Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model

Background  

Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO). However, the annotation of genes is a labor-intensive process; and the vocabularies are generally incomplete, leaving some important biological domains inadequately covered.     

Characterisation of novel functionality within the Blastocystis tryptophanase gene

In recent years, the human gut microbiome has been recognised to play a pivotal role in the health of the host. Intestinal homeostasis relies on this intricate and complex relationship between the gut microbiota and the human host. While much effort and attention has been placed on the characterization of the organisms that inhabit the gut microbiome, the complex molecular cross-talk between the microbiota could also exert an effect on gastrointestinal conditions. Blastocystis is a single-cell eukaryotic parasite of emerging interest, as its beneficial or pathogenic role in the microbiota has been a subject of contention even to-date. In this study, we assessed the function of the Blastocystis tryptophanase gene (BhTnaA), which was acquired by horizontal gene transfer and likely to be of bacterial origin within Blastocystis. Bioinformatic analysis and phylogenetic reconstruction revealed distinct divergence of BhTnaA versus known bacterial homologs. Despite sharing high homology with the E. coli tryptophanase gene, we show that Blastocystis does not readily convert tryptophan into indole. Instead, BhTnaA preferentially catalyzes the conversion of indole to tryptophan. We also show a direct link between E. coli and Blastocystis tryptophan metabolism: In the presence of E. coli, Blastocystis ST7 is less able to metabolise indole to tryptophan. This study examines the potential for functional variation in horizontally-acquired genes relative to their canonical counterparts, and identifies Blastocystis as a possible producer of tryptophan within the gut.     

TNF receptor 1 deficiency increases regulatory T cell function in nonobese diabetic mice

TNF has been implicated in the pathogenesis of type 1 diabetes. When administered early in life, TNF accelerates and increases diabetes in NOD mice. However, when administered late, TNF decreases diabetes incidence and delays onset. TNFR1-deficient NOD mice were fully protected from diabetes and only showed mild peri-insulitis. To further dissect how TNFR1 deficiency affects type 1 diabetes, these mice were crossed to β cell-specific, highly diabetogenic TCR transgenic I-A(g7)-restricted NOD4.1 mice and Kd-restricted NOD8.3 mice. TNFR1-deficient NOD4.1 and NOD8.3 mice were protected from diabetes and had significantly less insulitis compared with wild type NOD4.1 and NOD8.3 controls. Diabetic NOD4.1 mice rejected TNFR1-deficient islet grafts as efficiently as control islets, confirming that TNFR1 signaling is not directly required for β cell destruction. Flow cytometric analysis showed a significant increase in the number of CD4(+)CD25(+)Foxp3(+) T regulatory cells in TNFR1-deficient mice. TNFR1-deficient T regulatory cells were functionally better at suppressing effector cells than were wild type T regulatory cells both in vitro and in vivo. This study suggests that blocking TNF signaling may be beneficial in increasing the function of T regulatory cells and suppression of type 1 diabetes.     

Evaluation of a Prednisolone Acetate-Loaded Subconjunctival Implant for the Treatment of Recurrent Uveitis in a Rabbit Model

Aim

To assess the efficacy of a biodegradable, prednisolone acetate implant in a rabbit uveitis model.

Methods

Randomized, controlled study of biodegradable microfilms preloaded with prednisolone acetate (PA) in a rabbit uveitis model. Experimental uveitis was induced by unilateral intravitreal injection of Mycobacterium tuberculosis H37Ra antigen (50 ug; 1 ug/uL) in preimmunized rabbits. PA-loaded poly[d,l-lactide-co-ε-caprolactone] (PLC) microfilms (n = 10) and blank microfilms (n = 6) were implanted subconjunctivally. An estimate of PA release in vivo was calculated from measured residual PA amounts in microfilms after the rabbits were sacrificed. The eyes were clinically monitored for ocular inflammation for 28 days. Histopathological examination of the enucleated eyes was performed at the end of the study period.

Results

In vitro studies revealed that sandwich PA-loaded microfilm formulations exhibited higher release kinetic compared to homogenous PA-loaded microfilms. The 60–40–60% microfilm released an average of 0.034 mg/day of PA over the period of 60 days in vitro; and we found that approximately 0.12 mg/day PA was released in vivo. Animals implanted with the PA-loaded microfilms exhibited significantly lowered median inflammatory scores when compared against the control group in this model for recurrent uveitis (P<0.001). The implants were clinically well tolerated by all the animals. Histology results showed no significant scarring or inflammation around the PA-loaded microfilms.

