TRAMPR: AN R package for analysis and matching of terminal‐restriction fragment length polymorphism (TRFLP) profiles

trampr (TRFLP analysis and matching package for r ) is a package for matching multiple terminal restriction fragment length polymorphism (TRFLP) profiles between unknown samples and a database of known TRFLP profiles in order to infer the presence of species in environmental samples. It permits simultaneous analysis of multiple samples and facilitates direct workflow from electrophoresis output through to community analyses. trampr also resolves the issues of multiple TRFLP profiles within a species and (conversely) shared TRFLP profiles across species.     

NESTED AUTOMATED RIBOSOMAL INTERGENIC SPACER ANALYSIS: A RAPID AND ACCURATE METHOD FOR COMPARISON OF BACTERIAL COMMUNITY COMPOSITION

Nested automated ribosomal intergenic spacer analysis (ARISA) was used to examine the community structure of epilithic biofilms in freshwater streams experiencing different levels of human impact. This molecular fingerprinting technique generated reproducible profiles of bacterial community structure that varied significantly between stream sites. Nested ARISA was determined to be a cost-effective, high-throughput approach to assess bacterial community composition from very small sample volumes, requiring little sampling effort and without the need for taxonomic identification of individual organisms. In combination with multidimensional scaling, nested ARISA provides a rapid and sensitive method to carry out complex analyses of bacterial community structure.

PRACTICAL APPLICATIONS

Nested automated ribosomal intergenic spacer analysis (ARISA) provides a high-throughput molecular method with which to screen large numbers of environmental samples for differences in microbial community structure. This sensitive approach benefits assessments from small sample volumes or environments exhibiting reduced microbial biomass (both aquatic and terrestrial). Differences in bacterial community structure (obtained from ARISA profiles) could be used to characterize the impact of anthropogenic disturbance on freshwater systems, analogous to the current use of macroinvertebrate indicators of freshwater ecological health.     

Geographic and Environmental Sources of Variation in Lake Bacterial Community Composition

This study used a genetic fingerprinting technique (automated ribosomal intergenic spacer analysis [ARISA]) to characterize microbial communities from a culture-independent perspective and to identify those environmental factors that influence the diversity of bacterial assemblages in Wisconsin lakes. The relationships between bacterial community composition and 11 environmental variables for a suite of 30 lakes from northern and southern Wisconsin were explored by canonical correspondence analysis (CCA). In addition, the study assessed the influences of ARISA fragment detection threshold (sensitivity) and the quantitative, semiquantitative, and binary (presence-absence) use of ARISA data. It was determined that the sensitivity of ARISA was influential only when presence-absence-transformed data were used. The outcomes of analyses depended somewhat on the data transformation applied to ARISA data, but there were some features common to all of the CCA models. These commonalities indicated that differences in bacterial communities were best explained by regional (i.e., northern versus southern Wisconsin lakes) and landscape level (i.e., seepage lakes versus drainage lakes) factors. ARISA profiles from May samples were consistently different from those collected in other months. In addition, communities varied along gradients of pH and water clarity (Secchi depth) both within and among regions. The results demonstrate that environmental, temporal, regional, and landscape level features interact to determine the makeup of bacterial assemblages in northern temperate lakes.     

Coupling 16S-ITS rDNA clone libraries and automated ribosomal intergenic spacer analysis to show marine microbial diversity: development and application to a time series

We outline an approach to simultaneously assess multilevel microbial diversity patterns utilizing 16S-ITS rDNA clone libraries coupled with automated ribosomal intergenic spacer analysis (ARISA). Sequence data from 512 clones allowed estimation of ARISA fragment lengths associated with bacteria in a coastal marine environment. We matched 92% of ARISA peaks (each comprising >1% total amplified product) with corresponding lengths from clone libraries. These peaks with putative identification accounted for an average of 83% of total amplified community DNA. At 16S rDNA similarities <98%, most taxa displayed differences in ARISA fragment lengths >10 bp, readily detectable and suggesting ARISA resolution is near the 'species' level. Prochlorococcus abundance profiles from ARISA were strongly correlated (r2=0.86) to Prochlorococcus cell counts, indicating ARISA data are roughly proportional to actual cell abundance within a defined taxon. Analysis of ARISA profiles for 42 months elucidated patterns of microbial presence and abundance providing insights into community shifts and ecological niches for specific organisms, including a coupling of ecological patterns for taxa within the Prochlorococcus, the Gamma Proteobacteria and Actinobacteria. Clade-specific ARISA protocols were developed for the SAR11 and marine cyanobacteria to resolve ambiguous identifications and to perform focused studies. 16S-ITS data allowed high-resolution identification of organisms by ITS sequence analysis, and examination of microdiversity.     

