Global distribution and drivers of language extinction risk

Many of the world''s languages face serious risk of extinction. Efforts to prevent this cultural loss are severely constrained by a poor understanding of the geographical patterns and drivers of extinction risk. We quantify the global distribution of language extinction risk—represented by small range and speaker population sizes and rapid declines in the number of speakers—and identify the underlying environmental and socioeconomic drivers. We show that both small range and speaker population sizes are associated with rapid declines in speaker numbers, causing 25% of existing languages to be threatened based on criteria used for species. Language range and population sizes are small in tropical and arctic regions, particularly in areas with high rainfall, high topographic heterogeneity and/or rapidly growing human populations. By contrast, recent speaker declines have mainly occurred at high latitudes and are strongly linked to high economic growth. Threatened languages are numerous in the tropics, the Himalayas and northwestern North America. These results indicate that small-population languages remaining in economically developed regions are seriously threatened by continued speaker declines. However, risks of future language losses are especially high in the tropics and in the Himalayas, as these regions harbour many small-population languages and are undergoing rapid economic growth.     

Global risk transformative prioritization for prostate cancer candidate genes in molecular networks

Understanding the pathogenesis of complex diseases is aided by precise identification of the genes responsible. Many computational methods have been developed to prioritize candidate disease genes, but coverage of functional annotations may be a limiting factor for most of these methods. Here, we introduce a global candidate gene prioritization approach that considers information about network properties in the human protein interaction network and risk transformative contents from known disease genes. Global risk transformative scores were then used to prioritize candidate genes. This method was introduced to prioritize candidate genes for prostate cancer. The effectiveness of our global risk transformative algorithm for prioritizing candidate genes was evaluated according to validation studies. Compared with ToppGene and random walk-based methods, our method outperformed the two other candidate gene prioritization methods. The generality of our method was assessed by testing it on prostate cancer and other types of cancer. The performance was evaluated using standard leave-one-out cross-validation.     

Global transcription analysis of Krebs tricarboxylic acid cycle mutants reveals an alternating pattern of gene expression and effects on hypoxic and oxidative genes

To understand the many roles of the Krebs tricarboxylic acid (TCA) cycle in cell function, we used DNA microarrays to examine gene expression in response to TCA cycle dysfunction. mRNA was analyzed from yeast strains harboring defects in each of 15 genes that encode subunits of the eight TCA cycle enzymes. The expression of >400 genes changed at least threefold in response to TCA cycle dysfunction. Many genes displayed a common response to TCA cycle dysfunction indicative of a shift away from oxidative metabolism. Another set of genes displayed a pairwise, alternating pattern of expression in response to contiguous TCA cycle enzyme defects: expression was elevated in aconitase and isocitrate dehydrogenase mutants, diminished in alpha-ketoglutarate dehydrogenase and succinyl-CoA ligase mutants, elevated again in succinate dehydrogenase and fumarase mutants, and diminished again in malate dehydrogenase and citrate synthase mutants. This pattern correlated with previously defined TCA cycle growth-enhancing mutations and suggested a novel metabolic signaling pathway monitoring TCA cycle function. Expression of hypoxic/anaerobic genes was elevated in alpha-ketoglutarate dehydrogenase mutants, whereas expression of oxidative genes was diminished, consistent with a heme signaling defect caused by inadequate levels of the heme precursor, succinyl-CoA. These studies have revealed extensive responses to changes in TCA cycle function and have uncovered new and unexpected metabolic networks that are wired into the TCA cycle.     

