Biology subjects

Naik, N.

Publications and source records attributed to Naik, N..

8 recordsLinked to original sources

bioRxiv2026

Extended normothermic machine perfusion preserves viability and tissue integrity of ex vivo human intestine segments

Human intestinal diseases, including inflammatory bowel disease (IBD), are difficult to model because existing animal and in vitro systems do not capture the integrated vascular, immune, and metabolic complexity of the intestine. Here, we establish a custom normothermic machine perfusion platform that maintains surgically resected human intestinal tissue for up to 72 hours under near-physiological conditions. Discarded specimens from patients with nonmalignant intestinal diseases, including IBD and diverticulitis, were continuously perfused at 37{degrees}C with real-time hemodynamic and metabolic monitoring. Perfusion preserved tissue viability, mucosal architecture, epithelial integrity, oxygen consumption, metabolic activity, and vascular patency. Daily perfusate sampling enabled longitudinal profiling and experimental modulation of inflammatory mediators, revealing patient-specific immune states. This long-duration platform bridges reductionist in vitro models and clinical disease, enabling mechanistic studies of intestinal inflammation and future testing of personalized therapies in intact patient-derived tissue.

bioengineering↗

bioRxiv2026

The Dorsomedial Prefrontal Cortex Uses Reward Predictions to Regulate How Rewards Are Pursued

Reward-predictive cues regulate not only whether but how rewards are pursued, and their influence depends on the strength of their predictive relationship with reward. Whereas weak cues signaling sparse or uncertain reward invigorate instrumental reward seeking, cues signaling imminent reward produce little net invigoration of instrumental behavior and instead promote conditioned goal-approach. We propose that this reflects two opposing influences: an automatic motivational impulse to seek reward, and a top-down, expectancy-dependent control process that constrains it. On this view, the muted or even suppressive effect of imminent-reward cues on instrumental reward seeking reflects an active constraint on motivation rather than its absence. Consistent with this account, we found that selectively devaluing the predicted reward unmasked a latent motivational influence. Cues for imminent reward now invigorated rather than constrained instrumental seeking, suggesting the involvement of a goal-directed regulatory process. Separately, shifting rats from hunger to general satiety abolished the cue-specific regulation of instrumental seeking. We then tested whether the dorsomedial prefrontal cortex (dmPFC) mediates this control. Bulk calcium recordings revealed phasic dmPFC activity that encoded predicted reward probability, dipped when reward was omitted, and covaried with instrumental performance in a cue-dependent manner. Moreover, chemogenetic dmPFC inhibition disrupted the cue-specific regulation of instrumental seeking, leaving rats unable to use these predictions to determine how vigorously to press. These findings identify the dmPFC as a substrate for top-down, expectancy-dependent control over reward pursuit and suggest that its dysfunction may contribute to impulsive or maladaptive reward seeking.

neuroscience↗

bioRxiv2026

Spt5's central KOW domains and the Pol II Stalk Collaborate to Regulate Chromatin and 3'-End Processing

Spt5 is a universally conserved multidomain transcription elongation factor that acts as a component of all Pol II elongation complexes. Structural studies indicate that several of Spt5s central KOW domains lie adjacent to the Pol II stalk, composed of subunits Rpb4 and Rpb7. However, their in vivo functions are unknown. Here we show that Spt5 and Rpb4/7 jointly modulate 3-end formation and co-transcriptional chromatin integrity in Saccharomyces cerevisiae. We identify mutations in the SPT5 KOW2-3 domains and RPB7 that cause cryptic initiation of transcription and alter 3-end formation of RNA transcripts. Molecular readthrough assays reveal allele-specific changes at both GAL10 and SNR13, consistent with impacts on CPF/CF- and NNS-dependent termination. Proteomic experiments with isolated KOW2-3 domain enrich factors from both pathways as well as chromatin regulators, overlapping known Rpb7 interactors. Together, these findings support a model in which Spt5 KOW2-3/Pol II stalk region acts as a recruitment platform that coordinates pre-mRNA processing and chromatin dynamics during elongation, revealing new roles for the central KOW domains of Spt5. SummaryThis work describes a cooperative in vivo function for Spt5s central KOW domains and the Pol II stalk in Saccharomyces cerevisiae. Allele-specific genetics and reporter assays show cooperative effects of SPT5 and RPB4/7 on cryptic initiation and 3'-end formation; double-mutant analyses reveal synthetic interactions. RT-qPCR at GAL10 and SNR13 demonstrates regulation of both poly(A) and non-coding transcript termination. Spt5 KOW pull-down proteomics enrich poly(A) and non-coding termination factors, as well as chromatin regulators that overlap with known Rpb7 interactors. Together, the data support a model in which Spt5 and the Pol II stalk coordinate chromatin integrity and termination during elongation.

