Tropical forests are believed to be very harsh environments for human life. It is unclear whether human beings would have ever subsisted in those environments without external resources. It is therefore possible that humans have developed recent biological adaptations in response to specific selective pressures to cope with this challenge. To understand such biological adaptations we analyzed genome-wide SNP data under a Bayesian statistics framework, looking for outlier markers with an overly large extent of differentiation between populations living in a tropical forest, as compared to genetically related populations living outside the forest in Africa and the Americas. The most significant positive selection signals were found in genes related to lipid metabolism, the immune system, body development, and RNA Polymerase III transcription initiation. The results are discussed in the light of putative tropical forest selective pressures, namely food scarcity, high prevalence of pathogens, difficulty to move, and inefficient thermoregulation. Agreement between our results and previous studies on the pygmy phenotype, a putative prototype of forest adaptation, were found, suggesting that a few genetic regions previously described as associated with short stature may be evolving under similar positive selection in Africa and the Americas. In general, convergent evolution was less pervasive than local adaptation in one single continent, suggesting that Africans and Amerindians may have followed different routes to adapt to similar environmental selective pressures. Tropical forests are characterized by a high diversity of plants, with tall trees, dense canopies and low light penetration . Their cBesides food limitation, other characteristics of tropical forests may also contribute to the hostility of these environments. For instance, tropical areas harbor on average 70% higher human pathogen diversity as compared to more temperate areas . As a coHomo ).The six populations were combined into four distinct population sets (PS), each including one tropical forest and one control population per continent as follows:PS1: West Africa, Biaka; Pima, Surui.PS2: West Africa, Mbuti; Pima, Surui.PS3: West Africa, Biaka; Pima, Karitiana.PS4: West Africa, Mbuti; Pima, Karitiana.Each PS was analyzed separately to find loci and genomic regions that would putatively be under natural selection. The rationale behind the use of different population sets is to look for congruent signals of adaptation across data sets, and thus to eliminate signals potentially due to particular tropical forest populations, which have been shown to present high rates of genetic drift due to their small effective population sizes , 17.The results of this analysis are the core of this study, but since we had a single non-forest dwelling Amerindian population, we performed a supplementary analysis with a similar sampling design as described above, except that we replaced the Mexican Pima by the Zapotec. The rationale behind this approach is to check whether the signal observed using the core analyses is not related to any adaptive evolutionary phenomenon that would be restricted to Pima (non-forest) rather than to the forest dwellers Karitiana and Surui. Thus, by replacing Pima by Zapotec—another non-forest agriculturalist population—we should be able to check if the reported signals hold. This analysis is performed separately, because the number of shared SNP for this dataset  is much lower than that of the HGDP database . We therefore studied the following supplementary population sets (SPSs):SPS1: West Africa, Biaka; Zapotec, Surui.SPS2: West Africa, Mbuti; Zapotec, Surui.SPS3: West Africa, Biaka; Zapotec, Karitiana.SPS4: West Africa, Mbuti; Zapotec, Karitiana.ftp://ftp.cephb.fr/hgdp_supp1/>). We initially discarded 250 markers that were monomorphic in all populations, those that presented only missing data, and those located on either the Y-chromosome (due to low SNP density in the chip), the pseudoautosomal region of the sex chromosomes, or the mtDNA, leaving us with 660,668 SNPs. Within each PS defined above, those markers with minor allele frequency below 5% in all populations joined into one were discarded, yielding 582,074, 581,855, 584,205, and 577,345 SNPs for population sets PS1–PS4, respectively.Data on 660,918 SNPs (Illumina HumanHap 650K Beadchips) were downloaded from the Stanford University HGDP-CEPH SNP genotyping data supplement 1 , shared by all loci, and a locus-specific component (α), shared by all the populations [A modified version of BayeScan , 21 was ulations , 21, 23.ulations , 21 and ulations , 21. Thuulations –23.SC coefficient measures the differentiation of each population within the continental pool of migrants and an FCT coefficient measures the differentiation of each continent within the overall meta-population [q-values for each marker in order to control for the False Discovery Rate  considete  and disregard the results obtained for the highest hierarchical level , which could be due to other environmental differences between these two continents. By doing so, we still take into account the divergence between the two continents, but we avoid any particular bias or power decrease that would result in ignoring them [For an Fn humans –18 and Bn humans . In thising them . As for ing them , which iST-based natural selection tests are those based on Linkage Disequilibrium (LD). Those tests use information on homozygosity in single populations, which makes it difficult to formally test for differences in levels of LD and the length of homozygosity tracts over all populations. This is precisely the strength of the current approach, the integration of all populations in a single statistical analysis. A detailed comparison of this approach to other available methods can be found in ref [Alternatives to Fd in ref .q-values lower than 0.1 as potentially significant. This procedure yielded a list of outlier SNPs for each PS, which were then considered as candidate loci for natural selection targets. We finally assume hereafter that convergent selection occurred when selection presents a higher probability than neutrality and model 4 has a higher posterior probability than models 2 and 3.In this analysis, we considered all SNPs with http://pngu.mgh.harvard.edu/purcell/plink). SNPs not present in coding regions were assigned to a given gene if located less than 50 kb away from it. When more than one gene was within this range, the closest gene was chosen for the subsequent analyses. This software was also used for indicating the attributes of the functional SNPs, i.e. if they are nonsense (stop codon), missense (non-synonymous), frameshift or splice-site mutations, based on information from dbSNP build 129 available with PLINK v1.07. The amount of outlier SNPs falling in genic regions (or < 50 kb away from them) was compared to the amount of outlier SNPs in non-genic regions first taking into account only protein coding genes and then all regulatory elements as described below. This proportion was then compared to the distribution of the 660,668 HGDP SNPs in genic or non-genic regions with a Fisher exact test using R [All SNPs were assigned to genes using PLINK v1.07  sampled randomly the same number of SNPs as the counts of outlier SNPs from the population sets PS1-PS4 and counted the number of overlapping SNPs. We assessed the p-value by taking the proportion of occasions that resulted in an overlap equal or larger than the overlap we found. We followed the same procedure when testing the overlap of outlier genes.We tested whether the number of outlier SNPs and genes shared among the population sets was higher than expected. For this we repeatedly . Seventeen genes were annotated with multiple locations (caused by merging of gene records in the NCBI database); in these cases we took the outermost start and end positions.The hg19 assembly coordinates (NCBI Build 37.3) of 19,683 protein-coding genes located on the human autosomal and X chromosomes were obtained from the NCBI Entrez Gene website  were remapped on hg19 with the NCBI Genome Remapping Service (<http://www.ncbi.nlm.nih.gov/genome/tools/remap>). In doing so, we were not able to remap 74 SNPs, which were then excluded from the following analyses.The original positions of the SNPs on the hg18 reference genome (NCBI Built 36.3) obtained from the original dataset  for the hg19 assembly and added to the annotation file. These clusters show DNaseI hypersensitive areas assayed in 125 human cell types by the ENCODE Project [Information on the DNaseI Hypersensivity sites were obtained online  was plotted with R taking into account the physical position of the SNPs, their particular q-value, the window q-value, and the best supported model of selection  for each outlier SNPs. For the outlier SNPs, we also included information on their nearest gene if it was at most 50 kb apart.A sliding-window approach was implemented in R to identify significant clusters of candidate SNPs and remove isolated loci. We considered consecutive windows of size 500 kb wide with a shifting increment of 25 kb at each step. Despite the acknowledged differences in the extent of LD in the analyzed populations due to different time-scales and other particularities of the demographic history of the continent they are located –18, a siThe sliding-window approach yielded a second set of outlier markers. This SNP set is more refined than the one with the first candidates, since it ignores the low SNP density regions of the genome and those candidates that are isolated, highlighting genomic regions with a higher density of outlier SNP.paf), the Americas (pam), and both continents . We also added the score psl, which is the probability of any selection (psl = paf + pam +pco), which means selection in only one or both continents. As we use one value per gene in the enrichment test, we transformed the SNP-based scores to gene-based scores. First, SNPs were assigned to genes as described above: a SNP was assigned to a gene if the SNP location was within a gene transcript; otherwise it was assigned to the closest gene within 50 kb distance. For each gene and each selection model, we took the highest selection score of all SNPs assigned to this gene. After removing 1,922 genes with no SNPs assigned, a list of 17,761 genes remained for PS1 and PS4, 17,766 for PS2, and 17,769 for PS3.We applied a gene set enrichment analysis as described by Daub et al.  to find http://www.ncbi.nlm.nih.gov/biosystems, downloaded 3 Sep 2013). After removing genes that were not part of the gene list mentioned above, excluding gene sets containing less than 10 genes and pooling (nearly) identical gene sets into union sets, 1,216 (PS1 and PS2) and 1,217 (PS3 and PS4) sets remained and they were used as input in the enrichment tests.We collected 2,336 gene sets from the NCBI Biosystems database  were reported.R [We used R  and CytoR  to visuaR .ST-based Bayesian approach implemented via BayeScan [According to the FBayeScan , 21 1,48BayeScan  or a posBayeScan , or to tBayeScan , but notABLIM3, ACSS2, AKAP6, ANKRD26, ARHGEF10, ATIC, BCAT1, C20orf111, C2orf73, CBLN1, CNTN4, CNTNAP5, COL22A1, CPA5, CRTC3, CWH43, DCUN1D4, DHCR7, EPHB4, FAM188B, FKBP6, GALNT16, GLIS3, GLRB, GPC6, GRIK2, HLA-DPA1, HMG20B, HSF2, IQGAP1, KIAA1598, KLHL29, LRRC66, MASTL, MPST, NAALADL2, NRG1, NRP2, NSUN5, PARK2, PKIB, PPP2R2C, RABGGTB, RAD51B, RBFOX1, RBM9, RBMS3, RFX3, ROBO2, SCP2, SGCB, SHISA6, SPAG16, SPATA13, SPATA18, ST6GAL1, and TRG@). We confirmed with a random permutation test  that the number of outlier SNPs (75) and