Accurate assessments of biodiversity are crucial to advising ecosystem-monitoring programs and understanding ecosystem function. Nevertheless, a standard operating procedure to assess biodiversity accurately and consistently has not been established. This is especially true for meiofauna, a diverse community (>20 phyla) of small benthic invertebrates that have fundamental ecological roles. Recent studies show that metabarcoding is a cost-effective and time-effective method to estimate meiofauna biodiversity, in contrast to morphological-based taxonomy. Here, we compare biodiversity assessments of a diverse meiofaunal community derived by applying multiple taxonomic methods based on comparative morphology, molecular phylogenetic analysis, DNA barcoding of individual specimens, and metabarcoding of environmental DNA. We show that biodiversity estimates are strongly biased across taxonomic methods and phyla. Such biases affect understanding of community structures and ecological interpretations. This study supports the urgency of improving aspects of environmental high-throughput sequencing and the value of taxonomists in correctly understanding biodiversity estimates. Francesca Leasi et al. report a comparison of approaches for estimating meiofauna diversity, including metabarcoding and other morphological-based taxonomy methods. They show bias in estimates based on methods and phyla, highlighting the need for a standard procedure for assessing biodiversity. Meanwhile, the role of biodiversity in maintaining ecosystems should be considered prominent in climate change science and policy2. Awareness of current extinction rates has led to the establishment of large-scale conservation programs whose effectiveness relies upon the ability to thoroughly and rapidly assess biodiversity and ecosystem health3. Thus, there is a need for methods that accurately and efficiently appraise ecosystem biodiversity and temporal variations following natural or anthropogenic events. The improvement of molecular techniques in recent years has allowed for the development of genetic methods that help increase the rate and accuracy of species identification4. Large-scale DNA barcoding (metabarcoding) has become especially useful for analyses of environmental collections  for which biodiversity assessment is implemented at the community level5. Metabarcoding enables assessment of biodiversity where the molecular taxonomy can be communicated unambiguously. However, those sequences alone are not linked to a specific morphology. By contrast, morphology-based taxonomy is inherently linked to the characteristics of the organisms but it is extremely labor-intensive and difficult to communicate unambiguously.We are currently experiencing the fastest loss of biodiversity since the extinction of dinosaurs 66 million years ago6. Moreover, organisms typically excluded from morphological analyses, such as pieces of animals, juvenile stages, rare taxa, and cryptic species may be detected by metabarcoding7. Meiofauna include a heterogeneous collection of small (<1 mm) marine benthic invertebrates of almost all animal phyla, with a wide range of functional groups that have key roles in benthic energy flow9. Metabarcoding has been applied to meiofauna in a number of cases usually involving intentionally preserved samples for molecular analyses11. In contrast, morphology-based taxonomy for many groups of meiofauna requires observations on fresh/live specimens. Consequently, metabarcoding allows the reconstruction of community compositions from deep sea, polar regions, cold seeps, hydrothermal vents, etc., where investigation of live organisms would be difficult or impossible11. However, only a few studies have tested the relationship between metabarcoding and morphological assessment of diversity. For practical reasons, such studies have focused on mock communities of animals with an exoskeleton such as nematodes and copepods13. The other >20 phyla inhabiting the marine interstitial environment, most of which are soft-bodied, have been ignored.Metabarcoding is particularly useful to identify small-sized species, such as the meiofauna, for which morphological identification is time-consuming and requires rare taxonomic expertiseThe present study aims to investigate metabarcoding and morphological-based assessment of diversity across members of seven, mostly soft-bodied animals, phyla. In this assessment, we compared the results of a metabarcoding analysis across numerous samples with morphological and molecular assessments of individual specimens from those samples. The goal was to test for biases inherent in the taxonomic approach applied, and whether such biases affect broad ecological interpretations at the community level. Specifically, we tested whether different approaches result in significant differences in estimates of biological diversity; if such differences are consistently present in all seven phyla; and if the approach affects the ecological conclusions of a field study. Results showed that the taxonomic approach significantly biases biodiversity estimates, and such biases are inconsistent across phyla. Therefore, community structures and ecological correlations within and among community shift according to the taxonomic method applied.14 value was 38 for most of the samples except for one site with a salinity at 31 (station 9) and two sites at 36 . Sedimentological analyses indicated heterogeneous sampling, with a grain size ranging from fine to very coarse and distribution of size from well sorted to very poorly sorted. The sediments from three samples  were too coarse (mean grain size >2 mm) to analyze additional sedimentological parameters using our procedure .Samples were collected from 19 sites located in Panama .A total of 835 individuals belonging to seven phyla and allocated to 187 morphotypes were collected from the 19 sampled sites Fig. . Specifip = 0.773; Nemertea, p = 0.205; Xenacoelomorpha, p = 0.199), except for species of Gastrotricha (p = 0.0015) and Platyhelminthes (p = 0.016) whose 18S datasets seemed to better fit the GYMC model .DNA taxonomy obtained with the Generalized Mixed Yule Coalescent (GMYC) model revealed 116 evolutionary independent entities, among which 23 Annelida, 27 Gastrotricha, 6 Mollusca, 2 Nematoda, 9 Nemertea, 42 Platyhelminthes, and 7 Xenacoelomorpha. Results obtained by using the GMYC species delineation model were not significant .The same genetic dataset as described above was considered to determine the number of Operational Taxonomic Units (OTUs) for each phylum. A total of 211 OTUs were obtained, among which were 27 Annelida, 38 Gastrotricha, 17 Mollusca, 37 Nematoda, 34 Nemertea, 48 Platyhelminthes, and 10 Xenacoelomorpha .Meanwhile, biodiversity was assessed with metabarcoding for each of the 19 investigated sites. Taxonomic units obtained with the metabarcoding approach were categorized by groups in order to remove all organisms that are not meiofaunal invertebrates . The final dataset consisted of 120133 genetic reads and 1345 environmental OTUs (eOTUs) distributed in 20 phyla. The richest phylum was Arthropoda with 699 eOTUs, most of which (659) were Copepoda. By pruning the dataset to the seven focal phyla, we obtained a total of 32206 genetic reads and 547 eOTUs. In order of richness, 178 eOTUs were Annelida, 155 Nematoda, 120 Platyhelminthes, 36 Mollusca, 26 Xenacoelomorpha, 20 Gastrotricha, and 12 Nemertea .The same initial genetic dataset was analyzed and filtered to obtained sequence variants. Compared to eOTUs, we obtained a similar number of genetic reads  but these clustered in a lower number of unique units (496 sequence variants). The seven focused phyla were present with a total number of 29279 genetic reads and 293 sequence variants. In contrast to results obtained with eOTU, the richest taxon was Nematoda (89 sequence variants), followed by Platyhelminthes (87 sequence variants), Annelida (63 sequence variants), Mollusca (22 sequence variants), Gastrotricha and Xenacoelomorpha (both present with 12 sequence variants), and Nemertea with 8 sequence variants . Based on p-values and estimated confidence intervals associated with the pairwise comparisons of richness assessed by different methods, we identified that morphotypes, OTUs, and evolutionary independent entities obtained with GMYC model on single individuals were not statistically different to one another . Next, we identified that eOTUs and sequence variants obtained from environmental samples were statistically different from one another . Lastly, species richness estimated with both eOTUs and sequence variants were independently different from morphological units , OTUs , and evolutionary independent entities (p < 0.0001). Richness assessed by each taxonomic method is shown in Table Statistical analyses showed that the taxonomic method used to assess biodiversity was a significant predictor of whole community richness , although, because of the large number of comparisons, false negatives are quite possible. Richness of Annelida significantly differed between eOTUs and the three methods based on single individuals, namely morphotypes , OTUs , and evolutionary entities . For Nematoda, significant differences in predicted richness were observed between evolutionary entities and both eOTUs  and sequence variants .Pairwise tests for richness by phylum independently revealed significant differences only for Annelida and Nematoda .The total number of species belonging to the seven phyla estimated in the sampled area spanned between 365 and 609 depending on the species estimator function applied (chao2 and jack1) to the two taxonomic datasets considered , depth , grain size sorting , and the dichotomous (dummy) variable distinguishing morphology and one of the two DNA taxonomy methods (OTU) from the other  were statistically significant predictors of richness. Interestingly, none of the interactions with the dichotomous variable were found to be statistically significant . For metabarcoding analyses, on both selected phyla and the whole meiofaunal communities, our final model inferred that only the sample size , the dichotomous (dummy) variable distinguishing the two metabarcoding approaches , and the interaction between the dummy and the sample size  were statistically significant predictors of richness.The correlations between richness and environmental parameters differed across taxonomic methods. Specifically, for the datasets based on single individuals, our final model inferred that sample size .Values of Jaccard dissimilarity were comparable across the five taxonomic methods when biological communities were sampled from similar habitats . However, pairwise comparisons among the three investigated habitats showed values of Jaccard that were inconsistent across taxonomic methods . Specifically, for morphological taxonomy there were significant correlations between the community structure and depth (p = 0.003), mean grain size (p = 0.039), and the sedimentological parameter kurtosis (p = 0.027); and a nearly significant correlation with (iv) salinity (p = 0.082). Community compositions obtained via OTU showed a nearly significant correlation with depth (p = 0.064). For the metabarcoding datasets on focal phyla, a nearly significant correlation was found between both eOTU and sequence variant communities and the mean grain size . When the whole meiofaunal community was analyzed with metabarcoding  and the sedimentological parameter skewness (p = 0.039), and nearly significant correlations between eOTU communities and depth (p = 0.098). Sequence variant whole community structures were significantly correlated to depth (p = 0.05), and nearly significant correlated to the mean grain size (p = 0.069).The results of PERMANOVA suggested that the applied methodology strongly biased the correlations between community compositions and environmental parameters , taxonomic method , and the