BackgroundHeat shock proteins 90 (HSP90s) are a highly conserved protein family of cellular chaperones widely found in plants; they play a fundamental role in response to biotic and abiotic stresses. The genome-wide analysis of HSP90 gene family has been completed for some species; however, it has been rarely reported for the tobacco HSP90 genes.ResultsIn this study, we systematically conducted genome-wide identification and expression analysis of the tobacco HSP90 gene family, including gene structures, evolutionary relationships, chromosomal locations, conserved domains, and expression patterns. Twenty-one NtHSP90s were identified and classified into eleven categories (NtHSP90–1 to NtHSP90–11) based on phylogenetic analysis. The conserved structures and motifs of NtHSP90 proteins in the same subfamily were highly consistent. Most NtHSP90 proteins contained the ATPase domain, which was closely related to conserved motif 2. Motif 5 was a low complexity sequence and had the function of signal peptide. At least 6 pairs of NtHSP90 genes underwent gene duplication, which arose from segment duplication and tandem duplication events. Phylogenetic analysis showed that most species expanded according to their own species-specific approach during the evolution of HSP90s. Dynamic expression analysis indicated that some NtHSP90 genes may play fundamental roles in regulation of abiotic stress response. The expression of NtHSP90–4, NtHSP90–5, and NtHSP90–9 were up-regulated, while NtHSP90–6, and NtHSP90–7 were not induced by ABA, drought, salt, cold and heat stresses. Among the five treatments, NtHSP90s were most strongly induced by heat stress, and weakly activated by ABA treatment. There was a similar response pattern of NtHSP90s under osmotic stress, or extreme temperature stress.ConclusionsThis is the first genome-wide analysis of Hsp90 in N. tabacum. These results indicate that each NtHSP90 member fulfilled distinct functions in response to various abiotic stresses.Electronic supplementary materialThe online version of this article (10.1186/s12863-019-0738-8) contains supplementary material, which is available to authorized users.
The maturity of tobacco leaves directly affects their curing quality. However, no effective method has been developed for determining their maturity during production. Assessment of tobacco maturity for flue curing has long depended on production experience, leading to considerable variation. In this study, hyperspectral imaging combined with a novel algorithm was used to develop a classification model that could accurately determine the maturity of tobacco leaves. First, tobacco leaves of different maturity levels (unripe, under-ripe, ripe, and over-ripe) were collected. ENVI software was used to remove the hyperspectral imaging (HSI) background, and 11 groups of filtered images were obtained using Python 3.7. Finally, a full-band-based partial least-squares discriminant analysis (PLS-DA) classification model was established to identify the maturity of the tobacco leaves. In the calibration set, the model accuracy of the original spectrum was 88.57%, and the accuracy of the de-trending, multiple scattering correction (MSC), and standard normalization variable (SNV) treatments was 91.89%, 95.27%, and 92.57%, respectively. In the prediction set, the model accuracy of the de-trending, MSC, and SNV treatments was 93.85%, 96.92%, and 93.85%, respectively. The experimental results indicate that a higher model accuracy was obtained with the filtered images than with the original spectrum. Because of the higher accuracy, de-trending, MSC, and SNV treatments were selected as the candidate characteristic spectral bands, and a successive projection algorithm (SPA), competitive adaptive reweighted sampling (CASR), and particle swarm optimization (PSO) were used as the screening methods. Finally, a genetic algorithm (GA), PLS-DA, line support vector machine (LSVM), and back-propagation neural network (BPNN) classification and discrimination models were established. The combination SNV-SPA-PLS-DA model provided the best accuracy in the calibration and prediction sets (99.32% and 98.46%, respectively). Our findings highlight the efficacy of using visible/near-infrared (ViS/NIR) hyperspectral imaging for detecting the maturity of tobacco leaves, providing a theoretical basis for improving tobacco production.
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