A glutathione S-transferase GhTT19 determines flower petal pigmentation via regulating anthocyanin accumulation in cotton.

Anthocyanin accumulations in the flowers can improve seed production of hybrid lines, and produce higher commodity value in cotton fibre. However, the genetic mechanism underlying the anthocyanin pigmentation in cotton petals is poorly understood. Here, we showed that the red petal phenotype was introgressed from Gossypium bickii through recombination with the segment containing the R3 bic region in the A07 chromosome of Gossypium hirsutum variety LR compared with the near-isogenic line of LW with white flower petals. The cyanidin-3-O-glucoside (Cy3G) was the major anthocyanin in red petals of cotton. A GhTT19 encoding a TT19-like GST was mapped to the R3 bic site associated with red petals via map-based cloning, but GhTT19 homologue gene from the D genome was not expressed in G. hirsutum. Intriguingly, allelic variations in the promoters between GhTT19LW and GhTT19LR , rather than genic regions, were found as genetic causal of petal colour variations. GhTT19-GFP was found localized in both the endoplasmic reticulum and tonoplast for facilitating anthocyanin transport. An additional MYB binding element found only in the promoter of GhTT19LR , but not in that of GhTT19LW , enhanced its transactivation by the MYB activator GhPAP1. The transgenic analysis confirmed the function of GhTT19 in regulating the red flower phenotype in cotton. The essential light signalling component GhHY5 bonded to and activated the promoter of GhPAP1, and the GhHY5-GhPAP1 module together regulated GhTT19 expression to mediate the light-activation of petal anthocyanin pigmentation in cotton. This study provides new insights into the molecular mechanisms for anthocyanin accumulation and may lay a foundation for faster genetic improvement of cotton.

DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.

Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering clinical translation, lacking effective methods for multimodal and multisource data integration is becoming a bottleneck. Here we proposed DeepDRK, a machine learning framework for deciphering drug response through kernel-based data integration. To transfer information among different drugs and cancer types, we trained deep neural networks on more than 20 000 pan-cancer cell line-anticancer drug pairs. These pairs were characterized by kernel-based similarity matrices integrating multisource and multi-omics data including genomics, transcriptomics, epigenomics, chemical properties of compounds and known drug-target interactions. Applied to benchmark cancer cell line datasets, our model surpassed previous approaches with higher accuracy and better robustness. Then we applied our model on newly established patient-derived cancer cell lines and achieved satisfactory performance with AUC of 0.84 and AUPRC of 0.77. Moreover, DeepDRK was used to predict clinical response of cancer patients. Notably, the prediction of DeepDRK correlated well with clinical outcome of patients and revealed multiple drug repurposing candidates. In sum, DeepDRK provided a computational method to predict drug response of cancer cells from integrating pharmacogenomic datasets, offering an alternative way to prioritize repurposing drugs in precision cancer treatment. The DeepDRK is freely available via https://github.com/wangyc82/DeepDRK.

Design, synthesis, and biological evaluation of artemyrianolide H derivatives as potential antihepatoma agents.

Artemyrianolide H (AH) is a germacrene-type sesquiterpenolid isolated from Artemisia myriantha, and showed potent cytotoxicity against three human hepatocellular carcinoma cell lines HepG2, Huh7, and SK-Hep-1 with IC50 values of 10.9, 7.2, and 11.9 µM, respectively. To reveal structure-activity relationship, 51 artemyrianolide H derivatives including 19 dimeric analogs were designed, synthesized, and assayed for their cytotoxicity against three human hepatoma cell lines. Among them, 34 compounds were more active than artemyrianolide H and sorafenib on the three cell lines. Especially, compound 25 exhibited the most promising activity with IC50 values of 0.7 (HepG2), 0.6 (Huh7), and 1.3 µM (SK-Hep-1), which were 15.5, 12.0, and 9.2-fold higher than that of AH and 16.4, 16.3 and 17.5-fold higher than that of sorafenib. Cytotoxicity evaluation on normal human liver cell lines (THLE-2) demonstrated good safety profile of compound 25 with SI of 1.9 (HepG2), 2.2 (Huh 7) and 1.0 (SK-Hep1). Further studies revealed that compound 25 dose-dependently arrested cells at G2/M phase which was correlated with the up-regulation of both cyclin B1 and p-CDK1, and induced apoptosis through the activation of mitochondrial pathways in HepG2 cells. In addition, the migratory and invasive abilities in HepG2 cells after treatment with 1.5 µM of compound 25 were decreased by 89% and 86% with the increase of E-cadherin expression accompanied by the decrease of N-cadherin, vimentin expression. Bioinformatics analysis based on machine learning predicted that PDGFRA and MAP2K2 might be acting targets of compound 25, and SPR assays demonstrated compound 25 were bound with PDGFRA and MAP2K2 with KD value of 0.168 nM, and 8.49 µM, respectively. This investigation proposed that compound 25 might be considered as a promising lead compound for the development of antihepatoma candidate.

