Microbial transformation of geniposide in Gardeniae Fructus under the fermentation with Aspergillus niger DQWM-G11.

Gardeniae Fructus, the dried fruit of Gardenia jasminoides, was fermented with Aspergillus niger DQWM-G11. The antibacterial activities of the fermented and non-fermented products were measured and the transformation of chemical constituents was detected. The results revealed that A. niger DQWM-G11 fermented Gardeniae Fructus (AFGF) possessed a stronger antibacterial effect with a minimal inhibitory concentration (MIC) value of 256 µg/mL, compared to the raw material (MIC: > 1024 µg/mL). An undescribed microbial transformation reaction was discovered, where geniposide (1) was transformed into 1ß-methoxyl-4-epigardendiol (2), which was then verified. The produced component exhibited a stronger antibacterial effect (MIC: 256 µg/mL) than raw geniposide (1) (MIC: >1024 µg/mL), indicating that the increased activity of Gardeniae Fructus was due to the biotransformation. The discovery of this microbial transformation reaction will provide an important theoretical basis for further developing and applying Gardeniae Fructus and geniposide.

M1ARegpred: Epitranscriptome Target Prediction of N1-methyladenosine (m1A) Regulators Based on Sequencing Features and Genomic Features.

BACKGROUND: N1-methyladenosine (m1A) is a reversible post-transcriptional modification in mRNA, which has been proved to play critical roles in various biological processes through interaction with different m1A regulators. There are several m1A regulators existing in the human genome, including YTHDF1-3 and YTHDC1. METHODS: Several techniques have been developed to identify the substrates of m1A regulators, but their binding specificity and biological functions are not yet fully understood due to the limitations of wet-lab approaches. Here, we submitted the framework m1ARegpred (m1A regulators substrate prediction), which is based on machine learning and the combination of sequence-derived and genome-derived features. RESULTS: Our framework achieved area under the receiver operating characteristic (AUROC) scores of 0.92 in the full transcript model and 0.857 in the mature mRNA model, showing an improvement compared to the existing sequence-derived methods. In addition, motif search and gene ontology enrichment analysis were performed to explore the biological functions of each m1A regulator. CONCLUSIONS: Our work may facilitate the discovery of m1A regulators substrates of interest, and thereby provide new opportunities to understand their roles in human bodies.

Prevalence of and Risk Factors for Type 2 Diabetes Mellitus in Hyperlipidemia in China.

BACKGROUND: We explored the prevalence of and risk factors for type 2 diabetes in the adult population of Shanghai (China) with and without dyslipidemia. MATERIAL AND METHODS: We conducted a cross-sectional survey including 14 385 adults (aged 16 to 88 years) in Shanghai using a stratified, multistage cluster sampling approach. RESULTS: Type 2 diabetes and hyperlipidemia were found in 1456 (10.1%) and 4583 (31.9%) subjects, respectively. Type 2 diabetes was more common in males (11.4%) than in females (9.2%, P<0.01), in the elderly (> or =65 years, 22.5%) than in younger (<55 years, <10%, P<0.01) individuals, and in urban (12.8%) than in rural populations (5.2%, P<0.01). Diabetes incidence was higher among patients with hyperlipidemia than in controls (16.9% vs. 7.0%, P<0.01; OR=2.72, 95% CI 2.44-3.03). Compared with controls, the risk for diabetes in subjects with isolated hypertriglyceridemia, isolated hypercholesterolemia, and mixed hyperlipidemia increased 1.75-fold (95% CI 1.53-1.99), 1.53-fold (95% CI 1.17-2.01), and 2.93-fold (95% CI 2.37-3.63), respectively. The fasting plasma glucose (FPG) and 2h-postprandial plasma glucose (2h-PG) increased with age in both sexes. The age- and sex-adjusted FPG and 2h-PG levels in hyperlipidemia were significantly higher than in controls (P<0.01). CONCLUSIONS: A high prevalence of type 2 diabetes in hyperlipidemia patients exists in Shanghai. Hyperlipidemia is associated with elevated blood glucose levels and therefore requires prompt intervention for prevention and treatment of diabetes in patients with dyslipidemia.

