Epidemiological and animal studies have shown that maternal diet can influence metabolism in adult offspring. However, the molecular mechanisms underlying these changes remain poorly understood. Here, we characterize the phenotypes induced by maternal obesity in a mouse model and examine gene expression and epigenetic changes induced by maternal diet in adult offspring.We analyzed genetically identical male mice born from dams fed a high- or low-fat diet throughout pregnancy and until day 21 postpartum. After weaning, half of the males of each group were fed a high-fat diet, the other half a low-fat diet. We first characterized the genome-wide gene expression patterns of six tissues of adult offspring - liver, pancreas, white adipose, brain, muscle and heart. We then measured DNA methylation patterns in liver at selected loci and throughout the genome.Maternal diet had a significant effect on the body weight of the offspring when they were fed an obesogenic diet after weaning. Our analyses showed that maternal diet had a pervasive effect on gene expression, with a pronounced effect in liver where it affected many genes involved in inflammation, cholesterol synthesis and RXR activation. We did not detect any effect of the maternal diet on DNA methylation in the liver.Overall, our findings highlighted the persistent influence of maternal diet on adult tissue regulation and suggested that the transcriptional changes were unlikely to be caused by DNA methylation differences in adult liver. Marked changes in human nutrition, behavior and lifestyle have resulted in escalating rates of obesity and type 2 diabetes mellitus Several animal models have been developed to study the metabolic consequences of nutritional stress during development. For example, energy-rich diets during pregnancy in mice significantly influence body weight, hyperphagia, adiposity, insulin resistance and hypertension in offspring While these animal models have provided a thorough description of phenotypic and metabolic consequences of maternal nutrition in adult offspring, we still know little about the molecular mechanisms underlying these changes. Several studies have investigated molecular changes induced by maternal diets in a specific offspring adult tissue ad libidum access to food and water. We only considered male offspring in all of the analyses described. Experimental procedures were approved by the Institutional Animal Care and Use Committees of Case Western Reserve University and the Cleveland Clinic.C57BL/6J mice were obtained from The Jackson Laboratory. Animals were housed in temperature-controlled rooms with 14–10 h light-dark cycling and Figure S1).We assigned three-week-old virgin females to either a low-fat high-carbohydrate diet  or a high-fat low-carbohydrate diet . These diets (Research Diets Inc. D12328 and D12331) are nutritionally balanced, isocaloric, have identical quality and quantity of proteins  and have been previously used to study diet-induced obesity in C57BL/6J After weaning, we killed all dams after a four-hour fast and measured fasting blood glucose and body weight.2 production using double-labeled water (n = 5 for each of the four groups) at five and a half months of age. CO2 production is an indirect measurement of energy expenditure and has been shown to correlate well with direct estimates. We injected each mouse with 2.25% of body weight in ml of double labeled water (3∶1 D2O:H218O) and collected 25 ul of blood at 2, 26, 50, 74, 98 and 122 hours after injection. Samples were stored at -80°C until analysis. Samples were processed on a Agilent 5973N-MSD mass spectrometer equipped with an Agilent 6890 GC system and a DB-17MS capillary column (30 m×0.25 mm×0.25 um), and the data analyzed by ANOVA. At six months and after a four-hour fast, we collected blood by cardiac puncture from all animals. We then measured fasting blood glucose level and determined the concentration of fasting insulin and leptin in plasma (n = 49) with ELISA assays .For the Metabolic Phenotyping Cohort, we recorded the weight of each animal every week from weaning until 6 months of age. We measured blood glucose, for all animals (n = 47) after a four hour fast, at three time points  using a Bayer Contour glucose meter after nicking the animals' tail. For a subset of the animals (n = 5 for each of the four groups shown on Table S1).For the Molecular Phenotyping Cohort, we killed all offspring at 9 weeks of age and measured body weight, fasting blood glucose and fasting insulin and leptin in plasma as described above  samples of pancreas, skeletal muscle (thigh), white adipose tissues , whole brain, liver and heart from 40 nine-week-old males  using the DNeasy Blood and Tissue kit .First, we characterized genome-wide patterns of DNA methylation in 8 liver samples by Methyl-CpG Binding Domain (MBD) protein isolation followed by genome sequencing as described previously We also used 2 µg of DNA from each of the 40 liver tissues to characterize DNA methylation at individual CpGs using Reduced Representation Bisulfite Sequencing (RRBS) Figure S2. Briefly, we amplified each sample using 22 locus-specific primer pairs (Table S2) that include an identical 5′ oligonucleotide tail. After purification, we quantified the amplification products and pooled equal amount of all PCR products generated from a single sample. Each pool  was then PCR amplified by a second set of primers targeting the 5′ oligonucleotide tail and containing the Illumina adapter sequence and a unique barcode sequence (Table S2). We then pooled all individually barcoded libraries together and sequenced the resulting library pool on one lane of Illumina HiSeq 2000. We determined, for each sample and each locus, the average proportion of cytosine methylation using Bismark and analyzed the data by the same method as described above for the RRBS data.Finally, we also bisulfite treated 2 µg of DNA from each of the 40 liver tissues using the MethylCode Bisulfite Conversion kit  and PCR amplified 22 loci selected based on our gene expression analysis results. An overview of the experiment is presented in Figure S3).To test the effect of maternal diet on growth and metabolism of the offspring, we assigned virgin C57BL/6J females to high fat (HF) or low fat (LF) diet five weeks before pregnancy and until day 21 postpartum Figure 1From most breeding cages, we obtained litters without significant differences between the two diet groups in litter size  or offspring sex ratio . We followed up a total of 21 male offspring from 6 HF cages and 26 male offspring