The molecular characteristics of gastric cancer patients living in Qinghai-Tibetan Plateau.

Gastric cancer, or stomach cancer, that originates in the inner lining of the stomach, was the fifth most common cancer and the fourth mortality globally, with over one million new cases in 2020 and an estimated 769,000 deaths. The molecular characteristics of gastric cancer has been complicated by histological and intratumor heterogeneity. The incidence of gastric cancer shows wide geographical variation. As the largest and highest region in China, Qinghai-Tibetan Plateau is one of the important global biodiversity hotspots. Here, we collect tumour and paired normal bio-samples from 31 primary gastric cancer patients from Qinghai Provincial People's Hospital, and discuss the molecular characteristics for gastric cancer patients living in plateau. They have more single nucleotide polymorphisms (SNP) located in chromosome 7 with C → T and G → A as the most common alteration types, barely share the cancer driver genes with western patients, and have no significant differences in various Chinese nation. These characteristics offers a great opportunity to further understanding the divergent mechanism of gastric cancer, increase the efficacy for diagnosis and prognosis, finally lead the optimal targeted therapeutics.

Dissipation Dynamics of Doxycycline and Gatifloxacin and Accumulation of Heavy Metals during Broiler Manure Aerobic Composting.

In this study, broilers were fed with heavy-metal-containing diets (Zn, Cu, Pb, Cr, As, Hg) at three rates (T1: 5 kg premix/ton feed, T2: 10 kg premix/ton feed and T3: 15 kg premix/ton feed) and Doxycycline (DOX) and Gatifloxacin (GAT) at low or high doses (T4: 31.2 mg DOX/bird/day and 78 mg GAT/bird/day, T5: 15.6 mg DOX/bird/day and 48 mg GAT/bird/day) to assess the accumulation of various heavy metals and the fate of two antibiotics in broiler manure after 35 days of aerobic composting. The results indicated that the two antibiotics changed quite differently during aerobic composting. About 14.96-15.84% of Doxycycline still remained at the end of composting, while Gatifloxacin was almost completely removed within 10 days of composting. The half-lives of Doxycycline were 13.75 and 15.86 days, while the half-lives of Gatifloxacin were only 1.32 and 1.38 days. Based on the Redundancy analysis (RDA), the concentration of antibiotics was significantly influenced by physico-chemical properties (mainly temperature and pH) throughout the composting process. Throughout the composting process, all heavy metal elements remained concentrated in organic fertilizer. In this study the Cr content reached 160.16 mg/kg, 223.98 mg/kg and 248.02 mg/kg with increasing premix feed rates, similar to Zn, which reached 258.2 mg/kg, 312.21 mg/kg and 333.68 mg/kg. Zn and Cr concentrations well exceeded the United States and the European soil requirements. This experiment showed that antibiotic residues and the accumulation of heavy metals may lead to soil contamination and pose a risk to the soil ecosystem.

Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs.

Long noncoding RNA (lncRNA) transcripts have emerging impacts in cancer studies, which suggests their potential as novel therapeutic agents. However, the molecular mechanism behind their treatment effects is still unclear. Here, we designed a computational model to Associate LncRNAs with Anti-Cancer Drugs (ALACD) based on a bilevel optimization model, which optimized the gene signature overlap in the upper level and imputed the missing lncRNA-gene association in the lower level. ALACD predicts genes coexpressed with lncRNAs mean while matching drug's gene signatures. This model allows us to borrow the target gene information of small molecules to understand the mechanisms of action of lncRNAs and their roles in cancer. The ALACD model was systematically applied to the 10 cancer types in The Cancer Genome Atlas (TCGA) that had matched lncRNA and mRNA expression data. Cancer type-specific lncRNAs and associated drugs were identified. These lncRNAs show significantly different expression levels in cancer patients. Follow-up functional and molecular pathway analysis suggest the gene signatures bridging drugs and lncRNAs are closely related to cancer development. Importantly, patient survival information and evidence from the literature suggest that the lncRNAs and drug-lncRNA associations identified by the ALACD model can provide an alternative choice for cancer targeting treatment and potential cancer pognostic biomarkers. The ALACD model is freely available at https://github.com/wangyc82/ALACD-v1.

Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision.

Alternative splicing (AS) is a genetically and epigenetically regulated pre-mRNA processing to increase transcriptome and proteome diversity. Comprehensively decoding these regulatory mechanisms holds promise in getting deeper insights into a variety of biological contexts involving in AS, such as development and diseases. We assembled splicing (epi)genetic code, DeepCode, for human embryonic stem cell (hESC) differentiation by integrating heterogeneous features of genomic sequences, 16 histone modifications with a multi-label deep neural network. With the advantages of epigenetic features, DeepCode significantly improves the performance in predicting the splicing patterns and their changes during hESC differentiation. Meanwhile, DeepCode reveals the superiority of epigenomic features and their dominant roles in decoding AS patterns, highlighting the necessity of including the epigenetic properties when assembling a more comprehensive splicing code. Moreover, DeepCode allows the robust predictions across cell lineages and datasets. Especially, we identified a putative H3K36me3-regulated AS event leading to a nonsense-mediated mRNA decay of BARD1. Reduced BARD1 expression results in the attenuation of ATM/ATR signalling activities and further the hESC differentiation. These results suggest a novel candidate mechanism linking histone modifications to hESC fate decision. In addition, when trained in different contexts, DeepCode can be expanded to a variety of biological and biomedical fields.

Salt-adaptive strategies in oil seed crop Ricinus communis early seedlings (cotyledon vs. true leaf) revealed from proteomics analysis.

Soil salinity is a major abiotic stress affecting crop growth and productivity. Ricinus communis has good salt tolerance and is also an important oilseed crop throughout the world. Early seedling stage (such as cotyledon expansion stage) is the most vulnerable period for plant under stresses. However, little information exist concerning the physiological and molecular mechanisms of Ricinus communis seedlings and the role play by cotyledons and true leaf under salt stress. In the present study, biomass, photosynthesis, chlorophyll fluorescence, inorganic ions and organic solutes contents were measured, and two dimensional gel electrophoresis-based proteomic technology was employed to identify the differentially abundant proteins in the salt-treated Ricinus communis cotyledons and true leaves. The results showed that salt stress reduced growth and photosynthesis in the seedlings. With increasing salinity, the Na+ content increased and K+ content decreased in both cotyledons and leaves, but the true leaves had lower Na+ and higher K+ contents. Soluble sugars and proline are the primary organic solutes to cope with osmotic stress. In addition, proteomic analysis revealed 30 and 42 differentially accumulated protein spots in castor cotyledon and true leaf under salt stress, respectively. Most of the identified proteins were involved in carbohydrate and energy metabolism, photosynthesis, genetic information process, reactive oxygen species metabolism, amino acid metabolism and cell structure. The physiological and proteomic results highlighted that cotyledons accumulated a large number of Na+ and provided more energy to help true leaves cope with salt stress. The true leaves saved carbon structures to synthesize osmotic substances, and the enhancement of chlorophyll synthesis and electron transfer in true leaves could also maintain photosynthesis under salt stress. These findings provide new insights into different physiological mechanisms in cotyledon and true leaf of Ricinus communis response to salt stress during early seedling stage.

Computational probing protein-protein interactions targeting small molecules.

MOTIVATION: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel drug targets. RESULTS: Here, we propose a machine learning method to predict PPI targets in a genomic-wide scale. Specifically, we develop a computational method, named as PrePPItar, to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs. First, we survey the databases and manually construct a gold-standard positive dataset for drug and PPI interactions. This effort leads to a dataset with 227 associations among 63 PPIs and 113 FDA-approved drugs and allows us to build models to learn the association rules from the data. Second, we characterize drugs by profiling in chemical structure, drug ATC-code annotation, and side-effect space and represent PPI similarity by a symmetrical S-kernel based on protein amino acid sequence. Then the drugs and PPIs are correlated by Kronecker product kernel. Finally, a support vector machine (SVM), is trained to predict novel associations between drugs and PPIs. We validate our PrePPItar method on the well-established gold-standard dataset by cross-validation. We find that all chemical structure, drug ATC-code, and side-effect information are predictive for PPI target. Moreover, we can increase the PPI target prediction coverage by integrating multiple data sources. Follow-up database search and pathway analysis indicate that our new predictions are worthy of future experimental validation. CONCLUSION: In conclusion, PrePPItar can serve as a useful tool for PPI target discovery and provides a general heterogeneous data integrative framework. AVAILABILITY AND IMPLEMENTATION: PrePPItar is available at http://doc.aporc.org/wiki/PrePPItar. CONTACT: ycwang@nwipb.cas.cn or ywang@amss.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Network predicting drug's anatomical therapeutic chemical code.