genes (57) shared by all population sets is higher than expected by chance .Using a threshold of 50 kb, 568, 474, 620, and 517 genes were associated with those significant SNPs from PS1 to PS4 respectively, of which 57 genes were found to be co-occurring in all four PSs  for genic SNPs in comparison to the whole HGDP SNP set. On average 72.8% of the outlier SNPs after the sliding-window procedure are located in or less than 50 kb away from protein-coding regions considering all four PSs, while for all SNPs in the HGDP database this proportion is only 63.6%. This enrichment is not due to potential LD between outlier SNPs, as it still holds when randomly sampling one outlier SNP and one non-outlier SNP per 100 kb window . Moreover, all outliers were located at maximum 45.8 kb apart from a gene or from a putative regulatory element indicated by a hypersensibility to DNaseI.q-values for the different PSs, as well as the best model for selection (color-coded) in the sliding-window approach. It was possible to identify seven clusters of outlier SNPs co-occurring in all four PSs  includes two SNPs, one located in a FKBP6 intron and the other located 0.12 kb apart from NSUN5 and 3.55 kb from TRIM50. The abovementioned 14 genes that include or are close (<50 kb) to SNPs that present signals of positive selection in the sliding-windows approach for all four PSs are described in For the first two clusters, the most likely model of selection for the majority of outlier SNPs is positive selection in the Americas; however, for a few SNPs convergent evolution cannot be ruled out . While tSCP2 ., the seSCP2 .. The neSCP2 .. Clusteclusters –S6 Figs.lyzed PS .. ClusteFive of the seven clusters showing signs of positive selection with the HGDP data sets also show signals of positive selection in the supplementary analysis. The exceptions are cluster 3 on chromosome 4, which was not covered by any SNP in the smaller data set, and cluster 7 on chromosome 7, for which the signal of positive selection disappears after replacing Pima by Zapotec. In the supplementary analysis, no additional clusters with signal of positive selection were found in all SPSs. The type of natural selection—convergent evolution or selection in a single continent—inferred from the supplementary data set is generally concordant with the analysis done on HGDP data, except for cluster 6 on chromosome 6, where selection in Africa is more important in the supplementary analysis. After performing the sliding-window approach, cluster 1 appears for SPS1 and SPS3, clusters 4 and 5 for all SPSs, and cluster 6 for SPS2 and SPS4.q-values in our study  in TRIP4. In a third region, we found two genes with positive selection signals considering West Africans in comparison to Biaka instead of Mbuti: USP46 and MLL3. An additional region suggested by Mendizabal et al. [NNT, while we observed a more diffuse significant signal also including CCL28 in some cases .As selection is acting on phenotypes instead of genotypes, we applied a gene set analysis  to detecTropical forests are believed to be very harsh habitats for human beings . In addiABCA1, a gene encoding a cholesterol efflux regulatory protein, was evolving under positive selection due to limited food resources that Native Americans encountered during their history. We also found significant signals of positive selection in genes that are related to lipid circulation and metabolism, such as SCP2 and CWH43  and the second includes the genes DCUN1D4, LRRC66, and SGCB, which are found in a region known to be associated to severe limb-girdle muscular Duchenne-like dystrophy [It is generally accepted that the pygmy phenotype might have evolved as an adaptation to life in dense tropical forests, to thermoregulation, and to food scarcity  or as a PPP3CB, which encodes a subunit of calcineurin, a protein that regulates bone formation by osteoblast differentiation [TRIP4, a gene with positive selection in pygmy populations [MLL3, involved in histone modification, a category associated with height in a genome-wide association study [Other regions of interest for the study of the pygmy phenotype are those four in which we found positive selection signals and are associated with the pygmy phenotype according to another study . From thntiation ; TRIP4, ulations  that relulations ; and MLLon study .In general, non-convergent positive selection signals were more pervasive than signals of convergent evolution at the SNP, gene and pathway levels. This suggests that the similar selective pressures imposed by the tropical forest environment in Africa and the Americas have targeted different genes and pathways. This could be a result of different genetic background of the populations from these two continents and also a result of the differences in the time-scales that adaptation took place, since the Americas were peopled more recently than Africa –18. As iST-based hierarchical Bayesian method used in our study enabled us to detect a number of regions with positive selection, suggesting that the following biological functions and pathways may play a role in human adaptations to tropical forest: lipid metabolism, immunology, body development, and heat stress response. The same signals found in different population sets suggest that they are due to environmental adaptation rather than to the demographic history of the sampled populations. Further refinement of these analyses with e.g. full genome or exome sequence information could reveal which particular mutations are responsible for these adaptations. Moreover, the few cases in which convergent evolution could be inferred contrast with the larger amount of genes with non-convergent positive selection signals, suggesting that Africans and Amerindians may have followed different routes to adapt to similar environmental selective pressures.The FS1 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S2 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S3 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S4 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S5 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S6 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S7 FigNodes are gene sets that score a q-value ≤ 20% (before pruning). Gene sets are connected when at least one of them shares >33% of its genes with the other set. The width of the connecting lines represents the amount of similarity between sets; the size of a node scales with the size of the gene set. Each quadrant of a node represents results on one of the four selection scores, while the four rings in a node correspond to the four population sets Click here for additional data file.S1 Fileq-value, and nearest gene (NA stand for those cases where no gene was found in < 50 kb) is organized by sheet for each population set (PS).Information on SNP coordinates, α values for each model of selection , (XLSX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Except for intI1 and qacE+qacEΔ1 the abundances of selected genes were below the detection limit in non-irrigated soil. Confirming the results of a previous study, the absolute abundance of 16S rRNA genes in the samples increased significantly over time  suggesting an increase in bacterial biomass due to repeated irrigation with wastewater. Correspondingly, all tested antibiotic resistance genes as well as intI1 and korB significantly increased in abundance over the period of 100 years of irrigation. In parallel, concentrations of the heavy metals Zn, Cu, Pb, Ni, and Cr significantly increased. However, no significant positive correlations were observed between the relative abundance of selected genes and years of irrigation, indicating no enrichment in the soil bacterial community due to repeated wastewater irrigation or due to a potential co-selection by increasing concentrations of heavy metals.Long-term irrigation with untreated wastewater can lead to an accumulation of antibiotic substances and antibiotic resistance genes in soil. However, little is known so far about effects of wastewater, applied for decades, on the abundance of IncP-1 plasmids and class 1 integrons which may contribute to the accumulation and spread of resistance genes in the environment, and their correlation with heavy metal concentrations. Therefore, a chronosequence of soils that were irrigated with wastewater from 0 to 100 years was sampled in the Mezquital Valley in Mexico in the dry season. The total community DNA was extracted and the absolute and relative abundance (relative to 16S rRNA genes) of antibiotic resistance genes ( Wastewater irrigation is a widely used practice worldwide, especially in arid and semiarid regions, to alleviate water shortages in agriculture  , as well as municipal wastewater associated resistance genes tet(W) and tet(Q)   copies were quantified using the primers BACT1369F and PROK1492R and the probe TM1389F . Multiple comparisons of means were performed using the GLIMMIX procedure .Total community (TC-) DNA was extracted from 0.5 g soil using the NucleoSpin® Soil Kit according to the manufacturer's protocol . DNA extracted from the four parcels was combined and target genes were quantified in triplicate by quantitative real-time PCR 5′-nuclease assays (qPCR) in a CFX96 real-time PCR detection system  as previously described for 3  and 2 ml of H2O2  were added. After 12 h of pre-hydrolysis vessels were locked and subjected to 30 min of microwave treatment  with a maximum power of 600 W and a maximum temperature of 110°C. After digestion, samples were quantitatively transferred to volumetric flasks, filled up to 100 ml, and afterwards stored in polythene containers. Analyses of total concentrations of Zn, Cu, Cd, Pb, Ni, Cr, Mn, S, and P were performed by Inductive Coupled Plasma Atomic Emission Spectroscopy  from soil samples of years 0, 1.5, 3, 6, 8, 85, and 100. All samples were digested and analyzed in duplicate and concentrations were expressed on dry weight basis.Soil samples were air-dried (<40°C), sieved to pass a 2 mm nylon mesh, and homogenized using a disc mill with agate stone beakers. Afterwards, 200 mg of homogenized and dried (105°C) samples were placed in clean Teflon vessels and 4 ml of HNO4NO3 extracts (soil:solution ratio 1:2.5) by graphite furnace atomic absorption spectroscopy , and the electric conductivities in suspensions with distilled water (soil:water ratio 1:2.5 wt/vol). Total organic carbon (TOC) was quantified in a CHNS/O analyzer . Bioaccessible P in samples of years 0, 8, 85, and 100 was quantified colorimetrically in 1 M NaHCO3 extracts .The Pearson product-moment correlations between the concentrations of metals, target genes and the years of irrigation were tested using the CORR procedure of the SAS statistical package (rrn copies per gram soil (dry weight) was quantified and revealed similar results as observed in the previous study by Dalkmann et al. , which coincides with previous findings of an increased microbial biomass in long-term irrigated soils , while no significant correlation was observed for tet(Q) , while a significant negative correlation was found for tet(Q). The abundance of qacE and qacEΔ1 genes in irrigated soils on average was three times higher than of intI1 , while the gene abundances of korB, intI1, and aadA were not significantly different. The mean abundances of tet(W) and tet(Q) were about 1 and 2 orders of magnitude lower than the abundance of korB, intI1, and aadA, respectively.The abundance of n et al.  for the 4NO3-extractable) concentrations of selected metals were analyzed in dry season samples and ranged from 0 until 0.8 mg/g soil  and potentially