Leymus chinensis leaf cells, the air particulate matter (PM) contents, and concentrations of the main heavy metals  in soil and foliar dust samples collected from seven experiment points at the core mining area and one control point 20 kilometers away from the core mining area in Inner Mongolia in 2013. Comet assay was used to test the genetic damage of the Leymus chinensis leaf cells; the Tail DNA% and Tail Moment were used to characterize the genetic damage degree of the plant cells. The comet assay results showed that the cell genetic damage ratio was up to 77.0% in experiment points but was only 35.0% in control point. The control point also had the slight Tail DNA% and Tail Moment values than other experiment groups. The cell damage degree of the control group was 0.935 and experiment groups were 1.299–1.815. The geo-accumulation index and comperehensive pollution index(CPI) were used to characterize heavy metal pollution in foliar dust samples, and single factor pollution index and CPI were used to characterize the heavy metal pollution in soil samples. The CPIfoliar dust of control group was 0.36 and experiment groups were 1.45–2.57; the CPIsoil of control group was 0.04 and experiment groups were 0.07–0.12. The results of correlation analyze showed that Air Quality Index (AQI) -CPIfoliar dust(r = 0.955**)>Damage degree-CPIfoliar dust(r = 0.923**)>Damage degree-AQI(r = 0.908**)>Damage degree-CPIsoil (r = 0.824*). The present research proved that mining activity had a high level of positive correlation with organism genetic damage caused by heavy metals through comparing with the control point; soil and atmosphere were both the important action pathway for heavy metal induced genetic damage in mining area. Furthermore, heavy metal contents in foliar dust showed a higher positive correlation with genetic damage than when compared with soil. This means the heavy metal contents that L.chinensis absorbed through respiration from the atmosphere could make more serious genetic damage than when absorbed by root systems from soil in the mining area. This study can provide theoretical support for research on plant genetic damage mechanisms and exposure pathways induced by environmental pollution.Air and soil pollution from mining activities has been considered as a critical issue to the health of living organisms. However, few efforts have been made in distinguishing the main pathway of organism genetic damage by heavy metals related to mining activities. Therefore, we investigated the genetic damage of  With the spread of heavy “smog and haze”, the problem of environmental health impact is regaining worldwide concern. Especially in the northwest of China, with the expansion of the industrial scale, the large-scale development of mineral resources caused serious environmental pollution, in particular the haze problem caused by airborne particulate matter. Surface mining operations, collection, transportation and handling of coal are the major sources of such fugitive emission, and theCharybdis japonica in order to illustrate genotoxicity of three heavy metal ions  on marine crabs, and this research indicated that the DNA damage can be used as the potential biomarkers of heavy metal pollution in marine environments. Plants can absorb heavy metals from the atmosphere and soil by respiration and root uptake, making them a great reception and collector for heavy metals from external environment and have a variety of reactions. Thus plants have been widely used as the indicator of the heavy metal pollutants wi2EDTA; 10 mmol/L Tris; pH = 10; 1% sodium sarcosinate; 1%TritonX-100 and 10% DMSO were added immediately before use) for 1h at 4°C. The slide was drained and placed in a horizontal electrophoresis tank filled with freshly prepared alkaline buffer  at 4°C for 30min. Electrophoresis was performed in this buffer for 20min by applying 20V and adjusting the current to 300mA. Finally, the slide was gently washed twice in a buffer solution , fixed in methanol for 15 minutes, dried at room temperature, and stained with 0.005% ethidium bromide for 10 min. All procedures were carried out under low artificial light conditions to minimize UV-induced DNA damage. Slides were analyzed using a fluorescence microscope with an excitation filter [The commonly used “sandwich” structure was chosen to perform the comet assay. Regular microscope slides were dipped into a solution of 1% normal melting point (NMP) agarose prepared with 1×PBS buffer at 65°C, and a cover slip was immediately placed on top. After the agarose layer solidified, the cover slip was removed. Then, 20μL of the cell suspension was mixed with 75μL of 0.8% LMP (low melting point) agarose and spread over the first layer. The cover slip was replaced on top, and the slide was incubated at 4°C for 10 min. Then, the cover slip was removed, 80μL of 1% LMP agarose was added as a third layer, and the cover slip was placed on top again until solidification. Then, the cover slip was removed, and the slide was immersed in freshly prepared ice-cold lysis solution , healthy cells were round with a fluorescent, smooth surface  were determined microscopically. Cell viability always exceeded 95% in all samples.P is the index for pollutant p; CP is the mass concentration of pollutant p; BPHi is the largest observed value of CP; BPLo is the lowest observed value of CP; IAQIHi is the individual AQI value corresponding to BPHi; and IAQILo is the individual AQI value corresponding to BPLo. The evaluation of AQI follow the Technical Regulation on Ambient Air Quality Index(HJ 633–2012). The specific classfications of the IAQI values are shown in Air quality index (AQI) is a quantitative description of air quality dimensionless index. In this research AQI was used to evaluate the dust pollution (PM2.5 and PM10) in study area, and using the following equations:4, HF, and HNO3 [3 and stored in plastic bottles. Concentrations of Cd, Pb, and Cr were determined by a direct-reading inductively coupled plasma emission spectrometer analyzer . Another 0.5g sample was passed through a 63mm sieve and digested for 2h with aqua regia  in a warm water bath (95°C). After cooling, the digestion solution was used for Hg measurement after being de-oxidated by a reducing agent (thiocarbamide and ascorbic acid). Concentrations of Hg were determined by an atomic fluorescence spectrophotometer . Blanks and sediment standard reference material  were performed synchronously in each analytical batch. The recoveries were between 86 and 109%.Foliar dust samples were freeze-dried  and mixed thoroughly using mortars and pestles. A small portion of each sample (0.5g) was passed through a 63mm sieve, weighed, and placed in a polytetrafluoroethylene vessel. The samples were hot digested with concentrated HClOl., 2010). The diggeo) was introduced by Müller and has been widely employed in European trace metal studies[geo for the foliar dust samples of examined mines was computed using the following equation:n is the measured concentration of every heavy metal found in the mine soil (mg/kg), and Bn is the geochemical background value of the heavy metals found in the soil (mg/kg). The constant 1.5 is used due to potential variations in the baseline data[geo≤0, uncontaminated (class 0); if 0<Igeo ≤ 1, uncontaminated to moderately contaminated (class 1); if 1<Igeo≤ 2, moderately contaminated (class 2); if 2<Igeo≤3, moderately to heavily contaminated (class 3); if 3<Igeo≤4, heavily contaminated (class 4); if 4<Igeo ≤5, heavily contaminated to extremely contaminated (class 5); if Igeo > 5, extremely contaminated (class 6).Evaluating pollution degree of the foliar dust samples. The geo-accumulation index , we can obtain the CPI values.In order to assess the soil contamination degrees, the comprehensive pollution index (CPI) was calculated. CPI can take into account all the functions of the pollutants in soil, and also highlights the influence of pollutants of high concentrations to the soil environmental quality, so we followed the same way to evaluate. Based oi was calculated using the following formula:i represents the single factor pollution index of the pollutant i in the soil; Ci is the measured contamination value of the pollutant i; and Si denotes the assessment standard of the pollutant i. Combined with the background of the study area, in this study, the Pis, calculated for four elements  based on the third criterion of Environmental Quality Standard for Soils of China.Pave represents the mean of various pollutant indices in the soil, and (Pi)max is the maximum pollution index of single factor pollutant in the soil. If CPI≤0.7, the pollution degree of soil is cleaning and safety level; if 0.7<CPI≤1, the pollution degree of soil is slight cleaning but meeting warning limit level; if CPI>1, the content of soil heavy metal is beyond the third criterion of Environmental Quality Standard for Soils, which would influence the growth of farm crops, and affect human health; with if 1<CPI≤2 for slightly polluted level, 2<CPI≤3 for moderately polluted level, and CPI>3 for seriously polluted level.CPI was adopted to calculate the comprehensive pollution index of soil heavy metals and evaluate the pollution degree of soil in this study, following the Nemerow Pollution Index evaluation method defined in The Technical Specification for Soil Environment Monitoring (HJ/T 166–2004). The formula is expressed below:All values are expressed as mean±S.E. of six replicates in one representative experiment. The analyses were carried out using SPSS software (Version13.0). Data were compared with one-way ANOVA and Dunnett’s test. Statistical significance was defined as P<0.05 throughout.According to the comet images, the number of cells with a tail out of every 200 cells from each sample was counted. Based on the visual scoring of comet appearance, we identified different magnitudes of damage in the sampled population. According to the results, the maximum value for Tail DNA% (TD%) was 78.32 and the TD% was divided into 16 groups, 0–5%, 5%-10%, 10%-15% …… 70%-75%, 75%-80%. .The TD% reflects the proportion of DNA damage in the assayed cells. When TD% value is between 0%-5%, it could be regarded that there was almost no DNA damage in these cells. According to Comparing the proportion of cells in slightly damage (TD%<30%), the control sample was more than 85%. And there was no cell’s TD% above 45% in the control sample. Among these seven experiment points, the 4# was the most serious damaged group, the proportion of cells in low damage was less than 25%, and the proportion of cells above slightly damage (TD%>30) was almost 35%. As a result, these seven experiment samples showed a greater number of nuclei with medium damage and some nuclei with substantial damage (TD%>50%).L.chinensis DNA damage.Compared to control samples (8#), the seven samples at the core mining area had much higher damage because of the difference in growing environment, and