A data management system for structural genomics

Background  

Structural genomics (SG) projects aim to determine thousands of protein structures by the development of high-throughput techniques for all steps of the experimental structure determination pipeline. Crucial to the success of such endeavours is the careful tracking and archiving of experimental and external data on protein targets.     

STRUCTURE AND FUNCTION OF BENTHIC ALGAL COMMUNITIES IN AN EXTREMELY ACID RIVER1

The composition of algal species and pigments and the structural and functional characteristics of the algal community were investigated in an acid stream of southwestern Spain, the Río Tinto. The algal community had low diversity and showed few seasonal differences. It was mainly made up of Klebsormidium flaccidum Kütz. (Silva, Mattox & Blackwell) that produced long greenish or purplish filaments, Pinnularia acoricola Hust. (producing brown patches) and Euglena mutabilis Schmitz. The algal filaments made up a consistent biofilm that also included fungal hyphae, iron bacterial sheaths, diatoms, and mineral particles. HPLC analyses on Río Tinto samples showed that undegraded chl accounted for 67% of the total chl in the filamentous patches but were a minority in the brown patch (2.6%). The brown patch had a concentration of carotenoids eight times lower than that observed in the green patch. When chl concentrations were weighted for the proportion of the different patches on the streambed, undegraded chl a accounted for 89.2 mg chl a·m ^{? 2} of stream surface area (5.4 g C·m ^{? 2}). This high algal biomass was supported by relatively high nutrient concentrations and by a high phosphatase activity (V_max = 137.7 nmol methylumbelliferyl substrate·cm ^{? 2}·h ^{? 1} 1 Received 15 July 2002. Accepted 17 February 2003. , K_m = 0.0045 μM). The remarkable algal biomass in Río Tinto potentially contributed to the bacterial–fungal community and to the macroinvertebrate community and emphasizes the role that the algae may have in the organic matter cycling and energy flow in extreme systems dominated by heterotrophic microorganisms.     

Validated predictive modelling of the environmental resistome

Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.     

Suitability of flow cytometry for estimating bacterial biovolume in natural plankton samples: comparison with microscopy data

The relationship between flow cytometry data and epifluorescence microscopy measurements was assessed in bacterioplankton samples from 80 lakes to estimate bacterial biovolume and cell size distribution. The total counts of 4',6'-diamidino-2-phenylindole-stained cells estimated by both methods were significantly related, and the slope of their linear regression was not significantly different from 1, indicating that both methods produce very similar estimates of bacterial abundance. The relationships between side scatter (SSC) and 4',6'-diamidino-2-phenylindole fluorescence and cell volume (microscopy values) were improved by binning of the data in three frequency classes for each, but further increases in the number of classes did not improve these relationships. Side scatter was the best cell volume predictor, and significant relationships were observed between the SSC classes and the smallest (R2 = 0.545, P < 0.001, n = 80) and the largest (R2 = 0.544, P < 0.001, n = 80) microscopy bacterial-size classes. Based on these relationships, a reliable bacterial biomass estimation was obtained from the SSC frequency classes. Our study indicates that flow cytometry can be used to properly estimate bacterioplankton biovolume, with an accuracy similar to those of more time-consuming microscopy methods.     

Relationship of aerobic and anaerobic parameters with 400 m front crawl swimming performance

The aims of the present study were to investigate the relationship of aerobic and anaerobic parameters with 400 m performance, and establish which variable better explains long distance performance in swimming. Twenty-two swimmers (19.1±1.5 years, height 173.9±10.0 cm, body mass 71.2±10.2 kg; 76.6±5.3% of 400 m world record) underwent a lactate minimum test to determine lactate minimum speed (LMS) (i.e., aerobic capacity index). Moreover, the swimmers performed a 400 m maximal effort to determine mean speed (S400m), peak oxygen uptake (V.O2PEAK) and total anaerobic contribution (C_ANA). The C_ANA was assumed as the sum of alactic and lactic contributions. Physiological parameters of 400 m were determined using the backward extrapolation technique (V.O2PEAK and alactic contributions of C_ANA) and blood lactate concentration analysis (lactic anaerobic contributions of C_ANA). The Pearson correlation test and backward multiple regression analysis were used to verify the possible correlations between the physiological indices (predictor factors) and S400m (independent variable) (p < 0.05). Values are presented as mean ± standard deviation. Significant correlations were observed between S400m (1.4±0.1 m·s^-1) and LMS (1.3±0.1 m·s^-1; r = 0.80), V.O2PEAK (4.5±3.9 L·min^-1; r = 0.72) and C_ANA (4.7±1.5 L·O₂; r= 0.44). The best model constructed using multiple regression analysis demonstrated that LMS and V.O2PEAK explained 85% of the 400 m performance variance. When backward multiple regression analysis was performed, C_ANA lost significance. Thus, the results demonstrated that both aerobic parameters (capacity and power) can be used to predict 400 m swimming performance.