxiv2021

Characterizing stochastic cell cycle dynamics in exponential growth

Two powerful and complementary experimental approaches are commonly used to study the cell cycle and cell biology: One class of experiments characterizes the statistics (or demographics) of an unsynchronized exponentially-growing population, while the other captures cell cycle dynamics, either by time-lapse imaging of full cell cycles or in bulk experiments on synchronized populations. In this paper, we study the subtle relationship between observations in these two distinct experimental approaches. We begin with an existing model: a single-cell deterministic description of cell cycle dynamics where cell states (i.e. periods or phases) have precise lifetimes. We then generalize this description to a stochastic model in which the states have stochastic lifetimes, as described by arbitrary probability distribution functions. Our analyses of the demographics of an exponential culture reveal a simple and exact correspondence between the deterministic and stochastic models: The corresponding state lifetimes in the deterministic model are equal to the exponential mean of the lifetimes in the stochastic model. An important implication is therefore that the demographics of an exponential culture will be well-fit by a deterministic model even if the state timing is stochastic. Although we explore the implications of the models in the context of the Escherichia coli cell cycle, we expect both the models as well as the significance of the exponential-mean lifetimes to find many applications in the quantitative analysis of cell cycle dynamics in other biological systems.

biophysics↗

bioRxiv2021

Genome-wide protein-DNA interaction site mapping using a double strand DNA-specific cytosine deaminase

DNA-protein interactions (DPIs) are central to such fundamental cellular processes as transcription and chromosome maintenance and organization. The spatiotemporal dynamics of these interactions dictate their functional consequences; therefore, there is great interest in facile methods for defining the sites of DPI within cells. Here, we present a general method for mapping DPI sites in vivo using the double stranded DNA-specific cytosine deaminase toxin DddA. Our approach, which we term DddA-sequencing (3D-seq), entails generating a translational fusion of DddA to a DNA binding protein of interest, inactivating uracil DNA glycosylase, modulating DddA activity via its natural inhibitor protein, and DNA sequencing for genome-wide DPI detection. We successfully applied this method to three Pseudomonas aeruginosa transcription factors that represent divergent protein families and bind variable numbers of chromosomal locations. 3D-seq offers several advantages over existing technologies including ease of implementation and the possibility to measure DPIs at single-cell resolution.

genetics↗

bioRxiv2020

An interbacterial DNA deaminase toxin directly mutagenizes surviving target populations

When bacterial cells come in contact, antagonism mediated by the delivery of toxins frequently ensues. The potential for such encounters to have long-term beneficial consequences in recipient cells has not been investigated. Here we examined the effects of intoxication by DddA, a cytosine deaminase delivered via the type VI secretion system (T6SS) of Burkholderia cenocepacia. Despite its killing potential, we observed that several bacterial species resist DddA and instead accumulate mutations installed by the toxin, indicating that even in the absence of killing, interbacterial toxins can have profound consequences on target populations. Investigation of additional toxins from the deaminase superfamily revealed that mutagenic activity is a common feature of these proteins, including a representative we show targets single-stranded DNA and displays a markedly divergent structure. Our findings suggest that a surprising consequence of antagonistic interactions between bacteria could be the promotion of adaptation via the action of directly mutagenic toxins.

microbiology↗

bioRxiv2016

The replisomes remain spatially proximal throughout the cell cycle in bacteria

The positioning of the DNA replication machinery (replisome) has been the subject of several studies. Two conflicting models for replisome localization have been proposed: In the Factory Model, sister replisomes remain spatially colocalized as the replicating DNA is translocated through a stationary replication factory. In the Track Model, sister replisomes translocate independently along a stationary DNA track and the replisomes are spatially separated for the majority of the cell cycle. Here, we used time-lapse imaging to observe and quantify the position of fluorescently labeled processivity-clamp (DnaN) complexes throughout the cell cycle in two highly-divergent bacterial model organisms: Bacillus subtilis and Escherichia coli. Because DnaN is a core component of the replication machinery, its localization patterns should be an appropriate proxy for replisome positioning in general. We present automated statistical analysis of DnaN positioning in large populations, which is essential due to the high degree of cell-to-cell variation. We find that both bacteria show remarkably similar DnaN positioning, where any potential separation of the two replication forks remains below the diffraction limit throughout the majority of the replication cycle. Additionally, the localization pattern of several other core replisome components is consistent with that of DnaN. These data altogether indicate that the two replication forks remain spatially colocalized and mostly function in close proximity throughout the replication cycle.The conservation of the observed localization patterns in these highly divergent species suggests that the subcellular positioning of the replisome is a functionally critical feature of DNA replication.\n\nAuthor SummaryCell proliferation depends on efficient replication of the genome. Bacteria typically have a single origin of replication on a circular chromosome. After replication initiation, two replisomes assemble at the origin and each copy one of the two arms of the chromosome until they reach the terminus. There have been conflicting reports about the subcellular positioning and putative co-localization of the two replication forks during this process. It has remained controversial whether the two replisomes remain relatively close to each other with the DNA being pulled through, or separate as they translocate along the DNA like a track. Existing studies have relied heavily on snapshot images and these experiments cannot unambiguously distinguish between these two models: i.e. two resolvable forks versus two pairs of co-localized forks. The ability of replication to re-initiate before cell division in bacterial cells further complicates the interpretation of these types of imaging studies. In this paper, we use a combination of snapshot imaging, time-lapse imaging, and quantitative analysis to measure the fraction of time forks are co-localized during each cell cycle. We find that the forks are co-localized for the majority ( 80%) of the replication cycle in two highly-divergent model organisms: B. subtilis and E. coli. Our observations are consistent with proximal localization of the two forks, but also some transient separations of sister forks during replication. The conserved behavior of sub-cellular positioning of the replisomes in these two highly divergent species implies a potential functional relevance of this feature.

cell biology↗