Shlomchik, M. J. | bioRxiv Science

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↗