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Peas (Pisum sativum L.) serve as a vital model for plant development and stress research. The kinesin (KIN) gene family, encoding essential motor proteins, remains understudied in peas. Our research conducted a comprehensive genomic analysis of the KIN genes in peas, identifying 105 genes categorized into seven subfamilies based on evolutionary relationships, gene structures, conserved motifs, and interaction networks. A comparative analysis with Arabidopsis and soybean KIN gene families showed a non-uniform distribution of PsKIN genes across subfamilies. Homology analysis revealed that the PsKIN family has undergone segmental duplication and is under negative selection pressures, with conserved genes on chromosomes Ps5, Ps6, and Ps7 playing a significant role in pea evolution. Transcriptomics revealed 38 PsKIN genes with distinct tissue-specific expression, with PsKIN76, PsKIN96, PsKIN82, and PsKIN103 showing significant levels in roots, lateral roots, stems, petals, and seeds, respectively. Differential expression under drought and saline stress was observed, with PsKIN8, PsKIN11, PsKIN54 upregulated under drought, and PsKIN47 and PsKIN51 under saline stress. These genes are potential candidates for improving plant stress tolerance. This study offers insights into the pea KIN gene family, highlighting their potential in enhancing plant stress tolerance and setting a stage for future research.

Abstract Background Vegetable soybean is an important vegetable crop in world. Seed size and soluble sugar content are considered crucial indicators of quality in vegetable soybean, and there is a lack of clarity on the molecular basis of grain quality in vegetable soybean. Results In this context, we performed a comprehensive comparative transcriptome analysis of seeds between a high-sucrose content and large-grain variety (Zhenong 6, ZN6) and a low-sucrose content and small-grain variety (Williams 82, W82) at three developmental stages, i.e. stage R5 (Beginning Seed), stage R6 (Full Seed), and stage R7 (Beginning Maturity). The transcriptome analysis showed that 17,107 and 13,571 differentially expressed genes (DEGs) were identified in ZN6 at R6 (vs. R5) and R7 (vs. R6), respectively, whereas 16,203 and 16,032 were detected in W82. Gene expression pattern and DEGs functional enrichment proposed genotype-specific biological processes during seed development. The genes participating in soluble sugar biosynthesis such as FKGP were overexpressed in ZN6, whereas those responsible for lipid and protein metabolism such as ALDH3 were more enhanced in W82, exhibiting different dry material accumulation between two genotypes. Furthermore, hormone-associated transcriptional factors involved in seed size regulation such as BEH4 were overrepresented in ZN6, exhibiting different seed size regulation processes between two genotypes. Conclusions Herein, we not only discovered the differential expression of genes encoding metabolic enzymes involved in seed composition, but also identified a type of hormone-associated transcriptional factors overexpressed in ZN6, which may regulate seed size and soluble content. This study provides new insights into the underlying causes of differences in the soybean metabolites and appearance, and suggests that genetic data can be used to improve its appearance and textural quality.

This study aimed to explore the role of the trehalose-6-phosphate synthase (TPS) gene family in the adaptation of peas to environmental stress. A comprehensive analysis of the PsTPS gene family identified 20 genes with conserved domains and specific chromosomal locations. Phylogenetic analysis delineated evolutionary relationships, while gene structure analysis revealed compositional insights, and motif analysis provided functional insights. Cis-regulatory element identification predicted gene regulation patterns. Tissue-specific and stress-induced expression profiling highlighted eight genes with ubiquitous expression, with PsTPS15 and PsTPS18 displaying elevated expression levels in roots, nodules, and young stems, and PsTPS13 and PsTPS19 expression downregulated in seeds. Transcriptome analysis identified a differential expression of 20 PsTPS genes, highlighting the significance of 14 genes in response to drought and salinity stress. Notably, under drought conditions, the expression of PsTPS4 and PsTPS6 was initially upregulated and then downregulated, whereas that of PsTPS15 and PsTPS19 was downregulated. Salinity stress notably altered the expression of PsTPS4, PsTPS6, and PsTPS19. Taken together, these findings elucidate the regulatory mechanisms of the PsTPS gene family and their potential as genetic targets for enhancing crop stress tolerance.