bioaccessible , while soil pH, electrical conductivity, and plant available P contents are not significantly correlated with the years of irrigation.Total soil organic carbon contents also increased significantly with the duration of irrigation , and tet(Q) were correlated with total concentrations of heavy metals Zn, Cu, Pb, Ni, Cr, and Mn as well as with total concentrations of P and S (Table tet(Q) showed significant negative correlations to Zn, Cu, Pb, Ni, Cr, and P, absolute abundances of korB and tet(W) showed positive correlations to Zn, Cu, Pb, Ni, Cr (only korB), P, and S, as well as negative correlations to Mn , but not with tet(Q).Absolute and relative abundances of genes  S Table . While fMn Table . The conintI1, qacE+qacEΔ1, aadA, korB, tet(W), and tet(Q) and potentially bioaccessible concentrations of heavy metals Zn, Cu, and Cd, as well as with soil electrical conductivity, pH, and TOC were also analyzed (Table tet(Q) showed a significant correlation to bioaccessible concentrations of Zn, Cu, and Cd, but also to TOC. For the absolute abundance of genes only korB and tet(W) were correlated to the bioaccessible concentrations of Cu. Except for tet(Q) all absolute abundances of tested genes were positively correlated to TOC concentrations. The electrical conductivity showed a significant positive correlation to tet(Q) absolute abundance only. No significant correlations were observed between gene abundances and soil pH.Correlations between absolute and relative abundances of genes ed Table . SimilarWastewater typically contains a diverse mixture of pharmaceuticals, pathogenic bacteria, antibiotic resistant bacteria, resistance genes, and heavy metals, which can reach and affect the environment and might pose a risk for human health when wastewater is not properly treated or directly applied as fertilizer  genes with the duration of irrigation (Table tet(Q) which was detected in very low abundance. Additionally, confirming the results of previous studies  and tet(Q) were below detection limit in non-irrigated soils, which is in agreement with a previous study reporting low abundances of tet(W) and tet(Q) in agricultural soils without manure fertilization , and the bioaccessible concentration of Cu , and Zn were significant for the total concentrations of this heavy metal but not for its bioaccessible fraction, which might indicate a less relevant association compared to Cu.Positive correlations were observed between absolute abundances of Cu Table , which fNot much data are available regarding minimum co-selective concentrations (MCCs) of heavy metals in soil, that potentially drive co-selection of antibiotic resistance  relative abundance. However, similar to the present study, Knapp et al. (tet(W) relative abundance.MCCs for Zn ranged from 19.61 μg/L in water samples to 22.75 mg/kg and 46.1 mg/kg in manure and sediment samples, respectively , suggesting that more than one copy of these genes was associated with class 1 integrons or that these genes are carried by other integron classes, MGEs, or located chromosomally. The mean abundances of korB, intI1, and aadA genes were not significantly different, which might suggest that class 1 integrons and the streptomycin resistance gene aadA were associated with IncP-1 plasmids.Confirming a previous study by Gaze et al.  for pig Besides changes in metal concentrations, a significant increase was also observed for S and P concentrations along with extended duration of irrigation. Although Shi et al.  could shintI1, qacE+qacEΔ1, aadA, korB, and tet(W) genes. In parallel, concentrations of Zn, Cu, Pb, Ni, Cr, P, and S significantly increased. However, no significant positive correlations were observed between the relative abundance of selected genes and years of irrigation or concentration of heavy metals, indicating no enrichment in the soil bacterial community due to wastewater irrigation or due to a potential selection by increasing concentrations of heavy metals and antibiotic compounds. Nevertheless, the increase in gene copy numbers per gram of soil along with extended duration of irrigation might lead to a higher exposure of field crops and farmers to resistant bacteria, resistance genes and MGEs. Further epidemiological surveys are needed to address if the increased presence of antibiotic resistance genes in the soils of this region poses a health risk for the inhabitants of the region and the consumers of the crops.In summary, wastewater irrigation in Mezquital Valley, Mexico, over a period of 100 years led to an increase in bacterial biomass in soil and, accordingly, to an increase in absolute abundance of The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Background. Renal cell carcinoma accounts for about 2-3% of all malignant tumors. The prevalence of brain metastases from RCC is less than 20% of cases. Traditionally, whole brain radiotherapy as well as the latest stereotactic radiosurgery improves both survival and local tumor control. These treatments also allow stabilization of clinical symptomatology. However, validated treatment guidelines for RCC patients with brain metastases are not yet available on account of the frequent exclusion of such patients from clinical trials. Moreover, limited data about the sequential use of three therapies, changing the class of agent, have been published up to now. Case Report. We report the case of a patient with metastatic RCC who developed disease progression after sunitinib and everolimus as first-line and second-line therapy, respectively. Thus, he underwent a multimodality treatment with pazopanib, as third-line therapy, to control systemic disease and radiosurgery directed on the new brain metastasis. To date, the patient is still receiving pazopanib, with progression-free survival and overall survival of 43 and 103 months, respectively. Conclusion. In a context characterized by different emerging options, with no general consensus on the optimal treatment strategy, the use of pazopanib in pretreated patients could be a suitable choice. Renal cell carcinoma (RCC) accounts for about 2-3% of all adult, malignant tumors. Metastatic disease frequently occurs, about 50% of the cases, and a large analysis shows that the most common sites of metastases are lung, bone, lymph nodes, and liver . Adrenal and brain metastases are rarely diagnosed (about 8–10% of the patients) [According to the Memorial Sloan-Kettering Cancer Center (MSKCC), three prognostic risk groups can be distinguished: favorable, intermediate, and poor . PatientHowever, in the presence of brain metastases (BM), the prognosis of RCC patients worsens, with a median OS not reaching 20 months using traditional whole-brain radiotherapy (WBRT) alone .Here we describe a case of long-surviving patient who experienced progression of disease after two previous lines of treatment. He underwent a multimodality treatment, consisting of stereotactic radiosurgery (SRS) of the BM and pazopanib as third-line therapy, with a good clinical outcome.We report the case of a 76 year-old man who underwent a radical left nephrectomy for clear cell RCC  in February 2007. Follow-up was negative until February 2009 when a whole-body computed tomography (CT) scan revealed lung nodule with a major diameter of 2.8 cm in the inferior left lobe. Considering the long disease-free survival time (24 months) along with the presence of single metastasis, a left lung wedge resection was performed. The histology confirmed the diagnosis of metastasis from clear cell RCC. About 5 months later, a spiral CT showed a new malignant micronodule in the right superior lung lobe and nodules ranging from 2.8 to 1.8 cm in the left and right adrenal glands, respectively. Patient's Karnofsky Performance Scale score was 90% and he was classified in the favorable risk group according to both MSKCC and Heng's score criteria. In August 2009 he started a tyrosine-kinase inhibitor (TKI), sunitinib 50 mg per day (4 weeks on and 2 weeks off). After 2 cycles of treatment, a grade 3 mucositis occurred but after 3 weeks of break it declined to grade 1. Thus, he resumed therapy at the lower dosage of 37.5 mg per day (4 weeks on and 2 weeks off). The whole-body CT scan, regularly performed every 3 months, demonstrated a stable disease (SD) as best response until June 2011 when, after 15 cycles of therapy, it revealed a progressive disease (PD) at the left adrenal gland which was confirmed by magnetic resonance imaging. Therefore, in August 2011, a second-line therapy with everolimus 10 mg per day was started. The treatment was stopped after just 4 months because of the fact that CT scan had showed a new cerebellar lesion of 6 mm in the right side and a further progression of the left adrenal nodule (4 cm) . The remBrain metastases occur in less than 20% of patients with RCC and are generally related to a very poor prognosis . Shuch eThe WBRT and the latest SRS are demonstrated to improve survival, local tumor control, and clinical symptomatology . HoweverWe report a case of patient who developed BM after sunitinib as first-line therapy and everolimus as second-line therapy, with PFS of 24 and 4 months, respectively. We referred him to multimodality treatment with SRS of the cerebellar metastasis and pazopanib as third-line therapy. In our opinion, his excellent OS of more than 8 years raises particular interest. Indeed, to the best of our knowledge, none of the few previous studies about second- and third-line metastatic RCC (mRCC) reported similar outcome.P = 0.059) and OS . However, other retrospective studies have showed equal efficacy of these two sequences of treatments, with median OS generally not exceeding 30 months [Although there are limited published data on the sequential use of three therapies in the treatment of mRCC, several studies showed an increased disease control as well as improvements in quality of life by changing the class of agent. First of all, the RECORD-1 trial showed a better outcome with everolimus (mTOR inhibitor) versus placebo  in patients progressing on VEGF inhibitors (VEGFi) . In deta0 months –16.According to an algorithm treatment decision of changing the class of agent, having the patient received previous VEGFi-mTORi therapy, he should have been treated with another VEGFi, such as sorafenib.Sorafenib is VEGFi-TKI approved to treat mRCC patients who have failed or cannot bear other therapies. Its main toxicity typically is hand-foot-skin (HFS) reaction that may be severe (grade 3-4) in about 15% of patients . Particu inverse decision-making algorithm, as suggested by GOAL authors, we selected the best therapy by limiting toxicity and improving at the same time the treatment compliance [Our patient was a musician; thus, considering the high risk of HFS occurrence, another target molecule was offered. Effectively, applying anmpliance .Compared to other target therapies, such as everolimus, or axitinib, pazopanib showed clinical benefit as salvage therapy only in few studies , 20.The first one included 28 patients treated with pazopanib after two previous therapies: median PFS was 16.5 months with grade 3-4 hypertension and proteinuria as the most common toxicities . In the Our experience, coupled with data of literature, raises the question whether patients' clinical outcome was completely influenced by the presence of BM or rather by their exclusion from the most effective treatments such as the multimodality approach. Thus, in the evolving scenario of mRCC therapy, with no general consensus on the optimal sequence of therapies, the use of pazopanib in VEGFi-mTORi pretreated patients could represent a promising option.