MOTIVATION: Discovering drug's Anatomical Therapeutic Chemical (ATC) classification rules at molecular level is of vital importance to understand a vast majority of drugs action. However, few studies attempt to annotate drug's potential ATC-codes by computational approaches. RESULTS: Here, we introduce drug-target network to computationally predict drug's ATC-codes and propose a novel method named NetPredATC. Starting from the assumption that drugs with similar chemical structures or target proteins share common ATC-codes, our method, NetPredATC, aims to assign drug's potential ATC-codes by integrating chemical structures and target proteins. Specifically, we first construct a gold-standard positive dataset from drugs' ATC-code annotation databases. Then we characterize ATC-code and drug by their similarity profiles and define kernel function to correlate them. Finally, we use a kernel method, support vector machine, to automatically predict drug's ATC-codes. Our method was validated on four drug datasets with various target proteins, including enzymes, ion channels, G-protein couple receptors and nuclear receptors. We found that both drug's chemical structure and target protein are predictive, and target protein information has better accuracy. Further integrating these two data sources revealed more experimentally validated ATC-codes for drugs. We extensively compared our NetPredATC with SuperPred, which is a chemical similarity-only based method. Experimental results showed that our NetPredATC outperforms SuperPred not only in predictive coverage but also in accuracy. In addition, database search and functional annotation analysis support that our novel predictions are worthy of future experimental validation. CONCLUSION: In conclusion, our new method, NetPredATC, can predict drug's ATC-codes more accurately by incorporating drug-target network and integrating data, which will promote drug mechanism understanding and drug repositioning and discovery. AVAILABILITY: NetPredATC is available at http://doc.aporc.org/wiki/NetPredATC. CONTACT: ycwang@nwipb.cas.cn or ywang@amss.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of posttranslational modification sites from amino acid sequences with kernel methods.

Post-translational modification (PTM) is the chemical modification of a protein after its translation and one of the later steps in protein biosynthesis for many proteins. It plays an important role which modifies the end product of gene expression and contributes to biological processes and diseased conditions. However, the experimental methods for identifying PTM sites are both costly and time-consuming. Hence computational methods are highly desired. In this work, a novel encoding method PSPM (position-specific propensity matrices) is developed. Then a support vector machine (SVM) with the kernel matrix computed by PSPM is applied to predict the PTM sites. The experimental results indicate that the performance of new method is better or comparable with the existing methods. Therefore, the new method is a useful computational resource for the identification of PTM sites. A unified standalone software PTMPred is developed. It can be used to predict all types of PTM sites if the user provides the training datasets. The software can be freely downloaded from http://www.aporc.org/doc/wiki/PTMPred.

Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles.

Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually change expression level of downstream gene. Here, we propose a computational framework, PERD, to Predict the Enhancers Responsive to Drug. A machine learning model was trained to predict the genome-wide chromatin accessibility from transcriptome data using the paired expression and chromatin accessibility data collected from ENCODE and ROADMAP. Then the model was applied to the perturbed gene expression data from Connectivity Map (CMAP) and Cancer Drug-induced gene expression Signature DataBase (CDS-DB) and identify drug responsive enhancers with significantly altered chromatin accessibility. Furthermore, the drug responsive enhancers were related to the pharmacogenomics genome-wide association studies (PGx GWAS). Stepping on the traditional drug-associated gene signatures, PERD holds the promise to enhance the causality of drug perturbation by providing candidate regulatory element of those drug associated genes.

Compound-protein interaction prediction based on heterogeneous network reveals potential antihepatoma agents.

