Characterization of near-field temporal and spatial variations of pesticide residues using honeybee specimens as bio-sensing matrices.

This study was prompted by recent reports of the ubiquity of neonicotinoids (neonics) in environment and the likelihood of exposures and health hazards to non-target organisms. We aimed to quantify neonics levels in time- and location-match pollen and nectar samples foraged by honeybees (Apis mellifera) and characterized the temporal and spatial variations using a relative potency factor method to determine the total neonic levels, expressed as the imidacloprid-adjusted total neonics, IMIRPF (ng/g). Six pairs of pollen and nectar samples, a total of twelve samples, were collected from each of the thirty-two experimental hives during the active foraging months of March, April, and June and analyzed for eight neonics. We found 59% and 64% of pollen and nectar contained at least one neonic, respectively. Among those neonic-detected pollen and nectar samples, 45% and 77% of them contained more than one neonic, respectively. Imidacloprid and acetamiprid in pollen and clothianidin and thiamethoxam in nectar accounted for 60% and 83% detection, respectively. The highest 3-month average of IMIRPF in pollen (6.56 ng/g) and nectar (11.19 ng/g) were detected in a location with the predominant production of citrus fruit. The temporal and spatial variations of IMIRPF levels demonstrated the robustness of using paired pollen and nectar data as the bio-sensing matrices to facilitate the assessment of near-field exposure to total neonics and the delineation of risks.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes.

Rationale: Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. Objectives: Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics. Methods: We defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups. Measurements and Main Results: We examined two high-risk omics-defined groups in non-overlapping test sets (n=1,133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "High activity" (low PRS/high TRS) and "severe risk" (high PRS/high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signaling processes compared to a low-risk (low PRS, low TRS) reference subgroup. "High activity" but not "severe risk" participants had greater prospective FEV 1 decline (COPDGene: -51 mL/year; ECLIPSE: - 40 mL/year) and their proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors. Conclusions: Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Cluster analysis identifies novel real-world lung disease-pulmonary hypertension subphenotypes: implications for treatment response.

Background: Clinical trials repurposing pulmonary arterial hypertension (PAH) therapies to patients with lung disease- or hypoxia-pulmonary hypertension (PH) (classified as World Health Organization Group 3 PH) have failed to show a consistent benefit. However, Group 3 PH clinical heterogeneity suggests robust phenotyping may inform detection of treatment-responsive subgroups. We hypothesised that cluster analysis would identify subphenotypes with differential responses to oral PAH therapy. Methods: Two k-means analyses were performed on a national cohort of US veterans with Group 3 PH; an inclusive model (I) of all treated patients (n=196) and a haemodynamic model (H) limited to patients with right heart catheterisations (n=112). The primary outcome was organ failure or all-cause mortality by cluster. An exploratory analysis evaluated within-cluster treatment effects. Results: Three distinct clusters of Group 3 PH patients were identified. In the inclusive model (C1I n=43, 21.9%; C2I n=102, 52.0%; C3I n=51, 26.0%), lung disease and spirometry drove cluster assignment. By contrast, in the haemodynamic model (C1H n=44, 39.3%; C2H n=43, 38.4%; C3H n=25, 22.3%), right heart catheterisation data surpassed the importance of lung disease and spirometry. In the haemodynamic model, compared to C3H, C1H experienced the greatest hazard for respiratory failure or death (HR 6.1, 95% CI 3.2-11.8). In an exploratory analysis, cluster determined treatment response (p=0.006). Conclusions regarding within-cluster treatment responses were limited by significant differences between select variables in the treated and untreated groups. Conclusions: Cluster analysis identifies novel real-world subphenotypes of Group 3 PH patients with distinct clinical trajectories. Future studies may consider this methodological approach to identify subgroups of heterogeneous patients that may be responsive to existing pulmonary vasodilatory therapies.

Exploring Intrinsic Discrimination and Consistency for Weakly Supervised Object Localization.

