Biology subjects

Hollenbach, J. A.

Publications and source records attributed to Hollenbach, J. A..

7 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↗

bioRxiv2024

High KIR diversity in Uganda and Botswana children living with HIV

Killer-cell immunoglobulin-like receptors (KIRs) are essential components of the innate immune system found on the surfaces of natural killer (NK) cells. The KIRs encoding genes are located on chromosome 19q13.4 and are genetically diverse across populations. KIRs are associated with various disease states including HIV progression, and are linked to transplantation rejection and reproductive success. However, there is limited knowledge on the diversity of KIRs from Uganda and Botswana HIV-infected paediatric cohorts, with high endemic HIV rates. We used next-generation sequencing technologies on 312 (246 Uganda, 66 Botswana) samples to generate KIR allele data and employed customised bioinformatics techniques for allelic, allotype and disease association analysis. We show that these sample sets from Botswana and Uganda have different KIRs of different diversities. In Uganda, we observed 147 vs 111 alleles in the Botswana cohort, which had a more than 1 % frequency. We also found significant deviation towards homozygosity for the KIR3DL2 gene for both rapid (RPs) and long-term non-progressors (LTNPs)in the Ugandan cohort. The frequency of the bw4-80I ligand was also significantly higher among the LTNPs than RPs (8.9 % Vs 2.0%, P-value: 0.032). In the Ugandan cohort, KIR2DS4*001 (OR: 0.671, 95 % CI: 0.481-0.937, FDR adjusted Pc=0.142) and KIR2DS4*006 (OR: 2.519, 95 % CI: 1.085-5.851, FDR adjusted Pc=0.142) were not associated with HIV disease progression after adjustment for multiple testing. Our study results provide additional knowledge of the genetic diversity of KIRs in African populations and provide evidence that will inform future immunogenetics studies concerning human disease susceptibility, evolution and host immune responses.

genomics↗

bioRxiv2024

Antigen-specificity of clonally-enriched CD8+ T cells in multiple sclerosis

CD8+ T cells are the dominant lymphocyte population in multiple sclerosis (MS) lesions where they are highly clonally expanded. The clonal identity, function, and antigen specificity of CD8+ T cells in MS are not well understood. Here we report a comprehensive single-cell RNA-seq and T cell receptor (TCR)-seq analysis of the cerebrospinal fluid (CSF) and blood from a cohort of treatment-naive MS patients and control participants. A small subset of highly expanded and activated CD8+ T cells were enriched in the CSF in MS that displayed high activation, cytotoxicity and tissue-homing transcriptional profiles. Using a combination of unbiased and targeted antigen discovery approaches, MS-derived CD8+ T cell clonotypes recognizing Epstein-Barr virus (EBV) antigens and multiple novel mimotopes were identified. These findings shed vital insight into the role of CD8+ T cells in MS and pave the way towards disease biomarkers and therapeutic targets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/611010v2_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1a0b738org.highwire.dtl.DTLVardef@5cf5c2org.highwire.dtl.DTLVardef@10abdccorg.highwire.dtl.DTLVardef@32c7b2_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical SummaryC_FLOATNO Created in BioRender. Sabatino, J. (2024) BioRender.com/e66l598 C_FIG

immunology↗

bioRxiv2024

MHConstructor: A high-throughput, haplotype-informed solution to the MHC assembly challenge

The extremely high levels of genetic polymorphism within the human major histocompatibility complex (MHC) limit the usefulness of reference-based alignment methods for sequence assembly. We incorporate a short read de novo assembly algorithm into a workflow for novel application to the MHC. MHConstructor is a containerized pipeline designed for high-throughput, haplotype-informed, reproducible assembly of both whole genome sequencing and target-capture short read data in large, population cohorts. To-date, no other self-contained tool exists for the generation of de novo MHC assemblies from short read data. MHConstructor facilitates wide-spread access to high quality, alignment-free MHC sequence analysis.

bioinformatics↗

bioRxiv2023

High-throughput complement component 4 genomic sequence analysis with C4Investigator

