Dataset Information

IgTreeZ, A Toolkit for Immunoglobulin Gene Lineage Tree-Based Analysis, Reveals CDR3s Are Crucial for Selection Analysis.

ABSTRACT: Somatic hypermutation (SHM) is an important diversification mechanism that plays a part in the creation of immune memory. Immunoglobulin (Ig) variable region gene lineage trees were used over the last four decades to model SHM and the selection mechanisms operating on B cell clones. We hereby present IgTreeZ (Immunoglobulin Tree analyZer), a python-based tool that analyses many aspects of Ig gene lineage trees and their repertoires. Using simulations, we show that IgTreeZ can be reliably used for mutation and selection analyses. We used IgTreeZ on empirical data, found evidence for different mutation patterns in different B cell subpopulations, and gained insights into antigen-driven selection in corona virus disease 19 (COVID-19) patients. Most importantly, we show that including the CDR3 regions in selection analyses - which is only possible if these analyses are lineage tree-based - is crucial for obtaining correct results. Overall, we present a comprehensive lineage tree analysis tool that can reveal new biological insights into B cell repertoire dynamics.

SUBMITTER: Neuman H

PROVIDER: S-EPMC9643157 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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IgTreeZ, A Toolkit for Immunoglobulin Gene Lineage Tree-Based Analysis, Reveals CDR3s Are Crucial for Selection Analysis.

Neuman Hadas H Arrouasse Jessica J Kedmi Meirav M Cerutti Andrea A Magri Giuliana G Mehr Ramit R

Frontiers in immunology 20221026

Somatic hypermutation (SHM) is an important diversification mechanism that plays a part in the creation of immune memory. Immunoglobulin (Ig) variable region gene lineage trees were used over the last four decades to model SHM and the selection mechanisms operating on B cell clones. We hereby present IgTreeZ (Immunoglobulin Tree analyZer), a python-based tool that analyses many aspects of Ig gene lineage trees and their repertoires. Using simulations, we show that IgTreeZ can be reliably used fo ...[more]

PMID: 36389731

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Project description:BackgroundIn the typical setting of gene-selection problems from high-dimensional data, e.g., gene expression data from microarray or next-generation sequencing-based technologies, an enormous volume of high-throughput data is generated, and there is often a need for a simple, computationally-inexpensive, non-parametric screening procedure than can quickly and accurately find a low-dimensional variable subset that preserves biological information from the original very high-dimensional data (dimension p > 40,000). This is in contrast to the very sophisticated variable selection methods that are computationally expensive, need pre-processing routines, and often require calibration of priors.ResultsWe present a tree-based sequential CART (S-CART) approach to variable selection in the binary classification setting and compare it against the more sophisticated procedures using simulated and real biological data. In simulated data, we analyze S-CART performance versus (i) a random forest (RF), (ii) a fully-parametric Bayesian stochastic search variable selection (SSVS), and (iii) the moderated t-test statistic from the LIMMA package in R. The simulation study is based on a hierarchical Bayesian model, where dataset dimensionality, percentage of significant variables, and substructure via dependency vary. Selection efficacy is measured through false-discovery and missed-discovery rates. In all scenarios, the S-CART method is seen to consistently outperform SSVS and RF in both speed and detection accuracy. We demonstrate the utility of the S-CART technique both on simulated data and in a control-treatment mouse study. We show that the network analysis based on the S-CART-selected gene subset in essence recapitulates the biological findings of the study using only a fraction of the original set of genes considered in the study's analysis.ConclusionsThe relatively simple-minded gene selection algorithms like S-CART may often in practical circumstances be preferred over much more sophisticated ones. The advantage of the "greedy" selection methods utilized by S-CART and the likes is that they scale well with the problem size and require virtually no tuning or training while remaining efficient in extracting the relevant information from microarray-like datasets containing large number of redundant or irrelevant variables.AvailabilityThe MATLAB 7.4b code for the S-CART implementation is available for download from https://neyman.mcg.edu/posts/scart.zip.

Project description:IntroductionTranscriptomic analyses from early human immunodeficiency virus (HIV) infection have the potential to reveal how HIV causes widespread and lasting damage to biological functions, especially in the immune system. Previous studies have been limited by difficulties in obtaining early specimens.MethodsA hospital symptom-based screening approach was applied in a rural Mozambican setting to enrol patients with suspected acute HIV infection (Fiebig stage I-IV). Blood samples were collected from all those recruited, so that acute cases and contemporaneously recruited, uninfected controls were included. PBMC were isolated and sequenced using RNA-seq. Sample cellular composition was estimated from gene expression data. Differential gene expression analysis was completed, and correlations were determined between viral load and differential gene expression. Biological implications were examined using Cytoscape, gene set enrichment analysis, and enrichment mapping.ResultsTwenty-nine HIV infected subjects one month from presentation and 46 uninfected controls were included in this study. Subjects with acute HIV infection demonstrated profound gene dysregulation, with 6131 (almost 13% of the genome mapped in this study) significantly differentially expressed. Viral load was correlated with 1.6% of dysregulated genes, in particular, highly upregulated genes involved in key cell cycle functions, were correlated with viremia. The most profoundly upregulated biological functions related to cell cycle regulation, in particular, CDCA7 may drive aberrant cell division, promoted by overexpressed E2F family proteins. Also upregulated were DNA repair and replication, microtubule and spindle organization, and immune activation and response. The interferome of acute HIV was characterized by broad activation of interferon-stimulated genes with antiviral functions, most notably IFI27 and OTOF. BCL2 downregulation alongside upregulation of several apoptotic trigger genes and downstream effectors may contribute to cycle arrest and apoptosis. Transmembrane protein 155 (TMEM155) was consistently highly overexpressed during acute infection, with roles hitherto unknown.DiscussionOur study contributes to a better understanding of the mechanisms of early HIV-induced immune damage. These findings have the potential to lead to new earlier interventions that improve outcomes.

Dataset Information

IgTreeZ, A Toolkit for Immunoglobulin Gene Lineage Tree-Based Analysis, Reveals CDR3s Are Crucial for Selection Analysis.

Publications

IgTreeZ, A Toolkit for Immunoglobulin Gene Lineage Tree-Based Analysis, Reveals CDR3s Are Crucial for Selection Analysis.

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