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      The Expression of Chemosensory Genes in Male Maxillary Palps of Anopheles coluzzii (Diptera: Culicidae) and An. quadriannulatus

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          Abstract

          Because of its importance as a malaria vector, Anopheles coluzzii’s Coetzee & Wilkerson olfactory system has been studied extensively. Among this work is a series of studies comparing the expression of chemosensory genes in olfactory organs in females and/or males of these species. These have identified species- and female-biased chemosensory gene expression patterns. However, many questions remain about the role of chemosensation in male anopheline biology. To pave the way for future work we used RNAseq to compare chemosensory gene expression in the male maxillary palps of An. coluzzii and its sibling species An. quadriannulatus Theobald. As expected, the chemosensory gene repertoire is small in the male maxillary palps. Both species express the tuning receptors Or8 and Or28 at relatively high levels. The CO 2 receptor genes Gr22-Gr24 are present in both species as well, although at much lower level than in females. Additionally, several chemoreceptors are species-specific. Gr37 and Gr52 are exclusive to An. coluzzii, whereas Or9 and Gr60 were detected only in An. quadriannulatus. Furthermore, several chemosensory genes show differential expression between the two species. Finally, several Irs, Grs, and Obps that show strong differential expression in the female palps, are absent or lowly expressed in the male palps. While many questions remain about the role of chemosensation in anopheline male biology, these results suggest that the male maxillary palps could have both a sex- and species-specific role in the perception of chemical stimuli. This work may guide future studies on the role of the male maxillary palp in these species.

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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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            featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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                Author and article information

                Contributors
                Role: Subject Editor
                Journal
                J Med Entomol
                J Med Entomol
                jme
                Journal of Medical Entomology
                Oxford University Press (US )
                0022-2585
                1938-2928
                May 2021
                12 February 2021
                12 February 2021
                : 58
                : 3
                : 1012-1020
                Affiliations
                [1 ] Department of Poultry Science, Texas A&M University , College Station, TX
                [2 ] Department of Entomology, Texas A&M University , College Station, TX
                [3 ] Laboratory of Entomology, Wageningen University & Research , Wageningen, The Netherlands
                Author notes
                Corresponding author, e-mail: maslotman@ 123456tamu.edu

                These authors contributed equally.

                Author information
                https://orcid.org/0000-0002-1976-0726
                https://orcid.org/0000-0002-6318-5781
                Article
                tjaa290
                10.1093/jme/tjaa290
                8122237
                33576414
                e8482466-39ab-4b2c-b844-a89133cb6f61
                © The Author(s) 2021. Published by Oxford University Press on behalf of Entomological Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 22 May 2020
                : 06 December 2020
                Page count
                Pages: 9
                Funding
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: R01 AI085079
                Categories
                Behavior, Chemical Ecology
                AcademicSubjects/SCI01382
                AcademicSubjects/MED00860

                chemosensory gene,male behavior,maxillary palp,malaria vector

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