Primers were designed based on multiple alignments of phaC gene sequences from 218 species that have their genomes already analyzed and deposited at Biocyc databank. The combination of oligos phaCF3/phaCR1 allowed the amplification of the expected product (PHA synthases families types I and IV) from reference organisms used as positive control (PHA producer). The method was also tested in a multiplex system with two combinations of initiators, using 16 colonies of marine bacteria (pre-characterized for PHA production) as a DNA template. All amplicon positive organisms (n = 9) were also PHA producers, thus no false positives were observed. Amplified DNA was sequenced (n = 4), allowing for the confirmation of the phaC gene identity as well its diversity among marine bacteria. Primers were also tested for screening purposes using 37 colonies from six different environments. Almost 30% of the organisms presented the target amplicon. Thus, the proposed primers are an efficient tool for screening bacteria with potential for the production of PHA as well to study PHA genetic diversity.The use of molecular diagnostic techniques for bioprospecting and microbial diversity study purposes has gained more attention thanks to their functionality, low cost and quick results. In this context, ten degenerate primers were designed for the amplification of polyhydroxyalkanoate synthase ( Bioplastics polymers have emerged as an alternative to the excessive use of polymers from petrochemical origin, which represent a problem in terms of waste management and environmental impact ,2. PolyhphaC of Cupriavidus necator, and class II by Pseudomonas, where PHA synthase is encoded by phaC1 and phaC2 [phaC and phaE and can be found in the model organism Allochromatium vinosum [Bacillus megaterium, in which the main genes are phaC and phaR [phaC is the most important since it encodes the key enzyme for PHA synthesis, thus justifying its choice as an indicative of possible producers of these biopolymers [The genes responsible for PHA synthesis can be classified into four different classes, according to the organization of gene locus and the structural and functional properties of enzymes PHA synthase ,13. Clasnd phaC2 . Class I vinosum . Class Iand phaR . Among tpolymers .PHA-producing organisms can be identified and evaluated by different methods . Among tBacillus pumilus ATCC 14884, Bacillus thurigiensis var. israelensis 4Q2-72, Bacillus megaterium ATCC 14581, Bacillus cereus ATCC 14579, Chromobacterium violaceum (CV11), and Cupriavidus necator DSM 545, were used as positive controls for the presence of the gene phaC and a negative control was made by using DNA from Escherichia coli DH5α. Genomic DNA was isolated , quantified after agarose gel electrophoresis , and used as a DNA template for PCR (approximately 50 ng per reaction).The reference bacteria strains Pseudomonas sp. (LAMA 572), Halomonas hydrothermalis (LAMA 685), Micrococcus luteus (LAMA 702), Brevibacterium sp. (LAMA 758), Halomonas sp. (LAMA 761), LAMA 677, LAMA 726, LAMA 729, LAMA 737, LAMA 748, LAMA 760, LAMA 765, LAMA 790, and LAMA 896, previously recognized (tested by staining with Nile Red in seven culture media) as PHA producers, were used in the developed PHA PCR. Also, negative marine bacteria were applied; Idiomarina loihiensis and Terribacillus saccharophilus. To check the efficiency in pre-selection of PHA-producer bacteria isolated from the environment, 37 newly isolated bacteria from soils were tested. Genomic DNA of these bacteria was obtained as described, and similarly used for amplifications.Genomic DNA from twelve marine bacteria, phaC gene were retrieved from the BioCyc Database Collection [The protocol for primer design was similar to that previously described Lima & Garcês . A totalllection  and analllection . Sequencllection . The desllection  at Uniprllection . The besThe primer sequences were determined with the online program OligoAnalyzer version 3.1 , in which important parameters for the efficiency of PCR reaction were defined, such as the melting temperature (Tm) and the percentage of C+G. The primers drawn were also evaluated at CLC Genomics Workbench 4.8 , enabling the visualization of which primer would produce more annealing results to all the gene sequences used during primer design. The parameter considered in this evaluation was the possibility of up to two degenerate bases for each primer.phaC gene fragments with all primer combinations was a cycle of 94 °C for 4 min, followed by 35 cycles of 94 °C for 45 s, 61 °C for 20 s, 72 °C for 10 s and a final extension of 72 °C for 2 min. As a template, extracted DNA or bacteria cells isolated from the environment were used for phaC amplification. Once the best set of primers was established, the program used for phaC gene amplification using reference strains was adjusted to one cycle of 94 °C for 4 min, followed by 10 cycles of 94 °C for 30 s, 68 °C for 20 s, as well as 25 cycles of 94 °C for 12 s, 65 °C for 10 s, 72 °C for 7 s and a final extension of 72 °C for 2 min. All reactions were held in the thermocycler Eppendorf Mastercycler Gradient, consisting of 20 μL containing 1× PCR amplification buffer (Invitrogen), 0.2 mM of each dNTP, 0.5 μM of each primer, 1U Taq DNA polymerase (Invitrogen), 2 mM MgCl2, and the template DNA. PCR amplicons were observed by electrophoresis in 2% agarose gel further stained with ethidium bromide, and viewed under a UV transilluminator.The program used for the amplification of C. violaceum were purified  and sequenced in an ABI-Prism 3100 Genetic Analyzer at ACTGene . The identity of the sequences was evaluated through the Genomics Workbench 4.8 program accessing the tool of comparison BLASTX  [The amplified fragments obtained from marine bacteria LAMA 677, LAMA 737, LAMA 748, LAMA 760, and the reference bacteria A, USA ) . The genA, USA )  in GeneiphaC (1 sequence = 1 specie) used for primer design indicated a high percentage of organisms belonging to the phylum Proteobacteria  (The phylogenetic classification of the 218 sequences of the gene d gamma) . The phyd gamma) . The anaphaC. For instance, primers phaCF3 and phaCR1 were selected due to their capacity to anneal with a large number of sequences. Also, primer phaCF1 was picked due to its relative position to phaCR1 and its ability to be used in multiplex PCR  and compatibility for annealing with the 218 used sequences . Among tplex PCR . These tphaCF1, phaCF3, and phaCR1) were revealed to be attractive for phaC amplification during in silico analysis, their efficiency and specificity has to be determined in vitro. Therefore, a total of seven pairs of primers were tested using LAMA 677 (PHA producer). Positive results were observed for three of these combinations (phaCF3/phaCR1) was further tested using genomic DNA from already known phaC carrier species: the model organisms B. pumilus (ATCC 14884), B. thuringiensis var. israelensis (4Q2-72), B. megaterium (ATCC 14581), B. cereus (ATCC 14579), and C. necator (DSM 545). The expected fragments of 239bp were generated for all organisms tested (E. coli DH5α (data not shown).Although a particular set of primers  were used. The use of these primers in multiplex reactions allows for the increase of coverage for the detection of PHA producers. In this context, 16 marine organisms (14 positive and two negative PHA producers) and 37 environmental isolates (unknown PHA production) were screened. The proposed PCR protocol was able to detect phaC in nine marine isolates. No false positives were identified, highlighting the specificity of the primers designed. The positive control C. violaceum was amplified efficiently. When applying the phaC multiplex-PCR with the 37 environmental isolates, phaC amplicons were observed in approximately 30% of them (data not shown), revealing the great potential of this method for the screening of PHA producers. Once it was verified that primers were efficient in recognizing PHA-producing bacteria, they were also tested for the detection of potential new polymer producers isolated from environmental samples. For this purpose, two sets of primers (phaC genes/proteins previously described. This indicates the specificity and efficiency of the proposed method. The originated sequences also allowed the taxonomic classification of organisms harboring the phaC gene (phaC gene diversity that resides in bacterial cells belonging to distinct taxa.Amplicons from different reactions were sequenced and compared to the Genbank database. The sequences identities were compatible with haC gene . The difphaC gene sequences, which subsidizes our primer design [B. megaterium, not obtaining a large breadth of results, and this approach was also employed by Solaiman & Ashby [phaC gene sequences from 13 gram-positive bacteria to design primers, and presented a greater breadth of results, which were capable of detecting organisms belonging PHA synthase class I and II. The present study used a wide variety of gene sequences belonging to different organisms, resulting in the generation of a precise tool for the detection of organisms with the potential for the production of PHAs from classes I and IV. This method was tested in reference organisms, and was also employed for the screening of new isolates, working in both systems for the detection of the targeted gene.The use of molecular tools for the detection of organisms with particular features can aid in the field of biotechnology. These methodologies have been used for the determination of the microbial potential to produce PHA at the genetic level as well as to determine how much PHA can be produced by a given organism . Here, wr design ,35. An ir design  drew pri & Ashby , as a re & Ashby  used thephaC gene. Sasidharam et al. [Vibrio azureus BTKB33 isolated from marine sediments through PCR confirmation of PHA synthase class I. The use of the PCR technique considerably reduced the number of isolates and thus optimized the process. In addition to the traditional PCR, a multiplex PCR was performed. This methodology used more than one combination of primers to obtain a wider range of results and did not generate false positives, indicating that the use of specific primers for the samples and the chosen conditions were appropriate for the technique [Colony PCR proved efficient, as this method used amplified regions of interest without necessary DNA extraction methods, which has also been suggested by Sheu et al. , making m et al.  identifiechnique . Castrovechnique  showed technique  proved tphaA, phaB, and phaC genes of the PHA biosynthesis have led to the suggestion that horizontal gene transfer may be a major contributor for their evolution [phaC genes in limestone soil using primers targeting the class I and II PHA synthases, reassuring the relevance of the approach used in our study.Molecular detection of genes involved in PHA synthesis also allows for the prospection of PHA-producing organisms, as well as furthers the understanding and study of gene diversity and evolution ,38. Discvolution . In thisvolution , degenervolution  successfThe related sequences found in studies of diversity still have the potential to be used in genetic improvement programs by site-directed mutations, such as the DNA shuffling technique. Wang et al.  pointed phaC gene, as well as for use in analyses of environmental diversity. This study presents a powerful molecular tool for the identification and bioprospecting of bacteria that have the potential to produce PHAs. The tool also shows high potential for the identification of marine bacteria and pre-screening of environmental bacteria that have


