Bacterial exchange in household washing machines

Household washing machines (WMs) launder soiled clothes and textiles, but do not sterilize them. We investigated the microbial exchange occurring in five household WMs. Samples from a new cotton T-shirt were laundered together with a normal laundry load. Analyses were performed on the influent water and the ingoing cotton samples, as well as the greywater and the washed cotton samples. The number of living bacteria was generally not lower in the WM effluent water as compared to the influent water. The laundering process caused a microbial exchange of influent water bacteria, skin-, and clothes related bacteria and biofilm-related bacteria in the WM. A variety of biofilm-producing bacteria were enriched in the effluent after laundering, although their presence in the cotton sample was low. Nearly all bacterial genera detected on the initial cotton sample were still present in the washed cotton samples. A selection for typical skin- and clothes related microbial species occurred in the cotton samples after laundering. Accordingly, malodour-causing microbial species might be further distributed to other clothes. The bacteria on the ingoing textiles contributed for a large part to the microbiome found in the textiles after laundering

Testing the limits of 454 pyrotag sequencing: reproducibility, quantitative assessment and comparison to T-RFLP fingerprinting of aquifer microbes.

The characterization of microbial community structure via 16S rRNA gene profiling has been greatly advanced in recent years by the introduction of amplicon pyrosequencing. The possibility of barcoding gives the opportunity to massively screen multiple samples from environmental or clinical sources for community details. However, an on-going debate questions the reproducibility and semi-quantitative rigour of pyrotag sequencing, similar to the early days of community fingerprinting. In this study we demonstrate the reproducibility of bacterial 454 pyrotag sequencing over biological and technical replicates of aquifer sediment bacterial communities. Moreover, we explore the potential of recovering specific template ratios via quantitatively defined template spiking to environmental DNA. We sequenced pyrotag libraries of triplicate sediment samples taken in annual sampling campaigns at a tar oil contaminated aquifer in Düsseldorf, Germany. The abundance of dominating lineages was highly reproducible with a maximal standard deviation of ~4% read abundance across biological, and ~2% across technical replicates. Our workflow also allows for the linking of read abundances within defined assembled pyrotag contigs to that of specific 'in vivo' fingerprinting signatures. Thus we demonstrate that both terminal restriction fragment length polymorphism (T-RFLP) analysis and pyrotag sequencing are capable of recovering highly comparable community structure. Overall diversity was roughly double in amplicon sequencing. Pyrotag libraries were also capable of linearly recovering increasing ratios (up to 20%) of 16S rRNA gene amendments from a pure culture of Aliivibrio fisheri spiked to sediment DNA. Our study demonstrates that 454 pyrotag sequencing is a robust and reproducible method, capable of reliably recovering template abundances and overall community structure within natural microbial communities

Reproducibility and comparison of Shannon diversity (<i>H</i>’) and selected OTU abundance in T-RFLP fingerprinting and pyrotag libraries.

<p>Reproducibility and comparison of Shannon diversity (<i>H</i>’) and selected OTU abundance in T-RFLP fingerprinting and pyrotag libraries.</p

Read abundance standard deviation (SD) for dominating and less abundant taxa in pyrotag libraries.

<p>Averaged and maximal SDs for taxa within specified abundance ranges are given. All taxa resolved by the RDP-classifier and recovered at 1 read in average or more over 9 sediment pyrotag libraries were included in the calculation.</p

Comparison of bacterial community structure as recovered in T-RFLP fingerprinting and pyrotag libraries.

<p>Results are shown for selected aquifer sediment DNA extracts (replicate <i>b</i> from each year). For pyrotag ‘fingerprints’, the abundance of dominating assembled contigs is illustrated via T-RFs predicted <i>in silico</i> along with the total number of reads contributing to that contig. (*) illustrates the 282 bp pseudo T-RF of specific <i>Spirochaete</i> populations detected as <i>in vivo</i> T-RF only.</p

Semi-quantitative recovery of spiked <i>A. fisheri</i> 16S rRNA genes in pyrotag libraries.

<p>Semi-quantitative recovery of spiked <i>A. fisheri</i> 16S rRNA genes in pyrotag libraries.</p

Reproducibility of pyrotag read abundance over biological replicates of aquifer DNA extracts.

<p>Error bars indicate standard deviation (positive only) of averaged triplicate samples for each year. Taxon classification was recorded at phylum/class level (A) for entire libraries, and for selected abundant taxa (B) detected at the site <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040467#pone.0040467-Pilloni1" target="_blank">[11]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040467#pone.0040467-Winderl1" target="_blank">[13]</a>.</p

Characterization of Staphylococcus and Corynebacterium clusters in the human axillary region.

The skin microbial community is regarded as essential for human health and well-being, but likewise plays an important role in the formation of body odor in, for instance, the axillae. Few molecular-based research was done on the axillary microbiome. This study typified the axillary microbiome of a group of 53 healthy subjects. A profound view was obtained of the interpersonal, intrapersonal and temporal diversity of the human axillary microbiota. Denaturing gradient gel electrophoresis (DGGE) and next generation sequencing on 16S rRNA gene region were combined and used as extent to each other. Two important clusters were characterized, where Staphylococcus and Corynebacterium species were the abundant species. Females predominantly clustered within the Staphylococcus cluster (87%, n = 17), whereas males clustered more in the Corynebacterium cluster (39%, n = 36). The axillary microbiota was unique to each individual. Left-right asymmetry occurred in about half of the human population. For the first time, an elaborate study was performed on the dynamics of the axillary microbiome. A relatively stable axillary microbiome was noticed, although a few subjects evolved towards another stable community. The deodorant usage had a proportional linear influence on the species diversity of the axillary microbiome

Dynamics (moving window analysis) of 7 subjects of the DGGE results.

<p>LA = left axilla; RA = right axilla. Left axis indicates the similarity (based on Pearson correlation) of the axillary sample compared to the previous axillary sample. The higher the curve, the more similar the samples. The axillary microbiome was relatively constant throughout time, even on a longer timescale (9 months). Two followed-up subjects experienced an community shift from one cluster to the other, after which the microbiome again was stable (subject 4 and 11).</p

Clustering of individual axillary samples analyzed by means of DGGE, where 69% of the subjects clustered into the <i>Staphylococcus</i> cluster and 31% into the <i>Corynebacterium</i> cluster.

<p>Right: Subject indices from S1 till S53 (not all subjects were shown); gender of the subject; pyrosequenced samples indicated with MID (multiplex identifiers). Above: Identified band: bands A were identified as <i>Staphylococcus epidermidis</i> (100% identity), bands B were identified as <i>Staphylococcus</i> spp. (99% identity), bands C were identified as <i>Staphylococcus hominis</i> (100% identity), bands D and E were identified as <i>Proteobacteria</i> (from pyrosequencing results), band G was identified as <i>Corynebacterium</i> spp. (99% identity), bands H were identified as <i>Corynebacterium</i> spp. (99% identity), and bands F, I, J and K were identified as <i>Corynebacterium</i> spp. (from pyrosequencing results). Left: Clustering of the samples, based on Pearson correlation and unweighted pair group with mathematical averages dendrogram method. Under: indication of GC% of the bacterial bands. <i>Firmicutes</i> have a low GC%, and bands are generally situated left on the gel; <i>Actinobacteria</i> have a high GC%, with bands situated generally on the right side of the gel.</p