Biology subjects

Shukla, D.

Publications and source records attributed to Shukla, D..

At least 19 recordsLinked to original sources

bioRxiv2026

β-Hairpin Fold Peptide Brush Polymers

Peptide-brush polymers prepared via graft-through polymerization of peptide-based olefinic monomers form Protein-Like Polymers (PLPs) with demonstrated utility as chemical biology tools for modulating intracellular protein--protein interactions and are in development as a emerging class of therapeutic. This favorable bioactivity has been achieved in part by mimicking some of the structural and functional features of proteins, such as their dynamic, globular solution-phase structures, ability to insert into lipid membranes and enter cells, and tendency to enforce -helicity in peptide side chains. However, because structure leads to function, insight into the structure of these proteomimetic polymers, particularly their overall tertiary structure and the extent to which this influences the brush peptide structure, is incomplete. To further this understanding, we prepared a set of brush polymers incorporating the trpzip2 peptide sequence, commonly used as a model system for protein folding. We examine these polymers via circular dichroism spectroscopy, infrared spectroscopy, and small-angle X-ray scattering, coupled with insights from molecular dynamics simulations. Here, a loss of the {beta}-hairpin structure of the monomer is observed regardless of polymer composition, and simulations corroborate that the polymer backbone destabilizes local secondary structure, restricting peptide motion and altering the accessible free-energy landscape. This study provides insight into design rules governing these synthetic polymer-based proteomimetics.

biochemistry↗

bioRxiv2026

ALS/FTD-linked TDP-43 alterations prevent HSV-1 infection by disrupting cell-adhesion pathways

TAR DNA-binding protein-43 (TDP-43) alterations are a key hallmark of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and other neurodegenerative disorders. Increasing evidence links these conditions to viral infections, but it is not known if and how viral pathogens may be affected by disease-associated TDP-43. We found that ALS/FTD-linked forms of human TDP-43 prevent infection by herpes simplex virus type-1 (HSV-1), a common neurotropic virus. This cell-autonomous protective effect was conserved across different TDP-43 mutations, sporadic ALS, and C9ORF72-ALS, in neural and non-neural cell types, and in patient-derived cells and animal models. In addition to restricting infection, TDP-43 mutation prevented inflammatory and cell stress-related responses to HSV-1 exposure. TDP-43 knockdown but not overexpression was sufficient to protect cells from HSV-1 infection, implicating a loss-of-function mechanism. Neurons with TDP-43 mutation had disruptions in adhesion-related pathways and, accordingly, had reduced early-stage viral binding and entry. Further analyses revealed alternative RNA splicing and impairment of focal adhesion kinase (FAK/PTK2), a key mediator of cell-adhesion pathways. Inhibition of FAK was sufficient to decrease HSV-1 infection, and genetic enhancement of functional but not inactive FAK promoted infectivity in cells with TDP-43 mutation. Together, these data indicate that TDP-43 affects virus-host interactions by influencing adhesion pathways required for viral infection.

neuroscience↗

bioRxiv2026

Substrate binding reorganizes the energetic landscape of Plasmodium falciparum hexose transporter PfHT1

Malaria parasites depend on the Plasmodium falciparum hexose transporter PfHT1 for sugar uptake, yet how substrate binding reshapes transporter energetics and kinetics of sugar transport remains poorly understood. Here, we investigate how glucose reorganizes the conformational landscape, transition pathways, and residue interaction network underlying membrane transport. Using over 800 s of adaptive molecular dynamics simulations combined with Markov state models, transition-path theory, residue-contact analysis, and graph attention learning, we reconstruct the apo and glucose-bound conformational cycles. We show that glucose selectively stabilizes productive outward-facing, occluded, and inward-facing conformations, reshapes transition kinetics, and channels reactive flux through the occluded state. We identify TM7b helix cracking as a local structural transition coupled to extracellular-gate closure and substrate progression, providing a flexible connection between the binding pocket and global alternating access. Experimental testing of mechanistically critical residues validated their functional importance in PfHT1-dependent sugar utilization. Together, these results show how substrate binding reorganizes the energetic, kinetic, and interaction landscape of membrane transport and establish a transferable framework for studying transporter mechanisms.

