Selection of genes involved in metabolic pathways could target them differently depending on the position of genes in the pathway and on their role in controlling metabolic fluxes. This hypothesis was tested in the carotenoid biosynthesis pathway using population genetics and phylogenetics.IPI, PDS, CRTISO, LCYB, LCYE, CHXE and ZEP, were compared in seven dicot taxa. A survey of deviations from neutrality expectations at these genes was also undertaken in cultivated carrot (Daucus carota subsp. sativus), a species that has been intensely bred for carotenoid pattern diversification in its root during its cultivation history. Parts of sequences of these genes were obtained from 46 individuals representing a wide diversity of cultivated carrots. Downstream genes exhibited higher deviations from neutral expectations than upstream genes. Comparisons of synonymous and nonsynonymous substitution rates between genes among dicots revealed greater constraints on upstream genes than on downstream genes. An excess of intermediate frequency polymorphisms, high nucleotide diversity and/or high differentiation of CRTISO, LCYB1 and LCYE in cultivated carrot suggest that balancing selection may have targeted genes acting centrally in the pathway.Evolutionary rates of seven genes distributed along the carotenoid biosynthesis pathway, Our results are consistent with relaxed constraints on downstream genes and selection targeting the central enzymes of the carotenoid biosynthesis pathway during carrot breeding history. One of the most important objectives of molecular evolution studies is to understand which factors influence genetic variations in the genome. Many genes are organized in signaling or metabolic pathways and are therefore related to protein-protein interactions or product-substrate relationships. Understanding how selection acts on genes involved in pathways or networks has received increasing attention in the study of molecular evolution in recent years DrosophilaEnzymes acting directly downstream from metabolic nodes and therefore controlling metabolic allocation to subsequent metabolic branches are expected to experience more selective constraints than other enzymes in the pathway. Selection was thus found to be directed to genes encoding enzymes located at metabolic nodes in central metabolism in ω or Nd/Sd) and the position of genes along the pathway, suggesting progressive relaxation of selective constraints along metabolic pathways Arabidopsis thalianaOryzeae tribe Oryza sativaGenes encoding upstream enzymes are expected to face stronger selective constraints and therefore to evolve more slowly than genes encoding downstream enzymes, maybe owing to differential pleiotropic effects The carotenoid biosynthesis pathway is also suitable network topology to investigate the effect of pathway position on gene evolution, as this pathway involves about ten enzymes acting at different positions and contains two metabolic nodes . GeranylNd/Sd ratio in six dicots Y1 encoding the upstream enzyme PSY has experienced positive selection during the evolution of grasses Carotenoids act as accessory pigments and play a photoprotective role in the photosynthetic apparatus. They are also accumulated in large quantities in many fruits and flowers to attract animals required for pollination or seed dissemination Daucus carota L. ssp. sativus) is a good model for such a study as this species exhibits a range of root colors that mainly depend on variable carotenoid profiles, except for the purple type, which is colored by anthocyanins th century th century Carrot . Therefore, the results of the neutrality tests were not equal between the seven genes.To test the relationship between the nucleotide variation and the position of genes in carotenoid biosynthesis pathway in carrot, we first checked for heterogeneity in the results of the neutrality tests performed on Tajima’s IPI and PDS, showed the biggest trend toward highest p-values  had higher p-values than genes located downstream from lycopene  . P-values associated with the three tests taken globally correlated negatively with pathway position . Considering the three tests individually, only p-values associated with STF showed a significant correlation with pathway position . These results showed that polymorphism patterns in downstream genes deviated more from neutral expectations than those of upstream genes.The two genes located upstream in the pathway, p-values . Genes lNd/Sd = ω) in dicots. According to the M0 model, which assumes a constant ω in all branches and all codons, the estimated ω ratio varied from 0.040 in LCYB to 0.091 in ZEP , indicating that the Nd/Sd of these two genes was not significantly different. Similar results were obtained with comparisons of ω between IPI, CRTISO and CHXE (constrained ω tested = 0.053), between PDS, CRTISO and CHXE (constrained ω tested = 0.064), between PDS and LCYE (constrained ω tested = 0.078), and between LCYE and ZEP (constrained ω tested = 0.089). The groups of significance are summarized in ω values were obtained for LCYB and IPI, while the highest values were obtained for LCYE and ZEP.In order to test if this observation is specific to carrot or could be extended to other species, we tested the carotenoid biosynthesis genes for variations in evolutionary rates  and others with 1ω = 1 , significantly improved the likelihood in comparison with the M0 model which assumed all codons evolved neutrally  was high, but varied from 87% in ZEP to 94% in IPI  and between PDS and CRTISO, LCYB1 or LCYE (P<0.001). The results of all other pairwise comparisons were not significant (data not shown). This result was confirmed by the ML-HKA test PDS showed highly significant improvement to the likelihood compared with the neutral model . The maximum likelihood estimate of the selection parameter k was 0.16, suggesting a six-fold decrease in diversity over neutral expectation at this locus, in comparison with other genes. PDS showed low nucleotide diversity (π = 0.003) in the carrot set but high sequence divergence between the carrot and the tuberous-rooted chervil, with 216 fixed differences between the two species for the 911 sites compared. These marked differences between species suggest a selective sweep or background selection in the carrot lineage, or a modification in local mutation rates around PDS after divergence of the two species. In order to test these hypotheses, pairwise HKA tests were then applied to coding regions only (153 sites). Among all comparisons, a single significant departure from neutrality was revealed in the PDS-LCYE pairwise comparison (P<0.05), suggesting that the specific ratio between polymorphism and divergence shown for PDS in both introns and exons was less convincing for exonic regions only. The main difference between the effect of background selection and selective sweep is that the latter results in a deviation toward an excess of low-frequency alleles, while the former does not PDS showed the lowest Tajima’s D statistic , it did not significantly deviate from the expectations under the divergence model, as may be expected with selective sweep. However, the Tajima’s test may fail to detect recent selective sweeps because of a lack of regeneration of polymorphism since the selection event. We thus cannot conclude on whether the reduced polymorphism at PDS was obtained by background selection or by a selective sweep during carrot domestication.Carrot domestication has led to a change between an uncolored root in wild carrots to a root with sometimes high carotenoid levels in cultivated carrots. This change should have been obtained by positive selection and should be linked to a reduction of diversity for targeted genes. In order to detect nucleotide diversity variations in carotenoid biosynthesis genes, we used HKA neutrality tests. Pairwise HKA tests gave significant results for all comparisons implicating PDS experienced positive selection in carrot, the phylogenetic analysis should reveal an accelerated evolutionary rate for PDS in carrot by comparison to other dicots. We analyzed the ratios of nonsynonymous (Nd) to synonymous substitutions (Sd) in protein coding regions within carotenoid biosynthesis genes in several dicots in order to detect positive selection (Nd/Sd (ω) ratios among lineages were detected for PDS, CHXE  and for ZEP (P<0.001). The carrot lineage did not exhibit a significantly different Nd/Sd ratio from other background branches . However, the PDS gene gave a result close to the 5% threshold (P = 0.058). Carrot-lineage specific 1ω = 0.0937 was higher than background lineages 0ω =  0.0620. This tendency to an acceleration of the non-synonymous rate compared with the synonymous rate of substitution in the carrot is congruent with the low polymorphism/divergence ratio found for PDS in HKA tests and suggests positive selection at PDS in the carrot.If election . DiffereCRTISO, LCYE and LCYB1.In addition to pathway position, pathway reticulation can influence the evolution of metabolic pathway genes. Departures from expectations under the divergence model were tested for the seven carotenoid biosynthesis genes ; Table 3CRTISO gene showed a significant positive Tajima’s D in the pooled sample , showing an excess of intermediate-frequency polymorphisms in this group. Only the Western group showed a similar pattern  in CRTISO while Tajima’s D was significantly negative in the Eastern group , suggesting an excess of low-frequency polymorphisms in this group for CRTISO. A highly significant differentiation was found between Western and Eastern groups for CRTISO . Interestingly, we found a significantly negative normalized Fay and Wu’s H in the Eastern group , indicating an excess