Biology subjects

Toprak, E.

Publications and source records attributed to Toprak, E..

12 recordsLinked to original sources

bioRxiv2025

Saturation mutagenesis map of generalist versus specialist adaptations of β-lactamase to novel antibiotics

The evolution of {beta}-lactamase proteins is shaped by the need to maintain enzymatic activity against previously prevalent {beta}-lactam antibiotics while expanding substrate range against new classes of antibiotics. Using saturation mutagenesis and sequence-barcoding-based quantification, we comprehensively mapped the response of the fitness landscape of TEM-1 {beta}-lactamase, which evolved against penicillin-class antibiotics, to mutational perturbations against six diverse {beta}-lactam antibiotics. This systematic panel of antibiotic substrates, including representatives from penicillin, cephalosporin, and monobactam classes, allowed us to classify resistance mutations into two categories. Generalist mutations conferred resistance to multiple antibiotics and were consistently restricted to three positions critical for substrate recognition and catalytic function R164, G238, and E240. These substitutions produced broad spectrum resistance through mechanisms such as expansion of the active site and improved substrate accommodation. In contrast, specialist mutations conferred resistance to only a single antibiotic and exhibited much wider positional diversity. Ceftazidime selection yielded the greatest number of distinct specialist mutations, which were frequently found in flexible or peripheral regions including the omega loop. One especially unexpected finding was the identification of the E166P variant. E166 is a catalytic residue required for deacylation during hydrolysis, and substitutions at this site are generally assumed to abolish function. However, E166P conferred a significant increase in ceftazidime resistance despite eliminating activity against penicillins. Molecular dynamics simulations and mutational analysis revealed that the E166P mutant employs an alternative catalytic mechanism, involving residue N132, rather than the canonical pathway. Together, our findings reveal, at the molecular level, how specialist mutations open up a wide range of diverse and idiosyncratic solutions at the expense of generalizability. These insights may inform strategic design of antibiotic administration protocols to systematically lower pathogenic evolvability.

evolutionary biology↗

bioRxiv2025

Counting-based inference of mutant growth rates from pooled sequencing across growth regimes

Time-resolved sequencing of pooled mutants is widely used to track their frequencies under selection pressure, thereby revealing variants that are enriched or depleted. Here, we address how to quantify variant growth rates by analyzing the temporal dimension of the counts data through a model of growth. For exponential growth, we first study weighted least-squares fitting and show that non-linear fitting based on the softmax transformation exhibits more favorable properties than the currently employed linear regression. We then argue that direct maximization of the likelihood of the noise model should be preferred over least-squares fitting. For a multinomial model of counting noise, we adopt variational Bayesian inference to additionally quantify uncertainties in the estimated growth rates. We provide closed-form expressions for the experimentally practical case of sequencing only at the beginning and at the end of the experiment. Finally, we extend maximum-likelihood estimation and variational Bayesian inference to logistic and Gompertz growth, which serve as illustrative examples of general, non-exponential growth models formulated in terms of a small number of parameters per variant. The ability to incorporate arbitrary growth models within the developed inference framework opens new opportunities for high-throughput estimation of diverse biochemical parameters that influence growth. Author summarySimultaneously tracking the relative abundances of thousands of genetically distinct cellular variants over time is now possible through deep sequencing and other counting-based methods. This capability provides a quantitative window into increasingly broad regions of the combinatorial fitness landscape--well beyond those explored by natural evolution. While identifying a few engineered variants that survive under extreme selection is valuable, quantitative mapping of the fitness landscape requires accurate inference of growth rates for the entire variant population. Here, we revisit three approaches for estimating growth rates from sequencing count data: least-squares fitting, maximum likelihood estimation, and variational Bayesian inference. By clearly delineating the probabilistic model of counting noise and the deterministic model of variant growth--both jointly required for inferring growth rates from time-resolved sequencing counts--we show how exponential growth can be replaced by any alternative growth model. Applying the developed analysis framework to models in which growth rates are expressed in terms of microscopic biochemical parameters will enable the high-throughput inference of fundamental kinetic and biophysical constants from sequencing data.

bioinformatics↗

bioRxiv2025

Discrete protein dynamics enable long-range communication

Allostery, in which perturbations at an input protein site tune the activity at a distant output site, allows proteins to serve as molecular logic gates. Often, information is transmitted without altering the structure outside of the input and output sites. This focalized allostery requires correlated motion between protein backbone dihedral angles that are separated by distances many times longer than the scale of electrostatic interactions. What physical properties of folded proteins enable such long-distance information sharing despite thermal noise is unclear. To address this question, we introduce a Variable-Well Dihedral (VWD) model Hamiltonian which removes dependence on chemical details and forces, instead only tuning the degree of nonlinearity of purely local interactions within a densely-packed polymer. We show that tuning the physical parameters of the model gives rise to focalized allostery in so far that doing so increases the discreteness of the internal degrees of freedom, with real proteins occupying the highly discrete regime. These results parallel, at the molecular scale, the superiority of digital compared to analog signal processing for telecommuncations under noisy conditions.

