永兴岛白穗软珊瑚共附生放线菌筛选及部分活性次级代谢产物的鉴定

【目的】从白穗软珊瑚中分离和鉴定共附生放线菌,运用PCR技术对所分离的放线菌进行I型聚酮合酶(PKS)筛选,研究其次级代谢产物。【方法】使用11种培养基对白穗软珊瑚共附生放线菌进行分离、鉴定,构建16S rRNA基因系统发育进化树,以基于I型PKS的KS基因设计的简并引物对所分离放线菌进行基因筛选,对阳性菌株用3种培养基发酵检测,对目标菌株进行放大规模发酵分离鉴定次级代谢产物。【结果】从白穗软珊瑚中分离到20株共附生放线菌,包括链霉菌属10株、迪茨氏菌属2株和盐水孢菌属8株,筛选获得18株I型PKS阳性菌株,并从菌株Salinospora arenicola SH04中分离到化合物rifamycin S和rifamycin W。【结论】首次从珊瑚共附生环境中分离得到海洋专属性稀有放线菌盐水孢菌属,并以I型PKS基因筛选为指导,分离鉴定了聚酮类化合物rifamycins,为研究软珊瑚共附生可培养放线菌的多样性和基于基因筛选指导分离次级代谢产物提供了可靠依据。     

Finding the generalized molecular principles of protein thermal stability

Are there any generalized molecular principles of thermal adaptation? Here, integrating the concepts of structural bioinformatics, sequence analysis, and classical knot theory, we develop a robust computational framework that seeks for mechanisms of thermal adaptation by comparing orthologous mesophilic-thermophilic and mesophilic-hyperthermophilic proteins of remarkable structural and topological similarities, and still leads us to context-independent results. A comprehensive analysis of 4741 high-resolution, non-redundant X-ray crystallographic structures collected from 11 hyperthermophilic, 32 thermophilic and 53 mesophilic prokaryotes unravels at least five “nearly universal” signatures of thermal adaptation, irrespective of the enormous sequence, structure, and functional diversity of the proteins compared. A careful investigation further extracts a set of amino acid changes that can potentially enhance protein thermal stability, and remarkably, these mutations are overrepresented in protein crystallization experiments, in disorder-to-order transitions and in engineered thermostable variants of existing mesophilic proteins. These results could be helpful to find a precise, global picture of thermal adaptation.     

Characterization of free radicals formed from COX-catalyzed DGLA peroxidation

Like arachidonic acid (AA), dihomo-γ-linolenic acid (DGLA) is a 20-carbon ω-6 polyunsaturated fatty acid and a substrate of cyclooxygenase (COX). Through free radical reactions, COX metabolizes DGLA and AA to form well-known bioactive metabolites, namely, the 1 and 2 series of prostaglandins (PGs1 and PGs2), respectively. Unlike PGs2, which are viewed as proinflammatory, PGs1 possess anti-inflammatory and anticancer activities. However, the mechanisms linking the PGs to their bioactivities are still unclear, and radicals generated in COX-DGLA have not been detected. To better understand PG biology and determine whether different reactions occur in COX-DGLA and COX-AA, we have used LC/ESR/MS with a spin trap, α-(4-pyridyl-1-oxide)-N-tert-butyl nitrone (POBN), to characterize the carbon-centered radicals formed from COX-DGLA in vitro, including cellular peroxidation. A total of five types of DGLA-derived radicals were characterized as POBN adducts: m/z 266, m/z 296, and m/z 550 (same as or similar to COX-AA) and m/z 324 and m/z 354 (exclusively from COX-DGLA). Our results suggest that C-15 oxygenation to form PGGs occurs in both COX-DGLA and COX-AA; however, C-8 oxygenation occurs exclusively in COX-DGLA. This new finding will be further investigated for its association with various bioactivities of PGs, with potential implications for inflammatory diseases.     

Analyzing Large Gene Expression and Methylation Data Profiles Using StatBicRM: Statistical Biclustering-Based Rule Mining

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.     

Breakthrough Carbon Nanotube–Silicon Heterojunction Solar Cells

The latest advances in carbon nanotube–silicon heterojunction solar cells are combined with a new doping protocol based on the outstanding electron withdrawing properties and excellent silicon surface passivation ability of sulfonated polytetrafluoroethylene (Nafion). Using this new dopant for carbon nanotube–silicon solar cells, advanced substrate design, and an optimized antireflective texture fast etch with organic base, breakthrough performance is obtained from research grade devices with active areas of 1 and 5 cm², which yield power conversion efficiencies of 17.2 and 15.5%, respectively.     