Weakly supervised object localization (WSOL) is a challenging and promising task that aims to localize objects solely based on the supervision of image category labels. In the absence of annotated bounding boxes, WSOL methods must employ the intrinsic properties of the image classification task pipeline to generate object localizations. In this work, we propose a WSOL method for exploring the Intrinsic Discrimination and Consistency in the image classification task pipeline, and call it as IDC. First, we develop a Triplet Metrics Based Foreground Modeling (TMFM) framework to directly predict object foreground regions using intrinsic discrimination. Unlike Class Activation Map (CAM) based methods that also rely on intrinsic discrimination, our TMFM framework alleviates the problem of only focusing on the most discriminative parts by optimizing foreground and background regions synergistically. Second, we design a Dual Geometric Transformation Consistency Constraints (DGTC2) training strategy to introduce additional supervision and regularization constraints for WSOL by leveraging intrinsic geometric transformation consistency. The proposed pixel-wise and object-wise consistency constraint losses cost-effectively provide spontaneous supervision for WSOL. Extensive experiments show that our IDC method achieves significant and consistent performance gains compared to existing state-of-the-art WSOL approaches. Code is available at: https://github.com/vignywang/IDC.

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis.

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.

Drugst.One - A plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Elevated Pulmonary Arterial Compliance Is Associated with Survival in Pulmonary Hypertension: Results from a Novel Network Medicine Analysis.

Rationale: Predictors of adverse outcome in pulmonary hypertension (PH) are well established; however, data that inform survival are lacking. Objectives: We aim to identify clinical markers and therapeutic targets that inform the survival in PH. Methods: We included data from patients with elevated mean pulmonary artery pressure (mPAP) diagnosed by right heart catheterization in the U.S. Veterans Affairs system (October 1, 2006-September 30, 2018). Network medicine framework was used to subgroup patients when considering an N of 79 variables per patient. The results informed outcome analyses in the discovery cohort and a sex-balanced validation right heart catheterization cohort from Vanderbilt University (September 24, 1998-December 20, 2013). Measurements and Main Results: From an N of 4,737 complete case patients with mPAP of 19-24 mm Hg, there were 21 distinct subgroups (network modules) (all-cause mortality range = 15.9-61.2% per module). Pulmonary arterial compliance (PAC) drove patient assignment to modules characterized by increased survival. When modeled continuously in patients with mPAP ⩾19 mm Hg (N = 37,744; age, 67.2 yr [range = 61.7-73.8 yr]; 96.7% male; median follow-up time, 1,236 d [range = 570-1,971 d]), the adjusted all-cause mortality hazard ratio was <1.0 beginning at PAC ⩾3.0 ml/mm Hg and decreased progressively to ∼7 ml/mm Hg. A protective association between PAC ⩾3.0 ml/mm Hg and mortality was also observed in the validation cohort (N = 1,514; age, 60.2 yr [range = 49.2-69.1 yr]; 48.0% male; median follow-up time, 2,485 d [range = 671-3,580 d]). The association was strongest in patients with precapillary PH at the time of catheterization, in whom 41% (95% confidence interval, 0.55-0.62; P < 0.001) and 49% (95% confidence interval, 0.38-0.69; P < 0.001) improvements in survival were observed for PAC ⩾3.0 versus <3.0 ml/mm Hg in the discovery and validation cohorts, respectively. Conclusions: These data identify elevated PAC as an important parameter associated with survival in PH. Prospective studies are warranted that consider PAC ⩾3.0 ml/mm Hg as a therapeutic target to achieve through proven interventions.

[Difference of lipid-lowering efficacy of "Xinjianqu" before and after fermentation and its mechanism based on LKB1-AMPK pathway and 16S rDNA sequencing technology].

