Biology subjects

Salnikov, M.

Publications and source records attributed to Salnikov, M..

4 recordsLinked to original sources

bioRxiv2026

A mammalian fibroblast zoo reveals barriers to cross-species virus replication and differential regulation of antiviral immunity

Virus zoonosis represents a continual threat to humans. Viruses emerge in humans directly or indirectly through interactions with mammals. However, our understanding of virus-host susceptibility across the diversity of mammals is limited and experimental model systems are restricted to a very small set of model organisms. Mammals are highly diverse and different habitats, lifestyles, diets, and exposures to different pathogens have driven substantial genetic diversity. We generated a library of primary fibroblasts across 86 mammalian species and used these cells to build an atlas of virus-host susceptibility across a panel of arboviruses that infect diverse mammals including humans. Perturbing the innate immune system revealed some virus-host pairs where restriction is controlled by the immune system and others where there is an incompatibility with host factors. Transcriptomic profiling of the library in response to virus sensing mimicked by poly(I:C) or antiviral signaling with type I interferon reveals a core set of conserved genes that respond as well as substantial species- and order-specific responses. Together these data provide a comparative atlas of innate immune responses across mammals and offer a window into how these systems have evolved across animals.

immunology↗

bioRxiv2026

A chicken interferon stimulated gene screen reveals limited genes restricting influenza replication

Avian influenza A virus (IAV) is a major global health threat. Not only does it cause significant economic losses to the agricultural industry, but it also presents a risk of spillover into the human population. However, several obstacles must be overcome for a successful cross-species transmission event to occur. One of these obstacles is the innate immunity of the host. While the innate immune response to IAV in humans has been well-studied, the innate immune response to IAV in birds is not as well understood. Here, we explore the interferon-induced immune response by identifying interferon-stimulated genes (ISGs) in chicken, quail, and duck cells, and find that they are largely species-specific. Using this data, we performed a CRISPR knockout ISG screen against two strains of IAV, the mouse-adapted PR8, and the low pathogenicity avian IAV isolate WF10. We find that for PR8, IFITM3 is the dominant restrictor of viral replication with several additional hits with modest effects, consistent with previous work in mammals. For avian WF10, IFITM3 was also a hit however there were two additional hits absent for PR8, the HSP70 co-chaperone protein BAG5 and deubiquitinase OTUD4. Overall, these findings confirm the divergence of interferon-mediated immunity between species and establish a screening system that can be used to identify ISGs important for viral restriction in avian species. IMPORTANCEWild birds, especially waterfowl and shore birds, are the primary natural reservoir for influenza A viruses. These birds can transmit IAV to domestic poultry, where the spillover risk to humans increases. Chickens are the most common species of domestic poultry world-wide and are frequently infected with avian influenza viruses. Despite this, relatively little is known about how their immune system responds to influenza infection. This lack of knowledge creates a barrier to finding ways of preventing or mitigating IAV infection in chicken flocks. In this study, we characterized the interferon-induced immune response to IAV in chickens and found that only a small subset of ISGs were responsible for restricting IAV in chicken cells.

microbiology↗

bioRxiv2022

SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning

One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1b, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the RBD domain of SARS-CoV-2. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.

bioinformatics↗

bioRxiv2021

Revisiting the recombinant history of HIV-1 group M with dynamic network community detection

A new abundance of full-length HIV-1 genome sequences provides an opportunity to revisit the standard model of HIV-1/M diversity that clusters genomes into largely non-recombinant subtypes, which is not consistent with recent evidence of deep recombinant histories for SIV and other HIV-1 groups. Here we develop an unsupervised non-parametric clustering approach, which does not rely on predefined non-recombinant genomes, by adapting a community detection method developed for dynamic social network analysis. We show that this method (DSBM) attains a significantly lower mean error rate in detecting recombinant breakpoints in simulated data (quasibinomial GLM, P < 8 x 10-8), compared to other reference-free recombination detection programs (GARD, RDP4 and RDP5). Applied to a representative sample of n = 525 actual HIV-1 genomes, we determined k = 25 as the optimal number of DSBM clusters, and used change point detection to estimate that at least 95% of these genomes are recombinant. Further, we identified both known and novel recombination hotspots in the HIV-1 genome, and evidence of inter-subtype recombination in HIV-1 subtype reference genomes. We propose that clusters generated by DSBM can provide an informative new framework for HIV-1 classification.

evolutionary biology↗