ABSTRACT Ascochyta blight caused by Ascochyta pisi is a major constraint to pea (Pisum sativum L.) production worldwide. Deciphering the pathogenic mechanism of A. pisi on peas will help in breeding resistant pea varieties and developing effective approaches for disease management. However, little is known about the genomic features and pathogenic factors of A. pisi. In this study, we first report that A. pisi is one of the causal agents of ascochyta blight disease of pea in China. The genome of the representative isolate A. pisi HNA23 was sequenced using PacBio and Illumina sequencing technologies. The HNA23 genome assembly is almost 41.5 Mb in size and harbors 10,796 putative protein-encoding genes. We predicted 555 carbohydrate-active enzymes (CAZymes), 1,008 secreted proteins, 74 small secreted cysteine-rich proteins (SSCPs), and 26 secondary metabolite biosynthetic gene clusters (SMGCs). A comparison of A. pisi genome features with the features of 6 other available genomes of Ascochyta species showed that CAZymes, the secretome, and SMGCs of this genus are considerably conserved. Importantly, the transcriptomes of HNA23 during infection of peas at three stages were further analyzed. We found that 245 CAZymes and 29 SSCPs were upregulated at all three tested infection stages. SMGCs were also trigged, but most of them were induced at only one stage of infection. Together, our results provide important genomic information on Ascochyta spp. and offer insights into the pathogenesis of A. pisi. IMPORTANCE Ascochyta blight is a major disease of legumes worldwide. Ascochyta pisi and other Ascochyta species have been identified as pathogens of ascochyta blight. Here, we first report that A. pisi causes ascochyta blight of pea in China, and we report the high-quality, fully annotated genome of A. pisi. Comparative genome analysis was performed to elucidate the differences and similarities among 7 Ascochyta species. We predict abundant CAZymes (569 per species), secreted proteins (851 per species), and prolific secondary metabolite gene clusters (29 per species) in these species. We identified a set of genes that may be responsible for fungal virulence based on transcriptomes in planta, including CAZymes, SSCPs, and secondary metabolites. The findings from the comparative genome analysis highlight the genetic diversity and help in understanding the evolutionary relationship of Ascochyta species. In planta transcriptome analysis provides reliable information for further investigation of the mechanism of the interaction between Ascochyta spp. and legumes.

Background: Craving is the predictor of relapse, and insula cortex (IC) is a critical neural substrate for craving and drug seeking. This study investigated whether IC abnormalities among MA users can detect craving state and predict relapse susceptibility.Methods: A total of 142 subjects with a history of MA dependence completed structural MRI (sMRI) scans, and 30 subjects (10 subjects relapsed) completed 4-month follow-up scans. MA craving was measured by the Visual Analog Scale for Craving. Abnormalities of IC gray matter volume (GMV) between the subjects with and without craving were investigated by voxel-based morphometry (VBM). The receiver operating characteristic (ROC) analysis was performed for the region-of-interest (ROI) of IC GMV to assess the diagnostic accuracy.Results: By comparing whole-brain volume maps, this study found that subjects without craving (n = 64) had a significantly extensive decrease in IC GMV (family-wise error correction, p < 0.05) than subjects with craving group (n = 78). The ROI of IC GMV had a significantly positive correlation with the craving scores reported by MA users. The ROC analysis showed a good discrimination (area under curve is 0.82/0.80 left/right) for IC GMV between the subjects with and without craving. By selecting Youden index cut-off point from whole model group, calculated sensitivity/specificity was equal to 78/70% and 70/75% for left and right IC, respectively. By applying the above optimal cut-off values to 30 follow-up subjects as validations, the results showed a similar sensitivity (73–80%) and specificity (73–80%) for detecting craving state as model group. For predicting relapse susceptibility, the sensitivity (50–55%) was low and the specificity (80–90%) was high.Conclusions: Our study provides the first evidence that sMRI may be used to diagnosis the craving state in MA users based on optimal cut-off values, which could be served as MRI bio-markers and an objective measure of craving state.

Wheat end-use quality mainly derives from two interrelated characteristics: the compositions of gluten proteins and grain hardness. The composition of gluten proteins determines dough rheological properties and thus confers the unique viscoelastic property on dough. One group of gluten proteins, high molecular weight glutenin subunits (HMW-GS), plays an important role in dough functional properties. On the other hand, grain hardness, which influences the milling process of flour, is controlled by Puroindoline a (Pina) and Puroindoline b (Pinb) genes. However, little is known about the combined effects of HMW-GS and PINs on dough functional properties. In this study, we crossed a Pina-expressing transgenic line with a 1Ax1-expressing line of durum wheat and screened out lines coexpressing 1Ax1 and Pina or lines expressing either 1Ax1 or Pina. Dough mixing analysis of these lines demonstrated that expression of 1Ax1 improved both dough strength and over-mixing tolerance, while expression of PINA detrimentally affected the dough resistance to extension. In lines coexpressing 1Ax1 and Pina, faster hydration of flour during mixing was observed possibly due to the lower water absorption and damaged starch caused by PINA expression. In addition, expression of 1Ax1 appeared to compensate the detrimental effect of PINA on dough resistance to extension. Consequently, coexpression of 1Ax1 and PINA in durum wheat had combined effects on dough mixing behaviors with a better dough strength and resistance to extension than those from lines expressing either 1Ax1 or Pina. The results in our study suggest that simultaneous modulation of dough strength and grain hardness in durum wheat could significantly improve its breadmaking quality and may not even impair its pastamaking potential. Therefore, coexpression of 1Ax1 and PINA in durum wheat has useful implications for breeding durum wheat with dual functionality (for pasta and bread) and may improve the economic values of durum wheat.

