Duplication and Functional Specialization of the Telomere-capping Protein Cdc13 in Candida Species

The budding yeast G-tail binding complex CST (Cdc13-Stn1-Ten1) is crucial for both telomere protection and replication. Previous studies revealed a family of Cdc13 orthologues (Cdc13A) in Candida species that are unusually small but are nevertheless responsible for G-tail binding and the regulation of telomere lengths and structures. Here we report the identification and characterization of a second family of Cdc13-like proteins in the Candida clade, named Cdc13B. Phylogenetic analysis and sequence alignment indicate that Cdc13B probably arose through gene duplication prior to Candida speciation. Like Cdc13A, Cdc13B appears to be essential. Deleting one copy each of the CDC13A and CDC13B genes caused a synergistic effect on aberrant telomere elongation and t-circle accumulation, suggesting that the two paralogues mediate overlapping and nonredundant functions in telomere regulation. Interestingly, Cdc13B utilizes its C-terminal OB-fold domain (OB4) to mediate self-association and binding to Cdc13A. Moreover, the stability of the heterodimer is evidently greater than that of either homodimer. Both the Cdc13 A/A homodimer and A/B heterodimer, but not the B/B homodimer, recognized the telomere G-tail repeat with high affinity and sequence specificity. Our results reveal novel evolutionary elaborations of the G-tail-binding protein in Saccharomycotina yeast, suggesting a drastic remodeling of CDC13 that entails gene duplication, fusion, and functional specialization. The repeated and independent duplication of G-tail-binding proteins such as Cdc13 and Pot1 hints at the evolutionary advantage of having multiple G-tail-binding proteins.     

GENETIC DIFFERENTIATION AND SELECTION AGAINST MIGRANTS IN EVOLUTIONARILY REPLICATED EXTREME ENVIRONMENTS

We investigated mechanisms of reproductive isolation in livebearing fishes (genus Poecilia) inhabiting sulfidic and nonsulfidic habitats in three replicate river drainages. Although sulfide spring fish convergently evolved divergent phenotypes, it was unclear if mechanisms of reproductive isolation also evolved convergently. Using microsatellites, we found strongly reduced gene flow between adjacent populations from different habitat types, suggesting that local adaptation to sulfidic habitats repeatedly caused the emergence of reproductive isolation. Reciprocal translocation experiments indicate strong selection against immigrants into sulfidic waters, but also variation among drainages in the strength of selection against immigrants into nonsulfidic waters. Mate choice experiments revealed the evolution of assortative mating preferences in females from nonsulfidic but not from sulfidic habitats. The inferred strength of sexual selection against immigrants (RI_s) was negatively correlated with the strength of natural selection (RI_m), a pattern that could be attributed to reinforcement, whereby natural selection strengthens behavioral isolation due to reduced hybrid fitness. Overall, reproductive isolation and genetic differentiation appear to be replicated and direct consequences of local adaptation to sulfide spring environments, but the relative contributions of different mechanisms of reproductive isolation vary across these evolutionarily independent replicates, highlighting both convergent and nonconvergent evolutionary trajectories of populations in each drainage.     

The geometry of the Pareto front in biological phenotype space

When organisms perform a single task, selection leads to phenotypes that maximize performance at that task. When organisms need to perform multiple tasks, a trade‐off arises because no phenotype can optimize all tasks. Recent work addressed this question, and assumed that the performance at each task decays with distance in trait space from the best phenotype at that task. Under this assumption, the best‐fitness solutions (termed the Pareto front) lie on simple low‐dimensional shapes in trait space: line segments, triangles and other polygons. The vertices of these polygons are specialists at a single task. Here, we generalize this finding, by considering performance functions of general form, not necessarily functions that decay monotonically with distance from their peak. We find that, except for performance functions with highly eccentric contours, simple shapes in phenotype space are still found, but with mildly curving edges instead of straight ones. In a wide range of systems, complex data on multiple quantitative traits, which might be expected to fill a high‐dimensional phenotype space, is predicted instead to collapse onto low‐dimensional shapes; phenotypes near the vertices of these shapes are predicted to be specialists, and can thus suggest which tasks may be at play.     

