Biology subjects

Kolli, S.

Publications and source records attributed to Kolli, S..

6 recordsLinked to original sources

bioRxiv2025

Design and Optimization of Multifunctional Peptide Candidates for Cosmeceutical Applications: Combining Anti-Inflammatory and Collagen-Boosting Properties

Skin aging is multifactorial and involves inflammation, oxidative stress, collagen degradation, and impaired metal ion homeostasis, which requires holistic approaches for dermal rejuvenation. Conventional cosmeceutical peptides often target singular pathways, limiting their efficacy. In this study, we present a computational strategy for designing and evaluating multifunctional peptides that can simultaneously modulate collagen remodelling, exhibit anti-inflammatory responses, and maintain extracellular matrix (ECM) stability. We focused on three important skin-associated proteins: TGF-{beta} receptor II, Integrin 5{beta}1, and MMP-1, and generated peptide sequences specific to them through a de novo sequence generation method that utilized protein-specific profiles alongside machine learning-based property screening. Over 15,000 peptides were generated and filtered using physicochemical and synthesis-based criteria (charge, hydrophobicity, pI, solubility, and synthesis rules). A multi-parametric composite score was developed to visualize peptide favourability in a 3D physicochemical space. The top peptides from each target set were subjected to structure prediction and docking, resulting in binding affinities that ranged from -19.8 to -34.5 kcal/mol. Cross-target docking analysis demonstrated a significant potential for dual or pan-target applications, indicating the broad-spectrum utility of the cosmeceutical. This study firmly establishes a foundation for multifunctional peptide candidates that combine collagen-boosting, anti-inflammatory, and MMP-inhibitory effects. These candidates are poised to lead the way for innovative skincare formulations, paving the path for future validation and experimental evidence that will undeniably strengthen our findings.

bioinformatics↗

bioRxiv2024

Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization

Gene therapies have the potential to treat disease by delivering therapeutic genetic cargo to disease-associated cells. One limitation to their widespread use is the lack of short regulatory sequences, or promoters, that differentially induce the expression of delivered genetic cargo in target cells, minimizing side effects in other cell types. Such cell-type-specific promoters are difficult to discover using existing methods, requiring either manual curation or access to large datasets of promoter-driven expression from both targeted and untargeted cells. Model-based optimization (MBO) has emerged as an effective method to design biological sequences in an automated manner, and has recently been used in promoter design methods. However, these methods have only been tested using large training datasets that are expensive to collect, and focus on designing promoters for markedly different cell types, overlooking the complexities associated with designing promoters for closely related cell types that share similar regulatory features. Therefore, we introduce a comprehensive framework for utilizing MBO to design promoters in a data-efficient manner, with an emphasis on discovering promoters for similar cell types. We use conservative objective models (COMs) for MBO and highlight practical considerations such as best practices for improving sequence diversity, getting estimates of model uncertainty, and choosing the optimal set of sequences for experimental validation. Using three relatively similar blood cancer cell lines (Jurkat, K562, and THP1), we show that our approach discovers many novel cell-type-specific promoters after experimentally validating the designed sequences. For K562 cells, in particular, we discover a promoter that has 75.85% higher cell-type-specificity than the best promoter from the initial dataset used to train our models.

synthetic biology↗

bioRxiv2024

Phagolysosomes break down the membrane of a non-apoptotic corpse independent of macroautophagy

Cell corpses must be cleared in an efficient manner to maintain tissue homeostasis and regulate immune responses. Ubiquitin-like Atg8/LC3 family proteins promote the degradation of membranes and internal cargo during both macroautophagy and corpse clearance, raising the question how macroautophagy contributes to corpse clearance. Studying the clearance of non-apoptotic dying polar bodies in Caenorhabditis elegans embryos, we show that the LC3 ortholog LGG-2 is enriched in the polar body phagolysosome independent of membrane association or autophagosome formation. We demonstrate that ATG-16.1 and ATG-16.2, which promote membrane association of lipidated Atg8/LC3 proteins, redundantly promote polar body membrane breakdown in phagolysosomes independent of their role in macroautophagy. We also show that the lipid scramblase ATG-9 is needed for autophagosome formation in early embryos but is dispensable for timely polar body membrane breakdown or protein cargo degradation. These findings demonstrate that macroautophagy is not required to promote polar body degradation, in contrast to recent findings with apoptotic corpse clearance in C. elegans embryos. Determining how membrane association of Atg8/LC3 promotes the breakdown of different types of cell corpses in distinct cell types or metabolic states is likely to give insights into the mechanisms of immunoregulation during normal development, physiology, and disease.