the biggest difference is whether the plant is growing around the coalmine plot. Therefore, the results indicated that living near the coal mine could cause The same cells from each group were chosen for tail moment analysis. As Tail Moment (TM) is an integrated value of the tail DNA density multiplied by the migration distance, different magnitudes of DNA damage are depicted in The TM values represent the degree of damage in each cell. The seven testing sample groups exhibited higher TM values compared to the control sample. Among the seven test samples, there was some variation, although the differences were not significant (p>0.05).Considering the damage to each cell, this study calculated each group’s DNA damage degree. TM values were divided into five grades: Grade 0, when TM = 0; Grade 1, when TM ≤ 2; Grade 2, when TM ≤ 3; Grade 3, when TM ≤ 4; Grade 4, when TM > 4. Grade 0 represents the healthy cells.The ratio of damaged cells in each grade was calculated as the damaged cell count divided by the total cell count. The damage degree of each sample was calculated using the formula below, and the results are shown in The results of the Damage degree in the leaf sample cells showed that the sample plant cells near the core mining area were more damaged than control samples (8#). The damage degree of the eight sample groups decreased in the order: 4#(1.815)>7#(1.765)>1#(1.742)>2#(1.701)>6#(1.678)>3#(1.349) >5#(1.299) >8#(0.935). The avarage damage degree of these seven test samples is 1.621, much more than the damage degree of 8#(0.935). Therefore, we can deem that mining activity could make a certain degree of influence on the genetic damage of plants around coalmine.The results of the cell DNA damage degree further verified the mining activities can induce genetic damage to surrounding plants.The PM results and AQI values are listed in geo values of the foliar dust samples are listed in geo values of the four heavy metals in foliar dust samples based on the soil background values of Inner Mongolia. The comprehensive pollution index (CPI) of the foliar dust for each sample points was presented in The heavy metal contents and the Ifoliar dust values of 4#, 2#, 1# and 7# were much higher than other experiment groups, meaning the degree of damage caused by heavy metals in foliar dust were much more serious in 4#, 2#, 1# and 7#. In view of the Igeo values, Hg has the highest pollution degree; Pb is next; the lightest pollution element is Cr, which means plant leaves may be damaged by Pb and Hg particulate matters enriched in foliar dust through respiration at these sampling areas. Besides, through the correlation analysis, the concentrations of the Pb, Cd, and Hg in these foliar dust sample groups significantly correlate with each other (P<0.05).The average contents of Pb, Cd, Cr, and Hg in foliar dust of the seven testing samples were much higher than those in the control group. The CPIThe total concentrations of heavy metals in soil samples from the coal mine are show in soil for these eight sample groups were all less than 0.15. It showed that according to the national third grade standard of soil environmental quality evaluation, soil in the study area was not polluted by these four kinds of heavy metals. Sample 8# had the lowest CPIsoil value(0.04), meaning the control group was in the lowest soil pollution degree compared with other experiment groups. The CPIsoil values of 3#, 6#, 1# and 2# were much higher than other experiment groups; meaning the damage degree of heavy metals in soil was much more serious in 3#, 6#, 1# and 2#. According to the single factor pollution index, different heavy metals had different distribution in each sample point, and the same heavy metal also had different distribution in different sample points. Through the correlation analysis on Pb, Cd, Cr, and Hg concentrations, the concentrations of the Pb, Cd, and Hg in these soil sample groups are also correlated significantly with each other (P<0.01).The soil heavy metal pollution analyses showed that, the CPIThe correlation environment factors have among each other provided valuable information on their sources and behaviours. Due to soil (r = 0.824), it means the damage degree of sampling L.chinensis leaf cells has a higher correlation with the air quality compared with the soil pollution degree in the coal mining area. Considering the main air pollutants are heavy metal particles and coal fly dust in the mining area, and these particulates could settle on the blade surface of vegetation. Existing research[L.chinensis leaf surface porosity between 37~42μm, and the width between 19~23μm, which means that the leaf surface stomatas of the L.chinensis could permit some nanoscale particles to access. Therefore, only nanoscale heavy metal particles may attach onto the surface of the leaf and will enter the leaf stomata, futher into the mesophyll tissue through respiration, and induce the genetic damage of plants.The positive correlations between the pairs of factors are Damage degree-AQI (r = 0.908), and Damage degree-CPI research shows thfoliar dust (r = 0.955), and Damage degree-CPIfoliar dust (r = 0.923) has shown that the heavy metal contents in the foliar dust samples can be considered to be the main air pollutants in the study mining area. These heavy metals settled on foliar as dust also have a high correlation with the damage degree of sampling L.chinensis leaf cells. Which confirmed that lots of nanoscale heavy metal particles in the atmosphere can be absorbed by plant leaves and cause genetic damage to plants.Analyzing the positive correlations between the pairs of AQI-CPIfoliar dust (r = 0.923) and Damage degree-CPIsoil (r = 0.824), it can be said that heavy metals contained in the foliar dust have a higher level correlationship with leaf cells damage degree compared with heavy metals contained in soil. This may be because the heavy metal particles in the fly dust in mining areas accumulated in high concentration in a short time on the blade surface, and with the enrichment, lots of nanoscale particles could be absorbed by the leaf through micrometer size leaf stomatas during respiration, leading to enriched mesophyll tissue and induced genetic damage.Comparing the positive correlations between the pairs of Damage degree-CPIL.chinensis leaf cells. This showed that the soil and the atmosphere are both important pathways for plant genetic damage induced by heavy metals. Comparing these two pathways, heavy metals within the atmosphere have a more serious genetic toxicity than when found in soil.In general, these heavy metals contained both in foliar dust and in soil all exhibited a high positive correlation with the damage degree of sampling Our results firstly focus on whether the atmosphere or the soil is the main pathway of plants genetic damage in mining areas. According to Comet Assay analysis, the damage ratio in the control group was 36.0%, in experiment (sample) groups up to 77.0%; and the damage degree in control group was 0.935, in experiment groups up to 1.815. The results proved that coal-mining activities dose relevant to plants genetic damage nearby coalmine in certain degree.foliar dust(r = 0.923**)) had a more severe genetic toxicity than in soil (Damage degree-CPIsoil (r = 0.824*)). This may be because the heavy metal particles in the fly dust in mining areas accumulated. This shows that the genetic toxicity induced by heavy metals in plants through leaf uptake is much more serious than root uptake. Futhermore, due to the aggravating atmospheric pollution degree, the genetic damage induced by heavy metal contents on plant health through respiration exposure pathways would exceed root system absorbtion pathways, thereby elevating respiration to the principal action pathway to plant cell damage.By analyzing the correlation coefficient of the genetic damage degree, the particulate matter contents, and the heavy metal pollution of the foliar dust and soil, it can indicate that the soil and atmosphere are both important pathways for plant genetic damage induced by heavy metals in mining areas. Comparing these two pathways, heavy metals contained in the atmosphere  should be further investigated. Overall, the results can lay a foundation for further study of heavy metal pollution pathway related to mining activities, and provide a certain theoretical guidance for working direction on pollution control, plants and human health protection in and around mining areas.S1 Fig(PDF)Click here for additional data file.S2 FigThe number of damaged cells was counted, and statistical analysis of Tail DNA% and Tail Moment was conducted as the DNA damage measures.(PDF)Click here for additional data file.S3 FigIf the cells were damaged, the DNA structure exhibits a fracture phenomenon, and the speed of fragment movement to the anode side is faster than the large segments during electrophoresis. Then, the DNA forms the comet phenomenon and has a clear tail.(PDF)Click here for additional data file.S4 FigAfter dipping the samples in a strong alkaline electrophoresis solution (pH>13), healthy cells were round with a fluorescent, smooth surface . If the cells were damaged, the DNA structure exhibits a fracture phenomenon, and the speed of fragment movement to the anode side is faster than the large segments during electrophoresis. Then, the DNA forms the comet phenomenon and has a clear tail .(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableNot: 1#~7# are sample sites, located in the core mining area; 8# is the control site, away from the mining area.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


