Biology subjects

Omelchenko, A. A.

Publications and source records attributed to Omelchenko, A. A..

5 recordsLinked to original sources

bioRxiv2025

A unified network systems approach uncovers a core novel program underlying T follicular helper cell differentiation

T follicular helper (Tfh) cells are central to the adaptive immune response and exhibit remarkable functional diversity and plasticity. The complex nature of Tfh cell populations, inconsistent findings across experimental systems and potential differences across species have fueled ongoing debate regarding core regulatory pathways that govern Tfh differentiation. Many studies have experimentally investigated individual proteins and circuits involved in Tfh differentiation in limited contexts, each providing only a partial understanding of the process. To address this, we adopted a novel multi-scale network systems approach that incorporates both regulatory and protein-protein interactions. Our approach integrates diverse data types, captures regulation across multiple levels of immune system organization, and recapitulates known drivers. Further, we discover a core Tfh gene set that is conserved across tissue types and disease contexts, and is consistent across data modalities - bulk, single-cell and spatial. While components of this set have been individually reported, a novel aspect of our work lies in the discovery, characterization, and connectivity of this core signature using a single unbiased approach. Using this method, we also uncover a novel function of IL-12, a molecule with reported conflicting functions, in the regulation of Tfh differentiation. Notably, we find that, in both humans and mice, IL-12 is permissive for the differentiation of Tfh precursors, but blocks subsequent differentiation into GC Tfh cells. Overall, this work elucidates novel networks with unexplored roles in governing Tfh cell differentiation across species and tissues, paving the way for novel -therapeutic interventions.

immunology↗

bioRxiv2024

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LMs representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWINGs generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

systems biology↗

bioRxiv2021

Responses to Temperatures of Different Drosophila Species

Temperature is a critical environmental variable that affects the distribution, survival, and reproduction of most animals. Although temperature receptors have been identified in different animals, how these receptors respond to temperatures is largely unknown. Here we use modified single-fly thermotactic assays to analyze movements and temperature preferences of nine Drosophila species. The ability/inclination to move varies among these species and at different temperatures. Importantly, different species prefer various ranges of temperatures. While wild-type D. melanogaster flies avoid the warm temperature in the warm avoidance assay and the cool temperature in the cool avoidance assay, D. bipectinata and D. yakuba avoid neither warm nor cool temperatures and D. biarmipes and D. mojavensis do not avoid the warm temperature in the warm avoidance assay. These results demonstrate that Drosophila species have different mobilities and temperature preferences, thereby benefiting the research on molecular mechanisms of temperature responsiveness. Summary statementThe ability to move and the preference for temperatures vary among fly species when flies are exposed to steep temperature gradients.

animal behavior and cognition↗

bioRxiv2021

An open-source method for analysis of confocal calcium imaging with sparse cells

Research in the field of neuroscience has evolved to use complex imaging and computational tools to extract comprehensive information from data sets. Calcium imaging is a widely used technique that requires sophisticated software to obtain reproducible results, but many laboratories struggle to adopt computational methods when updating protocols to meet modern standards. Difficulties arise due to the lack of computational knowledge and paywalls for software. In addition, most calcium imaging analysis approaches ignore motion on the z-axis. Here, we described a workflow to use ImageJ to analyze 3D calcium imaging. We applied TrackMate, an open-source ImageJ plugin, to track neurons in the lateral (x/y) direction, detect regions of interest (ROIs), and extract fluorescence intensities. To track motion on the z-axis, we developed a new ImageJ plugin, TrackMate Analysis of Calcium Imaging (TACI). For neurons appearing on multiple z-positions, maximum fluorescence values were identified to represent neurons intensities of corresponding z-stacks. This workflow does not require coding ability, avoids human bias, and increases reproducibility. We validated this workflow using fly larval thermosensitive neurons that displayed movements in all directions during temperature fluctuation and a 3D calcium imaging dataset acquired from the fly brain.

neuroscience↗

bioRxiv2021

Using TrackMate to Analyze Drosophila Larval and Adult Locomotion

Drosophila adult and larvae exhibit sophisticated behaviors that are widely used in development, synaptic transmission, sensory physiology, and learning and memory research. Many of these behaviors depend on locomotion, the ability of an animal to move. However, the statistical analysis of locomotion is not trivial. Here we use an open-source Fiji plugin TrackMate to track the locomotion of Drosophila adults and larvae. We build optimal experimental setups to rapidly process recordings by Fiji and analyze by TrackMate. We also provide tips for analyzing non-optimal recordings. TrackMate extracts the X and Y positions of an animal on each frame of an image sequence or a video. This information allows for generating moving trajectories, calculating moving distances, and determining preference indices in two-choice assays. Notably, this free-cost analysis method does not require programming skills. Summary statementThis study uses an open-source Fiji plugin TrackMate to computationally analyze Drosophila adult and larval behavioral assays, which does not require programming skills.

animal behavior and cognition↗