of high-frequency derivate polymorphisms and suggesting a selective sweep at CRTISO in the Eastern group.The LCYE also showed a significant positive Tajima’s D in the pooled sample . Contrary to CRTISO, this excess of intermediate frequency polymorphisms was independent of population structure, as significant positive Tajima’s D values were also found for this gene in both Western and Eastern groups . This result was confirmed by a significantly low differentiation between Western and Eastern populations . LCYE may have been subjected to balancing selection at the subspecies level, as population structure-independent balancing selection is expected to decrease population differentiation The gene LCYB1 is the only one with a significant STF for color groups . This result suggests that the polymorphism at this gene is structured by root color and may be related to breeding for color diversification.The gene CRTISO and LCYE as well as the high differentiation of color groups for LCYB1 make feel that these three genes surrounding the metabolic node lycopene may have experienced balancing selection in carrot. Balancing selection generally leads to an increase of diversity. HKA test was used to test for specific nucleotide diversity levels in these three genes. A model that assumes selection at these genes significantly improved the likelihood in comparison with the neutral model . The maximum likelihood estimate of the selection parameter k for CRTISO (k = 2.62), LCYB1 (k = 2.38) and LCYE (k = 2.88) suggests a twofold increase in diversity over neutral expectations at these loci, in comparison with the other carotenoid biosynthesis genes analyzed. The excess of variability and the deviation of allele frequency spectrum toward intermediate frequency suggest that CRTISO, LCYE and LCYB1, acting at the center of the carotenoid pathway and surrounding the metabolic node lycopene, may have been evolving non-neutrally in a pattern consistent with balancing selection.The excess of intermediate frequency polymorphisms in Nd /Sd ratio revealed variations in the level of purifying selection in the pathway. Our results are consistent with a relaxed constraint on downstream carotenoid biosynthesis genes, in comparison with more upstream genes, and complement those of Livingstone and Anderson in the same pathway ZEP has more codons evolving under relaxed constraints than three more upstream genes PDS, ZDS, and LCYB. Similar conclusions were reached in studies of the Nd/Sd ratio in the anthocyanin LCYB. LCYB had the lowest Nd /Sd ratio, yet was located at the same level of the pathway as LCYE . This result is consistent with constraints preventing haplotype differentiation in these two upstream genes. A second possible explanation is that downstream genes are more prone to positive or balancing selection than upstream genes. In the context of artificial selection in carrot, this pattern may be expected, as the major carotenoids that accumulate in carrot germplasm  are products of central or downstream enzymes , LCYB1 (silπ = 0.0297) and LCYE (silπ = 0.0273) LCYE (average 2r = 0.93) and CRTISO (average 2r = 0.86), while LCYB1 (average 2r = 0.52) showed intermediate LD In addition to the upstream, central or downstream position of genes in the metabolic pathway, the position of the genes with respect to metabolic nodes has been postulated to influence their selection patterns CRTISO, LCYB1 and LCYE is challenging. These three genes surround lycopene in the carotenoid biosynthesis pathway , each one represented by a single individual Chaerophyllum bulbosum L.), a related Apiaceae, was used for analyses requiring an outgroup.For population genetics analyses, we used a sample of 46 cultivars of carrot  and 5′-gctcactgaaggatttgatttgaa-3′ (Reverse) for JW3D; 5′-agcgcattgaaatggaggtttt-3′ (Forward) and 5′-aggctagcattgctctcttgatca-3′ (Reverse) for SB4A. The same PCR conditions as in B1D and JW3D, and of 55°C for SB4A. These three anonymous DNA sequences, and 17 microsatellite loci already genotyped for this sample Seven carotenoid biosynthesis genes were used:  and ZEP . We choswθπDNA sequences were computed using DnaSP 4.9 One major drawback of signatures of selection is the confounding effect of demographic events and selection. For example, an excess of intermediate frequency variation is consistent with balancing selection but may also be driven by population scale events like population subdivision WN and EN respectively. At dT generations in the past, these two populations diverged from an ancestral population of an effective population size AN. Following this model, datasets including 17 autosomal diploid microsatellites and three autosomal haploid DNA sequences were simulated. Microsatellite loci were simulated using the generalized stepwise mutation model with the mean mutation rate SSRµ and the parameter of the geometric distribution SSRP. The same motif size and allele range as in observed data were used for the simulations. DNA sequences were simulated using the Jukes-Cantor model seqµ. Prior distribution of parameters is described in th and the 12th centuries dT follow a normal distribution such as X ∼ N truncated such that 350 ≤ X ≤ 750. A total of 106 approximate Bayesian computation simulations were released by DIYABC v.1.0 software STF between the two populations STF between the two populations −2  and corresponds to the number of different enzymes involved between IPI and the gene considered. If a gene, e.g. LCYB1, LCYE and CHXE, was involved at different metabolic steps in the carotenoid pathway, to calculate its position in the pathway, we considered the most upstream step. Pathway position indexes for each of the seven genes are shown in The p-values obtained for neutrality tests based on Tajima’s Nd) and synonymous substitutions (Sd) in protein coding regions within carotenoid biosynthesis genes in several species Solanum lycopersicum L., Vitis vinifera L., Populus trichocarpa Torr. & A.Gray, Ricinus communis L., Arabidopsis thaliana (L.) Heynh. and Arabidopsis lyrata (L.) O’Kane & Al-Shehbaz  and M2a (Positive Selection), allowing the ω ratio to vary among sites. M1a assumes that the sequence analyzed displays some codons with 0<ω<1 and other codons with ω = 1. M2a assumes that the sequence analyzed displays three classes of codons with 0<ω<1, ω = 1 and ω>1. The fit of the null model M0 versus a branch or a site model was evaluated by the likelihood ratio test. To check if carotenoid biosynthesis genes evolved under differential selective constraints, we tested the significance of differences in ω by comparing the likelihood obtained with the model M0 with the same model but constraining ω, as described in 1ω and 2ω respectively have overlapped confidence intervals if there is no given fω such as 1<ωf<ω2ω, giving a higher likelihood than 1ω or 2ω. In the opposite case, the confidence intervals of 1ω and 2ω do not overlap, and consequently the two compared genes have statistically different ω values.An unrooted phylogenetic tree was built for each gene, based on the neighbor joining method and the Jukes-Cantor nucleotide model using MEGA 5.05 software Figure S1Prior (dashed line) and posterior (solid line) distribution of approximate Bayesian computation model parameters. Population sizes for Western group (WN), Eastern group (EN) and ancestral population (AN) are expressed as the absolute number of individuals and are assumed to be constant. Divergence time (dT) between Western and Eastern groups is expressed as the number of generations since divergence. Mean mutation rate for microsatellites seqµ is expressed as the number of mutations per site per generation. SSRP is the parameter of the geometric distribution in a generalized stepwise mutation model for microsatellites. Mean mutation rate seqµ for sequences is expressed as the number of substitutions per site per generation.(TIF)Click here for additional data file.Figure S2Model checking. Principal Component Analysis in the space of summary statistics was done for the observed dataset, prior distributions of parameters, and posterior predictive distribution of parameters. Only 105 points were plotted for prior distributions.(TIF)Click here for additional data file.Figure S3Distribution of Tajima’s D and normalized Fay and Wu’s H simulated from posterior model parameters for pooled sample and geographical groups. Dashed lines delineate the 95% confidence interval. Observed values for the seven carotenoid biosynthesis genes are shown. I: IPI; P: PDS; C: CRTISO; B: LCYB1; E: LCYE; X: CHXE; Z: ZEP; y-axis: distribution density.(TIF)Click here for additional data file.Figure S4Distribution of STF and wθ under the divergence model for comparison between Western and Eastern groups. Observed values for the seven carotenoid biosynthesis genes are shown (filled circles). I: IPI; P: PDS; C: CRTISO; B: LCYB1; E: LCYE; X: CHXE; Z: ZEP.(TIF)Click here for additional data file.Table S1Set of carrot cultivar samples used for population genetics analyses.(DOC)Click here for additional data file.Table S2Prior distributions of parameter values with the divergence model used during the approximate Bayesian computation analysis.(DOC)Click here for additional data file.Table S3Pearson correlation coefficients r between summary statistics and model parameters.(DOC)Click here for additional data file.Table S4Model checking by comparison of observed dataset and posterior predictive distribution.(DOC)Click here for additional data file.Table S5Accession number of genes used for the phylogenetic analysis.(DOC)Click here for additional data file.Text S1Construction and validation of the divergence model, and neutral expectations.(DOCX)Click here for additional data file.


