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      Whole-Blood Transcriptome Analysis of Feedlot Cattle With and Without Bovine Respiratory Disease

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          Abstract

          Bovine respiratory disease (BRD) is one of the main factors leading to morbidity and mortality in feedlot operations in North America. A complex of viral and bacterial pathogens can individually or collectively establish BRD in cattle, and to date, most disease characterization studies using transcriptomic techniques examine bronchoalveolar and transtracheal fluids, lymph node, and lung tissue as well as nasopharyngeal swabs, with limited studies investigating the whole-blood transcriptome. Here, we aimed to identify differentially expressed (DE) genes involved in the host immune response to BRD using whole blood and RNA sequencing. Samples were collected from heifers (average arrival weight = 215.0 ± 5.3 kg) with ( n = 25) and without ( n = 18) BRD at a commercial feedlot in Western Canada. RNAseq analysis showed a distinct whole-blood transcriptome profile between BRD and non-BRD heifers. Further examination of the DE genes revealed that those involved in the host inflammatory response and infectious disease pathways were enriched in the BRD animals, while gene networks associated with metabolism and cell growth and maintenance were downregulated. Overall, the transcriptome profile derived from whole blood provided evidence that a distinct antimicrobial peptide-driven host immune response was occurring in the animals with BRD. The blood transcriptome of the BRD animals shows similarities to the transcriptome profiles obtained from lung and bronchial lymph nodes in other studies. This suggests that the blood transcriptome is a potential diagnostic tool for the identification of biomarkers of BRD infection and can be measured in live animals and used to further understand infection and disease in cattle. It may also provide a useful tool to increase the understanding of the genes involved in establishing BRD in beef cattle and be used to investigate potential therapeutic applications.

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          Most cited references52

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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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              Lipocalin 2: An Emerging Player in Iron Homeostasis and Inflammation

              Lipocalin 2 (Lcn2), an innate immune protein, has emerged as a critical iron regulatory protein during physiological and inflammatory conditions. As a bacteriostatic factor, Lcn2 obstructs the siderophore iron-acquiring strategy of bacteria and thus inhibits bacterial growth. As part of host nutritional immunity, Lcn2 facilitates systemic, cellular, and mucosal hypoferremia during inflammation, in addition to stabilizing the siderophore-bound labile iron pool. In this review, we summarize recent advances in understanding the interaction between Lcn2 and iron, and its effects in various inflammatory diseases. Lcn2 exerts mostly a protective role in infectious and inflammatory bowel diseases, whereas both beneficial and detrimental functions have been documented in neurodegenerative diseases, metabolic syndrome, renal disorders, skin disorders, and cancer. Further animal and clinical studies are necessary to unveil the multifaceted roles of Lcn2 in iron dysregulation during inflammation and to explore its therapeutic potential for treating inflammatory diseases.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                08 March 2021
                2021
                : 12
                : 627623
                Affiliations
                [1] 1Department of Agricultural, Food and Nutritional Science, Livestock Gentec, University of Alberta , Edmonton, AB, Canada
                [2] 2Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary , Calgary, AB, Canada
                [3] 3Simpson Ranch Chair in Beef Cattle Health and Wellness, University of Calgary , Calgary, AB, Canada
                [4] 4Ceva Santé Animale , Libourne, France
                [5] 5Department of Ecosystem and Public Health, Faculty of Veterinary Medicine, University of Calgary , Calgary, AB, Canada
                Author notes

                Edited by: Fabiana Quoos Mayer, Institute of Veterinary Research Desidério Finamor (IPVDF), Brazil

                Reviewed by: Ricardo Zanella, The University of Passo Fundo, Brazil; Jose Vargas, The University of Passo Fundo, Brazil

                *Correspondence: Graham Plastow, plastow@ 123456ualberta.ca

                This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2021.627623
                7982659
                33763112
                854fd4a9-e37d-4b82-8a2a-3389ec187319
                Copyright © 2021 Jiminez, Timsit, Orsel, van der Meer, Guan and Plastow.

                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
                : 09 November 2020
                : 08 February 2021
                Page count
                Figures: 3, Tables: 6, Equations: 0, References: 54, Pages: 13, Words: 0
                Funding
                Funded by: Genome Alberta 10.13039/501100010787
                Categories
                Genetics
                Original Research

                Genetics
                bovine respiratory disease,differentially expressed genes (degs),host immune response,innate immunity,rna sequencing

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