Biology subjects

Deeds, E. J.

Publications and source records attributed to Deeds, E. J..

16 recordsLinked to original sources

bioRxiv2026

Variation in the embryonic cell lineages of Caenorhabditis elegans natural populations

Developmental variation has often been appreciated as a major phenotypic substrate of evolutionary innovation in multicellular organisms. Although quantification of developmental variation in isogenic populations has a long history, it has been difficult to accomplish across natural populations until recently. Given its invariant cell lineage, Caenorhabditis elegans represents an ideal model system for studying how natural diversity in development is expressed at the resolution of single cells, cell lineages, and the whole embryo. Using embGAN, a deep-learning pipeline that enables label-free lineage tracing without the bottleneck and potential confounding effects of generating transgenic strains, we reconstructed the early embryonic lineage of 78 embryos spanning eight genetic backgrounds, including six wild strains, and quantified cell-cycle timing with a lineage-aligned graph metric that separates global rescaling of developmental rate from localized changes in lineage structure. We found that cell-cycle timing differs reproducibly among genotypes, with between-strain variation consistently exceeding within-strain variation. The magnitude of this variation was itself genotype-specific and not correlated with levels of genetic divergence. Unlike the global shifts in the tempo of development that dominate divergence between species, these differences are predominantly in lineage patterning, changing the timing of specific divisions, and they align to cell fate. Thus, the invariant lineage of C. elegans is built on a structured, genotype-specific landscape of developmental variation, a potential source of trait variation from which drift in developmental systems can emerge.

developmental biology↗

bioRxiv2026

Distinct extracellular matrix states uncouple collagen accumulation from pathological fibrosis in Duchenne muscular dystrophy

Fibrosis severity is routinely inferred from collagen abundance, although whether collagen quantity determines pathological fibrosis remains unclear. In Duchenne muscular dystrophy (DMD), chronic muscle injury and inflammation drive extracellular matrix accumulation, making these processes difficult to disentangle. We exploit sarcospan overexpression in mdx mice, a model of DMD (mdxTG), which improves membrane integrity and muscle function despite persistent matrix remodeling. mdxTG muscle accumulates more collagen than mdx yet lacks its dense macrophage-rich scars. Matrisome proteomics and spatial transcriptomics reveal compositionally and spatially distinct matrix states, while decellularized mdxTG matrix protects myotubes from membrane damage relative to mdx matrix. Despite these differences, both dystrophic matrices remain stiff and induce nuclear YAP in fibro-adipogenic progenitors. Verteporfin suppresses collagen production and reduces fibrosis in vivo, while nuclear YAP is increased in FAPs from patients with DMD. Thus, collagen abundance alone does not define pathological fibrosis; matrix organization, biological activity, and mechanosignaling distinguish functionally distinct fibrotic states.

physiology↗

bioRxiv2025

Life in the fast lane: Functional consequences of male-female dynamic differences in the renal autoregulation of flow

Tubuloglomerular feedback (TGF) is essential for the renal auto-regulation of flow. TGF is known to induce spontaneous oscillations in single-nephron tubular fluid flow in male kidneys. However, male-female differences in this dynamic behavior have not been studied. Leveraging intravital two-photon microscopy, resting-state magnetic resonance imaging, ultrasound-based and transdermal recordings, we found TGF-mediated oscillations across spatial scales in the rodent kidney, from single-nephron to whole-organ levels, and that male kidneys exhibited higher operating frequencies than females. To understand the mechanisms involved, we developed a dynamical systems model of TGF that agrees with physiological observations. Analysis of the mathematical model indicated that higher reabsorption rate and fluid flow efficiency in male proximal tubules not only result in higher frequencies, but also render male nephrons more susceptible to lose TGF-mediated oscillations. Furosemide abolished TGF-mediated oscillations in male kidneys and upregulated tubular injury marker KIM-1, suggesting that the propensity to lose TGF-mediated oscillations underlies the heightened risk for injury in males. Our analysis also indicated that SGLT-2 inhibition confers renoprotection by preserving TGF-mediated oscillations in hyperglycemia. Combining quantitative imaging and mathematical modeling, this study provides mechanistic insights into the transition from normal physiology to pathophysiology in the kidney.