On the basis of establishing the prescription of Xinjianqu and clarifying the increase of the lipid-lowering active ingredients of Xinjianqu by fermentation, this paper further compared the differences in the lipid-lowering effects of Xinjianqu before and after fermentation, and studied the mechanism of Xinjianqu in the treatment of hyperlipidemia. Seventy SD rats were randomly divided into seven groups, including normal group, model group, positive drug simvastatin group(0.02 g·kg~(-1)), and low-dose and high-dose Xinjianqu groups before and after fermentation(1.6 g·kg~(-1) and 8 g·kg~(-1)), with ten rats in each group. Rats in each group were given high-fat diet continuously for six weeks to establish the model of hyperlipidemia(HLP). After successful modeling, the rats were given high-fat diet and gavaged by the corresponding drugs for six weeks, once a day, to compare the effects of Xinjianqu on the body mass, liver coefficient, and small intestine propulsion rate of rats with HLP before and after fermentation. The effects of Xinjianqu before and after fermentation on total cholesterol(TC), triacylglyceride(TG), high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-C), alanine aminotransferase(ALT), aspartate aminotransferase(AST), blood urea nitrogen(BUN), creatinine(Cr), motilin(MTL), gastrin(GAS), and the Na~+-K~+-ATPase levels were determined by enzyme-linked immunosorbent assay(ELISA). The effects of Xinjianqu on liver morphology of rats with HLP were investigated by hematoxylin-eosin(HE) staining and oil red O fat staining. The effects of Xinjianqu on the protein expression of adenosine 5'-monophosphate(AMP)-activated protein kinase(AMPK), phosphorylated AMPK(p-AMPK), liver kinase B1(LKB1), and 3-hydroxy-3-methylglutarate monoacyl coenzyme A reductase(HMGCR) in liver tissues were investigated by immunohistochemistry. The effects of Xinjianqu on the regulation of intestinal flora structure of rats with HLP were studied based on 16S rDNA high-throughput sequencing technology. The results showed that compared with those in the normal group, rats in the model group had significantly higher body mass and liver coefficient(P<0.01), significantly lower small intestine propulsion rate(P<0.01), significantly higher serum levels of TC, TG, LDL-C, ALT, AST, BUN, Cr, and AQP2(P<0.01), and significantly lower serum levels of HDL-C, MTL, GAS, Na~+-K~+-ATP levels(P<0.01). The protein expression of AMPK, p-AMPK, and LKB1 in the livers of rats in the model group was significantly decreased(P<0.01), and that of HMGCR was significantly increased(P<0.01). In addition, the observed＿otus, Shannon, and Chao1 indices were significantly decreased(P<0.05 or P<0.01) in rat fecal flora in the model group. Besides, in the model group, the relative abundance of Firmicutes was reduced, while that of Verrucomicrobia and Proteobacteria was increased, and the relative abundance of beneficial genera such as Ligilactobacillus and Lachnospiraceae＿NK4A136＿group was reduced. Compared with the model group, all Xinjianqu groups regulated the body mass, liver coefficient, and small intestine index of rats with HLP(P<0.05 or P<0.01), reduced the serum levels of TC, TG, LDL-C, ALT, AST, BUN, Cr, and AQP2, increased the serum levels of HDL-C, MTL, GAS, and Na~+-K~+-ATP, improved the liver morphology, and increased the protein expression gray value of AMPK, p-AMPK, and LKB1 in the liver of rats with HLP and decreased that of LKB1. Xinjianqu groups could regulate the intestinal flora structure of rats with HLP, increased observed＿otus, Shannon, Chao1 indices, and increased the relative abundance of Firmicutes, Ligilactobacillus(genus), Lachnospiraceae＿NK4A136＿group(genus). Besides, the high-dose Xinjianqu-fermented group had significant effects on body mass, liver coefficient, small intestine propulsion rate, and serum index levels of rats with HLP(P<0.01), and the effects were better than those of Xinjianqu groups before fermentation. The above results show that Xinjianqu can improve the blood lipid level, liver and kidney function, and gastrointestinal motility of rats with HLP, and the improvement effect of Xinjianqu on hyperlipidemia is significantly enhanced by fermentation. The mechanism may be related to AMPK, p-AMPK, LKB1, and HMGCR protein in the LKB1-AMPK pathway and the regulation of intestinal flora structure.

Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations.

COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.

