Gneg-mPLoc: A top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins

By incorporating the information of gene ontology, functional domain, and sequential evolution, a new predictor called Gneg-mPLoc was developed. It can be used to identify Gram-negative bacterial proteins among the following eight locations: (1) cytoplasm, (2) extracellular, (3) fimbrium, (4) flagellum, (5) inner membrane, (6) nucleoid, (7) outer membrane, and (8) periplasm. It can also be used to deal with the case when a query protein may simultaneously exist in more than one location. Compared with the original predictor called Gneg-PLoc, the new predictor is much more powerful and flexible. For a newly constructed stringent benchmark dataset in which none of proteins included has ≥25% pairwise sequence identity to any other in a same subset (location), the overall jackknife success rate achieved by Gneg-mPLoc was 85.5%, which was more than 14% higher than the corresponding rate by the Gneg-PLoc. As a user friendly web-server, Gneg-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/Gneg-multi/.     

Novel miRNA markers for the diagnosis and prognosis of endometrial cancer

As endometrial cancer (EC) is a major threat to female health worldwide, the ability to provide an accurate diagnosis and prognosis of EC is promising to improve its treatment guidance. Since the discovery of miRNAs, it has been realized that miRNAs are associated with every cell function, including malignant transformation and metastasis. This study aimed to explore diagnostic and prognostic miRNA markers of EC. In this study, differential analysis and machine learning were performed, followed by correlation analysis of miRNA‐mRNA based on the miRNA and mRNA expression data. Nine miRNAs were identified as diagnostic markers, and a diagnostic classifier was established to distinguish between EC and normal endometrium tissue with overall correct rates >95%. Five specific prognostic miRNA markers were selected to construct a prognostic model, which was confirmed more effective in identifying EC patients at high risk of mortality compared with the FIGO staging system. This study demonstrates that the expression patterns of miRNAs may hold promise for becoming diagnostic and prognostic biomarkers and novel therapeutic targets for EC.     

Evaluating the invasion risk of longhorn crazy ants (Paratrechina longicornis) in South Korea using spatial distribution model

The longhorn crazy ant (Paratrechina longicornis) is a globally distributed ant species with a high invasion risk, suggesting the need to use species distribution modeling to evaluate its potential distribution. Therefore, this study aimed to predict the potential distribution of longhorn crazy ants in response to climate change by using CLIMEX and Maxent and identifying the climatic factors that influence their habitat. Then, the model outcomes were used to construct an ensemble map to evaluate invasion risk in South Korea. The results indicated that temperature-related variables mainly affect the distribution of the longhorn crazy ant, and the two models showed consensus regions in South America, Africa, Australia, and Southeast Asia. Due to climate change, it was expected that the northern limit would somewhat rise. In South Korea, high-risk areas were predicted to be located along the coasts, but they would expand as a consequence of climate change. Since the invasion of longhorn crazy ants has occurred via commercial trades, a relatively high risk in coastal areas demands a high level of attention. We expect that this study will provide initial insight into selecting areas for longhorn crazy ant quarantine with ensemble species distribution modeling.     

Classification of fish samples via an integrated proteomics and bioinformatics approach

There is an increasing demand to develop cost‐effective and accurate approaches to analyzing biological tissue samples. This is especially relevant in the fishing industry where closely related fish samples can be mislabeled, and the high market value of certain fish leads to the use of alternative species as substitutes, for example, Barramundi and Nile Perch (belonging to the same genus, Lates). There is a need to combine selective proteomic datasets with sophisticated computational analysis to devise a robust classification approach. This paper describes an integrated MS‐based proteomics and bioinformatics approach to classifying a range of fish samples. A classifier is developed using training data that successfully discriminates between Barramundi and Nile Perch samples using a selected protein subset of the proteome. Additionally, the classifier is shown to successfully discriminate between test samples not used to develop the classifier, including samples that have been cooked, and to classify other fish species as neither Barramundi nor Nile Perch. This approach has applications to truth in labeling for fishmongers and restaurants, monitoring fish catches, and for scientific research into distances between species.     

ApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa

Background

Computational identification of apicoplast-targeted proteins is important in drug target determination for diseases such as malaria. While there are established methods for identifying proteins with a bipartite signal in multiple species of Apicomplexa, not all apicoplast-targeted proteins possess this bipartite signature. The publication of recent experimental findings of apicoplast membrane proteins, called transmembrane proteins, that do not possess a bipartite signal has made it feasible to devise a machine learning approach for identifying this new class of apicoplast-targeted proteins computationally.