Cd14 (cluster of differentiation 14) gene, which is involved in the immune system through a proinflammatory cascade. Expression was evaluated in BXD mice treated with saline or acute 1.8 g/kg i.p. ethanol (12.5% v/v). Hippocampal gene expression data were generated to examine differential expression and to perform systems genetics analyses. The Cd14 gene expression showed significant changes among the BXD strains after ethanol treatment, and eQTL mapping revealed that Cd14 is a cis-regulated gene. We also identified eighteen ethanol-related phenotypes correlated with Cd14 expression related to either ethanol responses or ethanol consumption. Pathway analysis was performed to identify possible biological pathways involved in the response to ethanol and Cd14. We also constructed a genetic network for Cd14 using the top 20 correlated genes and present several genes possibly involved in Cd14 and ethanol responses based on differential gene expression. In conclusion, we found Cd14, along with several other genes and pathways, to be involved in ethanol responses in the hippocampus, such as increased susceptibility to lipopolysaccharides and neuroinflammation.Alcohol consumption affects human health in part by compromising the immune system. In this study, we examined the expression of the  Numerous studies have shown that the consumption of alcohol affects human health. One of the many ways that alcohol can exert its effects is by altering and subsequently lowering immune function . AlcoholCd14 (cluster of differentiation 14) is a gene vital in the immune system’s inflammatory cascade. It is a cell membrane-bound glycoprotein that is largely expressed in monocytes, macrophages, and microglial cells. This protein is also a co-receptor of TLR4 , particularly in the detection and binding of lipopolysaccharides, or LPS, which are found on the membranes of Gram-negative bacteria. Many studies have shown strong evidence linking Cd14 to alcohol. Deletion of Cd14, along with other neuroimmune genes such as Il6, decreased alcohol consumption in null mutant mice, which suggest that neuroimmune signaling plays a role in alcohol-related behaviors [Cd14’s function is altered by ethanol consumption. However, the mechanisms through which Cd14 regulates ethanol response and interacts with other genes are still unclear.ehaviors . Alcoholehaviors , which iehaviors  and stimehaviors . MultiplCd14 and neuroinflammation is Alzheimer’s disease, which is heavily implicated with the hippocampus and can be detected in the hippocampal formation at an early stage before further symptoms arise [Cd14 potentially regulates ethanol responses. Further, the examination of acute ethanol treatment may allow for the identification of mechanisms, including gene networks that are also important in the cascade of events that lead to addiction and other chronic illnesses.Either acute or chronic ethanol exposure is considered as a stressor. The sensitivity of the hippocampus to various stressors is well documented –14. At bms arise , 19. Perms arise , 21. Othms arise . Thus, tRecombinant inbred (RI) strains of mice are a very useful resource to identify the genetic basis of phenotypes, including the regulation of gene expression and genetic networks. The largest panel of these strains–the BXD family–consists of the inbred progeny of a cross between C57BL/6J (B6) and DBA2/J (D2) mice. Each BXD line is a discrete population with fixed genotypes at each locus and the parental B6 and D2 alleles segregating among the strains. Because individuals within each RI line are isogenic, the genotype of each line can be used to develop a map of complex traits ranging from DNA variation to phenotype. The BXD lines have been used extensively in genetic and genomic studies of many phenotypes and diseases, including ethanol consumption and anxiety phenotypes . ImportaCd14 in ethanol responses using two sets of existing hippocampal gene expression data that we generated. In this study we sought to identify expression quantitative trait loci (eQTL) for Cd14, analyze potential pathways through which Cd14 interacts with ethanol, and construct a genetic network that plays a part in ethanol responses.The purpose of this study was to combine the power of RI strains of mice and a systems genetics approach to explore the role of Two groups of BXD recombinant inbred (RI) strains of mice were used in this study. The first set consisted of 67 BXD RI strains, which were used to generate hippocampal gene expression data for system genetics analysis. The second set consisted of 26 BXD RI strains that were used for saline or alcohol treatment.All mice were group housed (2–5 mice in same sex cages) and maintained on a 12:12 light/dark cycle at University of Tennessee Health and Science Center (UTHSC) for the first set of 67 BXD RI mice and at University of Memphis for the second set of 26 BXD RI mice. When mice were sacrificed for tissue harvest, they were anesthetized using Avertin  and killed via cervical dislocation. All animal work and experimental protocols for this specific study were approved by the UTHSC Institutional Animal Care and Use Committee (IACUC) and the Univeristy of Memphis Institutional Animal Care and Use Committee (IACUC) following NIH guidelines.The B6, D2, and 26 BXD strains of mice  were divided into two groups: 1) saline group, treated with iusovolumetric saline via IP injection, or 2) ethanol group, treated with an IP injection of 1.8 g/kg i.p. ethanol (12.5% v/v). These conditions allowed us to test for differences in gene expression both in the presence and absence of alcohol. Four hours after treatment, these mice were sacrificed for tissue harvest.The 67 untreated BXD RI strains used to generate hippocampal expression data and the ethanol or saline treated animals (26 BXD RI strains) described above, were killed and harvested for their tissue according to previous methods [www.tel-test.com) as per the manufacturer’s instructions. A spectrophotometer  was used to measure RNA concentration and purity, and the Agilent 2100 Bioanalyzer was used to evaluate RNA integrity. The RNA integrity values had to be greater than 8 to pass quality control. The majority of samples had values between 8 and 10.RNA was extracted from the hippocampus using RNA STAT-60 . Detailed information for these data sets, including strain, age, sex, experimental protocol, data quality control, etc. can be found in the “info” pages.The gene expression data of the first set of 67 BXD RI strains used for system genetics analysis were collected using the Affymetrix Mouse Genome M430 2.0 array based on the manufacturer’s protocol. The gene expression data of the second set of 26 BXD RI strains under saline and ethanol treatment used for analysis of the effect of ethanol on gene expression were collected using the Illumina Sentrix Mouse-6 v1.1 arrays based on the manufacturer’s protocol. Both the first (“Hippocampus Consortium M430v2”) and second data sets  RankInv” and “UTHSC BXD Hippocampus ILM v6.1 NOE Balanced (Feb17) RankInv”) are listed in our GeneNetwork website  method . Raw micHippocampal gene expression data from saline or alcohol treatment were evaluated using a two-variable  Analysis of Variance (ANOVA).2) of gene expression was calculated using the broad sense heritability method [The heritability  according to our published methods [Cd14 expression levels and to estimate the significance at each location using known genotypic data for those sites. Composite interval mapping was also used to control for genetic variance associated with major eQTLs and therefore identify any secondary eQTLs that may have been otherwise masked. Each of these analyses produced a likelihood ratio statistic (LRS) score, providing us with a quantitative measure of confidence of linkage between the observed phenotype—in this case variation in the expression level of Cd14—and known genetic markers. The significance of the eQTLs was calculated using more than 2000 permutations tests. Loci were considered statistically significant if genome-wide p < 0.05. Sequence variability between B6 and D2 was then determined using the SNP variant browser link on GeneNetwork.Among the BXD strains, differential expression of any given gene can be attributed to polymorphisms (referred to as expression quantitative trait loci or eQTLs) driving that gene’s expression. eQTL mapping was used to identify chromosomal locations associated with differential expression of genes. We performed eQTL analysis using the WebQTL module on GeneNetwork . Genes meeting these criteria were then selected for further analysis.Genetic correlation analysis was performed on GeneNetwork to identify transcriptional abundance relationships between cis-modulated genes within upstream regulatory regions. In order to find genes significantly correlated with Cd14, we performed partial correlation analysis controlling for the genotype at the Cd14 locus after genetic correlation analysis. Genes that were connected only genetically to the Cd14 locus were eliminated, leaving genes that were both genetically and biologically related to Cd14 to be further analyzed.Partial correlation is the correlation found between two variables that is presents after controlling for and removing the effect of one or more other variables. It can be used to determine the most likely set of r value for genes that are described by similar terminology in published papers. After genetic and partial correlation, we performed literature correlations using the Semantic Gene Organizer [Cd14 and other genes. Genes with higher correlation values (r > 0.3) were selected for further analysis.Literature correlation examines the rganizer  to find p < 0.05), and literature correlations (r > 0.3) were then selected for gene set enrichment analysis. After removing Riken clones, intergenic sequences, predicted genes, and probes not associated with functional mouse genes, the remaining list of correlates with mean hippocampal expression levels above baseline were uploaded to Webgestalt  for gene ontology (GO) and pathway analyses [p-values from the hypergeometric test were automatically adjusted to account for multiple comparisons using the Benjamini and Hochberg correction [p-value of less than 0.05 indicated that the set of submitted genes was significantly over-represented in those categories.Genes with significant genetic (p < 0.05), partial . Phenotypes, including behaviors related to ethanol, were analyzed for correlations to Cd14 using Spearman’s product correlations value (p < 0.05).To identify phenotypes highly correlated with variation in the http://binf1.memphis.edu/gcat/index.py). The nodes in the network represent genes and the edge between two nodes represent cosine scores of Latent Semantic Indexing (LSI) determines if the functional coherence of gene sets is larger than 0.6. The significance of the functional cohesion is evaluated by the observed number of gene relationships above a cosine threshold of 0.6 in the LSI model. The literature p-value (LP) is calculated using Fisher’s exact test by comparing the cohesion of the given gene set to a random one [The gene network was constructed and visualized using the Cytoscape utility through “Gene-set Cohesion Analysis Tool (GCAT)” (ndom one .Cd14 gene (1417268_at) targeting the exon and 3’-UTR of the Cd14 gene in the Affymetrix M430 dataset was used. Cd14 expression varied widely among 67 BXD RI strains, with a fold-change of 1.84  of 18.2 on chromosome 18 at the Cd14 gene location  in the Affymetrix dataset. However, only 524 known transcripts were left after filtering by genetic, partial, and literature correlations (The expression of 5218 transcripts in the hippocampus was significantly correlated with that of elations . Among telations . These thttps://www.wikigenes.org/) database was used to identify pathways in which these genes are involved. Gene pathway analysis for the whole gene set resulted in 75 significant pathways  for functional coherence analysis and gene network construction. These genes showed significant functional cohesion with a literature p value of < 8.10365e-17 . The expression of Ly86, Cd68, and Il18 were decreased by 42% , 37% , and 31%  respectively in the ethanol group compared to the control group.Genes in the ne group , which wCd14 is directly associated with ethanol-related phenotypes, we performed correlational analyses with phenotypes archived in our GeneNetwork database, and identified 18 ethanol-related phenotypes significantly correlated with Cd14 expression, including ethanol consumption, neurodegeneration, and degree of locomotor stimulation following exposure to alcohol  have