interaction between phylum and method  were all statistically significant predictors of phylogenetic diversity . For each phylum, we identified significant differences only between the phylogenetic diversity obtained from single individual datasets and the one obtained from one or both metabarcoding datasets , and no significant differences between the two metabarcoding approaches . The phylogenetic diversity of Gastrotricha and Nemertea did not differ significantly across methods .Our fitted model inferred that phylum . Specifically, our fitted model identified that phylum , method  and the interaction between phylum and method  were both statistically significant predictors of MPD. Similarly, our fitted model identified that phylum , method , and the interaction between phylum and method  were all statistically significant predictors of MNTD.Similar results were obtained for both Mean Phylogenetic Diversity (MPD) and Mean Nearest Taxon Distance (MNTD). The ANOVA model fit to MPD data exhibited a lack of Normality in the residuals, therefore, a robust ANOVA model was pursued. The fitted results and their associated hypothesis tests based on this robust model were similar to those obtained via an ordinary least-squares regression, and we can be confident that ANOVA results based on the ordinary least-squares model are valid, that phylum, method, and the interaction between phylum and method were all significant predictors of MPD (p = 0.0488). However, the relationship between phylogenetic diversity and sorting depended on the animal phylum in question; the interaction between sorting and phylum was a significant predictor of phylogenetic diversity .Among all the investigated ecological parameters and across all taxonomic methods, only one sedimentological parameter (sorting) was found to be a significant predictor of phylogenetic diversity , Mollusca , Nematoda , Nemertea , Platyhelminthes , and Xenacoelomorpha .In all the investigated phyla, the average percent identity of the V1–V2 variable region of the 18S rRNA was higher compared to the V9 variable regionHere, we have shown estimates of meiofauna biodiversity obtained via different methods, namely morphology-based taxonomy, DNA taxonomy, and metabarcoding. Statistical analyses revealed that the taxonomic method used to assess biodiversity is a strong predictor of biodiversity, which was measured in species richness, community structure, and phylogenetic diversity. In addition to revealing that biodiversity estimates can be biased by the methodology used to define taxonomic units, we have shown that different phyla respond differently to such a bias. Therefore, predictions of species composition within communities change, greatly affecting ecological conclusions.17. This is likely due to the observation of genotypes of hidden taxa, such as cryptic species, juvenile forms not linked to a morphology, and sequencing artifacts18. However, in our work, the number of taxa disclosed for four of the seven investigated phyla  was lower based on metabarcoding compared to morphological taxonomy. Surprisingly, for both gastrotrichs and nemerteans even the total estimated richness based on metabarcoding data was lower than the actual observed number of species obtained by morphological and molecular analyses of individuals. This discrepancy occurred for all 19 investigated sites supporting a consistent bias in assessing biodiversity using metabarcoding approaches for at least these phyla.Previous studies using metabarcoding generally report higher species richness compared to morphological taxonomy19, while in other instances, no variation was detected even among species20. With respect to this discrepancy, it is important to note that the analysis of individual specimens included both morphology and molecular analysis of 18S rRNA. In our analysis, metabarcoding employed highly conserved eukaryotic primers (V9 region), whereas individual specimens used less broadly conserved primers designed for invertebrates (V1–V2 regions). The cost of using highly conserved primers may represent a tradeoff between the loss of individual species for the amplification of phylogenetically diverse groups. Our observed low species richness for the metabarcoding relative to the analysis of individual specimens could simply reflect a lack of sequence divergence for the V9 region relative to the V1–V2 region in these phyla. However, comparisons of these two regions among complete published sequences for representatives of all phyla shows that the rate of divergence of V9 region is systematically higher than the V1–V2 region.Several potential reasons exist that could explain the apparent discrepancy in species estimation using metabarcoding for certain phyla. One possible factor is the choice of the target gene(s) used for metabarcoding. Given the variability in the number of mutations in a gene within and among species, and variance in lineage histories, it is nearly impossible to establish a commonality among taxa. Even within the same marker , extensive variation was observed within-species21, population density, extractability of both organisms and DNA, or ribosomal DNA copy numbers. The observation in nemerteans is unlikely to be due to low body mass, however, small population size is a possible factor. The opposite is true for gastrotrichs. Further, there is no evidence for a systematic bias against extractability for either nemerteans or gastrotrichs. With respect to the copy number of the ribosomal RNA gene arrays, while we do not have direct estimates of copy numbers for these taxa, they are known to be highly variable across species and unlikely to explain the observed phylum wide biases.Regardless of the genetic region, additional possible factors could be the characteristics of these phyla that limit the availability of DNA templates. This could include relatively low body mass23. The lack of difference in the phylogenetic diversity between eOTU and sequence variant datasets supports that eOTU clustering does not increase the actual species diversity, but only overestimates the number of biological units in the samples. For nematodes, richness was significantly different between metabarcoding and individual DNA taxonomy predictions obtained with the GMYC model . However, the number of species estimated by that model was not statistically supported (p = 0.773). Moreover, the application of this species delineation model to the 18S rRNA locus has been shown to underestimate diversity in many meiofaunal taxa25.Only the richness of annelids and nematodes were significantly increased by metabarcoding . However, the richness of annelids is significantly higher only when the eOTU approach is applied . The application of eOTU clustering analysis is intended to eliminate the effect of sequencing errors and variations among individual ribosomal repeats. By contrast, sequence variant methods are developed with specific pipelines to reduce such errors, they have demonstrated sensitivity and specificity as good as or better than eOTU methodsp = 0.003) only when single specimens were collected by the taxonomists, likely due to a biased effort in investigating samples morphologically. Taxonomists are constrained to work on fresh sediments within a limited timeframe before samples are no longer suitable for morphological investigations. Therefore, the number of individuals investigated and species revealed by morphology from each depth may, not surprisingly, reflect the number of samples collected. This bias is avoided with the application of metabarcoding, which has the potential to provide an inclusive view of diversity regardless of the time invested on each sample. Preliminary surveys of diversity across sites using environmental DNA methods would be extremely helpful to drive, in advance, the focus of taxonomists toward specific sites with a higher and/or particularly interesting diversity. More evidence of bias by the taxonomic method applied is the strong correlation between biodiversity and the grain size of the sediment that was found only in the morphological dataset. Grain size is considered among the most important factors that affect meiofaunal biodiversity26, and such biases need to be removed in order to reach an inclusive understanding of the ecological effect in marine sediments.Uneven differences in richness and incidence across taxa greatly affect the correlations with environmental parameters, especially when biodiversity is investigated at the community level. For example, biodiversity was significantly correlated to depth  is applied to either the focal or complete meiofaunal dataset. Meiofaunal organisms belong to diverse groups characterized by having intimate correlations with the surrounding environment30. Hence, although metabarcoding enables untangling of great phylogenetic diversity, it is not possible in most cases to classify species, and most remain known only at the phylum level31. Accurate low-level taxonomic placement of metabarcoding sequences improves many kinds of assessments of the structure and function of communities, and how these change over space, time, or environmental gradients. Nonetheless, the choice of the genetic loci, bioinformatics, and sampling protocol represent variables in metabarcoding32. As suggested by other works33 and the present study, employment of highly conserved primers likely yields uneven amplification efficiencies among PCR products. To overcome this, the integration of multiple taxon-specific primers is pivotal to ensure accurate estimates of biodiversity at the species level. Going forward, this issue may be addressed by a multi-barcode approach , which could help to improve taxonomic coverage and resolution of metabarcoding studies. To avoid primer biases and gain access to potential metabolic functions, metagenomics has the potential to provide a holistic view of the ecosystem diversity and function by disclosing genetic information from any genome present in the environment, including bacteria/archaea, eukaryotes, and viruses. However, a key step for the development of such an approach is building a reliable reference library of DNA sequences35. As such, the success of metagenomics (as well as metabarcoding) largely depends on the taxonomic diversity of sequences deposited in the reference library. Currently, there are active collaborations involving large groups of researchers to create better reference libraries across all groups35. This goal will only be achieved with the integrated effort of both morphology-based taxonomists and computational biologists36.In conclusion, the application of metabarcoding, compared to collecting single individuals, did not substantially improve estimates of biodiversity in our investigated samples, although metabarcoding is expected to disclose the taxonomy of specimens usually neglected by morphology-based taxonomists. Moreover, while morphological taxonomy allows identification at low taxonomic ranks , most of the meiofaunal taxa represented in general reference libraries for DNA sequence data remain poorly classified and some represent misidentifications or contaminations15). From each sampled site, geographic coordinates were registered; salinity and depth were measured with, respectively, a VWR International Brand Hand Held Refractometer and a Suunto Zoop Dive computer, and sedimentological parameters were obtained. According to the Scientific Committee on Oceanic Research working group, salinity has to be unit-less, as the measurement is based on conductivity and is not precisely related to the mass of dissolved material14.Nineteen sites in Panama (collecting permit No SE/A-2-16) were sampled between February 22 and March 9, 2016  and the sediment particle. Sedimentological parameters were calculated with the software Microtrac FLEX 10.6.2, which uses Mie's compensation theory to describe the effect of spherical particle shapes on light. As