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The aim of this study was to investigate the lactic acid bacteria (LAB) and yeast community from home-made sauerkraut collected from Southwest China through culture-dependent and culture-independent technology. Forty-eight samples of home-made sauerkraut were collected from households at three different locations in Southwest China. The pH, total acidity and salt contents among these fermented vegetables were 3.69 ± 0.42, 0.86 ± 0.43 g/100 ml, and 3.86 ± 2.55 g/100 ml, respectively. The number of lactic acid bacteria (LAB) and yeasts were 7.25 ± 1.05 log10 colony-forming units (CFU)/ml and 3.74 ± 1.01 log CFU/ml, respectively. A total of 182 LAB and 81 yeast isolates were identified. The dominant isolates were Lactobacillus plantarum, L. brevis, Pediococcus ethanolidurans, Pichia membranifaciens, P. fermentans and Kazachstania bulderi. Denaturing gradient gel electrophoresis (DGGE) showed that L. plantarum, uncultured Lactobacillus sp, P. ethanolidurans, and K. exigua were the predominant microflora. Our studies demonstrated that the DGGE technique combined with a culture-dependent method is very effective for studying the LAB and yeast community in Chinese traditional fermentation vegetables. The results will give us an understanding of LAB and yeast community of Chinese sauerkraut and improve the knowledge of LAB and yeast community of Chinese sauerkraut.

Ischemic cardiomyopathy (ICM) is a common human heart disease that causes death. No effective biomarkers for ICM could be found in existing databases, which is detrimental to the in-depth study of this disease. In the present study, ICM susceptibility biomarkers were identified using a proposed strategy based on RNA-Seq and miRNA-Seq data of ICM and normal samples. Significantly differentially expressed competing endogenous RNA (ceRNA) triplets were constructed using permutation tests and differentially expressed mRNAs, miRNAs and lncRNAs. Candidate ICM susceptible genes were screened out as differentially expressed genes in significantly differentially expressed ceRNA triplets enriched in ICM-related functional classes. Finally, eight ICM susceptibility genes and their significantly correlated lncRNAs with high classification accuracy were identified as ICM susceptibility biomarkers. These biomarkers would contribute to the diagnosis and treatment of ICM. The proposed strategy could be extended to other complex diseases without disease biomarkers in public databases.

Genetic stability of bacterium as a starter culture is vital for product quality in fermentation industry. The commercial strain Lactobacillus plantarum NCU116 widely used in fruit and vegetable fermentation was induced with various stressors to investigate the stability of potential prophages. PHAge Search Tool (PHAST) identified three potential prophages in bacterial genome. By spectrophotometric analysis, mitomycin C (MMC), lactic acid, and bile salt were found to inhibit the growth of L. plantarum NCU116 while ethanol and hydrogen peroxide had no notable impacts. Transcriptions of four phage-synthesizing genes (phaR, phacap, phaada, phatail) and four phage-resistant genes (cas116, helR, hsd1, hsd2) under stressors were investigated by quantitative reverse transcription PCR. MMC was found to most significantly upregulated transcriptions of phage-synthesizing genes, followed by lactic acid and bile salt. By transmission electron microscopy, no virus particles from the lysates of strain NCU116 treated by MMC were observed, corresponding to the result that no phage nucleic acids could be extracted from the supernatants of strain NCU116 treated by MMC. This study suggested that no prophages could be induced from L. plantarum NCU116 by strong inducer MMC, indicating its genetic stability, which supports the comprehensive application of strain NCU116 in industry without causing fermentation failure.

Fermented vegetables have a long history in many cultures. Jiang-shui and Suan-cai are two of the most well-known instances in North China. They are made by a process of natural lactic acid fermentation. However, they have the different characteristics, i.e. acidity, taste and flavor, which are influenced by the specific bacterial community. Therefore, we used high-throughput sequencing methods to identify the bacterial community structure of Jiang-shui and Suan-cai in this study. Subsequently, we figured out the bacterial differences of these two products using the statistical analysis. Firmicutes and Proteobacteria were the dominant phyla in both Jiang-shui and Suan-cai. However, Lactobacillus was the main genus in Jiang-shui samples, whereas both Lactobacillus and Pediococcus were the major genera in the Suan-cai samples. At the species level, Lactobacillus amylolyticus, Lactobacillus fermentum and Lactobacillus pontis were the major species in Jiang-shui samples, while Pediococcus parvulus, Lactobacillus coryniformis, Lactobacillus pentosus and Lactobacillus parabrevis were the dominant species in the Suan-cai samples. These results suggested that Jiang-shui and Suan-cai had their own unique bacterial community, leading to the specific characteristics. Furthermore, the bacterial communities of both fermented vegetables varied at different locations. This study revealed the flora present in the Jiang-shui and the Suan-cai, providing a deep insight of the microbial species of Chinese fermented vegetables and guidance for the production of the Jiang-shui and the Suan-cai.

Complex diseases, such as obesity, type II diabetes and chronic obstructive pulmonary disease (COPD) as metabolic disorder-related diseases are major concern for worldwide public health in the 21st century. The identification of these disease risk genes has attracted increasing interest in computational systems biology. In this paper, a novel method was proposed to prioritize disease risk genes (PDRG) by integrating functional annotations, protein interactions and gene expression information to assess similarity between genes in a disease-related metabolic network. The gene prioritization method was successfully carried out for obesity and COPD, the effectiveness of which was superior to those of ToppGene and ToppNet in both literature validation and recall rate by LOOCV. Our method could be applied broadly to other metabolism-related diseases, helping to prioritize novel disease risk genes, and could shed light on diagnosis and effective therapies.