biophysics↗

bioRxiv2025

The nucleotide analog bemnifosbuvir inhibits hepatitis E virus replication in preclinical models

BackgroundHepatitis E virus infections remain a global health concern. Immunocompromised patients are at an increased risk to develop chronic HEV infection and thereby severe liver disease. Current off-label regimens are suboptimal with treatment failure being reported. Therefore, there is an urgent need for an effective anti-HEV treatment. ObjectiveIn this study, we aimed to identify potent inhibitors of HEV replication. DesignWe developed a rapid, image-based screening platform based on a full-length HEV fluorescence reporter virus and screened a nucleotide/nucleoside analog library. The identified lead candidate was validated in authentic hepatocyte culture systems, as well as in a gerbil infection model. ResultsBemnifosbuvir (BEM), previously characterized as a nucleotide analog with activity against other RNA viruses, efficiently suppressed HEV replication in vitro and in vivo in a dose-dependent manner, with minimal cytotoxicity at effective concentrations. Combining BEM with ribavirin, the off-label drug given to chronic HEV patients, resulted in an additive antiviral effect against HEV. We found that HEV-3 remains susceptible to inhibition by BEM over an extended treatment period, reducing concerns about the rapid development of viral resistance. Importantly, BEM significantly reduced HEV viral loads and liver inflammation in a gerbil infection model. ConclusionsGiven BEMs favorable safety profile in preclinical and clinical settings, our results suggest investigating its efficacy in patients with chronic HEV infection.

microbiology↗

bioRxiv2025

Higher-order epistasis drives evolutionary unpredictability toward novel antibiotic resistance

The rapid evolution of extended-spectrum {beta}-lactamases (ESBLs) represents a global health threat, undermining the efficacy of {beta}-lactams, the most extensively used antibiotic class. To elucidate the evolutionary dynamics underlying {beta}-lactam resistance, we constructed a comprehensive combinatorial mutant library comprising all 55,296 possible TEM-1 {beta}-lactamase variants integrating 18 clinically observed mutations across 13 key residues. Over eight million empirical fitness measurements were obtained under selection pressure with both a native antibiotic substrate (ampicillin) and a novel antibiotic (aztreonam), establishing the largest experimentally determined fitness landscape for antibiotic resistance to date. Through graph-theoretic and epistatic analyses, we discovered that selection with ampicillin resulted in weak epistasis, with mutants rarely surpassing the fitness of the wild-type enzyme. Conversely, aztreonam selection elicited extensive higher-order epistasis, generating a rugged fitness landscape characterized by increased phenotypic unpredictability. Interpretable machine-learning analyses identified context-dependent epistatic interactions necessary for achieving high-level aztreonam resistance. Further evolutionary statistical analyses, including direct coupling analysis and latent generative landscapes, showed that top-performing TEM-1 variants consistently adhered to conserved epistatic patterns found in naturally occurring {beta}-lactamases. Our findings demonstrate that higher-order epistasis critically shapes fitness landscape ruggedness when enzymes adapt to novel substrates, whereas adaptations to native substrates exhibit predictably smoother landscapes. This integrated experimental and computational framework provides a foundation for predictive evolutionary pharmacology, enabling assessments of newly developed {beta}-lactams or emerging {beta}-lactamase variants for their potential contribution to ESBL evolution. Importantly, incorporating graph-theoretically informed evolutionary constraints can strategically disrupt evolutionary pathways, presenting a viable approach to mitigate the rise of antibiotic resistance.

evolutionary biology↗

bioRxiv2025

Context-dependent evolutionary dynamics toward bacterial survival during sequential antibiotic treatment

To overcome the antibiotic resistance problem, exploiting reproducible evolutionary tradeoffs is considered key for designing evolution-proof antibiotic therapies. Yet the predictability of resistance evolution has been viewed as limited, given the often idiosyncratic genetic trajectories observed in laboratory evolution experiments. To address this, we partially mimicked clinical antibiotic pharmacodynamics by imposing strong selection and evolved Escherichia coli under single or sequential antibiotic treatment. Under single-antibiotic selection, endpoint resistance, persistence, and tolerance phenotypes were reproducible, but populations evolving in parallel frequently followed divergent genetic trajectories. Remarkably, sequential antibiotic use redirected these divergent paths toward genotypic and phenotypic convergence, driven by extinction of resistance-conferring mutations when switching to the next effective antibiotic. Single-cell RNA sequencing demonstrated that evolved cultures contain cells occupying distinct metabolic niches and include more cells in states with lower translational activity and higher expression of toxin-antitoxin genes. These findings provide evolutionary insights to inform clinically effective antibiotic treatment strategies that employ sequential treatment.