been confirmed to have significant changes in expression after ethanol treatment. Among these genes with ethanol-related changes in gene expression, only IL18 was significantly correlated with ethanol based on previous literature. We present these genes as potential players in the Cd14 gene network and its involvement in regulating ethanol’s effects on the immune system. Interleukin 18 (Il18) is a proinflammatory cytokine involved in cell immunity. IL18 responds to LPS, and its secretion is CD14-dependent [We used the top 20 genes correlated with ependent , but ethependent .Cluster of differentiation 68 (CD68), similar to CD14, is present on macrophages and is often used as a marker of macrophage activation. The effects of ethanol on macrophage activation are well described . CD68 mRLy86 gene has been associated with obesity, insulin resistance, and inflammatory markers [Ly86 gene, given ethanol’s effects on inflammation, it is possible that ethanol could affect expression of the Ly86 gene as well.Methylation of the  markers . AlthougTlr2, Il18, Cst3, Csf3, and Smpd1) are significantly correlated with alcohol exposure based on literature reports. Thus, they may interact with Cd14 to regulate ethanol responses.Among the 20 genes identified in the network, five of them  is a membrane protein expressed on microglia, monocytes, macrophages, B-, C- and T-cells that aids in recognizing foreign substances and inducing a proinflammatory cytokine cascade . It is dCst3), a marker gene for kidney function, was identified in our network Cst3 is primarily a marker of kidney function, although it is found in many organs. It is a cysteine protease inhibitor, but has protective functions as well, as decreased expression has been associated with neurodegenerative diseases [Cst3 has previously been found to be upregulated in the midbrain after ethanol exposure [Cystatin C (diseases . Like madiseases . Cst3 haexposure .Csf3) stimulates the release granulocytes and stem cells from bone marrow into the bloodstream. Ethanol has been shown to downregulate Csf3 [Smpd1, or sphingomyelin phosphodiesterase, codes for acid sphingomyelinase, which is a lipid hydrolase. Mutations in the Smpd1 gene cause types A and B Niemann-Pick disease, which is a family of metabolic disorders. While Smpd1 mRNA levels have been shown to increase with chronic alcoholic liver disease and following alcohol exposure [Smpd1 mRNA levels in alcohol-fed mice [The remaining genes have a variety of functions. Granulocyte-colony stimulating factor 3 Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.


The effects of climate change on oligotrophic rivers and their communities are almost unknown, albeit these ecosystems are the primary habitat of the critically endangered freshwater pearl mussel and its host fishes, salmonids. The distribution and abundance of pearl mussels have drastically decreased throughout Europe over the last century, particularly within the southern part of the range, but causes of this wide-scale extinction process are unclear. Here we estimate the effects of climate change on pearl mussels based on historical and recent samples from 50 rivers and 6 countries across Europe. We found that the shell convexity may be considered an indicator of the thermal effects on pearl mussel populations under warming climate because it reflects shifts in summer temperatures and is significantly different in viable and declining populations. Spatial and temporal modeling of the relationship between shell convexity and population status show that global climate change could have accelerated the population decline of pearl mussels over the last 100 years through rapidly decreasing suitable distribution areas. Simulation predicts future warming-induced range reduction, particularly in southern regions. These results highlight the importance of large-scale studies of keystone species, which can underscore the hidden effects of climate warming on freshwater ecosystems. The ranges of many terrestrial species are shifting rapidly in latitude or altitude in response to changing climate5. Inland surface waters are also affected by climate warming because of the close relationship between air temperature and surface water temperature8. However, predictions of freshwater biodiversity responses to climate change are difficult because detected range shifts are based on records from a small number of taxa from few regions9. Modeling processes in key aquatic habitats based on the identification and quantification of factors that control the distribution of biodiversity is therefore a topic of great importance10.Many ecosystems are threatened because of climate warming, which affects the status of populations of individual species, their ecological functions and interspecific interactions8. Recent studies reveal that among hydro-climatological variables, change in air temperature, which is a response to climate forcing, is the main driver of river temperature change because it had the highest correlation with river temperature irrespective of period11. The strongest impacts of warming water appear to be on cold-water ecosystems, such as oligotrophic streams and rivers. Oligotrophic rivers are scattered throughout Europe and are mostly concentrated in the northern region of the continent and locally present within mountain systems12. These rivers harbor unique and vulnerable communities with several keystone species, including salmonid fishes and freshwater pearl mussels14. Changes in the populations of keystone species have the greatest effect because of their critical role in ensuring the functioning of natural ecosystems16. Unfortunately, the effects of climate change on oligotrophic rivers and their communities are investigated only at local and regional scales20.Current knowledge of recent warming in rivers is scarce due to a lack of long-term and large-scale studies in Europe as well as globally. However, a rapid rise in water temperature in European lotic systems in recent decades has been confirmedMargaritifera margaritifera   and narrow host specialization22. The distribution and abundance of M. margaritifera have drastically decreased throughout Europe over the last century, particularly within the southern part of the range25 and the majority of its populations has lacked successful reproduction for last 30–50 years26. The indirect effects associated with anthropogenic transformations, such as habitat degradation, alteration and fragmentation as well as salmonid host overfishing, are considered the most important factors for the decline of the species27. The nitrate concentration positively correlated with mortality rates of the adult pearl mussels, while phosphate, calcium and BOD5 correlated with decreasing juvenile survival22. Progressive eutrophication of oligotrophic water bodies may also affect salmonid hosts, with an inhibition of their natural reproduction28. However, little attention has been paid to global climate change as a possible factor in population decline19.Our study species is the freshwater pearl mussel, 58) Fig. , which iUnder this gap of knowledge, the present study aims to: (i) perform a series of regression models describing the influence of climatic variables on the shell convexity, age and status of freshwater pearl mussel populations at the continental scale; (ii) produce a series of temperature dependent integrative models of climatically suitable areas for freshwater pearl mussels throughout Europe during past and future periods on the basis of morphometric, ecological and climatic data; (iii) discuss the possible influence of climate warming on freshwater pearl mussel population decline during the last century. Based on spatial and temporal modeling of the relationship between shell convexity and population status, we determined that global climate change could have accelerated the population decline of freshwater pearl mussels over the last 100 years through rapidly decreasing suitable distribution areas.rs = −0.20 (n.s.), n = 12, P = 0.53)    Fig. . In cont01) Fig. . The sep02) Fig. .Figure 2t = 6.3, df = 43, P < 0.0001) and in historical samples . This parameter does not show differences between recent viable populations and historical samples .Results of general linear models (GLMs) revealed that in recent populations the relative shell width is significantly influenced by the population status independently of shell size; i.e., the mean SCI in recent declining populations differs from that for recent viable populations Table . FurtherM. margaritifera samples are directly associated with mean air summer temperature :We discovered that the mean SCI values in I = 0.51, expected Moran’s I = −0.02, variance = 0.48, z-score = 0.77, p = 0.44). Our testing with different periods of temperature averaging reveals that correlation with the mean SCI increases slightly with extension of the period but the coefficients under 20-, 30-, 40- and 50-year averaging were quite similar  revealed that the training models fit with the actual observations: mean absolute percentage deviation (MAPE) ≤5.5%, Tofallis’s relative accuracy measure ≤0.04, and min-max accuracy ≥0.95 (Supplementary Table 4). The training models are close to each other and to equation  revealedth century and the maximum during the warm period of 1991–2010  using equation 010 using920 Fig. . In cont920 Fig. . Based o920 Fig.  predict 29. However, it could be used for climatic reconstructions because growth patterns of the freshwater pearl mussels are significantly influenced by temperature with faster rates of shell growth in warmer environments , likely because higher temperatures increase metabolic activity and hence rates of shell production31 .The SCI of freshwater mussels proved to be considerably influenced by allometric growth and therefore is not considered a suitable parameter for detecting trends in ecophenotypic variationM. margaritifera populations under warming climate conditions because it directly reflects shifts in mean summer temperatures and is significantly different in viable and declining populations. The effect of higher temperature on populations manifests itself in increased growth and metabolic rates, reduced longevity of adult mussels, high mortality of juveniles and restricted larval development .The three samples from the River Kamp system (Austria) revealed anomalous SCI values in relation to air temperature  that may potentially be used for ecological monitoring of the populations of other threatened freshwater mussel species.Although our simplified temperature based model does not take into account many other environmental and human-mediated limiting factors , the impact of which appears to be more significant in southern regions, this example of possible warming-driven species decline revealed that climate changes may have hidden effects on populations of individual species and on freshwater ecosystems. These effects may accelerate local climate-related extinctions, which are widespread among terrestrial animals, but are almost unknown in freshwater taxaMargaritifera margaritifera from 50 rivers and six countries across the freshwater pearl mussel range in Europe were used . Additionally, we used 294 shells from 13 historical samples  found in the museum collections. We assessed shell lots from various museum collections as well as field samples of live mussels and shells. The living mussels were measured and returned to their habitat. The museum collections used are as follows:In this study, 3279 shells of sed Fig. . A totalIPEE - A.N. Severtsov Institute of Problems of Ecology and Evolution of the Russian Academy of Sciences, Moscow, RussiaMNHN – Muséum National d’Histoire Naturelle, Paris, FranceNHMG – Göteborg Natural History Museum, Göteborg, SwedenRMBH – Russian Museum of Biodiversity Hotspots, Federal Center for Integrated Arctic Research of the Russian Academy of Sciences, Arkhangelsk, RussiaSMNH – Swedish Museum of Natural History, Stockholm, SwedenULMHST – Museum of History of Science and Technology, University of Latvia, Riga, LatviaZIN – Zoological Institute of the Russian Academy of Sciences, St. Petersburg, Russia27. To