from 5 LF cages. At weaning, male offspring from HF fed dams were significantly heavier than those born from LF fed dams , 21 weeks  and six months  and results of glucose tolerance tests performed at 21 weeks of age  (2/kg/day) was directly affected by the maternal diet : fat mass  or fat-free dry mass  were not significantly associated with maternal diet once adjusted for body weight differences  Table 1.These results suggest that, in our mouse model, maternal diet predisposed the response to an obesogenic diet in male offspring and significantly influenced body weight and energy expenditure in adulthood, but had little effect on glucose homeostasis.Table S1).To identify molecular changes induced by the maternal diet in adult tissues and differentiate them from changes caused by the resulting body weight differences, we investigated tissues of 9-week-old mice that had similar weights irrespective of their dam's diet. Note that the phenotypes of the mice of this “Molecular Phenotyping Cohort” are very similar to those of the previously described “Metabolic Phenotyping Cohort” at the same age .We randomly selected ten nine-week-old males for each of the combinations of maternal and post-weaning diet  and isolated RNA from each animal from pancreas, skeletal muscle, white adipose tissue, whole brain, liver and heart. We characterized the gene expression profile of each RNA sample by microarrays and used principal component analysis to identify possible outliers . Acute phase response signaling and RXR activation were also among the five pathways most affected by maternal diet in adipose tissue, pancreas and heart . To understand how maternal diet affected these molecular pathways, we investigated in more detail the expression patterns of genes involved in cholesterol synthesis in liver. Fourteen genes involved in the synthesis of cholesterol from acetyl-CoA were significantly affected (FDR<5%) by maternal diet and thirteen showed a similar pattern: feeding the animals HF versus LF diet after weaning led to over-expression of these genes but the magnitude of this over-expression was greater when mice were born from LF fed dams . These patterns of expression changes suggested that maternal diet affected regulation of a key transcriptional regulator. Sterol regulatory element-binding proteins (Srebp) constituted obvious candidates since changes in regulation of these proteins lead to expression differences affecting similar genes as we observed Srebf2 (the gene encoding for Srebp2) was not detected in our assay and diet did not significantly affect expression of Srebf1 (encoding Srebp1). However, several genes known to interact with Srebp1 or Srebp2 Pparg, CAR (Nr1i3), PAR (Nr1i2) or REV-ERBα (Nr1d1), acting with RXR and involved in homeostasis and metabolic regulation  activation . Indeed, of 662,451 100bp windows that were significantly methylated in at least one sample, none were consistently methylated in all samples from one group and lacked methylation in all samples from the other group. We also tested for quantitative differences in methylation between the two groups using all windows throughout the genome  but did not identify any significant differences after correction for multiple testing. Analyses of different window-size  yielded similar results . Note that while providing a true genome-wide perspective on DNA methylation, MBD-genome sequencing is probably semi-quantitative and may not be able to detect subtle difference in DNA methylation  or HF fed dams (n = 4). Our analyses identified 1–2% of the assembled mouse reference genome as densely methylated (FDR<5%), and these regions were similar across samples . Relaxing our coverage threshold to include more CpGs yielded similar results.We then used reduced representation bisulfite sequencing (RRBS) to obtain a quantitative characterization of the DNA methylation across a large fraction of the genome. After stringent quality controls, we were able to obtain robust assessment of DNA methylation at 400,298 CpGs for each of the 40 animals analyzed in the gene expression study. We did not detect any significant differences between animals born from mothers on different diets and maintained on the same adult diet FDR <5%, Figure 5Figure S13).Finally, to overcome the loss of power inherent to multiple testing correction in genomic studies, we analyzed DNA methylation at 22 loci selected based on our gene expression findings. We amplified bisulfite-converted DNA for each locus and each of the 40 male liver samples analyzed for gene expression and sequenced all PCR products simultaneously. Again, none of the loci investigated showed significant differences in DNA methylation at any individual CpG between groups after correction for multiple testing .To begin understanding the molecular mechanisms underlying these phenotypic differences, we studied younger mice under the same experimental conditions. We hypothesized that by studying molecular changes at an earlier age, and particularly before the weight differences between groups become significant, we would enrich for pathways directly dysregulated by maternal diet rather than those affected by the resulting phenotypic differences. Our analyses revealed pervasive changes in gene expression associated with maternal diet across most tissues. The effect of the maternal diet on gene expression was typically modest (∼1.2 fold in expression level), but statistically significant due to the large number of biological replicates and consistency (in direction and intensity) across genes within a molecular pathway . Coconut oil also contains less than 6% (wt/wt) monounsaturated and 1.8% polyunsaturated fatty acids, compared to 45.1% and 11.2% for lard. In addition, lard contains 95 mg of cholesterol which is absent in coconut oil. Composition in choline and various vitamins also differs between coconut oil and lard. It is possible that mice respond differently to intrauterine exposure to different fatty acids or other nutrients present in the two diets but further research will be required to better understand the specific role of these nutrients.The effect of the maternal diet on gene expression was most pronounced in liver, where more than 5% of the genes were affected. The gene expression changes induced by maternal nutrition depended heavily on the offspring's diet after weaning, mirroring the phenotypic patterns observed in 6-month-old animals. The number of genes affected by maternal diet was four-fold greater when the animals were fed HF diet after weaning compared to animals