microbiology↗

bioRxiv2025

A Simple, Low-Cost, and Efficient Protocol for Rapid Isolation of Pathogenic Bacteria from Human Blood

Bacteremia is a serious clinical condition in which pathogenic bacteria enter the bloodstream, putting patients at risk of septic shock and necessitating antibiotic treatment. Choosing the most effective antibiotic is crucial not only for resolving the infection but also for minimizing side effects, such as dysbiosis in the healthy microbiome and reducing the selection pressure for antibiotic resistance. This requires prompt identification of the pathogen and antibiotic susceptibility testing, yet these processes are inherently slow in standard clinical microbiology labs due to reliance on growth-based assays. Although alternative methods exist, they are rarely adopted in clinical settings because they involve complex protocols and high costs for training and infrastructure. Here, we present an optimized, simple protocol for rapidly and efficiently isolating bacterial pathogens from blood without altering typical laboratory workflows. Our method is cost-effective and compatible with commonly available laboratory instruments, offering the advantage of isolating bacterial cells directly, which bypasses the delays associated with traditional blood culture methods and enables faster diagnostic results. The protocol achieved over 70% efficiency within 30 minutes, remained effective at low bacterial concentrations (1-10 bacteria/0.3 mL blood), and preserved bacterial viability with no notable change in growth lag times. We validated the protocol on several clinically relevant bacterial strains, including Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. These findings highlight our protocols potential utility in clinical and research settings, facilitating timely cultures and minimizing diagnostic delays.

microbiology↗

bioRxiv2024

Synergistic and antagonistic drug-drug interactions are prevalent but not conserved across acute myeloid leukemia cell lines

Acute myeloid leukemia (AML) is the most prevalent type of leukemia in adults. Despite advancements in medicine, the standard treatment that utilizes a combination of cytarabine and daunorubicin for AML has remained the same for decades. Combination drug therapies are proven to be an effective way to achieve targeted efficacy while minimizing drug dosage along with the unintended side effects. However, a systematic survey of synergistic potential of drug-drug interactions in the context of AML pathology is currently lacking. Here we examine the interactions between 15 frequently used cancer drugs across distinct AML cell lines and demonstrate that synergistic and antagonistic drug-drug interactions are widespread but not conserved across these cell lines. Notably, enasidenib (AG-221) and venetoclax (ABT-199), recently approved anticancer agents, exhibited the highest counts of synergistic interactions and the fewest antagonistic ones. In contrast, 6-Thioguanine (6-TG), a purine analog, was involved in the highest number of antagonistic interactions. The interactions we report here cannot be attributed solely to the inherent synergistic or antagonistic natures of these three drugs, as each drug we examined was involved in several synergistic or antagonistic interactions in the cell lines we tested. Moreover, we observed that these drug-drug interactions are not conserved across cell lines, suggesting that the success of combination therapies might vary depending on AML genotypes. For instance, we found that a single mutation in the TF1 cell line could dramatically alter drug-drug interactions, even turning synergistic interactions into antagonistic ones, as seen with AG-221 and cladribine A (2CdA). Our findings provide a preclinical survey of the potential synergistic effects revealing the complexity of the problem in vitro. However, the exploitable synergistic regimes in clinical scenarios remain to be explored. We anticipate these results to be an insightful guideline for future clinical studies, aiming to refine chemotherapy regimens and ultimately enhance patient outcomes.

cancer biology↗

bioRxiv2024

Elucidating TolC Protein Dynamics: Structural Shifts Facilitate Efflux Mediated β-lactam Resistance

Efflux-mediated {beta}-lactam resistance represents a significant public health challenge, limiting the efficacy of various {beta}-lactam antibiotics against numerous clinically relevant pathogenic bacteria. Structural and functional analyses have revealed that the efflux protein TolC in several Gram-negative bacteria serves as a conduit for antibiotics, bacteriocins, and phages, affecting bacterial susceptibility and virulence. In this study, we conducted a comprehensive examination of the efflux of {beta}-lactam drugs mediated by TolC, employing extensive experimental and computational analyses. Our computational investigations into the molecular dynamics of drug-free TolC revealed critical unidirectional movements of the trimeric TolC and identified residues significantly involved in TolC opening. To corroborate these findings, we performed a whole-gene-saturation mutagenesis assay, systematically mutating each residue of TolC to 19 other amino acids and measuring the fitness effects of these mutations under {beta}-lactam-induced selection. The {beta}-lactams oxacillin, piperacillin, and carbenicillin were selected for this study because they are effluxed by the AcrAB-TolC complex with varying efficiencies. This approach clarified the similarities and differences in the efflux processes of the three {beta}-lactam antibiotics through the trimeric TolC. Further analysis of TolCs efflux mechanism for these {beta}-lactam antibiotics via steered molecular dynamics simulations revealed the existence of general and drug-specific mechanisms employed by TolC. We identified key positions at the periplasmic entry of TolC whose altered dynamics influence long-range efflux motions as allosteric modulators. Our findings provide valuable insights into the structural dynamics of TolC, establishing a foundation for understanding the key mechanisms behind multidrug resistance and principles for designing new antibiotics and antibiotic derivatives capable of circumventing the bacterial efflux mechanism.