recommended by Tanner et al.37, final sedimentological values were calculated via the Method of Moments, a parameter estimation technique that involves equating sample moments with theoretical moments. Textural classification of the sediment was based on size ranges and names were assigned according to the Wentworth scale38.Sediment texture was evaluated by passing 1 g of sample (dried sediment) through a Microtrac Bluewave S54000 laser granulometer that uses light refraction technology. The determination of grain size by this equipment is based on the interaction between three red laser beams (2), sampled by scooping the top layer of sand with a jar or a bucket. The sediments were collected by hand from littoral beaches , by apnea freediving in the sublittoral zone ; and with SCUBA divers offshore . Immediately after collection, samples were taken to the Achotines Bay Laboratory Facility (www.iattc.org/AchotinesLab/AchotinesResearchFacilitiesENG.htm), where they were processed immediately. Each sample was divided into two parts; one of which was used for investigations of single individuals, and the other for metabarcoding analyses.Samples consisted of at least two replicates, each of which consisted of about 2–4 L of sediment from the upper 5 cm of the sea bottom collected over a homogeneously sandy area  using an automated DNA extraction system . Tissue digestion was performed at 56 °C in a shaker incubator with 150 µL of M2 buffer mixed to 150 µL of M1 buffer and Proteinase K. DNA extraction was performed using the Autogen Prep 956 Extractor. The DNA was eluted in 100 µL of R9 buffer25 and is currently stored at the biorepository of the Smithsonian National Museum of Natural History (Washington D.C.) in tubes marked with unique barcodes. Manufacturer buffers were provided by Autogen, Inc.18S rRNA was carried out using the 18S EukF forward primer [AACCTGGTTGATCCTGCCAGT] and the SR7 reverse primer [GTTCAACTACGAGCTTTTTAA]39. Either a 25 µL final volume with 50 mM Tris–HCl pH 9.1, 16 mM (NH4)2SO4, 3.5 mM MgCl2, 150 mg/mL bovine serum albumin (BSA), 0.5 mM of each primer, 160 mM of each dNTP, and 0.05 U/μL of KlenTaq polymerase ; or a 10 μL final volume with 3 pmol of each primer, 500 μM dNTPS, 3 mM MgCl, 0.25 mg/μL BSA, and 0.05 U/μL of BiolaseTM DNA polymerase  with manufacturer provided buffers was the input to the thermal cycler. Thermal cycler parameters for the BiolaseTM DNA polymerase included an initial denaturing step of 95 °C for 3 min, followed by 40 cycles of 95 °C for 30 s, 50 °C for 60 s, 72 °C for 60 s, and a final extension step of 72 °C for 5 min. Thermo cycling using the KlenTaq polymerase comprised an initial 3-min denaturation at 95 °C, followed by 35 cycles of 30 s at 95 °C, 30 s at 52 °C, 45 s at 72 °C. The cycling ended with a 7-min sequence extension at 72 °C. PCR products were purified with Illustra Sephadex columns and then used for cycle sequencing with dye-terminators using BigDye chemistry (Perkin-Elmer) and standard cycling . Cycle-sequenced products were run on an ABI 3730xl 96-well capillary sequencer at the Laboratories of Analytical Biology at the Smithsonian National Museum of Natural History.Polymerase chain reaction (PCR) was performed on the DNA of single individuals. Specifically, amplification of the V1–V2 region of 18S rRNA, region V1 and V2), were checked for possible contaminations or misidentifications using BLAST. Sequences organized by phylum were aligned using MAFFT v7.01741, by implementing the Q-INS-I algorithm known as the optimal strategy for ribosomal markers42. Unique sequences were used to obtain ultrametric trees. The selected model of evolution for the phylogenetic reconstructions of each taxon was general time-reversible-plus-gamma-distribution (GTR+G) chosen by hierarchical likelihood ratio tests in ModelGenerator v. 2.14543. The model of evolution was run to reconstruct phylogenetic trees with BEAST 2.2.144 to obtain Bayesian ultrametric trees. Beast was run for 100 million generations and a tree was sampled every 10,000 generations. The summarized trees were calculated with the program TreeAnnotator (part of the BEAST package) after discarding the first 20% of the trees as burn-in. As outgroups for rooting, we used sequences obtained in the present work from the phylogenetically closest clade.Sequences obtained from single organisms, about 600 bp long (45 with the package splits (http://splits.r-forge.r-project.org/)46. The GMYC method is a likelihood method for delimiting species under coalescent theory by fitting within-species and between-species branching models to reconstructed ultrametric gene trees4.In order to quantify evolutionary independent entities, the ultrametric trees obtained with BEAST were used as input for the single-threshold GMYC approach in R 2.15.318S rRNA sequences were concatenated into a single FASTA file to mimic the input of a processed metabarcoding dataset. OTUs are based on sequence similarity47. The sequences were clustered into OTUs using the QIIME open-reference OTU picking pipeline and the SILVA (release 128) 99% identity SSU ribosomal sequence reference database47.To obtain OTUs, the amplified DNA extraction, amplification, and sequencing: DNA was extracted for metabarcoding from the sediments samples, for each of the 19 locations, at the Laboratories of Analytical Biology, Smithsonian Institution National Museum of Natural History. Each sample was vortexed for 10 s after which 10 mL were transferred to 2-mL tubes. The ethanol was evaporated in a 60 °C vacuum spinning centrifuge. 0.5 mL of extraction buffer 48 was added to the dry sample which was then incubated for 2–12 h at 56 °C. The samples were then re-suspended by vigorous pipetting and all the liquid was transferred to the microbead-tube of the commercial PowerSoil® DNA Isolation Kit , according to the manufacturer’s protocol. DNA was eluted in 100 µL water and quantified prior amplification.18S rRNA (region V9) Illumina Amplicon47. Specifically, the 18S rRNA (region V9) was amplified using Illumina_Euk_1391f forward primer [GTACACACCGCCCGTC] and Illumina_EukBr reverse primer, barcoded [TGATCCTTCTGCAGGTTCACCTAC]. The input to the thermal cycler was a final volume of 25 µL including 12 µL of PCR-grade water, 10 µL of PCR master mix, 1 µL of forward primer (5 µM), 1 µL of reverse primer (5 µM), and 1 µL of template DNA. Thermal cycler conditions included an initial denaturing step of 94 °C for 3 min, followed by 35 cycles of 94 °C for 45 s, 57 °C for 60 s, 72 °C for 60 s, and a final extension step of 72 °C for 10 min. The PCR products were purified with Rapid PCR Purification System Kit Marligen Bioscience, Inc.DNA amplification, purification, and sequencing for metabarcoding analyses were all conducted following the Earth Microbiome Project protocol for phred quality score less than Q3 were trimmed16. The merged sequencing reads were clustered into operational taxonomic units using the QIIME open-reference OTU picking pipeline and the SILVA (release 128) 99% identity SSU ribosomal sequence reference database47. Within the pipeline, reverse-strand matches were allowed and a percent identity threshold of 99 was used for UCLUST clustering49. The final result of the QIIME pipeline is an OTU table with per sample counts of each eOTU16. Sequence variant tables were constructed following the DADA2 V1.4 pipeline22. Forward and reverse reads were truncated to 130 base-pairs on quality profiles. Reads were then de-replicated and binned into unique sequence variants. Final sequence variants across the samples were then inferred using a parametric error model customized for each sample dataset. Forward and reverse reads were then merged and a ‘sequence table’ was constructed with per sample total counts for each sequence variant . Chimeric sequence variants were removed from the final table16.To reconstruct eOTU tables, forward and reverse reads were merged using PEAR V0.9.6; bases with a 51. To validate the taxonomic classifications of the sequences, a phylogenetic tree was constructed for each dataset and the phylum-level taxonomic identities for each clade were examined. Previously unidentified sequences were dubbed a certain phylum when belonging to a supported clade including at least five other sequences, all them belonging to that phylum.Each sequence from the dataset was compared to the SILVA 18S reference set using BLAST. The taxonomy from the top five BLAST hits were then compared and the best consensus taxonomy was obtained. BLAST hits were only considered if the percent identity of the match falls within 0.5% identity of the top hit and if the alignment of the hit spans >120 bp. Taxonomic identifications were made to the species, family, and phylum level if the percent identity of the best hit was >97, 93, and 90, respectively. These threshold values were arbitrarily chosen according to diverse literature sources and investigations that were mostly based on mock communities of selected taxa47. Maximum Likelihood phylogenies were reconstructed using RAxML v8.252. Finally, the separate phyla trees were combined into a single tree. The topology of the final tree was constrained following the animal phylogeny proposed by Dunn et al.53A constrained phylogenetic tree was reconstructed to show the taxonomic composition of the whole meiofaunal dataset in the sampled area. First, sequence variants were grouped in their respective phyla based on the taxonomic classifications. Separated alignments were performed with Uclast and PyNAST method, a python implementation of the NAST alignment18S rRNA sequences in the SILVA SSU database using blastn. Variable regions (V1–V2 and V9) were then extracted and separated into multi-fastas for each phylum. Each respective FASTA was then aligned using clustal omega with an option to output a full distance matrix. Average percent identity was then calculated for each phylum–primer pair using custom python scripts16.Primer locations were identified from each of the metazoan Our goal was testing whether different taxonomic methods provide significant differences in revealing the diversity of the seven focal phyla, and how possible differences affected the correlation with environmental parameters. The significance of explanatory variables in all regression analyses was based on two-sided tests of the associated regression coefficients. The effect of the methodology used was evaluated through three different types of community descriptors, which were included as response variables in the different sets of models.This was measured as the number of unique units (= putative species) from each of the five methods applied on (a) single individuals, namely morphotypes obtained with morphological taxonomy, evolutionary independent entities obtained with GMYC model, and OTUs; and on (b) environmental samples, namely eOTU and sequence variant. Analyses of data obtained with metabarcoding were performed for the seven focal phyla as well as the total number of eOTUs or sequence variants for all the meiofauna groups present in the sediments.