physiology↗

bioRxiv2025

DeepAtlas: a tool for effective manifold learning

Manifold learning builds on the "manifold hypothesis," which posits that data in high-dimensional datasets are drawn from lower-dimensional manifolds. Current tools generate global embeddings of data, rather than the local maps used to define manifolds mathematically. These tools also cannot assess whether the manifold hypothesis holds true for a dataset. Here, we describe DeepAtlas, an algorithm that generates lower-dimensional representations of the datas local neighborhoods, then trains deep neural networks that map between these local embeddings and the original data. Topological distortion is used to determine whether a dataset is drawn from a manifold and, if so, its dimensionality. Application to test datasets indicates that DeepAtlas can successfully learn manifold structures. Interestingly, many real datasets, including single-cell RNA-sequencing, do not conform to the manifold hypothesis. In cases where data is drawn from a manifold, DeepAtlas builds a model that can be used generatively and promises to allow the application of powerful tools from differential geometry to a variety of datasets.

systems biology↗

bioRxiv2024

Modeling reveals the strength of weak interactions in stacked ring assembly

Cells employ many large macromolecular machines for the execution and regulation of processes that are vital for cell and organismal viability. Interestingly, cells cannot synthesize these machines as functioning units. Instead, cells synthesize the molecular parts that must then assemble into the functional complex. Many important machines, including chaperones like GroEL and proteases like the proteasome, are comprised protein rings that are stacked on top of one another. While there is some experimental data regarding how stacked-ring complexes like the proteasome self-assemble, a comprehensive understanding of the dynamics of stacked ring assembly is currently lacking. Here, we developed a mathematical model of stacked trimer assembly, and performed an analysis of the assembly of the stacked homomeric trimer, which is the simplest stacked ring architecture. We found that stacked rings are particularly susceptible to a form of kinetic trapping that we term "deadlock," in which the system gets stuck in a state where there are many large intermediates that are not the fully-assembled structure, but that cannot productively react. When interaction affinities are uniformly strong, deadlock severely limits assembly yield. We thus predicted that stacked rings would avoid situations where all interfaces in the structure have high affinity. Analysis of available crystal structures indicated that indeed the majority - if not all - of stacked trimers do not contain uniformly strong interactions. Finally, to better understand the origins of deadlock, we developed a formal pathway analysis and showed that, when all the binding affinities are strong, many of the possible pathways are utilized. In contrast, optimal assembly strategies utilize only a small number of patwhays. Our work suggests that deadlock is a critical factor influencing the evolution of macromolecular machines, and provides general principles for not only understanding existing machines but also for the design of novel structures that can self-assemble efficiently. Statement of SignificanceUnderstanding the assembly macromolecular machines is important for understanding a wide range of cellular processes. Here, we use mathematical models to study the assembly of stacked rings, which are a common motif in these machines. Our models revealed that these complexes can readily get "stuck" during assembly when the binding affinity between subunits is too strong. This suggests an evolutionary pressure to favor weaker interactions, and our analysis of solved structures confirmed this prediction. Our findings not only contribute to the fundamental understanding of assembly but also offer insights into the evolutionary pressures shaping the architecture of stacked rings, and have implications for both cell and synthetic biology.

biophysics↗

bioRxiv2023

In vitro reconstitution of the M.tb proteasome core particle reveals conserved aspects of bacterial proteasome assembly

