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Insect neuropeptides play an important role in regulating physiological functions such as growth, development, behavior and reproduction. We identified temperature-sensitive neuropeptides and receptor genes of the cotton whitefly, Bemisia tabaci. We identified 38 neuropeptide precursor genes and 35 neuropeptide receptors and constructed a phylogenetic tree using additional data from other insects. As temperature adaptability enables B. tabaci to colonize a diversity of habitats, we performed quantitative polymerase chain reaction with two temperature stresses (low = 4 °C and high = 40 °C) to screen for temperature-sensitive neuropeptides. We found many neuropeptides and receptors that may be involved in the temperature adaptability of B. tabaci. This study is the first to identify B. tabaci neuropeptides and their receptors, and it will help to reveal the roles of neuropeptides in temperature adaptation of B. tabaci.
Pubmed ID: 31912953
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Tool to search translated nucleotide databases using a protein query.
View all literature mentionsA software which acquires and processes data sets, primarily through the Xcalibur system.
View all literature mentionsA portal to biomedical and genomic information. NCBI creates public databases, conducts research in computational biology, develops software tools for analyzing genome data, and disseminates biomedical information for the better understanding of molecular processes affecting human health and disease.
View all literature mentionsWeb application for the prediction of transmembrane helices in proteins using Hidden Markov Models. FASTA formatted sequences can be uploaded via file or copy-paste, and output can be formatted as extensive with graphics, extensive without graphics, or one line per protein. Submissions are limited to 10,000 sequences and 4,000,000 amino acids - each sequence is limited to no more than 8,000 amino acids.
View all literature mentionsWeb application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
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