Biology subjects

Baker, R. L.

Publications and source records attributed to Baker, R. L..

3 recordsLinked to original sources

bioRxiv2026

Cell-based epitope screen identifies a novel B chain-derived Hybrid Insulin Peptide recognized by human and mouse autoreactive CD4 T cells

Type 1 diabetes (T1D) is mediated by autoreactive CD4 T cells that recognize pancreatic {beta}-cell antigens MHC class II molecules. The B-chain of insulin is thought to be a primary autoantigen although native peptide sequences from the B-chain bind weakly to disease-associated MHC risk alleles. Hybrid Insulin Peptides (HIPs), consist of proinsulin fragments fused with peptides from other {beta}-cell proteins, and may endow B-chain peptides with C-terminal acid residues that allow strong binding to MHC risk alleles and subsequent stimulation of autoreactive T cells. Here we applied a high-throughput cell-based screening platform with Signaling and Antigen-presenting Bifunctional Receptors (SABRs) to interrogate a theoretical library of insulin B-chain HIPs against clonally expanded CD4 T cell receptors from NOD mouse islets. We identified a previously undescribed HIP containing a fragment of insulin B-chain combined with a calreticulin-derived peptide. I-Ag7 tetramers loaded with the InsB/Calr HIP bind to T cell clones as well as islet-infiltrating CD4 T cells and CD4 T cells from a recent-onset HLA-DQ2 T1D donor reacted exclusively to the InsB/Calr HIP. Collectively, these results demonstrate that insulin B-chain-derived peptides can undergo post-translational modification through HIP formation to generate neoepitopes with increased antigenicity, and underscore the utility of cell-based screening for identifying disease-relevant neoepitopes in T1D.

immunology↗

bioRxiv2019

Integrating transcriptomic network reconstruction and QTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development

Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and undergo selection. Therefore, characterizing the mechanistic connections between the genetic architecture governing plant development and the resulting ontogenetic dynamics of plants in field settings is critically important for agricultural production and evolutionary ecology. We use a hierarchical Bayesian Function-Valued Trait (FVT) approach to estimate Brassica rapa growth curves throughout ontogeny, across two treatments and in two growing seasons. We find that the shape of growth curves is relatively plastic across environments compared to final height, and that there are trade-offs between growth rate and duration. We determined that combining FVT Quantitative Trait Loci (QTL) and genes/eigengene expression identified via transcriptomic co-expression network reconstructions best characterized phenotypic variation. Further, targeted eQTL analyses identified regulatory hotspots that colocalized with FVT QTL and co-expression network identified genes and mechanistically link FVT QTL with structural trait variation throughout development in agroecologically relevant field settings.

genomics↗

bioRxiv2018

A framework for genomics-informed ecophysiological modeling in plants

Dynamic process-based plant models capture complex physiological response across time, carrying the potential to extend simulations out to novel environments and lend mechanistic insight to observed phenotypes. Despite the translational opportunities for varietal crop improvement that could be unlocked by linking natural genetic variation to first-principles based modeling, these models are challenging to apply to large populations of related individuals. Here we use a combination of model development, experimental evaluation, and genomic prediction in Brassica rapa L. to set the stage for future large-scale process-based modeling of intra-specific variation. We develop a new canopy growth sub-model for B. rapa within the process-based model Terrestrial Regional Ecosystem Exchange Simulator (TREES), test input parameters for feasibility of direct estimation with observed phenotypes across cultivated morphotypes and indirect estimation using genomic prediction on a Recombinant Inbred Line population, and explore model performance on an in silico population under non-stressed and mild water stressed conditions. We find evidence that the updated whole plant model has capacity to distill genotype by environment interaction (G x E) into tractable components. The framework presented offers a means to link genetic variation with environment-modulated plant response and serves as a stepping stone towards large-scale prediction of un-phenotyped, genetically-related individuals under un-tested environmental scenarios.

plant biology↗