Graph-based mitochondrial genomes of three foundation species in the Saccharum genus.

KEY MESSAGE: We reported the graph-based mitochondrial genomes of three foundation species (Saccharum spontaneum, S. robustum and S. officinarum) for the first time. The results revealed pan-structural variation and evolutionary processes in the mitochondrial genomes within Saccharum. Saccharum belongs to the Andropogoneae, and cultivars species in Saccharum contribute nearly 80% of sugar production in the world. To explore the genomic studies in Saccharum, we assembled 15 complete mitochondrial genomes (mitogenome) of three foundation species (Saccharum spontaneum, S. robustum and S. officinarum) using Illumina and Oxford Nanopore Technologies sequencing data. The mitogenomes of the three species were divided into a total of eight types based on contig numbers and linkages. All mitogenomes in the three species encoded 51 unique genes, including 32 protein-coding, 3 ribosomal RNA (rRNA) and 16 transfer RNA (tRNA) genes. The existence of long and short-repeat-mediated recombinations in the mitogenome of S. officinarum and S. robustum was revealed and confirmed through PCR validation. Furthermore, employing comparative genomics and phylogenetic analyses of the organelle genomes, we unveiled the evolutionary relationships and history of the major interspecific lineages in Saccharum genus. Phylogenetic analyses of homologous fragments between S. officinarum and S. robustum showed that S. officinarum and S. robustum are phylogenetically distinct and that they were likely parallel rather than domesticated. The variations between ancient (S. sinense and S. barberi) and modern cultivated species (S. hybrid) possibly resulted from hybridization involving different S. officinarum accessions. Lastly, this project reported the first graph-based mitogenomes of three Saccharum species, and a systematic comparison of the structural organization, evolutionary processes, and pan-structural variation of the Saccharum mitogenomes revealed the differential features of the Saccharum mitogenomes.

Quantitative Trait Loci Sequencing and Genetic Mapping Reveal Two Main Regulatory Genes for Stem Color in Wax Gourds.

Stem color is an important agronomic trait of wax gourds. However, its regulatory genes have not been identified. In this study, 105 inbred lines constructed from two parents (GX-71 and MY-1) were sequenced and quantitative trait loci sequencing was used to mine the genes that regulate stem color in wax gourds. The results identified two quantitative trait loci related to stem color, qSC5 and qSC12, located on Chr05 (11,134,567-16,459,268) and Chr12 (74,618,168-75,712,335), respectively. The qSC5 had a phenotypic variation rate of 36.9% and a maximum limit of detection of 16.9. And the qSC12 had a phenotypic variation rate of 20.9%, and a maximum limit of detection of 11.2. Bch05G003950 (named BchAPRR2) and Bch12G020400 were identified as candidate genes involved in stem color regulation in wax gourds. The chlorophyll content and expression of BchAPRR2 and Bch12G020400 were significantly higher in green-stemmed wax gourds than in white-stemmed ones. Therefore, BchAPRR2 and Bch12G020400 were considered the main and secondary regulatory genes for wax gourd stem color, respectively. Finally, InDel markers closely linked to BchAPRR2 were developed to validate the prediction of wax gourd stem color traits in 55 germplasm lines, with an accuracy of 81.8%. These findings lay the foundation for exploring the genetic regulation of wax gourd stem color and future research on wax gourd breeding.

Potential adverse events associated with sphingosine-1-phosphate (S1P) receptor modulators in patients with multiple sclerosis: an analysis of the FDA adverse event reporting system (FAERS) database.

