Biology subjects

Chang, R. L.

Publications and source records attributed to Chang, R. L..

5 recordsLinked to original sources

bioRxiv2026

Stochasticity in Metabolism: Oxidative Stress Response Phenotypes As Emergent Consequences of Network Topology

Reactive oxygen species (ROS) are ubiquitous yet elusive due to their reactivity and short half-lives, making simulation essential for mechanistic insight. Traditional ROS modeling has been confined to small ODE frameworks limited by kinetic-parameter scarcity. Here, we recast ROS dynamics as a flux-balance analysis (FBA) optimization problem using COBRA and CPLEX, augmenting the Escherichia coli iJO1366 genome-scale model with 84 spontaneous oxidation, 14 redox, and 9 detoxification reactions, targeting three ROS species: hydroxyl radicals, superoxide, and hydrogen peroxide (H2O2). We also formalize five fundamental rules of oxidation biology in the form of mixed-integer linear constraints, allowing us to dynamically incorporate reaction rate constants and let complex behavior emerge from first principles. Among these phenomena is the bimodal killing pattern under H2O2 stress, first announced over 40 years ago and never computationally recapitulated until now. We observe it by stochastically sampling our models with GapSplit, simulating bet-hedging and partitioning solutions into growers, persisters, and non-survivors. In our simulations, this killing effect arises solely from the metabolic network topology during random exploration of alternative metabolic configurations. The conservation of steady state creates a stochastic bias for detoxification when central carbon metabolism reaches its metabolic capacity to deal with ROS, causing the famous survival recovery phase. The recovery phase is thus part of Mode 1 metabolism, characterized by spare detoxification capacity and fulfilled by the Ahp reaction in E. coli. Mode 2 has no spare detoxification. We anticipate this to be an overlooked universal principle.

systems biology↗

bioRxiv2022

Making sense of disorder: Investigating intrinsically disordered proteins in the tardigrade proteome via a computational approach

Tardigrades, also known as water bears, are a phylum of microscopic metazoans with the extraordinary ability to endure environmental extremes. When threatened by suboptimal habitat conditions, these creatures enter a suspended animation-like state called cryptobiosis, in which metabolism is diminished, similar to hibernation. In this state, tardigrades benefit from enhanced extremotolerance, withstanding dehydration efficiently for years at a time in a type of cryptobiosis called anhydrobiosis. Recent studies have demonstrated that the tardigrade proteome is at the heart of cryptobiosis. Principally, intrinsically disordered proteins (IDPs) and tardigrade-specific intrinsically disordered proteins (TDPs) are known to help protect cell function in the absence of water. Importantly, TDPs have been successfully expressed in cells of other species experimentally, even protecting human tissue against stress in vitro. However, previous work has failed to address how to strategically identify TDPs in the tardigrade proteome holistically. The overarching purpose of this current study, consequently, was to generate a list of IDPs/TDPs associated with tardigrade cryptobiosis that are high-priority for further investigation. Firstly, a novel database containing 44,836 tardigrade proteins from 338 different species was constructed to consolidate and standardize publicly available data. Secondly, a support vector machine (SVM) was created to sort the newly constructed database entries on the binary basis of disorder (i.e., IDP versus non-IDP). Features of this model draw from disorder metrics and literature curation, correctly classifying 160 of the 171 training set proteins (~93.6%). Of the 5,415 putative IDPs/TDPs our SVM identified, we present 82 (30 having confident subclass prediction and 52 having experimental detection in previous studies). Subsequently, the role each protein might play in tardigrade resilience is discussed. By and large, this supervised machine learning classifier represents a promising new approach for identifying IDPs/TDPs, opening doors to harness the tardigrades remarkable faculties for biomaterial preservation, genetic engineering, astrobiological research, and ultimately, the benefit of humankind.

bioinformatics↗

bioRxiv2021

Natural and designed proteins inspired by extremotolerant organisms can form condensates and attenuate apoptosis in human cells

Many organisms can survive extreme conditions and successfully recover to normal life. This extremotolerant behavior has been attributed in part to repetitive, amphipathic, and intrinsically disordered proteins that are upregulated in the protected state. Here, we assemble a library of approximately 300 naturally-occurring and designed extremotolerance-associated proteins to assess their ability to protect human cells from chemically-induced apoptosis. We show that several proteins from tardigrades, nematodes, and the Chinese giant salamander are apoptosis protective. Notably, we identify a region of the human ApoE protein with similarity to extremotolerance-associated proteins that also protects against apoptosis. This region mirrors the phase separation behavior seen with such proteins, like the tardigrade protein CAHS2. Moreover, we identify a synthetic protein, DHR81, that shares this combination of elevated phase separation propensity and apoptosis protection. Finally, we demonstrate that driving protective proteins into the condensate state increases apoptosis protection, and highlight the ability for DHR81 condensates to sequester caspase-7. Taken together, this work draws a link between extremotolerance-associated proteins, condensate formation, and designing human cellular protection.

synthetic biology↗

bioRxiv2020

Unraveling Oxidative Stress Resistance: Molecular Properties Govern Proteome Vulnerability

AbstractOxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS-susceptibility essential. The radiation-resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by {gamma}-irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS-susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS-resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery and proteins protecting against ROS are more resistant in D. radiodurans. Our findings substantiate that protein-intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties. One Sentence SummaryProteins differ in intrinsic susceptibility to oxidation, a mode of evolutionary adaptation for stress tolerance in bacteria.

systems biology↗

bioRxiv2017

Systemic post-translational control of bacterial metabolism regulates adaptation in dynamic environments

Across all domains of life, elaborate control mechanisms regulate proteins, pathways, and cell phenotypes as organisms adapt to ever-changing environments. Post-translational modifications (PTMs) allow cells to rapidly and reversibly regulate molecular pathways, but it remains unclear how individual PTMs regulate fitness. Here, we studied >130 PTM sites in Escherichia coli to unravel how PTMs regulate cell metabolism and fitness in response to environmental changes, such as the glucose-acetate diauxie. Using a new metabolic modeling approach, we found a significant fraction of post-translationally modified enzymes are predicted to control shifts in pathway usage following evolutionarily-important environmental changes. Genetic screens using Multiplex Automated Genome Engineering confirmed that these PTMs impact cellular fitness, especially under dynamically changing environments. Finally, mechanisms of how individual PTMs impact protein function were detailed using molecular dynamics simulations and enzyme assays for enolase, transaldolase, and serine hydroxymethyltransferase. Thus, by integrating whole-cell data and pathway modeling with detailed biochemical analysis, we unraveled how individual PTMs regulate enzymes, pathways, and phenotypes to adapt to sudden environmental changes.

systems biology↗