MTMR8 gene in a sample of 111 sub-Saharan and 49 non-African X chromosomes was investigated to assess the early evolutionary history of sub-Saharan Africans and the out-of-Africa expansion. The analyses revealed a complex genetic structure of the Africans that contributed to the emergence of modern humans. We observed partitioning of two thirds of old lineages among southern, west/central and east African populations indicating ancient population stratification predating the out of Africa migration. Age estimates of these lineages, older than coalescence times of uniparentally inherited markers, raise the question whether contemporary humans originated from a single population or as an amalgamation of different populations separated by years of independent evolution, thus suggesting a greater antiquity of our species than generally assumed. While the oldest sub-Saharan lineages, ∼500 thousand years, are found among Khoe-San from southern-Africa, a distinct haplotype found among Biaka is likely due to admixture from an even older population. An East African population that gave rise to non-Africans underwent a selective sweep affecting the subcentromeric region where MTMR8 is located. This and similar sweeps in four other regions of the X chromosome, documented in the literature, effectively reduced genetic diversity of non-African chromosomes and therefore may have exacerbated the effect of the demographic bottleneck usually ascribed to the out of Africa migration. Our data is suggestive, however, that a bottleneck, occurred in Africa before range expansion.The genetic diversity within an 11 kb segment of the  In light of recent data, human evolutionary history looks much more complex than what geneticists postulated only a decade ago Is greater genetic diversity in Africa due only to older and larger ancestral African population sizes as compared to those outside of Africa, or does it reflect the impact primarily of the out of Africa population bottleneck(s) in reducing the genetic diversity outside Africa? If at the time of out-of-Africa migration Africans and migrant populations did not differ in their genetic structure, but African populations stayed larger than non-Africans, greater African diversity would be expected to result from an accumulation of new low frequency local variants. In contrast, a restricted sampling of alleles due to a stringent out-of-Africa bottleneck MTMR8), in the centromeric portion of the X chromosome, earlier found to have reduced sequence diversity in non-African populations MTMR8 diversity in sub-Saharan Africans can reveal much about the evolutionary history of these populations. Specifically, ancient population structure in the geographic partitioning reflects separate evolutionary histories of the southern, western/central and the eastern African clades. Our results are consistent with the enrichment of the African diversity through fragmentation of its populations over long periods of their history as well as by admixture among archaic populations. They also support the hypothesis of an early bottleneck in out of Africa migrants, prior to their range expansion outside of Africa.Here we studied an 11 kb DNA segment with low-recombination frequencies A total of 160 X chromosomes were analyzed, 73 drawn from DNA samples of the Human Genome Diversity Panel of Centre d'Étude du Polymorphisme Humain, CEPH-HGDP MTMR8 (NT011669), starting in intron 3 and ending in intron 5  and 30 s at 72°C, to end in 10 min incubation at 72°C. Both strands of the products were sequenced on ABI 3730 DNA sequencer. In addition to 118 chromosomes analyzed in this way we screened 21 female non-African samples  for the presence of polymorphisms by DNA temperature-melting heteroduplex-detecting analysis PCR-primers were designed to amplify 21 overlapping fragments covering an 11024 bp-long segment of intron 5 . AmplifiThe ancestral alleles of the MTMR8 polymorphisms were inferred by comparison with the primate outgroup sequences of chimpanzee, orangutan and macaque. The human allele identical by state with a chimpanzee or at least two other outgroup sequences was considered ancestral. All distinct haplotypes could have been derived from the data without any ambiguity because all but two African samples represented hemizygous males and all females were either homozygous or when heterozygous it was only in a single haplotype position . The hapgenetree, version 9.0, on the full data set (n = 160), on all Sub-Saharan Africans together (n = 111), their eight subpopulations, and Sub-Saharan Africans +1 (n = 112) by addition of haplotype 23, found in a single copy in Lebanon but possibly of African origin given its position in the network. Maximum likelihood estimates of Θ N (or 1.5 (0)N chromosomes), such that the size of the population at time t is (t) =  N(0) e−βtN (note that t = g/(0)1.5N, where g is the number of generations ago). Using genetree we jointly estimated ΘML and the growth parameter β. First, we explored different values of β at fixed ΘML . Subsequently, for the selected β, we explored the likelihood density of Θ, to finally obtain the maximum likelihood estimates of both parameters after few rounds of such simulations. The ρ-statistics MTMR8 segment was estimated at 1.49×10−4 per generation (or 1.35×10−8 per bp per generation) from the human-chimpanzee divergence of 0.0081 (±0.0009) per bp, assuming a separation time of 7.5 million years (My) and an average generation time along both lineages of 25 years Coalescence analysis was carried out according to the method of Griffiths and Tavaré k and diversity G, count of segregating sites S and nucleotide diversity as well as different estimates of the scaled mutation parameter Θ, global and pairwise FST) and to carry out neutrality tests such as Tajima's DSFH and carry out the Fay & Wu H test ARLEQUIN software, v. 3.1 MLSПSimulations were performed using the msHot software MTMR8 segment in a worldwide sample of 160 X chromosomes. Out of 24 haplotypes, 21 are observed in sub-Saharan Africa and only 4 are found outside of Africa (−3) and all other summary statistics (−3) falls within the range observed at other X-linked segments MTMR8 in sub-Saharan Africa  with ΘML = 8.7 ((0)MLN = 19,500) . Non-African samples were excluded from this analysis except for a single Lebanese chromosome carrying haplotype H23 because of its proximity to Africa and likely sub-Saharan origin . In Biaka Pygmies from the Central African Republic we find a structurally distinct, ancient haplotype H22, whose lineage originates directly from the root of the tree  and population growth from 7 000 to 20 000 over the last 15 000 generations and assuming (input) Θ-value of 7.5, we reproduced the three observed Θ estimates: ΘП = 5.39<ΘS = 6.33<ΘML = 7.20 (i.e. ΘML was estimated assuming constant population size). While the simulations do not provide true proof, they do show that both population subdivision and subsequent population growth can shape MTMR8 diversity in ways consistent with the data, thereby offering a plausible explanation. A moderate increase in population size in Africa is suggested by the shape of the western/central and eastern branches of the network  for non-Africans, which increased to H = −9.44 (p<0.001) when non-Africans and Ethiopians were analyzed (p = 0.03 Fisher exact test). And this, in spite of the fact that our Ethiopian sample partly represents non-African chromosomes due to effects of the Eurasian gene-flow on Ethiopian diversity, thus diluting its African component MTMR8, about two thirds of the haplotypes from each of the three geographic poles  represent distinct old lineages , in Bantu speakers from Gabon and from South Africa (5), in Yoruba's from Nigeria (3) and Mandenka from Senegal (2) CMAH, CDC209 and Xp21.1 segment gene trees these rare ancient haplotypes have driven the corresponding TMRCAs above 2 My ago, artificially elongating tree branches due to the presence of derived alleles not shared with all remaining worldwide haplotypes. Other examples of similarly distinct African haplotypes were also found in the 4qMB179, 13qMG107 and 18qMB60 regions on chromosomes 4q, 13q and 18q, respectively The Biaka haplotype H22 does not share any derived alleles with the remaining haplotypes and is absent outside Africa. Only this haplotype has the alternative allele at our mutation 6  thus driet al. (2000) which is considered as a good estimator MTMRC8 tree Click here for additional data file.Material S1Supplementary Material.(DOCX)Click here for additional data file.Table S1Samples and their Geographic/Ethnic Origin.(DOCX)Click here for additional data file.Table S2PCR Primer Sequences.(DOCX)Click here for additional data file.Table S3n – number of chromosomes; S – number of segregating sites (SNPs); k – number of haplotypes; G – gene (haplotype) diversity; (1-G) – haplotype homozygosity; Θ – estimator of population mutation rate 4Nµ ; Θπ – estimate from nucleotide diversity s – estimate from the number of segregating sites H – estimate from frequency of the derived alleles ML – maximum likelihood estimate by genetreek - estimate from the number of haplotypes G – estimator from haplotype diversity Summary Statistics of Populations Samples. Abbreviations: (DOCX)Click here for additional data file.Table S4Time estimates (Ky ± S.D.) marking the MTMRC8 segment history  and 2 ba(DOCX)Click here for additional data file.Table S5http://www.cephb.fr/en/hgdp/Extended haplotypes of a subset of our Sub-Saharan samples from HGDP project that were used in our study  and for (DOCX)Click here for additional data file.


Leucocoprinus gongylophorus for processing polysaccharides has remained controversial. We therefore used quantifiable DeepSAGE technology to obtain mRNA expression patterns of genes coding for secreted enzymes from top, middle, and bottom sections of a laboratory fungus-garden of Acromyrmex echinatior leaf-cutting ants.The fungus gardens of leaf-cutting ants are natural biomass conversion systems that turn fresh plant forage into fungal biomass to feed the farming ants. However, the decomposition potential of the symbiont in situ: cellulases , hemicellulases , pectinolytic enzymes , glucoamylase (GH15), α-galactosidase (GH27), and various cutinases, esterases, and lipases. In general, expression of these genes reached maximal values in the bottom section of the garden, particularly for an AA9 lytic polysaccharide monooxygenase and for a GH5 (endocellulase), a GH7 (reducing end-acting cellobiohydrolase), and a GH10 (xylanase), all containing a carbohydrate binding module that specifically binds cellulose (CBM1). Although we did not directly quantify enzyme abundance, the profile of expressed cellulase genes indicates that both hydrolytic and oxidative degradation is taking place.A broad spectrum of biomass-conversion-relevant enzyme genes was found to be expressed Acromyrmex leaf-cutting ants can degrade a large range of plant polymers, but the conversion of cellulose, hemicellulose, and part of the pectin occurs primarily towards the end of the decomposition process, i.e. in the bottom section of the fungus garden. These conversions are likely to provide nutrients for the fungus itself rather than for the ants, whose colony growth and reproductive success are limited by proteins obtained from ingesting fungal gongylidia. These specialized hyphal tips are hardly produced in the bottom section of fungus gardens, consistent with the ants discarding old fungal biomass from this part of the garden. The transcripts that we found suggest that actively growing mycelium in the bottom of gardens helps to maintain an optimal water balance to avoid hyphal disintegration, so the ants can ultimately discard healthy rather than decaying and diseased garden material, and to buffer negative effects of varying availability and quality of substrate across the seasons.The fungal symbiont of  Leucocoprinus gongylophorus that they culture for food in subterranean nest cavities—so-called fungus gardens . To validate the normalization of libraries the mean frequencies of mono-tags representing “housekeeping” genes were compared between the top, middle, and bottom sections of the fungus garden, which showed that there were no significant differences .After removing low-abundance ones, 29,732 unique mono-tags remained in the dataset across all libraries  and predicted to include a CBM1 and thus to specifically bind to cellulose microfibrils  and in this article and its additional files.The data sets supporting the results of this article are available in the European Nucleotide Archive (ENA) [AA: Auxiliary activity; ANOVA: Analysis of variance; CBH: Cellobiohydrolase; CBM: Carbohydrate-binding module; CE: Carbohydrate esterase; DeepSAGE: Deep serial analysis of gene expression; EST: Expressed sequence tag; GH: Glycoside hydrolase; PDA: Potato dextrose agar; PL: Polysaccharide lyase; PPR: Peptide pattern recognition; WBA: Wheat bran agar.The authors declare that they have no competing interests.JJB and LL conceived of the study. LL, MNG, and KLN designed and supervised the experimental work. TL carried out the experimental work. SN and TL annotated the mono-tags. MNG assembled all selected genes to full-length, manually curated computer-generated annotations, produced the figure and tables, and wrote the manuscript with input from JJB and LL. All authors read and approved the final manuscript.List of 29732 mono-tag sequences and their normalized tag counts. The first column is the tag id, followed by the 5′ to 3′ sequence of each mono-tag . The following 22 columns display the normalized tag counts in each of the 22 DeepSAGE libraries produced: three PDA L. gongylophorus pure culture libraries (1–1 to 1–3), five WBA L. gongylophorus pure culture libraries (2–1 to 2–5), five top section libraries (3–1 to 3–5), four middle section libraries (4–1 to 4–4), and five bottom section libraries (5–1 to 5–5). The Top, Middle, and Bottom columns display the mean tag counts for each of the three fungus garden sections.Click here for fileExpressed sequence tag library ‘contigs17’. The 36 bp Illumina reads were assembled using Velvet [g Velvet  with 17 Click here for fileExpressed sequence tag library ‘contigs19’. The 36 bp Illumina reads were assembled using Velvet [g Velvet  with 19 Click here for fileExpressed sequence tag library ‘contigs21’. The 36 bp Illumina reads were assembled using Velvet [g Velvet  with 21 Click here for fileExpressed sequence tag library ‘contigs23’. The 36 bp Illumina reads were assembled using Velvet [g Velvet  with 23 Click here for fileExpressed sequence tag library ‘contigs25’. The 36 bp Illumina reads were assembled using Velvet [g Velvet  with 25 Click here for fileExpressed sequence tag library ‘contigs27’. The 36 bp Illumina reads were assembled using Velvet [g Velvet  with 27 Click here for fileExpressed sequence tag library ‘contigs29’. The 36 bp Illumina reads were assembled using Velvet [g Velvet  with 29 Click here for fileExpressed sequence tag library ‘contigs31’. The 36 bp Illumina reads were assembled using Velvet [g Velvet  with 31 Click here for fileList of 683 mono-tag sequences for which we obtained one or more hits to proteins in the databases. The first column is the tag id, followed by the 5′ to 3′ sequence of each mono-tag . The following 22 columns display the normalized tag counts in each of the 22 DeepSAGE libraries produced: three PDA L. gongylophorus pure culture libraries (1–1 to 1–3), five WBA L. gongylophorus pure culture libraries (2–1 to 2–5), five top section libraries (3–1 to 3–5), four middle section libraries (4–1 to 4–4), and five bottom section libraries (5–1 to 5–5). The Top, Middle, and Bottom columns display the mean tag counts for each of the three fungus garden sections. The final column shows the inferred function based on the most significant BLASTX hit(s) obtained for each mono-tag after first extending the sequence by searching our EST library and a L. gongylophorus low coverage genome sequence [sequence ,8.Click here for file