Objective: Sphingosine-1-phosphate receptor (S1PR) modulators have recently attracted increasing attention for the treatment of multiple sclerosis (MS). Despite their preference in the clinic, multiple adverse events (AEs) continue to be reported every year. This study aimed to investigate the potential AEs as well as related important medical events (IMEs) signal associated with S1PR modulators, including fingolimod, siponimod and ozanimod in a real-world study using the FDA Adverse Event Reporting System (FAERS) database. Methods: All data were collected from the FAERS database, spanning from the fourth quarter of 2010(2010Q4) to the second quarter of 2023 (2023Q2). Potential AE and IME signals of S1PR modulators were identified based on a disproportionality analysis using the reporting odds ratio (ROR), proportional reporting ratio (PRR), and the bayesian confidence propagation neural network of information components (IC). Results: Overall, 276,436 reports of fingolimod, 20,972 reports of siponimod and 10,742 reports of ozanimod were analyzed from the FAERS database. Among reports, females were more prone to develop AEs (73.71% for females vs. 23.21% for males), and more than 50% of patients suffered from AEs were between 18 and 64 years. Subsequently, we investigated the top 20 AEs associated with the signal strength of S1PR modulators at the preferred term (PT) level, and identified 31 (8 vs. 11 vs. 12, respectively) unlabeled risk signals such as thrombosis, uterine disorder and reproductive system and breast disorders. Furthermore, we discovered that the S1PR modulator reported variations in the possible IMEs, and that the IMEs associated with ocular events were reported frequently. It's interesting to note that infection and malignancy are prominent signals with both fingolimod and siponimod in the top 20 PTs related to mortality reports. Conclusion: The present investigation highlights the possible safety risks associated with S1PR modulators. The majority of AEs are generally consistent with previous studies and are mentioned in the prescribing instructions, however, several unexpected AE signals have also been observed. Ozanimod showed the lowest signal intensity and a better safety profile than the other S1PR modulators. Due to the short marketing time of drugs and the limitations of spontaneous reporting database, further research is required to identify potential AEs related to S1PR modulators.

High-Entropy Metal Oxide-Coated Carbon Cloth as Catalysts for Long-Life Li-S Batteries.

Lithium-sulfur (Li-S) batteries with high specific energy density, low cost, and environmental friendliness of sulfur have been regarded as a competitive alternative to replace lithium-ion batteries. However, the shuttle effect and the sluggish conversion rate of lithium polysulfides (LiPSs) have seriously limited the practical application of Li-S batteries. Herein, high-entropy oxides grown on the carbon cloth (CC/HEO) are synthesized by a simple and ultrafast solution combustion method for the sulfur cathode. The as-prepared composites possess abundant HEO active sites for strong interaction with LiPSs, which can significantly promote redox kinetics. Besides, the carbon fiber substrate not only ensures high electrical conductivity but also accommodates large volume change, leading to a stable sulfur electrochemistry. Benefiting from the rational design, the Li-S batteries with CC/HEO as cathode skeleton exhibits good cyclability with a capacity decay rate of 0.057% per cycle after 1000 cycles at 2 C. More importantly, the Li-S batteries with 4.3 mg cm-2 high sulfur loading can still retain a high capacity retention of 78.2% after 100 cycles.

Construction of high-quality genomes and gene catalogue for culturable microbes of sugarcane (Saccharum spp.).

Microbes living inside or around sugarcane (Saccharum spp.) are crucial for their resistance to abiotic and biotic stress, growth, and development. Sequences of microbial genomes and genes are helpful to understand the function of these microbes. However, there is currently a lack of such knowledge in sugarcane. Here, we combined Nanopore and Illumina sequencing technologies to successfully construct the first high-quality metagenome-assembled genomes (MAGs) and gene catalogues of sugarcane culturable microbes (GCSCMs), which contained 175 species-level genome bins (SGBs), and 7,771,501 non-redundant genes. The SGBs included 79 novel culturable bacteria genomes, and 3 bacterial genomes with nitrogen-fixing gene clusters. Four single scaffold near-complete circular MAGs (cMAGs) with 0% contamination were obtained from Nanopore sequencing data. In conclusion, we have filled a research gap in the genomes and gene catalogues of culturable microbes of sugarcane, providing a vital data resource for further understanding the genetic basis and functions of these microbes. In addition, our methodology and results can provide guidance and reference for other plant microbial genome and gene catalogue studies.

Effect of nano-graphene lubricating oil on particulate matter of a diesel engine.