According to the WHO, one in three people in the world has a latent tuberculosis infection. Tuberculosis is caused by the bacterium Mycobacterium tuberculosis (M.tb). The development of multi-drug resistant (MDR) tuberculosis indicates a need for novel treatments. Hence, it is important to find a second line of treatment for patients infected with MDR tuberculosis. The proteasome is known to be necessary for survival under stress and pathogenicity in M.tb. However, our ability to use the proteasome as drug target has been limited by our abilities to screen for inhibitor compounds in vitro. The proteasome is a protease complex that degrades proteins and is crucial for the maintenance of protein homeostasis within cells. Like many protein complexes, the proteasome must assemble into a specific quaternary structure in order to be active. Specifically, the proteolytically-active proteasome Core Particle (CP) consists of 28 subunits (14 and 14 {beta}) that must assemble into a barrel-like structure in order to become catalytically active. Hence, understanding the assembly process in not only important from a basic cell biological perspective, but may also serve as the basis for the discovery of novel assembly inhibitors. In this study, we have established for the first time a protocol to express and purify the M.tb and {beta} subunits separately in vitro. The subunits are soluble monomers on purification and only assemble into active CPs upon reconstitution. Our assembly experiments revealed that M.tb CP assembly pathway is almost certainly identical to that seen in previous experiments on the CP from the bacterium Rhodococcus erythropolis (R.e), but assembly in M.tb is much slower. Interestingly, we found that subunits from M.tb and R.e spontaneously self-assembled into active hybrid proteasomes on reconstitution with each other, despite having only 65% sequence similarity. Our work thus strongly suggests that the CP assembly pathway is conserved across bacteria, and the ability to perform in vitro assembly experiments on the M.tb proteasome opens up the possibility of performing critical experiments, including screening for potential molecules that could inhibit assembly, directly in this clinically-relevant organism.

biochemistry↗

bioRxiv2023

Novel metrics reveal new structure and unappreciated heterogeneity in C. elegans development

High throughput experimental approaches are increasingly allowing for the quantitative description of cellular and organismal phenotypes. Distilling these large volumes of complex data into meaningful measures that can drive biological insight remains a central challenge. In the quantitative study of development, for instance, one can resolve phenotypic measures for single cells onto their lineage history, enabling joint consideration of heritable signals and cell fate decisions. Most attempts to analyze this type of data, however, discard much of the information content contained within lineage trees. In this work we introduce a generalized metric, which we term the branch distance, that allows us to compare any two embryos based on phenotypic measurements in individual cells. This approach aligns those phenotypic measurements to the underlying lineage tree, providing a flexible and intuitive framework for quantitative comparisons between, for instance, Wild-Type (WT) and mutant developmental programs. We apply this novel metric to data on cell-cycle timing from over 1300 WT and RNAi-treated Caenorhabditis elegans embryos. Our new metric revealed surprising heterogeneity within this data set, including subtle batch effects in WT embryos and dramatic variability in RNAi-induced developmental phenotypes, all of which had been missed in previous analyses. Further investigation of these results suggests a novel, quantitative link between pathways that govern cell fate decisions and pathways that pattern cell cycle timing in the early embryo. Our work demonstrates that the branch distance we propose, and similar metrics like it, have the potential to revolutionize our quantitative understanding of organismal phenotype.

developmental biology↗

bioRxiv2022

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, so far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions, but are relatively slow, if carried out at all-atom resolution, or are significantly coarse-grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (i.e., they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise Fast Fourier Transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution.

biophysics↗

bioRxiv2022

A lack of distinct cell identities in single-cell measurements: revisiting Waddington's landscape

The prevailing interpretation of Waddingtons landscape is that attractors in gene expression space produce and stabilize distinct cell types. This notion is often applied in single-cell omics, where transcriptome data is clustered to produce models of cell types. Here we apply graph theory to characterize the distribution of cells in epigenetic space, using data from various tissues, organisms, and single-cell omics technologies. We find that cells of different types exist in the same regions of epigenetic space, with highly heterogeneous density distributions that are inconsistent with expected densities near an attractor. The lack of attractor structure could not be explained by technical noise, scale variance among genes, nor the subset of genes that were used; nor could it be rescued by any standard set of transformations. These findings pose a challenge for the robust analysis of single-cell data and open the possibility for alternative explanations of canalization during development.

systems biology↗

bioRxiv2022

Understanding the Separation of Timescales in Rhodococcus erythropolis Proteasome Core Particle Assembly

