Multiplex approach for screening genetic markers of microbial indicators

FunGene: the functional gene pipeline and repository

Ribosomal RNA genes have become the standard molecular markers for microbial community analysis for good reasons, including universal occurrence in cellular organisms, availability of large databases, and ease of rRNA gene region amplification and analysis. As markers, however, rRNA genes have some significant limitations. The rRNA genes are often present in multiple copies, unlike most protein-coding genes. The slow rate of change in rRNA genes means that multiple species sometimes share identical 16S rRNA gene sequences, while many more species share identical sequences in the short 16S rRNA regions commonly analyzed. In addition, the genes involved in many important processes are not distributed in a phylogenetically coherent manner, potentially due to gene loss or horizontal gene transfer. While rRNA genes remain the most commonly used markers, key genes in ecologically important pathways, e.g., those involved in carbon and nitrogen cycling, can provide important insights into community composition and function not obtainable through rRNA analysis. However, working with ecofunctional gene data requires some tools beyond those required for rRNA analysis. To address this, our Functional Gene Pipeline and Repository (FunGene; http://fungene.cme.msu.edu/) offers databases of many common ecofunctional genes and proteins, as well as integrated tools that allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences. Additional FunGene tools are specialized to process coding gene amplicon data. For example, FrameBot produces frameshift-corrected protein and DNA sequences from raw reads while finding the most closely related protein reference sequence. These tools can help provide better insight into microbial communities by directly studying key genes involved in important ecological processes.

Survival and persistence of human and ruminant-specific faecal Bacteroidales in freshwater microcosms

Amplification of host-specific markers from Bacteroidales faecal anaerobes can rapidly identify the source of faecal pollution. It is necessary to understand persistence and survival of these markers and marker cells, both to interpret quantitative source-tracking data, and to use such data to predict pathogen occurrence. We measured marker persistence and cell survival of two human (HF134, HF183) and two ruminant (CF128, CF193) faecal Bacteroidales markers, compared with Escherichia coli and enterococci. Freshwater microcosms were inoculated with fresh cattle or human faeces and incubated at 13 degrees C in natural light or darkness. Marker persistence was measured by polymerase chain reaction (PCR) and quantitative PCR. Survival of marker cells was measured by real-time quantitative PCR. There was no difference in persistence between the two human-specific Bacteroidales DNA markers in the light and dark microcosms. Cell survival profiles of the two human markers were also similar; both were significantly affected by light. Ruminant markers persisted and survived longer than human markers (14 versus 6 days respectively). CF193 decreased more rapidly than CF128, and light significantly affected CF128 but not CF193. These results support use of host-specific faecal Bacteroidales markers as indicators of recent faecal pollution, but suggest that caution is needed in interpreting quantitative results to indicate proportional contribution of different sources, as individual markers differ in their survival, persistence and response to environmental variables. The survival and persistence profiles for Bacteroidales markers are consistent with survival profiles for several faecal pathogens.

Virulence factor activity relationships: challenges and development approaches

Virulence factor activity relationships (VFAR) is a predictive approach proposed by the National Research Council's Committee on Drinking Water Contaminants (Washington, D.C.) to classify and rank waterborne pathogens. It is based on the presumption that health threats of waterborne pathogens can be predicted from descriptors at different levels of cellular organization. This paper summarizes challenges that need to be addressed while developing VFAR, with a focus on genomics, such as genomic variability among related pathogens and the need to incorporate genetic descriptors for persistence and host susceptibility. Three key components of VFAR development and validation are also presented, including (1) compilation of a comprehensive VFAR database, (2) development of predictive mathematical models relating descriptors to health effects and other microbial responses, and (3) high-throughput molecular monitoring of drinking water supplies and sources. Bayesian approach and on-chip polymerase chain reaction are discussed as examples of mathematical models and molecular monitoring.