biophysics↗

bioRxiv2023

Susceptible bacteria survive antibiotic treatment in the mammalian gastrointestinal tract without evolving resistance

In vitro systems have provided great insight into the mechanisms of antibiotic resistance. Yet, in vitro approaches cannot reflect the full complexity of what transpires within a host. As the mammalian gut is host to trillions of resident bacteria and thus a potential breeding ground for antibiotic resistance, we sought to better understand how gut bacteria respond to antibiotic treatment in vivo. Here, we colonized germ-free mice with a genetically barcoded antibiotic pan-susceptible Escherichia coli clinical isolate and then administered the antibiotic cefepime via programmable subcutaneous pumps which allowed for closer emulation of human parenteral antibiotic pharmacokinetics/dynamics. After seven days of antibiotics, we were unable to culture E. coli from feces. We were, however, able to recover barcoded E. coli from harvested gastrointestinal (GI) tissue, despite high GI tract and plasma cefepime concentrations. Strikingly, these E. coli isolates were not resistant to cefepime but had acquired mutations - most notably in the wbaP gene, which encodes an enzyme required for the initiation of the synthesis of the polysaccharide capsule and lipopolysaccharide O antigen - that increased their ability to invade and survive within intestinal cells, including cultured human colonocytes. Further, these E. coli mutants exhibited a persister phenotype when exposed to cefepime, allowing for greater survival to pulses of cefepime treatment when compared to the wildtype strain. Our findings highlight a mechanism by which bacteria in the gastrointestinal tract can adapt to antibiotic treatment by increasing their ability to persist during antibiotic treatment and invade intestinal epithelial cells where antibiotic concentrations are substantially reduced.

microbiology↗

bioRxiv2020

The highly evolvable nature of the antibiotic efflux protein TolC limits use of phages and bacterial toxins as antibacterial agents

Bacteriophages and bacterial toxins are promising antibacterial agents to treat infections caused by multidrug resistant (MDR) bacteria. In fact, bacteriophages have recently been successfully used to treat life-threatening infections caused by MDR bacteria [1-3]. One potential problem with using these antibacterial agents is the evolution of resistance against them in the long term. Here, we studied the fitness landscape of the Escherichia coli TolC protein, an outer membrane protein that is exploited by a pore forming toxin called colicin E1 and by TLS-phage [4, 5]. By systematically assessing the distribution of fitness effects (DFEs) of ~9,000 single amino acid replacements in TolC using either positive (antibiotics and bile salts) or negative (colicin E1 and TLS-phage) selection pressures, we quantified evolvability of the TolC. We demonstrated that the TolC is highly optimized for the efflux of antibiotics and bile salts. In contrast, under colicin E1 and TLS phage selection, TolC sequence is very sensitive to mutation. Our findings suggest that TolC is a highly evolvable target limiting the potential clinical use of bacteriophages and bacterial toxins.

systems biology↗

bioRxiv2018

High-order epistasis in catalytic power of dihydrofolate reductase gives rise to a rugged fitness landscape in the presence of trimethoprim selection

Evolutionary fitness landscapes of certain antibiotic target enzymes have been comprehensively mapped showing strong high order epistasis between mutations, but understanding these effects at the biochemical and molecular levels remained open. Here, we carried out an extensive experimental and computational study to quantitatively understand the evolutionary dynamics of Escherichia coli dihydrofolate reductase (DHFR) enzyme in the presence of trimethoprim induced selection. Biochemical and structural characterization of resistance-conferring mutations targeting a total of ten residues spanning the substrate binding pocket of DHFR revealed distinct resistance mechanisms. Next, we experimentally measured biochemical parameters (Km, Ki, and kcat) for a mutant library carrying all possible combinations of six resistance-conferring DHFR mutations and quantified epistatic interactions between them. We found that the epistasis between DHFR mutations is high-order for catalytic power of DHFR (kcat and Km), but less prevalent for trimethoprim affinity (Ki). Taken together our data provide a concrete illustration of how epistatic coupling at the level of biochemical parameters can give rise to complex fitness landscapes, and suggest new strategies for developing mutant specific inhibitors.

evolutionary biology↗