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      Fasciola hepatica Infection in Cattle: Analyzing Responses of Peripheral Blood Mononuclear Cells (PBMC) Using a Transcriptomics Approach

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          Abstract

          The parasitic helminth Fasciola hepatica (liver fluke) causes economic loss to the livestock industry globally and also causes zoonotic disease. New control strategies such as vaccines are urgently needed, due to the rise of drug resistance in parasite populations. Vaccine development requires a comprehensive understanding of the immunological events during infection. Previous in vivo studies by our group have investigated global differentially expressed genes (DEGs) in ovine peripheral blood mononuclear cells (PBMC) in response to both acute and chronic F. hepatica infection. This work demonstrated that pathways involved in the pathogenesis of ovine fasciolosis included fibrosis, inhibition of macrophage nitric oxide production, and antibody isotype switching, among others. Transcriptomic changes in PBMC populations following F. hepatica infection in cattle, in which the disease phenotype is quite different, have not yet been examined. Using RNA sequencing we investigated gene expression changes in PBMC isolated from 9 non-infected and 11 F. hepatica-experimentally-infected calves immediately before infection, at 1 and at 14 weeks post-infection. Longitudinal time-course comparisons between groups revealed 21 and 1,624 DEGs driven exclusively by F. hepatica infection in cattle at acute and chronic stages, respectively. These results show that fewer DEGs at the acute stage of infection can be identified in cattle, as compared with sheep. In addition, the log 2 fold-changes of these DEGs were relatively low (−1 to 3) reflecting the different clinical presentation of F. hepatica infection in cattle. Gene pathways for hepatic fibrosis and hepatic cholestasis along with apoptosis of antigen-presenting cells were enriched at chronic stages. Our results reflect the major differences in the disease phenotype between cattle and sheep and may indicate pathways to target in vaccine development.

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          It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR.

          RNA sequencing (RNA-seq) is widely used to profile transcriptional activity in biological systems. Here we present an analysis pipeline for differential expression analysis of RNA-seq experiments using the Rsubread and edgeR software packages. The basic pipeline includes read alignment and counting, filtering and normalization, modelling of biological variability and hypothesis testing. For hypothesis testing, we describe particularly the quasi-likelihood features of edgeR. Some more advanced downstream analysis steps are also covered, including complex comparisons, gene ontology enrichment analyses and gene set testing. The code required to run each step is described, along with an outline of the underlying theory. The chapter includes a case study in which the pipeline is used to study the expression profiles of mammary gland cells in virgin, pregnant and lactating mice.
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            Calculating sample size estimates for RNA sequencing data.

            Given the high technical reproducibility and orders of magnitude greater resolution than microarrays, next-generation sequencing of mRNA (RNA-Seq) is quickly becoming the de facto standard for measuring levels of gene expression in biological experiments. Two important questions must be taken into consideration when designing a particular experiment, namely, 1) how deep does one need to sequence? and, 2) how many biological replicates are necessary to observe a significant change in expression? Based on the gene expression distributions from 127 RNA-Seq experiments, we find evidence that 91% ± 4% of all annotated genes are sequenced at a frequency of 0.1 times per million bases mapped, regardless of sample source. Based on this observation, and combining this information with other parameters such as biological variation and technical variation that we empirically estimate from our large datasets, we developed a model to estimate the statistical power needed to identify differentially expressed genes from RNA-Seq experiments. Our results provide a needed reference for ensuring RNA-Seq gene expression studies are conducted with the optimally sample size, power, and sequencing depth. We also make available both R code and an Excel worksheet for investigators to calculate for their own experiments.
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              Concomitant infections, parasites and immune responses.

              Concomitant infections are common in nature and often involve parasites. A number of examples of the interactions between protozoa and viruses, protozoa and bacteria, protozoa and other protozoa, protozoa and helminths, helminths and viruses, helminths and bacteria, and helminths and other helminths are described. In mixed infections the burden of one or both the infectious agents may be increased, one or both may be suppressed or one may be increased and the other suppressed. It is now possible to explain many of these interactions in terms of the effects parasites have on the immune system, particularly parasite-induced immunodepression, and the effects of cytokines controlling polarization to the Th1 or Th2 arms of the immune response. In addition, parasites may be affected, directly or indirectly, by cytokines and other immune effector molecules and parasites may themselves produce factors that affect the cells of the immune system. Parasites are, therefore, affected when they themselves, or other organisms, interact with the immune response and, in particular, the cytokine network. The importance of such interactions is discussed in relation to clinical disease and the development and use of vaccines.
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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
                29 August 2019
                2019
                : 10
                : 2081
                Affiliations
                [1] 1UCD School of Veterinary Medicine, University College Dublin , Dublin, Ireland
                [2] 2Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin , Dublin, Ireland
                [3] 3UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin , Dublin, Ireland
                Author notes

                Edited by: Jayne Hope, University of Edinburgh, United Kingdom

                Reviewed by: Parul Sharma, University of Liverpool, United Kingdom; John Graham-Brown, University of Liverpool, United Kingdom

                *Correspondence: Grace Mulcahy grace.mulcahy@ 123456ucd.ie

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

                Article
                10.3389/fimmu.2019.02081
                6727689
                31555289
                093d74b2-eab6-4ac2-8c0b-1bdf5b252ce1
                Copyright © 2019 Garcia-Campos, Correia, Naranjo-Lucena, Garza-Cuartero, Farries, Browne, MacHugh and Mulcahy.

                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
                : 04 June 2019
                : 16 August 2019
                Page count
                Figures: 7, Tables: 4, Equations: 0, References: 75, Pages: 16, Words: 11716
                Funding
                Funded by: Science Foundation Ireland 10.13039/501100001602
                Funded by: Horizon 2020 Framework Programme 10.13039/100010661
                Funded by: Department of Agriculture, Food and the Marine 10.13039/501100001584
                Categories
                Immunology
                Original Research

                Immunology
                fasciola,transcriptomics,vaccines,immunoregulation,cattle,pbmcs,apoptosis
                Immunology
                fasciola, transcriptomics, vaccines, immunoregulation, cattle, pbmcs, apoptosis

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