Thyroid cancer is a common malignant disease of the endocrine system with increasing incidence rates over the last few decades. In this study, we sought to analyze the possible association of 45 single nucleotide polymorphisms (SNPs) with thyroid cancer in a population from Rio Grande do Norte, Brazil.Based on histological analysis by a pathologist, 80 normal thyroid specimens of tissue adjacent to thyroid tumors were obtained from the biobank at the Laboratory of Pathology of Liga Norte Riograndense Contra o Câncer, Natal, RN. Patient samples were then genotyped using the MassARRAY platform  followed by statistical analysis employing the SNPassoc package in R program. The genotypic frequencies of all 45 SNPs obtained from the International HapMap Project database and based on data from the ancestral populations of European and African origin were used to compose the control study group.p = 6.27 × 10e−5) and 2.91 (p = 8.27 × 10e−5), respectively, after Bonferroni’s correction for multiple comparisons.In our study, the following 9 SNPs showed significant differences in their frequency when comparing the study and control groups: rs3744962, rs258107, rs1461855, rs4075022, rs9943744, rs4075570, rs2356508, rs17485896, and rs2651339. Furthermore, the SNPs rs374492 C/T and rs258107 C/T were associated with a relative risk for thyroid carcinoma of 3.78  contains supplementary material, which is available to authorized users. Thyroid cancer (TC) is the most common endocrine malignancy, and its incidence rate has been increasing noticeably for decades . Hence, The thyroid gland is mainly composed of two cell types: follicular cells and parafollicular cells. Based on histological and clinical parameters, follicular cell-derived carcinomas, which correspond to more than 90% of thyroid carcinomas, are typically divided into well differentiated, poorly differentiated, and undifferentiated (or anaplastic) carcinomas. Well differentiated thyroid carcinomas (WDTC) are further subdivided histologically as papillary thyroid carcinoma and follicular thyroid carcinoma, which correspond to 80% and 10% of thyroid carcinomas, respectively . Both thThe recent progress in understanding the molecular pathogenesis of TC has paved the way for developing more effective treatment strategies. This has mainly resulted from identification of numerous genetic and epigenetic changes including mutations, gene copy-number gain, gene translocation, and aberrant gene methylation, resulting in alterations of the signaling pathways involved in regulating cell proliferation and survival, such as the MAPK and PI3K–AKT pathways among others, which are reshaping TC medicine , 9. In aRecent high-throughput genotyping (GWAS) studies sought to identify single nucleotide polymorphisms (SNPs) associated with increased risk of thyroid tumorigenesis and to better explain the role of genetic variations in predisposition to TC –22. HoweTherefore, this study aimed to investigate whether the selected SNPs could be associated with predisposition to TC in patients diagnosed with this neoplasia in the state of Rio Grande do Norte. Considering that the population of this region is characterized by a marked ethnic mixture, this study may significantly contribute to elucidating the molecular basis underlying both predisposition to TC and the effect of interbreed populations on SNP-based association studies.In this retrospective study, 80 tumor formalin-fixed paraffin-embedded (FFPE) samples of tissue adjacent to thyroid tumors were selected from patients diagnosed with thyroid cancer at the Laboratory of Pathology, Hospital Liga Norte Riograndense Contra o Câncer  between 2003 and 2011. Among these 80 cases, 77 had papillary thyroid carcinoma and 3 had follicular thyroid carcinoma, as confirmed based on histological analysis by a pathologist. Ten 20-μm sections were cut from blocks containing non-tumor tissue using a Rotary Microtome YD-335 . Samples were placed in 1.5 mL microtubes at room temperature until processing. For each patient sample, a unique High Profile 818 disposable blade  was used to avoid contamination of genomic DNA.The control group was obtained from the International HapMap project and consisted of genotype data from 180 healthy individuals: 90 with European ancestry and 90 with African ancestry .This research was approved by the Ethics Committee in Research from Hospital Liga Norte Riograndense Contra o Câncer (protocol number 558788).FFPE tissue samples were deparaffinized with 2.4 mL of xylene  and washed with 2.4 mL of 100% ethanol . DNA was then isolated using the QIAamp® DNA FFPE Tissue Kit , according to the manufacturer’s instruction. DNA quantification was performed using the NanoDrop ND-1000 spectrophotometer. DNA samples were diluted to a final concentration of 20 ng/μL.www.usc.es/cegen/), coordinated by Dr. Angel Carracedo, Professor of the Faculty of Medicine, University of Santiago de Compostela (USC) [A panel of 45 SNPs was selected through previous studies performed at the Genotyping National Center (CEGEN) in Santiago de Compostela – Spain (la (USC) .SNP genotyping of the case group was performed using the MassARRAY SNP genotyping system  according to manufacturer’s instructions at the National Genotyping Center (CEGEN), on a panel of 45 SNP assays. The primers for amplification and extension were designed using the Extend Primer Assay Design software v4. Sequenom iPLEX GOLD chemistry was used for locus-specific amplification, followed by a single-base primer extension reaction, which generated products of different masses that were quantitatively analyzed using MALDI-TOF mass spectrometry. The resulting data were analyzed using TyperAnalyzer software v 4, followed by manual inspection of the spectra by trained personnel . All assP values. A P value <0.05 was considered statistically significant.Statistical tests were performed using the SNPassoc function in the R software v2.14.1 statistical package  . For eacEighty TC patients from Brazil were genotyped for 45 SNPs, to identify potential molecular markers of predisposition to this neoplasia. Among all the patients studied, 77 were diagnosed with papillary thyroid carcinoma and 3 with follicular thyroid carcinoma. The age of one patient was not determined, and the staging, location, and size of tumor were also not determined in some patients. Among the 80 patients, 61 lymph node metastases were not evaluated. These data are shown in Table p value for the Hardy-Weinberg equilibrium (HWE) of each SNP are shown in Table p < 0.05). The allelic frequencies from the Brazilian population were compared with those available in the HapMap database for 180 individuals of European and African ancestry, which constituted the control group.The allelic frequencies of 45 SNPs were analyzed and the minimum cutoff value for missing genotypes was defined as 10%. The allelic composition, allele major frequency and the p-value greater than 0.05 for Hardy-Weinberg equilibrium is shown in Table p = 6.27 × 10e−5) and 2.91 (p = 8.27 × 10e−5), respectively after Bonferroni’s correction for multiple comparisons.The association analysis between patients and control groups for SNPs that showed a p-values. Among the 9 SNPs analyzed , 7 showed OR values greater than 1, meaning that those alleles represent a risk for thyroid cancer development. In particular, the SNP rs3744962 showed the greatest risk with an OR value of 3.78. On the other hand, 2 SNPs (rs1461855 and rs2356508) were associated with a protective effect, with OR values less than 1.Figure The genetic ancestry profile of Brazilian population samples have been largely investigated in the last decades, mainly using uniparental inherited markers such mitochondrial DNA and Y-chromosome polymorphisms –29, as wAll these previous studies conclude that the genetic ancestry of the Brazilian population is widely heterogeneous and is characterized by extensive admixture from three different ancestral roots: Amerindians, Europeans, and Africans. Furthermore, the composition of the Brazilian population ancestry follows a clear trend related to historical facts , 38. EurBrazil is the fifth largest country in the world and is divided into five geographical regions. In all these regions, the European ancestry is predominant with proportions ranging from 60.6% in the Northeast to 77.7% in the South , 39–42. The heterogeneity and admixture of Brazilians constitutes an important issue to be considered in genetic association studies . BesidesAn extensive review of literature showed that considerable progress towards understanding complex diseases has been made in recent years due to development of high-throughput genotyping technologies . FurtherIn this regard, a growing number of studies have identified several SNPs associated with TC risk , 46–49. The association of rs944289 and rs965513 with TC risk was also shown in genetic studies conducted in Asian populations , 50, 51.Similarly, another study has demonstrated that SNPs rs2910164, rs965513, rs1867277, rs6983267, and rs944289 showed a significant association with TC risk in patients from the United Kingdom . FurtherA correlation study between Forkhead box E1 (FOXE1) gene variants rs894673, rs1867277, and rs3758249, and histopathological features of TC, suggested that FOXE1 variations generate a higher risk for poor histopathological features in papillary thyroid carcinoma . In addiFinally, Lidral  showed tAn important challenge faced by molecular epidemiological association studies of candidate disease-susceptibility genes is to define variants that are functionally implicated in the disease. In addition to identifying polymorphic genetic variants and their possible association with diseases, it is necessary to understand the functional relevance of these SNPs. This is particularly urgent because the amount of genomic information that is available greatly exceeds the information about the function of variants that are presented in human disease studies . ImportaAs an example, the association of polymorphisms in DNA repair genes XRCC1  and XRCC3 (rs861539) with thyroid cancer risk and progression can be considered. As Yan and collaborators showed, the XRCC1 variant can interact with the XRCC3 variant to significantly increase differentiated thyroid carcinoma (DTC) susceptibility . SimilarIn our study for molecular markers of predisposition to TC, we assessed the allele and genotype frequency of 45 SNPs, including four of the aforementioned SNPs. We observed that SNPs rs2910164 and rs965513, despite being in HWE, did not show associated risk with TC for the Brazilian population. Furthermore, SNPs rs6983267 and rs944289 were not in Hardy-Weinberg equilibrium in our studied groups. As observed, the frequencies of these particular SNPs differ from those observed for European, American and Asian populations, where some of these markers were associated with an increased risk of developing TC. These results reinforce the concept that the genetic constitution of a population, and the contribution of ancestral roots, is an important and influencing parameter in genetic association studies.Other studies evaluated potential markers for predisposition to thyroid cancer in larger sample sizes , 46. TheOf a total of 35 SNPs that were found in HWE in the Brazilian population, logistic regression analysis of 7 SNPs showed statistical significance when evaluated according to the genetic model of inheritance. For the first time, our results suggest that a new panel constituted of SNPs rs3744962, rs258107, rs4075022, rs9943744, rs4075570, rs17485896, and rs2651339 could represent predisposition to TC development in the population of Rio Grande do Norte. Furthermore, they may be considered suitable molecular markers for early diagnosis of the disease. These SNPs could be useful models to predict risk and for genetic counseling in clinical practice, thus avoiding invasive methods of analysis and often inconclusive diagnoses. In addition, SNPs rs1461855 and rs2356508 might possibly be associated with a protective effect against TC development. Nevertheless, complementary studies with a larger patient population as well as a control group composed of samples from the state of Rio Grande do Norte should be conducted to confirm these results.


