QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density information

Background  

The selection of the most accurate protein model from a set of alternatives is a crucial step in protein structure prediction both in template-based and ab initio approaches. Scoring functions have been developed which can either return a quality estimate for a single model or derive a score from the information contained in the ensemble of models for a given sequence. Local structural features occurring more frequently in the ensemble have a greater probability of being correct. Within the context of the CASP experiment, these so called consensus methods have been shown to perform considerably better in selecting good candidate models, but tend to fail if the best models are far from the dominant structural cluster. In this paper we show that model selection can be improved if both approaches are combined by pre-filtering the models used during the calculation of the structural consensus.     

Factors influencing in the response of <i>Schizolobium parahybum</i> (Vell) Blake to <i>Ceratocystis paradoxa</i> and <i>C. moniliformis</i>

In the Ecuadorian coast one of the most destructive diseases of the pachaco is vascular wilt or stem rot caused by Ceratocystis complex, so the aim of this study was to determine the factors that affect the efficiency of the reaction of bark pachaco to this disease. This research was conducted under laboratory conditions, using trees pachaco S38, S41, S98, AE-1, AE-2 and AE-3, and pathogenic species Ceratocystis paradoxa and C. moniliformis. The method utilized was tissue stem bark,with bark sections with 4.5 cm², and a suspension of 3x10⁴ units infection and remained in a humid chamber for 96 hours at 25 ± 5 °C. Were determined grades of resistance/ susceptibility using a scale from 0 to 4, depending on the amount of mycelia and peritecio in each plant sample. Three factors were used: four colonies obtained by several transfers from each fungal specie, four ages of colonies of each fungal specie and four volumes of inoculum applied (units of infection), using for each experiment separately Completely Randomized Design with 4 replications factorial arrangement. For comparison between treatment means was used Tukey test at 5% probability of error. For future trials using this technique, you could use 30-day colonies for C. paradoxa and 40 days for C. moniliformis, and an application volume of 100 μL/cm², it would improve the level of response for the formation of perithecium and mycelia in samples cortex.     

QTL detection for coccidiosis (Eimeria tenella) resistance in a Fayoumi?×?Leghorn F2 cross,using a medium-density SNP panel

Background

Coccidiosis is a major parasitic disease that causes huge economic losses to the poultry industry. Its pathogenicity leads to depression of body weight gain, lesions and, in the most serious cases, death in affected animals. Genetic variability for resistance to coccidiosis in the chicken has been demonstrated and if this natural resistance could be exploited, it would reduce the costs of the disease. Previously, a design to characterize the genetic regulation of Eimeria tenella resistance was set up in a Fayoumi × Leghorn F₂ cross. The 860 F₂ animals of this design were phenotyped for weight gain, plasma coloration, hematocrit level, intestinal lesion score and body temperature. In the work reported here, the 860 animals were genotyped for a panel of 1393 (157 microsatellites and 1236 single nucleotide polymorphism (SNP) markers that cover the sequenced genome (i.e. the 28 first autosomes and the Z chromosome). In addition, with the aim of finding an index capable of explaining a large amount of the variance associated with resistance to coccidiosis, a composite factor was derived by combining the variables of all these traits in a single variable. QTL detection was performed by linkage analysis using GridQTL and QTLMap. Single and multi-QTL models were applied.

Results

Thirty-one QTL were identified i.e. 27 with the single-QTL model and four with the multi-QTL model and the average confidence interval was 5.9 cM. Only a few QTL were common with the previous study that used the same design but focused on the 260 more extreme animals that were genotyped with the 157 microsatellites only. Major differences were also found between results obtained with QTLMap and GridQTL.

Conclusions

The medium-density SNP panel made it possible to genotype new regions of the chicken genome (including micro-chromosomes) that were involved in the genetic control of the traits investigated. This study also highlights the strong variations in QTL detection between different models and marker densities.     