biophysics↗

bioRxiv2026

Mapping the sequence preference of the generalist class II lanthipeptide synthetase ProcM by mRNA display

The biosynthetic machineries of ribosomally synthesized and post-translationally modified peptides (RiPPs) are often substrate tolerant. A remarkable example is the class II lanthipeptide synthetase ProcM, which naturally functions as a generalist enzyme that has not evolved to use a specific substrate during its evolutionary history. Although ProcM has been studied extensively, the sequence features associated with productive modification remain underexplored. In this study, we use the ultrahigh-throughput mRNA display technique to map the sequence compatibility of ProcM across a focused library. This approach expands the landscape of ProcM reactivity beyond native substrates and individually characterized variants. Machine learning (ML) is used as a tool to demonstrate that the selected dataset contains learnable signatures and classification architectures revealed a balanced accuracy of 0.73. This performance contrasts sharply with the near-perfect accuracy of specialized enzyme models as the sequence-fitness landscape of the generalist enzymes are characterized by class imbalance and limited by intrinsic dataset features. Our results provide a high-throughput view of ProcM reactivity and highlight differences with previous high-throughput studies on substrate selectivity of RiPP modification enzymes. Future studies will need to assess whether these differences are common when comparing generalist with specialist enzymes.

biochemistry↗

bioRxiv2026

Pi-Ensemble: Sequence-guided generation of interpolated protein conformational ensembles

Proteins are critical biomolecular machines that populate ensembles of interconverting conformations. Many biological processes depend on transitions between metastable states. Although molecular dynamics (MD) simulations provide a physically grounded route to characterize these motions, routine sampling of large-scale conformational transitions remains computationally demanding. Recent advances in protein structure prediction have created new opportunities for ensemble generation, but many existing approaches require noising inputs, task-specific training, supervised fitting on extensive MD data, or experimentally-informed restraints. Here, we introduce Pi-Ensemble (Predicting Interpolated Ensemble), a sequence-guided framework for generating protein conformational ensembles interpolating between two structural anchor states. Unlike previous methods, Pi-Ensemble alternately leverages inverse-folding and structure-prediction models to propose intermediate conformations between known protein states, generating diverse ensembles without additional training. We evaluate Pi-Ensemble across diverse protein systems, including enzymes, transporters, receptors, and benchmark cases with reference MD simulations or experimental Double Electron-Electron Resonance (DEER) data. Pi-Ensemble recovers physically plausible intermediate conformations, captures transition pathways observed in large-scale MD simulations, and generates structures consistent with experimental distance distributions. Furthermore, Pi-Ensemble-generated conformations provide effective starting seeds for parallel MD simulations, improving conformational exploration and accelerating convergence relative to simulations initiated only from endpoint structures. These results establish sequence-guided structural interpolation as a practical strategy for probing protein conformational landscapes. By generating diverse and physically reasonable conformational proposals without long-timescale MD or model retraining, Pi-Ensemble provides an extensible framework for studying protein flexibility, guiding adaptive sampling, and accelerating mechanistic investigations of protein function.

biophysics↗

bioRxiv2026

Coevolution-informed Bayesian optimization for sample-efficient protein design

Protein engineering is limited less by generating variants than by the cost of evaluating them, so designing under a tight budget demands sequence features that let a model learn fitness from very few examples. We introduce ALSEBO (Active Learning Sequence Exploration via Bayesian Optimization), which couples a generative latent sequence landscape to Bayesian optimization and featurizes candidates with direct-coupling-analysis (DCA) coevolutionary statistics. This representation carries a specific inductive bias: it places the dominant organizer of the fitness landscape along a single linear coordinate, producing a smooth, funnel-like objective that a low-data surrogate navigates efficiently. On a virtual avGFP fluorescence benchmark, ALSEBO reaches the optimum in [~]40 evaluations and outpaces protein-language-model embeddings and raw latent coordinates; controls with representation-neutral oracles confirm that the advantage is intrinsic, not an artifact of the benchmark. Molecular dynamics of the optimized variant recovers structural hallmarks of fluorescence, and ALSEBO transfers to divergent GFP orthologs and to a non-GFP enzyme, establishing a data-efficient route to protein design.

