Mosquito transcriptome changes and filarial worm resistance in Armigeres subalbatus

<p>Abstract</p> <p>Background</p> <p><it>Armigeres subalbatus </it>is a natural vector of the filarial worm <it>Brugia pahangi</it>, but it rapidly and proficiently kills <it>Brugia malayi </it>microfilariae by melanotic encapsulation. Because <it>B. malayi </it>and <it>B. pahangi </it>are morphologically and biologically similar, the <it>Armigeres-Brugia </it>system serves as a valuable model for studying the resistance mechanisms in mosquito vectors. We have initiated transcriptome profiling studies in <it>Ar. subalbatus </it>to identify molecular components involved in <it>B. malayi </it>refractoriness.</p> <p>Results</p> <p>These initial studies assessed the transcriptional response of <it>Ar. subalbatus </it>to <it>B. malayi </it>at 1, 3, 6, 12, 24, 48, and 72 hrs after an infective blood feed. In this investigation, we initiated the first holistic study conducted on the anti-filarial worm immune response in order to effectively explore the functional roles of immune-response genes following a natural exposure to the parasite. Studies assessing the transcriptional response revealed the involvement of unknown and conserved unknowns, cytoskeletal and structural components, and stress and immune responsive factors. The data show that the anti-filarial worm immune response by <it>Ar. subalbatus </it>to be a highly complex, tissue-specific process involving varied effector responses working in concert with blood cell-mediated melanization.</p> <p>Conclusion</p> <p>This initial study provides a foundation and direction for future studies, which will more fully dissect the nature of the anti-filarial worm immune response in this mosquito-parasite system. The study also argues for continued studies with RNA generated from both hemocytes and whole bodies to fully expound the nature of the anti-filarial worm immune response.</p

Persistence of Brugia malayi DNA in vector and non-vector mosquitoes: Implications for xenomonitoring and transmission monitoring of lymphatic filariasis

Abstract. Xenomonitoring (detection of filarial larvae or their DNA in mosquitoes) is a sensitive marker for as-sessing the endemicity of filariasis and a useful tool for evaluating elimination programs. To examine the fate of microfilariae (mf) and filarial DNA in vector competent and non-competent mosquito strains, we compared the detec-tion of Brugia malayi parasites by dissection and by quantitative real-time polymerase chain reaction (PCR) in three different mosquito strains. We conclude that PCR is much more sensitive than dissection for detecting filarial larvae, especially their remnants in mosquitoes. However, parasite DNA can be detected in both vector and non-vector mosquitoes for two weeks or longer after they ingest mf-positive blood. Thus, although xenomonitoring with vector and non-vector mosquito species may be a sensitive method for indirectly detecting filarial parasites in human populations, positive test results for parasite DNA in mosquitoes do not necessarily prove that transmission is ongoing in the study area

Construction and characterization of an expressed sequenced tag library for the mosquito vector Armigeres subalbatus

<p>Abstract</p> <p>Background</p> <p>The mosquito, <it>Armigeres subalbatus</it>, mounts a distinctively robust innate immune response when infected with the nematode <it>Brugia malayi</it>, a causative agent of lymphatic filariasis. In order to mine the transcriptome for new insight into the cascade of events that takes place in response to infection in this mosquito, 6 cDNA libraries were generated from tissues of adult female mosquitoes subjected to immune-response activation treatments that lead to well-characterized responses, and from aging, naïve mosquitoes. Expressed sequence tags (ESTs) from each library were produced, annotated, and subjected to comparative analyses.</p> <p>Results</p> <p>Six libraries were constructed and used to generate 44,940 expressed sequence tags, of which 38,079 passed quality filters to be included in the annotation project and subsequent analyses. All of these sequences were collapsed into clusters resulting in 8,020 unique sequence clusters or singletons. EST clusters were annotated and curated manually within ASAP (A Systematic Annotation Package for Community Analysis of Genomes) web portal according to BLAST results from comparisons to Genbank, and the <it>Anopheles gambiae </it>and <it>Drosophila melanogaster </it>genome projects.</p> <p>Conclusion</p> <p>The resulting dataset is the first of its kind for this mosquito vector and provides a basis for future studies of mosquito vectors regarding the cascade of events that occurs in response to infection, and thereby providing insight into vector competence and innate immunity.</p

A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function

Prevalence of sexual dimorphism in mammalian phenotypic traits.

The role of sex in biomedical studies has often been overlooked, despite evidence of sexually dimorphic effects in some biological studies. Here, we used high-throughput phenotype data from 14,250 wildtype and 40,192 mutant mice (representing 2,186 knockout lines), analysed for up to 234 traits, and found a large proportion of mammalian traits both in wildtype and mutants are influenced by sex. This result has implications for interpreting disease phenotypes in animal models and humans

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease

HIV-1 Vpu Neutralizes the Antiviral Factor Tetherin/BST-2 by Binding It and Directing Its Beta-TrCP2-Dependent Degradation

Host cells impose a broad range of obstacles to the replication of retroviruses. Tetherin (also known as CD317, BST-2 or HM1.24) impedes viral release by retaining newly budded HIV-1 virions on the surface of cells. HIV-1 Vpu efficiently counteracts this restriction. Here, we show that HIV-1 Vpu induces the depletion of tetherin from cells. We demonstrate that this phenomenon correlates with the ability of Vpu to counteract the antiviral activity of both overexpressed and interferon-induced endogenous tetherin. In addition, we show that Vpu co-immunoprecipitates with tetherin and β-TrCP in a tri-molecular complex. This interaction leads to Vpu-mediated proteasomal degradation of tetherin in a β-TrCP2-dependent manner. Accordingly, in conditions where Vpu-β-TrCP2-tetherin interplay was not operative, including cells stably knocked down for β-TrCP2 expression or cells expressing a dominant negative form of β-TrCP, the ability of Vpu to antagonize the antiviral activity of tetherin was severely impaired. Nevertheless, tetherin degradation did not account for the totality of Vpu-mediated counteraction against the antiviral factor, as binding of Vpu to tetherin was sufficient for a partial relief of the restriction. Finally, we show that the mechanism used by Vpu to induce tetherin depletion implicates the cellular ER-associated degradation (ERAD) pathway, which mediates the dislocation of ER membrane proteins into the cytosol for subsequent proteasomal degradation. In conclusion, we show that Vpu interacts with tetherin to direct its β-TrCP2-dependent proteasomal degradation, thereby alleviating the blockade to the release of infectious virions. Identification of tetherin binding to Vpu provides a potential novel target for the development of drugs aimed at inhibiting HIV-1 replication

Soft windowing application to improve analysis of high-throughput phenotyping data.

MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce \u27soft windowing\u27, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Identification of genetic elements in metabolism by high-throughput mouse phenotyping.

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe