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      A rodent model for Dirofilaria immitis, canine heartworm: parasite growth, development, and drug sensitivity in NSG mice

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          Abstract

          Heartworm disease, caused by Dirofilaria immitis, remains a significant threat to canines and felines. The development of parasites resistant to macrocyclic lactones (ML) has created a significant challenge to the control of the infection. The goal of this study was to determine if mice lacking a functional immune response would be susceptible to D. immitis. Immunodeficient NSG mice were susceptible to the infection , sustaining parasites for at least 15 weeks, with infective third-stage larvae molting and developing into the late fourth-stage larvae. Proteomic analysis of host responses to the infection revealed a complex pattern of changes after infection, with at least some of the responses directed at reducing immune control mechanisms that remain in NSG mice. NSG mice were infected with isolates of D. immitis that were either susceptible or resistant to MLs, as a population. The susceptible isolate was killed by ivermectin whereas the resistant isolate had improved survivability, while both isolates were affected by moxidectin. It was concluded that D. immitis survives in NSG mice for at least 15 weeks. NSG mice provide an ideal model for monitoring host responses to the infection and for testing parasites in vivo for susceptibility to direct chemotherapeutic activity of new agents.

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          Most cited references43

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Adjusting batch effects in microarray expression data using empirical Bayes methods.

            Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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              Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *

              Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.
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                Author and article information

                Contributors
                david.abraham@jefferson.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 January 2023
                18 January 2023
                2023
                : 13
                : 976
                Affiliations
                [1 ]GRID grid.265008.9, ISNI 0000 0001 2166 5843, Department of Microbiology and Immunology, Sidney Kimmel Medical College, , Thomas Jefferson University, ; Philadelphia, PA USA
                [2 ]Social Circle, GA 30025 USA
                [3 ]GRID grid.428240.8, ISNI 0000 0004 0553 4650, Evotec, ; Anna-Sigmund-Straße 5, 82061 Neuried, Germany
                [4 ]GRID grid.418412.a, ISNI 0000 0001 1312 9717, Boehringer Ingelheim Animal Health USA Inc., ; 6498 Jade Road, Fulton, MO USA
                [5 ]GRID grid.418412.a, ISNI 0000 0001 1312 9717, Boehringer Ingelheim Animal Health USA Inc., ; 1730 Olympic Dr, Athens, GA USA
                Article
                27537
                10.1038/s41598-023-27537-z
                9849205
                36653420
                acc4e1af-4547-46e0-a8f1-5432016932dd
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 October 2022
                : 4 January 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;
                Award ID: ABRA13OV
                Funded by: FundRef http://dx.doi.org/10.13039/100001003, Boehringer Ingelheim;
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                parasitic infection,proteomics,pharmacology
                Uncategorized
                parasitic infection, proteomics, pharmacology

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