biophysics↗

bioRxiv2026

AdaptivePy: a unified Python framework for adaptive sampling in molecular dynamics

Adaptive sampling accelerates the exploration of conformational space in molecular dynamics (MD) simulations by repeatedly analyzing the accumulated trajectories and seeding a new round of simulations from informative configurations. A growing collection of adaptive sampling policies has been proposed, each built around a particular notion of what makes a configuration informative, yet these methods are scattered across separate and often incompatible implementations, which complicates their systematic comparison and their combined use in meta adaptive sampling schemes. Here, we present AdaptivePy, a compact and extensible Python framework that implements nine seed-selection policies behind a single configuration-driven interface, spanning simple population-based baselines, several established machine-learning and geometry-based methods, and two ensemble or meta sampling policies introduced in this work. We show that the shared implementation reproduces the characteristic selection behavior of each policy on a series of analytic benchmark landscapes. We also introduce a new adaptive sampling scheme that employs TS-DAR, a deep learning framework originally designed to identify transition states, into an acquisition criterion that drives the discovery of an entire multi-basin landscape starting from a single basin. We further demonstrate that the common interface enables meta adaptive sampling policies, which aggregate the rankings of several policies into a single set of seeds. AdaptivePy thereby provides a unified testbed for the adoption, benchmarking, and continued development of adaptive sampling methods for biomolecular MD simulations.

biophysics↗

bioRxiv2026

Variational Autoencoder-enabled High-throughput Drug Screening for HIV Latency Modulators predicted through Noise in Gene Expression

Due to its ability to establish a pool of undetectable and latently infected cells that can initiate viral production through random reactivation, a cure to human immunodeficiency virus (HIV) infections has remained elusive. Many approaches have been proposed, including the "shock and kill" method where latency reversing agents (LRAs) are administered to reactivate latently infected cells out of latency and remove them through immune targeting and clearance, and the "block and lock" method where latency promoting agents (LPAs) are administered to inhibit reactivation and potentially induce a "deep latency" state where infected cells can no longer reactivate. Previous large scale drug screen studies have demonstrated a correlation between a compounds capability to modulate the fluctuations (or "noise") in HIV gene expression and its potential to modulate HIV latency. However, measurements of gene expression noise are labor-and cost-intensive. To circumvent these drawbacks, we trained a variational autoencoder (VAE) on a previously published large scale time-lapse fluorescence microscopy dataset, and performed an in silico screening of [~]175,000 compounds for HIV latency modulators. Out of the top 113 predicted modulators that were experimentally tested, 16 latency reversing agent (LRA) synergizers and 2 latency promoting agents (LPAs) were confirmed, yielding an overall experimental hit rate of 15.9%. Our work demonstrates that in silico drug screening modalities, guided by existing large-scale experimental datasets, can yield high experimental hit rates, reducing costs incurred from labor-intensive wet lab-focused methodologies.

biochemistry↗

bioRxiv2026

De novo Folding Mechanisms of Lasso Peptides

Lasso peptides adopt a distinctive [1]rotaxane conformation, yet the principles governing the folding of this kinetically trapped structure have remained elusive. Here, we integrated extensive molecular dynamics simulations and deep learning to elucidate the de novo folding mechanism of 20 lasso peptides lacking secondary post-translational modifications. We constructed Multi-Ensemble Markov Models for each lasso peptide and uncovered a universal uphill folding landscape with spontaneous folding probabilities consistently below 0.8%. Loop stability strongly correlated with folding propensity, and targeted experiments further validated that enhancing loop {beta}-hairpin formation promotes folding of microcin J25, the well-studied lasso peptide extensively characterized as an in vitro model. Additionally, the substantial entropy cost opposed lasso peptide folding. Simulations mimicking enzymatic spatial confinement reduced this penalty and stabilize folding. Leveraging Variational AutoEncoder-based pathway clustering, we resolved distinct pathway channels and representative folding pathways. Together, these findings establish representative folding models and fundamental thermodynamic and kinetic principles for rational engineering of lasso peptides.