M. longissimus with divergent marbling phenotype (marbling scores 2 to 7) using qRT-PCR. The results demonstrate that transmembrane protein 60 (TMEM60) and dihydropyrimidine dehydrogenase (DPYD) are associated with increasing marbling fat. We suggest that the network-based approach in livestock may be an important method for analyzing the complex effects of candidate genes associated with complex traits like marbling or tenderness.Marbling is an important trait in characterization beef quality and a major factor for determining the price of beef in the Korean beef market. In particular, marbling is a complex trait and needs a system-level approach for identifying candidate genes related to the trait. To find the candidate gene associated with marbling, we used a weighted gene coexpression network analysis from the expression value of bovine genes. Hub genes were identified; they were topologically centered with large degree and BC values in the global network. We performed gene expression analysis to detect candidate genes in  The power adjacency function aij = |cor|β was used to construct a weighted network as the connection strength between two genes. We investigated soft thresholding with the power adjacency function and selected a power of beta (β) = 7. A major aim of coexpression network analysis is to determine subsets of nodes (modules) that are tightly connected to each other. To organize genes into modules, we used a module identification method based on a topological overlap dissimilarity measure [Ω = [ωij] provides a similarity measure, which has proven useful in biological networks [lij = ∑uaiuauj and ki = ∑uaiu is the node connectivity as follows:In coexpression networks, we refer to nodes as genes whose degrees indicate the number of links connected by the node. We extracted expression values for 844 genes and evaluated pairwise correlations between the gene expression profiles of each pair of genes using Pearson's correlation coefficients  in a hierarchical cluster analysis. Each module represents a group of genes with similar expression profiles across the samples and the expression profile pattern is distinct from those of other modules. The weighted gene coexpression analysis (WGCNA) software packages for R were used to identify coexpression values associated with marbling score [In the case of our network, ng score .p(k) ~ kr−, which is the defining property of a scale-free network [k, BC is the fraction of the number of shortest paths that pass through each node [gi→j is the number of the shortest geodesic paths from node i to node j and gi→jk is the number of geodesic paths among gi→j from node i to node j that pass through node k. We calculated BC as global properties according to all nodes in a network. From the results of the network topology analysis, we selected high-degree nodes and high-centrality nodes as key drivers that are most associated with our trait of interest in the network.To characterize the overall network topology, we used node degree (or connectivity), betweenness centrality (BC) . The deg network . The gen network . A node ach node  and is dhttp://david.abcc.ncifcrf.gov/). Each module was also analyzed separately, regardless of whether the gene module was significantly enriched with known ontology or pathway terms. The software calculates a Fisher's exact test P-value and provides a corrected P-value to avoid multiple test issues.We performed functional enrichment analysis against the 844 genes that were associated with marbling score enrichment in the Gene Ontology and KEGG pathway terms using the database for Annotation, Visualization, and Integrated Discovery (DAVID) tool (M. longissimus tissue in Korean cattle (Hanwoo). All experimental procedures and care of animals were conducted in accordance with the guidelines of the Animal Care and Use Committee of the National Institute of Animal Science in Korea. Twelve steers from each of low-marbled group (9.54 ± 1.35%) and high-marbled group (20.84 ± 1.52%) were used in this study for real-time PCR and statistical analyses (A260/A280) was over 1.90. For cDNA synthesis, 2 μg RNA was reverse transcribed in a 20 μL reaction volume using random primers  and reverse transcriptase . Reactions were incubated at 65°C for 5 min, 42°C for 50 min, and then at 70°C for 15 min to inactivate the reverse transcriptase. Real-time PCR was performed using a 2× Power SYBR Green PCR Master mix  with a 7500 real-time PCR system (Applied Biosystems) using 10 pM of each primer. PCR was run for 2 min at 50°C and 10 min at 95°C, followed by 40 cycles at 95°C for 10 s and then at 60°C for 1 min. Following amplification, a melting curve analysis was performed to verify the specificity of the reactions. The endpoint used in the real-time PCR quantification, Ct, was defined as the PCR threshold cycle number. We selected 11 hub genes (6 genes with large degree and 5 with large BC) from the network topology analysis. To determine major patterns in the 11 gene expression data, we performed principal component analysis (PCA) for the nodes with large degree and BC. A regression model was used to examine the association between gene expression value and intramuscular fat content using the “lm” function in R. This produced the following equation:μ is an overall mean, IMFij is the intramuscular fat content of each animal from gene i   and animal j  , and Ageij is slaughtering age in months, which was included as a covariate; the mRNA level of the β-actin, ribosomal protein, large, P0 (RPLP0) gene was also introduced as a covariate [We determined whether any associations existed between expression levels and intramuscular fat content in analyses . Total Rovariate .The nodes represent candidate genes obtained from the animal QTL database and microarray data, and the links between the nodes represent the association between expression profiles across all microarray samples. The absolute value of Pearson's correlation coefficient was calculated for all pairwise comparisons.τ, 0.7) and a “soft” threshold , producing a gene coexpression network. The network follows a power-law (P(k) ~ kr−) degree distribution, where r is the degree exponent and ~ indicates “proportional to.” We examined whether the coexpression network followed a power-law distribution with an exponent of approximately −1.8 [p(k)) and log(k), that is, the model fitting index, R2 of the linear module that regresses log(p(k)) and log(k). Figures 10(p(k)) and log10(k)k shows that the network approximately follows a scale-free topology . We also found that the connectivity distribution p(k) was better modeled using an exponentially truncated power-law (k) ~ kr−exp(−αk), where R2 = 0.98 in the unweighted network and R2 = 0.97 in the weighted network [We constructed a weighted gene coexpression network associated with the marbling score using soft threshold. A comparison with the weighted and unweighted gene coexpression network is required before decision making. This correlation matrix was transformed into a matrix of adjacency using a “hard” threshold  is an indicator of network structure, which quantifies network modularity and how close the node and its neighbors are. We observed an inverse relationship or a triangular region between the clustering coefficient and connectivity in the unweighted network . The decMost biological networks are characterized by a small number of highly connected nodes, while most of the other nodes have few connections . The higTo find clusters (gene modules) of highly correlated genes, we used average linkage hierarchical clustering, which uses TOM as dissimilarity. We choose a height cutoff of 0.99 to identify modules using a dynamic cut-tree algorithm. Connectivity is the number of nearest neighbors of a node and the effective mean degree is the average degree of all nodes except isolated nodes. We are able to identify seven distinct modules  for groups of genes with high topological overlap: turquoise, black, yellow, brown, blue, green, and red. P-value = 0.01); yellow, oxidative phosphorylation ; blue, hematopoietic cell lineage ; brown, PPAR signaling pathway ; green, dilated cardiomyopathy ; red, natural killer cell-mediated cytotoxicity ; and black, no significant term. Marbling (intramuscular fat)-related genes have been identified which are directly involved in lipid and fatty acid metabolism. These genes are not independently associated with marbling but interact in functionally important pathways [P = 0.002), and the lipid metabolic process . The lipid biosynthetic process involved the following genes: TECR, PMVK, LASS4, HMGCS2, APOA2, MGST2, FDFT1, and FDPS. The lipid metabolic process included the following genes: CROT, PI4KB, LASS4, HMGCS2, APOA2, HPGD, MGST2, FDFT1, and FDPS. Investigations on lipid metabolism in harvested animals have centered on research into adipose tissues [γ, CCAAT-enhancer binding proteins , and sterol regulatory element binding proteins (SREBP 1c) are reportedly directly or indirectly related to the regulation of adipogenesis [γ is known as a master regulator of adipogenesis [γ signaling pathway in the brown module, that is, APOA2, ANGPTL4, FABP5, and ACSL6. APOA2, ANGTPTL4, and ACSL6 are involved in lipid metabolism. ANGPTL4 is a well-known PPAR target gene and has multiple metabolic effects such as glucose and lipid metabolism [γ activation both in vitro and in vivo [Gene modules are important for identifying genes related to the trait of interest because they may be highly correlated in biological pathways. Each module was analyzed through functional enrichment analysis using gene ontology or KEGG pathway terms to understand the biological significance of the module genes and to determine putative pathways. The seven modules and their representative pathway terms were turquoise, other glycan degradations . The Pearson's correlation coefficients of marbling and two gene's expression levels are highly correlated and also statistically significant by regression analysis . Therefore, we identified candidate genes associated with marbling and then confirmed candidate genes in IMF phenotype.To further confirm gene expressions and relationships, 11 genes (6 genes with large degree and 5 with large BC) were selected after network topology analysis. Then, we conducted experimental validation of whether large degree and large BC nodes were related to marbling (intramuscular fat). We investigated the expression levels of eleven candidate genes in  reports , 48. Ourγ and CEBPα, as indicators of fat accumulation, which are the major transcription factors regulating adipogenesis [γ and CEBPα were more highly expressed in the high-marbled group (P ≤ 0.01), In the present study, we identified two genes, TMEM60 and DPYD, which were approximately 2.1 and 3.2 times higher in the high-marbled group and also upregulated with intramuscular fat content increases (P < 0.05)  . These gγ, CEBPα, MAEL, TMEM60, S100A11, and DPYD genes have a negative relationship with PC2. Our experimental results suggest that these genes warrant further investigation as metabolic indicators of marbling.PCA is a useful tool for data simplification and visualization of relationships. Therefore, we applied PCA to the 11-gene expression data set. In this study, we extracted gene list related to the marbling score trait from the Animal QTL database and microarray experiments from the GEO database. We subsequently constructed a global network and a weighted gene coexpression network based on Pearson's correlation matrix that displayed degrees using a power-law distribution, with an exponent of approximately −2. Hub genes were identified; they were topologically centered with large degree and BC values in the global network. Moreover, they were significantly correlated with three  gene modules. Finally, we confirmed that the expressions of hub (TMEM60) and nodes with large BC values (DPYD) were consistent with the network topology analysis. These genes have not been reported previously in bovine gene expression studies on marbling. Further studies should be conducted to identify biological mechanism of the genes in the network associated with bovine marbling.Table S1 shows “Summary of microarray data sets”.Figure S1 shows “the distribution before and after normalization”.Click here for additional data file.