Strontium stratigraphy of the Oligocene–Early Miocene shellbeds of the Kutch Basin,western India,and its implications

The Kutch Basin is unique among the western Indian sedimentary basins because of its near-complete sequence of post-Palaeozoic rocks. Due to extensive marine influence, the Oligocene–Early Miocene formations of the basin, namely Maniyara Fort, Khari Nadi and Chhasra, contain numerous shellbeds. Although age assignments of these formations exist based on foraminiferal biostratigraphy, detailed numerical age of the lithounits are yet to be established. We have identified a total of eleven distinct shellbeds (oldest SB 01 to youngest SB11) from this interval primarily containing bivalve fossils. Using ⁸⁷Sr/⁸⁶Sr of selected oyster and pectinid shells with pristine shell characteristics, we report the age of four shellbeds. The ages of SB 01, SB 04, SB 06 and SB 10 are 24.37, 17.31, 16.85 and 15.38 Ma, respectively. Our dates suggest a Chattian (24.37 Ma) age for SB 01 from the Bermoti Member, validating the previous biostratigraphical estimates from the Maniyara Fort Formation. The Chhasra Formation, however, shows a younger range of ages (17.31–15.38 Ma) characterized by a transition from the Burdigalian (SB 04–SB 06) to the Langhian (SB 10) stages. These dates have important implications in the study of sequence stratigraphy, Palaeobiogeography and tectonic history of the Kutch Basin. A surface with subaerial exposure is found in SB 08 (between 16.85 and 15.38 Ma) that corresponds to a global eustatic sea-level decrease (Mi2). Our new dates will also help evaluate the response of marine fauna to the closure of the Tethyan seaway around 19 Ma due to the formation of ‘Gomphotherium Landbridge’. The dated shellbeds enable us to identify pre- and post-closure fauna and assess the effect of biogeographical separation on these fauna. These dates have important implications in evaluating the regional geological record of western India in the context of various global events.     

A two-step genetic study on quantitative precursors of coronary artery disease in a homogeneous Indian population: Case–control association discovery and validation by transmission-disequilibrium test

In spite of its strong familiality, gene identification for coronary artery disease (CAD) has not yielded a consistent picture. One major reason for this is that families or cases and controls were not recruited from a homogeneous population. We, therefore, attempted to map genes underlying 10 quantitative traits (QTs) that are known precursors of CAD in a homogeneous population (Marwari) of India. The QTs are apolipoprotein B (ApoB), C-reactive protein (CRP), fibrinogen (FBG), homocysteine (HCY), lipoprotein (a) (LPA), cholesterol – total (CHOL-T), cholesterol – HDL (CHOL-H), cholesterol – LDL (CHOL-L), cholesterol – VLDL (CHOL-V) and triglyceride (TG). We assayed 209 SNPs in 31 genes among members of Marwari families. After log-transformation and covariate-adjustment of the QTs, a two-step analysis was performed. In Step-1, data on unrelated individuals were analysed for association with the SNPs. In Step-2, for validation of Step-1 results, a quantitative transmission-disequilibrium test on parent–offspring data was performed for each SNP found to be significantly associated with a QT in Step-1 on an independent sample set drawn from the same population. Statistically significant results found for the various QTs and SNPs were: rs3774933, rs230528, rs230521, rs1005819 and rs1609798 (intronic, NFKB1) with APOB; rs5361 (Missense, R > S, SELE) and rs4648004 (Intronic, NFKB1) with FBG; rs4220 (Missense, K > R, FGB) with HCY; and rs3025035 (Intronic, VEGFA) with CHOL-H. SNPs in SELE, VEGFA, FGB and NFKB1 genes impact significantly on levels of quantitative precursors of CAD in Marwaris.     

Optimization of process parameters for ethanol production from sugar cane molasses by Zymomonas mobilis using response surface methodology and genetic algorithm

Ethanol is a potential energy source and its production from renewable biomass has gained lot of popularity. There has been worldwide research to produce ethanol from regional inexpensive substrates. The present study deals with the optimization of process parameters (viz. temperature, pH, initial total reducing sugar (TRS) concentration in sugar cane molasses and fermentation time) for ethanol production from sugar cane molasses by Zymomonas mobilis using Box–Behnken experimental design and genetic algorithm (GA). An empirical model was developed through response surface methodology to analyze the effects of the process parameters on ethanol production. The data obtained after performing the experiments based on statistical design was utilized for regression analysis and analysis of variance studies. The regression equation obtained after regression analysis was used as a fitness function for the genetic algorithm. The GA optimization technique predicted a maximum ethanol yield of 59.59 g/L at temperature 31 °C, pH 5.13, initial TRS concentration 216 g/L and fermentation time 44 h. The maximum experimental ethanol yield obtained after applying GA was 58.4 g/L, which was in close agreement with the predicted value.