Molecular profiling of indoor microbial communities in moisture damaged and non-damaged buildings

Epidemiological studies have shown an elevation in the incidence of asthma, allergic symptoms and respiratory infections among people living or working in buildings with moisture and mould problems. Microbial growth is suspected to have a key role, since the severity of microbial contamination and symptoms show a positive correlation, while the removal of contaminated materials relieves the symptoms. However, the cause-and-effect relationship has not been well established and knowledge of the causative agents is incomplete. The present consensus of indoor microbes relies on culture-based methods. Microbial cultivation and identification is known to provide qualitatively and quantitatively biased results, which is suspected to be one of the reasons behind the often inconsistent findings between objectively measured microbiological attributes and health. In the present study the indoor microbial communities were assessed using culture-independent, DNA based methods. Fungal and bacterial diversity was determined by amplifying and sequencing the nucITS- and16S-gene regions, correspondingly. In addition, the cell equivalent numbers of 69 mould species or groups were determined by quantitative PCR (qPCR). The results from molecular analyses were compared with results obtained using traditional plate cultivation for fungi. Using DNA-based tools, the indoor microbial diversity was found to be consistently higher and taxonomically wider than viable diversity. The dominant sequence types of fungi, and also of bacteria were mainly affiliated with well-known microbial species. However, in each building they were accompanied by various rare, uncultivable and unknown species. In both moisture-damaged and undamaged buildings the dominant fungal sequence phylotypes were affiliated with the classes Dothideomycetes (mould-like filamentous ascomycetes); Agaricomycetes (mushroom- and polypore-like filamentous basidiomycetes); Urediniomycetes (rust-like basidiomycetes); Tremellomycetes and the family Malasseziales (both yeast-like basidiomycetes). The most probable source for the majority of fungal types was the outdoor environment. In contrast, the dominant bacterial phylotypes in both damaged and undamaged buildings were affiliated with human-associated members within the phyla Actinobacteria and Firmicutes. Indications of elevated fungal diversity within potentially moisture-damage-associated fungal groups were recorded in two of the damaged buildings, while one of the buildings was characterized by an abundance of members of the Penicillium chrysogenum and P. commune species complexes. However, due to the small sample number and strong normal variation firm conclusions concerning the effect of moisture damage on the species diversity could not be made. The fungal communities in dust samples showed seasonal variation, which reflected the seasonal fluctuation of outdoor fungi. Seasonal variation of bacterial communities was less clear but to some extent attributable to the outdoor sources as well. The comparison of methods showed that clone library sequencing was a feasible method for describing the total microbial diversity, indicated a moderate quantitative correlation between sequencing and qPCR results and confirmed that culture based methods give both a qualitative and quantitative underestimate of microbial diversity in the indoor environment. However, certain important indoor fungi such as Penicillium spp. were clearly underrepresented in the sequence material, probably due to their physiological and genetic properties. Species specific qPCR was a more efficient and sensitive method for detecting and quantitating individual species than sequencing, but in order to exploit the full advantage of the method in building investigations more information is needed about the microbial species growing on damaged materials. In the present study, a new method was also developed for enhanced screening of the marker gene clone libraries. The suitability of the screening method to different kinds of microbial environments including biowaste compost material and indoor settled dusts was evaluated. The usability was found to be restricted to environments that support the growth and subsequent dominance of a small number microbial species, such as compost material.