The aim of this study was to verify the effects of programmed and self-selected physical activities on the physical fitness of adolescents. High school adolescents, aged between 15 and 17 years, were divided into two experimental groups: a) a self-selected physical activity group (PAS) with 55 students (aged 15.7 ± 0.7 years), who performed physical activities with self-selected rhythm at the following sports: basketball, volleyball, handball, futsal and swimming; and b) a physical fitness training group (PFT) with 53 students (aged 16.0 ± 0.7 years), who performed programmed physical fitness exercises. Both types of activity were developed during 60 min classes. To assess physical fitness the PROESP-BR protocol was used. The statistical analysis was performed by repeated measures ANOVA. The measurements of pre and post-tests showed significantly different values after PFT in: 9 minute running test, medicine ball throw, horizontal jump, abdominal endurance, running speed and flexibility. After PAS differences were detected in abdominal endurance, agility, running speed and flexibility. The intervention with programmed physical activity promoted more changes in the physical abilities; however, in the self-selected program, agility was improved probably because of the practice of sports. Therefore, physical education teachers can use PFT to improve cardiorespiratory fitness and power of lower and upper limbs and PAS to improve agility of high school adolescents. Morphological changes during adolescence are associated with several components of physical fitness  which, aMoreover, the American College of Sports Medicine  also staNevertheless, the programmed and systematized high intensity practice of physical activity seems to hamper exercise adherence, especially in school. This may be due to dissatisfaction and lack of pleasure in performing physical activity. Studies on intensity of self-selected exercises during physical activity and on the concurrent physiological changes are not novel. Since only few studies have addressed the programmed vs. self-selected physical activity in the context of high-school physical education classes, performing such research seemed necessary. Therefore, the aim of this study was to verify the effects of programmed and self-selected physical activities on the physical fitness of adolescents. We hypothesized that practice of programmed physical activities had larger effects on physical performance compared to self-selected physical activity.Research Ethics Committee (HREC) of the Federal University of Paraíba – UFPB, Protocol No. CEP / CCS No. 0269, 2011 in accordance with the Declaration of Helsinki and resolution No. 196/96 of the National Health Council. A total of 108 high school students , aged between 15 and 17 years, from the Federal Institute of Education, Science and Technology of Paraíba (IFPB) / João Pessoa, Brazil, participated in the study. All of them were pubescent and post-pubescent, according to Tanner Stages of Sexual Development This study was approved by the Human After explaining the procedures, students were invited to participate in the study, being randomly assigned to develop two types of activities: a) self-selected physical activity, which was performed three times per week for 60 min and comprised of 20–25 min stretching as a warm up, dynamic joint movements followed by various sports chosen by the students . From the selected sports, competitive recreational games were applied and they lasted between 25 and 35 minutes. During the games, each student was replaced when showed either low physical performance (fatigue or continuous error) or psychological stress (unfair attitude). Finally, the students had 5 to 10 min of relaxation activities such as stretching, relaxation through static sound stimuli (sounds of nature) or just walking slowly, as decided by the group; b) programmed physical activity (fitness) was performed with the intensity level of 70 to 80% of the maximum heart rate (220-age) , followiThe PROESP-BR battery test described by 2), values greater than 0.20, 0.50, 0.80 and 1.30 represented small, medium, high and very high end, respectively , group (PAS × PFT), and the interaction effect (group × time). Additionally, a t test was run within groups with percentage variation (Δ%) and to estimate the effect size (ηectively . The lev2 = 0.50), small in the LMEabd  and FLEXw/b  tests, and were non-significant in SP20m  and LLEP  tests. The effect of time was not observed in the ULEP test nor in the AGILst test. As to the group effect, it was non-significant and only the 9MIN  and ULEP  variables presented significant changes. Other variables that were not sensible to the group effect were the LLEP, LMEabd, AGILst, SP20m and FLEXw/b tests. The analysis of interactive effect (time × group) proved significant for two variables: the 9MIN with small  and to RMLabd with trivial . There was no interactive effect for the ULEP, LLEP, AGILst, SP20m and FLEXw/b tests.The analysis of effect of time on the physical performance variables  indicateactual TET proves to be significantly shorter than the predicted TET only after sleep (EME[Retest 1]: t (11) = −3.901, p[one-tailed] = .001, d = 1.13; MEM[Retest 2]: t (11) = −5.019, p[one-tailed] < .001, d = 1.41). Also, in the EME-group this sleep-induced advantage of actual over estimated TET-performance appears to be preserved during the The comparisons of pre vs. post-test measures in the various physical fitness tests  vs. self-selected physical activity (PAS) on adolescents’ physical fitness. We hypothesized that practice of programmed physical activities had larger effects on physical fitness compared to self-selected physical activity.) between the administration of the two programs were observed in the cardiorespiratory fitness and in abdominal endurance. It is noteworthy that the self-selected practice of various sports (including game sports) was not able to promote cardiorespiratory adaptations. Hence, this may be the physical fitness component which requires extra engagement other than typical physical education classes. The changes observed in the remaining variables were less prominent and (or) may be mostly due to growth and maturation phenomena, despite the type of the program applied. Though, the magnitude of the changes may be influenced by hormonal release, which in turn may depend on the amount of physical activity  within the school daily environment. In summary, the present study showed differences between the two interventions regarding cardiorespiratory fitness in both genders, with higher values in the group that performed the prescribed exercise. Furthermore, in both groups and genders, applying programmed or self-selected activities, strength, endurance, speed and flexibility values increased. Nevertheless, it was perceived that the fitness training program was more effective, for both genders, since it was able to produce changes in more variables. These results recommend that the program of physical activities in high school should be diverse, stimulating both the engagement in fitness training and that in sports. Therefore, it is our opinion that high school syllabuses should be designed as to address the various types of physical activity. Poorly structured physical activity for these age groups can result in injuries, fractures, osteochondrosis, and menstrual dysfunction .We concluded that the efficacy of both program was not significantly different. However, the programmed physical activity indicated to cause more changes in physical fitness. Thus, it may be useful for physical education teachers to include physical fitness exercises in every class in order to enhance physical abilities of students  that need to be stimulated during adolescence.