The 20S proteasome Core Particle (CP) is a molecular machine that is a key component of cellular protein degradation pathways. Like other molecular machines, it is not synthesized in an active form, but rather as a set of subunits that assemble into a functional complex. The CP is conserved across all domains of life and is composed of 28 subunits, 14 and 14 {beta}, arranged in four stacked 7-member rings (7{beta}7{beta}77). While details of CP assembly vary across species, the final step in the assembly process is universally conserved: two half proteasomes (HP: 7{beta}7) dimerize to form the CP. In the bacterium Rhodococcus erythropolis, experiments have shown that the formation of the HP is completed within minutes, while the dimerization process takes hours. The N-terminal propeptide of the {beta} subunit, which is autocatalytically cleaved off after CP formation, plays a key role in regulating this separation of time scales. However, the detailed molecular mechanism of how the propeptide achieves this regulation is unclear. In this work, we used Molecular Dynamics (MD) simulations to characterize HP conformations and found that the HP exists in two states: one where the propeptide interacts with key residues in the HP dimerization interface and likely blocks dimerization, and one where this interface is free. We found that a propeptide mutant that dimerizes extremely slowly is essentially always in the non-dimerizable state, while the WT rapidly transitions between the two. Based on these simulations, we designed a propeptide mutant that favored the dimerizable state in MD simulations. In vitro assembly experiments confirmed that this mutant dimerizes significantly faster than WT. Our work thus provides unprecedented insight into how this critical step in CP assembly is regulated, with implications both for efforts to inhibit proteasome assembly and for the evolution of hierarchical assembly pathways.

biophysics↗

bioRxiv2021

Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) aims to characterize how variation in gene expression is distributed across cells in tissues and organisms. Yet, effective comprehension of these extremely high-dimensional datasets remains a critical barrier to progress in biological research. In standard analyses of scRNA-seq data, feature selection steps aim to reduce the dimensionality of the data by focusing on a subset of genes that are the most biologically variable across a set of cells. Ideally, these features provide the genes that are the most informative for partitioning groups of transcriptionally distinct cells, each representing a different cell type or identity. In this work, we propose a simple feature selection model where a binomial sampling process for each mRNA species produces a null model of technical variation. To compare our model to existing methods, we use scRNA-seq data where cell identities have been established a priori for each cell, and characterize whether different feature sets retain biologically varying genes, distort neighborhood structures, and allow popular clustering algorithms to partition groups of cells into their established classes. We find that our model of biological variation, which we term "Differentially Distributed Genes" or DDGs, outperforms existing methods, and enables dimensionality reduction without loss of critical structure within the data set.

bioinformatics↗

bioRxiv2020

Machine learning classification can reduce false positives in structure-based virtual screening

With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discoverys search for active chemical matter. Modern virtual screening methods are still, however, plagued with high false positive rates: typically, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because none of the studies reporting new scoring methods have validated their model prospectively within the same study. Here, we report a new strategy for building a training dataset (D-COID) that aims to generate highly-compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework of gradient-boosted decision trees. In retrospective benchmarks, our new classifier shows outstanding performance relative to other scoring functions. We additionally evaluate the classifier in a prospective context, by screening for new acetylcholinesterase inhibitors. Remarkably, we find that nearly all compounds selected by vScreenML show detectable activity at 50 {micro}M, with 10 of 23 providing greater than 50% inhibition at this concentration. Without any medicinal chemistry optimization, the most potent hit from this initial screen has an IC50 of 280 nM, corresponding to a Ki value of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.

bioinformatics↗

bioRxiv2019

Robustness and the evolution of length control strategies in the type III secretion system and flagellar hook

Bacteria construct many structures, like the flagellar hook and the type III secretion system, that aid in crucial processes such as locomotion and pathogenesis. Experimental work has suggested two competing mechanisms bacteria could use to regulate length in these structures: the \"ruler\" mechanism and the \"substrate switching\" mechanism. In this work, we constructed a mathematical model of length control based on the ruler mechanism, and found that the predictions of this model are consistent with experimental data not just for the scaling of the average length with the ruler protein length, but also the variance. Interestingly, we found that the ruler mechanism allows for the evolution of needles with large average lengths without the concomitant large increase in variance that occurs in the substrate switching mechanism. These findings shed new light on the trade-offs that may have lead to the evolution of different length control mechanisms in different bacterial species.

