A network-based approach to identify substrate classes of bacterial glycosyltransferases

Background

Bacterial interactions with the environment- and/or host largely depend on the bacterial glycome. The specificities of a bacterial glycome are largely determined by glycosyltransferases (GTs), the enzymes involved in transferring sugar moieties from an activated donor to a specific substrate. Of these GTs their coding regions, but mainly also their substrate specificity are still largely unannotated as most sequence-based annotation flows suffer from the lack of characterized sequence motifs that can aid in the prediction of the substrate specificity.

Results

In this work, we developed an analysis flow that uses sequence-based strategies to predict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these predicted GTs. Our analysis flow was benchmarked with the well-documented GT-repertoire of Campylobacter jejuni NCTC 11168 and applied to the probiotic model Lactobacillus rhamnosus GG to expand our insights in the glycosylation potential of this bacterium. In L. rhamnosus GG we could predict 48 GTs of which eight were not previously reported. For at least 20 of these GTs a substrate relation was inferred.

Conclusions

We confirmed through experimental validation our prediction of WelI acting upstream of WelE in the biosynthesis of exopolysaccharides. We further hypothesize to have identified in L. rhamnosus GG the yet undiscovered genes involved in the biosynthesis of glucose-rich glycans and novel GTs involved in the glycosylation of proteins. Interestingly, we also predict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-349) contains supplementary material, which is available to authorized users.     

Gene expression in cortex and hippocampus during acute pneumococcal meningitis

Background  

Pneumococcal meningitis is associated with high mortality (~30%) and morbidity. Up to 50% of survivors are affected by neurological sequelae due to a wide spectrum of brain injury mainly affecting the cortex and hippocampus. Despite this significant disease burden, the genetic program that regulates the host response leading to brain damage as a consequence of bacterial meningitis is largely unknown.     

Postprandial transformations of enzymatic and hormonal properties of saliva and blood

In 14 volunteers, saliva from both parotid, submandibular and sublingual glands were collected by capsules under stimulation of sialosis with citric acid or alimentary trial breakfast. It was taken immediately and on the 1st and 3rd hours of postprandial response. In saliva and the blood serum, alpha-amylases, trypsin, common protein, thyrotropin, thyroxine, triiodthyronin, luteinizing hormone, follicle-stimulating hormone, prolactin, progesterone, oestradiol and hydrocortisone were assessed by means of immuno-assay technique. All but oestradiol hormones had a lower concentration in the saliva than in the blood serum. The concentration and deficits of hormones and trypsin in saliva of submandibular and sublingual glands is higher, than in saliva of parotid glands, the latter having a higher alpha-amylolytic activity. The share of p-amylase in comparison with s-amylase in saliva of parotid glands is lesser than in saliva of submandibular and sublingual glands. In alimentary stimulation of sialosis, the saliva with higher amylolytic and tryptic activity, higher concentration of thyrotropin and thyroxine was found than under a non-alimentary stimulation. After the 1st and the 3rd hours following a trial breakfast, in response to a non-alimentary stimulation of sialosis the saliva was found to preserve properties of a postprandial saliva.     

Zerumbone induced apoptosis in liver cancer cells via modulation of Bax/Bcl-2 ratio

Background  

Zerumbone is a cytotoxic component isolated from Zingiber zerumbet Smith, a herbal plant which is also known as lempoyang. This new anticancer bioactive compound from Z. zerumbet was investigated for its activity and mechanism in human liver cancer cell lines.     

Predicting host tropism of influenza A virus proteins using random forest

Background

Majority of influenza A viruses reside and circulate among animal populations, seldom infecting humans due to host range restriction. Yet when some avian strains do acquire the ability to overcome species barrier, they might become adapted to humans, replicating efficiently and causing diseases, leading to potential pandemic. With the huge influenza A virus reservoir in wild birds, it is a cause for concern when a new influenza strain emerges with the ability to cross host species barrier, as shown in light of the recent H7N9 outbreak in China. Several influenza proteins have been shown to be major determinants in host tropism. Further understanding and determining host tropism would be important in identifying zoonotic influenza virus strains capable of crossing species barrier and infecting humans.

Results

In this study, computational models for 11 influenza proteins have been constructed using the machine learning algorithm random forest for prediction of host tropism. The prediction models were trained on influenza protein sequences isolated from both avian and human samples, which were transformed into amino acid physicochemical properties feature vectors. The results were highly accurate prediction models (ACC>96.57; AUC>0.980; MCC>0.916) capable of determining host tropism of individual influenza proteins. In addition, features from all 11 proteins were used to construct a combined model to predict host tropism of influenza virus strains. This would help assess a novel influenza strain's host range capability.

Conclusions

From the prediction models constructed, all achieved high prediction performance, indicating clear distinctions in both avian and human proteins. When used together as a host tropism prediction system, zoonotic strains could potentially be identified based on different protein prediction results. Understanding and predicting host tropism of influenza proteins lay an important foundation for future work in constructing computation models capable of directly predicting interspecies transmission of influenza viruses. The models are available for prediction at http://fluleap.bic.nus.edu.sg.

     

Molecular phylogenetics reveal multiple tertiary vicariance origins of the African rain forest trees

Background  

Tropical rain forests are the most diverse terrestrial ecosystems on the planet. How this diversity evolved remains largely unexplained. In Africa, rain forests are situated in two geographically isolated regions: the West-Central Guineo-Congolian region and the coastal and montane regions of East Africa. These regions have strong floristic affinities with each other, suggesting a former connection via an Eocene pan-African rain forest. High levels of endemism observed in both regions have been hypothesized to be the result of either 1) a single break-up followed by a long isolation or 2) multiple fragmentation and reconnection since the Oligocene. To test these hypotheses the evolutionary history of endemic taxa within a rain forest restricted African lineage of the plant family Annonaceae was studied. Molecular phylogenies and divergence dates were estimated using a Bayesian relaxed uncorrelated molecular clock assumption accounting for both calibration and phylogenetic uncertainties.     

Eicosapentaenoic and docosahexaenoic acids production by and okara-utilizing potential of thraustochytrids

Nine thraustochytrid strains isolated from subtropical mangroves were screened for their eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) production potential in a glucose yeast extract medium. Their ability to utilize okara (soymilk residue) for growth and EPA and DHA production was also evaluated. EPA yield was low in most strains, while DHA level was high on glucose yeast extract medium, producing 28.1–41.1% of total fatty acids, for all strains, with the exception of Ulkenia sp. KF13. The DHA yield of Schizochytrium mangrovei strains ranged from 747.7 to 2778.9 mg/l after 52 h of fermentation at 25°C. All strains utilized okara as a substrate for growth, but DHA yield was lower when compared with fermentation in a glucose yeast extract medium. Journal of Industrial Microbiology & Biotechnology (2001) 27, 199–202. Received 11 December 2000/ Accepted in revised form 29 June 2001