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      Immunogenomic profile at baseline predicts host susceptibility to clinical malaria


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          Host gene and protein expression impact susceptibility to clinical malaria, but the balance of immune cell populations, cytokines and genes that contributes to protection, remains incompletely understood. Little is known about the determinants of host susceptibility to clinical malaria at a time when acquired immunity is developing.


          We analyzed peripheral blood mononuclear cells (PBMCs) collected from children who differed in susceptibility to clinical malaria, all from a small town in Mali. PBMCs were collected from children aged 4-6 years at the start, peak and end of the malaria season. We characterized the immune cell composition and cytokine secretion for a subset of 20 children per timepoint (10 children with no symptomatic malaria age-matched to 10 children with >2 symptomatic malarial illnesses), and gene expression patterns for six children (three per cohort) per timepoint.


          We observed differences between the two groups of children in the expression of genes related to cell death and inflammation; in particular, inflammatory genes such as CXCL10 and STAT1 and apoptotic genes such as XAF1 were upregulated in susceptible children before the transmission season began. We also noted higher frequency of HLA-DR+ CD4 T cells in protected children during the peak of the malaria season and comparable levels cytokine secretion after stimulation with malaria schizonts across all three time points.


          This study highlights the importance of baseline immune signatures in determining disease outcome. Our data suggests that differences in apoptotic and inflammatory gene expression patterns can serve as predictive markers of susceptibility to clinical malaria.

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          Most cited references68

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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              Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

              DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

                Author and article information

                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                03 July 2023
                : 14
                : 1179314
                [1] 1 Institute for Genome Sciences, University of Maryland School of Medicine , Baltimore, MD, United States
                [2] 2 Center for Vaccine Development and Global Health, University of Maryland School of Medicine , Baltimore, MD, United States
                [3] 3 Malaria Research and Training Center, International Centers for Excellence in Research (NIH), University of Science Techniques and Technologies of Bamako , Bamako, Mali
                [4] 4 Department of Microbiology and Immunology, University of Maryland School of Medicine , Baltimore, MD, United States
                [5] 5 Global Health and Tropical Medicine, Instituto deHigiene e Medicina Tropical, Universidade Nova de Lisboa (GHTM, IHMT, UNL) , Lisboa, Portugal
                Author notes

                Edited by: Irene S. Soares, University of São Paulo, Brazil

                Reviewed by: Katherine Rose Dobbs, Case Western Reserve University, United States; Isaac Ssewanyana, Infectious Diseases Research Collaboration, Uganda

                *Correspondence: Joana C. Silva, jcsilva@ 123456som.umaryland.edu ; Kirsten E. Lyke, klyke@ 123456som.umaryland.edu

                †These authors have contributed equally to this work and share senior authorship

                Copyright © 2023 Mbambo, Dwivedi, Ifeonu, Munro, Shrestha, Bromley, Hodges, Adkins, Kouriba, Diarra, Niangaly, Kone, Coulibaly, Traore, Dolo, Thera, Laurens, Doumbo, Plowe, Berry, Travassos, Lyke and Silva

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 03 March 2023
                : 19 June 2023
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 69, Pages: 14, Words: 6485
                Funded by: National Institutes of Health , doi 10.13039/100000002;
                This project was funded in part by federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services under grant number U19AI110820. Site development and the conduct of the original clinical trial were supported by contract N01AI85346 and cooperative agreement U19AI065683 from the National Institute of Allergy and Infectious Diseases, grant D43TW001589 from the Fogarty International Center, National Institutes of Health and contract W81XWH-06-1-0427 from the United States Department of Defense and the United States Agency for International Development for site development and the conduct of the trial.
                Original Research
                Custom metadata
                Parasite Immunology

                rna sequencing,mass cytometrý,immunoinformatic analysis,baseline immunity,malaria susceptibility,malaria immunity,human parasite immunology


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