The complete chloroplast genome sequence of Gentiana triflora and comparative analysis with its congeneric species

Gentiana triflora is an important medicinal plant in China with economic and medicinal value. Here, we report the complete chloroplastsequences of G. trifloral. The cp genome of G. triflora of 149, 125 bp contains 130 unique genes, including 85 protein-coding genes,8 rRNA genes, and 37 tRNA genes. The analysis of repeat showed that palindromic had the highest frequency. Besides, a total numberof 45 SSR were identified, most of which were mononucleotide adenine-thymine. Comparative genome analysis of Gentiana speciesrevealed that the pair of the inverted repeat was more conserved than the single-copy region. This analysis resulted in identification of 8 hypervariable regions (trnH-GUG, trnG-UCC-intron, atpI, trnD-GUC, trnL-UAA, rpl32-trnL-UAG, petA and ycf1). Phylogenetic analysis revealed that G. triflora was most closely related to Gentiana manshurica. In conclusion, this study enriched the genomic resources of the Gentiana genus and provided a basis for evolution and phylogeny analyses

The complete chloroplast genome sequence of Hyssopus cuspidatus Boriss. and analysis of phylogenetic relationships

Hyssopus cuspidatus is a member of the Lamiaceae family, members of which are often used to treat cough and asthma by the Uigurs. However, the Hyssopus genus has a limited number of known chloroplast genomes, making it difficult to compare species within the genus and to classify species within and outside the genus accurately. The introduction of the chloroplast genome method would therefore help improve the classification of the Hyssopus genus. This report presents the complete chloroplast sequences of Hyssopus cuspidatus. The chloroplast genome of H. cuspidatus is 149,678 bp long and contains 129 genes, including 85 protein-coding genes, 36 tRNA genes, and 8 rRNA genes. We identified 46 single sequence repeats (SSRs), most of which were mononucleotide adenine–thymine. The analysis of the repeat sequences, codon usage, and comparison of chloroplast genomes showed a high degree of conservation. The plastid genomes exhibited a typical quartile structure. Four hypervariable regions were identified: accD–psal, psbZ–trnG–GCC, trnH–GUG–psbA, and atpH–atpI. Phylogenetic analysis revealed that the Hyssopus genus was closely related to the adjacent genus Dracocephalum. Our research conducted a comprehensive analysis of the characteristics of the Hyssopus genus and provided a detailed comparison of the differences between species within and outside of this genus. Through IR comparison, phylogenetic analysis, and variation region analysis, we discovered a close relationship between the genera Hyssopus and Dracocephalum and propose a new perspective on the phylogenetic classification of H. cuspidatus. These findings will support the continued identification, classification, and evolutionary analysis of this genus

Revealing ecotype influences on Cistanche sinensis: from the perspective of endophytes to metabolites characteristics

IntroductionPlant microorganism is critical to plant health, adaptability, and productive forces. Intriguingly, the metabolites and microorganisms can act upon each other in a plant. The union of metabolomics and microbiome may uncover the crucial connections of the plant to its microbiome. It has important benefits for the agricultural industry and human being health, particularly for Chinese medical science investigation.MethodsIn this last 2 years study, on the strength of the UPLC–MS/MS detection platform, we accurately qualitatively, and quantitatively measured the Cistanche sinensis fleshy stems of two ecotypes. Thereafter, through high-throughput amplicon sequencing 16S/ITS sequences were procured.ResultsPhGs metabolites including echinacoside, isoacteoside, and cistanoside A were significantly downregulated at two ecotypes of C. sinensis. Add up to 876 metabolites were monitored and 231 differential metabolites were analyzed. Further analysis of 34 core differential metabolites showed that 15 compounds with up-regulated belonged to phenolic acids, flavonoids, and organic acids, while 19 compounds with down-regulated belonged to phenolic acids, flavonoids, alkaloids, amino acids, lipids, and nucleotides. There was no noteworthy discrepancy in the endophytic bacteria’s α and β diversity between sandy and loam ecotypes. By comparison, the α and β diversity of endophytic fungi was notably distinct. The fungal community of the loam ecotype is more abundant than the sandy ecotype. However, there were few such differences in bacteria. Most abundant genera included typical endophytes such as Phyllobacterium, Mycobacterium, Cistanche, Geosmithia, and Fusarium. LEfSe results revealed there were 11 and 20 biomarkers of endophytic bacteria and fungi in C. sinensis at two ecotypes, respectively. The combination parsing of microflora and metabolites indicated noteworthy relativity between the endophytic fungal communities and metabolite output. Key correlation results that Anseongella was positive relation with Syringin, Arsenicitalea is negative relation with 7-methylxanthine and Pseudogymnoascus is completely positively correlated with nepetin-7-O-alloside.DiscussionThe aim of this research is: (1) to explore firstly the influence of ecotype on C. sinensis from the perspective of endophytes and metabolites; (2) to investigate the relationship between endophytes and metabolites. This discovery advances our understanding of the interaction between endophytes and plants and provides a theoretical basis for cultivation of C. sinensis in future

Ecotype Division and Chemical Diversity of <i>Cynomorium songaricum</i> from Different Geographical Regions

Cynomorium songaricum is an important endangered plant with significant medicinal and edible values. However, the lack of resources and quality variation have limited the comprehensive developments and sustainable utilization of C. songaricum. Here, we evaluated the chemical and genetic traits of C. songaricum from the highly suitable habitat regions simulated with species distribution models. The PCA and NJ tree analyses displayed intraspecific variation in C. songaricum, which could be divided into two ecotypes: ecotype I and ecotype II. Furthermore, the LC-MS/MS-based metabolomic was used to identify and analyze the metabolites of two ecotypes. The results indicated that a total of 589 compounds were detected, 236 of which were significantly different between the two ecotypes. Specifically, the relative content and the kind of flavonoids were more abundant in ecotype I, which were closely associated with the medicinal activities. In contrast, amino acids and organic acids were more enriched in ecotype II, which may provide better nutritional quality and unique flavor. In summary, our findings demonstrate the ecotype division and chemical diversity of C. songaricum in China from different geographical regions and provide a reference for the development of germplasm and directed plant breeding of endangered medicinal plants