Nano-graphene lubricating oil with appropriate concentration shows excellent performance in reducing friction and wear under different working conditions of diesel engines, and has been widely concerned. Lubricating oil has a significant impact on particulate matter (PM) emissions. At present, there are few studies on the impact of nano-graphene lubricating oil on the physicochemical properties of PM. In order to comprehensively evaluate the impact of nano-graphene lubricating oil on diesel engines, this paper mainly focused on the effects of lubricating oil nano-graphene additives on the particle size distribution and physicochemical properties of PM. The results show that, compared with pure lubricating oil, the total number of nuclear PM and accumulated PM of nano-graphene lubricating oil is significantly increased. The fractal dimension of PM of nano-graphene lubricating oil increases and its structure becomes more compact. The average fringe separation distance of basic carbon particles decreases, the average fringe length increases. The degree of ordering and graphitization of basic carbon particles are higher. The fringe tortuosity of basic carbon particles decreases, and the fluctuation of carbon layer structure of basic carbon particles decreases. Aliphatic substances in PM are basically unchanged, aromatic components and oxygen functional groups increase. The initial PM oxidation temperature and burnout temperature increase, the maximum oxidation rate temperature and combustion characteristic index decrease, and the activation energy increases, making it more difficult to oxidize. This was mainly caused by the higher graphitization degree of PM of nano-graphene lubricating oil and the increased content of aromatic substances.

Population genomics highlights structural variations in local adaptation to saline coastal environments in woolly grape.

Structural variations (SVs) are a feature of plant genomes that has been largely unexplored despite their significant impact on plant phenotypic traits and local adaptation to abiotic and biotic stress. In this study, we employed woolly grape (Vitis retordii), a species native to the tropical and subtropical regions of East Asia with both coastal and inland habitats, as a valuable model for examining the impact of SVs on local adaptation. We assembled a haplotype-resolved chromosomal reference genome for woolly grape, and conducted population genetic analyses based on whole-genome sequencing (WGS) data from coastal and inland populations. The demographic analyses revealed recent bottlenecks in all populations and asymmetric gene flow from the inland to the coastal population. In total, 1,035 genes associated with plant adaptive regulation for salt stress, radiation, and environmental adaptation were detected underlying local selection by SVs and SNPs in the coastal population, of which 37.29% and 65.26% were detected by SVs and SNPs, respectively. Candidate genes such as FSD2, RGA1, and AAP8 associated with salt tolerance were found to be highly differentiated and selected during the process of local adaptation to coastal habitats in SV regions. Our study highlights the importance of SVs in local adaptation; candidate genes related to salt stress and climatic adaptation to tropical and subtropical environments are important genomic resources for future breeding programs of grapevine and its rootstocks.

Targeting P2Y14R protects against necroptosis of intestinal epithelial cells through PKA/CREB/RIPK1 axis in ulcerative colitis.

Purinergic signaling plays a causal role in the pathogenesis of inflammatory bowel disease. Among purinoceptors, only P2Y14R is positively correlated with inflammatory score in mucosal biopsies of ulcerative colitis patients, nevertheless, the role of P2Y14R in ulcerative colitis remains unclear. Here, based on the over-expressions of P2Y14R in the intestinal epithelium of mice with experimental colitis, we find that male mice lacking P2Y14R in intestinal epithelial cells exhibit less intestinal injury induced by dextran sulfate sodium. Mechanistically, P2Y14R deletion limits the transcriptional activity of cAMP-response element binding protein through cAMP/PKA axis, which binds to the promoter of Ripk1, inhibiting necroptosis of intestinal epithelial cells. Furthermore, we design a hierarchical strategy combining virtual screening and chemical optimization to develop a P2Y14R antagonist HDL-16, which exhibits remarkable anti-colitis effects. Summarily, our study elucidates a previously unknown mechanism whereby P2Y14R participates in ulcerative colitis, providing a promising therapeutic target for inflammatory bowel disease.

SnS/SnS2 Heterostructures Embedded in Hierarchical Porous Carbon as Polysulfides Immobilizer for High-Performance Lithium-Sulfur Batteries.

Driven by the strong adsorptive and catalytic ability of metal sulfides for soluble polysulfides, it is considered as a potential mediator to resolve the problems of shuttle effect and slow reaction kinetics of polysulfides in lithium-sulfur (Li-S) batteries. However, their further development is limited by poor electrical conductivity and bad long-term durability. Herein, one type of new catalyst composed of SnS/SnS2 heterostructures on hierarchical porous carbon (denoted as SnS/SnS2-HPC) by a simple hydrothermal method is reported and used as an interlayer coating on the conventional separator for blocking polysulfides. The SnS/SnS2-HPC integrates the advantages of a porous conductive network for promoting the transport of electrons and an enhanced electrocatalyst for accelerating polysulfides conversion. As a result, such a cell coupled with a SnS/SnS2-HPC interlayer exhibits a long-term lifespan of 1200 cycles. This work provides a new cell configuration by using heterostructures with a built-in electric field formed from a p-n heterojunction to improve the performance of Li-S batteries.

Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer.

BACKGROUND: Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA. METHODS: We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS). RESULTS: The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups. CONCLUSIONS: In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

Dysbiosis of gut microbiota and metabolites during AIDS: implications for CD4 + T cell reduction and immune activation.

OBJECTIVE: Identifying the gut microbiota associated with host immunity in the AIDS stage. DESIGN: We performed a cross-sectional study. METHODS: We recruited people with HIV (PWH) in the AIDS or non-AIDS stage and evaluated their gut microbiota and metabolites by using 16S ribosomal RNA (rRNA) sequencing and liquid chromatography-mass spectrometry (LC-MS). Machine learning models were used to analyze the correlations between key bacteria and CD4 + T cell count, CD4 + T cell activation, bacterial translocation, gut metabolites, and KEGG functional pathways. RESULTS: We recruited 114 PWH in the AIDS stage and 203 PWH in the non-AIDS stage. The α-diversity of gut microbiota was downregulated in the AIDS stage ( P  < 0.05). Several machine learning models could be used to identify key gut microbiota associated with AIDS, including the logistic regression model with area under the curve (AUC), sensitivity, specificity, and Brier scores of 0.854, 0.813, 0.813, and 0.160, respectively. The decreased key bacteria ASV1 ( Bacteroides sp.), ASV8 ( Fusobacterium sp.), ASV30 ( Roseburia sp.), ASV37 ( Bacteroides sp.), and ASV41 ( Lactobacillus sp.) in the AIDS stage were positively correlated with the CD4 + T cell count, the EndoCAb IgM level, 4-hydroxyphenylpyruvic acid abundance, and the predicted cell growth pathway, and negatively correlated with the CD3 + CD4 + CD38 + HLA-DR + T cell count and the sCD14 level. CONCLUSION: Machine learning has the potential to recognize key gut microbiota related to AIDS. The key five bacteria in the AIDS stage and their metabolites might be related to CD4 + T cell reduction and immune activation.

Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis.

BACKGROUND: The studies on SARS-CoV-2 and human microbiota have yielded inconsistent results regarding microbiota α-diversity and key microbiota. To address these issues and explore the predictive ability of human microbiota for the prognosis of SARS-CoV-2 infection, we conducted a reanalysis of existing studies. METHODS: We reviewed the existing studies on SARS-CoV-2 and human microbiota in the Pubmed and Bioproject databases (from inception through October 29, 2021) and extracted the available raw 16S rRNA sequencing data of human microbiota. Firstly, we used meta-analysis and bioinformatics methods to reanalyze the raw data and evaluate the impact of SARS-CoV-2 on human microbial α-diversity. Secondly, machine learning (ML) was employed to assess the ability of microbiota to predict the prognosis of SARS-CoV-2 infection. Finally, we aimed to identify the key microbiota associated with SARS-CoV-2 infection. RESULTS: A total of 20 studies related to SARS-CoV-2 and human microbiota were included, involving gut (n = 9), respiratory (n = 11), oral (n = 3), and skin (n = 1) microbiota. Meta-analysis showed that in gut studies, when limiting factors were studies ruled out the effect of antibiotics, cross-sectional and case-control studies, Chinese studies, American studies, and Illumina MiSeq sequencing studies, SARS-CoV-2 infection was associated with down-regulation of microbiota α-diversity (P < 0.05). In respiratory studies, SARS-CoV-2 infection was associated with down-regulation of α-diversity when the limiting factor was V4 sequencing region (P < 0.05). Additionally, the α-diversity of skin microbiota was down-regulated at multiple time points following SARS-CoV-2 infection (P < 0.05). However, no significant difference in oral microbiota α-diversity was observed after SARS-CoV-2 infection. ML models based on baseline respiratory (oropharynx) microbiota profiles exhibited the ability to predict outcomes (survival and death, Random Forest, AUC = 0.847, Sensitivity = 0.833, Specificity = 0.750) after SARS-CoV-2 infection. The shared differential Prevotella and Streptococcus in the gut, respiratory tract, and oral cavity was associated with the severity and recovery of SARS-CoV-2 infection. CONCLUSIONS: SARS-CoV-2 infection was related to the down-regulation of α-diversity in the human gut and respiratory microbiota. The respiratory microbiota had the potential to predict the prognosis of individuals infected with SARS-CoV-2. Prevotella and Streptococcus might be key microbiota in SARS-CoV-2 infection.