minimize morphological differences between populations resulting from the ontogenetic heterogeneity of their individuals35, all mussels with shell lengths less than 5 cm were excluded from the compiled data set.We measured three shell dimensions for each specimen using calipers (±0.1 mm): the length (L), height (H), and width (W) of the shell, all taken at the maximal diameterI:36.Using primary shell measurements, we calculated the basic morphometric index, namely the shell convexity index, SCI (W:L ratio × 100). In addition, we computed the integrated shell convexity index, SCII was found to be a function of the traditional SCI  and was not used as a separate parameter. Additionally, we checked the assumption regarding the influence of sample size (number of mussels measured) on the index values using Spearman’s correlation test. We determined that the variability of both the mean SCI and SCII values are not correlated with sample size .However, the SCI6, the status of recent populations was estimated as follows: (i) viable  populations with high mussel density, and high or moderate recruitment, and (ii) declining  populations with moderate or low mussel density, and low or no recruitment .Using the approach of Geistn = 45 in total). For all samples, at least 16 annual rings for each shell were measured. The growth constants were calculated using a data approximation based on the recursive form of the Bertalanffy equation, i.e., the Ford-Walford equation13. The maximum age of individuals in a sample was calculated using a shell with the maximum length. Additionally, we incorporated a sclera-chronological approach by counting the annual rings in a thin transverse section of a valve31. In some cases, a logarithmic equation describing the trend line on the annual growth length vs. age plot was also applied.To estimate the relationship between the growth constant (K) and mean summer temperature, shell samples from 9 rivers  were investigated. Five specimens were used from each sample  were used. We used shell width plotted against shell length as a covariate and population status as a factor with two levels  using the mean SCI as a dependent variable, latitude as a continuous predictor, and two-level time interval as a categorical predictor. Correlation of morphometric parameters with climatic and geographic variables was calculated using Pearson’s and Spearman’s coefficients depending on sample size, type of variables and normality test results.To test the hypothesis of climate-induced morphometric differences between 38. For each location of the samples, we calculated the mean summer temperature (MST) values for the 10-, 20-, 30-, 40- and 50-year periods before the year of each sample collection (number of available samples varied depending on the period of averaging from 49 to 55) in accordance with the slow growth rate and large longevity of the freshwater pearl mussels30. These parameters were estimated as possible predictors for the mean SCI values in pearl mussel populations using a simple linear regression model algorithm of STATISTICA 10 34. The spatial auto-correlation in the modeling SCI data set  and thermal growing season length index . The six samples from the River Kamp system (Austria) were excluded from the models in accordance with available recommendations34 because the three of them revealed outlier  SCI values in relation to air temperature  and R language, i.e., mean absolute percentage deviation (MAPE), Tofallis’s relative accuracy measure, and min-max accuracy41.Monthly mean air temperatures were obtained from the CRU TS v. 3.23 climate database  as gridded variables (0.5° resolution), which were based on weather station records during the period from 1 January 1901 to 31 December 2014M. margaritifera was processed using published mapping data21, which was transformed to a gridded digital map (0.5 × 0.5°) using ESRI ArcGIS 10  of the CMIP5 centennial simulations, which are based on the HadGEM2-ES global climate model, inferred from the WorldClim v. 1.4 database43.The general range model of  10 Fig. . FurtherA1 represents the estimated 95% upper confidence boundary of the mean SCI in declining populations which indicates disappeared populations under unsuitable thermal conditions for their existence; A2 represents the mean SCI in declining populations; A3 represents a threshold SCI value between viable and declining populations; A4 represents the mean SCI in viable populations; s.e.m. represents the standard error of the mean.Using the spatial models outlined above, climatically suitable areas were mapped based on the estimated threshold SCI values in viable and declining populations. These threshold values for the SCI interval in declining populations were as follows:M. margaritifera is a cold-adapted species with the most abundant populations living in northern regions16.Accordingly, the estimated threshold value for the SCI interval in viable freshwater pearl mussel populations was as follows:n = 45, P = 0.05). The SCI values >30.04 indicate warm thermal conditions that likely unsuitable to populations’ survival at a 95% confidence level. These ranges were assigned for each grid cell located within the range model on a digital map.Based on these intervals, we calculated the ranges of mean SCI values, i.e., SCI ≤28.37 and 28.38 ≤SCI ≤30.04 in viable and declining populations, respectively (Supplementary InformationSupplementary Dataset 1


Measuring viral DNA methylation in human papillomavirus (HPV) infected women showed promise for accurate detection of high-grade cervical lesions and cancer. Methylation status has been widely investigated for HPV16, sporadically for other HPV types.Objective of this methodological study was to set up molecular methods to test the methylation levels in the twelve oncogenic HPV types by pyrosequencing, minimizing the number of HPV type-specific PCR protocols. Target CpGs were selected on the HPV L1  and L2 genes.Study samples included DNA stored at Turin, Italy, purified by cervical cells collected in Standard Transport Medium or PreservCyt from women who participated in two studies (N = 126 and 140) nested within the regional organized screening programme.PCR consensus primers were designed by PyroMark Assay Design software to be suitable for amplification of many different oncogenic HPV types.Generation of consensus primers was successful for L1 I and II regions, unsuccessful for L2 region, for which HPV type-specific primers remained necessary. The difference between replicated tests on the same sample was ≤4% in 88%, 77% and 91% of cases when targeting the L1 I, L1 II and L2 regions, respectively. The corresponding intra-class correlation coefficients (ICC) were 0.94, 0.87 and 0.97 respectively. When comparing methylation measures based on consensus and type-specific primers, ICC was 0.97 for the L1 I region and 0.99 the for L1 II region.The proposed protocols, applying consensus primers suitable to amplify the oncogenic HPV types and minimize the number of PCR reactions, represent a promising tool to quantify viral methylation in women positive for any high risk HPV type.Potential application of these methylation protocols in screening settings can be explored to identify women with high probability of progression to high grade lesions. Persistent infection by oncogenic high risk human papillomavirus (hrHPV) types drives the development of high-grade cervical intraepithelial neoplasia (hgCIN) which may, if untreated, progress to invasive cervical cancer.In hrHPV positive women, measuring DNA methylation in the HPV genome has shown promise for accurate detection of hgCIN and cancer –9. The HA commonly used method for assessing methylation status of DNA is pyrosequencing. This entails a preliminary amplification of bisulfite-modified DNA, until now performed by type-specific primers. The present paper reports about the development of consensus primers for multiple HPV genotypes and their use to evaluate methylation status of the L1 and L2 viral regions by pyrosequencing. This approach is able to minimize the number of PCR protocols needed to investigate the methylation status in the twelve hrHPV types.We report here about its development and analytical validation, including the reproducibility of methylation values on the same sample and the consistency of such values with those obtained by type-specific primers. The association of such values with the current or future detection of high-grade cervical intraepithelial neoplasia (CIN) is not included. This would take as itself the space of another full article.CpG sites were selected for HPV16, based on their location in the L1 or L2 regions and the strong reported association of their methylation with hgCIN occurrence. For the remaining hrHPV types the CpGs closest to those selected for HPV16 were identified. Type-specific and consensus primers for the defined sequences were generated and checked to avoid artefact sequences. PCR and pyrosequencing conditions were optimised. Consensus primers were validated, under such optimised conditions, as for the reproducibility of the estimated methylation and its consistency with that obtained by applying type-specific primers.We used two series of DNA samples, obtained from women participating to: (1) the NTCC study  and Only samples from HPV positive women with infection by a single hrHPV type at the screening test were included.DNA samples from the HPV pilot project (N = 126) were used to develop and set up the novel methylation assays. Ten to thirteen samples for each hrHPV type were randomly retrieved from storage. They had been extracted from cervical scrapes collected in STM  and had been positive to the Hybrid Capture 2  HPV screening test.DNA samples from the NTCC trial (Turin centre) (N = 140) were used to assess the reproducibility of the newly developed quantitative methylation assays. They had been extracted from cervical scrapes collected either in STM or in PreservCyt  and had been HC2 positive. As they had been already used to analyse methylation status in selected host cell genes  residual bisulfite modified DNA stored at -80°C was used for the current reproducibility evaluation, with each sample tested twice. For each hrHPV type five to twenty-one samples were randomly retrieved from bisulfite modified DNA storage.As methylation events had been suggested to involve most of the HPV CpGs in hgCIN –7,11, weFor all the HPV oncogenic types the CpG sites to analyse were selected on the L1 and L2 capsid genes on the basis of the following considerations: i) in some HPV types (e.g. HPV18) methylation events poorly involve other viral genes, such as the URR (untranslated regulatory region) and E6 ; ii) thewww.ncbi.nlm.nih.gov web site and are listed in Reference sequences and CpG positions for each HPV type were found at HPV16 genome sequence was used as reference and three regions, hereafter denoted as L1 I  –15,20,21www.ebi.ac.uk/ Tools/msa/clustalo/), which allowed visualizing the sequence homology and identifying, along each HPV type sequence, the CpG closest to each selected HPV16 CpG. Sequence alignment was performed following conversion to bisulfite modified sequences by the PyroMark Assay Design 2.0. software (Qiagen). The correct format to fit Clustal Omega tool was obtained by the Emboss Seqret software (www.ebi.ac.uk/Tools/sfc/emboss seqret).For non-HPV16 oncogenic types, CpG sites were identified through the multiple sequence alignment tool Clustal Omega  and “18 family” .Primers for the selected target regions L1 I, L1 II and L2 of the “16 family” and “18 family” were designed through the PyroMark Assay Design 2.0 software (Qiagen). Software stringent criteria, such as the mandatory requirement to avoid primer overlap of CpG sites and, when possible, to avoid homopolymers and the generation of amplicons longer than 300 bp, were taken into account. Sets of three primers, two for the amplification of the sequence that contains the selected CpGs and one for its sequencing, were generated for each hrHPV type.Quality checks were performed throughout pyrosequencing to avoid artefact sequences: either the