Biology subjects

Vierra-Green, C.

Publications and source records attributed to Vierra-Green, C..

4 recordsLinked to original sources

bioRxiv2026

Autoantibody Repertoires in Healthy Individuals Vary According to HLA Class II Genotype

The link between HLA genotype and the presence of pathogenic autoantibodies has previously been established across different autoimmune disorders. However, the functional process linking specific antibodies to specific HLA remains unclear. Additionally, autoantibodies - usually associated with autoimmune disease - are also present in healthy individuals. To this end, we sought to determine the spectrum of self-antigen antibody specificity (the "autoreactome") in healthy individuals, stratified by HLA genotypes. We utilized Phage ImmunoPrecipitation Sequencing (PhIP-Seq), a programmable phage display for interrogation of antibody specificity, which encompasses over 700,000 peptides tiled across the entire human proteome. Serum from 741 donors without diagnosed autoimmune disease were grouped by their five homozygous HLA-DRB1 genotypes and analyzed for differences in autoreactivity profiles. We applied a custom filter to the PhIP-Seq normalized data and obtained a set of enriched peptides for each HLA-DRB1 genotype. We found that the autoreactome in healthy individuals with different HLA-DRB1 genotypes are generally distinct. Binary logistic regression successfully identified whether a sample belongs to a specific HLA-DRB1 genotype (HLA-DRB1*01, HLA-DRB1*03, HLA-DRB1*04, HLA-DRB1*07, and HLA-DRB1*15) with the following accuracies: 96%, 92%, 90%, 94%, and 90%, and multinomial models predicted HLA-DRB1 genotype with up to 90% accuracy. Finally, gene-level analysis suggests that individuals with specific HLA autoimmune risk alleles may harbor potentially pathogenic autoantibodies in the absence of, or prior to the establishment of, overt disease. Our analysis demonstrates that autoreactivity profiles in healthy people vary according to HLA class II genotype, and may provide insight into the pathological processes associated with development of autoimmune and other immune-mediated diseases.

immunology↗

bioRxiv2020

A Detailed View of KIR Haplotype Structures and Gene Families as Provided by a New Motif-based Multiple Sequence Alignment

Human chromosome 19q13.4 contains genes encoding killer-cell immunoglobulin-like receptors (KIR). Reported haplotype lengths range from 67 to 269 kilobases and contain 4 to 18 genes. The region has certain properties such as single nucleotide variation, structural variation, homology, and repetitive elements that make it hard to align accurately beyond single gene alleles. To the best of our knowledge, a multiple sequence alignment of KIR haplotypes has never been published or presented. Such an alignment would be useful to precisely define KIR haplotypes and loci, provide context for assigning alleles (especially fusion alleles) to genes, infer evolutionary history, impute alleles, interpret and predict co-expression, and generate markers. In order to extend the framework of KIR haplotype sequences in the human genome reference, 27 new sequences were generated including 24 haplotypes from 12 individuals of African American ancestry that were selected for genotypic diversity and novelty to the reference, to bring the total to 68 full length genomic KIR haplotype sequences. We leveraged these data and tools from our long-read KIR haplotype assembly algorithm to define and align KIR haplotypes at <5 kb resolution on average. We then used a standard alignment algorithm to refine that alignment down to single base resolution. This processing demonstrated that the high-level alignment recapitulates human-curated annotation of the human haplotypes as well as a chimpanzee haplotype. Further, assignments and alignments of gene alleles were consistent with their human curation in haplotype and allele databases. These results define KIR haplotypes as 14 loci containing 9 genes. The multiple sequence alignments have been applied in two software packages as probes to capture and annotate KIR haplotypes and as markers to genotype KIR from WGS.

bioinformatics↗

bioRxiv2019

Iterative feature selection method to discover predictive variables and interactions for high-dimensional transplant genomic data

After allogeneic hematopoietic stem cell transplantation (allo-HCT), donor-derived immune cells can trigger devastating graft-versus-host disease (GVHD). The clinical effects of GVHD are well established; however, genetic mechanisms that contribute to the condition remain unclear. Candidate gene studies and genome-wide association studies have shown promising results, but they are limited to a few functionally derived genes and those with strong main effects. Transplant-related genomic studies examine two individuals simultaneously as a single case, which adds additional analytical challenges. In this study, we propose a hybrid feature selection algorithm, iterative Relief-based algorithm followed by a random forest (iRBA-RF), to reduce the SNPs from the original donor-recipient paired genotype data and select the most predictive SNP sets in association with the phenotypic outcome in question. The proposed method does not assume any main effect of the SNPs; instead, it takes into account the SNP interactions. We applied the iRBA-RF to a cohort (n=331) of acute myeloid leukemia (AML) patients and their fully 10 of 10 (HLA-A, -B, -C, -DRB1, and -DQB1) HLA-matched healthy unrelated donors and assessed two case-control scenarios: AML patients vs healthy donor as case vs control and acute GVHD group vs non-GVHD group as case vs control, respectively. The results show that iRBA-RF can efficiently reduce the size of SNPs set down to less than 0.05%. Moreover, the literature review showed that the selected SNPs appear functionally involved in the pathologic pathways of the phenotypic diseases in question, which may potentially explain the underlying mechanisms. This proposed method can effectively and efficiently analyze ultra-high dimensional genomic data and could help provide new insights into the development of transplant-related complications from a genomic perspective.

bioinformatics↗

bioRxiv2017

Revealing Complete Complex KIR Haplotypes Phased By Long-Read Sequencing Technology

The killer cell immunoglobulin-like receptor (KIR) region of human chromosome 19 contains up to sixteen genes for natural killer (NK) cell receptors that recognize human leukocyte antigen (HLA)/peptide complexes and other ligands. The KIR proteins fulfill functional roles in infections, pregnancy, autoimmune diseases, and transplantation. However, their characterization remains a constant challenge. Not only are the genes highly homologous due to their recent evolution by tandem duplications, but the region is structurally dynamic due to frequent transposon-mediated recombination. A sequencing approach that precisely captures the complexity of KIR haplotypes for functional annotation is desirable.\n\nWe present a unique approach to haplotype the KIR loci using Single Molecule, Real-Time (SMRT(R)) Sequencing. Using this method, we have - for the first time - comprehensively sequenced and phased sixteen KIR haplotypes from eight individuals without imputation. The information revealed four novel haplotype structures, a novel gene-fusion allele, novel and confirmed insertion/deletion events, a homozygous individual, and overall diversity for the structural haplotypes and their alleles.\n\nThese KIR haplotypes augment our existing knowledge by providing high-quality references, evolutionary informers, and source material for imputation. The haplotype sequences and gene annotations provide alternative loci for the KIR region in the human genome reference GrCh38.p8.\n\nAuthor contributionsDR, CVG, SS, SR, DEG, MM designed the project. CWP, KE, RH performed the preparation and sequencing experiments. DR, CVG, RK, SR, DWG wrote the majority of the manuscript.

genomics↗