biophysics↗

bioRxiv2019

A novel metric reveals previously unrecognized distortion in dimensionality reduction of scRNA-Seq data

High-dimensional data are becoming increasingly common in nearly all areas of science. Developing approaches to analyze these data and understand their meaning is a pressing issue. This is particularly true for single-cell RNA-seq (scRNA-seq), a technique that simultaneously measures the expression of tens of thousands of genes in thousands to millions of single cells. Popular analysis pipelines significantly reduce the dimensionality of the dataset before performing downstream analysis. One problem with this approach is that dimensionality reduction can introduce substantial distortion into the data, particularly by disrupting the local neighborhoods of certain points. Since many scRNA-seq analyses like cell type clustering or trajectory inference rely on these near-neighbor relationships, distortion in this aspect of the data could significantly influence the outcomes of these analyses. Here, we introduce a straightforward approach to quantifying this distortion by comparing the local neighborhoods of points before and after dimensionality reduction. We found that popular techniques like t-SNE and UMAP introduce substantial distortion even for simple simulated data sets. For scRNA-seq data, we found the distortion in local neighborhoods was often greater than 95%, and that there was no consistent set of neighborhoods across the various steps in the consensus scRNA-seq analysis pipeline. We also found that this distortion had profound impacts on the outcomes of cell type clustering and other downstream analyses. Our findings suggest that caution must be applied when interpreting results in terms of 2-D visualizations produced by tools like UMAP, and that there is a critical need for new dimensionality reduction tools that more effectively preserve the local topological structure of the data.

bioinformatics↗

bioRxiv2019

Crosstalk and Ultrasensitivity in Protein Degradation Pathways

Protein turnover is vital to protein homeostasis within the cell. Many proteins are degraded efficiently only after they have been post-translationally \"tagged\" with a polyubiquitin chain. Ubiquitylation is a form of Post-Translational Modification (PTM): addition of a ubiquitin to the chain is catalyzed by E3 ligases, and removal of ubiquitin is catalyzed by a De-UBiquitylating enzyme (DUB). Over three decades ago, Goldbeter and Koshland discovered that reversible PTM cycles function like on-off switches when the substrates are at saturating concentrations. Although this finding has had profound implications for the understanding of switch-like behavior in biochemical networks, the general behavior of PTM cycles subject to synthesis and degradation has not been studied. Using a mathematical modeling approach, we found that simply introducing protein turnover to a standard modification cycle has profound effects, including significantly reducing the switch-like nature of the response. Our findings suggest that many classic results on PTM cycles may not hold in vivo where protein turnover is ubiquitous. We also found that proteins sharing an E3 ligase can have closely related changes in their expression levels. These results imply that it may be difficult to interpret experimental results obtained from either overexpressing or knocking down protein levels, since changes in protein expression can be coupled via E3 ligase crosstalk. Understanding crosstalk and competition for E3 ligases will be key to ultimately developing a global picture of protein homeostasis.

systems biology↗

bioRxiv2019

Signal integration and information transfer in an allosterically regulated network

A biological reaction network may serve multiple purposes, processing more than one input and impacting downstream processes via more than one output. These networks operate in a dynamic cellular environment in which the levels of network components may change within cells and across cells. Recent evidence suggests that protein concentration variability could explain cell fate decisions. However, systems with multiple inputs, multiple outputs, and changing input concentrations have not been studied in detail due to their complexity. Here, we take a systems biochemistry approach, combining physiochemical modeling and information theory, to investigate how cyclooxygenase-2 (COX-2) processes simultaneous input signals within a complex interaction network. We find that changes in input levels affect the amount of information transmitted by the network, as does the correlation between those inputs. This, and the allosteric regulation of COX-2 by its substrates, allows it to act as a signal integrator that is most sensitive to changes in relative input levels.

systems biology↗