Single cell measurements of microbial stoichiometry and phylogeny

Die ökologische Stöchiometrie beschäftigt sich mit den Verhältnissen von Elementen in einzelnen Organismen und Ökosystemen sowie dem Gleichgewicht zwischen den Elementen in ökologischen Prozessen und Interaktionen. In der vorliegenden Arbeit habe ich Unterschiede zwischen verschiedenen Methoden untersucht, die häufig für die Messung der Stöchiometrie (C:N:P) von Bakterien verwendet werden (1. Kapitel). Des Weiteren habe ich versucht, stöchiometrische Messungen einzelner Bakterien mit phylogenetischen Bestimmungen mittels Fluoreszenz in situ Hybridisierung (FISH) zu verbinden (2. Kapitel). Für die Untersuchungen wurden die beiden Bakterienarten Pectobacterium carotovorum und Verrucomicrobium spinosum verwendet. Diese wurden vorab in mehreren Ansätzen bei unterschiedlichen Nährstoffverhältnissen angezogen und sowohl in der stationären als auch in der logarithmischen Wachstumsphase für weitere Analysen fixiert wurden. Mittels energiedispersiver Röntgenmikroanalyse (EDX) wurden die C:N:P Verhältnisse von einzelnen Bakterien gemessen. Die Stöchiometrie vieler Bakterien zusammen („Bulk“) wurde mit Hilfe eines CHN Analysators (C, N) und Phosphoraufschluss (P) bestimmt. Ich habe ferner versucht mit Elektronenenergieverlustsspektroskopie (EELS) die Stöchiometrie einzelner Bakterien zu messen, jedoch waren diese Messungen aufgrund der Dicke der Bakterien nicht erfolgreich. Im Mittel wurde ein C:N:P Verhältnis von 365:67:1 für EDX Ergebnisse ohne Standardkorrektur durch Koenzym A, 228:33:1 für standardkorrigierte EDX Ergebnisse (EDX_S) und 148:24:1 für Bulk bestimmt. Fast 80 % der EDX_S Ergebnisse, sowie 36 % der nicht standardkorrigierten EDX Ergebnisse, waren statistisch nicht signifikant unterschiedlich von den Bulk Ergebnissen (p < 0.05). Diese Resultate haben gezeigt, dass sowohl EDX als auch Bulk Analysen ähnliche Ergebnisse erzielen, sofern für die EDX Auswertung ein Standard verwendet wird. Welche Methode für die verbleibenden Unterschiede verantwortlich ist, ist dabei noch ungeklärt. Signifikante Unterschiede wurden darüber hinaus zwischen den Elementverhältnissen der verschiedenen Wachstumsphasen sowie den beiden untersuchten Arten gefunden, was darauf hindeutet, wie wichtig diese beiden Faktoren bei stöchiometrischen Analysen sind. Die Varianz zwischen EDX_S Ergebnissen einzelner Zellen war signifikant höher für C:P und N:P als bei nicht standardisierten EDX Ergebnissen. EDX_S Ergebnisse haben, verglichen mit den Bulk Ergebnissen, eine signifikant höhere Varianz gezeigt. Dies könnte durch die deutlich kleinere Probenanzahl bei Bulk Messungen (3 – 4 Proben pro Ansatz) im Vergleich zu den EDX Messungen (10 – 37 Zellen pro Ansatz) bedingt sein. Im zweiten Teil der Arbeit habe ich versucht Messungen der Stöchiometrie einzelner Bakterien mit deren phylogenetischer Bestimmung zu verbinden und habe dafür brommarkierte FISH Sonden sowie CARD-FISH mit brom- oder fluormarkierten Tyramiden verwendet. Brom ließ sich mit EDX nicht in den Zellen nachweisen und die gemessene Menge an Fluor war in den fluormarkierten Zellen nicht signifikant höher als in den brommarkierten Zellen. Etliche Möglichkeiten sind denkbar um diese Methode zu verbessern, so zum Beispiel eine Markierung der Zellen mit Immunogold, Zellsortierung mittels Durchflusszytometrie (FACS), ein verändertes FISH-Protokoll, bei dem die Zellen direkt auf dem TEM Grid markiert werden, sowie Schnitte von eingebetteten Bakterien um dann mittels EELS eine genau Messung von F durchzuführen. Weitere Analysen müssen ferner durchgeführt werden um die Veränderung der Stöchiometrie, welche bei FISH-markierten Zellen gemessen wurde, zu minimieren.Ecological stoichiometry is the study of the relative proportions of elements in organisms and ecosystems and the relationship of balance between elements in ecological processes and interactions. In this study, I investigated how different methods to measure the stoichiometry (C:N:P) of bacteria compare, and if it is possible to combine measurements of single cell stoichiometry to species identification with Fluorescent in situ hybridization (FISH). I used two bacterial species, Pectobacterium carotovorum and Verrucomicrobium spinosum, grown in several treatments at different resource ratios and harvested each in stationary and logarithmic growth phase. Single cell measurements were conducted with Energy Dispersive X-ray spectroscopy (EDX) and bulk cell analyses were performed with a CHN analyzer and phosphorus digestion. In addition, I tried to measure single cell stoichiometry with Electron Energy Loss Spectroscopy, but due to the thickness of the cells, these measurements were not successful. The average C:N:P was 365:67:1 for unstandardized EDX results, 228:33:1 for standardized EDX (EDX_S), and 148:24:1 for bulk measurements. EDX results were compared to bulk results and only 22 % of the EDX results, when standardized with Coenzyme A, were significantly different from the bulk cell analyses (p < 0.05). When unstandardized EDX results were compared to bulk results, 64 % of the elemental ratios differed significantly between the two methods. These results indicate that, if EDX results are standardized, both methods may be used for microbial elemental analysis and it is currently unclear which of the two methods drives the 22 % of significant differences. Significant differences were also found between the elemental ratios of cells harvested at different growth phases and between two species, pointing out the importance of these variables when microbial stoichiometry is studied. Standardized EDX results furthermore had significantly lower variance in C:P and N:P as compared to unstandardized EDX results. In addition, the variance of the bulk results differed significantly from the EDX results, which might be an effect of low sample number for the bulk analyses (3 – 4) compared to single cell measurements (10 – 37). In the second part of the study I aimed to combine measurements of single cell stoichiometry to phylogenetic identification and used FISH with bromine (Br) labeled probes and CARD-FISH with Br or fluorine (F) containing tyramides in order to identify these rare elements in labeled cells with EDX. Unfortunately, the Br label was not detectable with EDX, and no significantly higher amount of F could be measured for F labeled cells. However, there are various possibilities to improve the method, such as immunogold labeling, cell sorting by Flow Cytometry, performing FISH labeling directly on TEM grids, or the slices of resin-embedded cells for EELS measurements of F. Future research also has to address the significant change of single cell stoichiometry found after cell labeling when the elemental ratios of FISH and CARD-FISH labeled cells were compared to ratios of unlabeled cells