Geographic and environmental sources of variation in lake bacterial community composition

This study used a genetic fingerprinting technique (automated ribosomal intergenic spacer analysis [ARISA]) to characterize microbial communities from a culture-independent perspective and to identify those environmental factors that influence the diversity of bacterial assemblages in Wisconsin lakes. The relationships between bacterial community composition and 11 environmental variables for a suite of 30 lakes from northern and southern Wisconsin were explored by canonical correspondence analysis (CCA). In addition, the study assessed the influences of ARISA fragment detection threshold (sensitivity) and the quantitative, semiquantitative, and binary (presence-absence) use of ARISA data. It was determined that the sensitivity of ARISA was influential only when presence-absence-transformed data were used. The outcomes of analyses depended somewhat on the data transformation applied to ARISA data, but there were some features common to all of the CCA models. These commonalities indicated that differences in bacterial communities were best explained by regional (i.e., northern versus southern Wisconsin lakes) and landscape level (i.e., seepage lakes versus drainage lakes) factors. ARISA profiles from May samples were consistently different from those collected in other months. In addition, communities varied along gradients of pH and water clarity (Secchi depth) both within and among regions. The results demonstrate that environmental, temporal, regional, and landscape level features interact to determine the makeup of bacterial assemblages in northern temperate lakes.     

Comparative analysis of polychlorinated biphenyl-dechlorinating communities in enrichment cultures using three different molecular screening techniques

The catalysts for many microbially mediated environmental processes such as the dechlorination of polychlorinated biphenyls (PCBs) have been difficult to identify by traditional isolation techniques. Numerous, as yet unsuccessful, attempts have been made to isolate and culture the dechlorinating species. To overcome this limitation, amplified rDNA restriction analysis (ARDRA) of a clone library, denaturing gradient gel electrophoresis (DGGE) and terminal restriction fragment length polymorphism (TRFLP) were used concurrently to compare their effectiveness for characterizing an enriched microbial community. These methods were applied to enrichment cultures that selectively dechlorinated double-flanked chlorines in the PCB congener 2,3,4,5 chlorinated biphenyl. The methods have different biases, which were apparent from discrepancies in the relative clone frequencies (ARDRA), band intensities (DGGE) or peak heights (TRFLP) from the same enrichment culture. However, each method was effectively qualitative and identified the same organisms: a low G + C Gram-positive eubacterium, an organism most similar to the green non-sulphur bacteria, an Aminobacterium sp. and a Desulfovibrio sp. Overall, in community fingerprinting and preliminary identification, DGGE proved to be the most rapid and effective tool for the monitoring of microorganisms within a highly enriched culture. TRFLP results corroborated DGGE fingerprint analysis; however, identification required the additional step of creating a clone library. ARDRA provided an in-depth analysis of the community and this technique detected slight intraspecies sequence variation in 16S rDNA. These molecular methods are common in environmental microbiology, but rarely are they compared with the same sample site or culture. In general, all three methods detected similar community profiles, but inherent biases resulted in different detection limits for individual OTUs (operational taxonomic units).     

Spatial and temporal heterogeneity of the bacterial communities in stream epilithic biofilms

The spatial and temporal variability in bacterial communities within freshwater systems is poorly understood. The bacterial composition of stream epilithic biofilms across a range of different spatial and temporal scales both within and between streams and across the profile of individual stream rocks was characterised using a community DNA-fingerprinting technique (Automated Ribosomal Intergenic Spacer Analysis, ARISA). The differences in bacterial community structure between two different streams were found to be greater than the spatial variability within each stream site, and were larger than the weekly temporal variation measured over a 10-week study period. Greater variations in bacterial community profiles were detected on different faces of individual stream rocks than between whole rocks sampled within a 9-m stream section. Stream temperature was found to be the most important determinant of bacterial community variability using distance-based redundancy analysis (dbRDA) of ARISA data, which may have broad implications for riparian zone management and ecological change as a consequence of global warming. The combination of ARISA with multivariate statistical methods and ordination, such as multidimensional scaling (MDS), permutational manova and RDA, provided rapid and effective methods for quantifying and visualising variation in bacterial community structure, and to identify potential drivers of ecological change.     

