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      CCR5 and Biological Complexity: The Need for Data Integration and Educational Materials to Address Genetic/Biological Reductionism at the Interface of Ethical, Legal, and Social Implications

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          Abstract

          In the age of genomics, public understanding of complex scientific knowledge is critical. To combat reductionistic views, it is necessary to generate and organize educational material and data that keep pace with advances in genomics. The view that CCR5 is solely the receptor for HIV gave rise to demand to remove the gene in patients to create host HIV resistance, underestimating the broader roles and complex genetic inheritance of CCR5. A program aimed at providing research projects to undergraduates, known as CODE, has been expanded to build educational material for genes such as CCR5 in a rapid approach, exposing students and trainees to large bioinformatics databases and previous experiments for broader data to challenge commitment to biological reductionism. Our students organize expression databases, query environmental responses, assess genetic factors, generate protein models/dynamics, and profile evolutionary insights into a protein such as CCR5. The knowledgebase generated in the initiative opens the door for public educational information and tools (molecular videos, 3D printed models, and handouts), classroom materials, and strategy for future genetic ideas that can be distributed in formal, semiformal, and informal educational environments. This work highlights that many factors are missing from the reductionist view of CCR5, including the role of missense variants or expression of CCR5 with neurological phenotypes and the role of CCR5 and the delta32 variant in complex critical care patients with sepsis. When connected to genomic stories in the news, these tools offer critically needed Ethical, Legal, and Social Implication (ELSI) education to combat biological reductionism.

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          Most cited references134

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            The mutational constraint spectrum quantified from variation in 141,456 humans

            Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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              Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

              We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                02 December 2021
                2021
                02 December 2021
                : 12
                : 790041
                Affiliations
                [1] 1 Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University , Grand Rapids, MI, United States
                [2] 2 HudsonAlpha Institute for Biotechnology , Huntsville, AL, United States
                [3] 3 Infectious Disease, Helen DeVos Children’s Hospital , Grand Rapids, MI, United States
                [4] 4 Department of Mathematics, University of North Alabama , Florence, AL, United States
                [5] 5 Department of Biology, Grand Valley State University , Allendale, MI, United States
                [6] 6 Department of Science, Davenport University , Grand Rapids, MI, United States
                [7] 7 Office of Research, Spectrum Health , Grand Rapids, MI, United States
                [8] 8 Department of Biology, Athens State University , Athens, AL, United States
                [9] 9 Allergy & Immunology, Spectrum Health , Grand Rapids, MI, United States
                [10] 10 Department of Pharmacology and Toxicology, Michigan State University , East Lansing, MI, United States
                [11] 11 Department of Philosophy, The University of Akron , Akron, OH, United States
                [12] 12 Department of Critical Care Medicine and Pediatrics, Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine , Pittsburgh, PA, United States
                [13] 13 Pediatric Intensive Care Unit, Helen DeVos Children’s Hospital , Grand Rapids, MI, United States
                [14] 14 Medical Genetics, Spectrum Health , Grand Rapids, MI, United States
                Author notes

                Edited by: Joel Henrique Ellwanger, Federal University of Rio Grande do Sul, Brazil

                Reviewed by: Jose Artur Chies, Federal University of Rio Grande do Sul, Brazil; Leonardo Augusto Luvison Araujo, University of São Paulo, Brazil

                *Correspondence: Jeremy W. Prokop, jprokop54@ 123456gmail.com

                This article was submitted to Microbial Immunology, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2021.790041
                8674737
                9a952daf-e5cd-4457-ac17-63252e9120bf
                Copyright © 2021 Bauss, Morris, Shankar, Olivero, Buck, Stenger, Hinds, Mills, Eby, Zagorski, Smith, Cline, Hartog, Chen, Huss, Carcillo, Rajasekaran, Bupp and Prokop

                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.

                History
                : 06 October 2021
                : 18 November 2021
                Page count
                Figures: 7, Tables: 5, Equations: 0, References: 134, Pages: 20, Words: 10570
                Categories
                Immunology
                Original Research

                Immunology
                ccr5,viral infections,expression analysis,evolutionary profiling,molecular dynamic simulations,microglia,educational material generation

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