Dataset Information

A Haystack Heuristic for Autoimmune Disease Biomarker Discovery Using Next-Gen Immune Repertoire Sequencing Data.

ABSTRACT: Large-scale DNA sequencing of immunological repertoires offers an opportunity for the discovery of novel biomarkers for autoimmune disease. Available bioinformatics techniques however, are not adequately suited for elucidating possible biomarker candidates from within large immunosequencing datasets due to unsatisfactory scalability and sensitivity. Here, we present the Haystack Heuristic, an algorithm customized to computationally extract disease-associated motifs from next-generation-sequenced repertoires by contrasting disease and healthy subjects. This technique employs a local-search graph-theory approach to discover novel motifs in patient data. We apply the Haystack Heuristic to nine million B-cell receptor sequences obtained from nearly 100 individuals in order to elucidate a new motif that is significantly associated with multiple sclerosis. Our results demonstrate the effectiveness of the Haystack Heuristic in computing possible biomarker candidates from high throughput sequencing data and could be generalized to other datasets.

SUBMITTER: Apeltsin L

PROVIDER: S-EPMC5509648 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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A Haystack Heuristic for Autoimmune Disease Biomarker Discovery Using Next-Gen Immune Repertoire Sequencing Data.

Apeltsin Leonard L Wang Shengzhi S von Büdingen H-Christian HC Sirota Marina M

Scientific reports 20170713 1

Large-scale DNA sequencing of immunological repertoires offers an opportunity for the discovery of novel biomarkers for autoimmune disease. Available bioinformatics techniques however, are not adequately suited for elucidating possible biomarker candidates from within large immunosequencing datasets due to unsatisfactory scalability and sensitivity. Here, we present the Haystack Heuristic, an algorithm customized to computationally extract disease-associated motifs from next-generation-sequenced ...[more]

PMID: 28706301

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Project description:To gain insight into the functional antibody repertoire of rabbits, the VH and VL repertoires of bone marrow (BM) and spleen (SP) of a naïve New Zealand White rabbit (NZW; Oryctolagus cuniculus) and that of lymphocytes collected from a NZW rabbit immunized (IM) with a 16-mer peptide were deep-sequenced. Two closely related genes, IGHV1S40 (VH1a3) and IGHV1S45 (VH4), were found to dominate (~90%) the VH repertoire of BM and SP, whereas, IGHV1S69 (VH1a1) contributed significantly (~40%) to IM. BM and SP antibodies recombined predominantly with IGHJ4. A significant proportion (~30%) of IM sequences recombined with IGHJ2. The VK repertoire was encoded by nine IGKV genes recombined with one IGKJ gene, IGKJ1. No significant bias in the VK repertoire of the BM, SP and IM samples was observed. The complementarity-determining region (CDR)-H3 and -L3 length distributions were similar in the three samples following a Gaussian curve with average length of 12.2 ± 2.4 and 11.1 ± 1.1 amino acids, respectively. The amino acid composition of the predominant CDR-H3 and -L3 loop lengths was similar to that of humans and mice, rich in Tyr, Gly, Ser and, in some specific positions, Asp. The average number of mutations along the IGHV/KV genes was similar in BM, SP and IM; close to 12 and 15 mutations for VH and VL, respectively. A monoclonal antibody specific for the peptide used as immunogen was obtained from the IM rabbit. The CDR-H3 sequence was found in 1,559 of 61,728 (2.5%) sequences, at position 10, in the rank order of the CDR-H3 frequencies. The CDR-L3 was found in 24 of 11,215 (0.2%) sequences, ranking 102. No match was found in the BM and SP samples, indicating positive selection for the hybridoma sequence. Altogether, these findings lay foundations for engineering of rabbit V regions to enhance their potential as therapeutics, i.e., design of strategies for selection of specific rabbit V regions from NGS data mining, humanization and design of libraries for affinity maturation campaigns.

Project description:BackgroundSmall ncRNAs (sncRNAs) offer great hope as biomarkers of disease and response to treatment. This has been highlighted in the context of several medical conditions such as cancer, liver disease, cardiovascular disease, and central nervous system disorders, among many others. Here we assessed several steps involved in the development of an ncRNA biomarker discovery pipeline, ranging from sample preparation to bioinformatic processing of small RNA sequencing data.MethodsA total of 45 biological samples were included in the present study. All libraries were prepared using the Illumina TruSeq Small RNA protocol and sequenced using the HiSeq2500 or MiSeq Illumina sequencers. Small RNA sequencing data was validated using qRT-PCR. At each stage, we evaluated the pros and cons of different techniques that may be suitable for different experimental designs. Evaluation methods included quality of data output in relation to hands-on laboratory time, cost, and efficiency of processing.ResultsOur results show that good quality sequencing libraries can be prepared from small amounts of total RNA and that varying degradation levels in the samples do not have a significant effect on the overall quantification of sncRNAs via NGS. In addition, we describe the strengths and limitations of three commercially available library preparation methods: (1) Novex TBE PAGE gel; (2) Pippin Prep automated gel system; and (3) AMPure XP beads. We describe our bioinformatics pipeline, provide recommendations for sequencing coverage, and describe in detail the expression and distribution of all sncRNAs in four human tissues: whole-blood, brain, heart and liver.ConclusionsUltimately this study provides tools and outcome metrics that will aid researchers and clinicians in choosing an appropriate and effective high-throughput sequencing quantification method for various study designs, and overall generating valuable information that can contribute to our understanding of small ncRNAs as potential biomarkers and mediators of biological functions and disease.

Dataset Information

A Haystack Heuristic for Autoimmune Disease Biomarker Discovery Using Next-Gen Immune Repertoire Sequencing Data.

Publications

A Haystack Heuristic for Autoimmune Disease Biomarker Discovery Using Next-Gen Immune Repertoire Sequencing Data.

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