Detection of QTL controlling digestive efficiency and anatomy of the digestive tract in chicken fed a wheat-based diet

Background

Improving digestive efficiency is a major goal in poultry production, to reduce production costs, make possible the use of alternative feedstuffs and decrease the volume of manure produced. Since measuring digestive efficiency is difficult, identifying molecular markers associated with genes controlling this trait would be a valuable tool for selection. Detection of QTL (quantitative trait loci) was undertaken on 820 meat-type chickens in a F2 cross between D- and D+ lines divergently selected on low or high AMEn (apparent metabolizable energy value of diet corrected to 0 nitrogen balance) measured at three weeks in animals fed a low-quality diet. Birds were measured for 13 traits characterizing digestive efficiency (AMEn, coefficients of digestive utilization of starch, lipids, proteins and dry matter (CDUS, CDUL, CDUP, CDUDM)), anatomy of the digestive tract (relative weights of the proventriculus, gizzard and intestine and proventriculus plus gizzard (RPW, RGW, RIW, RPGW), relative length and density of the intestine (RIL, ID), ratio of proventriculus and gizzard to intestine weight (PG/I); and body weight at 23 days of age. Animals were genotyped for 6000 SNPs (single nucleotide polymorphisms) distributed on 28 autosomes, the Z chromosome and one unassigned linkage group.

Results

Nine QTL for digestive efficiency traits, 11 QTL for anatomy-related traits and two QTL for body weight at 23 days of age were detected. On chromosome 20, two significant QTL at the genome level co-localized for CDUS and CDUDM, i.e. two traits that are highly correlated genetically. Moreover, on chromosome 16, chromosome-wide QTL for AMEn, CDUS, CDUDM and CDUP, on chromosomes 23 and 26, chromosome-wide QTL for CDUS, on chromosomes 16 and 26, co-localized QTL for digestive efficiency and the ratio of intestine length to body weight and on chromosome 27 a chromosome-wide QTL for CDUDM were identified.

Conclusions

This study identified several regions of the chicken genome involved in the control of digestive efficiency. Further studies are necessary to identify the underlying genes and to validate these in commercial populations and breeding environments.     

Isocitrate dehydrogenase 1 R132C mutation occurs exclusively in microsatellite stable colorectal cancers with the CpG island methylator phenotype

The CpG Island Methylator Phenotype (CIMP) is fundamental to an important subset of colorectal cancer; however, its cause is unknown. CIMP is associated with microsatellite instability but is also found in BRAF mutant microsatellite stable cancers that are associated with poor prognosis. The isocitrate dehydrogenase 1 (IDH1) gene causes CIMP in glioma due to an activating mutation that produces the 2-hydroxyglutarate oncometabolite. We therefore examined IDH1 alteration as a potential cause of CIMP in colorectal cancer. The IDH1 mutational hotspot was screened in 86 CIMP-positive and 80 CIMP-negative cancers. The entire coding sequence was examined in 81 CIMP-positive colorectal cancers. Forty-seven cancers varying by CIMP-status and IDH1 mutation status were examined using Illumina 450K DNA methylation microarrays. The R132C IDH1 mutation was detected in 4/166 cancers. All IDH1 mutations were in CIMP cancers that were BRAF mutant and microsatellite stable (4/45, 8.9%). Unsupervised hierarchical cluster analysis identified an IDH1 mutation-like methylation signature in approximately half of the CIMP-positive cancers. IDH1 mutation appears to cause CIMP in a small proportion of BRAF mutant, microsatellite stable colorectal cancers. This study provides a precedent that a single gene mutation may cause CIMP in colorectal cancer, and that this will be associated with a specific epigenetic signature and clinicopathological features.     

三种兰科植物的保护遗传学研究初探

采用随机扩增多态 DNA(RAPD)分析研究了中国3种珍稀濒危兰科植物硬叶兜兰(Paphiopedilum micranthum Tang et Wang)、麻栗坡兜兰(P. malipoense S.C.Chen et Tsi)和独花兰(Changnienia amoena Chien)的遗传多样性与群体遗传结构.12个RAPD引物在2种兜兰中共扩增出131条带.对4个硬叶兜兰群体的检测表明其物种水平的多态条带百分率(PPB)为 71.6%,Nei 的基因多样度(h)为 0.217 1,Shannon多样性指数 (I) 为 0.330 1;4个群体的平均多样性水平为 PPB = 45.2%,h = 0.145 7,I = 0.220 4,低于远交兰花的平均水平.在总遗传变异中,群体间遗传变异占20.31%,略高于远交物种的平均水平.在物种水平上,麻栗坡兜兰的PPB为49.5%,h为0.117 4,I为0.176 4,均大大低于硬叶兜兰.对11个独花兰群体采用16个RAPD引物共扩增出119条带.物种水平PPB=76.5%,h=0.194 1,I=0.305 8;在群体水平上,上述3个指标的平均值则分别为37.2%、0.119 7和0.181 0,均低于远交兰花的平均水平.群体间的遗传变异占45.27%,遗传分化明显高于远交物种的平均水平.导致3个物种遗传多样性偏低而群体间遗传分化较高的主要原因在于人为的过度采挖和生境的片断化.研究结果为兰花保护策略和措施的制定提供了理论基础.     

