Adaptive landscape genetics and malaria across divergent island bird populations

Environmental conditions play a major role in shaping the spatial distributions of pathogens, which in turn can drive local adaptation and divergence in host genetic diversity. Haemosporidians, such as Plasmodium (malaria), are a strong selective force, impacting survival and fitness of hosts, with geographic distributions largely determined by habitat suitability for their insect vectors. Here, we have tested whether patterns of fine‐scale local adaptation to malaria are replicated across discrete, ecologically differing island populations of Berthelot's pipits Anthus berthelotii. We sequenced TLR4, an innate immunity gene that is potentially under positive selection in Berthelot's pipits, and two SNPs previously identified as being associated with malaria infection in a genome‐wide association study (GWAS) in Berthelot's pipits in the Canary Islands. We determined the environmental predictors of malaria infection, using these to estimate variation in malaria risk on Porto Santo, and found some congruence with previously identified environmental risk factors on Tenerife. We also found a negative association between malaria infection and a TLR4 variant in Tenerife. In contrast, one of the GWAS SNPs showed an association with malaria risk in Porto Santo, but in the opposite direction to that found in the Canary Islands GWAS. Together, these findings suggest that disease‐driven local adaptation may be an important factor in shaping variation among island populations.     

Effects of in vivo exposure to DEET on blood feeding behavior and fecundity in Anopheles quadrimaculatus (Diptera: Culicidae)

This study determined the effects of contact with DEET on guinea pig skin on mortality, probing time, blood feeding rate, engorgement time, and fecundity responses in female Anopheles quadrimaculatus Say. Exposure, in this manner, to 10% DEET (in ethanol) for 5 min, resulted in 98% mortality in mosquitoes after 24h. The median probing time (PT(50)) required by females, when exposed to 0.1%, 1.0%, and 10% DEET, was significantly (P<0.0001) longer (12.5, 12.1, and 19.1s, respectively) than the 6.8s required by females to probe ethanol-treated skin (control). Similarly, mean blood feeding rates in populations of females exposed to 1.0% DEET for < or = 5 min (14.4%) was 6x lower (P<0.001) (85.5%) than in females exposed to ethanol-treated skin, whereas the mean engorgement time on skin treated with 1.0% DEET (66.3s) was significantly shorter (P<0.0001) than for females feeding on the control guinea pigs (105.9s). The mean number of mature o?cytes per female (fecundity) in treatment (1.0% DEET) and control mosquitoes was not significantly different. The responses to DEET observed in this study suggest that repeated exposure of female A. quadrimaculatus populations to this repellent, in laboratory bioassays, could result in confounding of toxicant and repellent effects and inaccurate estimates of DEET repellency.     

Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions

Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)-based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking.     

Interaction of rice dwarf virus outer capsid P8 protein with rice glycolate oxidase mediates relocalization of P8

Yeast two-hybrid and coimmunoprecipitation assays indicated that P8, an outer capsid protein of Rice dwarf phytoreovirus (RDV), interacts with rice glycolate oxidase (GOX), a typical enzyme of peroxisomes. Confocal immunofluorescence microscopy revealed that P8 was colocalized with GOX in peroxisomes. Time course analysis demonstrated that the localization of P8 in Spodoptera frugiperda cells changed from diffuse to discrete, punctuate inclusions during expression from 24 to 48 h post inoculation. Coexpression of GOX with P8 may target P8 into peroxisomes, which serve as replication sites for a number of viruses. Therefore, we conclude that the interaction of P8 with the GOX of host cells leads to translocation of P8 into peroxisomes and we further propose that the interaction between P8 and GOX may play important roles in RDV targeting into the replication site of host cells. Our findings have broad significance in studying the mechanisms whereby viruses target appropriate replication sites and begin their replication.     

VPMCD: variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.     

Learning the drug target-likeness of a protein

Current drug discovery and development approaches rely extensively on the identification and validation of appropriate targets; for example, those with marketable and robust therapeutics. Wide-ranging efforts have been directed at this problem and various approaches have been developed to identify disease-associated genes as candidates. In this work, we show with statistical significance that successful drug targets, in addition to their linkage to disease, share common characteristics that are disease-independent. For example, marked differences in functional category, tissue specificity, and sequence variability are observed between known targets and average proteins. These results lead to an interesting hypothesis: potentially good drug targets shall have some desired properties, which we refer to as "drug target-likeness" that are beyond their disease-associations. Because of the limited availability of comprehensive protein characteristics data, we tried to learn the drug target-likeness property at the sequence level. Results show that a support vector machine model is able to accurately distinguish targets from nontargets entirely with sequence features. It is our hope that these encouraging results will invite future systematic proteomic scale experiments to gather necessary protein characteristics data for the accurate and predictive definition of "drug target-likeness", providing a new perspective toward understanding and pursuing effective therapeutics.     

Adhesive thermosensitive gels for local delivery of viral vectors

Local delivery of viral vectors can enhance the efficacy of therapies by selectively affecting necessary tissues and reducing the required vector dose. Pluronic F127 is a thermosensitive polymer that undergoes a solution–gelation (sol–gel) transition as temperature increases and can deliver vectors without damaging them. While pluronics can be spread over large areas, such as the surface of an organ, before gelation, they lack sufficient adhesivity to remain attached to some tissues, such as the surface of the heart or mucosal surfaces. Here, we utilized blends of pluronic F127 and polycarbophil (PCB), a mucoadhesive agent, to provide the necessary adhesivity for local delivery of viral vectors to the cardiac muscle. The effects of PCB concentration on adhesive properties, sol–gel temperature transition and cytocompatibility were evaluated. Rheological studies showed that PCB decreased the sol–gel transition temperature at concentrations >1% and increased the adhesive properties of the gel. Furthermore, these gels were able to deliver viral vectors and transduce cells in vitro and in vivo in a neonatal mouse apical resection model. These gels could be a useful platform for delivering viral vectors over the surface of organs where increased adhesivity is required.     

Machine Learning in Healthcare

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.     

Application of partial restriction procedure in both shotgun and non-random strategies for nucleotide sequencing

Partial digestion of a target DNA fragment with 4-bp-recognition restriction enzymes followed by a forced ligation to an M13 vector was employed for the construction of a subfragment library. The library can be used for either shotgun or non-random nucleotide sequencing. Application of the partial digests generated with the 4-bp recognition restriction enzymes instead of DNase I in the improved non-random strategy for nucleotide sequencing (Li and Wu, 1987) made the procedure as easy as that of the random strategy. The library can also be used in shotgun nucleotide sequencing directly, and few self-ligated subfragments were found. The usefulness of this procedure was demonstrated by the sequencing of a goat 6.5-kb EcoRI fragment, which is located 5' to the globin gene.     

A vector with the downstream box of the initiation codon can highly enhance protein expression in Escherichia coli

An expression vector, pET-DB, with a perfectly matching downstream box of the initiation codon has been constructed on the basis of the pET system. Any gene of interest can then be inserted into the vector. Four genes were used to test the expression efficiency of the vector. The results show that the vector pET-DB can further increase protein expression level at least up to 35–70% as compared with the initial T7 expression system, indicating that the downstream box can enhance protein expression in Escherichia coli.