The complement component 4 gene locus, composed of the C4A and C4B genes and located on chromosome 6, encodes for C4 protein, a key intermediate in the classical and lectin pathways of the complement system. The complement system is an important modulator of immune system activity and is also involved in the clearance of immune complexes and cellular debris. The C4 gene locus exhibits copy number variation, with each composite gene varying between 0-5 copies per haplotype, C4 genes also vary in size depending on the presence of the HERV retrovirus in intron 9, denoted by C4(L) for long-form and C4(S) for short-form, which modulates expression and is found in both C4A and C4B. Additionally, human blood group antigens Rodgers and Chido are located on the C4 protein, with the Rodger epitope generally found on C4A protein, and the Chido epitope generally found on C4B protein. C4 copy number variation has been implicated in numerous autoimmune and pathogenic diseases. Despite the central role of C4 in immune function and regulation, high-throughput genomic sequence analysis of C4 variants has been impeded by the high degree of sequence similarity and complex genetic variation exhibited by these genes. To investigate C4 variation using genomic sequencing data, we have developed a novel bioinformatic pipeline for comprehensive, high-throughput characterization of human C4 sequence from short-read sequencing data, named C4Investigator. Using paired-end targeted or whole genome sequence data as input, C4Investigator determines gene copy number for overall C4, C4A, C4B, C4(Rodger), C4(Ch), C4(L), and C4(S), additionally, C4Ivestigator reports the full overall C4 aligned sequence, enabling nucleotide level analysis of C4. To demonstrate the utility of this workflow we have analyzed C4 variation in the 1000 Genomes Project Dataset, showing that the C4 genes are highly poly-allelic with many variants that have the potential to impact C4 protein function.

bioinformatics↗

bioRxiv2021

High-throughput Interpretation of Killer-cell Immunoglobulin-like Receptor Short-read Sequencing Data with PING

The killer-cell immunoglobulin-like receptor (KIR) complex on chromosome 19 encodes receptors that modulate the activity of natural killer cells, and variation in these genes has been linked to infectious and autoimmune disease, as well as having bearing on pregnancy and transplant outcomes. The medical relevance and high variability of KIR genes makes short-read sequencing an attractive technology for interrogating the region, providing a high-throughput, high-fidelity sequencing method that is cost-effective. However, because this gene complex is characterized by extensive nucleotide polymorphism, structural variation including gene fusions and deletions, and a high level of homology between genes, its interrogation at high resolution has been thwarted by bioinformatic challenges, with most studies limited to examining presence or absence of specific genes. Here, we present the PING (Pushing Immunogenetics to the Next Generation) pipeline, which incorporates empirical data, novel alignment strategies and a custom alignment processing workflow to enable high-throughput KIR sequence analysis from short-read data. PING provides KIR gene copy number classification functionality for all KIR genes through use of a comprehensive alignment reference. The gene copy number determined per individual enables an innovative genotype determination workflow using genotype-matched references. Together, these methods address the challenges imposed by the structural complexity and overall homology of the KIR complex. To determine copy number and genotype determination accuracy, we applied PING to European and African validation cohorts and a synthetic dataset. PING demonstrated exceptional copy number determination performance across all datasets and robust genotype determination performance. Finally, an investigation into discordant genotypes for the synthetic dataset provides insight into misaligned reads, advancing our understanding in interpretation of short-read sequencing data in complex genomic regions. PING promises to support a new era of studies of KIR polymorphism, delivering high-resolution KIR genotypes that are highly accurate, enabling high-quality, high-throughput KIR genotyping for disease and population studies. Author summaryKiller cell immunoglobulin-like receptors (KIR) serve a critical role in regulating natural killer cell function. They are encoded by highly polymorphic genes within a complex genomic region that has proven difficult to interrogate owing to structural variation and extensive sequence homology. While methods for sequencing KIR genes have matured, there is a lack of bioinformatic support to accurately interpret KIR short-read sequencing data. The extensive structural variation of KIR, both the small-scale nucleotide insertions and deletions and the large-scale gene duplications and deletions, coupled with the extensive sequence similarity among KIR genes presents considerable challenges to bioinformatic analyses. PING addressed these issues through a highly-dynamic alignment workflow, which constructs individualized references that reflect the determined copy number and genotype makeup of a sample. This alignment workflow is enabled by a custom alignment processing pipeline, which scaffolds reads aligned to all reference sequences from the same gene into an overall gene alignment, enabling processing of these alignments as if a single reference sequence was used regardless of the number of sequences or of any insertions or deletions present in the component sequences. Together, these methods provide a novel and robust workflow for the accurate interpretation of KIR short-read sequencing data.

immunology↗

bioRxiv2019

Multiple Measures Reveal The Value of Both Race And Geographic Ancestry For Self-Identification

There is long-standing tension regarding whether and how to use race or geographic ancestry in biomedical research. We examined multiple self-reported measures of race and ancestry from a cohort of over 100,000 U.S. residents alongside genetic data. We found that these measures are often non-overlapping, and that no single self-reported measure alone provides a better fit to genetic ancestry than a combination including both race and geographic ancestry. We also found that patterns of reporting for race and ancestry appear to be influenced by participation in direct-to-consumer genetic ancestry testing. Our results demonstrate that there is a place for the language of both race and geographic ancestry as we seek to empower individuals to fully describe their family history in research and medicine.\n\nOne Sentence SummarySelf-identification in the United States according to both racial and geographic terms best reflects genetic ancestry in individuals.

genetics↗