fed LF diet. In addition, many genes influenced by the maternal diet were also affected by the post-weaning diet. For example, cholesterol synthesis genes displayed higher expression in animals fed HF than LF diet, and the magnitude of this difference depended on the diet of their dams: animals exposed to high fat during development presented a lesser response to HF diet after weaning Figure 3Srebf1 or Srebf2, but cannot rule out that these proteins were directly responsible for the patterns observed since they are primarily regulated post-transcriptionally In our setting, many of the gene expression changes induced by maternal diet in liver could result from a primary/upstream change in Srebp regulation which is established during development and tends to be responsible for long-term gene expression silencing, while histone modifications have a more dynamic role through nine weeks . Fourth, we analyzed genomic DNA extracted from whole liver and the DNA methylation patterns therefore likely represent the epigenetic marks of hepatocytes (which constitute the overwhelming majority of the liver). It is possible that DNA methylation changes occurred in another cell population (e.g. Kupffer cell) and were missed by our analyses . Finally, it is possible that the translation of maternal nutritional stress into molecular mechanisms in our model is independent of DNA methylation.We attempted to identify the primary molecular changes induced by maternal diet and tested whether epigenetic mechanisms could be responsible for the pervasive changes in gene expression. We focused on DNA methylation in separating the pre-conception, in utero, and early postnatal effects of the maternal diet. These are confounded in our experimental design: dams were fed the same diet for 5 weeks before mating, throughout pregnancy and until day 21 postpartum. To preliminarily assess these effects, we analyzed a small group of animals whose dams were switched from a LF diet to HF diet upon delivery. The adult animals were phenotypically similar to animals born from mothers fed a LF diet throughout pregnancy and lactation, which suggests that most of the phenotype changes we observed were indeed induced by intrauterine effects.A limitation of our study is the difficulty Overall, our experimental design and the findings presented here provide a framework to better understand the molecular mechanisms induced by maternal nutrition. Future studies of this animal model could identify promising pharmacological avenues to overcome developmental programming and could contribute to reduce the risk for offspring to develop obesity.MBD data can be found under SRA# GSE52266. Microarray data are available on the GEO database . RRBS and locus specific bisulfite sequencing are available on the GEO database (GSE52268).Figure S1Experimental design with dams whose diet was switched at birth. Dams are displayed in dark grey, offspring in light grey. LF and HF designate, respectively, low fat and high fat diet. Females on the left were fed a LF diet until birth when the diet was switched to HF until postpartum day 21 (weaning).(TIF)Click here for additional data file.Figure S2Description of locus specific bisulfite PCR library protocol. Samples are first amplified by multiple locus specific PCR primers in separate reactions (“Locus specific PCR”) and subsequently pooled. Adapter and barcoding sequence are added to each pool in a second round of PCR (“barcoding PCR”). After barcoding, the libraries are pooled into a single tube and sequenced.(TIF)Click here for additional data file.Figure S3Maternal weight. Average weight  of dams is presented from three weeks of age through weaning. Mating at eight weeks and weaning are noted.(TIF)Click here for additional data file.Figure S4Male offspring weights for all five groups of animals. Animal weights in grams (±SE) are presented from one week through six months of age. Animals are separated by maternal and adult diets. The maternal diet labeled “LowSwitch” represents the cohort of animals for which the dams' diet was changed from LF to HF upon delivery and offspring were fed on a HF diet after weaning.(TIF)Click here for additional data file.Figure S5Energy expenditure in adult offspring is influenced by maternal diet. Energy expenditure  is increased in male offspring of mothers fed a high fat diet during pregnancy.(TIF)Click here for additional data file.Figure S6Principal component analysis of the gene expression patterns across tissues. Each dot represents one tissue from one male mouse. Red dots represent pancreas samples; light blue, liver samples; yellow, adipose tissues; green, brains; dark blue, muscle and pink, heart.(TIF)Click here for additional data file.Figure S7Heatmap of gene expression data for six tissues for the comparison of animals fed a high fat diet post-weaning, but from mothers fed a different diet.(TIF)Click here for additional data file.Figure S8Liver gene expression level for 14 genes involved in cholesterol synthesis. See legend of (TIF)Click here for additional data file.Figure S9Comparison of the genome-wide DNA methylation patterns obtained by MBD-Genome sequencing of two liver samples. Each dot of the graph represents one 100 bp window of the genome and is displayed according to the number of reads obtained after MBD isolation in the liver sample of a male born from a HF fed dams (x-axis) and a male born from a LF fed dam (y-axis) (both mice being fed HF post-weaning).(TIF)Click here for additional data file.Figure S10Comparison of the DNA methylation patterns obtained by MBD-GS across ∼200 kb in four liver samples. The figure shows the DNA methylation patterns across 200 kb of chromosome 6. The top track shows in blue the genomic coordinates of genes based on the UCSC annotation. The next tracks show the methylation patterns observed from MBD isolation and genome sequencing for two liver samples from males born from HF fed dams (top two tracks) and two liver samples from males born from LF fed dams (bottom two tracks). Each brown vertical line display the number of reads obtained per 100 bp window for each sample.(TIF)Click here for additional data file.Figure S11Volcano plots of MBD-GS results. The volcano plots show the fold change in MBD read counts (x-axis) and associated p-value  for each tested genomic locus (window sizes are indicated above the volcano plots). After FDR correction, no windows were significantly differentially methylated.