Comparison of primer sets for use in automated ribosomal intergenic spacer analysis of aquatic bacterial communities: an ecological perspective

Two primer sets for automated ribosomal intergenic spacer analysis (ARISA) were used to assess the bacterial community composition (BCC) in Lake Mendota, Wisconsin, over 3 years. Correspondence analysis revealed differences in community profiles generated by different primer sets, but overall ecological patterns were conserved in each case. ARISA is a powerful tool for evaluating BCC change through space and time, regardless of the specific primer set used.     

Automated approach for ribosomal intergenic spacer analysis of microbial diversity and its application to freshwater bacterial communities.

An automated method of ribosomal intergenic spacer analysis (ARISA) was developed for the rapid estimation of microbial diversity and community composition in freshwater environments. Following isolation of total community DNA, PCR amplification of the 16S-23S intergenic spacer region in the rRNA operon was performed with a fluorescence-labeled forward primer. ARISA-PCR fragments ranging in size from 400 to 1,200 bp were next discriminated and measured by using an automated electrophoresis system. Database information on the 16S-23S intergenic spacer was also examined, to understand the potential biases in diversity estimates provided by ARISA. In the analysis of three natural freshwater bacterial communities, ARISA was rapid and sensitive and provided highly reproducible community-specific profiles at all levels of replication tested. The ARISA profiles of the freshwater communities were quantitatively compared in terms of both their relative diversity and similarity level. The three communities had distinctly different profiles but were similar in their total number of fragments (range, 34 to 41). In addition, the pattern of major amplification products in representative profiles was not significantly altered when the PCR cycle number was reduced from 30 to 15, but the number of minor products (near the limit of detection) was sensitive to changes in cycling parameters. Overall, the results suggest that ARISA is a rapid and effective community analysis technique that can be used in conjunction with more accurate but labor-intensive methods (e.g., 16S rRNA gene cloning and sequencing) when fine-scale spatial and temporal resolution is needed.     

Comparison of Primer Sets for Use in Automated Ribosomal Intergenic Spacer Analysis of Aquatic Bacterial Communities: an Ecological Perspective

Two primer sets for automated ribosomal intergenic spacer analysis (ARISA) were used to assess the bacterial community composition (BCC) in Lake Mendota, Wisconsin, over 3 years. Correspondence analysis revealed differences in community profiles generated by different primer sets, but overall ecological patterns were conserved in each case. ARISA is a powerful tool for evaluating BCC change through space and time, regardless of the specific primer set used.     

Automated Approach for Ribosomal Intergenic Spacer Analysis of Microbial Diversity and Its Application to Freshwater Bacterial Communities

An automated method of ribosomal intergenic spacer analysis (ARISA) was developed for the rapid estimation of microbial diversity and community composition in freshwater environments. Following isolation of total community DNA, PCR amplification of the 16S-23S intergenic spacer region in the rRNA operon was performed with a fluorescence-labeled forward primer. ARISA-PCR fragments ranging in size from 400 to 1,200 bp were next discriminated and measured by using an automated electrophoresis system. Database information on the 16S-23S intergenic spacer was also examined, to understand the potential biases in diversity estimates provided by ARISA. In the analysis of three natural freshwater bacterial communities, ARISA was rapid and sensitive and provided highly reproducible community-specific profiles at all levels of replication tested. The ARISA profiles of the freshwater communities were quantitatively compared in terms of both their relative diversity and similarity level. The three communities had distinctly different profiles but were similar in their total number of fragments (range, 34 to 41). In addition, the pattern of major amplification products in representative profiles was not significantly altered when the PCR cycle number was reduced from 30 to 15, but the number of minor products (near the limit of detection) was sensitive to changes in cycling parameters. Overall, the results suggest that ARISA is a rapid and effective community analysis technique that can be used in conjunction with more accurate but labor-intensive methods (e.g., 16S rRNA gene cloning and sequencing) when fine-scale spatial and temporal resolution is needed.     

