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A new unbiased and highly automated approach to find new prognostic markers in preclinical research.

ABSTRACT: Data acquisition in (pre)clinical studies is often based on a hypothesis. Numerical algorithms, however, may help to find biomarkers from existing data without formulating any hypothesis. By simply assessing whether a statistical relationship exists between two parameters from a (unlimited) database, every (in)conceivable combination of data becomes a hypothesis. The aim was to create an unbiased and highly automated approach for secondary analysis of (pre)clinical research, including the possibility of a non-linear functional relationship. In our example, an almost homogeneous database was formed by overall 45 parameters (vital, blood and plasma parameters) measured in 11 individual experimental studies at 6 different time points using 57 rats without and 63 rats with systemic inflammation following lipopolysaccharide infusion. For each rat, four group classifiers (treatment, survival, study, ID) were used to get valid samples by a later filtering of the statistical base. Any information about the hypothesis leading to the respective studies was suppressed. In order to assess whether a statistical relationship exists, a total of six different functional prototypes (linear and non-linear) were postulated and examined for their regression. Regression quality, correlation and significance were obtained in form of matrices. In our example, ultimately 510?300 regressions were optimized, automatically evaluated and filtered. The developed algorithm is able to reveal statistical relationships from a nearly crude database with low effort by systematic and unbiased analysis. The finding of well-known correlations proves its reliability, whose validity could be increased by clean aggregation of different studies. In addition, new interesting hints for future research could be gained. Thus, unknown markers can be found which are associated with an increased risk of death during systemic inflammation and sepsis. A further development of the program is planned including multiple regressions (more than two parameters could be related to each other) or cluster analysis.

SUBMITTER: Neidnicht M

PROVIDER: S-EPMC6859257 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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A new unbiased and highly automated approach to find new prognostic markers in preclinical research.

Neidnicht Martin M Mittermüller Daniela D Effenberger-Neidnicht Katharina K

Database : the journal of biological databases and curation 20190101

Data acquisition in (pre)clinical studies is often based on a hypothesis. Numerical algorithms, however, may help to find biomarkers from existing data without formulating any hypothesis. By simply assessing whether a statistical relationship exists between two parameters from a (unlimited) database, every (in)conceivable combination of data becomes a hypothesis. The aim was to create an unbiased and highly automated approach for secondary analysis of (pre)clinical research, including the possib ...[more]

PMID: 31735952

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Project description:Developing B lymphocytes undergo V(D)J recombination to assemble germline V, D, and J gene segments into exons that encode the antigen-binding variable region of immunoglobulin (Ig) heavy (H) and light (L) chains. IgH and IgL chains associate to form the B cell receptor (BCR), which upon antigen binding activates B cells to secrete BCR as an antibody. Each of the huge number of clonally independent B cells expresses a unique set of IgH and IgL variable regions. Ability of V(D)J recombination to generate vast primary B cell repertoires results from combinatorial assortment of large numbers of different V, D, and J segments, coupled with diversification of the junctions between them to generate the complementary determining region 3 (CDR3) for antigen contact. Approaches to evaluate in depth the content of primary antibody repertoires and, ultimately, to study how they are further molded by secondary mutation and affinity maturation processes are of great importance to the B cell development, vaccine, and antibody fields. We now describe an unbiased, sensitive, and readily accessible assay, referred to as HTGTS repertoire sequencing (HTGTS-Rep-seq), to quantify antibody repertoires. HTGTS-Rep-seq quantitatively identifies the vast majority of IgH and IgL V(D)J exons, including their unique CDR3 sequences, from progenitor and mature mouse B lineage cells via the use of specific J primers. HTGTS-Rep-seq also accurately quantifies DJH intermediates and V(D)J exons in either productive or non-productive configurations. HTGTS-Rep-seq should be useful for studies of human samples, including clonal B-cell expansions and also for following antibody affinity maturation processes. We employed high-throughput genome-wide translocation sequencing adapted repertoire sequencing (HTGTS-Rep-seq) to study antibody repertoires. For HTGTS-Rep-seq libraries, we utilize bait coding ends of J segments to identify, in unbiased fashion, mouse IgH DJH repertoires [processed tlx files] along with both productive and non-productive IgH V(D)J repertoires from both pro-B and peripheral B cells [processed xls files of samples 1-18, 21-51]. Similarly, we also identify mouse productive and non-productive Igk repertoires from peripheral B cells [processed xls files of samples 19,20,52-57].

Dataset Information

A new unbiased and highly automated approach to find new prognostic markers in preclinical research.

Publications

A new unbiased and highly automated approach to find new prognostic markers in preclinical research.

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