Genome-Wide Association Study Unravels Quantitative Trait Loci and Genes Associated with Yield-Related Traits in Sugarcane.

Sugarcane, a major sugar and energy crop worldwide faces an increasing demand for higher yields. Identifying yield-related markers and candidate genes is valuable for breeding high-yield varieties using molecular techniques. In this work, seven yield-related traits were evaluated in a diversity panel of 159 genotypes, derived from Tripidium arundinaceum, Saccharum spontaneum, and modern sugarcane genotypes. All traits exhibited significant genetic variance with high heritability and high correlations. Genetic diversity analysis reveals a genomic decay of 23 kb and an average single nucleotide polymorphism (SNP) number of 25,429 per genotype. These 159 genotypes were divided into 4 subgroups. Genome-wide association analysis identified 47 SNPs associated with brix, spanning 36 quantitative trait loci (QTLs), and 138 SNPs for other traits across 104 QTLs, covering all 32 chromosomes. Interestingly, 12 stable QTLs associated with yield-related traits were identified, which contained 35 candidate genes. This work provides markers and candidate genes for marker-assisted breeding to improve sugarcane yields.

Alterations of the gut microbiota associated with the occurrence and progression of viral hepatitis.

Background: Gut microbiota is the largest population of microorganisms and is closely related to health. Many studies have explored changes in gut microbiota in viral hepatitis. However, the correlation between gut microbiota and the occurrence and progression of viral hepatitis has not been fully clarified. Methods: PubMed and BioProject databases were searched for studies about viral hepatitis disease and 16S rRNA gene sequencing of gut microbiota up to January 2023. With bioinformatics analyses, we explored changes in microbial diversity of viral hepatitis, screened out crucial bacteria and microbial functions related to viral hepatitis, and identified the potential microbial markers for predicting risks for the occurrence and progression of viral hepatitis based on ROC analysis. Results: Of the 1389 records identified, 13 studies met the inclusion criteria, with 950 individuals including 656 patient samples (HBV, n = 546; HCV, n = 86; HEV, n = 24) and 294 healthy controls. Gut microbial diversity is significantly decreased as the infection and progression of viral hepatitis. Alpha diversity and microbiota including Butyricimonas, Escherichia-Shigella, Lactobacillus, and Veillonella were identified as the potential microbial markers for predicting the risk of development of viral hepatitis (AUC>0.7). Microbial functions including tryptophan metabolism, fatty acid biosynthesis, lipopolysaccharide biosynthesis, and lipid metabolism related to the microbial community increased significantly as the development of viral hepatitis. Conclusions: This study demonstrated comprehensively the gut microbiota characteristics in viral hepatitis, screened out crucial microbial functions related to viral hepatitis, and identified the potential microbial markers for predicting the risk of viral hepatitis.

Genome-wide analysis of NBS-LRR genes revealed contribution of disease resistance from Saccharum spontaneum to modern sugarcane cultivar.