amplicon alone, or the biotinylated primer or the sequencing primer were combined with the annealing buffer and tested, expecting no sequencing. For the same purpose, each biotinylated primer was combined with the sequencing primer. Moreover, primers were tested on hrHPV negative samples to assess their specificity for the HPV genome and exclude cross-reaction with host cell genome. Primers were first tested on samples containing the specific hrHPV type. Further, within each HPV family, each forward primer was coupled with each reverse primer and tested for amplification of all the types included in the family, in the aim to identify potential consensus primers favoured by sequence homology. Similarly, each sequencing primer was tested by pyrosequencing with amplicons from all the hrHPV types of the same family. Optimization of PCR and pyrosequencing was performed both on DNA samples and DNA controls .The methylation status of a DNA sequence can be determined after DNA treatment with sodium bisulfite, which deaminates unmethylated cytosine residues on single-stranded DNA molecules and converts them to uracils, whereas 5-methyl cytosines remain protected from conversion. DNA polymerase in PCR driven assays converts the uracil residues of bisulfite modified DNA to thymidines. Different DNA strands, distinguishable by sequencing, are therefore generated from methylated and unmethylated CpGs.DNA from clinical samples (≤ 1 μg), as well as from SiHa and HeLa cell line, used as methylation positive controls for the HPV16 and HPV18 family respectively, underwent bisulfite modification using the Epitect Bisulfite Kit (Qiagen) according to the manufacturer’s instructions, except for the incubation time extended to 16 h, optimized in line with suggestions by Izzi and coll. .Synthetic HPV16 and HPV18 plasmidic DNA containing the complete genomes  were used as unmethylated controls. These synthetic HPV plasmids were also used as fully methylated controls after treatment with CpG methylase  according to the manufacturer’s instructions. Both CpG methylase-treated and -untreated HPV plasmids underwent two cycles of bisulfite modification with incubation time extended to 16 h to achieve a complete conversion. Modified DNA was used immediately or stored at -80°C until methylation assessment.2, 0.8 mM dNTPs, 0.5 μM of each primer, 1.75U Taq polymerase and 2 μl of bisulfte modified DNA. The cycling profile was as follows: 95°C for 10 min followed by 45 cycles of denaturation at 95°C for 30 s, annealing for 1 min at the specific temperature set for each hrHPV type (Methylation assays were performed by pyrosequencing onto a PyroMark Q24 MDx system (Qiagen). For regions L1 I and L1 II preliminary PCR reactions were performed in a total volume of 35 μl containing 1X PCR buffer, 2mM MgClHPV type , extensiFor the L2 region PCRs were performed employing the PyroMark PCR Kit (Qiagen) following manufacturer’s instructions, except for the annealing temperature set for each hrHPV type as listed in Two μl of bisulfite modified DNA were added to the amplification mix.Amplicons were analyzed by gel electrophoresis on a 2% agarose gel stained with DNA intercalating dye (e.g. Syber Safe) and visualized by ultraviolet trans-illumination. Twenty μl of PCR product were added, in a 24-well microplate, to 60 μl of a mix composed by 18 μl distilled water, 40 μl binding buffer pH 7.6 , and 2 μl of sepharose beads covered by streptavidin, and incubated under shaking. PCR products were then washed with ethanol 70%, denatured with NaOH 0.2 M and re-washed with Tris-Acetate 10 mM pH 7.2) and 0.15 μl of 50 μM type-specific sequencing primer . Assays were created according to the manufacturer’s instructions.Pyrosequencing was performed in a total volume of 25 μl, including 24.85 μl of annealing buffer  which shows high performance in quantitative evaluation of the proportion of methylated cytosines at each CpG site through the C/(C+T) ratio . Based oExamples of pyrograms are available in the supplementary data, Comparison was carried out for L1 I and L1 II regions by testing with the two different assays a total of 90 samples from the pilot project , some ofWe evaluated the protocols performance by applying the assays to a series of samples (N = 140) from the NTCC Turin cohort that included long-stored DNA. Half of them had been obtained from cells originally collected not in STM but in a different transport medium (PreservCyt). Bisulfite modified DNA samples were tested. To assess the reproducibility of methylation, the procedure was replicated by performing for each sample a second PCR run along with the subsequent correspondent pyrosequencing assay. Again, methylation on the L1 I, L1 II and L2 regions was compared separately, considering the mean percentage of CpGs measured as methylated in each region and afterwards computing the difference between the mean percent methylation. Because we used residual modified samples from previous analysis on human genes, some samples did not have sufficient material to perform replicates on all the three target regions. Therefore, replicates on the L2 region were 105 instead of 140.The agreement between the percentage of methylated CpGs measured by testing two aliquots of the same sample with consensus and type specific primers and that obtained by replicate testing with consensus primers were evaluated by the intra-class correlation coefficient (ICC). For this purpose, we used a log transformation of methylation values, so to normalise their distribution . ICC does not assume different accuracy of compared variables and takes into account systematic differences between measurements . Wilcoxon test was used to compare methylation levels by cytology groups.Sequence variability was found not only among types but also within the same type, and several primers had to be designed and tested before setting up the final assays. At first primers dedicated to each hrHPV type were generated. The sequences of HPV type-specific primers for the L1 I and L1 II regions, with the corresponding PCR profile, are listed in Similarly, for the L1 II region consensus primers and annealing temperature were set for PCR targeting HPV16, 33, 35, 52, 56, 58 of the “16 family” and HPV18, 51 and 45, 39, 59 of the “18 family”. For HPV31 consensus primers were not efficient, and dedicated primers and annealing temperature were maintained.Generation of consensus primers for the L2 region was not successful, and dedicated primers for each HPV type were maintained, along with specific annealing temperatures .Methylation levels for each region and the corresponding PyroMark Q24 score for the 126 tested DNA samples (Pilot project) are reported at supplementary A total of 90 samples from the pilot project were tested with two different assays employing either consensus or type-specific primers in the preliminary PCR preceding pyrosequencing. The comparison, carried out for L1 I and L1 II regions and limited to types HPV16, 18, 31, 33, 45, 52, 56 and 58, showed a highly comparable quantification of the mean percentage of methylated CpGs in each region by using the two kinds of primers, with intra-class correlation coefficient between log (methylation) values of 0.97 and 0.99 for L1 I and L1 II respectively .Cytology was available for the 126 samples listed in We sought the feasibility to design consensus primers for hrHPV methylation analysis of all hrHPV types. Although we targeted the highly conserved HPV capsidic genes, homology among viral types was not sufficient to allow simultaneous testing of all the twelve hrHPV types recognized as oncogenic for cervical mucosas. Nevertheless, we succeeded in the generation of consensus primers able to target two regions on the L1 gene (L1 I and II) for groups of HPV types, aggregated according to their phylogenetic affinity. Generation of consensus primers was instead hampered in the L2 region, due to high density of CpGs and low sequence homology, thus forcing the use of type-specific primers to methylation analyses.The analytical specificity of the assays newly developed in this study may be in theory ensured by consistency between the expected sequence to analyse and the resulting pyrogram for each hrHPV type. Indeed, when targets different from those expected for the specific genotype were sequenced, failed or confused pyrograms were obtained. To further explore if the use of our consensus primers could provide results systematically different from those obtained with type-specific primers we carried out a comparison between the methylation levels measured starting with type-specific primers and with our consensus primers. The very high agreement obtained supported the analytical reliability of the consensus primers driven assays.It must be taken into account that samples of lower quality, having undergone repeated freeze and thaw episodes, were used to evaluate assays reproducibility. Despite low quality of these samples, however, good levels of reproducibility of the methylation quantitation by consensus primers were obtained with replicates for the L1 I and L2 regions, with ICC values above 90%. In addition, the difference between replicates was ≤4% in about 90% of cases. With such difference, would the average between replicates be calculated, then the difference between such average and the original results would be ≤2%, an acceptable value in line with other reports . ReproduThe analytical sensitivity of pyrosequencing allows to target viral DNA also in samples with low levels of positivity on the original HC2 screening test. The available DNA amount can marginally affect pyrosequencing results, at most leading to low peaks in the pyrograms, but does not affect the reliability of the methylation count at the single CpG site analysed. In preliminary PCR for pyrosequencing analyses a minimum of 1–10 ng of DNA is conventionally used. However, a standardization of DNA amount is not mandatory  as pyrosThe methylation protocols were developed and set up on DNA from cervical samples stored for short-time in STM (HPV pilot project sample series) and having undergone only one thaw, for genotyping assay. Nevertheless, the newly developed protocols showed a good performance also with the samples from the Turin NTCC biobank, which had a long-time storage and had been already subjected to multiple freezing and thawing episodes. They performed efficiently with DNA extracted from cervical cells originally collected and stored either in STM or in PreservCyt transport mediums. Even better efficiency is expected in clinical setting by using fresh material.We considered only samples with single HPV infection, as others before . Taking Our validation of the method was substantially analytical. In a first, very rough, clinical evaluation, we also compared, in the specimens used for developing and setting up the novel assays, the methylation value obtained by our method to cytology, an imprecise predictor of histology. Point estimates suggested an association but, given the small size, the difference was statistically significant only for one region (L2). In-depth analyses to investigate viral methylation association with histologically confirmed current or future hgCIN detection have been conducted in a wide controlled cohort and will be described elsewhere.This is, to our knowledge, the first attempt to develop consensus primers for multiple HPV genotypes to use in the assessment of methylation of the L1 and L2 viral regions by pyrosequencing in cervical scrape samples.We designed and set up such primers and the overall procedure, and assessed their analytical performance in providing a reproducible quantitative estimate similar to that obtained by type-specific PCR. Such data show that the developed consensus primers can be considered equivalent to type-specific primers in order to provide a quantitative evaluation of the methylation of the considered CpGs by pyrosequencing.HPV methylation protocols covering the majority of oncogenic types could provide an efficient risk stratification tool to quantify viral methylation in HPV positive women.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.


