Molecular sex-typing in shorebirds: a review of an essential method for research in evolution, ecology and conservation

Knowing the correct sex of individuals is essential both for research in evolutionary ecology and for practical conservation. Recent molecular advances have produced cheap, quick and reliable methods for sexing birds including chicks, juveniles, immatures and adults. Shorebird researchers have not yet fully utilised these advances. Here we provide an overview of work in this area to date with two objectives: (i) to review the major applications of molecular sexing and findings of shorebird research so far, and (ii) to provide an essential guide on how to carry out molecular sexing using current methods whilst avoiding methodological pitfalls. We encourage shorebird researchers to make better use of molecular sex-typing techniques in studies of conservation, migration, foraging ecology and breeding behaviour.

Immobilization of bacterial recombinant proteins

Recombinant α-L Rhamnosidase has several potential applications in citrus fruit juice processing industries. Immobilized recombinant α-L Rhamnosidase further provides an added advantage to this industrially important enzyme. Various techniques have been used to immobilize native rhamnosidase from fungal origin and applications were explored in great details by several workers. (Puri et al., 1996, 2000, 2001)

A recombinant rhamnosidase from a bacterial source was expressed in E.coli has been immobilized in calcium alginate beads (entrapment method). A batch bioreactor was created for the hydrolysis of naringin using immobilized recombinant α-L Rhamnosidase under shaking and stationary conditions and it was found to hydrolyze naringin effectively. The system was efficient to hydrolyze narigin under shaking conditions and was operationally stable up to 9 days. A high percent hydrolysis of naringin was achieved at pH 7.5 and 60˚C by immobilized rhamnosidase. Entrapped rhamnosidase was able to hydrolyze naringin content in kinnow juice repeatedly and this feature makes this technique economically suitable for debittering of fruit juices.

Insights into bacterial genome composition through variable target GC content profiling

This study presents a new computational method for guanine (G) and cytosine (C), or GC, content profiling based on the idea of multiple resolution sampling (MRS). The benefit of our new approach over existing techniques follows from its ability to locate significant regions without prior knowledge of the sequence, nor the features being sought. The use of MRS has provided novel insights into bacterial genome composition. Key findings include those that are related to the core composition of bacterial genomes, to the identification of large genomic islands (in Enterobacterial genomes), and to the identification of surface protein determinants in human pathogenic organisms (e.g., Staphylococcus genomes). We observed that bacterial surface binding proteins maintain abnormal GC content, potentially pointing to a viral origin. This study has demonstrated that GC content holds a high informational worth and hints at many underlying evolutionary processes. For online Supplementary Material, see www.liebertonline.com.

Bacterial genomic G + C composition-eliciting environmental adaptation

Bacterial genomes reflect their adaptation strategies through nucleotide usage trends found in their chromosome composition. Bacteria, unlike eukaryotes contain a wide range of genomic G + C. This wide variability may be viewed as a response to environmental adaptation. Two overarching trends are observed across bacterial genomes, the first, correlates genomic G + C to environmental niches and lifestyle, while the other utilizees intra-genomic G + C incongruence to delineate horizontally transferred material. In this review, we focus on the influence of several properties including biochemical, genetic flows, selection biases, and the biochemical-energetic properties shaping genome composition. Outcomes indicate a trend toward high G + C and larger genomes in free-living organisms, as a result of more complex and varied environments (higher chance for horizontal gene transfer). Conversely, nutrient limiting and nutrient poor environments dictate smaller genomes of low GC in attempts to conserve replication expense. Varied processes including translesion repair mechanisms, phage insertion and cytosine degradation has been shown to introduce higher AT in genomic sequences. We conclude the review with an analysis of current bioinformatics tools seeking to elicit compositional variances and highlight the practical implications when using such techniques.

BacHbpred: support vector machine methods for the prediction of bacterial hemoglobin-like proteins

The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.