However, the severe adverse effects including fluid retention, cardiovascular complications, and bone loss greatly limited their use in clinic. Recently, numerous novel PPARγ agonists involving the endogenous PPARγ ligands and selective PPARγ modulators (SPPARMs) are emerging as the promising candidates of the next generation of antidiabetic drugs instead of TZDs. Due to the higher selectivity of these novel PPARγ agonists on the regulation of the antidiabetes-associated genes than that of the side effect-associated genes, they present fewer adverse effects than TZDs. The present review was undertaken to address the advancements and the therapeutic potential of these newly developed PPARγ agonists in dealing with diabetic kidney disease. At the same time, the new insights into the therapeutic strategies of DN based on the PPARγ agonists were fully addressed.Diabetic nephropathy (DN) is a severe complication of diabetes and serves as the leading cause of chronic renal failure. In the past decades, angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs) based first-line therapy can slow but cannot stop the progression of DN, which urgently requests the innovation of therapeutic strategies. Thiazolidinediones (TZDs), the synthetic exogenous ligands of nuclear receptor peroxisome proliferator-activated receptor- In the past decades, a number of studies demonstrated the critical role of PPARs in the regulation of metabolic homeostasis, inflammation, cell differentiation and proliferation, fluid balance, and so on [γ was best characterized and its high-affinity ligands of TZDs were widely used in clinic for the treatment of type-2 diabetes mellitus (T2DM). PPARγ is expressed in various organs with the most abundant expression in adipose tissue. It heterodimerizes with retinoid X receptor (RXR) and then binds to PPAR responsive element (PPRE) to regulate a number of target genes. TZDs including rosiglitazone, pioglitazone, and troglitazone are synthetic exogenous PPARγ ligands with high efficacy in treating T2DM via enhancing the insulin sensitivity [γ ligand, demonstrated a potent renal-protective role under diabetic and nondiabetic situations possibly via PPARγ dependent and independent mechanisms with no obvious side effects seen in TZDs [γ agonists, also termed as selective PPARγ modulators (SPPARγMs), are being generated and some of them are under the clinical trials for the treatment of T2DM [γ agonists and the SPPARγMs in the protection of DN. Meanwhile, the therapeutic strategies via manipulating the use of various PPARγ agonists will be fully addressed.PPARs are nuclear receptors consisting of three PPAR isoforms of PPARnd so on –3. Amongsitivity . Besidessitivity –7. Moreositivity –11. Althsitivity –14. Inte in TZDs –20. More of T2DM , 21. TheWith the profound increase of obesity, the prevalence of T2DM is rapidly rising worldwide. Among the patients with T2DM, about 10% of them developed DN . In Nortγ is located in all three types of glomerular cells with a prominent expression in podocytes [γ2 is significantly associated with a reduced risk of albuminuria among patients with type-2 diabetes [γ in glomeruli, particularly in the podocytes. In agreement with this concept, numerous reports including a meta-analysis of 15 original clinical studies involving 2860 patients convincingly demonstrated the significant efficacy of rosiglitazone or pioglitazone on diabetic proteinuria [PPARodocytes , 27. Sevdiabetes . These rγ in diabetic kidney disease [γ in treating diabetic kidney disease was extensively investigated since PPARγ was discovered, the chief mechanism is roughly focused on the inhibition of inflammation and oxidative stress [In addition to the clinical evidence mentioned above, numerous basic studies performed in diabetic animals and in vitro cells also proved the beneficial action of PPAR disease , 27, 29.e stress  with pooγ benefits all kinds of kidney cells including the glomerular mesangial cells, endothelial cells, podocytes, and tubular epithelial cells under the diabetic condition [γ activation remarkably improved the mitochondria dysfunction induced by aldosterone in podocytes [γ in opposing the diabetic podocyte injury. However, a direct link between the PPARγ and mitochondria function in podocytes and other kidney cells under the diabetic condition does need a great deal of experimental evidence.A number of in vivo and in vitro studies demonstrated that PPARondition  with morondition , 31, 32.ondition , blockadondition , and antondition , 34. It odocytes . These nγ in TZDs-induced fluid retention [γ in the vasculature also played a crucial role in mediating the fluid retaining effect [γ-mediated fluid retention and also suggested some potential targets to overcome the TZD-induced fluid volume expansion. In addition to the fluid retaining effect, TZDs also cause the cardiomyocytes hypertrophy and coronary artery lesions with elusive mechanisms [γ agonists or therapeutic strategies.Although there is much evidence from clinical trials and basic studies pronounced the protective role of TZDs in DN, the severe side effects greatly restricted their use in patients. Troglitazone had to quit the market owing to the severe hepatotoxicity. Rosiglitazone has been found to be significantly associated with the increased risk of cardiovascular complications including heart failure and myocardial infarction leading to the restriction or withdrawal from the markets. As for the pioglitazone, it has been thought to have a different safety profile with no increase of cardiovascular disease as compared with other TZDs . But, itetention , 38, PPAg effect , 40. Allchanisms . In genechanisms . CollectEvidence from studies demonstrated the dose-dependent response of TZDs in antagonizing hyperglycemia of T2DM , 43. Accγ include unsaturated and oxidized fatty acids, nitrated fatty acids, eicosanoids, and prostaglandins [γ ligands and received attention from a number of studies [γ agonists may play a similar role as TZDs in protecting DN with no significant side effects shown by TZDs. Although nitro-oleic acid definitely activates PPARγ [Endogenous ligands for PPARglandins . 15-Deox studies , 46, 47. studies , 47. Inf studies . Most re studies . In contes PPARγ . Furtherγ agonists, nonselectively regulate the expressions of antidiabetic efficacy-associated and adverse effect-associated genes in similar proportion [γ modulators (SPPARγMs) are being actively pursued as the second generation of PPARγ agonists. Presumably, SPPARγMs preserve greater capability in the regulation of antidiabetic genes than that of adverse-effect-associated genes, which could effectively limit the side effects seen in TZDs, particularly the fluid retention. By now, numerous synthetic SPPARγMs have been generated [γMs convincingly demonstrated the antihyperglycemia effect with fewer side effects, their efficacy in treating DN is still unclear. We believe, with better recognition on the importance of SPPARγMs and the research progression of this field, this question will be answered soon.TZDs, as full PPARoportion , which lenerated . Among tenerated . Data frenerated . The preenerated . Besidesenerated . Human senerated . Althougγ in protecting DN, TZDs partnering with ACEI and/or ARB served to be a better option for the nephrologists. However, the unacceptable side effects of TZDs unfortunately interrupted such an ideal marriage. Even so, we still believe that with the discovery and the clinical application of novel PPARγ agonists including endogenous PPARγ agonists and SPPARγMs, this marriage between RAS blocker and PPARγ agonist will be rebuilt in the near future. Currently, an exciting example is telmisartan with dual properties of AT1 blocker and selective PPARγ modulator [RAS blockers including ACEIs and ARBs served as the cornerstone therapy of DN in the past decades. Although their efficacy in reducing the proteinuria and retarding the DN progression was established, a large number of DN patients with the therapy of RAS blockade and glycemic control still stepped onto the stage of renal failure. This situation raised a serious request for more effective therapies of DN. Due to the established role of PPARodulator , 52. Butα is distributed in several tissues including the kidney and its agonists had shown the pivotal roles in regulating lipid metabolism, inflammation, and cardiovascular response [α agonists such as fenofibrate can effectively protect DN via reducing renal lipotoxicity and inhibiting renal inflammation and oxidative stress [α may serve as a new therapeutic target of DN. In addition, dual activation of PPARα and PPARγ may be a novel strategy against DN. In line with this concept, an animal study using combined low dose PPARα agonist fenofibrate and low dose PPARγ agonist rosiglitazone more remarkably attenuated the diabetic kidney injury than drug alone [α/γ agonist tesaglitazar markedly ameliorated the diabetic renal injury in db/db mice [α agonist with novel PPARγ agonists or low dose of TZDs could be a suitable strategy for the treatment of DN.PPARresponse . Recent e stress . These fug alone . Moreove/db mice  and obes/db mice . Based oγ agonists will cause greater extracellular fluid volume expansion and more severe cardiovascular complications. However, low dose aspirin has been used for long-term primary or secondary prevention of vascular disease in clinic and the safety has been well evaluated. Although there is no convincing evidence showing the efficacy of low dose COX inhibitors in the therapy of DN, the combination of a lower dose COX inhibitor with SPPARγM or endogenous PPARγ agonist could be a feasible strategy in treating DN. Moreover, it is also worthwhile to investigate whether a low dose of COX inhibitor will strengthen the effect of RAS inhibitors in protecting the diabetic kidney. By reviewing the literatures, we did not find any clinical or animal reports demonstrating this notion.COX-2 was induced in the podocytes under the diabetic status . Inhibitγ agonist may provide additional protection from diabetic kidney disease. In addition, it is also expected that antagonism of specific PGE2 receptors partnering with a selective PPARγ agonist could achieve better outcome in DN treatment than PPARγ agonist alone. However, none of the mPGES-1 inhibitors or EP antagonists is available in clinic now. The investigations in animals or in vitro cells may be the current emphasis to validate the present hypothesis.mPGES-1 is one of three characterized prostaglandin E synthases . In the past decade, only mPGES-1 was evidenced as a functional PGE2 synthase in vivo and played important roles under various physiological and pathological conditions –71. Evidγ in treating human diseases. With the development of novel PPARγ agonists with minimal side effects, the PPARγ will gain the researcher's focus again. Actually, PPARγ activation not only ameliorates diabetic kidney disease, but it also protects kidneys from a variety of other acute and chronic insults. In the past decades, only RAS blockers stand on the first line in fighting against chronic kidney diseases (CKDs). With the generation and application of novel PPARγ agonists in the near future, we can conceive that the therapeutic outcome of DN and other CKDs will be significantly advanced.Except for the known side effects, TZDs have been so fantastic for the treatment of T2DM and diabetic kidney disease. We believe that the withdrawal or restriction of TZDs owing to their severe side effects only temporarily fades the light of PPAR


