Impact of Early Empiric Antibiotic Regimens on the Gut Microbiota in Very Low Birth Weight Preterm Infants: An Observational Study

Frequent use of antibiotics in preterm infants disturbs their gut microbial balance. In this preliminary observational study, we investigated the effect of different antibiotic regimens, administered during the first week of life, on microbial composition and diversity in very low birth weight (VLBW) preterm infants. We performed fecal sampling of breastfed VLBW infants on days 7, 14, and 30. After excluding stool samples from infants who received probiotics or who were administered antibiotics beyond the age of 7 days, we compared gut microbiota profiles between infants receiving a combination of ampicillin and gentamicin for 3 days (AG group, n = 10) and those receiving a combination of ampicillin and cefotaxime for 7 days (AC group, n = 14) using 16S ribosomal DNA community profiling. We also assessed the changes over time in each group. Compared to the AG group, Enterococcus species were significantly more abundant in the AC group (P = 0.002), especially in 7-day samples (12.3 vs. 0.6%, respectively, P = 0.032). No difference was observed at phylum and genus level over time within each group. Species richness in the AC group decreased significantly in the 14-day (P = 0.038) and 30-day (P = 0.03) samples compared to that in the 7-day sample. The same was observed for microbial evenness; in contrast, no significant difference in Shannon index and beta-diversity was detected between the two groups. Controlling for relevant confounding variables did not change the results. In conclusion, different antibiotic regimens affect the early development of gut microbiota in VLBW preterm infants. Prolonged use of ampicillin and cefotaxime might result in overabundance of Enterococcus. However, given that no significant differences were observed in 1-month samples, bacterial genera appear to continue colonizing the gastrointestinal tract despite previous exposure to antibiotics. The clinical relevance of these findings should be elucidated by further studies.

Carcinogenesis of Male Oral Submucous Fibrosis Alters Salivary Microbiomes

Most oral squamous cell carcinoma (OSCC) tumors arise from oral premalignant lesions. Oral submucous fibrosis (OSF), usually occurring in male chewers of betel quid, is a premalignant stromal disease characterized by a high malignant transformation rate and high prevalence. Although a relationship between the inhabited microbiome and carcinogenesis has been proposed, no detailed information regarding the oral microbiome of patients with OSF exists; the changes of the salivary microbiome during cancer formation remain unclear. This study compared the salivary microbiomes of male patients with OSCC and a predisposing OSF background (OSCC-OSF group) and those with OSF only (OSF group). The results of high-throughput sequencing of the bacterial 16S rRNA gene indicated that OSF-related carcinogenesis and smoking status significantly contributed to phylogenetic composition variations in the salivary microbiome, leading to considerable reductions in species richness and phylogenetic diversity. The microbiome profile of OSF-related malignancy was associated with increased microbial stochastic fluctuation, which dominated the salivary microbiome assembly and caused species co-occurrence network collapse. Artificial intelligence selection algorithms consistently identified 5 key species in the OSCC-OSF group: Porphyromonas catoniae, Prevotella multisaccharivorax, Prevotella sp. HMT-300, Mitsuokella sp. HMT-131, and Treponema sp. HMT-927. Robust accuracy in predicting oral carcinogenesis was obtained with our exploratory and validation data sets. In functional analysis, the microbiome of the OSCC-OSF group had greater potential for S-adenosyl-l-methionine and norspermidine synthesis but lower potential for l-ornithine and pyrimidine deoxyribonucleotide synthesis and formaldehyde metabolism. These findings indicated that the salivary microbiome plays important roles in modulating microbial metabolites during oral carcinogenesis. In conclusion, our results provided new insights into salivary microbiome alterations during the malignant transformation of OSF.

Inferring microbial interaction network from microbiome data using RMN algorithm

Background

Microbial interactions are ubiquitous in nature. Recently, many similarity-based approaches have been developed to study the interaction in microbial ecosystems. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. In addition, the strength of microbial interactions is difficult to be quantified by only using correlation analysis.

Results

In this study, a rule-based microbial network (RMN) algorithm, which integrates regulatory OTU-triplet model with parametric weighting function, is being developed to construct microbial regulatory networks. The RMN algorithm not only can extrapolate the cooperative and competitive relationships between microbes, but also can infer the direction of such interactions. In addition, RMN algorithm can theoretically characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial networks. Our results suggested that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. Furthermore, we inferred some possible microbial interactions, including the competitive relationship between Veillonella and Bacteroides, and the cooperative relationship between Veillonella and Clostridium XI.