genetics↗

bioRxiv2023

IMPROVING ANTIBODY AFFINITY USING LABORATORYDATA WITH LANGUAGE MODEL GUIDED DESIGN

AO_SCPLOWBSTRACTC_SCPLOWProtein design involves navigating vast sequence spaces to discover sequences with desired traits. Language models (LMs) pretrained on universal protein datasets have shown potential to make this search space tractable. However, LMs trained solely on natural sequences have limitations in creating proteins with novel functions. In this work, we used a combination of methods to finetune pretrained LMs on laboratory data collected in an anti-CD40L single domain antibody library campaign to develop an ensemble scoring function to model the fitness landscape and guide the design of new antibodies. Laboratory experiments confirmed improved CD40L affinity in the designed antibodies. Notably, the designs improved the affinities of four antibodies, originally ranging from 1 nanomolar to 100 picomolar, all to below 25 picomolar, approaching the limit of detection. This work is a promising step towards realizing the potential of LMs to leverage laboratory data to develop improved treatments for diseases.

synthetic biology↗

bioRxiv2023

Structure-based development of new cyclic compoundstargeting PSD-95 PDZ3 domain.

Aberrant BDNF signaling has been proposed to contribute to the pathophysiology of depression and other neurological disorders such as Angelman syndrome. We have previously shown that targeting the TrkB / PSD-95 nexus by peptidomimetic inhibitors is a promising approach for therapeutic intervention. Here we used structure-based knowledge to develop a new peptidomimetic compound series that fuses SynGAP-derived peptides to our prototype compound CN2097. These compounds target the PSD-95 PDZ3 domain and adjoining C helix to achieve bivalent binding that results in up to 7-fold stronger affinity compared to CN2097. These compounds were designed to improve CN2097 specificity for the PDZ3 domain and limited SAR studies have been performed to improve their resistance to proteolysis.

molecular biology↗

bioRxiv2021

Deep neural language modeling enables functional protein generation across families

Bypassing natures evolutionary trajectory, de novo protein generation--defined as creating artificial protein sequences from scratch--could enable breakthrough solutions for biomedical and environmental challenges. Viewing amino acid sequences as a language, we demonstrate that a deep learning-based language model can generate functional artificial protein sequences across families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. Our protein language model is trained by simply learning to predict the next amino acid for over 280 million protein sequences from thousands of protein families, without biophysical or coevolutionary modeling. We experimentally evaluate model-generated artificial proteins on five distinct antibacterial lysozyme families. Artificial proteins show similar activities and catalytic efficiencies as representative natural lysozymes, including hen egg white lysozyme, while reaching as low as 44% identity to any known naturally-evolved protein. The X-ray crystal structure of an enzymatically active artificial protein recapitulates the conserved fold and positioning of active site residues found in natural proteins. We demonstrate our language models ability to be adapted to different protein families by accurately predicting the functionality of artificial chorismate mutase and malate dehydrogenase proteins. These results indicate that neural language models successfully perform de novo protein generation across protein families and may prove to be a tool to shortcut evolution.

synthetic biology↗

bioRxiv2020

Predicting the Viability of Beta-Lactamase: How Folding and Binding Free Energies Correlate with Beta-Lactamase Fitness

One of the long-standing holy grails of molecular evolution has been the ability to predict an organisms fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 {beta}-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of {beta}-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon {beta}-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 {beta}-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software such as FoldX and PyRosetta designed to compute folding free energies can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in {beta}-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of {beta}-lactamases fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

biophysics↗

bioRxiv2020

ProGen: Language Modeling for Protein Generation

Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervised sequence generation problem in order to leverage the exponentially growing set of proteins that lack costly, structural annotations. We train a 1.2B-parameter language model, ProGen, on [~]280M protein sequences conditioned on taxonomic and keyword tags such as molecular function and cellular component. This provides ProGen with an unprecedented range of evolutionary sequence diversity and allows it to generate with fine-grained control as demonstrated by metrics based on primary sequence similarity, secondary structure accuracy, and conformational energy.

synthetic biology↗