Soluble TNFα receptor (sTNFRs) levels were determined by ELISA and genotyping was determined by PCR-RFLP. Homozygous TT individuals at SNP −609G/T TNFRI (rs4149570) showed lower levels of sTNFRI compared to GG genotype carriers. Homozygous carriers of CC genotype at SNP −1207G/C TNFRI (rs4149569) had lower expression densities of membrane-bound TNFRI on intact CD14+ monocytes compared to individuals with the GC genotype. The frequency differences in the CD3+ and CD19+ cells expressing TNFRII in relation to SNP −1709A/T TNFRII (rs652625) in healthy individuals were also determined. The genotype CC in SNP −3609C/T TNFRII (rs590368) was associated with a lower percentage of CD14+ cells expressing TNFRII compared to individuals with the CT genotype. Patients with rheumatoid arthritis had no significant changes in the frequencies of genotypes. Reduced frequency was identified for the combination TNFRI −609GT + TNFRII −3609CC only. The polymorphisms in genes represent one of cell type-specific mechanisms affecting the expression levels of membrane-bound TNFα receptors and TNFα-mediated signaling.The level of TNF receptors on various cells of immune system and its association with the gene polymorphism were investigated. Determining the levels of membrane-bound TNF This analysis also demonstrated that serum levels of sTNFRI in healthy individuals positively correlated with serum TNFα levels (0.669 [0–1.9] pg/mL) . The levels of TNFα negatively correlated with the absolute number of TNFRI expressed on CD3+ T cells and CD19+ B cells .The TNFα receptors on certain subpopulations of mononuclear cells, which may be indicative of different effector profiles of different immunocompetent cells in response to TNFα. These potentially different responses are affected by the percentage of TNFR positive cells in the context of the absolute number of TNFα receptors .TNFosibirsk . The genα receptor genes and serum levels of TNFα and sTNFRII. When analyzing data regarding serum concentrations of soluble TNFα receptors and respective genotypes, we observed that individuals homozygous at the T allele at position −609G/T TNFRI (rs4149570) presented with lower levels of soluble TNFα receptor type I compared to individuals presenting with the G allele  .We did not observe associations between SNPs present in the promotor region of TNF= 0.032) . The comα receptor type I and genotype was established for SNP −1207G/C TNFRI (rs4149569). The homozygous CC genotype was statistically more frequent in the group with lower densities of CD14+ monocytes expressing surface TNFRI  . The stimulation index was calculated as a simple ratio of absolute number of TNFRI receptors on CD14+ cells in cultures with and without LPS stimulation.The association between the expression level of membrane-bound TNF= 0.025) . We also TNFRII genotype frequencies at SNP −1709A/T (rs652625) we observed a statistically significant difference in the percentage of CD3+ and CD19+ cells expressing TNFRII in healthy individuals .When analyzing TNFRII had a lower percentage of intact CD14+ cells expressing TNFRII compared to individuals with the CT genotype   of= 0.041) .χ2 = 11.6, P = 0.0006). The Odds Ratio for this combination of genotypes was OR = 0,42 (CI95 = 0.25–0.71), and a relative risk of rheumatoid arthritis for carriers of this genotype was 10% lower. These combinations of genotypes comparative analysis are shown in The frequencies of alleles and genotypes of TNFRI promoter at positions −609 and −1207 and TNFRII at positions −3609 and −1709 had no statistically significant differences in RA patients and healthy individuals. However, the analysis revealed a combination of genotypes TNFRI-609GT + TNFRII-3609CC. The frequency of this combination in patients was 10% and was significantly lower than that in the group of population controls 22% . For this reason, the percentage of cells expressing TNFα receptors does not always correlate with the absolute number of receptors. For example, comparison of TNFRI expression by T and B lymphocytes and monocytes identified that CD19+ B lymphocytes expressed the lowest number of total TNFRI but as a population expressed the greatest density of receptors. By contrast, a greater percentage of CD3+ T lymphocytes expressed TNFRII at the lowest density of any cell type examined.Analyses of signaling mechanisms associated with TNF+ monocytes [α receptors in response to LPS action. These data confirmed that LPS significantly affected TNFRII expression on CD14+ monocytes from healthy individuals. In addition, comparison of freshly isolated (unstimulated) CD14+ monocytes to Mock-stimulated CD14+ monocytes cultured for 24 h revealed differences both in the percentage of positive cells and in the expression level of membrane-bound TNFα receptors likely associated with microenvironment changes.Previous work has demonstrated that cells cultured in the presence of LPS for 24 h resulted in a significant enhancement in TNRII expression compared to TNFRI expression in CD14onocytes . Data prα with its soluble receptors have resulted in conflicting observations. For example, Spinas et al. [α and sTNFRI levels but not with sTNFRII and Koga et al. [α and sTNFRII but did not establish a correlation between TNFα and levels of sTNFRI. Data presented in this report demonstrated that serum TNFα levels positively correlated with sTNFRI levels in the serum of healthy individuals. We also demonstrated that serum sTNFRI (weakly) negatively correlated with that of the density of membrane-bound TNFRI expressed on cell surfaces, suggesting an association with proteolytically derived membrane-bound receptors. TNFα levels also negatively correlated with the levels of membrane-bound TNFRI on cells, supporting previous reports demonstrating that TNFα decreased in the amount of mRNA encoding for TNFRI [Analyses of correlation of TNFs et al.  establisa et al.  establisor TNFRI .α receptor gene polymorphisms. A considerable number of SNPs located within the promoter region of TNF-TNFR superfamily gens can affect regulation by significantly impacting levels of gene expression [α receptor gene types I and II promoter regions and are therefore likely to affect TNFRs expression levels.Differences in the levels of receptor expression can also be affected by TNFpression , 37. The TNFRI −609G/T (rs4149570) locus with various pathologies. For example, the T allele was significantly associated with systemic lupus erythematous [ TNFRI −609G/T polymorphism was strongly associated with primary hepatocellular carcinoma and that the T allele repressed TNFRI expression. The present study demonstrated that individuals homozygous for the T allele of SNP −609G/T located within the TNFRI gene promoter presented with lower serum levels of soluble type I TNFα receptors. It has been demonstrated that soluble receptors inhibit the biologic effects of TNFα [+ B cells in individuals with the TT genotype  , a transcription factor that is involved in TNFRI-mediated activation of NF-κB signaling pathway [Several studies have examined the association of polymorphisms at thehematous , poor suhematous , and T chematous ; however609G/T rs49570 lochematous  and invahematous . Kim et hematous  found ou of TNFα ; therefo= 0.099) . Consideeceptors , 13, it  pathway . TNFRI −1207G/C (rs4149569), the C allele frequencies in patients with systemic lupus erythematous were significantly lower than the frequencies in control groups. The present study demonstrated that CC genotype carriers at position −1207G/C of the TNFRI gene presented with a reduced density of TNFRI on CD14+ monocytes. It has been demonstrated using the online AliBaba2.1  program that this SNP  was associated with lack of transcription factor binding sites and that the G allele was associated with transcription factor binding sites for C/EBPalpha , AP-2alpha , and Sp1. It is quite probable that the differences in expression of TNFRI on cells of individuals with different genotypes are associated with one of these transcription factors.Miyagawa et al.  demonstr TNFRII −1709A/T (rs652625) with pathology [ TNFRII gene increased the risk of severe infusion reactions to infliximab in Crohn's disease patients. We examined the frequency of allelic variants of TNFRI and TNFRII genes in patients with rheumatoid arthritis and demonstrated that RA patients (compared to controls) were significantly less likely to present with TNFRI −609GT + TNFRII −3609CC combination of genotypes. Individual's predisposition to developing of disease may be determined by the individual characteristics of the expression regulation of TNF-α and its receptors in the cells of the immune system. The present study identified statistically significant frequency differences in the percentage of CD3+ and CD19+ cells expressing TNFRII in individuals carrying AA genotype in SNP TNFRII −1709A/T (rs652625). Individuals homozygous for the C allele in SNP −3609C/T (rs590368) of the TNFRII gene had low percentage of CD14+ cells expressing TNFRII. Using AliBaba2.1 we demonstrated a difference at the binding site defined by the −1709A/T of TNFRII allele. Specifically, transcription factors did not bind to the sequence encoded by the T allele and the sequence encoded by the A allele resulting in CFT binding . The biologic effects of TNFα result from interactions with two types of membrane-bound receptors: TNFRI and TNFRII. It is known that simultaneous expression of TNFRI and TNFRII results in the degradation of TRAF2 resulting in increased TNFRI-mediated cytotoxicity [A number of studies have established an association between SNPathology , 46. Steathology  determintoxicity , 48. The TNFRI gene promoter and −1709A/T and −3609C/T of TNFRII gene promoter are associated with expression level of TNFα receptors what specifies that these polymorphisms are functional. Association of SNPs −1207G/C, −1709A/T, and −3609C/T of TNFR genes promoters with expression levels of membrane-bound TNFα receptors types I and II in the absence of association with level of soluble TNFα receptors is established what testifies to existence of different mechanisms of regulation of soluble and membrane-bound receptors expression. Association of SNPs −1207C/T and −3609C/T with expression of TNFRs on CD14+ population in the absence of association with expression on CD3+ and CD19+ subpopulations testifies to a functional role of these SNPs for separate subpopulations of mononuclear cells. A possible mechanism for determining the expression of the receptor is a cell-specific transcriptional regulation of a set of factors (enhancers and repressors) [Thus, we have established that SNPs −609G/T and −1207G/C ofressors) , 50.Interesting results were obtained in the analysis of combinations of genotypes. Combination TNFRI −609GT (rs4149569) and TNFRII −3609CC rarely is detected in RA patients and is associated with increased levels of TNFRI and reduced level of TNFRII on the immune cells. Perhaps different levels of TNF receptors types I and II on the cells determine the relationship of genetic variants with rheumatoid arthritis. However, for certain output, a broader study is necessary.