Modelling disease dynamics is most useful when data are limited. We present a spatial transmission model for the spread of canine rabies in the currently rabies-free wild dog population of Australia. The introduction of a sub-clinically infected dog from Indonesia is a distinct possibility, as is the spillover infection of wild dogs. Ranges for parameters were estimated from the literature and expert opinion, or set to span an order of magnitude. Rabies was judged to have spread spatially if a new infectious case appeared 120 km from the index case. We found 21% of initial value settings resulted in canine rabies spreading 120km, and on doing so at a median speed of 67 km/year. Parameters governing dog movements and behaviour, around which there is a paucity of knowledge, explained most of the variance in model outcomes. Dog density, especially when interactions with other parameters were included, explained some of the variance in whether rabies spread 120km, but dog demography (mean lifespan and mean replacement period) had minimal impact. These results provide a clear research direction if Australia is to improve its preparedness for rabies. Canine rabies typically persists in developing countries where stray and unvaccinated, free-roaming domestic dogs account for a substantial proportion of the population. In this paper we investigate whether sustained canine rabies transmission can occur within the wild dog population of Australia, which comprises dingoes and their hybrids with domestic dogs. To do this we simulated the transmission of rabies between wild dogs across a sizeable landscape area. Whenever the disease spread 120 km we considered an epidemic as having occurred and recorded the length of time it took to do so. This allowed us to estimate the probability the sustained transmission of canine rabies will occur (21%) upon its introduction along with the speed at which it will spread (67 km/year). We also determined that wild dog movements and behaviour (for which there is the least amount of information available at present) together with wild dog density are the most important factors influencing these two outcomes, whereas wild dog demography (including average lifespan and average replacement period) had minimal impact. Future research should therefore focus on studying wild dog movements and behaviour to improve Australia’s readiness to control and prevent canine rabies transmission and establishment. Lyssavirus. Annually, it causes an estimated 61,000 human deaths in over 150 countries and territories where it is endemic . This range was selected as it includes all permissible integer values for the random number generator seed (MATLAB R2012b), thereby ensuring no bias was inadvertently introduced into model outcome distributions by falsely limiting the range of values the seed could assume . An important indication that sampling the random number generator seed has been done correctly is that the seed accounts for a non-negligible proportion of model outcome variance  and that when model outcomes are plotted against the seed value a horizontal trend line is observed (i.e. model outcomes should be independent of the seed).We note here that because the canine rabies model is a stochastic simulation model, even if all biological input variables were assigned fixed values, model outcomes would still vary from one simulation to the next. That is to say, model outcome variance is not completely explained by biological input variable uncertainty alone. This is important because this additional variance will affect the accuracy of global sensitivity indices estimated using the procedure above. To address this, the random number generator seed  must be treated as an additional input variable, sampled from its own unique distribution, and assigned a value at the start of each simulation . For the rabies model, we sampled the seed from a discrete uniform distribution with range [0,2Lastly, to estimate 95% confidence intervals for Sobol’s indices we employed the bootstrapping procedure prescribed in .Because many wild dogs are hybrids of dingoes and modern domestic dog breeds  we assumAt present, there are three published, field-data derived estimates for the mean incubation period of canine rabies. Hampson et al.  and HampThe infectious period of canine rabies is typically much shorter than the incubation period. Tepsumethanon et al.  reportedτ is defined as the transmission probability given contact. Rabies transmission, however, requires that an infectious dog inoculate a susceptible dog by biting it rather than by merely being in its vicinity. Thus, assuming independence, τ is equivalent to the probability a rabid dog bites another dog given contact  multiplied by the probability of successful transmission given a rabid dog bites a susceptible dog  and higher densities in higher-rainfall areas (0.14–0.3 dogs/km2) [2.No estimates for the population density of wild dogs (occupied nodes) in tropical northern Australia, where a rabid dog incursion is most likely, have been published to date. Therefore, to select a range of feasible values we considered wild dog population density estimates obtained for other regions around Australia . In general, wild dog density appears to depend largely on landscape carrying capacity, with lower densities observed in arid regions (0.08 dogs/kmogs/km2) , 39. Con2 [2/day). Assuming a wild dog’s sociability was equal to the square root of the area it traverses per day  whilst the sociability scale parameter was sampled from the range The literature provides no estimates for the area of land a wild dog traverses per day, reporting only home ranges, which are not rates and therefore not equivalent , 10. Nev per day  we then λ. Nor are there any appropriate data (e.g. proximity log or GIS tracking data) relating the contact rate of wild dogs to the distance between their mean positions that can be used to estimate λ. Wild dog contact distance distributions, however, are likely to be highly left-skewed [λ from a range of values spanning an order of magnitude (0.1–1.0 km-1), where for λ = 0.1 km-1 the wild dog contact rate declines by 50% every 7 km and for λ = 1.0 km-1 it declines by 50% every 0.7 km.There are no recorded estimates in the literature for the introduced spatial scale parameter t-skewed . From a The natural mortality of wild dogs is a complex parameter that is most likely age- (highest in juveniles <18 months) and density-dependent; the precise relationship, however, has not yet been quantified. It has been noted, though, that wild dogs live up to 10 years, with most dying by 5–7 years , 24. TheTo maintain model simplicity we assumed the replacement period was exponentially distributed and sampled the mean value from a range of 1–100 days based on expert opinion .M and M′, canine rabies percolated 30, 60, 90, and 120 km a total of 11,306 (23%), 10,782 (22%), 10,695 (21%), and 10,672 (21%) times respectively. We therefore estimate there is a 21% probability the introduction of a single sub-clinically infected dog into the wild dog population of Australia and subsequent transmission of rabies virus will result in a canine rabies epidemic. Furthermore, because there is little difference between the probabilities rabies percolates 30 km and 120 km, once an epidemic starts it is unlikely to stop naturally. We also note that for 99.4% of the simulations in which rabies percolated 120 km, rabies was still present within milestone 1 (30 km) at the time of percolation beyond milestone 4. This suggests the percolation of rabies 120 km or further is a reasonable indicator that canine rabies has become established in the wild dog population .Of the 50,000 transmission network realizations obtained from evaluating the model on the rows of matrices R0) was estimated as the mean number  of wild dogs infected by the index case over all 50,000 transmission network realizations.The basic reproduction number , given that it percolated 120km, to plot a baseline distribution of speeds the disease might spread across the Australian landscape . The disThe distributions of model outcomes for the 50,000 transmission network realizations were used to investigate the relationships between the outcomes and six input variables Figs –S6 Fig. 2 and a sociability scale parameter less than 36.0 the probability rabies will percolate beyond milestone 4 is less than 12% and 5% respectively. Once wild dog densities rise above 0.35 dogs/km2 and the sociability scale parameter increases to 67.0 the probability of percolation exceeds 40%. Lastly, the more territorial wild dogs are in their behaviour  the lower the probability of percolation.In λ, increases in value. Thirdly, we note that at low values for wild dog density and the sociability scale parameter the median time to percolation is highly variable. This is explained by the fact that in this region of parameter space canine rabies very seldom percolated beyond the milestone distances . This model may be applied in a similar manner to other wildlife populations for which there is either no contact network information or the observation thereof is difficult. This is true even when the host population is not territorial  since a sufficiently small value for λ would render each host capable of contacting every other host in the population.In recent years, wildlife contact networks have attracted increasing levels of attention . This isGlobal sensitivity analysis is an increasingly popular approach to performing sensitivity analysis of epidemiological models . This isD in a system of partial differential equations describing the spatial propagation of travelling infection waves. Furthermore, the velocity at which these travelling waves propagate is proportional to the square root of the diffusion co-efficient, The assumption that a wild dog’s sociability is equal to the square root of the area of land it traverses per day is a consequence of the wild dog contact rate proposed in cient, D . Thus ouλ, values. Predicting exactly how these parameters, and possibly even the