Uncovering common pathobiological processes between COVID-19 and pulmonary arterial hypertension by integrating Omics data.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to the current pandemic. Many factors, including age and comorbidities, influence the severity and mortality of COVID-19. SARS-CoV-2 infection can cause pulmonary vascular dysfunction. The COVID-19 case-fatality rate in patients with pulmonary arterial hypertension (PAH) is higher in comparison with the general population. In this study, we aimed to identify pathobiological processes common to COVID-19 and PAH by utilizing the human protein-protein interactome and whole-genome transcription data from peripheral blood mononuclear cells (PBMCs) and from lung tissue. We found that there are significantly more interactions between SARS-CoV-2 targets and PAH disease proteins than expected by chance, suggesting that the PAH disease module is in the neighborhood of SARS-CoV-2 targets in the human interactome. In addition, SARS-CoV-2 infection-induced changes in gene expression significantly overlap with PAH-induced gene expression changes in both tissues, indicating SARS-CoV-2 and PAH may share common transcriptional regulators. We identified many upregulated genes and downregulated genes common to COVID-19 and PAH. Interestingly, we observed different co-regulation patterns and dysfunctional signaling pathways in PBMCs versus lung tissue. Endophenotype enrichment analysis revealed that genes regulating fibrosis, inflammation, hypoxia, oxidative stress, immune response, and thromboembolism are significantly enriched in the COVID-19-PAH co-expression modules. We examined the network proximity of the targets of repositioned drugs for COVID-19 to the co-expression modules in PBMCs and lung tissue, and identified 42 drugs that can be potentially used for COVID-19 patients with PAH as a comorbidity. The uncovered common pathobiological pathways are crucial for discovering therapeutic targets and designing tailored treatments for COVID-19 patients who also have PAH.

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases.

Cardiovascular diseases (CVD) are the leading cause of death worldwide and display complex phenotypic heterogeneity caused by many convergent processes, including interactions between genetic variation and environmental factors. Despite the identification of a large number of associated genes and genetic loci, the precise mechanisms by which these genes systematically influence the phenotypic heterogeneity of CVD are not well understood. In addition to DNA sequence, understanding the molecular mechanisms of CVD requires data from other omics levels, including the epigenome, the transcriptome, the proteome, as well as the metabolome. Recent advances in multiomics technologies have opened new precision medicine opportunities beyond genomics that can guide precise diagnosis and personalized treatment. At the same time, network medicine has emerged as an interdisciplinary field that integrates systems biology and network science to focus on the interactions among biological components in health and disease, providing an unbiased framework through which to integrate systematically these multiomics data. In this review, we briefly present such multiomics technologies, including bulk omics and single-cell omics technologies, and discuss how they can contribute to precision medicine. We then highlight network medicine-based integration of multiomics data for precision medicine and therapeutics in CVD. We also include a discussion of current challenges, potential limitations, and future directions in the study of CVD using multiomics network medicine approaches.

Proline and glucose metabolic reprogramming supports vascular endothelial and medial biomass in pulmonary arterial hypertension.

In pulmonary arterial hypertension (PAH), inflammation promotes a fibroproliferative pulmonary vasculopathy. Reductionist studies emphasizing single biochemical reactions suggest a shift toward glycolytic metabolism in PAH; however, key questions remain regarding the metabolic profile of specific cell types within PAH vascular lesions in vivo. We used RNA-Seq to profile the transcriptome of pulmonary artery endothelial cells (PAECs) freshly isolated from an inflammatory vascular injury model of PAH ex vivo, and these data were integrated with information from human gene ontology pathways. Network medicine was then used to map all aa and glucose pathways to the consolidated human interactome, which includes data on 233,957 physical protein-protein interactions. Glucose and proline pathways were significantly close to the human PAH disease module, suggesting that these pathways are functionally relevant to PAH pathobiology. To test this observation in vivo, we used multi-isotope imaging mass spectrometry to map and quantify utilization of glucose and proline in the PAH pulmonary vasculature at subcellular resolution. Our findings suggest that elevated glucose and proline avidity underlie increased biomass in PAECs and the media of fibrosed PAH pulmonary arterioles. Overall, these data show that anabolic utilization of glucose and proline are fundamental to the vascular pathology of PAH.

[Investigating erythrocyte hemolysis assay use for proinflammatory potential prediction of silica particles].