The potential of microbial ecological indicators to guide ecosophisticated management of hydrocarbon-contaminated soils

Soil is an unrenewable natural resource under increasing anthropogenic pressure. One of the main threats to soils, compromising their ability to provide us with the goods and ecosystem services we expect, is pollution. Oil hydrocarbons are the most common soil contaminants, and they disturb not just the biota but also the physicochemical properties of soils. Indigenous soil micro-organisms respond rapidly to changes in the soil ecosystem, and are chronically in direct contact with the hydrophobic pollutants on the soil surfaces. Soil microbial variables could thus serve as an intrinsically relevant indicator of soil quality, to be used in the ecological risk assessment of contaminated and remediated soils. Two contrasting studies were designed to investigate soil microbial ecological responses to hydrocarbons, together with parallel changes in soil physicochemical and ecotoxicological properties. The aim was to identify quantitative or qualitative microbiological variables that would be practicable and broadly applicable for the assessment of the quality and restoration of oil-polluted soil. Soil bacteria commonly react on hydrocarbons as a beneficial substrate, which lead to a positive response in the classical microbiological soil quality indicators; negative impacts were accurately reflected only after severe contamination. Hydrocarbon contaminants become less bioavailable due to weathering processes, and their potentially toxic effects decrease faster than the total concentration. Indigenous hydrocarbon degrader bacteria, naturally present in any terrestrial environment, use specific mechanisms to improve access to the hydrocarbon molecules adsorbed on soil surfaces. Thus when contaminants are unavailable even to the specialised degraders, they should pose no hazard to other biota either. Change in the ratio of hydrocarbon degrader numbers to total microbes was detected to predictably indicate pollutant effects and bioavailability. Also bacterial diversity, a qualitative community characteristic, decreased as a response to hydrocarbons. Stabilisation of community evenness, and community structure that reflected clean reference soil, indicated community recovery. If long-term temporal monitoring is difficult and appropriate clean reference soil unavailable, such comparison could possibly be based on DNA-based community analysis, reflecting past+present, and RNA-based community analysis, showing exclusively present conditions. Microbial ecological indicators cannot replace chemical oil analyses, but they are theoretically relevant and operationally practicable additional tools for ecological risk assessment. As such, they can guide ecologically informed and sustainable ecosophisticated management of oil-contaminated lands.

Microbial identification by detection of ligation probes on DNA microarray

Microbes in natural and artificial environments as well as in the human body are a key part of the functional properties of these complex systems. The presence or absence of certain microbial taxa is a correlate of functional status like risk of disease or course of metabolic processes of a microbial community. As microbes are highly diverse and mostly notcultivable, molecular markers like gene sequences are a potential basis for detection and identification of key types. The goal of this thesis was to study molecular methods for identification of microbial DNA in order to develop a tool for analysis of environmental and clinical DNA samples. Particular emphasis was placed on specificity of detection which is a major challenge when analyzing complex microbial communities. The approach taken in this study was the application and optimization of enzymatic ligation of DNA probes coupled with microarray read-out for high-throughput microbial profiling. The results show that fungal phylotypes and human papillomavirus genotypes could be accurately identified from pools of PCR amplicons generated from purified sample DNA. Approximately 1 ng/μl of sample DNA was needed for representative PCR amplification as measured by comparisons between clone sequencing and microarray. A minimum of 0,25 amol/μl of PCR amplicons was detectable from amongst 5 ng/μl of background DNA, suggesting that the detection limit of the test comprising of ligation reaction followed by microarray read-out was approximately 0,04%. Detection from sample DNA directly was shown to be feasible with probes forming a circular molecule upon ligation followed by PCR amplification of the probe. In this approach, the minimum detectable relative amount of target genome was found to be 1% of all genomes in the sample as estimated from 454 deep sequencing results. Signal-to-noise of contact printed microarrays could be improved by using an internal microarray hybridization control oligonucleotide probe together with a computational algorithm. The algorithm was based on identification of a bias in the microarray data and correction of the bias as shown by simulated and real data. The results further suggest semiquantitative detection to be possible by ligation detection, allowing estimation of target abundance in a sample. However, in practise, comprehensive sequence information of full length rRNA genes is needed to support probe design with complex samples. This study shows that DNA microarray has the potential for an accurate microbial diagnostic platform to take advantage of increasing sequence data and to replace traditional, less efficient methods that still dominate routine testing in laboratories. The data suggests that ligation reaction based microarray assay can be optimized to a degree that allows good signal-tonoise and semiquantitative detection.