biophysics↗

bioRxiv2026

Sequence constraints predispose Class D GPCRs to follow an atypical activation mechanism

The biophysical principles underlying distinct conformational changes in proteins with similar topologies remain poorly understood. Class D G Protein-Coupled Receptors (GPCRs), fungal pheromone-sensing receptors essential for mating and survival, exhibit an atypical activation mechanism compared to other GPCR classes. Unlike Class A GPCRs, which activate through outward movement of TM6, Class D receptors undergo activation via outward displacement of TM7 coupled with inward movement of TM6. To investigate the origin of this atypical process, we employed state-specific generative AI sequence models to design protein sequences corresponding to unique active states, revealing that sequence constraints predispose Class D GPCRs toward this mechanism. We further explored the dynamic basis of activation through millisecond-scale atomistic simulations of STE2, a representative Class D GPCR and therapeutic target for fungal diseases. Using Maximum Entropy VAMPNets, an active learning based adaptive sampling strategy, we efficiently mapped the conformational free energy landscape of STE2. These simulations uncovered multiple intermediate states that have not yet been resolved experimentally and demonstrated that activation in these dimeric proteins involves fully decoupled monomers. Comparative simulations across Classes A, B, D, and F, totaling 4 milliseconds of all-atom molecular dynamics, revealed distinct patterns of electrostatic interactions. In Class D GPCRs, activation disrupts a dense TM7 interaction network while inward TM6 movement enhances electrostatic contacts. In contrast, Class A GPCRs such as CB1R display the opposite trend. Together, these findings show that sequence differences in TM6 and TM7 underlie the unique activation mechanism of STE2, offering new insights into GPCR conformational diversity.

biophysics↗

bioRxiv2026

All roads lead to Rome: Emergence of Mechanistic Divergence Shapes Activation of Class B1 G-Protein Coupled Receptors

Class B1 GPCRs are crucial to maintaining homeostasis along a multitude of vital biochemical pathways. Understanding the activation mechanism of these proteins at both a family and clade-specific level is particularly relevant for designing multi-target agonists, as exemplified by recently designed dual-agonists for GLP-1R and GIPR, for treating obesity. Here, we use 6 milliseconds of unbiased all-atom MD simulations of GCGR, GLP1R, PAC1R, SCTR, PTH1R and CALCR from the four different clades of Class B1 GPCRs to establish the universal mechanism of their activation. We show that the activation of Class B1 GPCRs involves a clade-independent intermediate state characterized by the outward movement of helix 6. We use a combination of Markov state models and transition path theory to show that the activation of these proteins occurs at a millisecond timescale. We identify characteristic molecular locks that are conserved at a clade-level, showcasing the uniqueness among the activation mechanisms of these proteins. We show that these proteins show similar inactive and active states, but show unique activation mechanisms at a residue level. These sites can be targeted directly or allosterically to design therapeutics targeting a specific clade of proteins. Thus, this study provides an integrated atomistic view of the activation for Class B1 GPCRs from a mechanistic, thermodynamic and kinetic perspective.

biophysics↗

bioRxiv2025

How minor sequence changes enable mechanistic diversity in MFS transporters? An atomic-level rationale for symport emergence in NarU