β-diversity) between samples were measured for units obtained by every single method using the Jaccard dissimilarity index55 calculated with the function beta.pair included in the package betapart 1.3 of R statistical software system56. Biological units obtained by each of the five methods were organized in presence/absence (incidence) datasets.Differences in community structure  and MNTD58, calculated with the functions ses.mpd and sesmntd, included in the R package picante 1.6-259. Datasets used are unique genetic sequences obtained from single individuals and environmental samples.A quantitative measure of phylogenetic diversity, defined as the minimum total length of all the phylogenetic branches required to span a given set of taxa on the phylogenetic tree, was measuredRichness estimation: In order to estimate the species richness for our sampled locality, we computed the species estimator functions chao2 and jack1 in the R package fossil61. These functions apply the algorithms Chao and Jackknife using incidence data. In fact, the number of individuals in our qualitative samples did not reflect the natural population abundance. Moreover, measuring abundance based on the number of genomic reads may be misleading because body size and cell numbers differ between species and individuals, the number of rRNA gene copies can vary within-species62, the occurrence of extensive intragenomic variation63, and possible PCR biases. The seven focal phyla and complete meiofaunal community were considered for the whole investigated area and sorted by three depth descriptors . To evaluate richness estimation, we did not consider datasets including evolutionary independent entities, OTUs, and eOTUs for the following reasons: the number of entities was not supported statistically for most phyla; the richness obtained via OTU analysis was comparable to the evaluation of distinct morphotypes, thus, we considered morphotypes in order to include also additional units, from which DNA amplification and/or sequencing failed. Lastly, in order to be more conservative in species estimation, we chose sequence variant approach which estimates a lower richness than eOTU.χ2 = 441.86, df = 1, p < 0.0001).To identify significant predictors of genetic richness (response), a generalized linear mixed-effects model was pursued. More specifically, a Poisson (log-linear) model form was assumed, a model form that is theoretically appropriate for count data such as species richness. Using the Poisson model form, our full model included a single random effect for the 19 station locations, and several fixed effects including a continuous variable for sample size; continuous ecological variables such as depth, salinity, grain size, sorting, skewness, and kurtosis; and a categorical variable for taxonomic method. The Poisson model form has a fairly restrictive assumption that the response follows a Poisson distribution , and our particular data did show evidence of overdispersion. Thus, the Negative Binomial model form, an alternative to the Poisson model, was pursued as this model, considered a variant of the Poisson form, included an additional parameter to account for such overdispersion (overdispersion parameter = 1.7). Furthermore, based on a likelihood ratio test, the negative binomial model form (for our best model) provided a statistically significant improvement over a comparable Poisson model  considered all main effects and interactions between continuous variables  and categorical variable distinguishing species identification method. Because, based on our fitted model, we identified a statistically significant interaction effect , we pursued a post-hoc analysis to compare levels of combinations of our categorical variables (phylum and methodology), or factors. To do this, we used the package lsmeans65 to calculate predicted values of each combination of factors. So, for our two factors, phylum and methodology, with 7 and 5 levels, respectively, we have 35 different combinations of factors. Then, for these 35 combinations, using a Tukey procedure for multiple comparisons to ensure a fixed family-wise error rate (α = 0.05), we performed all pairwise comparisons of predicted values66.All model fitting was performed using the package 66. In this case, no evidence of overdispersion (an assumption of the Poisson model form) was observed and subsequent fitting of a negative binomial model form to the data yielded a dispersion parameter estimate >400, an indication that the original Poisson assumption holds .Because the values related to the sample size from single individuals (number of specimens or genetic sequences) and environmental samples (number of genetic reads) were not comparable, we evaluated the effect of the sample size and ecological variables on richness by performing separate analyses. To identify significant predictors of richness (response), a generalized linear mixed effect model was pursued. More specifically, a Poisson (log-linear) model form was assumed as it is theoretically appropriate for count data such as species richness66.For the metabarcoding data associated with the whole meiofaunal community (not restricted to focused phyla), we followed the same basic approach as described above. However, for these data, we observed evidence of overdispersion and a negative binomial model form was assumedJaccard pairwise differences as a response variable, we assessed the percentage of the variability in community composition observed across samples using a permutational multivariate analysis of variance applied on distance matrices . In these models, we included continuous environmental variables to test the significance of differences between sediments in structuring differences in community structures. Moreover, meiofaunal community dissimilarities were calculated within and among different depths as described above using the R package betapart, and the functions beta.pair and beta.multi56.For models on community composition using matrices of nlme in the R statistical software system45. We note that the model, as specified above, did yield significantly non-Normal model residuals. To remedy this, we transformed the response and reran the model using the square-root of the phylogenetic diversity. This model specification met all ANOVA/regression assumptions , and a Levene test showed no evidence of non-constant variance between groups . For the analysis regarding the MPD, the transformation of the phylogenetic diversity did not remedy the violation of the regression assumption. So, in light of the heavy-tailed  residuals, we implemented a robust regression model form, which is less restrictive in terms of assumptions. Such a model was fit using the package robustlmm in R statistical software68.To analyze the relationship between phylogenetic diversity and both single animal / metabarcoding methodology and phylum, we use a mixed-effects linear model and analysis of variance (ANOVA). For our model, phylogenetic diversity was designated as our continuous response, and our explanatory variables included categorical fixed effects for sample methodology and phylum, the interaction between methodology and phylum, ecological variables, and a random effect for station location. All model fitting was performed package 15. Sequence data that support the findings of this study have been deposited in GenBank with the accession codes MH302536–MH303208 for sequences obtained from single individuals, sequences obtained via metabarcoding are in the SRA database with accession code SRP142495. Additional outcomes that support results obtained by comparing the 18S rRNA V1–V2 region with 18S rRNA V9 region are available in GitHub with the identifier 10.5281/zenodo.130882916. In consideration of partial funding by GOMRI, data are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org (10.7266/n7-szj8-1531).All data supporting the findings of this study are available within the article and its supplementary information files, which can be found at 10.6084/m9.figshare.679064016. Computer codes used for statistical analyses that support the findings of this study are available from the figshare record associated with this publication at 10.6084/m9.figshare.629506166.Computer codes used for bioinformatics analyses that support the findings of this study are available in GitHub at 10.5281/zenodo.1308829


There is no such case of whom had HMB in reproductive age with ACS, uremia and systemic lupus erythematosus under hemodialysis and antiplatelet therapy. More clinical safety data on HMB of reproductive age women who are under antithrombotic therapy are required.Heavy menstrual bleeding (HMB), previously known as menorrhagia, is in place with heavy flow and longer lasting days of bleeding during menstrual period, sequentially leading to anemia. We reported a rare case of HMB in a 33-year-old patient after percutaneous coronary intervention (PCI), who presented with acute coronary syndromes (ACS), uremia and systemic lupus erythematosus before PCI. This patient received three times of hemodialysis weekly . On the next day after PCI, this patient began to have menstruation. On the fifth day of menstruation, the patient complained of HMB and physical discomfort, with an urgent need for consultation of gynecologist. After gynecologist consultation, this patient was under oxytocin treatment. However, 2 days of oxytocin treatment did not significantly improve HMB. Afterward, the menstrual volume of patients was significantly reduced on eighth day of menstruation after once therapy of testosterone propionate and norethindrone. Regarding the reasons of HMB, heparin in hemodialysis and antiplatelet drugs utilized (aspirin and clopidogrel) after PCI may be contributors to the HMB. In addition, uterine myoma, cervical canal cyst, renal insufficiency and  From 1995 to 2010, the prevalence of ST-elevation myocardial infarction increased from 11.8 to 25.5% particularly in women younger than 60 years . As a grOur patient is a 33-year-old women suffering from ACS, uremia and systemic lupus erythematosus. The patient was hospitalized on February 26th 2018 for ACS. Before the introduction of PCI, she had been treated with hemodialysis weekly at Monday, Wednesday, and Friday for years , ∗3CYP2C19 (rs4986893) was detected via DFMH method using a commercial kit from Sino Era Genotech1 . 200 μL of whole blood was added to 1 mL of RBC lysis buffer and incubated for 5 min at indoor temperature. Centrifuged  the mixtures for 5 min then removed the supernatant. Afterward, 50 μL of preservation solution was added to the precipitate. 1.5 μL of sample was combined with detecting reagent that includes hybridization buffer and two molecular beacon probes labeled with FAM and HEX for wild-type or mutation. TL998A real-time PCR system  was used to perform the analysis. The detected process began with denaturation for 5 min at 95°C followed by 55 cycles of denaturation for 30 s at 95°C and hybridization for 75 s at 62°C. Based on CYP2C19 genotype, the patients were classified into three metabolizers, which were extensive metabolizer , intermediate metabolizer , or poor metabolizer . ABCB1 (rs1045642), PEAR1 (rs12041331), and PON1 (rs662) genotyping were also detected via DFMH method. Results indicated only a ∗2CYP2C19 heterozygous, lead to a classification of intermediate metabolizer in this patient  and ADP-induced (adenosine diphosphate-induced) platelet aggregation. Results indicated a high drug inhibition rate of platelet . 