Insulin from the β-cells of the pancreatic islets of Langerhans controls energy homeostasis in vertebrates, and its deficiency causes diabetes mellitus. During embryonic development, the transcription factor Neurogenin3 initiates the differentiation of the β-cells and other islet cell types from pancreatic endoderm, but the genetic program that subsequently completes this differentiation remains incompletely understood. Here we show that the transcription factor Rfx6 directs islet cell differentiation downstream of Neurogenin3. Mice lacking Rfx6 failed to generate any of the normal islet cell types except for pancreatic-polypeptide-producing cells. In human infants with a similar autosomal recessive syndrome of neonatal diabetes, genetic mapping and subsequent sequencing identified mutations in the human RFX6 gene. These studies demonstrate a unique position for Rfx6 in the hierarchy of factors that coordinate pancreatic islet development in both mice and humans. Rfx6 could prove useful in efforts to generate β-cells for patients with diabetes.

Although the universal genetic code exhibits only minor variations in nature, Francis Crick proposed in 1955 that ''the adaptor hypothesis allows one to construct, in theory, codes of bewildering variety.'' The existing code has been expanded to enable incorporation of a variety of unnatural amino acids at one or two nonadjacent sites within a protein by using nonsense or frameshift suppressor aminoacyl-tRNAs (aa-tRNAs) as adaptors. However, the suppressor strategy is inherently limited by compatibility with only a small subset of codons, by the ways such codons can be combined, and by variation in the efficiency of incorporation. Here, by preventing competing reactions with aa-tRNA synthetases, aa-tRNAs, and release factors during translation and by using nonsuppressor aa-tRNA substrates, we realize a potentially generalizable approach for template-encoded polymer synthesis that unmasks the substantially broader versatility of the core translation apparatus as a catalyst. We show that several adjacent, arbitrarily chosen sense codons can be completely reassigned to various unnatural amino acids according to de novo genetic codes by translating mRNAs into specific peptide analog polymers (peptidomimetics). Unnatural aa-tRNA substrates do not uniformly function as well as natural substrates, revealing important recognition elements for the translation apparatus. Genetic programming of peptidomimetic synthesis should facilitate mechanistic studies of translation and may ultimately enable the directed evolution of small molecules with desirable catalytic or pharmacological properties.

ObjectiveThe lack of standardized reference range for the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index has limited its clinical application. This study defines the reference range of HOMA-IR index in an adult Hispanic population based with machine learning methods.MethodsThis study investigated a Hispanic population of 1854 adults, randomly selected on the basis of 2000 Census tract data in the city of Brownsville, Cameron County. Machine learning methods, support vector machine (SVM) and Bayesian Logistic Regression (BLR), were used to automatically identify measureable variables using standardized values that correlate with HOMA-IR; K-means clustering was then used to classify the individuals by insulin resistance.ResultsOur study showed that the best cutoff of HOMA-IR for identifying those with insulin resistance is 3.80. There are 39.1% individuals in this Hispanic population with HOMA-IR>3.80.ConclusionsOur results are dramatically different using the popular clinical cutoff of 2.60. The high sensitivity and specificity of HOMA-IR>3.80 for insulin resistance provide a critical fundamental for our further efforts to improve the public health of this Hispanic population.

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Vitamin D deficiency is associated with risk in several diseases. Vitamin D status has high heritability, yet the genetic epidemiology of vitamin D or its metabolites has not been well studied. Our objective was to identify the relationship among three vitamin D-related genes (GC, CYP2R1 and DHCR7/NADSYN1) and the levels of 25(OH)D in northeastern Han Chinese children. A total of 506 northeastern Han Chinese children were enrolled in this study. Linear regression was used to examine the impact of 12 SNPs on 25(OH)D concentrations after adjustment for age, gender, BMI and regular usage of vitamin D, and Bonferroni's method was adopted for multiple corrections. The two SNPs in GC (rs222020, rs2298849), four SNPs in CYP2R1 (rs10741657, rs10766197, rs12794714 and rs1562902) and two SNPs in DHCR7/NADSYN1 (rs3829251, rs12785878) were significantly associated with plasma 25(OH)D concentrations under both additive and recessive models (P <0.05). The genotypes of the CYP2R1 rs2060793 polymorphism showed positive association with serum 25(OH)D status under all of the three genetic models even after correction for multiple comparison. This population-based study was the first to confirm the strong effects of the GC, CYP2R1 and DHCR7/NADSYN1 loci on circulating 25(OH)D concentrations in northeastern Han Chinese children.

Objective Adiponectin and leptin play critical roles in the development of Metabolic Syndrome (MetS). The study was designed to assess circulating levels of adiponectin and leptin in early diagnosis of Metabolic Syndrome (MetS). Methods This cross-sectional study was performed on 367 participants randomly selected from a well-characterized cohort of Mexican-Americans living at the US-Mexico border. Results Significant differences in circulating levels of adiponectin and leptin were observed between males and females. The adiponectin/leptin ratio significantly correlated with MetS in this population. A receiver-operator characteristic (ROC) analysis demonstrated that adiponectin/leptin ratio is a valuable biomarker for the diagnosis of MetS Conclusion Our study supported the central role of adiponectin and leptin in MetS, and demonstrated that adiponectin/leptin ratio can be used as a highly sensitive and specific biomarker for MetS.

Objectives This review considers the state of occupational injury surveillance and prevention among migrant workers in China and suggests areas of focus for future research on the topic. Methods Bibliographic databases were searched for qualitative and quantitative studies on surveillance of and interventions to prevent occupational injury among migrant workers in mainland China. Additional abstracts were identified from the citations of relevant articles from the database search. Studies fitting the inclusion criteria were evaluated, and findings were extracted and summarised. Results The search uncovered 726 studies in the English-language databases searched, and 3109 in the Chinese database. This article analyses a total of 19 research articles that fit the inclusion criteria with qualitative or quantitative data on occupational injury surveillance and prevention of migrant workers in China. Despite evidence of the vulnerability of migrant workers in the workplace, there is little systematic surveillance of occupational injury and few evaluated interventions. Conclusions Migrant workers account for a disproportionate burden of occupational injury morbidity and mortality in China. However, data are inconsistent and inadequate to detail injury incidence or to evaluate interventions. The following are suggestions to decrease injury incidence among migrants: strengthen the national system of occupational injury surveillance; focus surveillance and interventions on high-risk occupations employing migrants such as construction, manufacturing and small mining operations; improve occupational safety training and access to appropriate safety equipment; evaluate recent changes in occupational health and safety and evaluate outcome of multi-party interventions to reduce occupational injury among migrant workers.

The rapid progress of genomic technologies has been providing new opportunities to address the need of maturity-onset diabetes of the young (MODY) molecular diagnosis. However, whether a new mutation causes MODY can be questionable. A number of in silico methods have been developed to predict functional effects of rare human mutations. The purpose of this study is to compare the performance of different bioinformatics methods in the functional prediction of nonsynonymous mutations in each MODY gene, and provides reference matrices to assist the molecular diagnosis of MODY. Our study showed that the prediction scores by different methods of the diabetes mutations were highly correlated, but were more complimentary than replacement to each other. The available in silico methods for the prediction of diabetes mutations had varied performances across different genes. Applying gene-specific thresholds defined by this study may be able to increase the performance of in silico prediction of disease-causing mutations.