Zooplankton communities and Bythotrephes longimanus in lakes of the montane region of the northern Alps

Lakes in the Alps represent a considerable fraction of nutrient-poor lakes in Central Europe, with unique biodiversity and ecosystem properties. Although some individual lakes are well studied, less knowledge is available on large-scale patterns essential to general understanding of their functioning. Here, we aimed to describe crustacean zooplankton communities (Cladocera, Copepoda) and identify their environmental drivers in the pelagic zone of 54 oligotrophic lakes in the montane region of the Alps (400–1200 m) in Austria, Germany, and Switzerland, covering a spatial scale of 650 km. Moreover, we aimed to provide data on the distribution and ecological requirements of the North American invader Bythotrephes longimanus in its Central European native range. Communities were mainly dominated by widespread species typical of lowland habitats, and only a few true specialists of oligotrophic alpine lakes were present. The most frequent taxa were the Daphnia longispina complex and Eudiaptomus gracilis, with 48 and 45 occurrences, respectively. Species richness decreased with altitude and increased with lake area. The main structuring factors of community composition were chlorophyll a concentration and depth, which drove an apparent separation of mesotrophic and oligotrophic communities. Bythotrephes had 13 occurrences, showing a preference for deep oligotrophic lakes. Its presence was not coupled with lower crustacean species richness, as was repeatedly observed in North America. Additionally, it frequently co-occurred with the other large predatory cladoceran, Leptodora kindtii. B. longimanus might be considered a truly montane species in Central Europe, given its absence in lowland and alpine lakes.ISSN:2044-205XISSN:2044-204

Estimating Bacterial Diversity for Ecological Studies: Methods, Metrics, and Assumptions

<div><p>Methods to estimate microbial diversity have developed rapidly in an effort to understand the distribution and diversity of microorganisms in natural environments. For bacterial communities, the 16S rRNA gene is the phylogenetic marker gene of choice, but most studies select only a specific region of the 16S rRNA to estimate bacterial diversity. Whereas biases derived from from DNA extraction, primer choice and PCR amplification are well documented, we here address how the choice of variable region can influence a wide range of standard ecological metrics, such as species richness, phylogenetic diversity, β-diversity and rank-abundance distributions. We have used Illumina paired-end sequencing to estimate the bacterial diversity of 20 natural lakes across Switzerland derived from three trimmed variable 16S rRNA regions (V3, V4, V5). Species richness, phylogenetic diversity, community composition, β-diversity, and rank-abundance distributions differed significantly between 16S rRNA regions. Overall, patterns of diversity quantified by the V3 and V5 regions were more similar to one another than those assessed by the V4 region. Similar results were obtained when analyzing the datasets with different sequence similarity thresholds used during sequences clustering and when the same analysis was used on a reference dataset of sequences from the Greengenes database. In addition we also measured species richness from the same lake samples using ARISA Fingerprinting, but did not find a strong relationship between species richness estimated by Illumina and ARISA. We conclude that the selection of 16S rRNA region significantly influences the estimation of bacterial diversity and species distributions and that caution is warranted when comparing data from different variable regions as well as when using different sequencing techniques.</p></div

Barchart of the most abundant bacterial classes.