1 mL of whole blood was mixed with kaolin, inverted five times, and loaded in a heparinize-coated cup containing 20 μl of CaClet Table . These r∗2CYP2C19 lead to a intermediate metabolizer of clopidogrel. However, renal insufficiency was probably a contributing factor to an increased susceptibility to aspirin and clopidogrel. This may explain the high reactivity of aspirin and clopidogrel in TEG. Another contributing factor for excessive HMB was the use of heparin during the process of hemodialysis. This may aggravate bleeding risk in female patients when using DAPT. On the other hand, uterine myoma and cervical canal cyst may also a contributing factor for AUB.Female sex is an independent factor of a higher bleeding risk than male sex during cardiac interventions. After the placement of DES, DAPT including aspirin and P2Y12 receptor antagonist is commonly used for the prevention of thrombosis. Clopidogrel, as a kind of P2Y12 receptor antagonist, remains the first-line antithrombotic drug in Asian patients post DES. The presence of inappropriate dosages of antiplatelet agents and uterine fibroids could cause AUB . HoweverCYP2C19 genotype was well established due to the influence of clopidogrel response (∗2CYP2C19 and ∗3 polymorphisms impair both antiplatelet effects and bioactivation of clopidogrel (CYP2C19 genotyping is feasible and well used in everyday clinical practice (PEAR1 are associated with cardiovascular events in patients undergoing PCI and treated with aspirin in combination with clopidogrel (PON1 gene variations may be a risk factor of bleeding after aspirin and clopidogrel therapy (CYP2C19, ABCB1, PEAR1, and PON1 gene variations which are predictors of clopidogrel platelet reactivity. A heterozygote of ∗2CYP2C19 lead to a intermediate metabolizer of clopidogrel, which indicated the gene variations are not responsible for the HMB in this patient.Aspirin, in combination with clopidogrel, reduces major adverse cardiovascular events in patients with ACS managed with PCI . There aresponse . The nonpidogrel . In the practice . Recentlpractice . There ipidogrel . PON1 ge therapy . In this∗2CYP 2C19 play an important role in the variability of clopidogrel’s curative effect and significantly affect the platelets inhibition ratio resulted from TEG (TEG offer a method of testing the efficiency of blood coagulation in patients with higher risks of bleeding or thrombus. The relationship between gene variation and TEG are still not clear. In Chinese patients treated with clopidogrel, polymorphisms of from TEG . In addifrom TEG . Hemodilfrom TEG . Howeverfrom TEG . The higfrom TEG .Oxytocin is often used as a medication to facilitate childbirth. There is still lack of evidence of oxytocin being used as a hemostatic in patients with HMB. On the contrary, some evidences indicated additional of an oxytocin infusion after cesarean delivery dose not affect the overall occurrence of major obstetric hemorrhage . FurtherThere were several data available on how to treat HMB associated with antithrombotic agents . BoonyawTo the best of our knowledge, there was a rare report of HMB of women on reproduction age who were under antiplatelet therapy. Furthermore, there was no such case of whom has HMB with ACS, uremia and systemic lupus erythematosus. More clinical safety data on HMB of reproductive age women who are under antithrombotic therapy are required.As shown in Figure This case report involved a human subject. Written informed consent was obtained before the preparation of this manuscript.CYP2C19, ABCB1, PEAR1, and PON1 genotyping. F-HS was involved in the care of the patient and interpreted the results, and wrote the manuscript. HL supervised the investigations, interpreted the results, and wrote the manuscript. All authors read and approved the manuscript.JZ, FW, and LS was in charge of the treatment of patient. Z-CG was responsible for collecting the patient’s information and performed the phenotyping test of The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Suckling piglets need more iron for rapid and healthy growth than is available from sow’s milk alone. Therefore, iron supplementation is common on both conventional and organic farms and is usually carried out by a single injection of 200 mg iron during the piglets’ first days of life. However, the long suckling periods in organic pig farming can still lead to iron deficiency, as we found in a study with 288 piglets on an experimental farm. A single subcutaneous injection of iron led to lower iron levels and slower growth until weaning, when compared with giving iron two or three times. We conclude that one iron injection is not enough to ensure adequate iron supply and unimpaired growth of organically reared piglets, and options of additional iron supply need to be explored. Suckling piglets cannot satisfy their demand for the essential trace element iron from sows’ milk alone, which is poor in iron. Therefore, iron is usually supplemented by injecting 200 mg iron (as iron dextran) on day 3 post natum. However, the longer suckling period in organic pig herds leads to iron intake from feed starting later. We hypothesized that one iron injection is not enough for organically reared piglets, and that a second and third injection would improve their iron status and growth. On an organically certified experimental farm, 288 piglets from 32 litters were allocated to three treatments with one iron injection on day 3, two injections on day 3 and 14 and three injections on day 3, 14 and 21 post natum, respectively. One injection resulted in the lowest hematocrit and serum iron levels until day 28 post natum and the lowest daily weight gains until weaning, while piglets receiving two or three iron injections did not differ from each other. Based on these observations, we conclude that one iron injection is not enough to ensure adequate iron supply and unimpaired growth of organically reared piglets, and additional iron supply is necessary.  The most common health disorder in suckling piglets is iron deficiency anemia . PigletsWith piglet mortality being one of the biggest challenges in organic pig husbandry , an adeqThe experiment was conducted between February and October 2016 on the experimental farm of Thünen Institute of Organic Farming in Trenthorst, Germany. Animal husbandry followed the rules of European Commission Regulation 889/2008 . The stu3+) per piglet under the folds of the knee .The experiment was designed as a completely randomized block design, with the single litter forming one block and the individual piglet as the experimental unit. Only litters consisting of at least 9 healthy piglets of more than 1 kg body weight on day 3 post natum were included in the experiment. In total, the experiment included 32 litters of 23 sows and 288 piglets. Within each litter, on day 3 post natum, nine piglets were randomly assigned to one of the three treatments of iron supplementation, resulting in at least three piglets of each litter receiving the same treatment. Piglets were assigned to treatments by generating a random number between 1 and 3 for each piglet ”). The three treatments were as follows: Iron1 consisted of a single dose of 200 mg iron dextran on day 3 post natum (3.0 ± 0.35); in treatment Iron2, piglets received iron twice, once on day 3, and again on day 14 post natum (13.7 ± 0.81); piglets assigned to Iron3 received iron three times: on day 3, 14 and 21 post natum (21.0 ± 1.2). Iron supplementation was always carried out by subcutaneous injection of an iron solution equivalent to 200 mg ferrous ions  until weaning at day 42 (±1.7). The group suckling system offered 7.8 m2 indoor and 3.8 m2 outdoor areas per sow and also included a creep area. The rules for grouping the sows were as follows: Piglets´ age differed no more than 5 days; sick sows remained in the individual farrowing pen; no single primiparous sow in a group. Although cross-suckling was not observed, the possibility cannot be completely ruled out because within each group the sows and piglets were free to mix. During gestation, sows were fed the gestation mixture shown in ante partum until weaning, sows were fed a lactation mixture . The piglets were born of crossbred sows (German Landrace × Large White), inseminated with Piétrain. At one week ante partum, sows were moved to individual straw-bedded farrowing pens of 7.8 mture see  twice a ture see . When fo−1 of the analgesic meloxicam . At the age of 21 days, all piglets were vaccinated against Mycoplasma hyopneumoniae  and Lawsonia intracellularis . Both vaccines did not contain any form of iron.Male piglets were castrated at the age of 7 days, under inhalation anesthesia with isoflurane . In addition, piglets received 2 mg animal® c111 system, Roche Diagnostics GmbH, Mannheim, Germany, 2014). Piglets were weighed individually once a week until weaning, and daily weight gain was calculated from individual body weight. All medical treatments and mortality were documented routinely during the experimental period.From every feed mixture, one bulk sample was collected and sent to a commercial laboratory for nutrient analysis according to EU regulation EC 152/2009 . Feed cok + dayl + treatmentk × dayl + litterm + pigletn(treatmentk) (legend see below). For a dataset of 30 litters (=270 piglets), the expected power was estimated at 0.95–0.97 for the effect of group, 1.00 for the effect of day and 0.70–0.96 for treatment × day. The final dataset included 32 litters.At the start of the experiment, the first three litters were used as a pilot study to estimate the necessary sample size for hematocrit, serum iron and body weight. The estimation followed the method described by Littell et al.  and was ® software [k = fixed effect of treatment ; dayl = fixed effect of day of life ; treatment × day = interaction between treatment and day; litterm = random effect of litter ; pigletn(treatmentk) = random effect of piglet  within treatment; b1 = regression coefficient of birth weight; weight_day3 = continuous linear effect of the live weight of piglet on the third day of life; ε = residual error.Statistical analysis was performed with SASsoftware . Using Pp values of the effects of treatment, day and treatment × day. Values within a row with different lowercase superscripts differ significantly at p < 0.05. Values within a column with different capital superscripts differ significantly at p < 0.05. Due to low incidences of medical treatments and mortalities during the experimental period, no statistical analysis was done. Instead, the numbers of piglets needing medical treatment and the numbers of lost piglets are given in Table 5.The effect of sex was initially included in the model but dropped due to a lack of significance. When analyzing daily weight gain until weaning, the effect of day and the interaction treatment × day were dropped from the model. For multiple comparisons of means, the Tukey test was used. Probability values <0.05 were interpreted as representing statistically significant differences. Tables 2–4 show least square estimates of the interaction treatment × day, the standard errors of the means (SEM) and The effects of different frequencies of iron supplementation on hematocrit in piglet blood are shown in Due to the experimental design , feed consumption of piglet starter feed could not be differentiated between treatments and was documented as the sum consumed by all piglets of a group in the group suckling system. Although the frequency of iron supplementation did not influence the body weight of piglets until the age of 28 days, both body weight at weaning and daily weight gain until weaning were significantly affected . While nThis study was conducted in order to assess whether one subcutaneous iron injection (200 mg iron as iron dextran) is enough to ensure adequate iron supply and unimpaired growth of suckling piglets on an organic farm, and whether administering iron two or three times improves iron status and weight gain. In total, 32 litters of at least nine healthy piglets of more than 1 kg body weight on day 3 post natum participated in the study. Within each litter, all three treatments were tested, resulting in at least three piglets of each litter receiving the same treatment.The main blood parameters used to detect imbalances in the iron metabolism of mammals are hemoglobin, hematocrit, serum iron, and the iron-binding capacity . Only thThe hematocrit is the volume percentage of erythrocytes in the whole blood. Since the synthesis of erythrocytes directly depends on the iron supply, erythrocyte production and, consequently, the hematocrit decreases when iron supply is limited ,30. The −1 as latent iron deficiency in piglets, and lower than 17.91 µmol L−1 as indicating a manifest deficiency [−1 [−1, which is between the above-mentioned recommendations [−1 serum iron recommended by Pfau et al. [Another blood parameter illustrating iron supply is serum iron, which is the concentration of iron bound to transferrin, the glycoprotein responsible for iron transport . In a sificiency . In 1983ency [−1 . The mosndations . On day ndations , and Irondations . The optu et al.  was neveSummarizing our findings on the effect of iron supplementation on the