Temporal microbial diversity changes in solvent-degrading anaerobic granular sludge from low-temperature (15 degrees C) wastewater treatment bioreactors

Anaerobic sludge granules were obtained from laboratory-scale anaerobic bioreactors used to treat pharmaceutical-like (methanol-, acetone- and propanol-contaminated) wastewater under low-temperature conditions (15 degrees C). The microbial diversity and diversity changes of the sludge samples were ascertained by applying 16S rRNA gene cloning and terminal restriction fragment length polymorphism (TRFLP) analyses, respectively, and using sludge samples from the inoculum, throughout and at the conclusion of the bioreactor trial. Data from genetic fingerprinting correlated well with those from physiological activity assays of the reactor biomass. Specifically, for example, TRFLP profiles indicated the dominance of hydrogenotrophic methanogens within the archaeal community, thus supporting the findings of specific methanogenic activity measurements. TRFLP data supported the hypothesis that the deviation between the replicated reactors, in terms of treatment efficiency, was associated with succession within the microbial communities present, and indicated that community development was linked to both operating temperature and wastewater composition. Fluorescence in situ hybridization (FISH) was also applied, to quantitatively assess the abundance of selected microbial groups, and revealed the underestimation of the abundance Methanosarcina by gene cloning analysis and demonstrated the spatial arrangement of these organisms within the architecture of the low-temperature solvent-degrading anaerobic biofilms.     

Deep Sequencing of Subseafloor Eukaryotic rRNA Reveals Active Fungi across Marine Subsurface Provinces

The deep marine subsurface is a vast habitat for microbial life where cells may live on geologic timescales. Because DNA in sediments may be preserved on long timescales, ribosomal RNA (rRNA) is suggested to be a proxy for the active fraction of a microbial community in the subsurface. During an investigation of eukaryotic 18S rRNA by amplicon pyrosequencing, unique profiles of Fungi were found across a range of marine subsurface provinces including ridge flanks, continental margins, and abyssal plains. Subseafloor fungal populations exhibit statistically significant correlations with total organic carbon (TOC), nitrate, sulfide, and dissolved inorganic carbon (DIC). These correlations are supported by terminal restriction length polymorphism (TRFLP) analyses of fungal rRNA. Geochemical correlations with fungal pyrosequencing and TRFLP data from this geographically broad sample set suggests environmental selection of active Fungi in the marine subsurface. Within the same dataset, ancient rRNA signatures were recovered from plants and diatoms in marine sediments ranging from 0.03 to 2.7 million years old, suggesting that rRNA from some eukaryotic taxa may be much more stable than previously considered in the marine subsurface.     

Impact of catchment land use on bacterial communities within stream biofilms

The bacterial community structure in epilithic biofilms within 18 different streams was characterised using a community DNA fingerprinting technique (automated ribosomal intergenic spacer analysis—ARISA). Each stream has previously been described in terms of the dominant catchment land use, relative level of human disturbance and using a broad suite of water quality variables. Combination of ARISA with multivariate statistical analysis and ordination revealed that bacterial communities in streams located within rural catchments were significantly different to those within urban catchments. Broad-scale catchment land use described the largest component of the observed variation with no single water quality variable found to be a dominant determinant of the observed bacterial community variability, assessed using distance based redundancy analysis (dbRDA) of the ARISA data. This study highlights the potential of bacterial ARISA to provide a rapid and cost-effective approach to monitor the impact of catchment land use on aquatic ecosystems, such as the influence of encroaching urban development on the ecological health of rural streams.     

A dynamic programming algorithm for binning microbial community profiles

MOTIVATION: A number of community profiling approaches have been widely used to study the microbial community composition and its variations in environmental ecology. Automated Ribosomal Intergenic Spacer Analysis (ARISA) is one such technique. ARISA has been used to study microbial communities using 16S-23S rRNA intergenic spacer length heterogeneity at different times and places. Owing to errors in sampling, random mutations in PCR amplification, and probably mostly variations in readings from the equipment used to analyze fragment sizes, the data read directly from the fragment analyzer should not be used for down stream statistical analysis. No optimal data preprocessing methods are available. A commonly used approach is to bin the reading lengths of the 16S-23S intergenic spacer. We have developed a dynamic programming algorithm based binning method for ARISA data analysis which minimizes the overall differences between replicates from the same sampling location and time. RESULTS: In a test example from an ocean time series sampling program, data preprocessing identified several outliers which upon re-examination were found to be because of systematic errors. Clustering analysis of the ARISA from different times based on the dynamic programming algorithm binned data revealed important features of the biodiversity of the microbial communities.     