Novel functional variants locus in PLCE1 and susceptibility to esophageal squamous cell carcinoma: Based on published genome-wide association studies in a central Chinese population

A novel functional single nucleotide polymorphism (SNP) rs2274223 located in the phospholipase C epsilon 1 (PLCE1) gene was found to be associated with the risk of esophageal squamous cell carcinoma (ESCC) by three large-scale genome-wide association studies (GWAS) in Chinese populations. In the present study, we validated this finding and also explored the risk of ESCC associated with other two unreported potentially functional SNPs (rs17417407 G > T and rs2274224 C > G) of PLCE1 in a population-based case–control study to investigate the association between these three potentially functional SNPs in PLCE1 and susceptibility to ESCC. A total of 381 ESCC cases and 420 controls matched by age and sex were recruited and successfully genotyped for three SNPs (rs17417407, rs2274223 and rs2274224) of the PLCE1 in a central Chinese population. SNP rs2274223 was independently associated with increased risk of ESCC (adjusted odds ratio [OR], 2.80; 95% confidence interval [95% CI], 1.45–5.39 for GG vs. AA), and SNP rs2274224 was found to be associated with decreased risk of ESCC (adjusted OR, 0.65; 95% CI, 0.46–0.91 for CG vs. CC). The combined effects of risk alleles for three SNPs (rs17417407T, rs2274223G and rs2274224G) were found to be associated with elevated risk of ESCC in a dose-dependent effect manner (P_trend = 0.005). The G_rs17417407A_rs2274223C_rs2274224 haplotype decreased the risk of ESCC (adjusted OR, 0.76; 95% CI, 0.62–0.93), meanwhile the G_rs17417407G_rs2274223C_rs2274224 and T_rs17417407G_rs2274223C_rs2274224 haplotypes could increase the risk of ESCC (adjusted OR, 1.75; 95% CI, 1.33–2.18 and OR, 2.51; 95% CI, 1.15–2.49). Gene–environment interaction analysis presented a best model consisted of four factors (rs2274223, rs2274224, family history, and smoking) with testing balance accuracy (TBA): 0.66 and cross validation consistency (CVC): 7/10, which could increase the esophageal cancer risk in the “high risk group” with 3.67-fold (OR: 3.67, 95% CI: 2.74–4.92), compared to the “low risk group”. Our results further confirmed that genetic variations in PLCE1 may contribute to ESCC risk associated with tobacco exposure in a central Chinese population. Further functional studies are needed to validate our results.     

基于SNP 互作识别潜在冠心病致病基因

目的:基于全基因组关联分析(Genomewideassociationstudy,GWAS)数据与生物信息学方法,识别冠心病潜在致病基因。方法:利用生物信息学方法和GWAS数据,对单核苷酸多态性(SingleNucleotidePolymorphisms,SNP)进行疾病风险打分,依据特定距离阈值内的SNP-SNP互作关系,筛选出疾病相关SNP显著风险模块,识别潜在致病基因。结果:设定阈值20kb,经筛选获得279个SNP显著风险模块,映射到79个基因,文献验证率为71.01%。结论:基于SNP互作识别的潜在致病基因,能更加准确的分析冠心病的发生发展过程。     

A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

As one large class of non-coding RNAs(nc RNAs), long nc RNAs(lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lnc RNAs have been implicated in human disorders. Many lnc RNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here,we introduce a computational method named lnc RNA–protein bipartite network inference(LPBNI). LPBNI aims to identify potential lnc RNA–interacting proteins, by making full use of the known lnc RNA–protein interactions. Leave-one-out cross validation(LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk(RWR)and protein-based collaborative filtering(Pro CF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lnc RNA–interacting proteins.     