iron status of piglets at our experimental farm, a single iron injection on day 3 led to both hematocrit level and serum iron concentration indicating latent to manifest iron deficiency until day 28 post natum. On day 14 post natum, however, hematocrit could be considered as normal. The treatments with two or three iron injections resulted in hematocrit levels within the normal range, and serum iron concentrations mostly indicated sufficient iron supply as well.On organic pig farms with suckling periods of at least 40 days, the transition to solid feed happens later than on conventional farms, where piglets are usually weaned at an age of 21–28 days. In accordance with previous observations at our organically certified experimental station , we founThe importance of adequate iron supply for unimpaired growth of piglets has been known for a long time ,35, but The average daily weight gains found in treatments Iron2 and Iron3 were 274 and 276 g, respectively. This level of growth is common for our experimental pig herd , but lowAlthough blood analysis of the piglets led to the conclusion that a single iron injection on day 3 post natum resulted in latent to manifest iron deficiency, no differences in the number of medical treatments and mortality during the experimental period could be observed.Mortality was generally low, and amounted to 3.1%, 3.0% and 5.4% of piglets in treatments Iron1, Iron2, and Iron3, respectively. Prunier et al. reported a considerably higher mortality of 13.0%–25.5% on organic pig farms in eight European countries, which can be explained by the fact that their report includes early losses during the first days of life, while our experimental period lasted from day 3 post natum until weaning [To summarize our observations, a single iron injection was not enough to ensure adequate iron supply and unimpaired growth in suckling piglets on our experimental farm. Considering that a single iron injection is the usual practice on organic pig farms, it seems that inadequate iron supply for suckling piglets is a problem that requires attention. Based on the differences in iron status and growth between the piglets receiving iron once or two or three times, additional iron supplementation seems highly recommendable. One option is a second (or third) injection as in our study. Oral application could be another option but would require a change in the regulations for organic agriculture.


Epinephelus moara♀ × Epinephelus lanceolatus♂). In this study we analyzed and compared the intestinal microbiota composition of healthy and diseased pond-reared fish to discern the functional profile of a healthy status. The richness and diversity of the intestinal microbiota did not differ significantly between diseased and healthy fish, yet the abundance of predominant phyla like the Proteobacteria were upregulated in the diseased Yunlong Grouper. At the genus level, a significant reduction of Cetobacterium was observed in the intestinal tracts of diseased fish, as Pseudomonas became the most dominant bacterium. To compare the intestinal microorganism abundances between the two health groups of fish, we first screened the gut bacteria and discerned 4 phyla and 12 genera to designate a healthy status in Yunlong Grouper. The environmental bacterial community influenced composition of the intestinal microbiota in Yunlong Grouper, and the intestinal microbiota of diseased fish was more susceptible to the influence of the culture water. In addition, the prediction of functional genes by phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) indicated that the intestinal microbiota of Yunlong Grouper is related mainly to the terms “metabolism, environmental information processing, genetic information processing, human diseases, and cellular processing; moreover, the functions of the intestinal microbiota differed between the different health states of this fish. The overall results indicate that the occurrence of disease can affect the composition and function of the intestinal microbiota in a cultured fish.Maintaining stabilization of the intestinal microbiota is important in preventing bacterial diseases in cultured fish. At present, there have been no reports on the composition and functional analysis of intestinal microbiota in Yunlong Grouper ( The intestinal microbiota comprise a complex and diverse ecosystem attached to the intestinal mucosa and they exist in a dynamic state. Once the balance of the intestinal microbiota is destroyed, the host becomes susceptible to innumerable bacterial diseases. Thus, stability of the intestinal microbiota community is an important factor in preventing bacterial diseases in animals (Round and Mazmanian Epinephelus moara♀ × E. lanceolatus♂) with fast growth, a high survival rate, and wide temperature range. Researchers have reported on their embryonic  is a bioinformatics software package used to predict metagenomic functional genes ; body weight (BW) was 684.13 ± 124.24 g for the H group vs. 374.23 ± 71.02 g for the D group . There were three replicates for each of the two health-status groups. Sterile water-collection bottles (2 L) were used to take culture water from four locations in each of the two 20-m3 culture ponds. Feed samples were collected using sterile bags. All samples were immediately transported to the laboratory.Yunlong Grouper and their feed and culture-water were sampled at the Tianjin Xingsheng Sea and Freshwater Aquaculture Co., Ltd. All sampled fish were of the same age (6 months old) and had been fed the same feed and reared in a similar environment (n = 3 per group). Two experimental groups were acquired from same recirculating aquaculture systems: healthy fish (H group) and diseased fish (D group); the latter were taken from a culture pond specifically maintained for producing diseased fish, and all the fish had obvious signs of disease by visual observation. The healthy samples were randomly removed from a culture pond and visually observed for signs of disease before being dissected. Mean (± SD) of body length (BL) was 38.33 ± 3.79 cm for the H group vs. 30.67 ± 1.53 cm for the D group  according to the manufacturer’s instructions. The V4–V5 variable region of 16S rRNA genes of gut microbiota were amplified by polymerase chain reaction (PCR) using the primers 515F [5′-GTGCCAGCMGCCGCGGTAA-3′ , according to the manufacturer’s instructions. The 2-L water samples were sequentially filtered through 0.22-μm filter paper  to collect as many bacterioplankton as possible; DNA was extracted from these organisms using the E.Z.N.A.All PCR products were recovered from the gel with an AxyPrep DNA Gel Recovery Kit  and then quantified using an FTC-3000™ Real-Time PCR system. The amplicons were sequenced with Illumina MiSeq 2 × 300 bp . Sequencing was carried out at TinyGene Bio-Tech Co., Ltd .https://sourceforge.net/projects/rdp-classifier) and the Greengenes Database (http://greengenes.lbl.gov) were used for taxonomic assignments. Rarefaction curves and Alpha diversity indices  were generated with Mothur  software. A P-value of less than 0.05 was considered significant.All sequences were clustered into operational taxonomic units (OTUs) by de novo OTU picking at the 97% similarity level, performed with the software Mothur  for biomarker discovery. The LEfSe method uses the Kruskal–Wallis rank sum test to detect features with significantly different abundances between the assigned taxa and performs the LDA to estimate the effect size of each feature with the normalized relative abundance matrix.Characterization of the healthy and diseased Yunlong Grouper in each treatment was performed using linear discriminant analysis (LDA) effect size (LEfSe) method .By sequencing the V4–V5 region of the 16s rRNA gene from the samples, the high-throughput sequencing data in each sample can be statistically analyzed. The effective tags of each sample were between 29,297 and 37,177, and the optimized clean data were between 21,091 and 34,280. A total of 521 OTUs were obtained, which identified to 18 phyla. The samples of healthy fish aquaculture water (HW), diseased fish aquaculture water (DW), feed (AF), healthy fish intestinal microbiota , and diseased fish intestinal microbiota , respectively, included 275, 280, 69, 155, 108, 50, 178, 198 and 145 OTUs (Table P > 0.05).The rarefaction curve was drawn with randomly selected sequence numbers and the number of OTUs they can represent Fig. . The resProteobacteria, Bacteroidetes, and Actinobacteria as the dominant phyla, and total relative abundance was > 80%. The microbiota composition of the feed was relatively simple, with Proteobacteria as the dominant phyla, with a relative abundance of 94% , Fusobacteria (23.30%), and Firmicutes (20.47%), while that of diseased fish comprised Proteobacteria (56.21%), Actinobacteria (16.00%), and Cyanobacteria (12.61%) , and the microbiota composition and proportions of each group were analyzed at the genus level Fig. c. The moFirmicutes; Actinobacteria and Planctomycetes were biomarkers for the diseased group; and Actinobacteria had the highest LDA score.LEfSe analyses , Fusobacteria (23.30%), and Firmicutes (20.47%); while in the intestinal microbiota of diseased fish they were Proteobacteria (56.21%), Actinobacteria (16.00%), and Cyanobacteria (12.61%). The composition of the intestinal microbiota of diseased fish had somewhat changed from the structure present in a healthy state. Proteobacteria was the most dominant phyla in the microbiota community of Yunlong Grouper. This finding is consistent with previous studies of other species of marine and freshwater fishes, including grass carp, yellow catfish, coho salmon, and rainbow trout  to proliferate; this situation may inhibit the growth of beneficial bacteria and lead to slow growth of the diseased fish. Nonetheless, the effects of Pseudomonas on the intestinal microbiota of fishes merits further study.To better understand the relationship between intestinal microbiota composition and fish health condition, the intestinal microbiota of Yunlong Grouper was examined. We found no significant difference between the taxonomic richness and diversity of the intestinal microbiota in diseased fish and healthy fish. The dominant phyla in the intestinal microbiota of healthy fish were illa Fan  and the Actinobacteria had the highest LDA score in the intestinal microbiota of diseased fish, while in the healthy fish the score was highest for Firmicutes. Actinobacteria have the ability to biosynthesize secondary metabolites as antibiotics against invasive pathogens , composition, distribution characteristics and influencing factors, and less research has been carried out on microbial community function. More recently, PICRUSt has been widely employed for function prediction of fish intestinal microbiota (Bairagi et al. In this study we found no significant differences in the taxonomic richness and diversity of intestinal microbiota between healthy and diseased Yunlong Grouper, though the composition of the intestinal microbiota had certainly changed. In the intestinal microbiota of healthy fish, probiotics known to promote the digestion and absorption of food and also promote general growth and development are enriched. Pathogenic bacteria may occupy a dominant position in the intestinal microbiota of diseased fish, thus altering the function of the intestinal microbiota, in turn influencing the related functions of digestion, absorption and energy metabolism; which is more likely to lead to appearing the intestinal tract inflammation of Yunlong Grouper, and appearing the abnormal phenomena of growth of diseased fish slower than the growth of healthy fish. These results provide some basis for early detection of infectious bacterial diseases in this newly cultivated hybrid fish. Future research should increase the number of samples to eliminate individual differences, adding accuracy and reliability to the data.