α receptors and in the absolute number of membrane-bound receptors expressed by PBMCs. Also we have established that the percentage of cells expressing TNFRs is not always associated with the absolute number of receptors. Furthermore, we determined that differences in expression levels of TNFα receptors types I and II could be associated with TNFRI and TNFRII gene polymorphisms. Associations of SNPs located within the promoter regions of TNFα type I and type II receptor genes were established in the context of expression levels of membrane-bound receptors present on subpopulations of mononuclear cells and with the serum levels of soluble type I TNFα receptors. These observations suggested that TNFα receptor gene alleles represent one of the factors that affects variability in the expression of membrane-bound receptors that may explain differences in the effects mediated by TNFα on different cell populations/subpopulations.This study identified differences in the percentage of cells expressing TNF


Salmo salar) to a high marine protein (MP) or low fishmeal, higher plant protein replacement diet (PP), formulated to the same nutritional specification within previously determined acceptable maximum levels of individual plant feed materials.Aquaculture of piscivorous fish is in continual expansion resulting in a global requirement to reduce the dependence on wild caught fish for generation of fishmeal and fish oil. Plant proteins represent a suitable protein alternative to fish meal and are increasingly being used in fish feed. In this study, we examined the transcriptional response of Atlantic salmon . The dietary comparison revealed large alteration in gene expression in all the tissues studied between fish on the two diets. Gene ontology analysis showed, in the mid intestine of fish fed PP, higher expression of genes involved in enteritis, protein and energy metabolism, mitochondrial activity/kinases and transport, and a lower expression of genes involved in cell proliferation and apoptosis compared to fish fed MP. The liver of fish fed PP showed a lower expression of immune response genes but a higher expression of cell proliferation and apoptosis processes that may lead to cell reorganization in this tissue. The skeletal muscle of fish fed PP vs MP was characterized by a suppression of processes including immune response, energy and protein metabolism, cell proliferation and apoptosis which may reflect a more energy efficient tissue.The PP diet resulted in significant effects on transcription in all the 3 tissues studied. Despite of these alterations, we demonstrated that high level of plant derived proteins in a salmon diet allowed fish to grow with equal efficiency as those on a high marine protein diet, and with no difference in biometric quality parameters. Salmo salar) production alone now reaches over 1.5 million tonnes per year × 100;W1 = start weight (g), W2 = final weight (g), days = days in the growth periodSFR (%) = SGR × FCRHSI, CSI, ISI (%) = [organ weight (g)/round weight (g)] × 100 Condition factor = [round weight (g) × 100]/[fork length (cm)]3Biometric parameters were analyzed for significant differences by Anova using InfoStat v 2009 software , with p <0.05 considered significant. Data is presented as means ± standard deviation for each dietary treatment.FCR = feed consumed (g)/biomass increase (g) SGR . Sections (5 μm) were stained with hematoxylin and eosin (H & E) and examined under a light microscope. Micrographs were examined “blind” by the same experienced pathologist. Liver sections were evaluated for lipid degeneration level (fatty change) and the integrity of the whole organ. Heart tissue was evaluated for the presence of inflammatory infiltrates, myodegeneration and other possible abnormalities. Intestinal morphology was evaluated according to the following criteria: a) widening and shortening of intestinal folds, 2) loss of supranuclear vacuolization in enterocytes in the intestinal epithelium, 3) widening of central lamina propria within the intestinal folds, and 4) infiltration of (mixed) leukocytes in the lamina propria and submucosa. The occ2O. The concentration was determined by spectrophotometry  and the integrity of the RNA was determined by electrophoresis . The RNA was then stored at −80° until required.RNA was extracted from 100 mg of tissue by homogenisation in 1 ml TRIZol (Invitrogen) using tungsten carbide beads  and shaking (300 times per min) following the manufacturer’s instructions. The RNA pellet was washed in 500 μl 80% ethanol, air dried and resuspended in RNase free HMicroarray platform:Oncorhynchus mykiss) sequences - the latter being selected when no homologues appeared to be available within Salmo salar datasets. Full details of the microarray platform and design are shown in Tacchi et al. 2011[Microarray experiments were performed using a custom-designed, Agilent-based microarray platform with 4 × 44 K probes per slide . The array contained primarily an Atlantic salmon resource with 34,441 features from Atlantic salmon coding sequences but additionally a further 9,111 features from rainbow trout  was hybridised against this reference sample (labelled with Cy5TM) in a 2-colour experiment. mRNA amplification, labelling and hybridization was performed as follows: mRNA was amplified using a MessageAMPTM aRNA Amplification Kit (Ambion). Briefly, 2 μg total RNA was reverse transcribed and the cDNA was used as a template for in vitro transcription in the presence of amino allyl modified dUTP, which allowed the generation of amplified antisense RNA (aRNA). For labelling, aRNA (3 μg) was denatured at 70°C for 2 min in a volume of 10 μl to which 3 μl of 0.5 M NaHCO3 and 2 μl Cy dye  was added. Incorporation of dyes was performed for 1 h in the dark, and after excess label was removed using a DyeExtm2.0 spin kit (QIAGEN) the amount incorporated was checked with a Nanodrop ND1000 (LabTech) spectrophotometer. Prior to hybridisation, 825 ng of each labelled template was fragmented in the presence of 11 μl of 10X blocking agent, 2.2 μl of 25X Fragmentation buffer (Agilent), and made up to a final volume of 20 μl with nuclease-free dH2O. The solution was then incubated in the dark at 60°C for 30 min, after which 57 μl of 2X GEx Hybridisation buffer (Agilent) was added to each sample and 103 μl of each hybridisation solution was dispensed on the Agilent 4x44K Atlantic salmon “Salmo salar2” oligo array . The hybridisations were performed in a Microarray Hybridisation Oven (Agilent) overnight (18 h) at 65°C. Following hybridisation, the slides were washed in Gene Expression Wash buffers 1 and 2 (Agilent) following the manufacturer’s instructions. The slides were then scanned using a GenePix personal 4100A Scanner (Axon Instruments) at a resolution of 5 μm and saved as *. TIF files. Images were extracted and initial analysis was performed by Feature extraction v9.5.3 (Agilent) performing background correction of feature intensities (within the software). A Lowess normalisation of background corrected data was next conducted and all intensity values <1.0 were set to 1.0. Statistical analysis of the arrays was performed using Genespring GX analysis platform . Quality control of the data was performed within Genespring and included removal of saturated probe features, non-uniform features, population outliers and those features showing intensities not significantly different from background in the Cy3 or Cy5 channels. After these relatively stringent procedures, 20,095 of the original 43,730 array features were maintained for subsequent analyses. The experimental hybridisations are at European Bioinformatics Institute archived under accession number E-MEXP: E-TABM-1207.For microarray analysis, 4 pools of RNA were produced for each tissue from fish fed PP and MP diets. Each RNA pool was an equimolar RNA mix from four different fish chosen randomly from each group. The microarray hybridisation was performed using a reference design, using a reference RNA sample, which comprised an equimolar mix of RNA extracted from all individual fish and tissue samples. Each experimental sample . Further filtering on fold change was conducted, and only transcripts showing more than two-fold change in expression were further characterised.Significant differential expression between fish fed the PP diet and fish fed the control diet was established by Enrichment for gene ontology (GO) biological processes was performed on all cDNA features that had GO identifiers associated using the GOEAST program. Fisher’17 (500 ng μl-1), left at room temperature for 5 min to allow annealing, then stored on ice. The resulting cDNA was diluted to a final volume of 50μl in RNA/DNA free water (Sigma). For real time PCR, 3μl of cDNA was used as template with gene specific primers (Table(−1/s) where s is the slope generated from the serial dilutions, when Log dilution is plotted against ΔCT (threshold cycle number). Primers were design to have a Tm of 55°C, and where possible, to cross an exon-exon junction sites to avoid amplification of genomic DNA. Exon-intorn junction sites were determined comparing Salmo salar cDNA with genomic sequences for orthologous genes from Danio rerio, Gasterosteus aculeatus, Oryzias latipes, Takifugu rubripes and Tetraodon nigroviridis obtained from Ensembl (http://ensembl.org/).The cDNA was synthesized using 2 μg of total RNA as previously described. Real tiT-test, performed using R software, with p < 0.05 considered significant. The expression data is presented as means ± standard error.The results obtained by real time PCR were analyzed using the Pfaffl method. The qPCThe authors declare that they have no competing interests.LT performed microarray experiments, analyzed the data, carried out the real time PCR and wrote the manuscript. MAC, CV and HT were involved in fish maintenance, feeding trial and sampling. SAMM, CJS, RB and MAC were involved in the experimental design and drafting of the manuscript. All the authors read and approved the final manuscript.