HMGB1 and its downstream receptors RAGE, TLRs and TREM-1 may be potential anticancer targets. In conclusion, HMGB1 plays an important role in oncogenesis and represents a novel therapeutic target, which deserves further study.The story of high mobility group protein B1 (HMGB1) in cancer is complicated and the function of HMGB1 in different cancers is uncertain. This review aims to retrieve literature regarding HMGB1 from English electronic resources, analyze and summarize the role of the HMGB1 signaling pathway in hepatocellular carcinoma (HCC), and provide useful information for carcinogenesis and progression of HCC. Results showed that HMGB1 could induce cell proliferation, differentiation, cell death, angiogenesis, metastasis, inflammation, and enhance immunofunction in  According to the database from GLOBOCAN 2012, liver cancer has the fifth highest incidence rate and is the second most life-threatening cancer in the world. There were an estimated 14.1 million new cases and 8.2 million cancer deaths worldwide in 2012, among which there were 782,500 new patients and 745,500 deaths caused by liver cancer [In this scenario, it is essential for scientists to pay great affords for HCC therapies. Targeting the hallmarks of cancer is usually one of the approaches to anchoring this issue. For HCC, hallmarks include sustaining proliferative signaling, evading growth suppressors, avoiding immune destruction, enabling replicative immortality, promoting inflammation, activating invasion and metastasis, inducing angiogenesis, mediating genome instability and mutation, resisting cell death and deregulating cellular energetic ,3. This N-methyl-d-aspartate receptor (NMDAR) [HMGB1 is one of the HMGB family members  with a molecular weight of 25 to 30 kDa , pancrea (NMDAR)  and the  (NMDAR) .hmgb1 gene in mammals [HMGB1 is expressed in all eukaryotic cells and highly conserved through evolution. There is 99% identity of the  mammals . To date mammals .However, the functions of HMGB1 in cancer are complicated and paradoxical because of its difference of intracellular and extracellular locations. In recent research, it has been documented as a regulator for a number of DNA events, cell differentiation, inflammatory response, cell migration, cell proliferation, cell deaths, cellular senescence, microRNA biogenesis, immune response, tissue regeneration, antibacterial, and so on , whereasHere, we reviewed the effects of HMGB1 on the oncogenesis and progression in HCC. The review aims to critically summarize the multiple functions of HMGB1, the receptors and the signaling pathways to unveil the hallmarks and the potential therapeutic targets of HCC.For HCC, some cell cycle proteins and proliferation cytokines are central in the proliferation, such as cyclin D1 and proliferating cell nuclear antigen (PCNA). HMGB1 not only increased cyclin D1 and PCNA to induce the proliferation of HCC , but alsin vitro [in vitro and in vivo [The signaling pathways for HMGB1 on the proliferation in HCC may involve its downstream, RAGE and TLRs. On the one hand, HMGB1 could increase the cellular proliferation by HMGB1/RAGE/NF-κB pathway. Knockdown of RAGE by siRNA inhibited cell growth in HCC in vitro ,51. Furtin vitro ,52. On t in vivo .Recently, HMGB1 has been recognized as a pro-angiogenesis factor leading to the generation of vascular endothelial growth factor (VEGF) in colon cancer ,55, whilN-acetyl-cysteine (NAC), the cell death was altered from autophagic to apoptotic [Autophagy and apoptosis are recognized as both the programmed cell deaths. In HCC, the release of HMGB1 from nuclei to cytoplasm was reported as an inducer for cell autophagic cell death, which may be associated with ROS and/or Beclin-1. In contrast, when this pathway was inhibited by administration of antioxidant poptotic . Such a poptotic . Howeverpoptotic ,59. HMGBpoptotic , as HMGBpoptotic . This mapoptotic ,61 or thpoptotic .As a nuclear non-histone protein response to various stimuli, HMGB1 is elucidated to be involved in the differentiation of HCC. HMGB1 was a higher level in HCC tissues than that in the normal tissues. For moderately differentiated cancer cells, the localization of HMGB1 was perinuclear. In contrast, in the low differentiated cancer cells, HMGB1 normally resided in the nucleus . In addiTo date, strategies for HCC radiotherapie, chemotherapie and surgical resection are inefficient due to high recurrence rate and metastasis. However, the recurrence after tumor rectomy at least partially stems from the distant metastasis to other tissues and organs . HMGB1 wHCC is definitely associated with hepatitis B and hepatitis C and other sterile liver injury such as alcoholic fatty liver disease (AFLD) . With reThe immune system including the innate and adaptive parts protects the body against pathogens, destroy cancer cells and foreign substances. In some case, HMGB1 regulates innate and adaptive immune responses by TLRs and RAGE pathways ,74, guidHowever, the role of HMGB1 in liver cancer immunity needs to be further studied. For the patients who suffered from HCC and had undergone transarterial chemoembolization (TACE) therapy, though the level of HMGB1 was found to increase after TACE, the level of HMGB1 was no different between the “progression group” and “no progression group” patients after TACE for 24 h. In contrast, the soluble receptor of advanced glycation end products (sRAGE) were significantly higher in the non-progression group than that in the progression group , indicatHMGB1 has attracted researchers since it was discovered in 1973, because it plays a critical role in various diseases and disorders, especially in inflammatory, immune responses, and hypoxia in cancer microenvironment. However, little is known about its role in HCC.In this review, we summarized HMGB1 in oncogenesis and progression in HCC. Indeed, HMGB1 can induce cell proliferation, angiogenesis and metastasis, cell death, differentiation and inflammation . While Hin vitro and in vivo HCC models. However, according to the research, intercellular HMGB1 also could interact with RB, a tumor suppressor in breast cancer [However, there are conflicting roles of HMGB1 acting as both a tumor suppressor and an oncogenic factor in cancer. On the one hand, in the transcriptional level, p53, the tumor suppressor, could down-regulate the activity of the HMGB1 gene promoter by binding to CTF2. In contrast, CTF2 was proved both to up-regulate the promoter of p21 by binding to p53 and to down-regulate the promoter of p21 by binding to p73, a homologue of p53 , which it cancer , and inct cancer . This mat cancer ,60,81. HAdditionally, more details of HMGB1 about its up- and down-regulation, alteration between different signaling pathways as well as strategies must constitute future potential approaches.In summary, HMGB1 plays a pivotal role in oncogenesis and progression in HCC which may be a potential target for therapies and is worthy of further study.


Leaf lateral asymmetry in width and thickness has been reported previously in rice. However, the differences between the wide and narrow sides of leaf blade in other leaf morphological and physiological traits were not known. This study was conducted to quantify leaf lateral asymmetry in leaf width, leaf thickness, specific leaf weight (SLW), leaf nitrogen (N) concentration based on dry weight (Nw) and leaf area (Na), and chlorophyll meter reading (SPAD). Leaf morphological and physiological traits of the two lateral halves of the top three leaves at heading stage were measured on 23 rice varieties grown in three growing seasons in two locations. Leaf lateral asymmetry was observed in leaf width, leaf thickness, Nw, Na, and SPAD, but not in SLW. On average, the leaf width of the wide side was about 17% higher than that of the narrow side. The wide side had higher leaf thickness than the narrow side whereas the narrow side had higher Nw, Na, and SPAD than the wide side. We conclude that the narrow side of leaf blade maintained higher leaf N status than the wide side based on all N-related parameters, which implies a possibility of leaf lateral asymmetry in photosynthetic rate in rice plant. Oryza sativa L.) is one of the most important staple foods in the world, providing 35–60% of the dietary calories consumed by more than 3 billion people [Rice  gene was reported to cause rice leaf lateral asymmetry by deleting a part or whole of one lateral half or forming additional midrib in one lateral half [Rice leaf blade is divided by midrib into two lateral halves. Ishii  reportedral half . Leaf laral half , oak [17ral half , and birral half . In thesChen et al.  reported2.Four field experiments were conducted in 2014 at Xiazhouyu Village, Dajin Township, Wuxue County  and the experimental farm of Huazhong Agricultural University, Wuhan City , Hubei Province, China. The field in Wuxue is owned by Caijin Xu, a farmer from Xiazhouyu Village. The field in Wuhan is owned by Huazhong Agricultural University. No specific permissions were required for conducting these field experiments because they are rice fields and our field studies did not involve endangered or protected species. For the Wuxue site, three field experiments were conducted in the early (April to July), middle (May to October), and late (July to November) growing seasons. Only one field experiment was conducted during the middle growing season in Wuhan. Four and 8 varieties were used in the early and late growing seasons, respectively, in Wuxue . The exp-1 in the early growing season, 154 kg ha-1 in the late growing season, and 100 kg ha-1 in the middle growing season in both locations. Phosphorus in the form of calcium superphosphate was applied only at basal with the P rate of 31 kg ha-1 in the early growing season, 24 kg ha-1 in the late growing season, and 40 kg ha-1 in the middle growing season in both locations. Potassium in the form of potassium chloride was applied at basal and panicle initiation with the total K rate of 93 kg ha-1 in the early growing season, 97 kg ha-1 in the late growing season, and 100 kg ha-1 in the middle growing season in both locations. Basal application of fertilizers was manually broadcast and incorporated 1 d before transplanting. Weeds, diseases, and insects were intensively controlled throughout the entire growing season according to farmers’ practices.To avoid nutrient deficiency during the entire growing season, sufficient amounts of N, phosphorus (P), and potassium (K) were applied in all four experiments. Nitrogen in the form of urea was applied at basal, tillering, and panicle initiation with the total N rate of 176 kg hand, and -3rd leaves). SPAD readings were taken in situ on the top three leaves of selected plants. On each side of midrib, 7 SPAD readings were taken at 1/8, 1/4, 3/8, 1/2, 5/8, 3/4, and 7/8 positions along leaf length using a chlorophyll meter . The 7 SPAD readings were averaged to obtain SPAD values for each side of leaf blade.Three representative plants were selected at heading stage from each replication for the measurements of leaf traits. For each plant, measurements were done on the top three