cell biology↗

bioRxiv2023

Pretraining strategies for effective promoter-driven gene expression prediction

The ability to deliver genetic cargo to human cells is enabling rapid progress in molecular medicine, but designing this cargo for precise expression in specific cell types is a major challenge. Expression is driven by regulatory DNA sequences within short synthetic promoters, but relatively few of these promoters are cell-type-specific. The ability to design cell-type-specific promoters using model-based optimization would be impactful for research and therapeutic applications. However, models of expression from short synthetic promoters (promoter-driven expression) are lacking for most cell types due to insufficient training data in those cell types. Although there are many large datasets of both endogenous expression and promoter-driven expression in other cell types, which provide information that could be used for transfer learning, transfer strategies remain largely unexplored for predicting promoter-driven expression. Here, we propose a variety of pretraining tasks, transfer strategies, and model architectures for modelling promoter-driven expression. To thoroughly evaluate various methods, we propose two benchmarks that reflect data-constrained and large dataset settings. In the data-constrained setting, we find that pretraining followed by transfer learning is highly effective, improving performance by 24 - 27%. In the large dataset setting, transfer learning leads to more modest gains, improving performance by up to 2%. We also propose the best architecture to model promoter-driven expression when training from scratch. The methods we identify are broadly applicable for modelling promoter-driven expression in understudied cell types, and our findings will guide the choice of models that are best suited to designing promoters for gene delivery applications using model-based optimization. Our code and data are available at https://github.com/anikethjr/promoter_models.

genomics↗

bioRxiv2020

Unique features of stimulus-based probabilistic reversal learning

Reversal learning paradigms are widely-used assays of behavioral flexibility with their probabilistic versions being more amenable to studying integration of reward outcomes over time. Prior research suggests differences between initial and reversal learning, including higher learning rates, a greater need for inhibitory control, and more perseveration after reversals. However, it is not well-understood what aspects of stimulus-based reversal learning are unique to reversals, and whether and how observed differences depend on reward probability. Here, we used a visual probabilistic discrimination and reversal learning paradigm where male and female rats selected between a pair of stimuli associated with different reward probabilities. We compared accuracy, rewards collected, omissions, latencies, win-stay/lose-shift strategies, and indices of perseveration across two different reward probability schedules. We found that discrimination and reversal learning are behaviorally more unique than similar: latencies to select the better option, win-stay strategies, and perseveration were of a different pattern in reversal learning compared to discrimination learning. Additionally, fit of choice behavior using reinforcement learning models revealed a lower sensitivity to the difference in subjective reward values (greater exploration) and higher learning rates for the reversal phase. Interestingly, a consistent reward probability group difference emerged with a richer environment associated with longer reward collection latencies than a leaner environment. We also replicated previous reports on sex differences in reversal learning. Future studies should systematically compare the neural correlates of fine-grained behavioral measures to reveal possible dissociations in how the circuitry is recruited in each phase.

animal behavior and cognition↗

bioRxiv2020

Sex-dependent effects of chronic intermittent voluntary alcohol consumption on attentional, not motivational, measures during probabilistic learning and reversal

BackgroundForced alcohol (ethanol, EtOH) exposure has been shown to cause significant impairments on reversal learning, a widely-used assay of cognitive flexibility, specifically on fully-predictive, deterministic versions of this task. However, previous studies have not adequately considered voluntary EtOH consumption and sex effects on probabilistic reversal learning. The present study aimed to fill this gap in the literature. MethodsMale and female Long-Evans rats underwent either 10 weeks of voluntary intermittent 20% EtOH access or water only (H2O) access. Rats were then pretrained to initiate trials and learn stimulus-reward associations via touchscreen response, and subsequently required to select between two visual stimuli, rewarded with probability 0.70 or 0.30. In the final phase, reinforcement contingencies were reversed. ResultsWe found significant sex differences on several EtOH-drinking variables, with females reaching a higher maximum EtOH consumption, exhibiting more high-drinking days, and escalating their EtOH at a quicker rate compared to males. During early abstinence, EtOH drinkers made more initiation omissions and were slower to initiate trials than H2O drinking controls, particularly during pretraining. EtOH drinking females were most affected. A similar pattern in trial initiations was also observed in discrimination, but not in reversal learning. EtOH drinking rats were unaffected in their reward collection and stimulus response times, indicating intact motivation and motor responding. Although there were sex differences in discrimination and reversal phases, performance improved over time. We also observed sex-independent drinking group differences in win-stay and lose-shift strategies specific to the reversal phase. ConclusionsFemales exhibit increased vulnerability to EtOH effects in early learning: there were sex-dependent EtOH effects on attentional measures during pretraining and discrimination phases. We also found sexindependent EtOH effects on exploration strategies during reversal. Future studies should aim to uncover the neural mechanisms for changes in attention and exploration in both acute and prolonged EtOH withdrawal.

pharmacology and toxicology↗