Methodology/principal findings

In this work, we develop a method for predicting apicoplast-targeted transmembrane proteins for multiple species of Apicomplexa, whereby several classifiers trained on different feature sets and based on different algorithms are evaluated and combined in an ensemble classification model to obtain the best expected performance. The feature sets considered are the hydrophobicity and composition characteristics of amino acids over transmembrane domains, the existence of short sequence motifs over cytosolically disposed regions, and Gene Ontology (GO) terms associated with given proteins. Our model, ApicoAMP, is an ensemble classification model that combines decisions of classifiers following the majority vote principle. ApicoAMP is trained on a set of proteins from 11 apicomplexan species and achieves 91% overall expected accuracy.

Conclusions/significance

ApicoAMP is the first computational model capable of identifying apicoplast-targeted transmembrane proteins in Apicomplexa. The ApicoAMP prediction software is available at http://code.google.com/p/apicoamp/ and http://bcb.eecs.wsu.edu.     

Exome sequencing identifies RDH12 compound heterozygous mutations in a family with severe retinitis pigmentosa

Objective

Retinitis pigmentosa (RP) is the most prevalent type of inherited retinal degeneration and one of the commonest causes of genetically determined visual dysfunction worldwide. To date, approximately 35 genes have been associated with nonsyndromic autosomal recessive RP (arRP), however the small contribution of each gene to the total prevalence of arRP and the lack of a clear genotype–phenotype correlation complicate the genetic analysis in affected patients. Next generation sequencing technologies are powerful and cost-effective methods for detecting causative mutations in both sporadic and familial RP cases.

Methods

A Mexican family with 5 members affected from arRP was studied. All patients underwent a complete ophthalmologic examination. Molecular methods included genome-wide SNP homozygosity mapping, exome sequencing analysis, and Sanger-sequencing confirmation of causal mutations.

Results

No regions of shared homozygosity among affected subjects were identified. Exome sequencing in a single patient allowed the detection of two missense mutations in the RDH12 gene: a c.446T>C transition predicting a novel p.L149P substitution, and a c.295C>A transversion predicting a previously reported p.L99I replacement. Sanger sequencing confirmed that all affected subjects carried both RDH12 mutations.

Conclusions

This study adds to the molecular spectrum of RDH12-related retinopathy and offers an additional example of the power of exome sequencing in the diagnosis of recessively inherited retinal degenerations.     

Structural organization of a chain molecule with specific charge distribution: A molecular dynamics study

We report the results of molecular dynamics simulations of a charged bead-monomer chain molecule with charge distribution adopted from immunoglobulin-binding domain B1 of protein-g. The beads of the model are connected by invariable bonds and interact with each other via the Coulomb potential. To study the low-temperature conformational space of the designed model we use standard canonical, microcanonical and multicanonical molecular dynamics simulations. We find that at low temperature T = T c the chain undergoes a continuous freezing transition into one of many low-energy conformations. Below T c the molecule is a compact globule composed of an inner core, containing mostly charged monomers, and an outer corona, consisting of all the rest neutral units. All frozen conformations have almost equal potential energy but differ in structure. The potential energy surface of the model does not posses a pronounced ground-state minimum--an essential feature of protein-like heteropolymers.     

An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes

Electrocardiogram (ECG) signals are difficult to interpret, and clinicians must undertake a long training process to learn to diagnose diabetes from subtle abnormalities in these signals. To facilitate these diagnoses, we have developed a technique based on the heart rate variability signal obtained from ECG signals. This technique uses digital signal processing methods and, therefore, automates the detection of diabetes from ECG signals. In this paper, we describe the signal processing techniques that extract features from heart rate (HR) signals and present an analysis procedure that uses these features to diagnose diabetes. Through statistical analysis, we have identified the correlation dimension, Poincaré geometry properties (SD2), and recurrence plot properties (REC, DET, L _mean) as useful features. These features differentiate the HR data of diabetic patients from those of patients who do not have the illness, and have been validated by using the AdaBoost classifier with the perceptron weak learner (yielding a classification accuracy of 86%). We then developed a novel diabetic integrated index (DII) that is a combination of these nonlinear features. The DII indicates whether a particular HR signal was taken from a person with diabetes. This index aids the automatic detection of diabetes, thereby allowing a more objective assessment and freeing medical professionals for other tasks.