Ecological niche partitioning of the invasive dinoflagellate Prorocentrum minimum and its native congeners in the Baltic Sea

This study analyses three decades of the peculiar bloom-formation history of the potentially toxic invasive planktonic dinoflagellates Prorocentrum minimum (Pavillard) Schiller in the SW Baltic Sea. We tested a research hypothesis that the unexpectedly long delay (nearly two decades) in population development of P. minimum prior to its first bloom was caused by competition with one or several closely related native dinoflagellate species due to ecological niche partitioning which hampered the spread and bloom-forming potential of the invader. We applied the ecological niche concept to a large, long-term phytoplankton database and analysed the invasion history and population dynamics of P. minimum in the SW Baltic Sea coastal waters using the data on phytoplankton composition, abundance and biomass. The ecological niche dimensions of P. minimum and its congener P. balticum were identified as the optimum environmental conditions for the species during the bloom events based on water temperature, salinity, pH, concentration of nutrients (PO₄³⁻; total phosphorus, TP; total nitrogen, TN; SiO₄⁴⁻), TN/TP-ratio and habitat type. The data on spatial distribution and ecological niche dimensions of P. minimum have contributed to the development of the “protistan species maximum concept”. High microplankton diversity at critical salinities in the Baltic Sea may be considered as a possible reason for the significant niche overlap and strong competitive interactions among congeners leading to prolonged delay in population growth of P. minimum preceding its first bloom in the highly variable brackishwater environment.     

How do ecologists select and use indicator species to monitor ecological change? Insights from 14 years of publication in Ecological Indicators

Indicator species (IS) are used to monitor environmental changes, assess the efficacy of management, and provide warning signals for impending ecological shifts. Though widely adopted in recent years by ecologists, conservation biologists, and environmental practitioners, the use of IS has been criticized for several reasons, notably the lack of justification behind the choice of any given indicator. In this review, we assess how ecologists have selected, used, and evaluated the performance of the indicator species. We reviewed all articles published in Ecological Indicators (EI) between January 2001 and December 2014, focusing on the number of indicators used (one or more); common taxa employed; terminology, application, and rationale behind selection criteria; and performance assessment methods. Over the last 14 years, 1914 scientific papers were published in EI, describing studies conducted in 53 countries on six continents; of these, 817 (43%) used biological organisms as indicators. Terms used to describe organisms in IS research included “ecological index”, “environmental index”, “indicator species”, “bioindicator”, and “biomonitor,” but these and other terms often were not clearly defined. Twenty percent of IS publications used only a single species as an indicator; the remainder used groups of species as indicators. Nearly 50% of the taxa used as indicators were animals, 70% of which were invertebrates. The most common applications behind the use of IS were to: monitor ecosystem or environmental health and integrity (42%); assess habitat restoration (18%); and assess effects of pollution and contamination (18%). Indicators were chosen most frequently based on previously cited research (40%), local abundance (5%), ecological significance and/or conservation status (13%), or a combination of two or more of these reasons (25%). Surprisingly, 17% of the reviewed papers cited no clear justification for their choice of indicator. The vast majority (99%) of publications used statistical methods to assess the performance of the selected indicators. This review not only improves our understanding of the current uses and applications of IS, but will also inform practitioners about how to better select and evaluate ecological indicators when conducting future IS research.     

Ecological total-factor energy efficiency of China’s energy intensive industries

This paper uses a meta-frontier slack-based DEA model to measure the ecological total-factor energy efficiency as well as the energy conservation potential of China’s four energy intensive subsectors. We incorporate both desirable and undesirable output together in the period, 2000–2013. The conclusions are: firstly, under the meta-frontier, the four subsectors of energy intensive industries have low average level of ecological total-factor energy efficiencies. They are 0.137, 0.212, 0.238, and 0.307 in the non-metallic mineral products manufacturing industry, raw chemical materials and chemical products manufacturing industry, smelting and pressing of ferrous metals industry, and smelting and pressing of non-ferrous metals industry, respectively. Secondly, the ecological energy efficiency in East China is the highest among three regions. Central China and West China are behind, but they are extremely close to each other. Thirdly, East China almost has no technology gap pertaining to energy efficiency, while Central China and West China almost have the same gap. Finally, Sichuan is considered to be the best province in West China under group frontier due to its perform in the three energy intensive subsectors. For Central and East China, no province has higher ecological energy efficiency in more than two energy intensive subsectors.