(PNG)Click here for additional data file.Figure S12RRBS Power Analysis. The figure shows the statistical power (y-axis) to detect a given difference in DNA methylation (x-axis) at conserved CpGs. To estimate power, we randomly sampled means and standard deviations from the RRBS data generated for the HH group (n = 10) and simulated RRBS data for two groups (each n = 10) with a fixed average difference in DNA methylation (x-axis). 10,000 simulations  were performed for each fixed difference in methylation and the percent of significant tests (p<0.05) calculated. Only CpGs with low DNA methylation variability within group (lowest 10% of standard deviation) were used in this analysis . The red line represents 80% power.(TIFF)Click here for additional data file.Figure S13DNA methylation patterns at 22 loci using bisulfite sequencing. The figure shows the average cytosine methylation of each CpG of 22 selected loci after bisulfite sequencing of 20 liver sample from 9-week-old offspring fed HF after weaning and born from LF fed dams  or HF fed dams .Each plot corresponds to a different locus. The y-axis shows the average DNA methylation (for 0 to 100%). The x-axis shows each CpG sequenced in a given locus. No difference remains significant after correction for multiple testing.(TIF)Click here for additional data file.Table S1Comparison of the offspring phenotypes for the “Molecular phenotyping” and “Metabolic phenotyping” cohorts at nine weeks. LF and HF stand for, respectively, low fat and high fat diet. p Mat and p Post indicate, respectively, the p-values associated with the influence of the maternal diet and diet after weaning. The influence of the maternal diet on dams' phenotypes and offspring's body weight at weaning was assessed using a Student's t-test. For all other offspring's phenotypes, the respective influence of the maternal and post-weaning diet was determined using a two-way ANOVA.(XLSX)Click here for additional data file.Table S2Primer sequences used for locus specific bisulfite sequencing. For primers used in the first round of PCRs, the primers were the combined sequences of the 5′ tail and priming sequence. Barcodes present in second primers are noted beside the primer sequences.(XLS)Click here for additional data file.Table S3Lists of the differentially expressed genes in each tissue and for each comparisons.(XLS)Click here for additional data file.Table S4Molecular pathways overrepresented in the differentially expressed genes. The table shows the five most differentially regulated pathways for each tissue and comparison based on Ingenuity Pathways Analysis. The comparisons made reveal the influence of either the maternal diet or post-weaning diet. These comparisons are broken down further by either post-weaning or maternal diets. The maternal diet and post-weaning diet are presented in a two letter code, with the first letter indicating the maternal diet, and second the post-weaning diet.(XLSX)Click here for additional data file.Table S5Nutritional content comparison of coconut oil and lard. Data were obtained from http://ndb.nal.usda.gov/.(XLSX)Click here for additional data file.


This study aims to investigate the strength of various sources of phylogenetic information that led to recent seemingly robust conclusions about higher-level arthropod phylogeny and to assess the role of excluding or downweighting synonymous change for arriving at those conclusions.The current study analyzes DNA sequences from 68 gene segments of 62 distinct protein-coding nuclear genes for 80 species. Gene segments analyzed individually support numerous nodes recovered in combined-gene analyses, but few of the higher-level nodes of greatest current interest. However, neither is there support for conflicting alternatives to these higher-level nodes. Gene segments with higher rates of nonsynonymous change tend to be more informative overall, but those with lower rates tend to provide stronger support for deeper nodes. Higher-level nodes with bootstrap values in the 80% – 99% range for the complete data matrix are markedly more sensitive to substantial drops in their bootstrap percentages after character subsampling than those with 100% bootstrap, suggesting that these nodes are likely not to have been strongly supported with many fewer data than in the full matrix. Data set partitioning of total data by (mostly) synonymous and (mostly) nonsynonymous change improves overall node support, but the result remains much inferior to analysis of (unpartitioned) nonsynonymous change alone. Clusters of genes with similar nonsynonymous rate properties  show some distinct patterns of node support but few conflicts. Synonymous change is shown to contribute little, if any, phylogenetic signal to the support of higher-level nodes, but it does contribute nonphylogenetic signal, probably through its underlying heterogeneous nucleotide composition. Analysis of seemingly conservative indels does not prove useful.Generating a robust molecular higher-level phylogeny of Arthropoda is currently possible with large amounts of data and an exclusive reliance on nonsynonymous change. The robust resolution of higher-level arthropod phylogeny has been a challenging problem, as evidenced by numerous publications with alternative proposals of relationships Taxon sampling is clearly important Features of gene analysis are also important in assessing phylogenetic informativeness. For example, while it is commonly acknowledged that not all genes are equally informative, what is to be made of the finding that six of the seven arthropod nodes mentioned above are never recovered with strong bootstrap support by any of the 62 genes (see Supplementary Multicrustacea) from 68% to 97% A second feature of gene analysis is that inclusion of more rapidly evolving genes can result in relatively lower node support values for higher-level groupings A third feature of gene analysis is that synonymous change can have a deleterious effect on inferring higher-level phylogeny noLRall1nt2 character set  A more thorough bootstrap analysis of individual gene segments has been performed than previously nt123_degen1 data matrix  and all but one of these groups (Oligostraca: Ichthyostraca + Mystacocarida) receive >70% bootstrap by at least one of four implemented approaches  without replacement from the complete data matrix has been undertaken to estimate the amount of data needed to achieve the observed levels of combined-gene node support , S3, basincrease with data set reduction relative to the original data set, and this is even more striking when one considers that 59 of the 71 nodes already have values ≥90% and, hence, are excluded from