Sapovirus genus, in the family Caliciviridae. They have been associated with gastroenteritis in humans and in pigs but not in other animals. In addition, some strains from pigs, chimpanzees and rodents show close sequence identity with human SaVs thereby suggesting the possibility of interspecies transmissions. Bats are known to be a major reservoir of zoonotic viruses, however, very little is known about the genetic diversity of SaVs in bats. To explore the genetic diversity of bat SaVs, fecal samples of Eidolon helvum and Epomophorus gambianus were treated according to the NetoVIR protocol and sequenced by Illumina technology. Nearly complete genome sequences of six highly divergent SaVs and one partial SaV (only VP1 region) were identified in Eidolon helvum and based on sequence identities and phylogenetic analysis, they potentially represent two novel genogroups, only distantly related to known SaVs. Furthermore, comparing these sequences with currently used screening primers and probes indicated that the novel SaVs would not be detected in routine epidemiological screening studies in humans in case an interspecies transmission would occur. Therefore, we designed and validated new primers that can detect both human and bat SaVs. In this study, we identified multiple novel bat SaVs, however, further epidemiological studies in humans are needed to unravel their potential role in gastroenteritis.Sapoviruses (SaVs) belong to the  Since 2002, the International Committee on the Taxonomy of Viruses (ICTV) has assigned these viruses to the Sapporo virus species, Sapovirus genus, in the family Caliciviridae,3 together with four other genera: Norovirus, Lagovirus, Vesivirus and Nebovirus (www.ictvonline.org/). SaV particles are made up of a non-enveloped icosahedral capsid of 30–38 nm in diameter, with typical cup-shaped depressions on the surface.4 Inside the particle resides the positive-sense, single-stranded RNA genome of ~7.1–7.7 kb with a 3’- poly (A) tail. Generally, SaV genome contains two open reading frames (ORFs): ORF1 encodes for a large polyprotein containing the nonstructural proteins ) followed by the major capsid protein, VP1; ORF2 is predicted to encode for the minor structural protein VP2.5 However, a third ORF has been found in some human11 and bat12 SaV strains, although a known function is yet to be assigned. Until recently, SaVs were classified into five genogroups (GI–GV).13 With the exception of SaV GIII, SaV genogroups were further subdivided in one or multiple genotypes based on the full-length nucleotide (nt) sequence of VP1.13 GI, GII, GIV and GV SaVs are known to infect humans, while GIII infects pigs.10 Recently, more SaVs have been detected in different animal species, and novel genogroups GVI–GXV were proposed on the bases that strains with ≥57% pairwise aa identity of VP1 should be in the same genogroup.14 A number of SaVs from animal hosts, such as chimpanzee15, pig18 and rodents19 have been found to cluster closely with human SaVs in GI, GV and GII, respectively. This clearly indicates the possibility of cross-species transmission of SaVs;20 however, this does not indicate the direction of the interspecies transmission. To further elucidate the zoonotic potential of SaVs, more human and animal samples need to be screened. Bats (order Chiroptera) constitute the second largest group of mammals after the order Rodentia21 and have recently been implicated as a reservoir of many different viruses including some highly pathogenic to humans.22 Little is known about the genetic diversity of SaVs in bats and currently bat SaVs have been described only in two studies conducted in China12 and Hungary.23 The bat SaVs found in these studies were highly divergent from other SaVs known thus far.Sapovirus (SaV) particles were first detected in human diarrheic stool samples in 1976 in the United Kingdom using electron microscopy.(Eidolon helvum) in Cameroon.Here, we describe the nearly complete sequences of six highly divergent SaVs and one partial SaV obtained from the fecal samples of straw-colored fruit bats Ethical authorization for the protocol and the use of animal samples was obtained from the Cameroon National Ethics Committee, Yaoundé. All animal experiments were performed in accordance with the Ministry’s National Ethics Committee guidelines. Administrative authorization (R.11/MINSANTE/SWR/RDPH/PS/290/157) was obtained from the Delegation of Public Health for South West Region, Cameroon.Eidolon helvum) between December 2013 and May 2014 by a previously described method developed by Donaldson et al.24 Briefly, bats were captured in three different regions  of the South West Region of Cameroon using mist nets around fruit trees and around human dwellings. Captured bats were retrieved from the net traps and held in paper sacks for 10–15 min, allowing enough time for the production of fresh feces. Sterile disposable spatulas were used to retrieve feces from the paper sacks, and placed into tubes containing 1 mL of universal transport medium . Labeled samples were put in ice and then transferred to the Molecular and Cell Biology Laboratory, Biotechnology Unit, University of Buea, Cameroon and stored at −20 °C. Later on, they were shipped to the Laboratory of Clinical and Epidemiological Virology, Leuven, Belgium and stored at −80 °C. Each captured bat was assessed to determine species, weight (g), forearm length (mm), sex, reproductive state, and age (adult or juvenile). Parameters such as body temperature, activity, photophobia were not measured. All captured bats were then marked by hair clipping to facilitate identification of recaptures. Trained zoologists used morphological characteristics to determine the species of the bats before they were released. None of the captured animals showed physical signs of disease.Fecal samples were collected from straw-colored fruit bats (Eidolon helvum and two from Epomophorus gambianus) based on age, sex and location, and the pools were treated to enrich for viral particles using the NetoVIR protocol.25 Fecal suspensions  were homogenized for 1 min at 3000 rpm with a MINILYS homogenizer  and filtered using a 0.8 μm PES filter . The filtrate was then treated with a cocktail of Benzonase   and Micrococcal Nuclease  at 37 °C for 2 h to digest free-floating nucleic acids. Samples were extracted using the QIAamp Viral RNA Mini Kit  according to the manufacturer’s instructions but without addition of carrier RNA to the lysis buffer. First and second strand synthesis and random PCR amplification for 17 cycles were performed using a slightly modified Whole Transcriptome Amplification (WTA2) Kit procedure . WTA2 products were purified with MSB Spin PCRapace spin columns  and the libraries were prepared for Illumina sequencing using a slightly modified version of the Nextera XT Library Preparation Kit . Sequencing of the samples was performed on a HiSeq 2500 platform (Illumina) for 300 cycles (2 × 150 bp paired ends).Briefly, 25 pools (of two to five samples each) were made from 87 collected samples (85 from 26 and were de novo assembled into contigs using SPAdes.27 Contigs were then annotated using DIAMOND with the sensitive option28 using the non-redundant (nr) database of GenBank. ORFs were identified with ORF Finder analysis tools and the conserved motifs in the amino acid (aa) sequences were identified with HMMER29 and NCBI's conserved domain database (CDD).30 According to the International Calicivirus Conference Committee, a complete VP1 region sequence is required to designate new genogroups or genotypes, and strains in the same genogroups have ≥57% pairwise aa identity of VP1.13 Based on these complete gene sequence (at least 1602 nt), our novel sequences and 51 other complete reference gene sequences were aligned in MEGA631 using muscle and a phylogenetic tree generated using maximum-likelihood method, based on the LG+G+I+F substitution model (after testing for the best DNA/protein model in MEGA6). Sequences used in the phylogenetic analysis were representatives of the different genogroups and/or genotypes, complemented with all known bat SaVs, and SaVs which had been previously reported as potentially involved in zoonotic events.33 Amino acid sequence pairwise distances were also calculated in MEGA6 using transition and transversion substitution models. Sequences from the novel SaVs were submitted to GenBank with the following accession numbers: KX759618–KX759624.Raw reads were trimmed for quality and adapters using TrimmomaticDegenerate screening primers (SaV4529F: CCN TCD GGV ATG CCW TTY AC and SaV5165R: TYR CCC TCC ATY WCA AAC AC) were designed based on an alignment of 26 human SaVs  and 12 bat SaVs. These primers were used to screen 12 SaV-positive samples (eight human samples and four bat samples) by PCR. The following PCR conditions were used: an initial reverse transcription step at 50 °C for 30 min was followed by a PCR activation step at 95 °C for 15 min, 39 cycles of amplification, and a final extension step for 10 min at 72 °C in a Biometra T3000 thermocycler (Biometra). The cycle conditions for denaturation, annealing and elongation were 94 °C for 45 s, 58 °C for 45 s and 72 °C for 1 min, respectively. PCR products were run in polyacrylamide gel, stained with EtBr and visualised under UV-light.Eidolon helvum) while the lone pool of Gambian epauletted fruit bat (Epomophorus gambianus) contain no SaV reads. These positive pools were from both sexes and age groups and were identified in samples from two out of three localities (Limbe and Lysoka)  based on sex age and location and sequenced by Illumina next-generation sequencing (NGS) technology. Fourteen out of the twenty-five pools contained SaV reads, ranging from 0.1% to 6.3% of the non-phage viral reads. All SaV-positive pools were from the straw-colored fruit bat  identified by Tse et al12 shares 99%–100% aa identity among each other, are only 37%–42% similar to other SaVs, and were previously assigned to genotype XIV.14 On the other hand, two unpublished Chinese strains, GX2012 (from Rhinolophus sinicus) and JX2010 (from Myotis myotis), are only 50% similar to each other on the aa level while M63 (a Hungarian strain from Myotis daubentonii)23 shares 71% identity (67% nt identity) with JX2010. Therefore, the genotype numbers GXVI and GXVII were tentatively assigned to the strains GX2012, and JX2010 and M63, respectively. The sequences of the seven bat SaVs identified in Cameroon have an aa identity ranging from 31% to 41% (40%–49% nt identity) with all other SaVs and only 37%–41% (46%–48% nt identity) with other bat SaVs. On the basis of sequence similarity, the Cameroonian strains fall into two groups. The first group has two nearly identical pairs: Limbe65 and Limbe899b; Limbe 25 and Limbe 899a (99%–100% nt and aa identity) and between these pairs, there is an aa similarity of 77% (71% nt similarity). The rest of the strains  are more closely related to each other, with an aa similarity ranging from 61% to 78% (62%–72% nt similarity) among each other. These two groups share only 49%–54% aa (53%–57% nt) similarity among them. According to the classification scheme proposed by Oka et al,14 our bat SaVs represent two new genogroup within the Sapovirus genus. We tentatively placed them into genogroups GXVIII and GXVIX, respectively.On the basis of the complete lculated . Hong KoPhylogenetic analysis was performed using the aa sequences of the entire VP1 region of our sequences and 55 sequences representing genogroup and/or potential