Technologies for integrating biological data

The process of building a new database relevant to some field of study in biomedicine involves transforming, integrating and cleansing multiple data sources, as well as adding new material and annotations. This paper reviews some of the requirements of a general solution to this data integration problem. Several representative technologies and approaches to data integration in biomedicine are surveyed. Then some interesting features that separate the more general data integration technologies from the more specialised ones are highlighted.     

血雉的产卵时间和产卵间隔

鸟类的产卵时间和产卵间隔因种而异 ,并会影响其繁殖成功。采用在巢内放置含有温度数据自动记录装置的假卵 ,通过记录其温度的变化来监测雌鸟产卵行为的方法 ,对分布于我国青藏高原森林鸟类血雉(Ithaginiscruentus)的产卵时间和产卵间隔进行了研究。结果表明 ,雌鸟每次入巢产卵的时间是在 15 :16± 2 2min (13 :30～ 18:5 1) ,产卵高峰集中在 14:0 0～ 16 :0 0 ,每次在巢中停留 (2 2± 0 2 6 )h。产卵间隔为 (46 4± 3 0 8)h ,大多数 (75 % )产卵间隔 45 8～ 48 9h。由此推测 ,血雉产卵时间较为集中很可能与避开夜行性捕食者的活动高峰有关 ;而隔天产卵则是对高原恶劣环境条件的适应。     

Fluorescence resonance energy transfer (FRET) microscopy imaging of live cell protein localizations

The current advances in fluorescence microscopy, coupled with the development of new fluorescent probes, make fluorescence resonance energy transfer (FRET) a powerful technique for studying molecular interactions inside living cells with improved spatial (angstrom) and temporal (nanosecond) resolution, distance range, and sensitivity and a broader range of biological applications.     

Integrated analysis of multiple data sources reveals modular structure of biological networks

It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks.     

Yellow band and dark spot syndromes in Caribbean corals: distribution,rate of spread,cytology, and effects on abundance and division rate of zooxanthellae

Yellow band and dark spot syndromes have been frequently observed to affect coral species throughout the Caribbean within the last 10 years. These syndromes significantly impair at least three important reef-building species. Yellow band (also known as yellow blotch) appears as rings or blotches on Montastrea annularis throughout the Caribbean. The coral tissue necrosis occurs at a rate of approximately 0.6 cm/month. Transect measurements at various locations indicated that as many as 90% of M. annularis were affected by yellow band during 1997–98. Tissue samples reveal a 41–96.9% decrease in zooxanthellae/sample compared to healthy specimens, depending on distance from healthy tissue. Mitotic indices (MI) of zooxanthellae (symbiotic algae appearing as doublets) for M. annularis are 2.5%. MI in yellow band samples directly bordering healthy tissue are less than 0.9%, and zooxanthellae directly within the band bordering exposed skeleton had a mitotic index of 0.0%. This indicates impairment of zooxanthellae cell division in yellow band specimens. Zooxanthellae are not expelled and appear vacuolated and devoid of organelles. Dark spot, characterized by tissue necrosis as well as a depression of the colony surface, affects Stephanocoenia michelinii and Siderastrea siderea throughout the Caribbean. Transects showed that as many as 56% of S. michelinii and S. siderea showed signs of dark spot during 1997–98. Affected tissues of S. siderea died at a rate of 4.0 cm/month. In dark spot samples from S. siderea, the total number of zooxanthellae was 56% of that in healthy tissue; dark spot-affected specimens of S. michelinii showed a 14% decrease in the number of zooxanthellae compared to healthy tissue samples. Mitotic indices of zooxanthellae from healthy specimens of S. sidereawere 1.20% compared to 0.40% in dark spot samples. Mitotic indices of healthy S. michelinii were 1.54% compared to 0.23% in dark spot samples, also indicating a decrease in algal cell division. Zooxanthellae from dark spot tissue are swollen and darker in pigment. Due to the changes that are evident in the symbiotic algae, we suggest that both syndromes act primarily on the zooxanthellae symbiont, and secondarily on the cnidarian host.     

Detection and Structural Characterization of Ether Glycerophosphoethanolamine from Cortical Lysosomes Following Traumatic Brain Injury Using UPLC‐HDMSE

The use of ultra performance liquid chromatography coupled to data independent tandem mass spectrometry with traveling wave ion mobility for detection and structural identification of ether‐linked glycerophosphoethanolamine is described. The experimental design generates 4D data (chromatographic retention time, precursor accurate mass, drift time with associated calculated collisional cross‐section, and time‐aligned accurate mass diagnostic product ions) for each ionization mode. Confident structure identification depends on satisfying 4D data confirmation in both positive and negative ion mode. Using this methodology, a number of ether‐linked glycerophosphoethanolamine lipids are structurally elucidated from mouse brain lysosomes. It is further determined that several ether‐linked glycerophosphoethanolamine structures are differentially abundant between lysosomes isolated from mouse cortex following traumatic brain injury as compared to that of sham animals. The combined effort of aligning multi‐dimensional mass spectrometry data with a well‐defined traumatic brain injury model lays the foundation for gaining mechanistic insight in the role lysosomal membrane damage plays in neuronal cell death following brain injury.     