OBJECTIVES: Crystalline silica, which is a causative agent of silicosis (an occupational disease), is manufactured in a variety of products (particles) with different particle characteristics, such as size and surface properties. In Japan, the products are currently uniformly controlled as crystalline silica, which is a substance subject to labeling and notification requirements. However, since the toxicity of silica particles reportedly varies depending on its characteristics, businesses are encouraged to conduct appropriate risk assessments for each product to prevent silicosis. Recently, silica particles have been reported to induce lysosomal membrane damage, leading to the activation of proinflammatory factors. An indirect method to evaluate lysosomal membrane damage known as the erythrocyte hemolysis assay, in which the erythrocyte membrane is assumed to be the lysosomal membrane, was performed. This study aimed to examine the possibility of constructing a screening system for proinflammatory potential prediction of silica particles based on their erythrocyte hemolytic activity. METHODS: Hemolysis assays were performed on the silica particles with different sizes, crystallinity, and surface functional groups using the erythrocytes from a healthy volunteer. Additionally, the hemolytic activity of other element particles was compared with that of the silica particles, and 27 types of commercially available crystalline silica particle products underwent screening trials. RESULTS: The hemolytic activity of silica particles was higher in crystalline than that in amorphous and increased with the decreasing size. The hemolytic reaction was particular to silica particles and rarely occurred in particles of other elements. Moreover, the hemolytic activity was significantly suppressed if the silica particles surface was modified with metal ions (Fe3+, Al3+). The hemolytic activities of the crystalline silica products used industrially significantly differed. CONCLUSIONS: This study revealed that particle properties, such as size, crystallinity, and surface functional groups, affect the hemolytic activity of silica particles. Particularly, the surface functional groups (silanol groups) that are unique to silica particles were considered to be strongly involved in hemolytic activities. Since grading the commercially available crystalline silica particle products based on the hemolytic rate was possible, hemolytic activity was suggested to be an evaluation index for predicting the proinflammatory potential of silica particles.

A viscoelastic-viscoplastic constitutive model for polymer bonded explosives under low impact loading.

Viscoplastic work is very important to explosive ignition under impact loading. At present, a large number of constitutive models only consider the viscoelastic and damage behavior of explosives, ignoring the plastic effect under low impact loading. A new viscoelastic-viscoplastic (VE-VP) model was developed and studied to describe the dynamic mechanical behaviors of polymer-bonded explosives (PBXs). The total strain was assumed to be the sum of the viscoelastic (VE) and viscoplastic (VP) components. A generalized Maxwell model was used to determine the VE responses. A VP model was developed by using the classical J2 rate-dependent model with isotropic hardening. Viscoplastic flow was considered in hyperbolic sinusoidal form. The explicit algorithms of VE model were proposed and assessed by using two different integration methods. The accuracy and efficiency of these two methods are similar at high strain rates. The coupled algorithms of VE-VP model were developed by referring to the classical elasto-viscoplasticity (EVP) provided and using the expression of incremental relaxation modulus. The proposed model was implemented in the ABAQUS using a user-subroutine (VUMAT) to predict the response behaviors of PBX 9501 under low impact loading. Several numerical simulations illustrated the computational efficiency and the accuracy of the proposed methods. The model predictions were compared with experimental data, and reasonable agreement was obtained.

Transcriptomic analysis of rapeseed (Brassica napus. L.) seed development in Xiangride, Qinghai Plateau, reveals how its special eco-environment results in high yield in high-altitude areas.

As one of the most important oil crops, rapeseed (Brassica napus) is cultivated worldwide to produce vegetable oil, animal feed, and biodiesel. As the population grows and the need for renewable energy increases, the breeding and cultivation of high-yield rapeseed varieties have become top priorities. The formation of a high rapeseed yield is so complex because it is influenced not only by genetic mechanisms but also by many environmental conditions, such as climatic conditions and different farming practices. Interestingly, many high-yield areas are located in special eco-environments, for example, in the high-altitude Xiangride area of the Qinghai Plateau. However, the molecular mechanisms underlying the formation of high yields in such a special eco-environment area remain largely unknown. Here, we conducted field yield analysis and transcriptome analysis in the Xiangride area. Compared with the yield and environmental factors in the Xinning area (a low-yielding area), we found that the relatively longer daylight length is the key to high rapeseed yield in the Xiangride area, which leads up to a 52.1% increase in rapeseed yield, especially the increase in thousand seed weight and silique number (SN). Combined with transcriptome H-cluster analysis and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses, we can assume that the grain development of rapeseed in the Xiangride area is ahead of schedule and lasts for a long time, leading to the high-yield results in the Xiangride area, confirmed by the expression analysis by quantitative real-time polymerase chain reaction (qRT-PCR) of yield-related genes. Our results provide valuable information for further exploring the molecular mechanism underlying high yield in special ecological environments and provide a helpful reference for studying seed development characteristics in special-producing regions for Brassica napus.