leaves . Wide and narrow sides of the leaf blade were designated based on their width. Leaf thickness was measured with a displacement sensor  as described by Li et al. . The preThen, each leaf was sliced along the leaf length to remove midrib from all top three leaves manually. For each leaf position, the wide and narrow sides of leaf blade of the three plants were pooled separately to measure leaf area, dry weight, and N concentration of each lateral half. Leaf area was measured by a leaf area meter . Dry weight was determined after oven-drying at 80°C to constant weight. SLW was calculated as the ratio of dry weight to leaf area. Nw was determined by an Elemental analyzer . The Na was calculated by the following equation:0.05) for each experiment.Statistical analysis was done separately for each leaf position. For leaf SPAD, width, and thickness, the values of the three plants were averaged to represent each replication. Analysis of variance was done for all parameters. Significance of differences between the wide and narrow sides in these parameter were tested by least significant difference at P = 0.05  between SLW and leaf thickness ranged from 0.43 to 0.67 in rice leaf [nd and -3rd leaves despite of large difference in leaf thickness between the two sides.Because the instrument for measuring leaf thickness is not readily available, many crop researchers use SLW as an indication for leaf thickness ,10. Corrrd leaf was the greatest followed by -2nd and flag leaves especially for N-related parameters. The cause for different magnitudes in leaf lateral asymmetry across leaf positions is not clear and it is beyond the scope of this study. Leaf lateral asymmetry in leaf width, leaf thickness, Nw, and SPAD was also observed on leaves at tillering and panicle initiation stages (7th to 11th leaves counting from the bottom of plants) in separate experiments.Leaf lateral asymmetry was studied only on the top three leaves at heading stage in this study because the top three leaves of rice plants have greater contribution to yield formation than other leaves ,23. AmonSPAD provides a simple, quick, and nondestructive method for estimating leaf N status . Therefo2 diffusion inside leaves [On average, the narrow side had 9.9%, 7.8%, and 7.7% higher Nw, Na, and SPAD than the wide side, respectively. To our knowledge, this is the first report on leaf lateral asymmetry in leaf N status. Close correlations between leaf photosynthetic rate and leaf N concentration have been reported in many species . All date leaves  and subse leaves  or even e leaves  of totale leaves . Therefoe leaves . Environe leaves –18. The rd leaf followed by -2nd and flag leaves.In summary, leaf lateral asymmetry was observed in leaf width, leaf thickness, Nw, Na, and SPAD while leaf lateral symmetry was observed in SLW in rice. The wide side had higher leaf thickness than the narrow side whereas the narrow side had higher Nw, Na, and SPAD than the wide side. The magnitude of leaf lateral asymmetry for leaf width, leaf thickness, Nw, Na, and SPAD was the largest in -3


Induced pluripotent stem cells (iPSCs) provide a reliable source for the study of regenerative medicine, drug discovery, and developmental biology. Despite extensive studies on the reprogramming of mouse and human fibroblasts into iPSCs, the efficiency of reprogramming is still low. Here, we used a bioinformatics and systems biology approach to study the two gene regulatory waves governing the reprogramming of mouse and human fibroblasts into iPSCs. Our results revealed that the maturation phase of reprogramming was regulated by a more complex regulatory network of transcription factors compared to the initiation phase. Interestingly, in addition to pluripotency factors, the polycomb repressive complex 2 (PRC2) members Ezh2, Eed, Jarid2, Mtf2, and Suz12 are crucially recruited during the maturation phase of reprogramming. Moreover, we found that during the maturation phase of reprogramming, pluripotency factors, via the expression and induction of PRC2 complex members, could silence the lineage-specific gene expression program and maintain a ground state of pluripotency in human and mouse naïve iPSCs. The findings obtained here provide us a better understanding of the gene regulatory network (GRN) that governs reprogramming, and the maintenance of the naïve state of iPSCs. A wide range of mouse and human somatic cells can acquire pluripotency characteristics by using a defined set of four transcription factors (TFs), including Oct4/Pou5f1, Sox2, Klf4, and Myc . During Although Oct4, Sox2, Klf4, and c-Myc are usually used as the gold standard TFs for reprogramming somatic cells into iPSCs, some other TFs could replace them. For instance, it has been shown that the combination of Bmi1 and Oct4 can successfully reprogram fibroblasts into iPSCs . Besidesin vivo counterparts based on their gene expression profiles, with an aim for increasing the efficiency of differentiation [Gene Regulatory Network (GRN) was plotted and analyzed using powerful bioinformatics approaches and systems biology tools which can efficiently provide precise predictions about the behavior of TFs during reprogramming, during the direct conversion of somatic cells, and during the differentiation of pluripotent stem cells ,13. Prevntiation . In our ntiation . In spitIn the present study, we investigated the GRNs underlying the initiation and maturation phases of mouse and human fibroblast reprogramming. To that end, six independent and highly qualified microarray expression data sets were analyzed using bioinformatics approaches to understand the regulatory role of TFs during the initiation and maturation phases of reprogramming. Our results showed that the regulation of the gene expression program in the maturation stage is much more complex in comparison to the initiation stage. In addition to pluripotency factors, for the first time, using systems biology and bioinformatics approaches we characterized the role of PRC2 members including Ezh2, Eed, Jarid2, Mtf2, and Suz12 during the maturation phase of reprogramming. The maturation phase of reprogramming has come to be known as the “major road block” to reprogramming. Here, we provided molecular details regarding the regulatory network that underlie the control of the gene expression program during this maturation phase of reprogramming. We believe that the “road map” depicted here can address some issues related to the low efficiency of reprogramming and can be used to improve our understanding of the maturation of reprogrammed cells.p-values were calculated for all six data sets and genes with p-values less than 0.05 were considered as DEGs.Microarray data sets for the conversion of mouse and human fibroblasts into iPSCs were obtained from Gene Expression Omnibus (GEO) using GSE42379, GSE21757, GSE47489, GSE18226, GSE14897, and GSE34309 accession numbers –18 Tabl. These sp-value<0.05) was accepted as a significantly altered biological process.In order to identify significantly altered biological processes during the reprogramming of mouse fibroblasts into iPSCs, the list of DEGs was submitted to the Database for Annotation, Visualization and Integrated Discovery (DAVID) ,21. Thisp-value and their expression. TFs with p-value<0.05 were selected. Based on the importance of TFs in gene regulation, we selected TFs with a fold change of more than 1.5 as differentially expressed (DE)-TFs.To identify regulators of DEGs involved during the reprogramming of mouse fibroblasts into iPSCs, we used experimentally based databases, including ChIP Enrichment Analysis (ChEA) and TFacts ,23. The Experimentally validated and predicted interactions were retrieved from BioGRID and STRING databases ,25. CurrProtein-protein interactions for DE-TFs were combined with expression data and visualized in Cytoscape software . Construp-value correction.To construct the GRN during reprogramming of mouse fibroblasts into iPSCs, different sources of data, including TF binding sites, TF protein-protein interactions, and expression data were integrated and visualized in Cytoscape. Constructed networks were subjected to gene ontology and centrality analyzes. To identify the most affected biological processes in the constructed regulatory networks, the ClueGO and CluePedia plugins of Cytoscape were used ,28. In tIn order to find the hubs of each constructed network, we used the degree parameter. Degree is the simplest connectivity index, which considers the neighbors of each gene. We used out-degree to explore the most important regulator and in-degree to find the most regulated genes. To achieve this aim, the CentiScaPe plugin of Cytoscape was used .Clustering of DEGs was done using Gene Cluster software and visualized using Java TreeView. We used hierarchical clustering which is a strong method for analyzing high throughput expression data. Similarity metrics were calculated for both genes and arrays. To measure the similarity of both arrays and genes, correlation (uncentered) was used ,31.During the reprogramming process, cells experience two dramatic changes in their gene expression profile. The first change occurs during the initiation stage and the next one during the maturation stage of iPSCs. In this investigation, our comprehensive analysis of gene expression profiles revealed more changes in the maturation stage compared to the initiation stage. In the following parts of this study, we mainly focused on the maturation of reprogrammed cells and on the identification of the main regulators of this stage.Cdh1, Cldn11, and Epcam had significantly increased expression during the initiation phase of reprogramming. At the same time, however, genes including Col7a1, Gjb2, Il11, Itga5, Olr1, Smoc2, Tgfb1i1, and Wisp1, which are involved in cell adhesion and cell-cell contacts, were significantly down-regulated during this stage.By comparing the gene expression profile of cells in the initial stage of reprogramming with that of mouse embryonic fibroblasts (MEFs) from two studies , 124 DEGs with the same expression pattern in both studies were identified  S1 Tabl. In thisNanog, c-Myc, and Sall4 were detected, but two markers of maturation, namely Oct4 and Sox2, were not found. Interestingly, our data showed that during the transition between MEFs and initiation stage cells, the TFs Suz12 and Mtf2, which are members of PRC2, played roles as main regulators of the transition. Despite the presence of these two factors, the core catalytic protein of PRC2, Ezh2 and other associated members were not found as regulators. In this network Nanog, Suz12 and Mtf2 were found to be top three hub genes. In addition, some genes such as Sall4, Etv5, Cdh13, and Zfp704 were highly regulated by the profile of TFs in this transition.To understand the contribution of the TFs involved in gene expression regulation, a GRN was constructed for DEGs. This network harbored 113 DEGs and Differentially Expressed-TFs (DE-TFs) that connected to each other by 254 regulatory and protein-protein (PPI) interactions. Collectively, eight up-regulated TFs were identified to be involved in the regulation