High-Quality Genome Assembly and Genome-Wide Association Study of Male Sterility Provide Resources for Flax Improvement.

Flax is an economic crop with a long history. It is grown worldwide and is mainly used for edible oil, industry, and textiles. Here, we reported a high-quality genome assembly for "Neiya No. 9", a popular variety widely grown in China. Combining PacBio long reads, Hi-C sequencing, and a genetic map reported previously, a genome assembly of 473.55 Mb was constructed, which covers ~94.7% of the flax genome. These sequences were anchored onto 15 chromosomes. The N50 lengths of the contig and scaffold were 0.91 Mb and 31.72 Mb, respectively. A total of 32,786 protein-coding genes were annotated, and 95.9% of complete BUSCOs were found. Through morphological and cytological observation, the male sterility of flax was considered dominant nuclear sterility. Through GWAS analysis, the gene LUSG00017705 (cysteine synthase gene) was found to be closest to the most significant SNP, and the expression level of this gene was significantly lower in male sterile plants than in fertile plants. Among the significant SNPs identified in the GWAS analysis, only two were located in the coding region, and these two SNPs caused changes in the protein encoded by LUSG00017565 (cysteine protease gene). It was speculated that these two genes may be related to male sterility in flax. This is the first time the molecular mechanism of male sterility in flax has been reported. The high-quality genome assembly and the male sterility genes revealed, provided a solid foundation for flax breeding.

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery.

BACKGROUND: The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate. RESULTS: Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28-32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server ( http://bioinfor.imu.edu.cn/placenta ). CONCLUSION: Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.

A computational framework of routine test data for the cost-effective chronic disease prediction.

Chronic diseases, because of insidious onset and long latent period, have become the major global disease burden. However, the current chronic disease diagnosis methods based on genetic markers or imaging analysis are challenging to promote completely due to high costs and cannot reach universality and popularization. This study analyzed massive data from routine blood and biochemical test of 32 448 patients and developed a novel framework for cost-effective chronic disease prediction with high accuracy (AUC 87.32%). Based on the best-performing XGBoost algorithm, 20 classification models were further constructed for 17 types of chronic diseases, including 9 types of cancers, 5 types of cardiovascular diseases and 3 types of mental illness. The highest accuracy of the model was 90.13% for cardia cancer, and the lowest was 76.38% for rectal cancer. The model interpretation with the SHAP algorithm showed that CREA, R-CV, GLU and NEUT% might be important indices to identify the most chronic diseases. PDW and R-CV are also discovered to be crucial indices in classifying the three types of chronic diseases (cardiovascular disease, cancer and mental illness). In addition, R-CV has a higher specificity for cancer, ALP for cardiovascular disease and GLU for mental illness. The association between chronic diseases was further revealed. At last, we build a user-friendly explainable machine-learning-based clinical decision support system (DisPioneer: http://bioinfor.imu.edu.cn/dispioneer) to assist in predicting, classifying and treating chronic diseases. This cost-effective work with simple blood tests will benefit more people and motivate clinical implementation and further investigation of chronic diseases prevention and surveillance program.

Integrating transcriptomics and metabolomics to characterise the response of Astragalus membranaceus Bge. var. mongolicus (Bge.) to progressive drought stress.

BACKGROUND: Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao (A. mongolicus) is an important traditional Chinese herb that is cultivated on a large scale in northwestern China. Understanding plant responses to drought has important effects on ecological environment recovery and local economic development. Here, we combined transcriptomics (Illumina Hiseq 2000) and metabolomics ((1)H-NMR) to investigate how the roots of two-year-old A. mongolicus responded to 14 days of progressive drought stress. RESULTS: The dried soil reduced the relative water content (RWC) of the leaves and biomass, induced the differential expression of a large fraction of the transcriptome and significantly altered the metabolic processes. PCA analysis demonstrated that the sucrose, proline, and malate metabolites contributed greatly to the separation. Strikingly, proline was increased by almost 60-fold under severe stress compared to the control. Some backbone pathways, including glycolysis, tricarboxylic acid (TCA) cycle, glutamate-mediated proline biosynthesis, aspartate family metabolism and starch and sucrose metabolism, were significantly affected by drought. An integrated analysis of the interaction between key genes and the altered metabolites involved in these pathways was performed. CONCLUSIONS: Our findings demonstrated that the expression of drought-responsive genes showed a strong stress-dose dependency. Analysis of backbone pathways of the transcriptome and metabolome revealed specific genotypic responses to different levels of drought. The variation in molecular strategies to the drought may play an important role in how A. mongolicus and other legume crops adapt to drought stress.