contact rate .This work has some important caveats to bear in mind. The first is that rabies infection could modify wild dog behaviour in a manner significant for the sociability and spatial scale parameter, act rate , might dA second caveat is that we assumed home ranges (nodes) remained unoccupied for an exponentially distributed period of time. In reality, the mean replacement period is likely to be a complex function of wild dog migration between home ranges , seasonal birth rates, and wild dog density. Future research should include investigating whether incorporating more realistic wild dog demography  will significantly alter the model outcomes and global sensitivity analysis results presented here. This will be especially important when comparing the risk of rabies spread in multiple locations, e.g. Arnhem Land and Cape York.Thirdly, when simulating the spread of rabies we chose to fix the values of the canine rabies pathogenesis parameters as they are relatively well defined in the literature. Importantly, though, just because they were assigned fixed values does not mean model outcomes are insensitive to them. The pathogenesis parameters were assigned fixed values with the specific aim of identifying which ecological parameters (for which there is little known at present) are worth exploring further. The global sensitivity analysis results therefore need to be interpreted in this light.A fourth caveat is that when parameterizing the model, we fixed the sociability shape parameter to an arbitrarily chosen value of 30. Choosing a larger value would have reduced the upper limit of the range of values from which the sociability scale parameter was sampled. This, in turn, would have reduced both the probability of percolation  Panel D In order for a disease to become established each infectious dog must on average produce at least one subsequent infectious dog. To do this an infectious dog must first come into contact with enough other dogs such that it has sufficient opportunity to successfully infect at least one of them. When a dog in our model is sociable (i.e. when it covers a large area of land per unit time) and when wild dog density is high this requirement is satisfied, and therefore it is reasonable that wild dog sociability and density explain whether a rabies epidemic will occur. This interpretation agrees well with the finding that the number of dogs infected by the index case was also most sensitive to these two input variables.λ, one has to first consider the implications of the territorial nature of wild dogs . When an infectious disease spreads in a territorial population  a localized depletion of susceptible hosts may occur [λ is relatively small). This finding supports the hypothesis in [To understand the nearly linear relationship between the rate of spread (time to milestone) and spatial scale parameter, ay occur , 25. Thuhesis in .λ) than raccoons, foxes, and badgers, but alternatively could just be a result of the wide parameter ranges we considered. In either case, the preparedness of Australia could be greatly improved by targeted field studies that aim to better understand wild dog movement and behaviour.Rate of spread is an important outcome to consider since it determines the level of intervention required to contain an epidemic and bring about disease elimination once detected. Our estimates for the epidemic rate of spread are similar to, if not slightly above, the 20–80 km/year published in the literature for other sylvatic rabies virus strains and host populations . This maS1 Appendix(DOCX)Click here for additional data file.S1 Figλ, and (F) random number generator seed, given rabies percolates  30km,  60 km,  90 km, or  120 km. The  curve is the mean number of dogs infected by the index case irrespective of whether rabies percolates or not, equivalent to the basic reproduction number R0.The mean number of dogs infected by the index case as a function of (A) wild dog density, (B) mean lifespan, (C) mean replacement period, (D) sociability scale parameter, (E) spatial scale parameter, (PDF)Click here for additional data file.S2 Figλ, and (F) random number generator seed, given rabies percolates  30 km,  60 km,  90 km, or  120 km. The  curve is the median number of nodes within milestone 1 irrespective of whether rabies percolates or not.The median number of nodes within milestone 1 as a function of (A) wild dog density, (B) mean lifespan, (C) mean replacement period, (D) sociability scale parameter, (E) spatial scale parameter, (PDF)Click here for additional data file.S3 Figλ, and (F) random number generator seed, given rabies percolates  30 km,  60 km,  90 km, or  120 km. The  curve is the median number of dogs within milestone 1 before rabies introduction irrespective of whether rabies percolates or not.The median number of dogs within milestone 1 before rabies introduction as a function of (A) wild dog density, (B) mean lifespan, (C) mean replacement period, (D) sociability scale parameter, (E) spatial scale parameter, (PDF)Click here for additional data file.S4 Figλ, and (F) random number generator seed, given rabies percolates beyond milestone 4 (120 km).The median reduction in wild dog density within milestone 1 as a function of (A) wild dog density, (B) mean lifespan, (C) mean replacement period, (D) sociability scale parameter, (E) spatial scale parameter, (PDF)Click here for additional data file.S5 Figλ, and (F) random number generator seed, given rabies percolates beyond milestone 4 (120 km).The median proportion of infected (exposed) dogs within milestone 1 as a function of (A) wild dog density, (B) mean lifespan, (C) mean replacement period, (D) sociability scale parameter, (E) spatial scale parameter, (PDF)Click here for additional data file.S6 Figλ, and (F) random number generator seed, given rabies percolates beyond milestone 4 (120 km).The median proportion of infectious dogs within milestone 1 as a function of (A) wild dog density, (B) mean lifespan, (C) mean replacement period, (D) sociability scale parameter, (E) spatial scale parameter, (PDF)Click here for additional data file.


Microbial communities play crucial roles in the global carbon cycle, particularly in peatland ecosystems under climate change. The peatlands of the Sanjiang Plain could be highly vulnerable to global warming because they are mainly located at the southern limit of northern peatlands. In this study, the alpha diversity and composition of bacterial communities in three different minerotrophic fens along a chronosequence were investigated. We captured a rich microbial community that included many rare operational taxonomic units (OTUs) but was dominated by a few bacterial classes that have frequently been detected in other peatland ecosystems. Notably, a large diversity of methanotrophs affiliated with Alpha- and Gammaproteobacteria was also detected. Bacterial alpha diversity and composition varied as a function of peat depth and its associated physical-chemical properties, such as total carbon, total nitrogen, pH and bulk density. We also found that bacterial community turnover (beta diversity) to be significantly correlated with soil age, whereas bacterial alpha diversity was not. Thus, as one of the largest atmospheric carbon sinks and CH4 sources, northern peatlands have played an important role in global C cycles and climate change throughout the Holocene (from approximately 11600 years ago to the present)3. In peatlands, the sequestration of C arises from an imbalance between inputs via primary productivity and losses through microbial decomposition, which releases C to the atmosphere and as dissolved organic carbon (DOC) exports5. Both environmental and physico-chemical conditions impact C cycling through primary production and decomposition processes. Methane production is a consequence of the permanently waterlogged condition of the peat layers, where anaerobic decomposition prevails, and many studies have shown that microorganisms play a crucial role in C decomposition and methane production process10. Although microbial communities in the peatlands of Europe, America, Canada and the UK have been investigated13. However, there are relatively few studies on the peatlands in more temperate regions16. The Sanjiang Plain, located in the temperate climate region, is the largest area of freshwater marshlands in China17. As these peatlands are mainly located at the southern limit of northern peatlands, they may be more vulnerable to global warming than other areas18. Thus, it is imperative that we deepen our understanding of the microorganisms in this ecosystem.Despite covering only 6–8% of the world’s terrestrial ecosystems, northern peatlands store approximately 30% of the global soil carbon (C)19, and they vary in age from 500 to 10,000 years20. Correspondingly, these areas differ with regard to C accumulation rate. Accordingly, this chronosequence of peatlands offers an exceptional opportunity for studying links between the chronological characterization (soil age), soil properties and microbial community structure in peatland ecosystems. The influences of vegetative communities, water hydrology and soil properties in peatland microbial communities have been examined in many studies24, though the association between chronological characterization and the distribution of the microbial community structure has been neglected to date.The peatlands of the Sanjiang Plain have developed under certain topographic conditions during the Holocene or earlierIn the present study, high-throughput Illumina sequencing of 16S rRNA genes was applied to examine the bacterial communities of the Sanjiang Plain, the southern edge of northern peatlands. Peat cores were collected from three fens that began to develop in this area during different periods. The greater sequencing depth achieved by high-throughput sequencing allows for the capture of the less abundant and uncultured taxa and thus supplies a more thorough characterization of peatland bacterial diversity. Moreover, such chronological characterization facilitates