Introduction: During plant evolution, nucleotide-binding sites (NBS) and leucine-rich repeat (LRR) genes have made significant contributions to plant disease resistance. With many high-quality plant genomes sequenced, identification and comprehensive analyses of NBS-LRR genes at whole genome level are of great importance to understand and utilize them. Methods: In this study, we identified the NBS-LRR genes of 23 representative species at whole genome level, and researches on NBS-LRR genes of four monocotyledonous grass species, Saccharum spontaneum, Saccharum officinarum, Sorghum bicolor and Miscanthus sinensis, were focused. Results and discussion: We found that whole genome duplication, gene expansion, and allele loss could be factors affecting the number of NBS-LRR genes in the species, and whole genome duplication is likely to be the main cause of the number of NBS-LRR genes in sugarcane. Meanwhile, we also found a progressive trend of positive selection on NBS-LRR genes. These studies further elucidated the evolutionary pattern of NBS-LRR genes in plants. Transcriptome data from multiple sugarcane diseases revealed that more differentially expressed NBS-LRR genes were derived from S. spontaneum than from S. officinarum in modern sugarcane cultivars, and the proportion was significantly higher than the expected. This finding reveals that S. spontaneum has a greater contribution to disease resistance for modern sugarcane cultivars. In addition, we observed allelespecific expression of seven NBS-LRR genes under leaf scald, and 125 NBS-LRR genes responding to multiple diseases were identified. Finally, we built a plant NBS-LRR gene database to facilitate subsequent analysis and use of NBSLRR genes obtained here. In conclusion, this study complemented and completed the research of plant NBS-LRR genes, and discussed how NBS-LRR genes responding to sugarcane diseases, which provided a guide and genetic resources for further research and utilization of NBS-LRR genes.

Insights on Phytohormonal Crosstalk in Plant Response to Nitrogen Stress: A Focus on Plant Root Growth and Development.

Nitrogen (N) is a vital mineral component that can restrict the growth and development of plants if supplied inappropriately. In order to benefit their growth and development, plants have complex physiological and structural responses to changes in their nitrogen supply. As higher plants have multiple organs with varying functions and nutritional requirements, they coordinate their responses at the whole-plant level based on local and long-distance signaling pathways. It has been suggested that phytohormones are signaling substances in such pathways. The nitrogen signaling pathway is closely associated with phytohormones such as auxin (AUX), abscisic acid (ABA), cytokinins (CKs), ethylene (ETH), brassinosteroid (BR), strigolactones (SLs), jasmonic acid (JA), and salicylic acid (SA). Recent research has shed light on how nitrogen and phytohormones interact to modulate physiology and morphology. This review provides a summary of the research on how phytohormone signaling affects root system architecture (RSA) in response to nitrogen availability. Overall, this review contributes to identifying recent developments in the interaction between phytohormones and N, as well as serving as a foundation for further study.

Correlation of microbiomes in "plant-insect-soil" ecosystem.

Introduction: Traditional chemical control methods pose a damaging effect on farmland ecology, and their long-term use has led to the development of pest resistance. Methods: Here, we analyzed the correlations and differences in the microbiome present in the plant and soil of sugarcane cultivars exhibiting different insect resistance to investigate the role played by microbiome in crop insect resistance. We evaluated the microbiome of stems, topsoil, rhizosphere soil, and striped borers obtained from infested stems, as well as soil chemical parameters. Results and Discussion: Results showed that microbiome diversity was higher in stems of insect-resistant plants, and contrast, lower in the soil of resistant plants, with fungi being more pronounced than bacteria. The microbiome in plant stems was almost entirely derived from the soil. The microbiome of insect-susceptible plants and surrounding soil tended to change towards that of insect-resistant plants after insect damage. Insects' microbiome was mainly derived from plant stems and partly from the soil. Available potassium showed an extremely significant correlation with soil microbiome. This study validated the role played by the microbiome ecology of plant-soil-insect system in insect resistance and provided a pre-theoretical basis for crop resistance control.

The subgenome Saccharum spontaneum contributes to sugar accumulation in sugarcane as revealed by full-length transcriptomic analysis.