Despite the development of an increasing number of multi-kinase and immune checkpoint inhibitors for hepatocellular carcinoma (HCC), improvement in cancer survival remains limited due to their similar structures and targets. Natural products (NPs) maintain diverse structures and activities and are important sources of drug discovery. Currently, most of active NPs exhibit ambiguous binding targets and mechanisms. Herein, we proposed the CIPHEN (compound-protein interactions prediction based on the heterogeneous network) to predict potential antihepatoma NPs and their targets. The evaluation of canonical compound-protein interactions (CPIs) databases and independent test demonstrated the good ability of CPIHN to reveal known and unreported CPIs. Both prediction and experiment results indicated that CIPHEN could unveil relationships between actively antihepatoma sesquiterpenoid dimers (SDs) and anti-HCC targets. In conclusion, the CIPHEN provides a promising choice to investigate the mode of action of compounds, which will help to accelerate the process of drug research and development against HCC.

Genome-wide investigation of the OR gene family in Helicoverpa armigera and functional analysis of OR48 and OR75 in metamorphosis development.

The cotton bollworm, Helicoverpa armigera, is a significant global agricultural pest, particularly detrimental during its larval feeding period. Insects' odorant receptors (ORs) are crucial for their crop-feeding activities, yet a comprehensive analysis of H. armigera ORs has been lacking, and the influence of hormones on ORs remain understudied. Herein, we conducted a genome-wide study and identified 81 ORs, categorized into 15 distinct groups. Analyses of protein motifs and gene structures revealed both conservation within groups and divergence among them. Comparative gene duplication analysis between H. armigera and Bombyx mori highlighted different duplication patterns. We further investigated subcellular localization and protein interactions within the odorant receptor family, providing valuable insights for future functional and interaction studies of ORs. Specifically, we identified that OR48 and OR75 were abundantly expressed during molting/metamorphosis and feeding stages, respectively. We demonstrated that 20E induced the upregulation of OR48 via EcR, while insulin upregulated OR75 expression through InR. Moreover, 20E induced the translocation of OR48 to the cell membrane, mediating its effects. Functional studies involving the knockdown of OR48 and OR75 revealed their roles in metamorphosis development, with OR48 knockdown resulting in delayed pupation and OR75 knockdown leading to premature pupation. OR48 can promote autophagy and apoptosis in fat body, while OR75 can significantly inhibit apoptosis and autophagy. These findings significantly contribute to our understanding of OR function in H. armigera and shed light on potential avenues for pest control strategies.

Artificial Vegetation for Sand Stabilization May Impact Sand Lake Dynamics in Dune Regions.

Vegetation on dunes regulates the water supply from the dunes to the inter-dune lowland, which is a crucial factor affecting lake water dynamics in the inter-dune lowland. Previous researchers have paid insufficient attention to the water regulation function of dunes on a landscape- and regional scale. To fill this gap, both remote sensing technology and field observations were used to analyze the variations in the lake area and their influence factors, such as vegetation coverage and precipitation in the lake watershed, on a multi-year scale (2000-2020) and one-year scale (2021), respectively. The results showed that precipitation is the main factor influencing the changes in lake water, and artificial sand vegetation can regulate the changes in lake water. On the multi-year scale, with the coverage of artificial sand-fixing vegetation increasing on sand dunes in the lake watershed, the areas of the lakes were gradually decreasing. On the one-year scale, with dune vegetation coverage increased, the water supply from dunes to lakes showed a decreasing trend. This model can provide a possibility for estimating and predicting the influence of water supply from dunes to lakes that is affected by sand-fixing vegetation. The findings have significant theoretical and practical utility for the rational utilization of water resources in sandy land, as well as for assisting in the selection of an optimized construction mode for desert control projects.

Artemyriantholides A-K, guaiane-type sesquiterpenoid dimers from Artemisia myriantha var. pleiocephala and their antihepatoma activity.

Artemyriantholides A-K (1-11) as well as 14 known compounds (12-25) were isolated from Artemisia myriantha var. pleiocephala (Asteraceae). The structures and absolute configuration of compounds 2 and 8-9 were confirmed by the single crystal X-ray diffraction analyses, and the others were elucidated by MS, NMR spectral data and electronic circular dichroism calculations. All compounds were chemically characterized as guaiane-type sesquiterpenoid dimers (GSDs). Compound 1 was the first example of the GSD fused via C-3/C-11' and C-5/C-13' linkages, and compounds 2 and 5 were rare GSDs containing chlorine atoms. Eleven compounds showed obvious inhibitory activity in HepG2, Huh7 and SK-Hep-1 cell lines by antihepatoma assay to provide the IC50 values ranging from 7.9 to 67.1 µM. Importantly, compounds 5 and 8 exhibited the best inhibitory activity with IC50 values of 14.2 and 18.8 (HepG2), 9.0 and 11.5 (Huh7), and 8.8 and 11.3 µM (SK-Hep-1), respectively. The target of compound 5 was predicted to be MAP2K2 by a computational prediction model. The interaction between compound 5 and MAP2K2 was conducted to give docking score of -9.0 kcal/mol by molecular docking and provide KD value of 43.7 µM by Surface Plasmon Resonance assay.