Data_Sheet_1_Revealing ecotype influences on Cistanche sinensis: from the perspective of endophytes to metabolites characteristics.zip

IntroductionPlant microorganism is critical to plant health, adaptability, and productive forces. Intriguingly, the metabolites and microorganisms can act upon each other in a plant. The union of metabolomics and microbiome may uncover the crucial connections of the plant to its microbiome. It has important benefits for the agricultural industry and human being health, particularly for Chinese medical science investigation.MethodsIn this last 2 years study, on the strength of the UPLC–MS/MS detection platform, we accurately qualitatively, and quantitatively measured the Cistanche sinensis fleshy stems of two ecotypes. Thereafter, through high-throughput amplicon sequencing 16S/ITS sequences were procured.ResultsPhGs metabolites including echinacoside, isoacteoside, and cistanoside A were significantly downregulated at two ecotypes of C. sinensis. Add up to 876 metabolites were monitored and 231 differential metabolites were analyzed. Further analysis of 34 core differential metabolites showed that 15 compounds with up-regulated belonged to phenolic acids, flavonoids, and organic acids, while 19 compounds with down-regulated belonged to phenolic acids, flavonoids, alkaloids, amino acids, lipids, and nucleotides. There was no noteworthy discrepancy in the endophytic bacteria’s α and β diversity between sandy and loam ecotypes. By comparison, the α and β diversity of endophytic fungi was notably distinct. The fungal community of the loam ecotype is more abundant than the sandy ecotype. However, there were few such differences in bacteria. Most abundant genera included typical endophytes such as Phyllobacterium, Mycobacterium, Cistanche, Geosmithia, and Fusarium. LEfSe results revealed there were 11 and 20 biomarkers of endophytic bacteria and fungi in C. sinensis at two ecotypes, respectively. The combination parsing of microflora and metabolites indicated noteworthy relativity between the endophytic fungal communities and metabolite output. Key correlation results that Anseongella was positive relation with Syringin, Arsenicitalea is negative relation with 7-methylxanthine and Pseudogymnoascus is completely positively correlated with nepetin-7-O-alloside.DiscussionThe aim of this research is: (1) to explore firstly the influence of ecotype on C. sinensis from the perspective of endophytes and metabolites; (2) to investigate the relationship between endophytes and metabolites. This discovery advances our understanding of the interaction between endophytes and plants and provides a theoretical basis for cultivation of C. sinensis in future.</p

Embedding-Graph-Neural-Network for Transient NOx Emissions Prediction

Recently, Acritical Intelligent (AI) methodologies such as Long and Short-term Memory (LSTM) have been widely considered promising tools for engine performance calibration, especially for engine emission performance prediction and optimization, and Transformer is also gradually applied to sequence prediction. To carry out high-precision engine control and calibration, predicting long time step emission sequences is required. However, LSTM has the problem of gradient disappearance on too long input and output sequences, and Transformer cannot reflect the dynamic features of historic emission information which derives from cycle-by-cycle engine combustion events, which leads to low accuracy and weak algorithm adaptability due to the inherent limitations of the encoder-decoder structure. In this paper, considering the highly nonlinear relation between the multi-dimensional engine operating parameters the engine emission data outputs, an Embedding-Graph-Neural-Network (EGNN) model was developed combined with self-attention mechanism for the adaptive graph generation part of the GNN to capture the relationship between the sequences, improve the ability of predicting long time step sequences, and reduce the number of parameters to simplify network structure. Then, a sensor embedding method was adopted to make the model adapt to the data characteristics of different sensors, so as to reduce the impact of experimental hardware on prediction accuracy. The experimental results show that under the condition of long-time step forecasting, the prediction error of our model decreased by 31.04% on average compared with five other baseline models, which demonstrates the EGNN model can potentially be used in future engine calibration procedures

Potential Global Distribution of the Habitat of Endangered Gentiana rhodantha Franch: Predictions Based on MaxEnt Ecological Niche Modeling

Continued global climate and environmental changes have led to habitat narrowing or migration of medicinal plants. Gentiana rhodantha Franch. ex Hemsl. is a medicinal plant for ethnic minorities in China, and it has a remarkable curative effect in the treatment of lung-heat cough. However, its habitat is gradually decreasing, and the species has been listed as an endangered ethnic medicine due to excessive harvesting. Here, based on CMIP6 bioclimatic data and 117 species occurrence records, the maximum entropy model (MaxEnt), combined with ArcGIS technology, was applied to predict the potentially suitable habitats for G. rhodantha under different climate scenarios. The results showed that the most critical bioclimatic variables affecting G. rhodantha are the precipitation of the warmest quarter (Bio18) and the mean temperature of the coldest quarter (Bio11). The highly suitable habitats of G. rhodantha are mainly concentrated in Belt and Road (&ldquo;B&amp;R&rdquo;) countries, including China, Bhutan, and Vietnam. However, under different climate change scenarios, the fragmentation extent of suitable habitats in China will generally increase, the suitable area will show a decreasing trend as a whole, the distribution center will shift to the northeast, and the distance will increase with time. Notably, the shrinkage of the high suitability area was the most obvious for the 2081&ndash;2100 SSP585 scenario, with a total of 358,385.2 km2. These findings contribute to the understanding of the geo-ecological characteristics of this species, and provide guidelines for the conservation, management, monitoring, and cultivation of G. rhodantha