Conclusions

The RMN algorithm provides the reconstruction of gut microbe networks, and can shed light on the dynamical interactions of microbes in the infant intestinal tract.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0199-2) contains supplementary material, which is available to authorized users.

Genetic characterization of enterovirus 71 isolated from patients with severe disease by comparative analysis of complete genomes

Enterovirus 71 (EV71) which causes mild illness in children is also associated with severe neurological complications. This study analyzed the complete genomes of EV71 strains derived from mild and severe diseases in order to determine whether the differences of EV71 genomes were responsible for different clinical presentations. Compared to complete genomes of EV71 strains derived from mild cases (less virulent strains), nucleotide differences in EV71 strains isolated from severe cases (more virulent strains) were observed primarily in the internal ribosomal entry site (IRES) of the 5'-untranslated region (UTR), which is vital for the cap-independent translation of viral proteins. In the protein-coding region, an E-Q substitution at amino acid position 145 of structural protein VP1 that occurred in more than one of more virulent strains was observed. This site is known to be related functionally to receptor binding and virulence in mice. Overall, strains (Group III) isolated from patients with fatal or severe sequelae outcomes had greater sequence substitutions in the 5'-UTR and/or protein-coding region and exhibited a relatively low-average homology to less virulent strains across the entire genome, indicating the possibility of significant genomic diversity in the most virulent EV71 strains. Further studies of EV71 pathogenesis should examine the significance of genomic diversity and the effects of multiple mutations in a viral population.

Enhancement of initial equivalency for protein structure alignment based on encoded local structures

Most alignment algorithms find an initial equivalent residue pair followed by an iterative optimization process to explore better near-optimal alignments in the surrounding solution space of the initial alignment. It plays a decisive role in determining the alignment quality since a poor initial alignment may make the final alignment trapped in an undesirable local optimum even with an iterative optimization. We proposed a vector-based alignment algorithm with a new initial alignment approach accounting for local structure features called MIRAGE-align. The new idea is to enhance the quality of the initial alignment based on encoded local structural alphabets to identify the protein structure pair whose sequence identity falls in or below twilight zone. The statistical analysis of alignment quality based on Match Index (MI) and computation time demonstrated that MIRAGE-align algorithm outperformed four previously published algorithms, i.e., the residue-based algorithm (CE), the vector-based algorithm (SSM), TM-align, and Fr-TM-align. MIRAGE-align yields a better estimate of initial solution to enhance the quality of initial alignment and enable the employment of a non-iterative optimization process to achieve a better alignment.

Identification of activated cryptic 5' splice sites using structure profiles and odds measure

The activation of cryptic 5′ splice sites (5′ SSs) is often related to human hereditary diseases. The DNA-based mutation screening strategies are commonly used to recognize the cryptic 5′ SSs, because features of the local DNA sequence can influence the choice of cryptic 5′ SSs. To improve the identification of the cryptic 5′ SSs, we developed a structure-based method, named SPO (structure profiles and odds measure), which combines two parameters, the structural feature derived from hydroxyl radical cleavage pattern and odds measure, to assess the likelihood of a cryptic 5′ SS activation in competing with its paired authentic 5′ SS. Compared to the current tools for identifying activated cryptic 5′ SSs, the SPO algorithm achieves higher prediction accuracy than the other methods, including MaxEnt, MDD, Markov model, weight matrix model, Shapiro and Senapathy matrix, R_i and ΔG. In addition, the predicted ΔSPO scores from the SPO algorithm exhibited a greater degree of correlation with the strength of cryptic 5′ SS activation than that measured from the other seven methods. In conclusion, the SPO algorithm provides an optimal identification of cryptic 5′ SSs, can be applied in designing mutagenesis experiments for various splicing events and may be helpful to investigate the relationship between structural variants and human hereditary diseases.

Influenza genome diversity and evolution

The influenza viruses contain highly variable genomes and are able to infect a wide range of host species. Large-scale sequencing projects have collected abundant influenza sequence data for assessing influenza genome diversity and evolution. This work reviews current influenza sequence databases characteristics and statistics, as well as recent studies utilizing these databases to unravel influenza virus diversity and evolution. Also discussed are the newest deep sequencing methods and their applications to influenza virus research.

