Perceived rather than objective weight status is associated with suicidal behaviors among Chinese adolescents: a school-based study.

BACKGROUND: We aimed to explore the relationship between body mass index (BMI) and body weight perception (BWP) with suicidal behaviors among mainland Chinese adolescents. METHODS: A nationally representative sample (N = 10 110) of Chinese adolescents was assessed in this study. Suicidal behaviors (ideation, plan and attempt) were evaluated by four self-reported questions. Generalized linear mixed model was used to estimate the adjusted odds ratios (ORs) for the association between BWP/BMI with suicidal behaviors. RESULTS: The prevalence of suicidal ideation, suicidal plan and suicidal attempt was 12, 5 and 2.1%, respectively. After adjusting potential covariates, perceiving oneself as obese was significantly associated with increased risks of suicidal ideation (OR: 2.4, 95% confidence intervals, CI: 1.6-4.0, P = 0.001), suicidal plan (OR: 3.1, 95% CI: 1.5-6.3, P = 0.002) and suicidal attempt (OR: 3.7, 95% CI: 1.5-9.1, P = 0.001) compared with perceiving as normal weight among male adolescents; the effect attenuated to null among female adolescents. Perceiving oneself as underweight and overweight both exhibited significant adverse effect on suicidal behaviors (only suicidal ideation and suicidal plan) compared with perceiving oneself as normal weight among male adolescents, but not among female adolescents. The actual measured BMI was not significantly associated with suicidal behaviors among neither gender. CONCLUSIONS: Self-perception of their body image rather than actual measured weight may have a gender-specific adverse effect on suicidal behaviors among Chinese adolescents.

Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds.

BACKGROUND: Tea is one of the most popular non-alcoholic beverages worldwide. However, the tea plant, Camellia sinensis, is difficult to culture in vitro, to transform, and has a large genome, rendering little genomic information available. Recent advances in large-scale RNA sequencing (RNA-seq) provide a fast, cost-effective, and reliable approach to generate large expression datasets for functional genomic analysis, which is especially suitable for non-model species with un-sequenced genomes. RESULTS: Using high-throughput Illumina RNA-seq, the transcriptome from poly (A)+ RNA of C. sinensis was analyzed at an unprecedented depth (2.59 gigabase pairs). Approximate 34.5 million reads were obtained, trimmed, and assembled into 127,094 unigenes, with an average length of 355 bp and an N50 of 506 bp, which consisted of 788 contig clusters and 126,306 singletons. This number of unigenes was 10-fold higher than existing C. sinensis sequences deposited in GenBank (as of August 2010). Sequence similarity analyses against six public databases (Uniprot, NR and COGs at NCBI, Pfam, InterPro and KEGG) found 55,088 unigenes that could be annotated with gene descriptions, conserved protein domains, or gene ontology terms. Some of the unigenes were assigned to putative metabolic pathways. Targeted searches using these annotations identified the majority of genes associated with several primary metabolic pathways and natural product pathways that are important to tea quality, such as flavonoid, theanine and caffeine biosynthesis pathways. Novel candidate genes of these secondary pathways were discovered. Comparisons with four previously prepared cDNA libraries revealed that this transcriptome dataset has both a high degree of consistency with previous EST data and an approximate 20 times increase in coverage. Thirteen unigenes related to theanine and flavonoid synthesis were validated. Their expression patterns in different organs of the tea plant were analyzed by RT-PCR and quantitative real time PCR (qRT-PCR). CONCLUSIONS: An extensive transcriptome dataset has been obtained from the deep sequencing of tea plant. The coverage of the transcriptome is comprehensive enough to discover all known genes of several major metabolic pathways. This transcriptome dataset can serve as an important public information platform for gene expression, genomics, and functional genomic studies in C. sinensis.