Sub-okra leaf shape conferred via chromosomal introgression from Gossypium barbadense L. improves photosynthetic productivity in short-season cotton (Gossypium hirsutum L.).

Leaf shape is a vital agronomic trait that affects plant and canopy architecture, yield, and other production attributes of upland cotton. Compared with normal leaves, lobed leaves have potential advantages in improving canopy structure and increasing cotton yield. A chromosomal introgression segment from Gossypium barbadense L. conferring sub-okra leaf shape to Gossypium hirsutum L. was identified on chromosome D01. To determine the effects of this transferred sub-okra leaf shape on the leaf anatomical characteristics, photosynthesis-related traits, and yield of short-season cotton, we performed a field experiment with three sets of near-isogenic lines carrying okra, sub-okra, and normal leaf shape in Lu54 (L54) and Shizao 2 (SZ2) backgrounds. Compared with normal leaves, sub-okra leaves exhibited reduced leaf thickness and smaller leaf mass per area; moreover, the deeper lobes of sub-okra leaves improved the plant canopy structure by decreasing leaf area index by 11.24%-22.84%. Similarly, the intercepted PAR rate of lines with sub-okra leaf shape was also reduced. The chlorophyll content of sub-okra leaves was lower than that of okra and normal leaf shapes; however, the net photosynthetic rate of sub-okra leaves was 8.17%-29.81% higher than that of other leaf shapes at most growth stages. Although the biomass of lines with sub-okra leaf shape was less than that of lines with normal leaves, the average first harvest yield and total yield of lines with the sub-okra leaf shape increased by 6.36% and 5.72%, respectively, compared with those with normal leaves. Thus, improvements in the canopy structure and photosynthetic and physiological characteristics contributed to optimizing the light environment, thereby increasing the yield of lines with sub-okra leaf shape. Our results suggest that the sub-okra leaf trait from G. barbadense L. may have practical applications for cultivating short-season varieties with high photosynthetic efficiency, and improving yield, which will be advantageous for short-season varieties.

Chloroplast phylogenomics and the taxonomy of Saxifraga section Ciliatae (Saxifragaceae).

Comprising ca. 200 species, Saxifraga sect. Ciliatae is the most species-rich section of Saxifraga s.str., whose center of diversity is in the Tibeto-Himalayan region. The infra-sectional classification of sect. Ciliatae is still in debate due to the high level of species richness, as well as remarkable variations of habitat, morphology, physiology and life cycles. Subdivisions of this section proposed in various taxonomic systems have not been adequately tested in previous phylogenetic studies, partly due to low taxonomic sampling density, but also to the use of few DNA markers. In order to achieve a more robust infra-sectional classification of sect. Ciliatae, complete chloroplast genomes of 94 taxa from this section were analyzed, of which 93 were newly sequenced, assembled and annotated. The length of the 94 plastomes of sect. Ciliatae taxa range from 143,479 to 159,938 bp, encoding 75 to 79 unique protein-coding genes (PCGs). Analyses of the 94 plastomes revealed high conservation in structural organization, gene arrangement, and gene content. Gene loss and changes of IR boundaries were detected but in extremely low frequency. The molecular phylogenetic tree from concatenated PCGs and complete chloroplast genome sequences exhibited high resolution and support values and confirms that sect. Ciliatae is monophyletic. Three well-supported clades were revealed within the section that agree relatively well with the subsectional taxonomy of Gornall (1987), but some minor modifications should be made. Firstly, the monotypic subsection Cinerascentes should be abandoned and its constituent species, S. cinerascens, assigned to subsect. Gemmiparae. Secondly, subsections Rosulares and Serpyllifoliae should be merged and become subsect. Rosulares. Section Ciliatae thus comprises: subsect. Hirculoideae Engl. & Irmsch.; subsect. Rosulares Gornall; subsect. Gemmiparae Engl. & Irmsch.; subsect. Flagellares (C. B. Clarke) Engl. & Irmsch. and subsect. Hemisphaericae (Engl. & Irmsch.) Gornall.

