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      The molecular basis of differential host responses to avian influenza viruses in avian species with differing susceptibility

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          Abstract

          Introduction

          Highly pathogenic avian influenza (HPAI) viruses, such as H5N1, continue to pose a serious threat to animal agriculture, wildlife and to public health. Controlling and mitigating this disease in domestic birds requires a better understanding of what makes some species highly susceptible (such as turkey and chicken) while others are highly resistant (such as pigeon and goose). Susceptibility to H5N1 varies both with species and strain; for example, species that are tolerant of most H5N1 strains, such as crows and ducks, have shown high mortality to emerging strains in recent years. Therefore, in this study we aimed to examine and compare the response of these six species, to low pathogenic avian influenza (H9N2) and two strains of H5N1 with differing virulence (clade 2.2 and clade 2.3.2.1) to determine how susceptible and tolerant species respond to HPAI challenge.

          Methods

          Birds were challenged in infection trials and samples (brain, ileum and lung) were collected at three time points post infection. The transcriptomic response of birds was examined using a comparative approach, revealing several important discoveries.

          Results

          We found that susceptible birds had high viral loads and strong neuro-inflammatory response in the brain, which may explain the neurological symptoms and high mortality rates exhibited following H5N1 infection. We discovered differential regulation of genes associated with nerve function in the lung and ileum, with stronger differential regulation in resistant species. This has intriguing implications for the transmission of the virus to the central nervous system (CNS) and may also indicate neuro-immune involvement at the mucosal surfaces. Additionally, we identified delayed timing of the immune response in ducks and crows following infection with the more deadly H5N1 strain, which may account for the higher mortality in these species caused by this strain. Lastly, we identified candidate genes with potential roles in susceptibility/resistance which provide excellent targets for future research.

          Discussion

          This study has helped elucidate the responses underlying susceptibility to H5N1 influenza in avian species, which will be critical in developing sustainable strategies for future control of HPAI in domestic poultry.

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          Most cited references52

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              Near-optimal probabilistic RNA-seq quantification.

              We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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                Author and article information

                Contributors
                Journal
                Front Cell Infect Microbiol
                Front Cell Infect Microbiol
                Front. Cell. Infect. Microbiol.
                Frontiers in Cellular and Infection Microbiology
                Frontiers Media S.A.
                2235-2988
                28 February 2023
                2023
                : 13
                : 1067993
                Affiliations
                [1] 1 The Roslin Institute and R(D)SVS, The University of Edinburgh , Edinburgh, United Kingdom
                [2] 2 National Institute of High Security Animal Diseases, Indian Council of Agricultural Research , Bhopal, India
                Author notes

                Edited by: Kegong Tian, Henan Agricultural University, China

                Reviewed by: Yuebang Yin, Nankai University, China; Anan Jongkaewwattana, National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand

                This article was submitted to Virus and Host, a section of the journal Frontiers in Cellular and Infection Microbiology

                Article
                10.3389/fcimb.2023.1067993
                10011077
                36926515
                de3d2708-fa20-463b-89fa-baff8f1dc233
                Copyright © 2023 Morris, Mishra, Raut, Gaunt, Borowska, Kuo, Wang, Vijayakumar, Chingtham, Dutta, Baillie, Digard, Vervelde, Burt and Smith

                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
                : 12 October 2022
                : 09 February 2023
                Page count
                Figures: 7, Tables: 4, Equations: 0, References: 55, Pages: 17, Words: 9755
                Funding
                Funded by: Biotechnology and Biological Sciences Research Council , doi 10.13039/501100000268;
                Funded by: Department of Biotechnology, Ministry of Science and Technology, India , doi 10.13039/501100001407;
                This work was supported by the Biotechnology and Biological Sciences Research Council (grant number BB/L004666/1) and Department of Biotechnology, Government of India (grant number BT/IN/Indo-UK/FADH/48/AM/2013). KM was partly supported by a National Health and Medical Research Council Overseas Postdoctoral Fellowship. The University of Edinburgh provided funding for open access publication fees. LV received funding from the European Union’s Horizon 2020 (VetBioNet) research and innovation program under grant agreement N° 731014. Additional funding was provided by BBSRC strategic program grant funding (BBS/E/D/20002173 and BBS/E/D/20002174) to LV.
                Categories
                Cellular and Infection Microbiology
                Original Research

                Infectious disease & Microbiology
                avian influenza,transcriptome,h5n1,chicken,duck,pigeon,crow,disease resistance

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