AnimalMetaOmics: a multi-omics data resources for exploring animal microbial genomes and microbiomes.

The Animal Meta-omics landscape database (AnimalMetaOmics, https://yanglab.hzau.edu.cn/animalmetaomics#/) is a comprehensive and freely available resource that includes metagenomic, metatranscriptomic, and metaproteomic data from various non-human animal species and provides abundant information on animal microbiomes, including cluster analysis of microbial cognate genes, functional gene annotations, active microbiota composition, gene expression abundance, and microbial protein identification. In this work, 55 898 microbial genomes were annotated from 581 animal species, including 42 924 bacterial genomes, 12 336 virus genomes, 496 archaea genomes and 142 fungi genomes. Moreover, 321 metatranscriptomic datasets were analyzed from 31 animal species and 326 metaproteomic datasets from four animal species, as well as the pan-genomic dynamics and compositional characteristics of 679 bacterial species and 13 archaea species from animal hosts. Researchers can efficiently access and acquire the information of cross-host microbiota through a user-friendly interface, such as species, genomes, activity levels, expressed protein sequences and functions, and pan-genome composition. These valuable resources provide an important reference for better exploring the classification, functional diversity, biological process diversity and functional genes of animal microbiota.

SoyMD: a platform combining multi-omics data with various tools for soybean research and breeding.

Advanced multi-omics technologies offer much information that can uncover the regulatory mechanisms from genotype to phenotype. In soybean, numerous multi-omics databases have been published. Although they cover multiple omics, there are still limitations when it comes to the types and scales of omics datasets and analysis methods utilized. This study aims to address these limitations by collecting and integrating a comprehensive set of multi-omics datasets. This includes 38 genomes, transcriptomes from 435 tissue samples, 125 phenotypes from 6686 accessions, epigenome data involving histone modification, transcription factor binding, chromosomal accessibility and chromosomal interaction, as well as genetic variation data from 24 501 soybean accessions. Then, common analysis pipelines and statistical methods were applied to mine information from these multi-omics datasets, resulting in the successful establishment of a user-friendly multi-omics database called SoyMD (https://yanglab.hzau.edu.cn/SoyMD/#/). SoyMD provides researchers with efficient query options and analysis tools, allowing them to swiftly access relevant omics information and conduct comprehensive multi-omics data analyses. Another notable feature of SoyMD is its capability to facilitate the analysis of candidate genes, as demonstrated in the case study on seed oil content. This highlights the immense potential of SoyMD in soybean genetic breeding and functional genomics research.

Effects of Tai Chi on patients with moderate to severe COPD in stable phase.

This study was designed to investigate the effects of Tai Chi training on moderate to severe Chronic obstructive pulmonary disease (COPD) in the stable phase. This was a 2-arm randomized clinical trial. A total of 226 COPD patients with moderate to severe in the stable phase were allocated to either the control group or the observation group. The observation of the frequency of acute exacerbation for both groups lasted for at least 52 weeks follow-up. Changes in lung function and symptom scores of health-related quality of life (St George's Respiratory Questionnaire score) were also compared between the 2 groups. The accompanying anxiety and depressive symptoms of the patients were evaluated using the Self-Rating Depression Scale and Self-Rating Anxiety Scale prior to the procedure and 52 weeks later. Patients with moderate to severe COPD in China were divided into the Tai Chi group (n = 116) or control group (n = 110). After excluding 10 patients who fell off, 108 patients were enrolled in each group. Evidently, the matched group had higher exacerbation rate than the Tai Chi group (P < .05). Both groups showed no significant improvement in lung function (P > .05) but showed significant improvement in morbidity of acute exacerbation and quality of life (P < .05) compared with their former performance. Compared with regular therapy, Tai Chi also improved health-related quality of life (P < .05). The Self-Rating Anxiety Scale and Self-Rating Depression Scale scores of the 2 groups of patients after treatment and 52-week after treatment showed a notable decrease (P < .05). Overall, Tai Chi treatment was well tolerated. For moderate to severe COPD patients, regular treatment with Tai Chi can not only improve their health-related quality of life but also reduce the exacerbation rate compared with regular treatment alone. Tai Chi is recommended for COPD rehabilitation.