<p>Relative abundances of the ten most abundant bacterial classes across the V3, V4 and V5 datasets. Each bar represents the relative class distribution in one lake and each group of bars represents the relative abundances for one of the tree variable regions (V3, V4, V5). Bars are ordered from left to right by alphabetical order (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125356#pone.0125356.s001" target="_blank">S1 Fig</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125356#pone.0125356.s010" target="_blank">S3 Table</a> for more information about the lakes). Appendant results of paired t-test statistics are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125356#pone.0125356.s012" target="_blank">S5 Table</a>. Square brackets indicate candidate class names.</p

Rank-abundance evaluation of the variable 16S rRNA regions from the lake survey dataset.

<div><p>A: Rank-abundance plot of the complete dataset for each of the three variable regions, where abundance data was added up for all of the 20 lakes, plotted on log-log scale. Vertical dashed lines show the range of the rank-abundance plot (ranks 12 to 440) for which we found a significant difference between the rank-abundance distributions of V4 to V3 and V5. For the same region, the V3 and V5 rank-abundance distributions did not differ significantly from each other (significant Kolomogorov-Smirnov (KS) test: p < 0.05).</p> <p>B: Example rank-abundance plot of the rarefied data for one lake (Murtensee), plotted on log-log scale. X-axis: OTU rank, y-axis: OTU abundance.</p> <p>C: Result of KS-test using rank-abundance data of the individual lakes. X-axis: compared regions, y-axis: p-value distribution of KS test, dashed line plotted at p-value of 0.05. Each dot represents the comparison of rank-abundance curves from two regions of the same lake. P-values below 0.05 indicate a significant difference between the the rank-abundance distributions, whereas p-values above 0.05 indicate that there are no significant differences between two rank-abundance distributions.</p></div

Abundance and diversity of bacteria, phyto- and zooplankton

Abundance and diversity of bacteria, phyto- and zooplankto

Time series data of biotic and DOC parameters

Time series data of biotic and DOC parameter

Number of observed species (SR; left side) and phylogenetic diversity (PD; right side) of the rarefied dataset from Illumina OTU data of the lake samples.

<div><p>A: SR and PD estimates for the three different regions. Points show the mean SR/PD of all lake samples and lines the standard error of the mean.</p> <p>B: SR of individual lakes from the V3 region plotted against SR of the same lake from the V4, respectively the V5 region dataset. The solid central line shows the 1-to-1 line, dashed lines show the Major Axis (MA) regressions of the two comparisons.</p> <p>C: SR (x-axis) plotted against PD (y-axis) for each of the three regions, where each dot represents one lake sample. The different symbols indicate the three different regions. Lines show the MA regression lines for each variable region dataset.</p></div

Raup-Crick (RC) comparisons between the three variable 16S rRNA regions from the lake survey dataset.

<div><p>A: Modified RC probability comparison of V3 and V4 (for rarefied data). Each dot represents the RC value of one pairwise dissimilarity comparison of the V3 region plotted against the same pairwise dissimilarity comparison of the V4 region. Values between -1 and -0.975 indicate that communities are significantly less dissimilar, and values between +0.975 and +1 that communities are significantly more dissimilar than expected by chance. Values between -0.975 and + 0.975 indicate that communities are not different from random expectation. Dashed lines show boundaries of significance (-0.975 and +0.975), where points falling between -1 and -0.975, respectively +0.975 and +1 indicate significant deviations from the null-model distribution. Dark areas in the plot represent high densities of points.</p> <p>B: Same as A, but for V3 plotted against V4 values.</p> <p>C: Conceptual figure illustrating the four different possible combinations when two RC-matrices are compared. a (white area): both regions come to the same conclusion about the dissimilarity among communities, b (dark grey): one of the regions estimates <i>β</i>-diversity of one lake pair to be significantly more similar than expected by chance while the other region estimates the <i>β</i>-diversity of the same lake pair to be not different from a random null-model distribution, c (light grey): one of the regions estimates <i>β</i>-diversity of one lake pair to be significantly more dissimilar than expected by chance while the other region estimates the <i>β</i>-diversity of the same lake pair to be not different from a random null-model distribution, d (black): cases where pairwise lake comparison of one region estimate <i>β</i>-diversity to be significantly more similar than expected by random chance, while the other region estimates <i>β</i>-diversity to be significantly more dissimilar than expected by chance.</p> <p>D: Barplot showing the number of cases where the compared regions come to the same (a) or different (b, c, d) conclusions about <i>β</i>-diversity. Coding is illustrated in panel C.</p></div