AbstractClosely related membrane transporters can diverge sharply in their modes of trans-port despite minimal sequence differences, underscoring how minor structural features can alter transport function. This divergence is exemplified in nitrate and nitrite trans-port across bacterial membranes, which supports anaerobic respiration and involves the major facilitator superfamily (MFS) transporters NarK and NarU. NarK operates as a nitrate/nitrite antiporter, whereas NarUs mechanism remains unresolved, with evidence suggesting potential symport activity. Using extensive adaptive molecular dynamics simulations and Markov State Modeling, we mapped NarUs conformational free-energy landscape and assessed how its behavior contrasts with mechanistic prin-ciples established for NarK. NarU follows a similar gating pathway but displays pro-nounced asymmetry favoring the outward-facing state and stabilizes an apo-occluded intermediate inaccessible to antiporters. This state arises from rotation of an argi-nine gating pair and a hinge glycine substitution that enhances gate flexibility. These sequence-dependent adaptations alter gating energetics and reprogram the scaffold to permit coupled co-transport. Our results show that a presence of a few strategic residue substitutions in the binding pocket and translocation pathway could alter the transport mechanism of transporters with high sequence and structural similarity.

biophysics↗

bioRxiv2025

Ampyrone (4-Aminoantipyrine) is a Direct Agonist of Human Tyrosinase and Potential Therapeutic for Oculocutaneous Albinism and Disorders of Hypopigmentation.

Significant loss of pigmentation can increase visual disability, skin cancer risk, and psychosocial stress. Tyrosinase (TYR) catalyzes the first and rate-limiting step of melanin synthesis. Inhibitors of TYR are well established and are currently used in clinical settings; however, there is a dearth of direct activators of TYR. Here, using a unique human TYR construct, high-throughput screening, and computational analysis techniques, we identified ampyrone as a TYR activator. Ampyrone increased the in vitro catalytic activity of the intramelanosomal domain of human TYR (hTYR) and its hypomorphic variant, P406L, a cause of oculocutaneous albinism type 1B (OCA1B). Moreover, ampyrone induced melanin synthesis in both wild-type and OCA1B human melanocytes, as well as 3-dimension (3D) human skin cultures. Our results reveal ampyrone as a lead compound for first-in-class TYR activators, potentially accelerating the discovery of novel therapies for patients with genetic and acquired diseases of hypopigmentation.

pharmacology and toxicology↗

bioRxiv2025

A Structure-Aware Generative Framework for Exploring Protein Sequence and Function Space

The rapid expansion of protein sequence databases has far outpaced experimental structure determination, leaving many unannotated sequences, particularly the more remote homologs with low sequence identity. Because protein folds are more conserved and functionally informative than sequences alone, structural information offers a powerful lens for analysis. Here, we introduce a generative, structure-aware framework that integrates geometric encoding and coevolutionary constraints to map, cluster, and design protein sequences. Our approach employs the 3D interaction (3Di) alphabet to convert local residue geometries into compact, 20-state discrete representations. Using ProstT5, we enable bidirectional translation between amino acid sequences and 3Di representations, facilitating sensitive homology detection and structure-guided sequence generation. We then augment the latent generative landscape methodology by combining 3Di-based alignments with direct coupling analysis (DCA) and variational autoencoders (VAE), imbuing tasks such as clustering, annotation, and design with structural information. This integrative framework enhances the detection of coevolutionary signals and enables rational sampling of structural variants, even without functional labels. We demonstrate the utility of our method across diverse protein families, including globins, kinases, and malate dehydrogenases, achieving improved contact prediction, homology inference, and sequence generation. Together, our approach offers a quantitative, generative view of protein structure space, advancing protein evolution and design studies. Significance StatementProtein sequence databases are growing far faster than our ability to experimentally determine structures, leaving much of protein space poorly annotated, especially for distant homologs. Because protein structure is more conserved and informative than sequence alone, new approaches are needed to exploit structural signals at scale. We present a generative framework that integrates compact structural representations with evolutionary constraints to map, cluster, and design protein sequences. By combining geometric encoding with coevolutionary modeling, our approach enables sensitive homology detection, improved inference of structural contacts, and rational exploration of sequence space without requiring functional labels. This work provides a quantitative bridge between protein sequence and structure, advancing our ability to interpret protein evolution and guide protein design.