the statistic. The complementary, paired bootstrap value for each of the seven instances is always ≥25% less than for the original data set, indicating that phylogenetic signal is not equally distributed across characters and that the most informative characters may be relatively few in number.An additional, intriguing result is that there are seven instances affecting five nodes in which bootstrap values show a >10% nt123_degen1 data matrix has been partitioned into two equal-sized submatrices consisting of faster and slower evolving gene segments (nonsynonymous changes only), and their maximum likelihood topologies and bootstrap values estimated  than those for which faster genes have higher values  matrices. Only one of these three nodes receives strong bootstrap support with the complete (nt123_degen1) data matrix, although all do in the medium analysis. Conversely, there are four cases for which the medium matrix yields bootstrap values that are ≥30% lower than in the fastest or slowest matrices. All four of these nodes fail to receive strong support in the medium analysis, but three receive strong support with the complete data set, and the fourth nearly so. Results with noLRall1nt2 data sets yield similar results  with the complete matrix; 2) 91% bootstrap support for Arthropoda minus Pycnogonida with the slower genes versus 74% bootstrap support for Chelicerata with the complete matrix; 3) 93% bootstrap support for Spirobolida + Polyzoniida with the slower genes versus 67% bootstrap support for Callipodida + Polyzoniida  with the complete matrix; and 4) 79% bootstrap support for Chilopoda + Diplopoda with the slower genes versus 67% bootstrap support for Progoneata  with the complete matrix.Does partitioning by rate lead to strong support for any groups that conflict with those recovered with the complete data set? Indeed, there are four instances of this , S4: 1) nt123_degen1 data matrix  into two or three linked submatrices that differ in their average rate of nonsynonymous change yields no nodes whose bootstrap values vary by >10% from the unpartitioned nt123_degen1 analysis , there is a substantial improvement over the unpartitioned nt123 results. In particular, seven nodes in the nt123 partitioned analysis yield bootstrap values that are >10% higher than nt123 unpartitioned, while there are none lower by >1%. Even so, the nt123_degen1, unpartitioned analysis yields 28 nodes that have >10% higher bootstrap values than in the nt123, partitioned analysis, while only one is >10% lower .Partitioning the analysis . By contnt123_degen1 data matrix, which yields 63 strongly supported nodes  ed nodes . Comparied nodes , 4–8.1 result . The matconflict . Althougs to one .nt3 data matrix was analyzed  nevertheless recovers 31 strongly supported nodes in agreement with nt123_degen1, plus only one that strongly conflicts , and the resulting nt3_4foldsynon data matrix was analyzed  + Podura (Hexapoda: Collembola);83% bootstrap: Armillifer (Oligostraca: Pentastomida) + Podura (Hexapoda: Collembola) + Heterometrus (Arachnida: Scorpiones) + Hadrurus (Arachnida: Scorpiones);66%: Streptocephalus (Branchiopoda: Anostraca) + Ammothea (Pycnogonida); and52% bootstrap: Cypridopsis (Ostracoda: Podocopa) + Metajapyx (Hexapoda: Japygidae).60%: Shared bias in composition may also at least partially explain the level of bootstrap support for many of the presumably correct groupings, includingHeterometrus + Hadrurus (both Arachnida: Scorpiones);100%: Stenochrus + Mastigoproctus + Phrynus ;87%: Peripatoides + Euperipatoides (both Onychophora: Peripatopsidae);100%: Peripatoides + Euperipatoides + Peripatus ;97%: Limulus + Carcinoscorpius (both Xiphosura);100%: Pedetontus + Machiloides (both Hexapoda: Archaeognatha);100%: Acanthocyclops + Mesocyclops .67%: An inspection was undertaken of a slightly realigned arthropod data matrix see .Morphology and gene-based studies of higher-level arthropod phylogeny have been ongoing for more than 75 and 20 years, respectively nt123_degen1 data set yielded posterior probabilities of 1.0 for all nodes within Mandibulata except one .While other artifacts remain possible, e.g., An alternative interpretation for the paucity of strong support at the single-gene level is that some or all of the genes contribute substantial conflicting signal that cancels the phylogenetic signal, keeping individual node support low and raising doubts about the ultimate source of the combined-gene support. But this seems unlikely to be a predominant explanation because individually the gene segments mostly strongly support groups present in Submatrices corresponding to different rate categories of genes similarly do not provide striking evidence for major internal conflict. Only four groups conflict with ones in the complete matrix . And only the first one yields strong support for both (conflicting) alternatives. More importantly for this report, none of these groups conflict with any of the six newly named groups, Symphyla + Pauropoda, Mandibulata, Oligostraca, or Hexapoda.degen1 and noLRall1nt2, analyze in a likelihood framework those nucleotides that undergo mostly nonsynonymous change. A related approach -- likelihood analysis of amino acid change -- is conceptually similar, but of course the underlying model is very different. The fourth approach -- likelihood analysis of codon change -- is different in that codons, not single nucleotides, are analyzed and no data, including those that undergo synonymous change, are excluded from the analysis. Importantly, all four methods largely recover the same groups, including Symphyla + Pauropoda, Mandibulata, Oligostraca, Hexapoda, and the six newly named pancrustacean groups.Although not a formal optimality criterion, consistency of results across different analytical approaches is also quite striking in Regier et al., 2010 all scientific conclusions are defeasible, it is clear that nodes recovered with the complete data matrix with <100% bootstrap support are more sensitive to data subsampling than those with 100% support. Furthermore, the definition of 80% bootstrap support as "strong" is a heuristic device only; it does not refer to some innate or even broadly accepted category. Therefore, it seems likely that strongly supported nodes in the range of 80 - 99% bootstrap can still be sensitive to changes in the amount of data. Had the amount of data been less, the probability of recovering that group with strong support would have been reduced  differences in their relative bootstrap support values of total data (nt123) analyses. Interestingly, 