A robust correlation estimator and nonlinear recurrent model to infer genetic interactions in Saccharomyces cerevisiae and pathways of pulmonary disease in Homo sapiens

In order to identify genes involved in complex diseases, it is crucial to study the genetic interactions at the systems biology level. By utilizing modern high throughput microarray technology, it has become feasible to obtain gene expressions data and turn it into knowledge that explains the regulatory behavior of genes. In this study, an unsupervised nonlinear model was proposed to infer gene regulatory networks on a genome-wide scale. The proposed model consists of two components, a robust correlation estimator and a nonlinear recurrent model. The robust correlation estimator was used to initialize the parameters of the nonlinear recurrent curve-fitting model. Then the initialized model was used to fit the microarray data. The model was used to simulate the underlying nonlinear regulatory mechanisms in biological organisms. The proposed algorithm was applied to infer the regulatory mechanisms of the general network in Saccharomyces cerevisiae and the pulmonary disease pathways in Homo sapiens. The proposed algorithm requires no prior biological knowledge to predict linkages between genes. The prediction results were checked against true positive links obtained from the YEASTRACT database, the TRANSFAC database, and the KEGG database. By checking the results with known interactions, we showed that the proposed algorithm could determine some meaningful pathways, many of which are supported by the existing literature.

Genomic splice site prediction algorithm based on nucleotide sequence pattern for RNA viruses

Splice site prediction on an RNA virus has two potential difficulties seriously degrading the performance of most conventional splice site predictors. One is a limited number of strains available for a virus species and the other is the diversified sequence patterns around the splice sites caused by the high mutation frequency. To overcome these two difficulties, a new algorithm called Genomic Splice Site Prediction (GSSP) algorithm, was proposed for splice site prediction of RNA viruses. The key idea of the GSSP algorithm was to characterize the interdependency among the nucleotides and base positions based on the eigen-patterns. Identified by a sequence pattern mining technique, each eigen-pattern specified a unique composition of the base positions and the nucleotides occurring at the positions. To remedy the problem of insufficient training data due to the limited number of strains for an RNA virus, a cross-species strategy was employed in this study. The GSSP algorithm was shown to be effective and superior to two conventional methods in predicting the splice sites of five RNA species in the Orthomyxoviruses family. The sensitivity and specificity achieved by the GSSP algorithm was higher than 99 and 94%, respectively, for the donor sites, and was higher than 96 and 92%, respectively, for the acceptor sites. Supplementary data associated with this work are freely available for academic use at http://homepage.ntu.edu.tw/ approximately d91548013/.

Inhibition of enterovirus 71-induced apoptosis by allophycocyanin isolated from a blue-green alga Spirulina platensis

Enterovirus 71 infection causes significant morbidity and mortality in children, yet there is no effective treatment. In this study, a protein-bound pigment, allophycocyanin purified from blue-green algae is first reported to exhibit anti-enterovirus 71 activity. Allophycocyanin neutralized the enterovirus 71-induced cytopathic effect in both human rhabdomyosarcoma cells and African green monkey kidney cells. The 50% inhibitory concentration of allophycocyanin for neutralizing the enterovirus 71-induced cytopathic effect was approximately 0.045 +/- 0.012 microM in green monkey kidney cells. The cytotoxic concentrations of allophycocyanin for rhabdomyosarcoma cells and African green monkey kidney cells were 1.653 +/- 0.003 microM and 1.521 +/- 0.012 microM, respectively. A plaque reduction assay showed that the concentrations of allophycocyanin for reducing plaque formation by 50% were approximately 0.056 +/- 0.007 microM and 0.101 +/- 0.032 microM, when allophycocyanin were added at the state of viral adsorption and post-adsorption, respectively. Antiviral activity was more efficient in cultures treated with allophycocyanin before viral infection compared with that in the cultures treated after infection. Allophycocyanin was also able to delay viral RNA synthesis in the infected cells and to abate the apoptotic process in enterovirus 71-infected rhabdomyosarcoma cells with evidence of characteristic DNA fragmentation, decreasing membrane damage and declining cell sub-G1 phase. It is concluded that allophycocyanin possesses antiviral activity and has a potential for development as an anti-enterovirus 71 agent.