Quantitative PCR analysis of house dust can reveal abnormal mold conditions

Indoor mold concentrations were measured in the dust of moldy homes (MH) and reference homes (RH) by quantitative PCR (QPCR) assays for 82 species or related groups of species (assay groups). About 70% of the species and groups were never or only rarely detected. The ratios (MH geometric mean : RH geometric mean) for 6 commonly detected species (Aspergillus ochraceus, A. penicillioides, A. unguis, A. versicolor, Eurotium group, and Cladosporium sphaerospermum) were >1 (Group I). Logistic regression analysis of the sum of the logs of the concentrations of Group I species resulted in a 95% probability for separating MH from RH. These results suggest that it may be possible to evaluate whether a home has an abnormal mold condition by quantifying a limited number of mold species in a dust sample. Also, four common species of Aspergillus were quantified by standard culturing procedures and their concentrations compared to QPCR results. Culturing underestimated the concentrations of these four species by 2 to 3 orders of magnitude compared to QPCR.     

Utilizing pyrosequencing and quantitative PCR to characterize fungal populations among house dust samples

Molecular techniques are an alternative to culturing and counting methods in quantifying indoor fungal contamination. Pyrosequencing offers the possibility of identifying unexpected indoor fungi. In this study, 50 house dust samples were collected from homes in the Yakima Valley, WA. Each sample was analyzed by quantitative PCR (QPCR) for 36 common fungi and by fungal tag-encoded flexible (FLX) amplicon pyrosequencing (fTEFAP) for these and additional fungi. Only 24 of the samples yielded amplified results using fTEFAP but QPCR successfully amplified all 50 samples. Over 450 fungal species were detected by fTEFAP but most were rare. Twenty-two fungi were found by fTEFAP to occur with at least an average of ≥0.5% relative occurrence. Many of these fungi seem to be associated with plants, soil or human skin. Combining fTEFAP and QPCR can enhance studies of fungal contamination in homes.     

Quantitative PCR analysis of molds in the dust from homes of asthmatic children in North Carolina

The vacuum bag (VB) dust from the homes of 19 asthmatic children in North Carolina (NC) was analyzed by mold specific quantitative PCR. These results were compared to the analysis of the VB dust from 176 homes in the HUD, American Healthy Home Survey of homes in the US. The Environmental Relative Moldiness Index (ERMI) was calculated for each of the homes. The mean and standard deviation (SD) of the ERMI values in the homes of the NC asthmatic children was 16.4 (6.77), compared to the HUD survey VB ERMI value mean and SD of 11.2 (6.72), and was significantly greater (t-test, p = 0.003) in the NC asthmatic children's homes. The molds Chaetomium globosum, Aspergillus fumigatus, and the Eurotium Group were the primary species in the NC homes of asthmatics, making the ERMI values significantly higher (p < 0.02 for each). Vacuum bag dust analysis may be a useful method for estimating the mold burden in a home.     

Association of Streptomyces community composition determined by PCR-denaturing gradient gel electrophoresis with indoor mold status

Both Streptomyces species and mold species have previously been isolated from moisture-damaged building materials; however, an association between these two groups of microorganisms in indoor environments is not clear. In this study, we used a culture-independent method, PCR-denaturing gradient gel electrophoresis (PCR-DGGE), to investigate the composition of the Streptomyces community in house dust. Twenty-three dust samples each from two sets of homes categorized as high-mold and low-mold based on mold-specific quantitative PCR analysis were used in the study. Taxonomic identification of prominent bands was performed by cloning and sequencing. Associations between DGGE amplicon band intensities and home mold status were assessed using univariate analyses as well as multivariate recursive partitioning (decision trees) to test the predictive value of combinations of bands intensities. In the final classification tree, a combination of two bands was significantly associated with mold status of the home (p?=?0.001). The sequence corresponding to one of the bands in the final decision tree matched a group of Streptomyces species that included Streptomyces coelicolor and Streptomyces sampsonii, both of which have been isolated from moisture-damaged buildings previously. The closest match for the majority of sequences corresponding to a second band consisted of a group of Streptomyces species that included Streptomyces hygroscopicus, an important producer of antibiotics and immunosuppressors. Taken together, the study showed that DGGE can be a useful tool for identifying bacterial species that may be more prevalent in mold-damaged buildings.