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Karpe, S. D.

Publications and source records attributed to Karpe, S. D..

3 recordsLinked to original sources

Sensing volatiles throughout the body: Geographic and tissue-specific olfactory receptor expression in the fig wasp Ceratosolen fusciceps

An essential adaptive strategy in insects is the evolution of olfactory receptors (ORs) to recognize important volatile environmental chemical cues. Our model species, Ceratosolen fusciceps, a specialist wasp pollinator of Ficus racemosa, likely possesses an OR repertoire that allows it to distinguish fig-specific volatiles in highly variable environments. Using a newly assembled genome-guided transcriptome, we annotated 63 ORs in the species and reconstructed the phylogeny of Ceratosolen ORs in conjunction with other hymenopteran species. Expression analysis showed that though ORs were mainly expressed in the antennae, 20 percent were also expressed in non-antennal tissues such as the head, thorax, abdomen, legs, wings, and ovipositor. Specific upregulated expression was observed in OR30C in the head and OR60C in the wings. We identified OR expression from all major body parts of C. fusciceps, suggesting novel roles of ORs throughout the body. Further examination of OR expression of C. fusciceps in widely separated geographical locations, i.e., south (urban) and northeast (rural) India, revealed distinct OR expression levels in different locations. This discrepancy likely parallels the observed variation in fig volatiles between these regions and provides new insights into the evolution of insect ORs and their expression across geographical locations and tissues.

genomics↗

Genome-wide survey of odorant-binding proteins in the dwarf honey bee Apis florea

Odorant binding proteins (OBPs) in insects bind to volatile chemical cue and help in their binding to odorant receptors. The odor coding hypothesis states that OBPs may bind with specificity to certain volatiles and aid the insect in various behaviours. Honeybees are eusocial insects with complex behaviour that requires olfactory inputs. Here, we have identified and annotated odorant binding proteins from the genome of the dwarf honey bee, Apis florea using an exhaustive homology-based bioinformatic pipeline and analyzed the evolutionary relationships between the OBP subfamilies. Our study suggests that Minus-C subfamily may have diverged from the Classic subfamily of odorant binding proteins in insects.

bioinformatics↗

InsectOR - webserver for sensitive identification of insect olfactory receptor genes from non-model genomes

Insect Olfactory Receptors (ORs) are diverse family of membrane protein receptors responsible for most of the insect olfactory perception and communication, and hence they are of utmost importance for developing repellents or pesticides. Hence, accurate gene prediction of insect ORs from newly sequenced genomes is an important but challenging task. We have developed a dedicated web-server, insectOR, to predict and validate insect OR genes using multiple gene prediction algorithms, accompanied by relevant validations. It is possible to employ this sever nearly automatically and perform rapid prediction of the OR gene loci from thousands of OR-protein-to-genome alignments, resolve gene boundaries for tandem OR genes and refine them further to provide more complete OR gene models. InsectOR outperformed the popular genome annotation pipelines (MAKER and NCBI eukaryotic genome annotation) in terms of overall sensitivity at base, exon and locus level, when tested on two distantly related insect genomes. It displayed more than 95% nucleotide level precision in both tests. Finally, given the same input data and parameters, InsectOR missed less than 2% gene loci, in contrast to 55% loci missed by MAKER for Drosophila melanogaster. The web-server is freely available on the web at http://caps.ncbs.res.in/insectOR/. All major browsers are supported. Website is implemented in Python with Jinja2 for templating and bootstrap framework which uses HTML, CSS and JavaScript/Ajax. The core pipeline is written in Perl.

bioinformatics↗