Variation in CAL among different plant organs reflects the energy utilization and distribution strategy of plant. Here, we explored how the CAL of different plant organs varies at the species and regional level in relation to vegetation type . The results showed that, in the forests and grasslands, CAL significantly differed among the different organs and functional types of plants. The CAL of different organs in forests was ordered as: leaf (19.71 ± 1.82 KJ g–1) > branch (19.33 ± 1.32 KJ g–1) > trunk (19.09 ± 1.44 KJ g–1) > root (19.02 ± 1.11 KJ g–1). For forests, the CAL of plant organs, except for the leaves of trees and shrubs, increased with increasing latitude (P < 0.01). In comparison, the CAL in the roots of shrubs and herbs decreased with increasing longitude (P < 0.01). Through delineating systematic references of CAL among different plant organs, our findings provide key parameters to improve estimates of biomass energy at regional and global scales.The calorific value (CAL, KJ g CAL is an important plant trait because it reflects the photosynthetic ability (gross primary productivity) and nutritional status of plants to some extent [The calorific value  have different morphological characteristics and functional traits –19. For To explore how CAL varies among different plant organs and different vegetation types at a large scale, we collected samples from vegetation in Chinese terrestrial ecosystems  to assimilate a CAL dataset from field sampling and literature collection . The maihttp://apps.webofknowledge.com) and China National Knowledge Internet (http://www.cnki.net) databases, using “plant” and “calorific value” as key words to search for articles. The articles were further screened based on the following criteria: 1) data on CAL should be obtained through field measurements rather than model simulations; and 2) basic information  should be clearly presented. Then, the two datasets were combined and the units were transformed to KJ g–1. To enhance the credibility of data and to reduce uncertainty, we excluded all samples with CAL exceeding a mean ± 3 times the standard deviation to remove the adverse impacts of outliers during data analysis [Chinese terrestrial ecosystem is the research area in this study. Data on CAL were collected through two approaches: the experimental measurement data by our team and data from the published literature. For the experimental data, we sampled the leaves of 1124 plant species that were healthy, fully expanded, and sun-exposed. The plant samples were collected along the North-South Transect of Eastern China (NSTEC) in July and August 2013 . We measanalysis . After dhttp://www.geodata.cn/). We used the tool “Extract Multi Values to points” in ArcGIS software to extract the corresponding MAT and MAP values for each site.Based on climate and topography , the terData on plant CAL were analyzed at the species and regional level. At the species level, we summarized all plants genotypes across China based on "Chinese Flora" . First, –1) of various organs of different vegetation types across eight ecological regions  of each sample, i represents organs , n represents the number of organ samples; CAL–region is the CAL of plant organs at the regional level, CALj represents the CAL of each sample in the region, and k represents the CAL of plant organs in the region.where, CALP = 0.05. All data were analyzed with ArcGIS , SPSS 18.0  and Sigmaplot 12.5  software.One-way analysis of variance (ANOVA) was used to analyze differences in CAL among various organs and regions. Least multiple comparison (LSD) was used to explore the difference in CAL among trees, shrubs, and herbs. The independent-samples T test was used to test the differences in CAL among the different plant life forms of trees. The latitudinal and longitudinal patterns of plant CAL for different organs were analyzed using the linear regression of the ordinary least squares method (OLS). Pearson correlation analysis was used to analyze the correlation between plant CAL at the organ level and climatic factors (MAT and MAP). The significant of the statistical tests was set as P < 0.05; P < 0.05; There were significant differences in the CAL of different plant organs in forests  > branch (19.33 ± 1.32 KJ g–1) > trunk (19.09 ± 1.44 KJ g–1) > root (19.02 ± 1.11 KJ g–1). Furthermore, the CAL of needle trees were higher than that of broadleaf trees  (–1) was significantly higher than that of the underground biomass (16.68 ± 1.95 KJ g–1) , whereas the CAL of roots decreased with increasing longitude , while the CAL of the underground biomass showed no longitudinal patterns ; however, there was no significant correlation between MAT and the leaves of trees and herbs  than for underground organs (root). Gao et al.  also repFor forest vegetation, CAL significantly differed among various plant functional groups (trees > shrubs > herbs). Lin et al.  and LongOur study showed that the CAL of needle trees was significantly higher than that of broadleaved trees. Wang and Sun  showed tWe found no significant variation in CAL of plant organs in different regions. A large number of studies have shown that environmental factors such as light intensity, soil type, moisture, and day length, directly and indirectly influence plant CAL –35. DiffThere was no consistent longitude and latitude pattern for the CAL of different organs. For forests, the CAL of plant organs in the tree and shrub layer (except leaves) increased with increasing latitude, while the CAL in the roots of shrubs and herbs decreased with increasing longitude. The differences in latitudinal and longitudinal patterns among various organs may result from different environment adaptation strategies, or from the different control mechanisms of climate, such as MAT and MAP . Our resCAL in different plant organs reflects the level of plant energy allocated to storage, and it is an important form of biomass energy . TherefoThis study established a comprehensive dataset of CAL for different plant organs in the terrestrial ecosystems of China. CAL significantly differed among plant organs  and vegetation types . As expected, different strategies were detected in how energy was distributed across different organs. For example, the CAL of the aboveground organs is significantly larger than that of the underground parts, indicating that plants tend to store more energy in organs that produce energy. Furthermore, CAL noticeably varied among PFGs  and plant life forms in forest vegetation. CAL was higher in needle trees compared to broadleaf tree, and was ordered: tree layer > shrub layer > herb layer. Therefore, due to the spatial niche differentiation in forests, plants have different strategies for using resources, and plants that receive sufficient light have higher CAL. However, there was no significant difference in the CAL of plant organs between different regions, indicating that the different patterns of energy distribution in the organs of different plants might offset the variation due to environmental differences. Overall, our findings demonstrate that CAL exhibits broad variation in different plant organs among different vegetation types, contrasting with the trends of carbon content in plant organs. Therefore, a set of CAL references for different organs across different vegetation types is required to improve current estimates of biomass energy storage at regional and global scales.S1 Table(PDF)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigDifferent letters indicate significant differences in each cluster at the P = 0.05 level.(PDF)Click here for additional data file.S4 FigDifferent letters indicate significant differences in each cluster at the P = 0.05 level.(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.