调控糖酵解和生脂的重要转录因子:碳水化合物反应元件结合蛋白

碳水化合物反应元件结合蛋白（carbohydrate response element binding protein,ChREBP）是最近发现和分离的一种重要的转录调控因子,它直接激活多个参与糖酵解和脂肪合成基因的表达,从而调控糖代谢和脂肪酸合成。研究ChREBP表达的调控机制及生物学效应,将有助于全面认识肥胖、糖尿病等代谢综合征的发病机制。本文综述了碳水化合物调控元件（carbohydrate response element,ChRE）及ChREBP的结构,ChREBPDNA结合活性的活化过程和分子机制,以及对靶基因的作用。     

Mapping the NPHP-JBTS-MKS protein network reveals ciliopathy disease genes and pathways

Nephronophthisis (NPHP), Joubert (JBTS), and Meckel-Gruber (MKS) syndromes are autosomal-recessive ciliopathies presenting with cystic kidneys, retinal degeneration, and cerebellar/neural tube malformation. Whether defects in kidney, retinal, or neural disease primarily involve ciliary, Hedgehog, or cell polarity pathways remains unclear. Using high-confidence proteomics, we identified 850 interactors copurifying with nine NPHP/JBTS/MKS proteins and discovered three connected modules: "NPHP1-4-8" functioning at the apical surface, "NPHP5-6" at centrosomes, and "MKS" linked to Hedgehog signaling. Assays for ciliogenesis and epithelial morphogenesis in 3D renal cultures link renal cystic disease to apical organization defects, whereas ciliary and Hedgehog pathway defects lead to retinal or neural deficits. Using 38 interactors as candidates, linkage and sequencing analysis of 250 patients identified ATXN10 and TCTN2 as new NPHP-JBTS genes, and our Tctn2 mouse knockout shows neural tube and Hedgehog signaling defects. Our study further illustrates the power of linking proteomic networks and human genetics to uncover critical disease pathways.     

Global methylation analysis identifies PITX2 as an upstream regulator of the androgen receptor and IGF-I receptor genes in prostate cancer

The insulin-like growth factor-I (IGF-IR) and androgen (AR) receptors are important players in prostate cancer. Functional interactions between the IGF-I and androgen signaling pathways have crucial roles in the progression of prostate cancer from early to advanced stages. DNA methylation is a major epigenetic alteration affecting gene expression. Hypermethylation of tumor suppressor promoters is a frequent event in human cancer, leading to inactivation and repression of specific genes. The aim of the present study was to identify the entire set of methylated genes ("methylome") in a cellular model that replicates prostate cancer progression. The methylation profiles of the P69 (early stage, benign) and M12 (advanced stage, metastatic) prostate cancer cell lines were established by treating cells with the demethylating agent 5-aza-2'-deoxycytidine (5-Aza) followed by DNA microarray analysis. Comparative genome-wide methylation analyses of 5-Aza-treated versus untreated cells identified 297 genes overexpressed in P69 and 191 genes overexpressed in M12 cells. 102 genes were upregulated in both benign and metastatic cell lines. In addition, our analyses identified the PITX2 gene as a master regulator upstream of the AR and IGF-IR genes. The PITX2 promoter was semi-methylated in P69 cells but fully methylated (i. e., silenced) in M12 cells. Epigenetic regulation of PITX2 during the course of the disease may lead to orchestrated control of the AR and IGF signaling pathways. In summary, our results provide new insights into the epigenetic changes associated with progression of prostate cancer from an organ confined, androgen-sensitive disorder to an aggressive, androgen-insensitive disease.     

Low duplicability and network fragility of cancer genes

We identified genomic and network properties of approximately 600 genes mutated in different cancer types. These genes tend not to duplicate but, unlike most human singletons, they encode central hubs of highly interconnected modules within the protein-protein interaction network (PIN). We find that cancer genes are fragile components of the human gene repertoire, sensitive to dosage modification. Furthermore, other nodes of the human PIN with similar properties are rare and probably enriched in candidate cancer genes.     

Integrated analysis of recurrent properties of cancer genes to identify novel drivers

The heterogeneity of cancer genomes in terms of acquired mutations complicates the identification of genes whose modification may exert a driver role in tumorigenesis. In this study, we present a novel method that integrates expression profiles, mutation effects, and systemic properties of mutated genes to identify novel cancer drivers. We applied our method to ovarian cancer samples and were able to identify putative drivers in the majority of carcinomas without mutations in known cancer genes, thus suggesting that it can be used as a complementary approach to find rare driver mutations that cannot be detected using frequency-based approaches.