柠檬酸合酶的分子生物学研究进展

柠檬酸合酶（citrate synthase,CS）是细胞内多种重要代谢途径的关键酶。CS可催化草酰乙酸和乙酰辅酶A之间的缩合反应生成柠檬酸和辅酶A。通常革兰氏阳性细菌、古菌以及真核细胞的CS为同源二聚体,而革兰氏阴性细菌的CS为同源六聚体。根据其在细胞内的定位不同,CS可分为线粒体CS、乙醛酸循环体CS、过氧化物酶体CS。这些同工酶在能量代谢、植物脂肪的代谢、脂肪酸的氧化及细胞解毒过程中起着重要作用。不同来源的CS空间结构、催化机制和动力学性质十分相似。针对其生化特性、空间结构特点、催化机制以及分子进化等研究进展进行综述。     

Characterisation of Structures in Salivary Secretion Film Formation. An Experimental Study with Atomic Force Microscopy

The purpose of the present study was to characterise the structure dynamics of pure salivary secretions retained on controlled surfaces with different surface energies in the early stage of salivary film formation. Germanium prisms prepared to have either low surface energy or medium surface energy were incubated in fresh secretions of either human parotid saliva (HPS) or human submandibular/sublingual saliva (HSMSLS) for 15, 90, and 180 min. After controlled rinsing with distilled water, the surfaces were air dried and thereafter imaged with atomic force microscopy (AFM). The amount of adsorbed material and the size of the structures detected increased with increased saliva exposure time. The film thicknesses varied from 10 to 150 nm, and both HPS and HSMSLS films contained structures with diameters varying from 40 nm to 2 μm. Some of these were clustered into special formations. The HPS films exhibited a more granular morphology than the HSMSLS films. Furthermore, branched lines were detected on the low surface energy germanium prisms incubated in saliva. The results indicate that exposure time, surface energy, and type of salivary secretion all are factors affecting the adsorption characteristics of salivary films.     

Comparison of whole‐genome (13X) and capture (87X) resequencing methods for SNP and genotype callings

The number of polymorphisms identified with next‐generation sequencing approaches depends directly on the sequencing depth and therefore on the experimental cost. Although higher levels of depth ensure more sensitive and more specific SNP calls, economic constraints limit the increase of depth for whole‐genome resequencing (WGS). For this reason, capture resequencing is used for studies focusing on only some specific regions of the genome. However, several biases in capture resequencing are known to have a negative impact on the sensitivity of SNP detection. Within this framework, the aim of this study was to compare the accuracy of WGS and capture resequencing on SNP detection and genotype calling, which differ in terms of both sequencing depth and biases. Indeed, we have evaluated the SNP calling and genotyping accuracy in a WGS dataset (13X) and in a capture resequencing dataset (87X) performed on 11 individuals. The percentage of SNPs not identified due to a sevenfold sequencing depth decrease was estimated at 7.8% using a down‐sampling procedure on the capture sequencing dataset. A comparison of the 87X capture sequencing dataset with the WGS dataset revealed that capture‐related biases were leading with the loss of 5.2% of SNPs detected with WGS. Nevertheless, when considering the SNPs detected by both approaches, capture sequencing appears to achieve far better SNP genotyping, with about 4.4% of the WGS genotypes that can be considered as erroneous and even 10% focusing on heterozygous genotypes. In conclusion, WGS and capture deep sequencing can be considered equivalent strategies for SNP detection, as the rate of SNPs not identified because of a low sequencing depth in the former is quite similar to SNPs missed because of method biases of the latter. On the other hand, capture deep sequencing clearly appears more adapted for studies requiring great accuracy in genotyping.