Biology subjects

Rodrigues, J. V.

Publications and source records attributed to Rodrigues, J. V..

7 recordsLinked to original sources

bioRxiv2026

Chaperonin recognition of protein dynamics drives drug resistance

The emergence of drug resistance is typically driven by mutations that alter drug-target affinity, yet the role of host cellular machinery regulating these processes remains unclear. Here, we reveal that the chaperonin GroEL/S promotes drug resistance through recognition of protein dynamics. Using directed evolution of E. coli DHFR under antibiotic stress and varying GroEL/S expression, we identify a well-folded resistance variant whose fitness, despite tight inhibitor binding, is critically potentiated by GroEL/S engagement. X-ray crystallography, NMR, molecular dynamics and kinetic modeling reveal that millisecond-timescale flipping of the M20 loop generates steric accessibility and a kinetic window for chaperonin interaction that forcibly displaces tightly bound inhibitors, thereby overriding thermodynamic equilibrium of inhibitor binding to restore the active enzyme pool and preserve metabolic flux. Our findings not only reveal a novel paradigm of "dynamic recognition" where both conformational kinetics and distribution govern chaperonin recognition but also establish chaperonins as "deligandases" that actively modulate in vivo drug binding, suggesting that chaperone surveillance of the cellular proteome extends beyond quality control to govern native protein function. This mechanism defines a previously unrecognized route for the rapid development of drug resistance, with implications for understanding therapeutics of microbial infections and human malignancies.

biophysics↗

bioRxiv2021

Switching an active site helix in dihydrofolate reductase reveals limits to sub-domain modularity

To what degree are individual structural elements within proteins modular such that similar structures from unrelated proteins can be interchanged? We study sub-domain modularity by creating 20 chimeras of an enzyme, E. coli dihydrofolate reductase (DHFR), in which a catalytically important, 10-residue -helical sequence is replaced by -helical sequences from a diverse set of proteins. The chimeras stably fold but have a range of diminished thermal stabilities and catalytic activities. Evolutionary coupling analysis indicates that the residues of this -helix are under selection pressure to maintain catalytic activity in DHFR. We performed molecular dynamics simulations using replica exchange with solute-tempering. Chimeras with low catalytic activity exhibit non-helical conformations that block the binding site and disrupt the positioning of the catalytically essential residue D27. Simulation observables and in vitro measurements of thermal stability and substrate binding affinity are strongly correlated. Several E. coli strains with chromosomally integrated chimeric DHFRs can grow, with growth rates that follow predictions from a kinetic flux model that depends on the intracellular abundance and catalytic activity of DHFR. Our findings show that although -helices are not universally substitutable, the molecular and fitness effects of modular segments can be predicted by the biophysical compatibility of the replacement segment. Statement of Significance-helices are ubiquitous components of protein structure that exhibit a degree of independent folding behavior, making them plausible structural modules within proteins. Here, we assess the effects of switching the sequence of an -helix in an essential enzyme for -helical sequences from evolutionarily unrelated proteins. The resultant chimeric proteins can still fold but enzymatic activity, stability, and cellular growth rates are negatively affected. Computational investigations reveal how residues in an -helix have been shaped by selection pressure to maintain catalytic activity and a specific, helical conformation of the protein. More broadly, we illustrate how molecular and fitness effects of switching protein segments depend on the protein and cellular context.

biophysics↗

bioRxiv2021

A systems-guided approach to discover the intracellular target of a novel evolution-drug lead

Elucidating intracellular drug targets has been a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we developed a systems-guided hierarchic workflow that utilizes metabolic and structural analysis to narrow in on specific targets suggested by statistical and machine learning analysis of metabolomics data. Utilizing a novel multi-valent DHFR-targeting antibiotic compound, CD15-3, as a case study, we first measured global metabolomics and applied statistics and machine learning to locate broad areas of metabolic perturbation under antibiotic stress. We then tested the ability of suggested compounds to rescue growth and performed metabolic modelling to identify pathways whose inhibition was consistent with growth rescue patterns. Next, we utilized protein structural similarity to further prioritize candidate drug targets within these pathways. Overexpression and in vitro activity assays of a top candidate target, HPPK (folK), showed complete recovery from drug induced growth inhibition and with microscopy. As interest in white-box machine learning methods continues to grow, this study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows.

systems biology↗

bioRxiv2020

Development of antibacterial compounds that block evolutionary pathways to resistance

Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC50 50-75 {micro}M against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3 resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC50) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which block evolutionary escape routes in pathogens.

microbiology↗

bioRxiv2019

Comparison Between Normobaric Hypoxia Altitude Simulation Test and Altitude Hypoxia Predictive Equations in Cystic Fibrosis Patients

BackgroundThe Hypoxia Altitude Simulation Test (HAST) is the Gold Standard to evaluate hypoxia in response to altitude and to decide on in-flight requirements for oxygen supplementation. Several equations are available to predict PaO2 in altitude (PaO2alt), but it remains unclear whether their predictive value is equivalent. We aimed to compare the results obtained by the available methods in a population of cystic fibrosis (CF) adults.\n\nMethodsEighty-eight adults (58 healthy controls and 30 CF patients) performed a spirometry followed by an HAST. HAST results were compared with the predicted PaO2alt made by five equations: 1st: PaO2alt= 0,410 x PaO2ground + 1,7652; 2nd: PaO2alt= 0,519 x PaO2ground + 11,855 x FEV1 (L) - 1,760; 3rd: PaO2alt= 0,453 x PaO2ground + 0,386 x FEV1 (%) + 2,44; 4th: PaO2alt= 0,88 + 0,68 x PaO2ground; 5th: PaO2alt= PaO2ground - 26,6.\n\nResultsNone of the controls required in-flight oxygen neither by HAST or by the five predictive equations. Eleven CF-patients had PaO2alt < 50 mmHg, accessed by HAST. The positive predictive value was 50% (1st), 87.5% (2nd and 3rd), 77.78% (4th) and 58.33% (5th). Areas under the curve were 78.95% (1st), 84.69% (2nd), 88.04% (3rd) and 78.95% (4th and 5th). FEV1 and PaO2ground were correlated with HAST results.\n\nConclusionsThe 3rd equation gave the best predictions in comparison with results obtained by HAST. However, because the individual differences found were substantial for all equations, we still recommend performing a HAST whenever possible to confidently access in-flight hypoxia and the need for oxygen.

pathology↗

bioRxiv2019

Evolutionary dynamics determines adaptation to inactivation of an essential gene.

Genetic inactivation of essential genes creates an evolutionary scenario distinct from escape from drug inhibition, but the mechanisms of microbe adaptations in such cases remain unknown. Here we inactivate E. coli dihydrofolate reductase (DHFR) by introducing D27G,N,F chromosomal mutations in a key catalytic residue with subsequent adaptation by serial dilutions. The partial reversal G27->C occurred in three evolutionary trajectories. Conversely, in one trajectory for D27G and in all trajectories for D27F,N strains adapted to grow at very low supplement folAmix concentrations but did not escape entirely from supplement auxotrophy. Major global shifts in metabolome and proteome occurred upon DHFR inactivation, which were partially reversed in adapted strains. Loss of function mutations in two genes, thyA and deoB, ensured adaptation to low folAmix by rerouting the 2-Deoxy-D-ribose-phosphate metabolism from glycolysis towards synthesis of dTMP. Multiple evolutionary pathways of adaptation to low folAmix converge to highly accessible yet suboptimal fitness peak.

evolutionary biology↗

bioRxiv2018

Proteostasis environment shapes higher-order epistasis operating on antibiotic resistance

Recent studies have shown that higher-order epistasis is ubiquitous and can have large effects on complex traits. Yet, we lack frameworks for understanding how epistatic interactions are influenced by basic aspects of cell physiology. In this study, we assess how protein quality control machinery--a critical component of cell physiology--affects epistasis for different traits related to bacterial resistance to antibiotics. Specifically, we attempt to disentangle the interactions between different protein quality control genetic backgrounds and two sets of mutations: (i) SNPs associated with resistance to antibiotics in an essential bacterial enzyme (dihydrofolate reductase, or DHFR) and (ii) differing DHFR bacterial species-specific amino acid background sequences (Escherichia coli, Listeria grayi, and Chlamydia muridarum). In doing so, we add nuance to the generic observation that non-linear genetic interactions are widespread and capricious in nature, by proposing a mechanistically-grounded analysis of how proteostasis shapes epistasis. These findings simultaneously fortify and demystify the role of environmental context in modulating higher-order epistasis, with direct implications for evolutionary theory, genetic modification technology, and efforts to manage antimicrobial resistance.

genetics↗