biophysics↗

bioRxiv2025

ESMDynamic: A Fast and Accurate Prediction of Protein Dynamic Contact Maps from Single Sequences

Understanding conformational dynamics is essential for elucidating protein function, yet most deep learning models in structural biology predict only static structures. Here, we present ESMDynamic, a deep learning model that predicts residue-residue contact dynamics directly from protein sequence. Built on the ESMFold architecture and trained on conformational variability from experimental structure ensembles and molecular dynamics (MD) simulations, ESMDynamic predicts dynamic contact probabilities, contact frequencies reflecting equilibrium populations, and coarse-grained kinetics of contact formation and dissociation across multiple temperature conditions. On large-scale MD benchmarks (mdCATH and ATLAS), ESMDynamic matches or outperforms state-of-the-art ensemble prediction methods (AlphaFlow, ESMFlow, BioEmu) while requiring orders-of-magnitude less computation. We demonstrate generalization to diverse systems, including membrane transporters, a de novo designed protein, and a homodimer complex. We show that predicted dynamic contacts enable automated selection of collective variables for Markov state model construction. Applied to the human proteome, ESMDynamic generates predictions for over 18,000 proteins, enabling large-scale analysis of conformational variability. Overall, ESMDynamic provides a scalable, sequence-based representation of protein dynamics to inform simulation, analysis, and design workflows.

biophysics↗

bioRxiv2025

Active learning-guided optimization of cell-free biosensors for lead testing in drinking water

Point-of-use diagnostics based on allosteric transcription factors (aTFs) are promising tools for environmental monitoring and human health. However, biosensors relying on natural aTFs rarely exhibit the sensitivity and selectivity needed for real-world applications, and traditional directed evolution struggles to optimize multiple biosensor properties at once. To overcome these challenges, we develop a multi-objective, machine learning (ML)-guided cell-free gene expression workflow for engineering aTF-based biosensors. Our approach rapidly generates high-quality sequence-to-function data, which we transform into an augmented paired dataset to train an ML model using directional labels that capture how aTF mutations alter performance. We apply our workflow to engineer the aTF PbrR as a point-of-use diagnostic for lead contamination in water. We tune the sensitivity of PbrR to sense at the U.S. Environmental Protection Agency (EPA) action level for lead and modify the selectivity away from zinc, a common metal found in water supplies. Finally, we show that the engineered PbrR functions in freeze-dried cell-free reactions, enabling a diagnostic capable of detecting lead in drinking water down to [~]5.7 ppb. Our ML-driven, multi-objective framework--powered by directional tokens-- can generalize to other biosensors and proteins, accelerating the development of synthetic biology tools for biotechnology applications.

synthetic biology↗

bioRxiv2025

Oral Cavity Serves as Long-Term COVID-19 Reservoir with Increased Periodontal and Viral Disease Risk