BnIR: A multi-omics database with various tools for Brassica napus research and breeding.

In the post-genome-wide association study era, multi-omics techniques have shown great power and potential for candidate gene mining and functional genomics research. However, due to the lack of effective data integration and multi-omics analysis platforms, such techniques have not still been applied widely in rapeseed, an important oil crop worldwide. Here, we report a rapeseed multi-omics database (BnIR; http://yanglab.hzau.edu.cn/BnIR), which provides datasets of six omics including genomics, transcriptomics, variomics, epigenetics, phenomics, and metabolomics, as well as numerous "variation-gene expression-phenotype" associations by using multiple statistical methods. In addition, a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets. BnIR is the most comprehensive multi-omics database for rapeseed so far, and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms.

Influence of drugs on the prospective diagnostic method for coronary heart disease with urine.

The morbidity of coronary heart disease (CHD) with high risks has been rising in recent years. A novel and noninvasive method based on surface-enhanced Raman spectroscopy (SERS) was proposed by Yang et al. (Analyst 143: 2235, 2018) to prospectively diagnose the arterial blockage by detecting platelet-derived growth factor-BB (PDGF-BB) in urine. Clinically, anti-platelet drugs (such as aspirin, statins and clopidogrel) are often used for ordinary CHD patients or patients with percutaneous coronary intervention (PCI). Therefore, whether the previous developed method can be applied to the CHD patients on long-term medication (more than 6 months) or post-PCI patients was investigated here. Firstly, urine samples of 13 CHD patients on long-term medication (aspirin, rosuvastatin, clopidogrel bisulfate) and 13 post-PCI patients were measured by the proposed method. Clinical data of coronary angiography results provided by Xin Hua Hospital and Yangpu District Central Hospital Antu Branch revealed that these 26 patients were with serious arterial blockage, however, characteristic Raman peak at 1509 cm-1 attributed to PDGF-BB was not observed in the SERS spectra of these 26 patients. In addition, an eight-day follow-up investigation was performed on a CHD patient with PCI three years ago and on long-term medication. It was found that the Raman peak at 1509 cm-1 could be only observed in the third and fourth day after suspending the drugs. Furthermore, SERS spectra of mixed solutions of PDGF-BB and aspirin, rosuvastatin, mixed solutions of these two drugs and clopidogrel bisulfate were analyzed. The Raman peak at 1509 cm-1 was not found in all these spectra, it indicated that all the three kinds of drugs could influence on the SERS signal of PDGF-BB. Therefore, the previous developed method is not suitable for CHD patients on long-term medication and post-PCI patients.

Noninvasive and prospective diagnosis of coronary heart disease with urine using surface-enhanced Raman spectroscopy.

A prospective diagnosis method for coronary heart disease (CHD) using human urine based on surface-enhanced Raman spectroscopy (SERS) is proposed, and could provide valuable information for judging whether to perform percutaneous coronary intervention (PCI) in clinics. Here, urine samples from 87 patients with CHD, including patients with PCI before operation (degree of cardiovascular congestion above 70%) and without PCI (degree of cardiovascular congestion under 70%), and 20 healthy humans were measured using SERS. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to analyze the SERS spectra, revealing that the classification sensitivity and specificity were 90% and 78.9%, respectively, and the absolute value for loading of PC1 at 1509 cm-1 was the largest. Since platelet-derived growth factor-BB (PDGF-BB) is closely related to CHD, PDGF-BB aqueous solutions with various concentrations (1, 0.5, 0.1, 0.05 and 0.01 ppm) and a mixture of healthy human urine and PDGF-BB aqueous solutions were then investigated in this work, and it was found that the Raman peak at 1509 cm-1 may be attributed to PDGF-BB. Moreover, the measured SERS spectra of all the urine samples from the 87 patients with CHD were compared with the clinical data provided by a hospital, and it was revealed that the appearance of a peak at 1509 cm-1 in the SERS spectra was in good agreement with the results of coronary angiography tests when cardiovascular congestion was above 70%. This indicated that the classification sensitivity and specificity were 87.9% and 87.0%, respectively, through identification of the Raman peak at 1509 cm-1.