9 of these groups are more strongly supported by the slower genes, and all 9 have longer node-to-terminal branch lengths than the other eight, supporting the idea that more slowly evolving genes are better for recovering deeper nodes faster genes can remain informative at deeper levels.To improve the statistical properties of the analyses, subsets of gene segments with similar rate properties were analyzed and compared . In the fastest, medium, slowest), additional resolution becomes apparent in that three nodes  receive strong support from the medium subset of genes, but mystifyingly four nodes  receive strong support from the fastest + slowest genes, excluding the medium genes.When gene segments are partitioned into three subsets  by the slowest genes.Progoneata is supported (74% bootstrap) by the medium genes, while Spirobolida + Polyzoniida is strongly supported by the slowest genes.Callipodida + Polyzoniida is strongly supported by the Based on these observations, it would seem prudent to abstain from any strong conclusion about these conflicting groups, although only one of these four (Ammotheidae + Endeidae) is strongly supported in the complete-gene analysis . No othedegen1 and noLRall1nt2 analyses when analyzing deep-level arthropod relationships nt123 and nt123_degen1 in this report reinforces this point  is likely to be incorrect, based on consistent, but modestly supported, recovery of Progoneata  and exclusion , we have tried partitioning nt123 into mostly nonsynonymous and most synonymous change , and this results in a modest improvement relative to nt123, unpartitioned  tend to support nodes that are closer to the backbone on the phylogram shown in As we have documented in this report, synonymous change, much more so than nonsynonymous change, can be misinformative of the correct phylogeny . While tincorrect nodes are almost entirely due to synonymous change. For example, while it has been stated that nt3 characters should not be removed because they contribute useful phylogenetic information nt3 strongly supports only 19 of 63 nodes strongly supported by nt123_degen1, plus 15 conflicting nodes (6 strongly conflicting), while nt3_degen1, which is almost entirely nonsynonymous and polymorphic, still strongly supports 31 of 63 nodes with only 3 conflicting nodes (1 strongly conflicting). When only nt3 characters of fourfold synonymous codons (nt3_4foldsynon) are analyzed, thereby largely eliminating the potential for nonsynonymous change, the number of strongly supported nodes drops to 10 out of 63 plus five conflicting nodes (1 strongly conflicting). Even this relatively small level of support is suspect, however, when coupled with information about nucleotide heterogeneity , provide additional support and explication of the phylogenetic results presented in Regier et al. (2010) Colossendeis sp., lab code name "Col") has now been shown to have been misidentified (see Acknowledgments). It has been reidentified as Nymphon sp. (Pycnogonida: Nymphonidae). The lab code name remains "Col". The original misidentification changes nothing as regards conclusions in those earlier publications about the position of Pycnogonida within Arthropoda. In the current report, we use "Nymphon sp." and "Nymphonidae" throughout.Taxon and gene sampling are identical to that reported in Regier et al., 2010 nt123) or after fully degenerating nucleotides (nt123_degen1) using IUPAC ambiguity codes at those sites that can potentially undergo synonymous change, thereby making synonymous change largely invisible and reducing the effect of compositional heterogeneity but leaving the inference of nonsynonymous change largely intact (summarized in nt123_degen1 CAC and CAT (His) are both coded CAY, while TTA, TTG, CTT, CTC, CTA, and CTG (Leu) are all coded YTN. The nt123_degen1 data matrix and the nt123 data matrix can both be downloaded as Supplementary Data of Regier et al., 2010 http://www.phylotools.com. A character-exclusion mask  of ambiguously aligned nucleotide characters was also invoked, and is identical to that in Regier et al., 2010 degen1 approach, one set of submatrices, called single-gene matrices, was derived from each of the 68 gene fragments. Another set consisted of gene fragments grouped by their average rate of nonsynonymous change  rather than nucleotide degeneration http://www.phylotools.com). With this approach, two complementary subsets, called noLRall1nt2 and LRall1nt3, are defined as follows: The noLRall1nt2 character subset consists of all characters at the second codon position (nt2) plus only those nt1 characters that encode no leucine or arginine codons . The LRall1nt3 character subset consists of all characters at the third codon position (nt3) plus any and all nt1 characters not present in noLRall1. Since leucine and arginine codons are the only ones that undergo synonymous change at nt1, their absence from noLRall1nt2 leads to a dramatic reduction in the amount of synonymous change inferred over a tree. Software to create noLRall1 and LRall1 character set definitions is available at http://www.phylotools.com. In this set of analyses, the noLR analysis of gene fragments 1–68 and of the five subsets with differing rates occurred with noLRall1nt2 only, after excluding LRall1nt3.These same collections of gene fragments, namely, 1–37 / 38–68 and 1–24 / 25–43 / 44–68, as well as all genes combined (1–68) were also analyzed in a distinct manner from LRall1nt3 has already been described, and it was compared with LRall1nt3_degen1. Analysis of the latter captures all of the nonsynonymous change in LRall1nt3 with very little or no synonymous change. Another pair of submatrices was nt3 and nt3_degen1. The final submatrix, called nt3_4foldsynon, included only those sites at nt3 that encode fourfold-degenerate codons, e.g., alanine and glycine; all other sites were converted to N. Analysis of the nt3_4foldsynon submatrix should capture entirely synonymous change. Degeneration of nt3_4foldsynon would yield all N's and hence was not performed.To explore the informativeness of synonymous change, progressively more restrictive data submatrices were constructed and analyzed with and without degeneration. degen1 submatrices, character order for the entire nt123_degen1 data matrix was first randomized using a Perl script (available at http://www.phylotools.com), which randomly resampled all the characters in the matrix without replacement. This reordering (or shuffling) was repeated in series 10,000 times to ensure randomness. Submatrices of differing sizes were constructed . Only one submatrix each of 100%, 85%, and 15% of the complete size was constructed. For the 50% matrices, five independently randomized 100% matrices were split into five pairs of complementary matrices . Likewise, two independently randomized 100% matrices were each split into three complementary 33% matrices .For another set of nt123). In one, the complete data matrix  was split into two complementary subsets, namely, noLRall1nt2 and LRall1nt3, in order to separate a major source of character variation, namely, nonsynonymous and synonymous change. In the phylogenetic analysis, GTR + G + I models were applied to each subset with unlinked parameters. In another partitioning scheme, the nt123_degen1 matrix was analyzed by applying GTR + G + I models with unlinked parameters to three subsets described above, namely, fastest, medium, slowest.Further, two types of character partitioning were undertaken for total data (Hexagenia limbata (codename: May) that was duplicated in silico from another species' RNA polmerase II (largest subunit) sequence, and this has now been replaced with Ns. The realigned, non-degenerate data matrix used for indel analysis (nt123) is included in Supporting Information as Dataset S1. Separately, the degenerate, realigned data matrix  was reanalyzed under likelihood using GARLI 1.0. There were no changes in the maximum likelihood topology relative to Regier et al., 2010 Insertion / deletion (indel) events were analyzed for phylogenetic informativeness, but only "high-quality" indels, which consisted of those that have relatively unambiguous alignments, defined as those with no staggered ends and identical lengths but not necessarily identical sequences. Before doing this, however, a slightly improved manual alignment relative to that in Regier et al. (2010) nt3_4foldsynon analysis, which was 236. The tree shown in All phylogenetic analyses are based on the maximum likelihood criterion applied to nucleotides, as implemented in GARLI  of the fragment, and then corrected for missing taxa by multiplying this fraction by (80 taxa / (80 taxa − number of missing taxa)). The calculated value of phylogenetic utility for each segment was plotted relative to its rate of nonsynonymous change . Two features of this metric are worth pointing out. First, it estimates the total number of nodes that could be recovered, but in most systematic studies not all nodes are of equal interest. Second, this metric estimates utility as information density , but in most systematic studies what matters in a practical sense is the total amount of information in a sequenceable gene fragment.To provide a quantitative estimate for how useful a particular gene segment is for phylogeny reconstruction, an approximate metric called "phylogenetic utility" was developed. Based on single-gene likelihood bootstrap analyses, the total number of taxonomic groups present in ange see . Linear http://www.phylotools.com). A separate script available from the same web site was used to restrict the total data matrix to fourfold degenerate codons, which were subsequently split by codon position to produce a matrix  of those nt3 characters that are encoded by fourfold degenerate codons.To describe compositional heterogeneity in a data matrix, we calculated Euclidean distances on the proportions of the four nucleotide frequencies treated as independent characters using a Perl script (available at Table S1nt123_degen1) for taxonomic groups (nodes) present in Single-gene bootstrap values Click here for additional data file.Table S2nt123_degen1) for taxonomic groups NOT present in Single-gene bootstrap values Click here for additional data file.Table S3Bootstrap values based on analysis of shuffled data matrices of varying sizes (100% to 15% of complete data matrix). This table lists bootstrap values after randomizing character order in the 100% data matrix and splitting it into portions of varying sizes for analysis (100% to 15% of complete data matrix) without replacement. A subset of the Table-S3 results are also shown in (DOC)Click here for additional data file.Table S4Bootstrap values based on analysis of data sets and subsets differing in their average rates of nonsynonymous change. The complete data set is split into two or three subsets based on average rates of nonsynonymous change of individual genes, and bootstrap analyses are performed to estimate the informativeness of the different rate category ranges. A subset of the Table-S4 results is also shown in (DOC)Click here for additional data file.Table S5GenBank accession numbers Click here for additional data file.Dataset S1nt123) for 80 taxa and 62 genes, slightly realigned relative to that in Regier et al., 2010 noLRall1 and LRall1. This data set can be degenerated using the degen1 script available at http://www.phylotools.com. The species codenames used in this realigned data set are also identified by their complete genus-species names in Table SM1 of Regier et al., 2010 A Nexus-formatted data set that includes nucleotide sequence data ((NEX)Click here for additional data file.


Some studies have shown that erythropoietin have some non-hematopoietic protective effects on ischemia-reperfusion injury in myocardial cells. We evaluated the effect of exogenous EPO infusion on reducing ischemia-reperfusion injuries and improvement of cardiac function by echocardiography shortly after coronary artery bypass graft surgery.43 patients were joined the study and randomly divided in two groups, EPO group: receiving standard medication and CABG surgery plus 700 IU/kg erythropoietin  and control group: receiving standard medication and CABG surgery plus 10cc normal saline as placebo. The cardiac function was assessed by Echocardiography in all patients in before, 4 days after and also 30 days after CABG operation.Echocardiography indicated that EF had no differences between EPO and control group at 4 days  or 30 days after surgery . There were no differences between EPO and control group in wall motion score index at 4 days (P=0.83) or 30 days after surgery (P=0.902). In EPO group: Left ventricle end systolic and diastolic diameter  had reduction, as compared to control group.Our data suggest that peri-operatively exogenous EPO infusion can’t improve ventricular function and Wall motion index in first weeks after surgery. But as compared to control group, reduction in LVEDD and LVESD at 4 days or 30 days after CABG surgery in EPO group suggested that EPO had correlation with reduction of myocytes remodeling and reperfusion injury early after CABG surgery.