The genetic diversity, replication, and transmission of 2009 pandemic H1N1 viruses in China.

Background: The 2009 pandemic H1N1 influenza A virus (pdm09) continue to evolve, and few studies have systemically analyzed the evolution, replication, and transmission of pmd09 viruses in China. Methods: To better understand the evolution and pathogenicity of pdm09 viruses, we systematically analyzed viruses that were confirmed in 2009-2020 in China and characterized their replication and transmission ability. We extensively analyzed the evolution characteristics of pdm/09 in China over the past decades. The replication ability of 6B.1 and 6B.2 lineages on Madin-Darby canine kidney (MDCK) and human lung adenocarcinoma epithelial (A549) cells and their pathogenicity and transmission in guinea pigs were also compared. Results: In total, 3,038 pdm09 viruses belonged to clade 6B.1 (62% of all pdm09 viruses) and clade 6B.2 (4%). Clade 6B.1 pdm09 viruses are the predominant clade, with proportions of 54.1%, 78.9%, 57.2%, 58.6%, 61.7%, 76.3%, and 66.6% in the North, Northeast, East, Central, South, Southwest, and Northeast regions in China, respectively. The isolation proportion of clade 6B.1 pdm/09 viruses was 57.1%, 74.3%, 96.1%, 98.2%, 86.7%, and 78.5% in 2015-2020, respectively. A clear differentiation time point appeared in 2015 before which the evolution trend of pdm09 viruses in China was similar to that in North America but then showed a different trend after that point. To characterize pdm09 viruses in China after 2015, we further analyzed 33 pdm09 viruses isolated in Guangdong in 2016-2017, among which A/ Guangdong/33/2016 and A/Guangdong/184/2016 (184/2016) belonged to clade 6B.2, and the other 31 strains belonged to clade 6B.1. A/Guangdong/887/2017 (887/2017) and A/Guangdong/752/2017 (752/2017) (clade 6B.1), 184/2016 (clade 6B.2) and A/California/04/2009 (CA04) replicated efficiently in MDCK cells and A549 cells, as well as the turbinates of guinea pigs. 184/2016 and CA04 could transmit among guinea pigs through physical contact. Conclusion: Our findings provide novel insights into the evolution, pathogenicity, and transmission of pdm09 virus. The results show that enhancing surveillance of pdm09 viruses and timely evaluation of their virulence are essential.

Belowground bud banks and land use change: roles of vegetation and soil properties in mediating the composition of bud banks in different ecosystems.

Introduction: Belowground bud banks play integral roles in vegetation regeneration and ecological succession of plant communities; however, human-caused changes in land use severely threaten their resilience and regrowth. Although vegetation attributes and soil properties mediate such anthropogenic effects, their influence on bud bank size and composition and its regulatory mechanisms under land use change have not been explored. Methods: We conducted a field investigation to examine impacts of land use change on bud bank size and composition, vegetation attributes, and soil properties in wetlands (WL), farmlands (FL), and alpine meadow (AM) ecosystems in Zhejiang Province, China. Results: Overall, 63 soil samples in close proximity to the vegetation quadrats were excavated using a shovel, and samples of the excavated soil were placed in plastic bags for onward laboratory soil analysis. The total bud density (1514.727 ± 296.666) and tiller bud density (1229.090 ± 279.002) in wetland ecosystems were significantly higher than in farmland and alpine meadow ecosystems [i.e., total (149.333 ± 21.490 and 573.647 ± 91.518) and tiller bud density (24.666 ± 8.504 and 204.235 ± 50.550), respectively]. While vegetation attributes critically affected bud banks in WL ecosystems, soil properties strongly influenced bud banks in farmland and alpine meadow ecosystems. In wetland ecosystems, total and tiller buds were predominantly dependent on soil properties, but vegetation density played a significant role in farmlands and alpine meadow ecosystems. Root sprouting and rhizome buds significantly correlated with total C in the top 0 - 10 cm layer of farmland and alpine meadow ecosystems, respectively, and depended mainly on soil properties. Discussion: Our results demonstrate that land use change alters bud bank size and composition; however, such responses differed among bud types in wetland, farmland, and alpine meadow ecosystems.