Engineering topics

Feng, Shihai

Publications and source records attributed to Feng, Shihai.

3 records

DOE OSTI2017

Streptococcus mutans Displays Altered Stress Responses While Enhancing Biofilm Formation by Lactobacillus casei in Mixed-Species Consortium

Like Streptococcus mutans , lactobacilli are commonly isolated from carious sites, although their exact role in caries development remains unclear. This study used mixed-species models to analyze biofilm formation by major groups of oral lactobacilli, including L. casei, L. fermentum, L. rhamnosus, L. salivarius ssp. salivarius, and L. gasseri . The results showed that lactobacilli did not form good biofilms when grown alone, although differences existed between different species. When grown together with S. mutans , biofilm formation by L. gasseri and L. rhamnosus was increased by 2-log ( P < 0.001), while biofilms by L. fermentum reduced by >1-log ( P < 0.001). L. casei enhanced biofilm formation by ~2-log when grown with S. mutans wild-type, but no such effects were observed with S. mutans deficient of glucosyltransferase GtfB and adhesin P1. Both S. mutans and L. casei in dual-species enhanced resistance to acid killing with increases of survival rate by >1-log ( P < 0.001), but drastically reduced the survival rates following exposure to hydrogen peroxide ( P < 0.001), as compared to the respective mono-species cultures. When analyzed by RNA-seq, more than 134 genes were identified in S. mutans in dual-species with L. casei as either up- or down-regulated when compared to those grown alone. The up-regulated genes include those for superoxide dismutase, NADH oxidase, and members of the mutanobactin biosynthesis cluster. Among the down-regulated genes were those for GtfB and alternative sigma factor SigX. These results further suggest that interactions between S. mutans and oral lactobacilli are species-specific and may have significant impact on cariogenic potential of the community.

Immunology↗

DOE OSTI2016

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform

Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools in pre-configured workflows. These workflows, coupled with the ease of use of the environment, provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks and, within the context of the same environment, to visualize and further interrogate their results. As a result, this bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications for microbial research.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

ADEPT, a dynamic next generation sequencing data error-detection program with trimming

Illumina is the most widely used next generation sequencing technology and produces millions of short reads that contain errors. These sequencing errors constitute a major problem in applications such as de novo genome assembly, metagenomics analysis and single nucleotide polymorphism discovery. In this study, we present ADEPT, a dynamic error detection method, based on the quality scores of each nucleotide and its neighboring nucleotides, together with their positions within the read and compares this to the position-specific quality score distribution of all bases within the sequencing run. This method greatly improves upon other available methods in terms of the true positive rate of error discovery without affecting the false positive rate, particularly within the middle of reads. We conclude that ADEPT is the only tool to date that dynamically assesses errors within reads by comparing position-specific and neighboring base quality scores with the distribution of quality scores for the dataset being analyzed. The result is a method that is less prone to position-dependent under-prediction, which is one of the most prominent issues in error prediction. The outcome is that ADEPT improves upon prior efforts in identifying true errors, primarily within the middle of reads, while reducing the false positive rate.

59 BASIC BIOLOGICAL SCIENCES↗