BackgroundSARS-CoV-2 infection can lead to long-term health problems affecting multiple body systems termed long COVID. Currently, limited information exists about long-term oral health manifestations in COVID-19 patients with limited healthcare access. MethodsWe conducted a sequential, cross-sectional study (December 2020-March 2024) to assess how racial/ethnic differences (Black/Hispanic vs White/Asian) and health disparities affect oral and non-oral long COVID symptoms and their relationship with COVID-19 vaccination. We retrospectively reviewed patients oral health record from University of Illinois Chicago dental clinics before vaccination (December 2020; N=1150; Covid+/- N=575/group) and after vaccination (December 2021; N=592; Covid+/- N=292/group). Participants were recruited in two separate prospective groups of COVID-19 positive subjects (February-April 2021; pre-vaccination: N=158; January-March 2024; post-vaccination: N=171), we examined clinical indicators of oral (periodontal and salivary glands) and non-oral (neurologic) sequalae 3-6 months after initial exposure. We measured viral S protein by flow cytometry and quantified inflammatory markers, viral entry receptors, and oral viral load to correlate molecular, and cellular changes in COVID-19 positive subjects before and after vaccination. ResultsOur results identified racial disparity indicating oral associated post-acute sequelae (PASC) primarily manifested as periodontal (gum) disease (COVID-19 positive: 73.1{+/-}18.9% vs COVID-19 negative: 33.1{+/-}14.3%) and correlated with higher rates of dry mouth (57.5%), taste disturbance (47%), and smell loss (20%). Vaccination reduced oral PASC in COVID-19 positive subjects; however, periodontal disease indicators persisted compared to the COVID-19 negative group. Notably, 3-6 months post-infection, while SARS-CoV-2 Spike (S) transcript was rarely detected in saliva ([~]6%), its protein was commonly detected ([~]70%) in the COVID-19 positive subjects indicating incomplete viral clearance. This correlates with significantly higher salivary expression of viral entry receptors (ACE2, and TRMPSS2), and inflammatory mediators (IL-6, IL-8 and MMP-8), in COVID-19 positive subjects. This finding was further supported by higher prevalence of other oral viruses including Epstein-Barr Virus (70.5%), Herpes Simplex Virus (8.1%), and Human Papillomavirus (17.5%) in COVID-19 positive subjects. InterpretationCOVID-19 history significantly correlates with severe oral health complications in predominantly Black communities, while vaccination reduced but did not eliminate these issues. The oral cavity serves as a long-term viral reservoir, and periodontal inflammation with increased oral viral presence in COVID-positive patients may increase susceptibility to oral and non-oral viral diseases and identify risk for long COVID.

microbiology↗

bioRxiv2025

Palmitoleate Protects against Zika virus infection-induced Endoplasmic Reticulum Stress and Apoptosis in Neurons

Zika virus (ZIKV) infection during pregnancy is associated with the development of fetal complications such as microcephaly. We have recently demonstrated that palmitoleate protects against ZIKV-induced apoptosis in placental trophoblasts. In the present study, we hypothesize that palmitoleate prevents ZIKV infection-induced endoplasmic reticulum (ER) stress and apoptosis in neurons. Neurons were infected with 0.1-1 multiplicity of infection of recombinant MR766 or PRVABC59 strains of ZIKV for an hour followed by treatment of palmitoleate (100 {micro}M-200 {micro}M) for different post-infection time points. Apoptosis was measured by nuclear morphological changes, caspase 3/7 activity, and immunoblot analysis of pro-apoptotic mediators. Activation of ER stress markers and viral envelope levels were detected using qRT-PCR and immunoblot analysis. Infectious virus particles were measured by using plaque assay. ZIKV infection to neuronal cells showed increased levels of pro-apoptotic markers like cleaved-PARP, cleaved caspase-3, Bim, and Puma, whereas decreased levels of anti-apoptotic markers such as Mcl-1, Bcl-1, and Bcl-xL. Further, we observed activation of three arms of ER stress namely: inositol requiring enzyme 1 alpha (IRE1), protein kinase-like ER kinase (PERK), and activating transcription factor (ATF6) pathways with ZIKV infection. Treatment of palmitoleate dramatically decreased ZIKV infection-induced increase in percent apoptotic nuclei and caspase 3/7 activity. Further, treatment of palmitoleate decreased cleaved PARP and PUMA protein expressions. Treatment of palmitoleate reduced ZIKV-induced ER stress activation as evidenced by decreased levels of phosphorylated forms of IRE1 and eukaryotic initiation factor 2 alpha; decreased expressions of cleaved ATF6, spliced X-box associated protein 1 and C/EBP homologous protein compared to ZIKV infection alone. Further, treatment of palmitoleate attenuated ZIKV envelope levels and infectious titer in SH-SY5Y and primary fetal cortical neurons isolated from humanized STAT2 knockin mice. These data suggest that palmitoleate supplementation protects against ZIKV-induced neuronal ER stress, apoptosis and decreases Zika viral load thereby mitigates neuronal damage.

microbiology↗