Biology subjects

Reyna, J.

Publications and source records attributed to Reyna, J..

7 recordsLinked to original sources

bioRxiv2026

An Open Benchmark for Systems Vaccinology: Insights from the CMI-PB Challenges

Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.

immunology↗

bioRxiv2024

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

immunology↗

bioRxiv2024

Rickettsia β-peptide Reactivity with Immune IgM

Spotted Fever Group (SFG) Rickettsia species maintain an s-layer with two major outer membrane proteins (OmpA and OmpB) aligned within a lipopolysaccharide (LPS) matrix external to the bacterial outer membrane. While the two typhus group species (R. typhi and R. prowazekii) maintain a similar s-layer containing only OmpB with its own specific form of LPS. A major component of these two types of OmpB is a specific and highly conserved beta peptide ({beta}-peptide) non-covalently attached to the respective OmpB passenger domain. This {beta}-peptide is initially translated as the c-terminus of the 168 kDa polypeptide and initially forms a membrane pore to allow the larger passenger domain to exit the bacterial cytoplasm through the outer membrane. Prior to the full exit of this passenger domain from the cytoplasm the long polypeptide chain is cleaved by a peptidase, leaving the c-terminus remaining as a beta-folded membrane pore and the N-terminal passenger domain exiting to the space between the bacterial membrane and the s-layer. The fate of this pore structure and the attachment of {beta}-peptide to the OmpB continues to be examined and will be discussed as another protective component of the Rickettsia. An EIA IgM assay has been developed to utilize this antigen for clinical diagnostic use to accurately detect acute rickettsial infection in testing labs and lead to the availability of rapid test (lateral flow) formats for more immediate testing.

immunology↗

bioRxiv2024

nipalsMCIA: Flexible Multi-Block Dimensionality Reduction in R via Nonlinear Iterative Partial Least Squares

MotivationWith the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised analysis for clustering, visualization, and feature selection is imperative. Joint dimensionality reduction methods can be applied to multi-omics datasets to derive a global sample embedding analogous to single-omic techniques such as Principal Components Analysis (PCA). Multiple co-inertia analysis (MCIA) is a method for joint dimensionality reduction that maximizes the covariance between block- and global-level embeddings. Current implementations for MCIA are not optimized for large datasets such such as those arising from single cell studies, and lack capabilities with respect to embedding new data. ResultsWe introduce nipalsMCIA, an MCIA implementation that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS), and shows significant speed-up over earlier implementations that rely on eigendecompositions for single cell multi-omics data. It also removes the dependence on an eigendecomposition for calculating the variance explained, and allows users to perform out-of-sample embedding for new data. nipalsMCIA provides users with a variety of pre-processing and parameter options, as well as ease of functionality for down-stream analysis of single-omic and global-embedding factors. AvailabilitynipalsMCIA is available as a BioConductor package at https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html, and includes detailed documentation and application vignettes. Supplementary Materials are available online.

bioinformatics↗

bioRxiv2024

Loop Catalog: a comprehensive HiChIP database of human and mouse samples

HiChIP enables cost-effective and high-resolution profiling of chromatin loops. To leverage the increasing number of HiChIP datasets, we developed Loop Catalog (https://loopcatalog.lji.org), a web-based database featuring loop calls from 1000+ distinct human and mouse HiChIP samples from 152 studies plus 44 high-resolution Hi-C samples. We demonstrate its utility for interpreting GWAS and eQTL variants through SNP-to-gene linking, identifying enriched sequence motifs and motif pairs, and generating regulatory networks and 2D representations of chromatin structure. Our catalog spans over 4.19M unique loops, and with embedded analysis modules, constitutes an important resource for the field.

bioinformatics↗

bioRxiv2023

A systems vaccinology resource to develop and test computational models of immunity

Computational models that predict an individuals response to a vaccine offer the potential for mechanistic insights and personalized vaccination strategies. These models are increasingly derived from systems vaccinology studies that generate immune profiles from human cohorts pre- and post-vaccination. Most of these studies involve relatively small cohorts and profile the response to a single vaccine. The ability to assess the performance of the resulting models would be improved by comparing their performance on independent datasets, as has been done with great success in other areas of biology such as protein structure predictions. To transfer this approach to system vaccinology studies, we established a prototype platform that focuses on the evaluation of Computational Models of Immunity to Pertussis Booster vaccinations (CMI-PB). A community resource, CMI-PB generates experimental data for the explicit purpose of model evaluation, which is performed through a series of annual data releases and associated contests. We here report on our experience with the first such dry run for a contest where the goal was to predict individual immune responses based on pre-vaccination multi-omic profiles. Over 30 models adopted from the literature were tested, but only one was predictive, and was based on age alone. The performance of new models built using CMI-PB training data was much better, but varied significantly based on the choice of pre-vaccination features used and the model building strategy. This suggests that previously published models developed for other vaccines do not generalize well to Pertussis Booster vaccination. Overall, these results reinforced the need for comparative analysis across models and datasets that CMI-PB aims to achieve. We are seeking wider community engagement for our first public prediction contest, which will open in early 2024.

systems biology↗

bioRxiv2019

In-depth genetic analysis of 6p21.3 reveals insights into associations between HLA types and complex traits and disease

The highly polymorphic major histocompatibility (MHC) region encodes the human leucocyte antigen (HLA) gene complex and is associated with many autoimmune and infectious diseases. Despite the importance of this interval, comprehensive genetic studies interrogating associations between HLA types, expression of non-HLA genes and disease, have not yet been conducted. To address this issue, we collected high-coverage whole genome sequence from 419 individuals and performed HLA typing at the highest resolution. Using RNA-seq from matched iPSC lines, we conducted an in-depth eQTL analysis using "personalized" transcripts, which significantly improved estimated expression levels of HLA genes, and showed HLA types have genetic associations independent from SNPs. We leveraged the eQTL results to examine associations between expression levels of non-HLA genes and disease. As a proof-of-principle, we investigated RNF5, whose protein product is a novel drug target in cystic fibrosis. We observed that decreased expression of RNF5 was associated with the 8.1 ancestral haplotype, which was previously found associated with protection against infection in cystic fibrosis. Overall, our study shows that genetically dissecting the MHC region provides novel insights into mechanisms underlying associations of this interval with disease.

genomics↗