of the gene expression program during the initiation of reprogramming. Among them, pluripotency factors including Nanog is a main regulator amongst other TFs and that Sall4 and Mtf2 are the most regulated TFs.Finally, to reveal the role of DE-TFs on the regulation of DEGs, we studied the regulatory interactions between DE-TFs. The constructed GRN showed that Comparing the results between the initiation stage and the maturation stage revealed more complexity in the regulation of the gene expression program during the maturation of iPSCs from MEFs. To provide a better insight on gene expression regulation, we also compared the expression profiles of mature iPSCs with the expression profiles of MEFs in addition to comparing mature iPSCs with initiation stage cells during the reprogramming procedure. This comparison led to the identification of genes in which their expression levels gradually but constantly increased or decreased during the reprogramming stages. Furthermore, this comparison allowed us to obtain a list of genes in which their expressions were significantly changed at a certain stage but remained constant until the maturation of iPSCs. We performed four different comparisons based on two datasets in two categories. In the first category, we compared the expression profiles of the transition from initiation to maturation stage, and in the second category, we compared the expression profile of mature cells with MEFs. To call a gene a DEG, its expression should be changed significantly in both data sets in at least one of the comparisons (i.e. in the comparison of initiation stage with maturation stage or in the comparison of MEFs with cells in the maturation stage). Beside significant changes in expression, the genes should have the same pattern in both examinations. Collectively, we identified 356 DEGs based on the criteria . In the Oct4, Sox2, Nanog, Myc, and Sall4 were found. In addition to pluripotency factors, all members of PRC2, including Ezh2, Eed, Suz12, Mtf2, and Jarid2 were detected. In order to clarify the importance and exact roles of these 41 DE-TFs, the constructed network was analyzed to identify the most affected processes, hub genes, protein complexes, and finally the regulatory relationship between these DE-TFs.The down-regulated genes are involved in dozens of biological events, including cell migration, regulation of locomotion, and regulation of cell motility. On the contrary, the up-regulated genes were mostly involved in stem cells and developmental properties, such as stemness maintenance, negative regulation of cell adhesion, and gastrulation processes. The most prominent characteristics of cells based on the results are that during the transition from initiation stage to maturation stage, cells acquired pluripotency characteristics and lost their fibroblast gene expression program. To obtain a deeper insight into the regulatory events that lead to this outcome, regulators of 356 DEGs were found using high throughput data. Interestingly, we identified 41 DE-TFs that have roles in the maturation of iPSCs from MEFs. This list was substantially more complex than the initiation stage of reprogramming. In this list, nearly all critical pluripotency factors, including Nanog, Oct4, Sox2, Sall4, and Myc were indwelled among the top fifteen regulators of the network. In addition to pluripotency factors, four members of PRC2, including Suz12, Mtf2, Ezh2, and Jarid2 were found between these fifteen regulators . To understand the actual role of PRC2 members on epigenetic gene expression regulation, we considered 45 common target genes which were controlled by Ezh2, Jarid2, and Mtf2 TFs .EED, JARID2, MTF2, and SUZ12. For EZH2, only 40 interactions from one study were retrieved, which was not significant for our comprehensive analysis. To evaluate the potential roles of PRC2 during the maturation of reprogramming in the initiation stage, we sought to find the expression of EZH2, EED, JARID2, MTF2, and SUZ12 TFs in the Tanabe et al., data set. In both the comparison of expression profile of initiation stage cells with maturation stage cells, as well as the comparison of mature cell expression profile with human fibroblast expression profile, we found that the expression of three members of PRC2 including EZH2, JARID2, and MTF2 were increased. To increase the validity of these results, three additional data sets that were generated independently and in different laboratories were analyzed , was used [, was useanalyzed . Interesroblasts .OCT4, NANOG, SOX2, and SALL4 , Doi et al., (2009), and Si-Tayeb et al., (2010) [The gene expression pattern of pluripotency factors in both mouse and human data sets were clustered . The clund SALL4 . This anata sets . The cor, (2010)  Fig 1A)OCT4, NAN, (2010) , because, (2010) , 72 percDespite the lack of high throughput data sets for binding sites of all human PRC2 members in the sources we used in this investigation, there are high correlations in expression among three PRC2 members, EZH2, JARID2, and MTF2, in both human and mouse data sets . In addiEzh2, Eed, Jarid2, Mtf2, and Suz12. Interestingly, the integration of gene expression data, gene regulation information, and gene ontology results showed that PRC2 was mainly involved in the repression of fibroblast-specific genes through tri-methylation of H3K27, whereas pluripotency factors up-regulated stem cell-specific genes. The same results were identified specially for EZH2, JARID2, and MTF2 during the maturation stage of human fibroblast reprogramming into hiPSCs.In the current study, the gene expression profiles of both mouse and human fibroblasts undergoing reprogramming into iPSCs were dissected. The results showed that in addition to pluripotency factors, maturation of reprogramming was also regulated by PRC2 members, including Our results showed that the early events of reprogramming are less stable and complicated than at the final stages. Low overlapping rates between DEGs in data sets, which were used for the analysis of the early events of reprogramming in comparison with late events, showed that significant variation occurs among cell populations during the first days of reprogramming. The same observation was previously reported, where it was observed that different cells have significant differences in gene expression profiles during the initiation of reprogramming . In contPrevious reports have clearly demonstrated the significant roles of different members of PRC2 in pluripotent stem cells as well as their ability to generate different cell types. However, it was not clear which phase PRC2 was mostly involved in during the reprogramming procedure. It has been reported that mouse embryo deficient for Ezh2 factor cannot properly complete the early stages of development . In addiMore interestingly, in recent years many reports have demonstrated the role of PRC2 members in reprogramming, besides their roles in development. For example, it was shown that the Ezh2 catalytic core of PRC2, which is also a H3K27 methyl-transferase, plays a crucial role during the reprogramming of somatic cells into pluripotency states ,33,39. IIt seems that the function and stability of PRC2 members are associated with the presence of other members. For example, it has been demonstrated that ES cells which are deficient for the Eed factor, also have dramatically lower levels of the Ezh2 protein . Our resAlthough PRC2’s role in reprogramming had been previously described, their major regulatory role in controlling gene expression during the two transition phases of reprogramming had yet to be determined. We found that the maturation stage is much more complex than the initiation stage and less variable in different data sets compared to the initiation stage, which showed more variation even in single cell analysis . As we sS1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)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 Table(XLSX)Click here for additional data file.


Halomonas, Halorhodospira, Salinisphaera, Salinibacterium, Rubrobacter, Streptomyces, Arthrobacter and nine fungal classes represented by 113 genera including Cladosporium, Acremonium, Alternaria, Engyodontium, Penicillium, Rhizopus, and Aureobasidium. Most of the identified sequences were characteristic of organisms implicated in deterioration of wood and brick. Metabolomic data indicated the activation of numerous metabolic pathways, including those regulating the production of primary and secondary metabolites, for example, metabolites associated with the production of antibiotics, organic acids and deterioration of organic compounds. The study demonstrated that a combination of electron microscopy imaging with metabolomic and genomic techniques allows to link the phylogenetic information and metabolic profiles of microbial communities and to shed new light on biodeterioration processes.Preservation of cultural heritage is of paramount importance worldwide. Microbial colonization of construction materials, such as wood, brick, mortar, and stone in historic buildings can lead to severe deterioration. The aim of the present study was to give modern insight into the phylogenetic diversity and activated metabolic pathways of microbial communities colonized historic objects located in the former Auschwitz II–Birkenau concentration and extermination camp in Oświecim, Poland. For this purpose we combined molecular, microscopic and chemical methods. Selected specimens were examined using Field Emission Scanning Electron Microscopy (FESEM), metabolomic analysis and high-throughput Illumina sequencing. FESEM imaging revealed the presence of complex microbial communities comprising diatoms, fungi and bacteria, mainly cyanobacteria and actinobacteria, on sample surfaces. Microbial diversity of brick specimens appeared higher than that of the wood and was dominated by algae and cyanobacteria, while wood was mainly colonized by fungi. DNA sequences documented the presence of 15 bacterial phyla representing 99 genera including  Identifying the conditions and causes governing biodeterioration of construction materials, in general, and heritage materials, in particular, is essential in aiding the development of effective prevention and mitigation strategies. Importantly, both disinfection and conservation treatments must be approached with the utmost caution. Incorrectly administered mitigation strategies may actually increase the severity of biodeterioration and result in the destruction of the materials they aim to protect . This climate is characterized by large fluctuations in temperature and humidity. Thus, temperatures can vary from: −30°C to +30°C and the relative humidity (RH) from 30 to >90%. This climate zone also experiences long wet seasons  and is subject to extreme weather events, such as floods and droughts. Such an environment promotes the growth of microorganisms that are tolerant to broad ranges of temperature and humidity.The investigation reported herein expands on the previous studies that (i) assessed the visual signs of biodeterioration of wood and bricks from selected barracks and (ii) reported