INTRODUCTION: Modern sugarcane cultivars (Saccharum spp. hybrids) derived from crosses between S. officinarum and S. spontaneum, with high-sugar traits and excellent stress tolerance inherited respectively. However, the contribution of the S. spontaneum subgenome to sucrose accumulation is still unclear. OBJECTIVE: To compensate for the absence of a high-quality reference genome, a transcriptome analysis method is needed to analyze the molecular basis of differential sucrose accumulation in sugarcane hybrids and to find clues to the contribution of the S. spontaneum subgenome to sucrose accumulation. METHODS: PacBio full-length sequencing was used to complement genome annotation, followed by the identification of differential genes between the high and low sugar groups using differential alternative splicing analysis and differential expression analysis. At the subgenomic level, the factors responsible for differential sucrose accumulation were investigated from the perspective of transcriptional and post-transcriptional regulation. RESULTS: A full-length transcriptome annotated at the subgenomic level was provided, complemented by 263,378 allele-defined transcript isoforms and 139,405 alternative splicing (AS) events. Differential alternative splicing (DA) analysis and differential expression (DE) analysis identified differential genes between high and low sugar groups and explained differential sucrose accumulation factors by the KEGG pathways. In some gene models, different or even opposite expression patterns of alleles from the same gene were observed, reflecting the potential evolution of these alleles toward novel functions in polyploid sugarcane. Among DA and DE genes in the sucrose source-sink complex pathway, we found some alleles encoding sucrose accumulation-related enzymes derived from the S. spontaneum subgenome were differentially expressed or had DA events between the two contrasting sugarcane hybrids. CONCLUSION: Full-length transcriptomes annotated at the subgenomic level could better characterize sugarcane hybrids, and the S. spontaneum subgenome was found to contribute to sucrose accumulation.

Haplotype variations of sucrose phosphate synthase B gene among sugarcane accessions with different sucrose content.

BACKGROUND: Sucrose phosphate synthase B (SPSB) gene encoding an important rate-limiting enzyme for sucrose synthesis in sugarcane is mainly expressed on leaves, where its alleles control sucrose synthesis. In this study, genetic variation of SPSB gene represented by different haplotypes in sugarcane was investigated in hybrid clones with high and low sugar content and various accessory species. RESULTS: A total of 39 haplotypes of SPSB gene with 2, 824 bp in size were identified from 18 sugarcane accessions. These haplotypes mainly distributed on Chr3B, Chr3C, and Chr3D according to the AP85-441 reference genome. Single nucleotide polymorphisms (SNPs) and insertion/deletion (InDels) were very dense (42 bp/sequence variation) including 44 transitional and 23 transversional SNPs among the 39 haplotypes. The sequence diversity related Hd, Eta, and Pi values were all lower in clones of high sucrose content (HS) than those in clones of low sucrose content (LS). The evolutionary network analysis showed that about half SPSB haplotypes (19 out of 39) were clustered into one group, including 6 (HAP4, HAP6, HAP7, HAP9, HAP17 and HAP20) haplotypes under positive selection in comparison to HAP26 identified in Badila (S. officinarum), an ancestry noble cane species and under purification selection (except HAP19 under neutral selection) in comparison to HAP18 identified from India1 (S. spontaneum), an ancestry species with low sugar content but high stress tolerance. The average number of haplotypes under positive selection in HS clones was twice as that in LS. Most of the SNPs and InDels sequence variation sites were positively correlated with sucrose and fiber content and negatively correlated with reducing sugar. CONCLUSIONS: A total of 39 haplotypes of SPSB gene were identified in this study. Haplotypes potentially associated with high sucrose synthesis efficiency were identified. The mutations of SPSB haplotypes in HS were favorable and tended to be selected and fixed. The results of this study are informative and beneficial to the molecular assisted breeding of sucrose synthesis in sugarcane in the future.

An urban crowd flow model integrating geographic characteristics.

Predicting urban crowd flow spatial distributions plays a critical role in optimizing urban public safety and traffic congestion management. The spatial dependency between regions and the temporal dynamics of the local crowd flow are two important features in urban crowd flow prediction. However, few studies considered geographic characteristic in terms of spatial features. To fill this gap, we propose an urban crowd flow prediction model integrating geographic characteristics (FPM-geo). First, three geographic characteristics, proximity, functional similarity, and road network connectivity, are fused by a residual multigraph convolution network to model the spatial dependency relationship. Then, a long short-term memory network is applied as a framework to integrate both the temporal dynamic patterns of local crowd flow and the spatial dependency between regions. A 4-day mobile phone dataset validates the effectiveness of the proposed method by comparing it with several widely used approaches. The result shows that the root mean square error decreases by 15.37% compared with those of the typical models with the prediction interval at the 15-min level. The prediction error increases with the crowd flow size in a local area. Moreover, the error reaches the top of the morning peak and the evening peak and slopes down to the bottom at night.