the description of potential links between bacterial communities and soil age as well as the C accumulation rates.Nine peat cores were retrieved from fens, S (Shenjiadian), H (Honghe), and Q (Qindelie), on the Sanjiang Plain, northeastern China Fig. . With a A summary of the soil physico-chemical characteristics is presented in Table Soil age , F = 7.320, p = 0.001), TC  and pH  differed vertically. However, the C accumulation rate , TN , C/N ratio  and bulk density  did not. Interestingly, we found that TN , TC  and the C accumulation rate , were negatively correlated with peat age, whereas pH  was positively correlated with peat age.Concentrations of DNA extracted from peat soils across different sites and depths were ranged from 88.2 to 240.9 ng/µL. After amplification of the V3-V4 region of the 16 rRNA, 1,789,830 sequences from 24 soil samples were sequenced using Illumina HiSeq , Actinobacteria (16.07%), Acidobacteria (10.10%), Bacteroidetes (6.06%), Firmicutes (5.57%), Chloroflexi 3.30%), Gemmatimonadetes (0.81%), Caldiserica (0.57%), TM7 (0.51%) and AD3 (0.28%)  were used for the calculations. The bacterial communities of the surface peat of the S, H, and Q fens shared 42 common OTUs Fig. , and bacBacterial communities differed across depths and sites. The relative abundance of the dominant phylum, Proteobacteria, increased with increasing depth across all sites and was higher in younger peatlands, in contrast, Actinobacteria and Acidobacteria sequences decreased with increasing depth Fig. . From toWe used constrained correspondence analysis (CCA) to analyze variations in bacterial community structure and its associations with environmental variables. Overall, the measured environmental variables were not significantly correlated with bacterial community structure , and only those environmental variables that showed a significant correlation (p < 0.05) with the bacterial community were plotted as vectors Fig. . On the Using multiple alpha diversity indices, we assessed whether soil age and the C accumulation rate are significantly related to different aspects of microbial alpha diversity. However, we observed no significant relationships, positive or negative, among these parameters . Nonetheless, principal coordinate analysis (PCoA) of unweighted UniFrac distance ordinations did show that the bacterial community structures across the 33 soil samples were affected by soil age , but not the C accumulation rate  Fig. . In regr26.We captured a rich microbial community from the peat cores collected from the Sanjiang Plain, at the southern edge of northern peatlands. However, we found that the bacterial community to be dominated by a few bacterial classes and OTUs that have frequently been detected in other peatland ecosystems. The most abundant phyla, Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes and Firmicutes, have frequently been detected as dominant groups in other peatland ecosystems 2) and other electron acceptors, such as SO42− and, NO3−, mentioned by others27, the peat soil provides vertically stratified heterogeneous habitats for bacteria. In the present study, bacterial alpha diversity and composition varied as a function of peat depth and its associated physico-chemical properties, and pH, TC, TN, and bulk density showed significant effects on bacterial community structure.Due to the vertical gradients in the soil properties we measured, such as TC, TN, pH, bulk density, and C/N ratio, as well as the availability of oxygen , and Verrucomicrobia (Chthoniobacter). Acidobacteria, which are reportedly dominant among soils rich in organic matter, are involved in degradation of lignocellulose and celluloseet al.17, potential peatlands cover an area of approximately 10,520 km2 on the Sanjiang Plain and currently store ~0.26 Pg C. In response to future climate change and CO2 fertilization, both net primary productivity and CH4 fluxes will increase33. Methanotrophic bacteria can act as a natural barrier capable of significantly reducing the flux of methane into the atmosphere34, and bacteria that perform aerobic and anaerobic methanotrophy were detected in our samples. Although the relative abundance of type I and type II methanotrophs detected in the present study was similar to that in other peatlands35, a broader sequence diversity (14 OTUs) of putative methanotrophs belonging to Alpha- and Gamma-proteobacteria was observed. The OTUs detected in this study, are most likely to act as key players in methane cycling in this environment. Further studies on the isolation and characterization of these microorganisms are needed and will facilitate our understanding of their physiological functions and ecological importance.According to the research of Xing 5, Nonetheless, when investigated the linkage of organic C accumulation and microbial community dynamics in a sandy loam soil, Zhang et al. found that C accumulation promoted the macroaggregation and reduced the effective diffusion coefficient of oxygen, causing changes in microhabitats and a shift in microbial communities36. We did not detect significant correlation between bacterial community turnover and the C accumulation rates. The role of bacteria in C cycling in these peatlands requires further verification via field experiments and laboratory ecophysiological studies.By comparing the AMS dating and soil properties of the cores of peat that developed at different ages, we found that soil properties, such as TN, TC, C accumulation rate and pH, were correlated with peat age. These peatlands initiated at different times, were affected by the climatic conditions, such as temperature and precipitation, etc, and underwent different initiation and decomposition processes, resulting in the respective soil properties. It is worth noting, that we also found that bacterial community turnover (beta diversity) to be significantly correlated with soil age, bacterial alpha diversity was not. We suggest that the effect of soil age on the bacterial community was due to its correlation with soil properties. The structure and activity of bacterial communities involved in C decomposition and release processes can directly affect the C accumulation in peatlands and other ecosystemsIn summary, we found that the peatlands on the Sanjiang Plain developed over a wide range of dates during the Holocene, with the peat cores being dated from 637 to 11,496 cal. yr BP. The relative abundance, distribution, and composition of the microbial communities in three different minerotrophic fens on the Sanjiang Plain, at the southern edge of northern peatlands, was investigated by next-generation sequencing. We captured a rich microbial community that included many rare OTUs but was dominated by a few bacterial taxa that have frequently been detected in other peatland ecosystems. Notably, a large diversity of methanotrophs affiliated with Alpha- and Gammaproteobacteria was detected. In addition, bacterial alpha diversity and composition varied as a function of peat depth and its associated physical-chemical properties, such as TC, TN, pH and bulk density. We also found that bacterial community turnover (beta diversity) to be significantly correlated with soil age, though this was not observed for bacterial alpha diversity.20. This region is a large alluvial plain that is crossed by three major rivers, the Heilong River, Ussuri River and Songhua River, and has a total area of 10.9 × 106 ha, an elevation of <200 m and a slope grade of <1:10,000. Over 70% of this region is dominated by freshwater wetlands that have developed in ancient riverbeds and waterlogged depressions37, and approximately 30% (or nearly 3.3 × 104 ha) of this region is covered by peatlands that developed under certain topographic conditions during the Holocene or earlier38. The present climate of the plain is a temperate humid or subhumid continental monsoon climate. The mean annual temperature ranges from 1.4 to 4.3 °C, with an average maximum of 22 °C in July and an average minimum of −18 °C in January. The mean annual precipitation is 500–650 mm, and 80% of the rainfall occurs between May and September39. In addition to precipitation, the fens receive water inputs from groundwater and they are primarily covered with sedges (Carex lasiocarpa). In May 2012, triplicate cores were collected from each fen using a Russian peat corer, the cores were subsampled for chronological, microbiological and physico-chemical analyses. The storage of the soil samples for chronological analysis is described in more detail in the work of Zhang et al.18. For the microbiological and physico-chemical analyses, peat cores were sectioned into depth intervals of 0 to 30, 30 to 60, 60 to100 and 100 to 200 cm and; homogenized in sterile bags. After subsampling, the peat soils were stored in a sample incubator with a cooling function (2 to −10 °C) before being transferred to the laboratory. Chronological and physico-chemical characterization was conducted immediately after the samples arrived. Samples for DNA extraction were immediately frozen at −80 °C.We collected peat cores from three different minerotrophic fens, Shenjiadian (S), Honghe (H), and Qindelie (Q), on the Sanjiang Plain , northeastern China  with sequential combustion at 500 °C to estimate the organic matter content40. The bulk density of a 1-cm interval of each peat core was calculated according to the dry weight and volume of each subsample. Ash-free (organic matter) bulk density was calculated from the measurements of the bulk density and organic matter contents. All subsamples for AMS dating were dated with an accelerator mass spectrometry system at the Institute of Earth Environment, CAS. The AMS 14C dates were calibrated into calendar ages using the program Calib 7.02 based on the INTCAL 13 calibration dataset41. Carbon accumulation rates were calculated by multiplying the organic carbon content (using 52% C in peat organic matter) by the bulk density and dividing by the age interval42. Soil pH was measured in a 1:5 soil/water suspension43. The total nitrogen (N) in the soil was determined