Conclusion

Our pilot study demonstrated that a subconjunctival PA-loaded implant is effective in suppressing inflammation in the rabbit model of uveitis, by providing therapeutic levels of PA that attenuated the inflammatory response even after a rechallenge. Longer term studies are now needed to establish the therapeutic potential of such a delivery system for treatment of ocular inflammation.     

Ensemble Positive Unlabeled Learning for Disease Gene Identification

An increasing number of genes have been experimentally confirmed in recent years as causative genes to various human diseases. The newly available knowledge can be exploited by machine learning methods to discover additional unknown genes that are likely to be associated with diseases. In particular, positive unlabeled learning (PU learning) methods, which require only a positive training set P (confirmed disease genes) and an unlabeled set U (the unknown candidate genes) instead of a negative training set N, have been shown to be effective in uncovering new disease genes in the current scenario. Using only a single source of data for prediction can be susceptible to bias due to incompleteness and noise in the genomic data and a single machine learning predictor prone to bias caused by inherent limitations of individual methods. In this paper, we propose an effective PU learning framework that integrates multiple biological data sources and an ensemble of powerful machine learning classifiers for disease gene identification. Our proposed method integrates data from multiple biological sources for training PU learning classifiers. A novel ensemble-based PU learning method EPU is then used to integrate multiple PU learning classifiers to achieve accurate and robust disease gene predictions. Our evaluation experiments across six disease groups showed that EPU achieved significantly better results compared with various state-of-the-art prediction methods as well as ensemble learning classifiers. Through integrating multiple biological data sources for training and the outputs of an ensemble of PU learning classifiers for prediction, we are able to minimize the potential bias and errors in individual data sources and machine learning algorithms to achieve more accurate and robust disease gene predictions. In the future, our EPU method provides an effective framework to integrate the additional biological and computational resources for better disease gene predictions.     

Advances in the molecular biology of plant seed storage proteins

Plant seed storage proteins were among the first proteins to be isolated (20); however, only recently, as a result of using molecular biology techniques, have the amino acid sequences of many of these proteins been determined. With the accumulation of amino acid sequence data for many vicilin-type storage proteins much has been learned concerning the location of conserved amino acid regions and other regions which can tolerate amino acid sequence variation. Combining this knowledge with recent advances in plant gene transfer technologies will allow molecular biologists to correct (by using amino acid replacement mutations) the sulfur amino acid deficiency inherent to bean seed storage proteins. The development of more nutritious soybean and common bean seeds will be of benefit to programs involving human and animal nutrition.     

Effects of sports training on sleep characteristics of Asian adolescent athletes

Adolescents are predisposed to poorer quality of sleep and experience shortened sleep durations, with these trends being more pronounced amongst Asians. Even though sleep is crucial for athletic recovery, there is a dearth of the literature on the sleep patterns of Asian adolescent athletes. The purpose of this study was to examine the effects of different intensities of sports training on sleep patterns in adolescent athletes, and to describe novel sleep data and daytime sleepiness amongst Asian adolescents who were high-level athletes. Those athletes (age 14.8 ± 0.9 years) in higher-intensity sports showed significantly more deep sleep, less light sleep and waketime after sleep onset. Actigraphically determined bedtimes and waketimes were significantly delayed on weekends, when mean total sleep time was also significantly longer. There was a large effect for an increased daytime sleepiness in high-intensity sport athletes. These findings highlight the phenomenon of social jet lag in Asian adolescent student-athletes.     

Long Span DNA Paired-End-Tag (DNA-PET) Sequencing Strategy for the Interrogation of Genomic Structural Mutations and Fusion-Point-Guided Reconstruction of Amplicons

Structural variations (SVs) contribute significantly to the variability of the human genome and extensive genomic rearrangements are a hallmark of cancer. While genomic DNA paired-end-tag (DNA-PET) sequencing is an attractive approach to identify genomic SVs, the current application of PET sequencing with short insert size DNA can be insufficient for the comprehensive mapping of SVs in low complexity and repeat-rich genomic regions. We employed a recently developed procedure to generate PET sequencing data using large DNA inserts of 10–20 kb and compared their characteristics with short insert (1 kb) libraries for their ability to identify SVs. Our results suggest that although short insert libraries bear an advantage in identifying small deletions, they do not provide significantly better breakpoint resolution. In contrast, large inserts are superior to short inserts in providing higher physical genome coverage for the same sequencing cost and achieve greater sensitivity, in practice, for the identification of several classes of SVs, such as copy number neutral and complex events. Furthermore, our results confirm that large insert libraries allow for the identification of SVs within repetitive sequences, which cannot be spanned by short inserts. This provides a key advantage in studying rearrangements in cancer, and we show how it can be used in a fusion-point-guided-concatenation algorithm to study focally amplified regions in cancer.