Soil Bacterial and Fungal Community Structure Across a Range of Unimproved and Semi-Improved Upland Grasslands

Changes in soil microbial community structure due to improvement are often attributed to concurrent shifts in floristic community composition. The bacterial and fungal communities of unimproved and semi-improved (as determined by floristic classification) grassland soils were studied at five upland sites on similar geological substrata using both broad-scale (microbial activity and fungal biomass) and molecular [terminal restriction fragment length polymorphism (TRFLP), automated ribosomal intergenic spacer analysis (ARISA)] approaches. It was hypothesized that microbial community structure would be similar in soils from the same grassland type, and that grassland vegetation classifications could thus be used as predictors of microbial community structure. Microbial community measurements varied widely according to both site and grassland type, and trends in the effect of grassland improvement differed between sites. These results were consistent with those from similar studies, and indicated that floristic community composition was not a stable predictor of microbial community structure across sites. This may indicate a lack of correlation between grassland plant composition and soil microbial community structure, or that differences in soil chemistry between sites had larger impacts on soil microbial populations than plant-related effects.     

Analyzing salt-marsh fungal diversity: comparing ARISA fingerprinting with clone sequencing and pyrosequencing

In a previous study from our laboratory we used automated ribosomal intergenic spacer analysis (ARISA) to assess salt-marsh fungal diversity (Torzilli et al. 2006). The results demonstrated that different salt-marsh plants harbor distinct fungal communities, thereby supporting the hypothesis that substratum type is an important factor in determining fungal community composition. However, ARISA of several pure cultures of salt-marsh fungi indicated that an operational taxonomic unit (OUT) in an ARISA community profile may represent more than one taxon. To assess the extent to which such ambiguity might have affected the interpretation of our ARISA fingerprinting, we have now fingerprinted and sequenced clones derived from the same fungal DNA used for our ARISA community profiles. Results from this confirmed that an ARISA OTU may represent multiple taxa and that a given taxon may be represented by more than one OTU. Nonetheless, sequencing still confirmed the importance of substratum in determining community composition, and indicated that despite ambiguities associated with OTU's, ARISA may be used to provide a quick snapshot of diversity which can be further refined using sequencing methods. In addition, we compared the fungal diversity from short-form Spartina alterniflora as revealed by clone sequencing with that obtained from pyrosequencing, which avoids the cloning biases of traditional sequencing, and provide greatly expanded depth of coverage. Pyrosequencing significantly enhanced the characterization of fungal diversity compared to traditional clone sequencing.     

Shift of bacterial community structure in two Thai soil series affected by silver nanoparticles using ARISA

In this study we examined the influence of silver nanoparticles (SNP) on the bacterial community and microbial processes in two soils from Thailand, a Ayutthaya (Ay) and Kamphaengsaen soil series (Ks). Results of this analysis revealed that SNP did not affect to pH, electrical conductivity, cation exchange capacity, and organic matter in both the Ay and Ks series. Automated ribosomal intergenic spacer analysis (ARISA) analysis profiles showed that bacterial community decreased with increasing SNP concentration. Pearson’s correlation coefficient and multidimensional scaling analyses indicated that the effects of SNP on the bacterial community structure depended more on soil types than SNP application rates and incubation periods. Additionally, the results showed that SNP application rates affected on amount of CO₂ emissions, while SNP application rates had no effect on N mineralization in both soil types. This study is the first investigation of the effects of SNP on bacterial community using ARISA analysis. Our results might be useful to evaluate the risk associated with the applications of SNP for consumer products and agricultural practices.     

Seasonal influences on fungal community structure in unimproved and improved upland grassland soils

Seasonal and management influences on the fungal community structure of two upland grassland soils were investigated. An upland site containing both unimproved floristically diverse (U4a) and improved mesotrophic (MG7b) grassland types was selected. Samples from both grassland types were taken at five times in one year. Soil fungal community structure was assessed using fungal automated ribosomal intergenic spacer analysis (ARISA), a DNA-profiling approach. A grassland management regime was found to strongly affect fungal community structure, with fungal ARISA profiles from unimproved and improved grassland soils differing significantly. The number of fungal ribotypes found was higher in unimproved than improved grassland soils, providing evidence that improvement may reduce the suitability of upland soil as a habitat for specific groups of fungi. Seasonal influences on fungal community structure were also noted, with samples taken in autumn (October) more correlated with change in ribotype profiles than samples from other seasons. However, seasonal variation did not obscure the measurement of differences in the fungal community structure that were due to agricultural improvement, with canonical correspondence analysis indicating grassland type had a stronger influence on fungal profiles than did season.