by dichromate oxidation using a Continuous Flow Analytical System .Subsamples with a volume of 3 cmTM SPIN Kit for soil  according to the manufacturer’s instructions. The DNA concentrations were measured using a NanoDrop 2000 spectrophotometer  and diluted to 1 ng/μL. The V3-V4 region of the bacterial 16S rRNA gene was amplified using the primer pair 341F (CCTAYGGGRBGCASCAG), 806R (GGACTACNNGGGTATCTAAT) combined with Illumina adapter sequences, and barcodes44. The specific barcodes used in this study are listed in Supplementary Table Genomic DNA was extracted from 0.5 g of peat soil using a FASTDNA27. Quality filtering of the raw tags was performed according to the QIIME  quality control process45, and all sequences shorter than 200 bp in length and with an average quality score lower than 25 in the raw reads were removed. The remaining sequences were subjected to chimera removal using the UCHIME algorithm . UPARSE46  was employed to classify the operational taxonomic units (OTUs) at the 97% similarity level. The longest sequence that had the largest number of hits to other sequences in each OTU was screened as a representative sequence. All the OTUs with sequence numbers ≤ 2 were removed in the subsequent analysis. The RDP classifier  was used to annotate taxonomic information for each representative sequence. Representative sequences were aligned using the Greengenes Database47 , with a minimum identity of 80%. To study the phylogenetic relationships among different OTUs and the differences between the dominant species in different samples (groups), multiple sequence alignments were performed using the MUSCLE software 46. For alpha and beta diversity analyses, the sequences were rarefied to depths of 21,811 sequences per sample, to minimize the effects of a different sampling efforts. Alpha diversity indices, including Chao1, Shannon, Simpson, ACE and PieLou equitability, were calculated. Beta diversity among the microbial communities was evaluated using both weighted and unweighted UniFrac distances. Evolutionary relationships were analyzed with neighbour-joining phylogenetic trees constructed in MEGA6. Pearson correlation analyses were applied to evaluate the relationships between the soil geochemical48. Differences in soil properties across samples were determined by ANOVA in IBM SPSS 49. Relationships between the taxonomic diversity and the soil properties were examined by linear regression analyses using SPSS. Principal coordinate analysis (PCoA) was conducted using the ape package (version 4.1) in R (version 3.3.3)50. Constrained correspondence analysis (CCA) was performed using the vegan package (version 2.4–1) in R48. A Mantel test was used to study the relationship between bacterial similarities and overall environmental factors. We also performed post hoc permutations using the function “envfit” in the vegan package to detect associations of the microbial community composition with environmental variables. A Mantel test was also used to examine the correlation between the UniFrac distances and soil age, and C accumulation rate. Heatmap plots were generated in R using the pheatmap package51.Raw sequences were divided into sample libraries via sample-specific barcodes and truncated after cutting off the barcode and the primer sequence. Forward and reverse reads with at least 10-bp overlaps and less than 5% mismatches were merged using FLASHThe Illumina sequencing data obtained in the present study have been deposited in the NCBI SRA database under the accession number SRP082472. The OTU table used for alpha and beta diversity analyses is provided as Supplementary Table Microbial communities in peatlands along a chronosequence on the Sanjiang Plain, ChinaDataset1Dataset 2Dataset 3


Diabetic nephropathy (DN) is one of the leading causes of death in patients with type 2 diabetes mellitus (T2DM). Several genome-wide association studies have introduced Engulfment and Cell Motility 1 (ELMO1) as a candidate gene which is associated with DN. This study assessed the association of ELMO1 gene polymorphisms with DN in order to investigate the effects of ELMO1 gene on susceptibility to DN in an Iranian population.In the present study, 100 patients with T2DM, 100 patients with DN and 100 healthy subjects who were matched for sex were selected. Allele and genotype frequencies were determined by Tetra-ARMS PCR technique. In all groups, levels of FBS, creatinine, urea, HbA1C, urine levels of albumin creatinine ratio and glomerular filtration rate were measured.p value = 0.005), and GG genotypes of rs741301  and DN. A significant association was not detected between allelic and genotypic frequencies of rs1345365 and DN. Linkage Disequilibrium (LD) between two variants was weak . rs1345365A/rs741301A haplotypes were more frequent in patients with T2DM as compared to DN . Also, genotypes of variant rs741301 in all subjects had significant difference with respect to the mean of ACR .A statistically significant association was shown between G allele of rs741301 (odds ratio (OR) = 1.7 [95 % CI 1.17–2.63]; This study first investigated the association of ELMO1 gene polymorphisms (rs741301) with DN in an Iranian population, supporting its key role as a candidate gene in the susceptibility to DN. Type 2 diabetes mellitus (T2DM) has become a silent pandemic over the past few decades, resulting in remarkable loss in government’s economy, and has endangered human health , 2. PatiNowadays, it is generally accepted that DN is a multifactorial progressive disorder caused by the cooperation between environmental factors and genetic factors. The most important risk factors such as hyperglycemia, hypertension, diabetic duration, hyperlipidemia, chronic inflammation, familial clustering and genetic determinants can make a diabetic patient susceptible to DN. However, no clear etiology and pathogenesis have been found –13. OutbGenome-Wide Association Studies (GWAS) have reported some important genes all around the globe for resistance or susceptibility to DN . ELMO1  of Tehran University of Medical Sciences (TUMS), Tehran, Iran. A total of 200 patients with T2DM were enrolled in the study that included 100 patients with nephropathy (DN) and 100 patients without nephropathy (DM). A control group consisting of 100 healthy controls (HC) was also included. T2DM was diagnosed by the criteria of the American Diabetes Association (ADA) . Subject2). Urine and fasting blood samples were obtained from the subjects for biochemical analysis and DNA extraction. Glycosylated hemoglobin (HbA1C)  and Fasting Blood Sugar, urea, creatinine, and also urine albumin creatinine ratio  were measured. Estimated glomerular filtration rate (eGFR) was calculated using Cockroft and Gault equation  p = 0.01) might predispose an Iranian patient with T2DM to DN. Over the last decade, GWAS and replication studies have produced impressive results upon the genetic basis of DN. GWAS in Japanese type 2 diabetic patients (546 DN cases and 334 type 2 diabetic controls) [In the present study, we investigated ELMO1as a candidate gene for susceptibility to DN in Iranian individuals; a population with a high incidence of DN. Our analysis suggested an association between allelic and genotypic frequencies of the variant rs741301 and susceptibility to DN. In fact, we detected that G allele  , replicaontrols)  and Amerontrols) , Africanontrols) , Chineseontrols)  and Indiontrols)  support p value < 0.05), which is consistent with our results [Most studies represented rs741301 polymorphism as the strongest SNP associated with DN. In 2005, GWAS was first done in Japanese T2DM patients and the obtained results identified the strongest association between rs741301polymorphism and DN. They showed that G allele in rs741301 and GG genotype are associated with risk of DN in Japanese patients, which is consistent with our results . America results . The marp value = 0.0006) for Iranian patients with T2DM against progressing to DN, which is inconsistent with the Chinese study as this haplotype has been identified as a risk haplotype for DN [p value = 0.006) for Iranian patients with T2DM progressing to DN. These results showed that rs741301 polymorphism may play an important role in increasing the risk of DN. Results of studies maybe conflicting and the reason may be related to unknown etiology of DN, multiple genetic factors, environmental and racial differences and inappropriate sample sizes.Also, in the present study, we did not find any significant difference between case and control groups with respect to allelic and genotypic frequencies of the variant rs1345365 of intron 13, which is consistent with the Chinese study , Mexicane for DN . Also, oIn 2006, Shimazaki and colleagues found that expression of ELMO1 gene increased in high glucose conditions. Subsequently, it terminated to increased expression of extracellular matrix genes and decreased expression of metalloproteinase gene and cell adhesive properties might make diabetes progress to DN. The exact mechanism of ELMO1 role in expression of these genes is still unknown, but last studies suggested that ELMO1 cooperates with Dock180 and they activate Rac-1 and this complex leads to increased expression of extracellular matrix gene. Shimazaki showed that there are not significant effects of these factors on the expression of extracellular matrix gene , 37.The present study evaluated for the first time the genetic variants of ELMO1 in an Iranian subjects with or without T2DM. The limitations of this study were the relatively small sample size, especially in DN group and also lack of diabetic patients with ESRD.ELMO1 may play a role in diabetic kidney disease in Iranian individuals as a population with high prevalence of T2DM, DN and ESRD. We found that G allele and GG genotypes of the marker rs741301 may predispose a diabetic patient to progressing to DN. Also, the association between the marker rs741301 and mean of ACR implicates this gene in susceptibility to DN. As a result, ELMO1 could be selected as an appropriate target in order to prevent and treat DN.