[Clinical significance of large-scale screening of A1555G mutation of mitochondria DNA for neonates].

OBJECTIVE: To explore the necessity of large-scale screening of mitochondria DNA (mtDNA) A1555G mutation for prevention of aminoglycoside antibiotic induced deafness in newborns. METHODS: One thousand blood filter samples were collected from neonates born in July 2008 in Shenzhen. DNA was extracted with Chelex-100 Resin and amplified by PCR. The mtDNA A1555G mutation was determined by denaturing high-performance liquid chromatography(DHPLC) for PCR products. The positive frequency was calculated. RESULTS: The mitochondrial DNA A1555G mutation was detected in 2 cases of 1000 neonates. The frequency of mutation was 0.2%. CONCLUSION: There is a high frequency of mtDNA A1555G mutation in neonates, the large-scale screening of mtDNAA1555G mutation in newborns might detect the individuals sensitive to aminoglycoside antibiotic, which is helpful to guide a rational medication for newborns and the maternal relatives at high-risk. Furthermore, it might be useful to prevent aminoglycoside antibiotic induced deafness.

The involvement of XPC protein in the cisplatin DNA damaging treatment-mediated cellular response.

Recognition of DNA damage is a critical step for DNA damage-mediated cellular response. XPC is an important DNA damage recognition protein involved in nucleotide excision repair (NER). We have studied the XPC protein in cisplatin DNA damaging treatment-mediated cellular response. Comparison of the microarray data from both normal and XPC-defective human fibroblasts identified 861 XPC-responsive genes in the cisplatin treatment (with minimum fold change > or = 1.5). The cell cycle and cell proliferation-related genes are the most affected genes by the XPC defect in the treatment. Many other cellular function genes, especially the DNA repair and signal transduction-related genes, were also affected by the XPC defect in the treatment. To validate the microarray data, the transcription levels of some microarray-identified genes were also determined by an RT-PCR based real time PCR assay. The real time PCR results are consistent with the microarray data for most of the tested genes, indicating the reliability of the microarray data. To further validate the microarray data, the cisplatin treatment-mediated caspase-3 activation was also determined. The Western blot hybridization results indicate that the XPC defect greatly attenuates the cisplatin treatment-mediated Caspase-3 activation. We elucidated the role of p53 protein in the XPC protein DNA damage recognition-mediated signaling process. The XPC defect reduces the cisplatin treatment-mediated p53 response. These results suggest that the XPC protein plays an important role in the cisplatin treatment-mediated cellular response. It may also suggest a possible mechanism of cancer cell drug resistance.

Characteristic pattern of human prostatic growth with age.

AIM: To study the characteristic pattern of the age-related growth of the human prostate gland. METHODS: The volume (weight) of the prostate in 1,601 males, aged from newborn to 92 years, was determined by B-ultrasonography. RESULTS: Prostatic volume determination by B-ultrasonography in 1601 males (1301 normal subjects and 300 BPH patients) pointed out that the age-stratified growth of human prostate could be categorized into 4 life stages: (1) the first slow growing phase (from newborn to 9 years): the prostate grows slowly at a rate of 0.14 g per year; (2) the first rapid growing phase (from 10 to 30 years): the prostate grows at a rate of 0.84 g per year; (3) the second slow growing phase (from 30 to 50 years), the prostate grows at a rate of 0.21 g per year; (4) the second rapid growing phase (from 50 to 90 years): the prostate grows at one of the following rates: in one group the growth rate is of 0.50 g per year and in the other 1.20 g per year, leading to benign prostatic hyperplasia (BPH). CONCLUSION: The volumes of the prostate are different in different age groups and it grows with age at different rates in four life phases. The prostate growth in phases can be expressed by the following equation: Y=19.36+1.36X'-0.58X'(2+0.33X'3), where Y = prostate volume, X = age (up to 70 years), X'=(X-35.5)/10.