zoonotic strains. The human SaVs were found in four clusters, representing four genogroups  while the porcine SaVs were found not only in genogroup GIII, but also in genogroups GV-GXI.RdRP gene  of the RdRPs were available in GenBank.The phylogenetic analysis revealed a single monophyletic clade of bat SaVs, containing two major clusters . The firdRP gene  confirme35 with sequences of the novel bat strains  and probes (SaV124TP and SaV5TP) strains . It show strains .VP1 gene,36 which was also detected in a previous study in bats, although their ORF3 did not overlap with the VP1 of ORF1.12 The fact that the third ORF was not identified in our novel strains and other animal SaVs18 points to the fact that the protein encoded by this ORF in bats and human GV strains might not be mandatory. To ascertain the function of the protein, cell culture propagation, functional and knockout studies are required. Furthermore, the GC content of the newly described bat SaVs falls within the typical GC range of the genus Sapovirus (49.0% to 53.6%). However, SaVs identified previously in Pomona roundleaf bat (Hipposideros pomona) showed a relatively high GC content (up to 60.2%).12 In addition, the overlap of ORF1 and ORF2 in the genome of Lysoka36  of SaVs in Cameroonian bats, each of which encoded two ORFs. SaVs of genogroup GI, GIV, and GV have been reported to encode a third ORF overlapping the Lysoka36 , to the 38 and in pigs,39 but not in other animals. It is not clear whether these viruses cause disease in bats, since the adult and young bats from which they were identified appeared healthy based on physical examination  and no signs of white-nose syndrome were observed. This is not surprising as most bat viruses known today were discovered in apparently healthy bats, suggesting that they may have specific immune system or antiviral activity against virus infections,40 making them effective reservoirs of viruses.SaVs have been associated with gastroenteritis in people of all ages,15 Also three porcine SaV strains  and one California sea lion SaV were identified independently and are relatively closely related to human SaV GV.33 Lastly, based on the pairwise nt distances between the rodent strain Manhattan/Ro-SaV2 and the closest human SaVs (GII.1 to GII.7), it can be concluded that this rodent strain also belongs to the genogroup GII SaV. This suggests that these human and rat strains have a common origin, most likely reflecting an interspecies transmission event in a (distant) past.19 All these data from animal studies suggest that interspecies transmission of these viruses might have occurred in the past and as a consequence, raise the possibility of circulating animal SaVs strains in humans. Given that the bats we sampled live in closely proximity to humans in the region, there might have been an ample opportunity for such interspecies transmission to occur between bats and humans. However, most of the epidemiological SaVs studies described so far used conserved screening primers in PCR assay to identify and sequence the VP1 or VP1-RdRp region. Due to the nt divergence of bat SaVs observed here, even if there were zoonotic strains from bats circulating in humans, they might not be detected by the existing primers. Therefore, the novel primers presented here will be able to detect both zoonotic strains especially from bats. Further, to fully delineate the zoonotic capacity of these bat SaVs, in vitro studies in cell cultures would be very useful, as well as screening human samples around the same region for bat SaVs.These novel African SaVs clearly formed two phylogenetic clusters: the first cluster is made of Limbe65, Limbe899a, Limbe899b and Limbe25 and the second is made of Limbe900, Limbe894 and Lysoka36. Each cluster differs from the other and all other SaV genogroups by at least 40% aa identity and therefore constitute two new genogroups within the SaV genus, tentatively assigned as genogroups GXVIII and GXVIX, respectively. SaV genogroups GI, GII, GIV and GV infect humans while all other SaV genogroups (GIII and GVI–GXVII) have been identified in animals. Phylogenetically, some of the strains isolated from animals are related to human SaVs. Two SaVs (IJC09 and IJC04) isolated from chimpanzees in The Republic of Congo clustered closely with human G1 SaVs. Given that these chimps were living in close contact to humans (in the sanctuary) the authors suggested that there might have been a recent cross-species transmission of these viruses.


Wetlands are an important carbon reservoir pool in terrestrial ecosystems. Light fraction organic carbon (LFOC), heavy fraction organic carbon (HFOC), and dissolved organic carbon (DOC) were fractionated in sediment samples from the four wetlands . Organic carbon (OC) from rivers and coasts of China were retrieved and statistically analyzed. At regional scale, HFOC stably dominates the deposition of OC (95.4%), whereas DOC and LFOC in ZR is significantly higher than in ZRCW. Concentration of DOC is significantly higher in XRCW (30.37 mg/l) than that in XR (13.59 mg/l). DOC and HFOC notably distinguish between two sampling campaigns, and the deposition of carbon fractions are limited by low nitrogen input. At the national scale, OC attains the maximum of 2.29% at precipitation of 800 mm. OC has no significant difference among the three climate zones but significantly higher in river sediments than in coasts. Coastal OC increases from Bohai Sea (0.52%) to South Sea (0.70%) with a decrease in latitude. This study summarizes the factors affecting organic carbon storage in regional and national scale, and have constructive implications for carbon assessment, modelling, and management. Wetland ecosystems can deposit a large amount of photosynthesized carbon (C) into sediments3, and in-depth research on organic carbon (OC) sequestration and distribution must be undertaken to understand the processes and factors affecting it. OC can be divided into three C fractions on the basis of the stability and solubility of C in soils or sediments4. These fractions are: heavy fraction organic carbon (HFOC), light fraction organic carbon (LFOC) and dissolved organic carbon (DOC). Among the three fractions, HFOC (density >  = 1.7 g cm−3) is relatively stable to climate change and other external environmental conditions5, and LFOC (density <  = 1.7 g cm−3) is sensitive to the change of environment and microbial activities6. Furthermore, DOC has been studied widely with regards to biochemical activities, such as nitrification and denitrification7, and C mineralization8. Thus, carbon storage and factors affecting the carbon storage can be indicated by the study on carbon fractions as they participate in many biochemical activities and are easily affected by environmental variables.Increasing in atmospheric concentration of carbon dioxide  indicated that constructed wetland has higher carbon storage than river wetland4. Previous reports showed that surface soil has higher concentrations than subsurface soil12. Guo et al. (2015) shows the microbial phylum Acidobacteria can inhibit the decomposition and mineralization of organic carbon13. And Xu (2015) also showed the advantages of summer on carbon mineralization over winter in wetland14. At large scale, Mitsch et al. (2014) reported that tropical wetlands have significantly higher OC than boreal wetlands15, and variation in precipitation, climate and landscape can also influence the C distribution and storage17. Therefore, the regional and large-scale factors such like wetland types, soil depths, seasons, climate and precipitation, etc. may affect the OC deposition more or less. It is pertinent to identify important factors in the assessment of C storage at regional and national scales. However, systematic analysis of these factors affecting wetland C storage has not been undertaken in China.At regional scale, Cao Therefore, a research project was implemented at regional and national scale to study the factors affecting deposition of OC in wetland. The principal objectives of this study were to: 1) Assessing the distribution difference of three C fractions in two wetland types, two sampling campaigns, and among the sampling stations, 2) Evaluating the distribution of OC at the national scale, and, 3) Determining the relevant factors  affecting the distribution and storage of OC in wetland ecosystems. These objectives are realized by testing the hypothesis that sampling season and wetland types can significantly affect the storage of C fractions at regional scale, and OC is also affected by precipitations and climatic zones at large scale.Neither TOC nor HFOC exhibited any significant differences among two study zones Fig. , two wetIn general, carbon fractions in summer June, 2014) had higher concentrations than those in autumn . Difference of HFOC was the least among the carbon fractions, whereas DOC was the largest , with the mean value (24.77) of LFOC to LFON ratio, which was lower than TOC to TON ratio (49.81)  in ZR and ZRCW, and Bacteroidetes was negatively correlated with HFON . Thiobacillus, Burkholderiales and Rhodocyclales were all positively associated with carbon fractions and LFON in XR and XRCW. Specific Pearson correlation analysis between microbial communities and carbon and nitrogen fractions were showed in the Supplementary file.Moisture content and bulk density were significantly correlated to HFOC, heavy fraction organic nitrogen  and DOC, while they did not have a marked effect on LFOC and light fraction organic nitrogen . A prominent linear relationship existed between LFOC and LFON (R81) Fig. . And PriRiver sediments were studied for four precipitation regions  and three climatic zones . The OC concentration differed significantly among the precipitation regions  reported that carbon deposition in soils or sediments is primarily derived from terrestrial plants if the C: N ratio is >2053. Therefore, the high C: N ratio observed in the present study  suggests that OC input is primarily from the terrestrial plants. Goldman et al. (1987) reported that 53:6 (or 8.83:1) is the optimum C: N ratio for microbial growth, and any deviation from this ratio would strongly limit the microbial growth54. Tan et al.6 and Xiang et al.55 also reported that microbial biomass carbon to nitrogen ratio is about 9.5:1 and 23:3 ( = 7.7:1), respectively. Thus, the high LFOC: LFON ratio observed in the present study may indicate that the C is primarily derived from the terrestrial plants rather than from the microbial residues. In addition, the significantly higher TOC: TON ratio in ZRCW (70.6:1) and up-XR (141.8:1) than that in other stations in the present study indicate that low N concentration limits microbial growth and activities. Furthermore, it could also limit C accumulation but increase C mineralization. These results are similar to those of Moore et al.56 who also observed that unbalanced C: N ratio can lead to N fixation but C mineralization until the dynamic balance is achieved. Therefore, under conditions of N insufficiency, available N sequestration is an important factor affecting the C deposition.Carbon: nitrogen ratio is an important factor which can be used to distinguish carbon sources from aquatic plants or terrestrial plants57. Elshahed et al. (2007) reported that Acidobacteria-6 impacts both carbon deposition and ammonia oxidation in sediments or soils58. Further, such a relationship is also observed between LFOC, HFON, DOC and Acidobacteria-6 in ZR and ZRCW. However, the negative