on isolation and identification, as well as seasonal variations in bacterial, fungal, cyanobacterial, algal, bryophyte and lichen populations associated with these materials  coupled to high-resolution mass spectrometry (HRMS), and in particular to quadruple time-of-flight mass spectrometry (QToF-MS) , and a wooden barrack (B-145), both of which are located in the former Auschwitz II–Birkenau concentration and extermination camp. Pictures of the buildings where the samples were collected are presented by Piotrowska et al. . Descrip24, and aZeiss SUPRA 55VP Ultra-High Resolution Scanning Electron Microscope. Images were obtained at magnifications between 649 × and 7.64 k ×, and at 1 kV for imaging.Scanning electron microscopy micrographs of wood and brick samples were acquired with a Small fragments of wood or brick specimens (0.5–1 g dry weight) were used for DNA extraction. Wood samples were shaved using a sterile scalpel. Brick fragments and wood shavings were ground into a fine powder under liquid nitrogen, using a mortar and pestle. DNA was extracted from 0.5 g wood powder and from 0.25 g brick powder, employing PowerBiofilm DNA extraction kit  following the manufacturer's instructions. Quantification of DNA was conducted using Qubit 2.0 Fluorometer, high sensitivity dsDNA assay as described in the manufacturer's protocol .The bacterial 16S region was amplified for high-throughput sequencing analysis using universal prokaryotic primers, 519F and 806R . Ten microliters of each purified PCR product was added to a second PCR reaction each containing specific 12nt barcodes attached to M13. Samples were “tagged” with barcoded primers by re-amplification for six cycles. Barcode sequences are provided in Tables http://greengenes.lbl.gov; McDonald et al., http://unite.ut.ee/repository.php; Kõljalg et al., USEARCH v7 (UPARSE) was used for filtering the chimeric sequences and clustering OTUs Edgar,  and QIIMp-value of 0.01 was used for all abundance comparisons between sets of triplicates.One gram of ground brick, or 0.20 g of ground wood, was added to 4.0 mL of 4 N HCl and the suspensions sonicated in a water bath for 30 min. The mixtures were extracted into 2 times 3 mL ethyl acetate . The organic phase was evaporated to dryness under a constant flow of ultrapure nitrogen gas and the residue reconstituted in 100 μL isopropanol (Sigma Aldrich). Volumes of 5 μL were injected into the UPLC. Analysis of each sample was performed in triplicate and An UPLC/HRMS system, i.e., an Agilent 1290 Infinity UPLC coupled to an Agilent 6538 quadrupole time-of-flight mass spectrometer, was used for metabolomic analysis. Samples were processed in both negative and positive ion mode. When the mass spectrometer was operated in negative ion mode, an Acquity UPLC® HSS C18 SB column  was used for separation. The flow rate was 0.4 mL/min and a linear gradient from 23.5 to 95.5% HPLC- grade acetonitrile over 35 min, followed by 5 min at 95.5%, was used. With positive ion mode mass spectrometry, a SeQuant® ZIC®-HILIC column  was used with a flow rate of 0.3 mL/min. A linear gradient from 80 to 20% acetonitrile was used for the first 30 min, followed by 5% acetonitrile for an additional 8 min. Solvents contained 0.1% formic acid to promote positive ion formation in the electrospray. For both positive and negative ion mode, MS parameters were as follow: ion-source gas temperature, 350°C; capillary voltage, 3500 V; fragmentor voltage, 160 V; m/z range, 50–1100; data acquisition rate, 4 GHz; and 1 spectrum recorded per second.Raw MS data was processed using IDEOM version 19  metabolite database  are common inhabitants of building, temples, monuments surfaces and was found on rock, limestone, marble, sandstone, wall paintings and painted or coated surfaces  revealed extensive development of actinomycetes as evidenced by characteristic hyphae of a diameter of approx. 1 μm, and the formation of spores Figure , arrow.The surface of heritage wood B124 (S5) showed extensive degradation Figure  when comFollowing activation, abundant proliferation of actinomycetes was observed on surfaces of wood B124 (S6), with wood fibers entwined and separated from each other by pseudomycelium Figure , arrow. Proteobacteria were dominant in brick B124 both prior to and following the activation process , Halomonas (1.5–2.7%) Halorhodospira and Salinisphaera (below 0.1%) represented the halophilic bacteria. DNA sequences associated with halophilic and halotolerant bacteria of the genera Salinibacterium and Rubrobacter were also identified in the class Actinobacteria. This latter group was dominated by sequences representing the genera Arthrobacter (1.0–6.5%) and Pseudonocardia (0.45–4.1%) (Table Agaricomycetes and the genus Volvariella (0.25–4.2%), while Sordariomycetes was the most abundant class in the activated specimen  and Alternaria (0.9–8.3%) and the class Dothideomycetes,  and Halomonas (1.7–2.7%), as well as Devosia (3.7–5.6%) and Chelativorans (0.5–1.4%)  , Penicillium (class Eurotiomycetes) and Engyodontium (class Sordariomycetes). The fungal species composition was not altered by specimen activation.DNA sequencing data revealed the presence of a total of 99 genera of bacteria, belonging to 15 different classes  and Penicillium (1.65%), belonging to the class Eurotiomycetes, as well as Alternaria (0.4%) and Cladosporium (0.6%) of the class Dothideomycetes, and Engyodontium of the Sordariomycetes class (0.45%) , bacterial DNA amplification proved to be unsuccessful. Solely fungal sequences were obtained. Prior to the activation, the DNA recovered from the surface of wood D2 (S7) was dominated by sequences of molds from the genera rnaria 0.% and ClaA large number of features were obtained from the analysis of the UPLC/HRMS data.  than in the non-activated ones . For example, of the 2908 features detected in the combined brick samples, 90% were detected in the activated sample and 36% in the non-activated ones. The same trend, though less pronounced, was observed for the activated wood samples  vs. the non-activated ones .Of the features detected in the wood and brick samples, 43 and 35%, respectively, were putatively identified by matching them with metabolites and other compounds listed in the KEGG database, as described in Materials and Methods. For the more than 80% of these that were detected in positive ion mode, the identification was based both on exact mass and on a retention time calculator that had been calibrated for the instrument using 33 internal standards, see Materials and Methods. The remaining identifications were based on exact mass only, but these putative metabolites made an insignificant contribution (about 3% of identifications) to the results presented here. The putatively identified metabolites in the brick and wood samples are listed in Table The putative metabolites were used for metabolic pathway analysis as described in Materials and Methods. Ideally, the metabolites used for the analysis should all be identified at Level 1  for which all metabolites were observed with a lower abundance in the activated wood samples  than in the non-activated ones . In addition to the 22 pathways listed in Table A total of 22 pathways, including both primary and secondary metabolism, were “detected” on the basis of a significant number (≥5) of detected, putative metabolites. These pathways are strong candidates for being active in the respective samples, and they are listed in Table ted ones , S7. In Active metabolic pathways can be illustrated in KEGG metabolic graphs by highlighting metabolites that are detected in a sample. Examples of such maps for the brick and wood samples, using the KEGG ipath map, are depicted in Images The majority of the metabolic pathways detected in the heritage brick and wood specimens  and cyanobacteria, actinomyces and fungi dominated the surface of brick B124 (S1), while actinomycetes and fungi grew readily on wood.Arthrobacter, Bacillus, Sporosarcina, and Streptomyces and fungi of the genera Cladosporium, Acremonium, Alternaria, Engyodontium, Penicillium, Rhizopus, and Aureobasidium. Unlike enrichments, genomic analysis revealed halophilic and halotolerant bacterial sequences representing genera Halomonas, Halorhodospira, Salinisphaera, Salinibacterium, and Rubrobacter. The biodeterioration potential of these genera has been described and discussed by Ortega-Morales et al.  over conventional microbiological techniques in the study of phylogenetic diversity of microbial populations associated with deteriorating heritage materials is undisputable. The NGSMs are increasingly employed to characterize bacterial and fungal community structure in soil , most likely caused by heterotrophic microorganisms. FESEM images confirmed severe damage of test specimens and the formation of microbial biofilms. It has been proposed that the presence of biofilms on building materials may lead to changes in their physical and chemical properties and contribute to the biodeterioration of the materials (Gaylarde and Morton, Within the 3 months of activating brick B124 specimen by laboratory exposure to high humidity and temperature, material-specific microbial succession was observed, analogous to that reported in the field studies. The growth of autotrophic pioneering organisms such as algae and cyanobacteria was followed by that of actinomycetes and heterotrophic fungi. The succession phenomenon is readily accepted and documented in the literature (Herrera et al., Apatococcus, Chlorella, Chlorococcum, Klebsormidium, Trebouxia, Stichococcus and Lyngbyga, Chroococcus, Scytonema, Nostoc, Microcoleus, Plectonema on the surfaces of buildings was accompanied by the production of organic acids. The secretion of 2-ketogluconic acid which initiates decomposition of silica by bacteria of the genera Bacillus, Arthrobacter, and Streptomyces has also been described (Papida et al., Aspergillus, Penicillium has been reported in a number of studies (Gu et al., Loose fragments of degraded wood and crumbling brick observed in FESEM images may result from microbial metabolic activity. Organic acids can deteriorate building materials by contributing to mass loss through material dissolution and the formation of soluble or insoluble salts (Gomez-Alarcon et al., However, it should be noted that the observed damage to wood specimens can also result from physical disruption of the wood fibers, caused by the penetration of the fungal hyphae into the wood matrix (Crispin and Gaylarde, Actinomycetes, demonstrated here not only with NGS (Table The release of secondary metabolites, in particular by molds, including mycotoxins which are dangerous to human health, has been reported on the surfaces of building materials (Flannigan et al., GS Table . The detGS Table  would inMetabolomic profiling has not been previously been employed in the study of biodeterioration of building materials. However, the method has been used to screen and compare profiles of chemical compounds produced by molds and higher fungi in mono- and co-cultures on solid growth media (Peiris et al., 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.