Saccharomyces cerevisiae significantly influence fermentation kinetics and the production of volatile compounds important for wine aroma. Amino acids are the most important nitrogen source and have been classified based on how well they support growth. This study evaluated the effect of single amino acids on growth kinetics and major volatile production of two phenotypically different commercial wine yeast strains in synthetic grape must. Four growth parameters, lag phase, maximum growth rate, total biomass formation and time to complete fermentation were evaluated. In contrast with previous findings, in fermentative conditions, phenylalanine and valine supported growth well and asparagine supported it poorly. The four parameters showed good correlations for most amino acid treatments, with some notable exceptions. Single amino acid treatments resulted in the predictable production of aromatic compounds, with a linear correlation between amino acid concentration and the concentration of aromatic compounds that are directly derived from these amino acids. With the increased complexity of nitrogen sources, linear correlations were lost and aroma production became unpredictable. However, even in complex medium minor changes in amino acid concentration continued to directly impact the formation of aromatic compounds, suggesting that the relative concentration of individual amino acids remains a predictor of aromatic outputs, independently of the complexity of metabolic interactions between carbon and nitrogen metabolism and between amino acid degradation and utilization pathways.Nitrogen availability and utilization by  During fermentation, yeast take up and metabolize amino acids and other nutrients to support growth and produce biomass. In the process, a range of volatile aroma compounds, including esters, higher alcohols, volatile fatty acids, carbonyls and sulfur compounds are produced. The production of many of these aroma impact compounds is directly dependent on the nitrogen sources that are present during fermentation  and BM45  were used, due to reported differences in fermentation kinetics and the resultant aroma profiles. The pre-culture procedure was as follows: A single yeast colony was inoculated into 100 mL YPD and incubated overnight with agitation at 30°C. Cultures were centrifuged and washed with sterile distilled water and used to inoculate 100 mL YPD broth at an OD600nm of 0.1 (approximately 106 CFU/mL) and again incubated overnight at 30°C. Cells were then centrifuged, washed, and resuspended in sterile distilled water. The fermentation media was then inoculated at an optical density of 0.1 at 600 nm.Two industrial Two fermentation media were used for this study; Yeast Nitrogen Base (YNB) without amino acids and ammonium (Difco™ Laboratories) and synthetic grape must (SGM)  were conducted in triplicate at 30°C. The medium contained 10% m/v glucose, to ensure that alcoholic fermentation would go to completion, and the initial pH was adjusted to 3.8 with KOH or HCl. The source of ammonium , trace elements , vitamins , anaerobic factors (10 mg/L ergosterol and 0.5 mL/L Tween 80), and nitrogen sources as specified. The SGM contained a total YAN of 21.43 mmol N/L containing a single BCAA; phenylalanine, isoleucine, leucine, or valine at concentrations of 7.14, 14.28, and 21.43 mmol N/L , salts , isoleucine (2.41 mg N/L), leucine (2.41 mg N/L), phenylalanine (2.41 mg N/L), tyrosine (2.41 mg N/L), and valine (2.41 mg N/L).A final set of fermentations was conducted in SGM, as described above, however in this instance more complex mixtures of nitrogen Table  were use600nm), was used as an indication of biomass accumulation during fermentation and samples were taken every 2–4 h for the first 20 h followed by every 6–24 h until weight loss ceased. Two millilitres of the medium was aseptically collected from the fermentation vessel, centrifuged at 5,000 rpm for 5 min, and the supernatant was removed. Pellets were resuspended in distilled water, centrifuged, and washed again. Samples were dried for 48 h at 100°C and weighed. To obtain a calibration curve for the biomass, the dry weight was plotted against the OD reading at each of these sampling points.Optical density, measured at a wavelength of 600 nm  with a split/splitless injector was used for major volatiles quantification. The split flow rate was set at 49.4 ml/min and the split ratio was set to 15:1 at a temperature of 200°C. The separation of compounds was done using a J and B DBFFAP capillary GC column  with the dimensions of 60 m × 0.32 mm and a 0.5 μl coating film thickness with the flow rate of the hydrogen carrier gas set at 3.3 ml/min. Once the FID oven temperature reached the temperature of 240°C, 3 μl of extracted sample was injected into the gas chromatograph at an initial temperature of 33°C and held for 8 min; the temperature was then increased by 21°C/min to 130°C and then held for 17 min; increased by 12°C/min to 170°C and held for 5 min; increased by 21°C/min to 240°C and held for 2.5 min. A post run step at the end of each sample was carried out at 240°C for 5 min. Each sample was injected in duplicate. The column was cleaned with an injection of hexane after every 20 samples. Manual data collection and peak integration were done using the HP ChemStation software [Rev. B01.03 (204)].At the end of alcoholic fermentation, samples underwent a liquid-liquid extraction as described by Louw et al.  for analTo assess the functional relationship between the final BCAAs concentration and their associated volatile metabolites accumulation, a linear relationship was assumed, and regression analysis was carried out to determine the association.The assumed relationship can be summarized by the following equation:Y = metabolite concentration; b = gradient of the slope; X = concentration of amino acid; a = metabolite concentration when no amino acid is added.Where Different concentrations of BCAAs were plotted against their corresponding aroma compounds and the strength of the regression model represents the predictability of aroma compound production as a function of initial concentration.Heatmaps of autoscaled GC-FID data was constructed using the package Complex Heatmap in R studio, where applicable, rows and columns were clustered using Ward.D and the Euclidean distance metric. Similarly, cluster analysis was applied to the four yeast growth parameters measured.Individual amino acids were evaluated for their ability to support the growth of VIN13 and BM45 in YNB. Differences in growth kinetics were observed due to both the amino acid treatment and, to a lesser extent, the yeast strain. Four growth parameters were assessed: the duration of the lag phase, exponential growth rate, total biomass formation and time to complete fermentation Figures . In mostGlutamate, a preferred nitrogen source, resulted in the shortest lag phase Figure  in both For some amino acids, the general correlation between the four parameters was not maintained. This is illustrated by isoleucine, which yielded biomass comparable to the preferred amino acids such as arginine but resulted in a low exponential growth rate and long duration of fermentation Figure .Tryptophan, threonine, and asparagine treatments resulted in the lowest biomass production levels for both yeast strains Figure . The yeas Figure .As summarized in the heatmap, isoleucine, tyrosine, methionine, proline, leucine, and tryptophan displayed a comparatively weaker performance in all four growth parameters evaluated when compared to the preferred amino acids seen in the first cluster.Asparagine, threonine, and ammonia are in the third cluster, as they displayed short lag phases, despite having low levels of biomass production. Ammonia is considered to be a preferred nitrogen source and therefore its poor performance was further investigated, and this deviant behavior was ascribed to the buffering capacity of the YNB media used (data not shown).Yeast strains also responded differently to certain nitrogen treatments, as shown by the longer lag phases observed for VIN13 when supplemented with only threonine, isoleucine, and tryptophan Figure . The higAs anticipated, the volatile compound composition was strongly influenced by the amino acid (21.43 mmol N/L) provided Figure . The leuThe effect of single BCAAs on aroma biosynthesis was further investigated using VIN13 in synthetic grape must. Three amino acid concentrations were applied while maintaining total available nitrogen through ammonium addition. To evaluate whether changing ammonium concentrations might impact the result, the same data were also produced using alanine instead of ammonium. Volatile acidity (acetic acid and ethyl acetate) displayed a proportional response to ammonium supplementation Table , whereasThe data suggested that a linear correlation existed, consequently, data were used to develop a simple regression model to evaluate aroma compound production based on the initial amino acid concentration Table .Irrespective of the addition of ammonia or alanine, the valine fermentations responded in a similar manner with respect to isobutanol and isobutyric acid, whereas butanol, and propionic acid levels were significantly impacted by ammonium levels Figures .2), the slopes were insignificant  concentration. Three concentrations were used for each of these amino acids: Absent (0), same as all other amino acids and present at twice (2) the concentration of the other amino acids. The impact of this omission or twice the concentration of BCAA's on the production of aroma compounds was evaluated, in a more complex synthetic grape must (20% sugars). The amino acid treatments resulted in similar fermentation kinetics, with the ammonium treatment being comparatively slower, nonetheless, all fermentations proceeded to dryness .The concentrations yielded differed from the predicted levels calculated, suggesting a loss of linear predictability. Nonetheless, the data indicates that these individual amino acid changes do indeed cause significant changes in the production compounds associated with their catabolism Figure . This isIn a winemaking context, nitrogen has a dual role, promoting yeast growth to achieve completion of alcoholic fermentation, but also playing a central role in the production of volatile aromas. Previous studies used generation time to evaluate the efficiency of nitrogen sources to support growth Cooper, . This stOverall, biomass formation was generally correlated with the duration of the lag phase and the exponential growth rate correlated with the fermentation time. Exceptions to these correlations, due to strain differences, were found for both the biomass and lag phase  and exponential growth rate and fermentation time . Furthermore, asparagine was found to be an exception due to its irregular performance for all parameters evaluated. In a winemaking context, the inclusion of fermentation time and biomass provides a greater understanding of the effect of these individual amino acids on fermentation performance.de novo (Crépin et al., The type and quantity of volatile compounds produced are largely dependent on the amino acid, the yeast strain, and their interaction. Overall, similar responses to the nitrogen treatments were observed for most compounds, however, strains often differed in the magnitude of their responses to some of the amino acid treatments employed. In agreement with literature, the degradation of BCAAs into their corresponding fusel alcohols and fusel acids was observed to be positively linked (Hazelwood et al., S. cerevisiae strains, known to have differing fermentation kinetics and volatile aroma profiles (Rossouw and Bauer, This study provides a novel evaluation of the impact of single amino acids on several growth parameters, in addition to its effect on the production of volatile metabolites by two genetically different industrial SF and AM performed experiments, analyzed data, and wrote the manuscript; HM and AF wrote the manuscript; FB designed the experiments, contributed to data interpretation, supervised the study, and co-wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.