correlation between HFON and Bacteroidales may suggest that activities of Bacteroidales limit the deposition of HFON in the present study13.Carbon storage from the above ground plants into sediments immediately impacts the microbial activities5, which accounts for 95.4% of total OC, leads to the non-significant differences of OC distribution among the studied four wetlands. Whereas the interaction effect between study zones and wetland types causes the prominent differences of LFOC and DOC distribution  and subsequently the microbial communities. Fasching et al.59 reported that DOC can influence the microbial activities, whereas Jiao et al. (2010) showed that dissolved organic matter (DOM) can be mineralized by microbes60. Therefore, DOC can be regulated and limited by microbes to some extent. LFOC is primarily related to land use, plant coverage62, microbial activities63 and C mineralization64. XRCW, with significantly higher plant coverage and species and microbial diversity than XR, whereas XRCW has similar level of LFOC with XR. Thus, the data presented herein show that plant coverage and microbial activities may be not the determining factors for LFOC. However, for LFON, which had very low content in the sediments, is significantly correlated with LFOC  in June than those in October. This result further suggests that summer has higher C storage than autumn. Whereas Sabrekov et al. (2014) proved that emission of greenhouse gases (GHGs) is mostly during the summer67. In addition, Xu et al. (2015) observed that CH4 content in summer of XRCW is 15.5 times higher than that in autumn14. Therefore, summer is an important season to assess whether wetland is a carbon source or sink. Emission of GHGs is also strongly affected by plant species and the relative surface covered68. Xu et al. (2014) also showed that GHGs emission in mud flat, which has no covered plants, exhibited no significant difference among seasons66. Thus, notably higher plant coverage and species in XRCW than in XR may contribute to the higher potential for GHGs emission in summer in XRCW than XR, and was also easily to be carbon source.The four seasons of a year, with different climates, air temperatures, water level fluctuations and precipitations, have strong impacts on C deposition and emission−1) is significantly higher than that in river wetlands of China (1.58% ± 0.011)9, so is the OC density (8.01 kg C/m2 in China to 10.60 kg C/m2 in the world)69. Lal (2004) reported that soil physical structure can affect the carbon sequestration significantly70. Therefore, the prominent relationships among HFOC, DOC and bulk density herein may indicate that soil structure significantly affects the deposition of HFOC and DOC but not the LFOC in the present study. Above the ground, carbon sequestration may also be influenced by plants and carbon dioxide (CO2) in atmosphere. Mitsch et al. (2014) reported that plant richness in wetland can notably increase carbon sequestration compared to increasing methane (CH4) emission15; Van Groenigen et al.  also reported that with elevated CO2 concentration in atmosphere, increased microbial decomposition rate and CH4 emission in natural wetland limits carbon sequestration process72. Thus, the significantly lower carbon sequestration in natural wetland of China than the world’s mean level suggests that Chinese natural wetlands still have great potential for carbon sequestration. Effective measures should be carried out to mitigate the increasing CO2 concentration in China73. Google Scholar showed that most field studies on organic carbon of ecosystem in China focused on the eastern China, where concentrated with primarily population, industries, precipitation, cultivated land and river wetland17. Semi-arid grassland soils (such as northern China) can also accumulate stable organic carbon without much land use74. In China, effective management and proper protection on semi-arid grassland may improve higher carbon sequestration than that on the eastern land, which endured substantially disturbance. Consequently, these management and protection to terrestrial ecosystems are essential to carbon storage in national level.Concentration of OC in worldwide natural wetlands  and Wang et al. (2014)74, were involved to analyze OC storage in this study. The significant different OC distribution across among precipitation regions and the non-significant OC distribution across the sampled four wetlands suggested that precipitation is one important factor affecting carbon storage in large scale. The higher carbon storage in precipitation about 800 mm than those about 600 mm, 1500 mm or 1600 mm suggested that proper precipitation prone to carbon storage. Areas with precipitation of 1500 and 1600 mm had relative high temperature. And Bauer et al.75 showed that dry sites are more inclined to be carbon sink than humid sites in tropical. A significant logistic relationship showed that OC increased with an increase in precipitation and moisture to some extent76. Therefore, the too much precipitation may go against the carbon storage. Carbon deposition trend among the climatic zones of China is also similar with the report of Bauer et al.75, who showed higher carbon sequestration in temperate wetland than in tropical and boreal wetland by balancing CO2 sink and CH4 emission. Fine fraction of soils prone to carbon accumulation73 and high temperature and water content would increase microbial decomposition rate to plant residues72. Therefore, OC differences among different climatic zones and precipitations may be also induced by proportions of particle sizes in sediments and microbial richness. The notably lower OC in marine sediments than inland river wetland may suggest that carbon accumulation in ocean is less than in river wetland. The OC distribution trend, increasing with the reduced latitude, differs from carbon distribution in river wetland. One source of the OC in coastal sediments is the water flow from terrestrial rivers77. The particle fluxes from terrestrial river into coast are related to the terrain, runoff amount, and other environmental conditions78. And Ni et al. (2008) also reported that the maximum fluxes of suspended particle are in coincidence with the largest precipitation79. However, specific contributing factors should be studied in further research.Three climatic zones and four precipitation regions, which were divided by Shi In conclusion, the hypothesis we established is proved in this study, and the results presented support the following conclusions: (1) Sampling season can affect the storage of carbon fractions in temporal scale significantly; (2) Imbalanced C: N ratio could hinder the carbon sequestration in wetland in regional scale; (3) Proper precipitation is beneficial to carbon deposition in large scale, and carbon storage in river wetlands is prominently higher than in the coastal China; (4) However, the effects of wetland types and climatic zones on OC storage is not prominent in the present study. Therefore, comprehensive work should to be done to further confirm the influence of wetland types and climate on OC deposition, and global studies on carbon storage are also needed in the next step.51,  and Zhaoniu River Constructed Wetland (ZRCW), Xinxue River (XR) and Xinxue River Constructed Wetland (XRCW)) in Shandong Province in the northern China. The factors affecting wetland C storage in regional scale were analyzed. ZR and XR are two tributaries of Tuhai River and Nansi Lake, respectively Fig. . Nansi L4.Surface sediments were sampled in June 2015 from upstream, midstream, and downstream of ZR and XR; and upstream and downstream of ZRCW and XRCW. A total of 40 sediment samples  were collected from the four wetlands . Thus, data published before 2006 are excluded from this research. Previous studies also reported that the concentration of OC in surface soils and sediments was higher than in subsurface or deep soils11. And OC in deep soils was relatively stable and not easy to be affected by environmental factors84. So sediments deeper than 30 cm were also excluded from the present study. To make sure the data we retrieved is reliable, the published papers those have high cited times are referred firstly. The specific process of data retrieve is listed below.China is a fast developing countryhttp://scholar.glgoo.org/), and the first 100 publications  and the data were screened for the following requirements: 1) the articles published after 2006; 2) the data of sediments sampled before 2000 were eliminated; 3) sediment samples of deeper than 30 cm were excluded from the collected data; 4) the research stations not relevant to river wetlands or coastal wetlands were eliminated; 5) the data reused in two or more publications were retrieved only once; 6) the OC contents which could specifically be transformed into percentile system were retrieved; and 7) the OC determined by elemental analyzer was used to avoid experimental error. Finally, 595 data from river sediment samples and 364 data from coastal sediment samples published in 38 articles were retrieved for this study. In total, sediment data of over 40 rivers and tributaries and four coastal seas  were used in the present study. The sampling stations and C distributions are described in Fig. 16.“The OC in river sediments of China” was searched with Google Scholar  for extraction of DOC87. The concentration of DOC was measured by a total-C analyzer  using a non-purgeable OC analysis procedure. The pH was measured in 1:2.5 sediment: water suspension. The 1.70 g mL−1 of sodium iodide solution was used to separate heavy fraction organic matter (HFOM) and light fraction organic matter (LFOM) from sediment samples84. LFOM and HFOM were weighed by an electronic balance (0.0000 g), and C and N contents  were determined by an elemental analyzer . Total carbon to nitrogen ratio (TC/TN), light fraction carbon to nitrogen ratio (LFOC/LFON), and heavy fraction carbon to nitrogen ratio (HFOC/HFON) were calculated for further analysis.Prior to further analysis, moisture content and bulk density were calculated by comparing the volume of sediment samples under room temperature and 105 °C88.The analyses of DNA extraction and Illumina MiSeq sequencing of the amplified DNA were conducted at Shanghai Paisennuo Biological Technology Co. Ltd . Microbial communities and populations were cited and analyzed to explain the distribution of C fractionsStatistical analyses of the data were performed by using the SPSS 21.0. Mean value analysis and one-way analysis of variance (ANOVA) were computed to compare the differences of OC in inland rivers and sea areas of China. In addition, mean value analysis, one-way and two-way ANOVA, cluster analysis and correlation analysis were performed for the data on the carbon fractions. Cluster analysis to LFOC and DOC: mean values of LFOC and DOC in the four wetlands  are as four variables for cluster analysis to LFOC or DOC. Moreover, linear-regression analysis in SPSS and Principal component analysis (PCA) in Canoco 4.5 were performed between C fractions and other characteristics of the sediments . Correlation analysis was also done between C, N fractions and main microbial taxonomies. Origin 9, ArcGIS 10.2 and Adobe Illustrator (version 16.0.0) were used to draw figures.The sample collection of our study was conducted with the official permission of the Environmental Protection Bureau of Weishan Country and the Xinxue River Constructed Wetland Management Committee.Supplementary file