Last glacial maximum biomes reconstructed from pollen and plant macrofossil data from northern Eurasia

Pollen and plant macrofossil data from northern Eurasia were used to reconstruct the vegetation of the last glacial maximum (LGM: 18,000 ± 2000 ¹⁴C yr bp ) using an objective quantitative method for interpreting pollen data in terms of the biomes they represent ( Prentice et al., 1996 ). The results confirm previous qualitative vegetation reconstructions at the LGM but provide a more comprehensive analysis of the data. Tundra dominated a large area of northern Eurasia (north of 57°N) to the west, south and east of the Scandinavian ice sheet at the LGM. Steppe‐like vegetation was reconstructed in the latitudinal band from western Ukraine, where temperate deciduous forests grow today, to western Siberia, where taiga and cold deciduous forests grow today. The reconstruction shows that steppe graded into tundra in Siberia, which is not the case today. Taiga grew on the northern coast of the Sea of Azov, about 1500 km south of its present limit in European Russia. In contrast, taiga was reconstructed only slightly south of its southern limit today in south‐western Siberia. Broadleaved trees were confined to small refuges, e.g. on the eastern coast of the Black Sea, where cool mixed forest was reconstructed from the LGM data. Cool conifer forests in western Georgia were reconstructed as growing more than 1000 m lower than they grow today. The few scattered sites with LGM data from the Tien‐Shan Mountains and from northern Mongolia yielded biome reconstructions of steppe and taiga, which are the biomes growing there today.     

大数据时代杂合酶的设计及其新趋势

酶分子的高效性和稳定性是工业广泛应用的物质基础。利用分子生物学技术可以将不同酶分子通过串联、插入、翻译后融合等方式构建成符合工业需求的杂合酶,但应用中多结构域杂合酶在表达量与酶活等方面仍存在弊端,而基于特定蛋白质结构域的多功能设计成为新趋势。高通量测序技术的发展,使得生物学家正面临着爆炸式增长的大数据集。近年来"蛋白质功能区"概念的提出,拓宽了人们对蛋白质结构与功能组织层次的认知,功能区残基聚簇的协同演化可导致同一家族不同蛋白质功能的差异。基于海量大数据分析可以快速定位特定功能区以及协同进化的关键位点,再利用合成生物学技术就可实现多种功能残基在同一蛋白质中的精准嫁接,完成天然酶分子的再设计。这将是杂合酶技术发展的新阶段,也会成为生物大数据时代下蛋白质设计的新趋势。     

Extensive gaps and biases in our knowledge of a well‐known fauna: implications for integrating biological traits into macroecology

Aim Ecologists seeking to describe patterns at ever larger scales require compilations of data on the global abundance and distribution of species. Comparable compilations of biological data are needed to elucidate the mechanisms behind these patterns, but have received far less attention. We assess the availability of biological data across an entire assemblage: the well‐documented demersal marine fauna of the United Kingdom. We also test whether data availability for a species depends on its taxonomic group, maximum body size, the number of times it has been recorded in a global biogeographic database, or its commercial and conservation importance. Location Seas of the United Kingdom. Methods We defined a demersal marine fauna of 973 species from 15 phyla and 40 classes using five extensive surveys around the British Isles. We then quantified the availability of data on eight key biological traits (termed biological knowledge) for each species from online databases. Relationships between biological knowledge and our predictors were tested with generalized linear models. Results Full data on eight fundamental biological traits exist for only 9% (n= 88) of the UK demersal marine fauna, and 20% of species completely lack data. Clear trends in our knowledge exist: fish (median biological knowledge score = six traits) are much better known than invertebrates (one trait). Biological knowledge increases with biogeographic knowledge and (to a lesser extent) with body size, and is greater in species that are commercially exploited or of conservation concern. Main conclusions Our analysis reveals deep ignorance of the basic biology of a well‐studied fauna, highlighting the need for far greater efforts to compile biological trait data. Clear biases in our knowledge, relating to how well sampled or ‘important’ species are suggests that caution is required in extrapolating small subsets of biologically well‐known species to ecosystem‐level studies.