The purpose of this study was to investigate the effect of six months strength training with or without supplementing protein and vitamins, on chromosomal integrity of buccal cells in institutionalized elderly.One hundred seventeen women and men (65–98 years) performed either resistance training (RT), RT combined with a nutritional supplement (RTS) or cognitive training (CT) twice per week for six months. Participants’ fitness was measured using the 6 min walking, the chair rise, and the handgrip strength test. Genotoxicity and cytotoxicity parameters were investigated with the Buccal Micronucleus Cytome (BMcyt) assay.2 = 0.466, p = 0.000).Six minutes walking and chair rise performance improved significantly, however, no changes of the parameters of the BMcyt were detected. Age and micronuclei (MN) frequency correlated significantly, for both women  and men . Squared regressions revealed a significant increase in the MN frequency of buccal cells with age (RInterestingly and contrary to what was shown in blood lymphocytes, chromosomal damage in buccal cells increases until very old age, which might qualify them as a valid biomarker for aging. Unexpectedly, in this group of institutionalized elderly, resistance training using elastic bands had no effect on chromosomal damage in buccal cells.  •Mutation in buccal cells increased until very old age – a new aging biomarker?•Chromosomal damage in buccal cells was age-dependent and equal for women and men.•Strength training improved fitness but not mutagenicity in buccal cells of elderly. Increased levels of chromosomal and DNA damage are associated with the development of these chronic diseases  and furtThe frequency of micronuclei (MN) in blood lymphocytes is a well-established marker to measure chromosomal damage . InteresThe primary aim of this secondary analysis of the Vienna Active Ageing Study was to investigate the effect of a six months lifestyle intervention, including strength training (using elastic bands) and the intake of a nutritional supplement (proteins and vitamins), on markers of the Buccal Micronucleus Cytome (BMcyt) assay in Austrian institutionalized elderly women and men. We hypothesized improvements in chromosomal stability through exercise with further enhanced effects in subjects, who took the supplement.A secondary aim was to further investigate the link between age and parameters of the BMcyt by combining our data with those of Wallner et al. , who anaWith the age of our study subjects  being at or above life expectancy in Austria and 75% of our study population being 80 years or older, we were able to generate novel data on chromosomal damage using the BMcyt assay.2ClinicalTrials.gov, NCT01775111.The presented data are secondary analyses and part of the Vienna Active Ageing Study, which is a multidisciplinary project of the Centre for Sport Science and University Sports, the Faculty of Life Sciences, the Research Platform Active Ageing , the Karl Landsteiner Institute for Remobilization and Functional Health  and the Curatorship of Viennese Retirement Homes. The study was approved by the ethics committee of the City of Vienna (EK-11–151–0811) and registered at 2.1One hundred seventeen institutionalized elderly women and men (aged 65–98 years), recruited from five different senior residencies in the area of Vienna (Curatorship of Viennese Retirement Homes), volunteered for the study . The sub2.2The present study design was described previously in Franzke et al. . Briefly2.3The resistance training groups (RT and RTS) received two weekly sessions of resistance training, conducted on two non-consecutive days and supervised by a sport scientist. Training attendance was recorded every session. Exercises were conducted using elastic bands, chairs and own body weight - for detailed training program see supplement of Oesen et al. . The pro2.4The RTS group performed the same exercises together with the RT group and additionally a liquid supplement every morning, as well as directly after each training session. Each drink supplied a total energy of 150 kcal and contained 20.7g protein (56 energy (En)%, 19.7g whey protein, 3.0g leucine, > 10g essential amino acids), 9.3g carbohydrates , 3.0g fat (18 En%), 1.2g roughage (2 En%), 800IU (20 μg) of vitamin D, 250 mg calcium, 31.5 mg vitamin C, 7.5 mg vitamin E, 0.8 mg vitamin B6, 3 μg vitamin B12, 199.5 μg folic acid and 37.5 mg magnesium . The intake of the nutritional supplement was controlled at breakfast (prepared by the kitchen staff and served ready-to-drink for the participants) as well as after the training sessions, where the trainers mixed the supplement and distributed it to the participants.2.5The CT groups served as our control group and performed coordinative or cognitive tasks two times per week, equally to the frequency of the RT and RTS groups. This was done to minimize the “bias” of being part of a social group activity.Participants of all groups were instructed to maintain their regular food intake.2.6All study participants were asked to rinse mouth twice thoroughly with 100 ml of tap water to remove excess debris. Then the participants used toothbrushes and rubbed them on the insides of both cheeks to collect buccal cells, which were processed and scored according to the method of Thomas et al. . 80 000 2.7The participants had to stand up from a chair (46 cm seat height) as often as possible within 30 s. The chair was placed backwards against a wall to ensure a safe test setting. For one valid repetition, participants had to fully stand up (hip and knee fully extended) and sit back, with their arms crossed over their chest. A last-second-attempt was considered valid, if the person had covered more than 50% of the range of motion .2.8All participants performed an isometric handgrip strength test (kg) using a dynamometer. The test was conducted in a sitting position and maximal isometric contraction within 4–5 s was measured . The better result of two trials (1 min break in between) for each hand was recorded .2.9The participants had to walk for 6 min as fast and as far as possible. This test is a valid tool to evaluate aerobic endurance in the elderly. Participants were allowed to slow down and even take short rests. Every subject performed the test separately without being disturbed by others. They had to walk back and forth on a 30 m shuttle track and the distance covered within 6 min was registered .2.10Statistical analyses were performed with IBM SPSS Statistics 21. After testing for distribution of the data, baseline differences were measured using the Mann-Whitney-U test. Linear and curve fitting regressions were calculated for MN-frequency and age. To analyze age effects, additional data generated by our working group were included . Both, t2.11https://figshare.com/s/c4c095d491ad1c33f15e.Underlying data for this publication can be accessed via the following source - 33.12. Baseline analyses for the BMcyt assay were performed for the 87 subjects who participated in the six months sampling time-point.At baseline, 96 subjects were included into the Active Aging study , 12.4% wThere was no statistical difference between women and men for the parameters of the BMcyt, age and BMI, however the number of cells with MN (p = 0.060) and the total number of MN (p = 0.064) showed a tendency for increased frequencies in female subjects .Data regarding the physical performance of our study population have been published in detail by Oesen et al. . These r3.2To assess the effect of age on the parameters of the BMcyt assay, we statistically merged our data with the study of Wallner et al.  who inve2 = 0.466, p = 0.000)  . For bot= 0.000) .Fig. 2SqBaseline data of age, BMI and the BMcyt assay are presented in 3.3To assess the effect of physical fitness on chromosomal damage and cytotoxicity, we compared the functional fitness parameters  with the endpoints of the BMcyt assay. We found no association between chromosomal stability, cytotoxicity parameters and physical fitness. Still, karyolytic cells correlated negatively with the chair raise test  and the 6 min walking test .3.4After six months of intervening with RT alone, with RT combined with a nutritional supplement or with cognitive training, we did neither observe statistically significant differences of the BMcyt parameters to baseline, nor between intervention groups .Table 2IBoth, RT and RTS groups significantly improved in the chair rise test and the 6 min walking test, but not in the handgrip test ; for further details please see Franzke et al.  and Oese4The primary aim of the current secondary analysis of the Vienna Active Ageing Study was to investigate the effect of six months strength training, strength training combined with a protein-vitamin supplement, or of cognitive training on parameters of chromosomal instability and/or various stages of cell death, measured by the BMcyt assay, in a cohort of elderly institutionalized women and men in Vienna. Previous research on MN in human PBMCs  guided o2 classified as “overweight”, which is rather supportive than detrimental for this advanced age group. In elderly a higher bodyweight was observed to be inversely associated with mortality [Our study cohort of institutionalized elderly  with a mean age around their current statistical life-expectancy  reflectsortality  and evenortality .The MN frequency in buccal mucosa cells correlated positively with age in healthy individuals in earlier studies and equally for both sexes ,31, whicOur merged data, with results from 163 subjects between 20 and 98 years, showed a significant squared regression between age and MN frequencies , with moTo our knowledge there are no published studies investigating the effect of exercise on MN formation in buccal cells. Exercise with its high oxygen demand increases the formation of reactive oxygen species, which, under normal physiological conditions, serve as essential signaling molecules in the adaptation process to physical training . HoweverWe did not observe any correlations between MN frequency in buccal cells and physical performance parameters in our study cohort at baseline. After six months of strength training alone or in combination with the intake of a supplement containing protein and vitamins no effect on MN frequency and other parameters of the BMcyt assay was observed.Several explanations may account for the lack of an effect of the exercise intervention. Our subjects were, relatively healthy, free of severe diseases and without severe nutrient deficiencies. In buccal mucosa cells it has been shown, that folate is linked to reduced MN frequencies, however this was only the case in folate deficient or diseased populations ,42. AlthIt is possible that our cohort of institutionalized elderly was probably and despite their high age “too healthy” to detect an effect of our interventions. Further, the high age of our subjects  produced a naturally given relatively heterogeneous study group, although we followed strict in- and exclusion criteria. Consequently, in future projects, an even larger sample size (if possible) would be helpful to investigate effects of lifestyle interventions on parameters of the BMcyt assay.5Despite observing no effect of the exercise and nutritional intervention regarding chromosomal stability in buccal cells of our oldest subjects, our baseline analyses of the combined cohorts (old and young) revealed interesting results. We observed a consistent increase in parameters of the BMcyt assay until very old age (90 years plus), yet contrary to what was seen in lymphocytes, where a leveling off after the age of 60–70 years in the MN frequency occurred. This steady increase in the MN frequency until old age was equally strong for women and men and could, after further research, qualify this marker as a valid biomarker for aging .Our analyses of chromosomal damage in buccal cells, in a cohort of institutionalized elderly around their statistical life-expectancy, add important and missing data to the field of DNA stability and mutagenesis. To our knowledge, we are the first to report about the effect of a lifestyle intervention in elderly, including nutrition and exercise, on parameters of the BMcyt assay.However, in general and especially in this steadily growing population of elderly people, more research is needed to investigate the underlying mechanisms and tissue related differences regarding chromosomal and DNA integrity to revise and develop further strategies for improved quality of life.University of Vienna, by funding the Research Platform Active Ageing, the Anniversary Fund of the Austrian National Bank (No. 14541) and the EU-IRSES-318962—BIOAGE project. This article was supported by the Open Access Publishing Fund of the University of Vienna.This work was supported by the The authors declare that there is no conflict of interest.