Ascochyta blight, an infection caused by a complex of Ascochyta pinodes, Ascochyta pinodella, Ascochyta pisi, and/or Phoma koolunga, is a destructive disease in many field peas (Pisum sativum L.)-growing regions, and it causes significant losses in grain yield. To understand the composition of fungi associated with this disease in Zhejiang Province, China, a total of 65 single-pycnidiospore fungal isolates were obtained from diseased pea samples collected from 5 locations in this region. These isolates were identified as Ascochyta pinodes by molecular techniques and their morphological and physiological characteristics. The mycelia of ZJ-1 could penetrate pea leaves across the stomas, and formed specific penetration structures and directly pierced leaves. The resistance level of 23 available pea cultivars was tested against their representative isolate A. pinodes ZJ-1 using the excised leaf-assay technique. The ZJ-1 mycelia could penetrate the leaves of all tested cultivars, and they developed typical symptoms, which suggested that all tested cultivars were susceptible to the fungus. Chemical fungicides and biological control agents were screened for management of this disease, and their efficacies were further determined. Most of the tested fungicides (11 out of 14) showed high activity toward ZJ-1 with EC50 <5 µg/mL. Moreover, fungicides, including tebuconazole, boscalid, iprodione, carbendazim, and fludioxonil, displayed more than 80% disease control efficacy under the recorded conditions. Three biocontrol strains of Bacillus sp. and one of Pantoea agglomerans were isolated from pea-related niches and significantly reduced the severity of disease under greenhouse and field conditions. To our knowledge, this is the first study on ascochyta blight in field peas, and results presented here will be useful for controlling the disease in this area. [ABSTRACT FROM AUTHOR]

Background: Illicit drug use/dependence has been recognized as a major problem. Clinical studies demonstrate that poor sleep quality is associated with increased frequency of drug use and relapse. However, few studies have addressed the issue of sleep quality among illicit drug dependent subjects. Methods: This cross-sectional study explored sleep quality in drug dependent subjects in China. We studied 2178 illicit drug dependent subjects from drug rehabilitation centres in Changsha and 2236 non-drug-using subjects, all of whom completed the self-report Pittsburgh Sleep Quality Index (PSQI). Results: We found that the prevalence of sleep disturbance was much higher in drug users (68.5%, PSQI >5; specifically, 80.24% in heroin users, 54.16% in methamphetamine users and 81.98% in ketamine users with PSQI >5) than non-users (26.4%, PSQI >5). Drug users had approximately twice the sleep latency than nondrug users (37.7 minutes V.S 18.4 minutes). Although drug users and non-users reported similar sleep duration (about 7.4 hours), drug users showed poorer subjective sleep quality and habitual sleep efficiency. They reported more sleep disturbance and need for sleep medications, more daytime dysfunction and poorer subjective sleep quality compared with nondrug users. The total PSQI score positively correlated with the duration of drug use (rp = 0.164, p < 0.001). We also found a link between sleep problems and cigarette smoking, alcohol drinking, and duration of drug use. Conclusions: Poor sleep quality is common among illicit drug dependent subjects. Long-term substance users had more sleep problems. Future research aiming at quantifying the benefits of treatment interventions should not neglect the influence of sleep problems. Gaining more insight into the impact of sleep quality on the addiction treatment could also help to target future intervention measures more effectively. [ABSTRACT FROM AUTHOR]

Background and purpose: Approximately 60% of patients with hepatocellular carcinoma (HCC) are diagnosed at an advanced stage, therefore cannot receive timely and effective treatment. At present, the sensitivity of serum biomarkers commonly used in the diagnosis of HCC is low. In this paper, through the differential expression analysis of plasma exosomal proteome, the candidate exosome protein markers which may be used in the diagnosis of HCC were identified. Methods: First, we performed shot-gun proteome mass spectrometry assays in the plasma exosomes from the discovery cohort [including 4 healthy control (HC) group and 4 HCC patients group], and identified differentially expressed proteins by qualitative and quantitative analyses. Then, we carried out enzyme-linked immunosorbent assay (ELISA) in plasma samples of an independent verification cohort [including 56 HCC patients group, 20 liver cirrhosis (LC) patients group and 48 HC group], and evaluated the diagnostic value of the candidate protein in HCC. Results: In the discovery cohort, we identified a total of 1 434 exosomal proteins, of which CD48 exhibited higher levels in HCC than in HC. We further verified in the validation cohort by ELISA that CD48 was markedly higher in HCC compared with LC and HC groups, with the area under receiver operating characteristic (ROC) curve (AUC) of 0.886. When combined with plasma alpha-fetoprotein (AFP), the AUC reached 0.970. Conclusion: The plasma exosomal CD48 protein can be used as a candidate diagnostic biomarker for HCC, and its combination with AFP may further improve the clinical diagnostic ability for HCC.