Synechocystis sp. PCC 6803 contains three genes coding for glutaredoxins: ssr2061 (grxA) and slr1562 (grxB) code for dithiolic glutaredoxins while slr1846 (grxC) codes for a monothiolic glutaredoxin. We have analyzed the expression of these glutaredoxins in response to different stresses, such as high light, H2O2 and heat shock. Analysis of the mRNA levels showed that grxA is only induced by heat while grxC is repressed by heat shock and is induced by high light and H2O2. In contrast, grxB expression was maintained almost constant under all conditions. Analysis of GrxA and GrxC protein levels by western blot showed that GrxA increases in response to high light, heat or H2O2 while GrxC is only induced by high light and H2O2, in accordance with its mRNA levels. In addition, we have also generated mutants that have interrupted one, two, or three glutaredoxin genes. These mutants were viable and did not show any different phenotype from the WT under standard growth conditions. Nevertheless, analysis of these mutants under several stress conditions revealed that single grxA mutants grow slower after H2O2, heat and high light treatments, while mutants in grxB are indistinguishable from WT. grxC mutants were hypersensitive to treatments with H2O2, heat, high light and metals. A double grxAgrxC mutant was found to be even more sensitive to H2O2 than each corresponding single mutants. Surprisingly a mutation in grxB suppressed totally or partially the phenotypes of grxA and grxC mutants except the H2O2 sensitivity of the grxC mutant. This suggests that grxA and grxC participate in independent pathways while grxA and grxB participate in a common pathway for H2O2 resistance. The data presented here show that glutaredoxins are essential for stress adaptation in cyanobacteria, although their targets and mechanism of action remain unidentified.Glutaredoxins are small redox proteins able to reduce disulfides and mixed disulfides between GSH and proteins.  E. coli (but not in yeast), in sulfate assimilation, as electron donors for 3′-phosphoadenosine 5′-phosphosulfate synthase or for methionine sulfoxide reductases  contains 3 genes coding for glutaredoxins: ssr2061  and slr1562  code for dithiolic glutaredoxins that belong to class I  codes for a monothiolic glutaredoxin that belongs to class II  and one from the monothiolic subgroup (class II)  and bubbled with a stream of 1% (v/v) CO2 in air. For plate cultures, the medium was supplemented with 1% (wt/vol) agar. Kanamycin, chloramphenicol and spectinomycin were added to a final concentration of 50 μg mL−1, 20 μg mL−1, and 5 μg mL−1, respectively. BG11C medium was supplemented with different concentrations of CuSO4, NiSO4, CdCl2, Na2SeO4, and Na2SeO3 when indicated. Experiments were performed using cultures from the mid-logarithmic phase (3–5 μg chlorophyll mL−1) cultivated without antibiotics. For high light conditions, cultures were illuminated with white light at an intensity of 500 μE·m−2·s−1, and the temperature was kept at 30°C by applying a 5-cm-thick water filter. For heat shock conditions, cells were grown in a water bath at 42°C. For oxidative stress conditions, 1 mM hydrogen peroxide was added. Synechocystis strains and their relevant genotypes are described in Table Synechocystis cultures in the mid-exponential growth phase (3–5 μg chlorophyll mL−1). Extractions were performed by vortexing cells in the presence of phenol-chloroform and acid-washed baked glass beads (0.25–0.3 mm diameter) as previously described  using α-[32P] dCTP (3000 Ci/mmol). Prehybridization, hybridization, and washes were in accordance with Amersham instruction manuals. All filters were stripped and re-hybridized with the constitutively expressed rnpB gene from Synechocystis as loading control  or anti-GrxC (1:3000). Anti-GSI (1:50.000) was used as loading control.Anti-GrxA and anti-GrxC antisera were obtained according to standard immunization protocols by injecting GrxA and GrxC purified proteins in rabbits. For analysis of GrxA and GrxC protein levels in Synechocystis mutants lacking each or all of the glutaredoxin genes but their phenotypes were not analyzed in detail  during the growth curve. Growth of the mutants was analyzed in cultures inoculated at OD750nm = 0.1  and growth was monitored following both chlorophyll content and OD750nm for 5 days when it leveled off around 30 μg chl mL−1  during the early exponential growth phase and both grxA and grxB remained almost constant during all phases  of single mutant strains although neither GrxA or GrxC protein levels changed  from our standard light intensity (50 μE·m−2·s−1) to 500 μE·m−2·s−1 and followed glutaredoxin gene expression in response to this change in light intensity. This light intensity was chosen because it did not induce photoinhibition  were diluted to 1 μg chl mL−1, shifted to 500 μE·m−2·s−1 and growth of the strains was monitored measuring chlorophyll content  and the SGRXC (grxC) mutant strains was markedly retarded after the shift to high light while that of SGRXB (grxB) was only slightly reduced  were diluted to 2 μg chl mL−1, shifted from 30 to 42°C and growth of the strains was monitored measuring chlorophyll content  both at mRNA and protein levels and on growth of the mutant strains. As a marker gene we used the isiAB operon, which has been reported to be induced after this treatment , then cultures were diluted to 1 μg chl mL−1 and 1 mM of H2O2 was added to the cultures. Growth was monitored for 4 days by measuring chlorophyll content  but had almost no effect on grxC mutation . The triple mutant SGRXABC showed a degree of sensitivity comparable to that of SGRXAC, which suggested that the grxB mutation was not able to suppress the stress sensitivity of the SGRXAC strain.In order to study the sensitivity of the different strains, cells were grown until they reached the exponential growth phase  and triple mutants  strains behaved like the WT. This suggests that compensatory mechanisms were activated in double and triple mutant strains that protect against the toxic effect of these metals. However, although these mutations were able to suppress grxC mutation, they did not enhance resistance in a WT background  proved to be hypersensitive to H2O2, which is in agreement with phenotypes previously described for this mutant , although the catalase activity was inhibited by GrxA in Synechocystis extracts  is able to transfer its Fe-S cluster to ferredoxin , which present homology to bacterial NADPH thioredoxin reductases. Which redoxins are reduced by NTR is controversial because both dithiolic glutaredoxins, GrxA and GrxB, and TrxA have been reported to be reduced by NTR  and NADPH dependent GR activity is not detected in extracts (Marteyn et al., Synechocystis as it has been shown in other organisms including plants and algae (Gelhaye et al., grxC shows that grxB was also able to suppress totally or partially the sensitivity phenotype of grxC single mutants to high light, heat shock and metal stress but not to oxidative stress. The relation between these two proteins is completely unknown but is possible that grxB affects GSH metabolism which will probable also alter GrxC function.In contrast the SGRXB strain did not display any obvious phenotype, neither were the Synechocystis and that GrxA and GrxC levels increase in response to stress treatments. This induction is mediated mainly by transcriptional induction for GrxC and by posttranscriptional mechanisms in the case of GrxA. Analysis of the different genetic interactions between the glutaredoxin genes suggest the existence of an interconnected genetic network that is perturbed in response to environmental changes. In this regard, it would be interesting to analyze mutants lacking both glutaredoxins and other components of antioxidative system, such as the thioredoxins, as some of the phenotypes we have observed are shared between them.In conclusion, we have shown that glutaredoxins are essential for adaptation to several stresses in 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.