Whole genome pathway analysis is a powerful tool for the exploration of the combined effects of gene-sets within biological pathways. This study applied Interval Based Enrichment Analysis (INRICH) to perform whole-genome pathway analysis of body-mass index (BMI). We used a discovery set composed of summary statistics from a meta-analysis of 123,865 subjects performed by the GIANT Consortium, and an independent sample of 8,632 subjects to assess replication of significant pathways. We examined SNPs within nominally significant pathways using linear mixed models to estimate their contribution to overall BMI heritability. Six pathways replicated as having significant enrichment for association after correcting for multiple testing, including the previously unknown relationships between BMI and the Reactome regulation of ornithine decarboxylase pathway, the KEGG lysosome pathway, and the Reactome stabilization of P53 pathway. Two non-overlapping sets of genes emerged from the six significant pathways. The clustering of shared genes based on previously identified protein-protein interactions listed in PubMed and OMIM supported the relatively independent biological effects of these two gene-sets. We estimate that the SNPs located in examined pathways explain ∼20% of the heritability for BMI that is tagged by common SNPs . Obesity greatly increases risk for many forms of pathology, including vascular disease, multiple forms of cancer, heart disease, and other serious health problems P<5×10−6. During stage two, 125,931 subjects from 34 additional studies were used examine the 42 loci with suggestive significance in the first stage. In a joint analysis of the first and second stage 32 SNPs were significantly associated with BMI at p<5×10−8, increasing the number of loci robustly associated with BMI from 10 to 32 In 2010, the GIANT Consortium (Genetic Investigation of ANthropometric Traits) performed the largest GWAS of BMI to date, a two-stage analysis on 249,796 individuals of European ancestry Exclusively analyzing pathways that contain significant individual SNP associations in a discovery set is an informed way to reduce the number of pathways being examined and decrease the rate of type II errors from among those pathways implicated by SNPs detected in the discovery set Alternatively, formal whole-genome pathway analysis has a much less restricted scope, examining pathways composed of SNPs within genes from across the entire genome The current study employed Interval Based Enrichment Analysis (INRICH) to perform whole-genome pathway analysis of body-mass index (BMI) We also examined gene overlap between significant pathways to gain a better understanding of the biological networks that influence BMI. Genes and their products often act in multiple pathways, meaning some degree of overlap is expected Regions of the genome within significant pathways may have a greater than expected influence on the heritability of a trait because pathways contain sets of genes with shared biological functions Initially, we performed whole-genome pathway analysis using Interval Based Enrichment Analysis (INRICH) to identify pathways that were significantly enriched for SNP associations in the GIANT discovery set. The first stage in INRICH analysis generates interval data based on patterns of linkage disequilibrium to construct independent regions of association. We used HapMap Phase 2 European-American as a reference panel for patterns of linkage-disequilibrium (LD), the same reference GIANT used to perform imputation on the original data At SNP α thresholds of 10.0%, 5%, 1%, and 0.5%, we identified 85, 51, 35, and 20 nominally significant pathways respectively –S4 in thTo winnow down the 191 nominally significant pathways identified in the GIANT discovery set to a smaller number of more robustly associated pathways, we used an independent replication set of 8,632 individuals to validate only those pathways detected as nominally significant in the initial analysis. In total, 47 of these pathways replicated as nominally significant (p<0.05) of the 191 examined: 23 of 85 at the 10% SNP threshold, 16 of 51 at the 5% SNP threshold, 5 of 35 at 1% SNP threshold, and 3 of 20 and 0.5% SNP threshold –S8. The p<.05) in the replication sample were significant after accounting for multiple testing, dependencies between pathways, and characteristics  of each pathway, we used the permutation approach employed in INRICH. INRICH compares the observed nominal p-values of pathways to a null distribution composed of the minimum nominal p-value observed across the 47 examined pathways from each iteration of a permutation p = 0.038), and the Reactome stabilization of P53 pathway (corrected p = 0.048), were significantly enriched for associations from the top 5% of SNPs and were not previously associated with BMI. The KEGG Lysosome pathway was enriched for associations from both the top 1% (corrected p = 0.016), and the top 0.5% of SNPs (corrected p = 0.043), which shows this pathway contained an excess of loci with relatively large effects that were distributed across the top 0.5% and top 1% of SNP associations. Of the pathways that did contain genes examined in previous studies, the KEGG Toll-like receptor-signaling pathway (corrected p = 0.049), and the KEGG Fc epsilon RI signaling pathway (corrected p = 0.025) were identified as enriched for associations from the top 10% of SNPs, enrichment at this threshold in combination with lack of significant enrichment at more stringent thresholds indicates these pathways contained an excess of loci with relatively small effects. The Signal Transduction KE ERK1/ERK2 MAPK pathway (corrected p = 0.041) was enriched for associations from the top 5% of SNPs, demonstrating enrichment for loci with relatively moderate effects compared to the other thresholds examined. Regional association plots for intervals in all significant pathways are presented in Figures S1–S7.To determine which of the 47 pathways that replicated (We investigated the overlap of genes between these six significantly associated pathways too better understand their inter-relationships. Two non-overlapping groups of pathways (pathways that do not share common genes) emerged. Details on the overlap at the gene level between significant pathways are provided in After investigating the degree of gene overlap between significant pathways, we used STRING 9.0  to examine previously identified protein-protein interactions among genes that were shared across significant pathways We used the GCTA software package to estimate SNP based heritability using linear mixed models in order to determine if SNPs within the examined pathways explained a disproportionate amount of the heritability for BMI Six pathways contained significant enrichment for associations with BMI after correcting for multiple testing. Three of these pathways did not contain genes located near previously associated loci. Two non-overlapping gene-sets emerged when we compared which genes were contained within the six significant pathways. Gene clusters based on identified protein-protein interactions listed in PubMed and OMIM suggested that the genes within the significant pathways fit into two sets of relatively independent biological effects. These two sets were highly concordant with the groups of genes shared between the six significant pathways, as well as the division between pathways containing loci that were previously associated with BMI and the novel pathway associations identified in this analysis , Table 2We identified novel associations between the Reactome regulation of ornithine decarboxylase pathway, and the Reactome stabilization of P53 pathway with BMI. Recent studies have found a relationship between the P53 tumor suppressor protein and adipogenic differentiation between white and brown fat cells, and has been directly implicated in protection against diet-induced obesity in both mice and humans Our results also provide support for the GIANT consortium‘s finding that pathways containing genes near significant loci are more likely to contain other loci with greater effects than is expected by chance. Pathways that were part the second protein-protein interaction cluster, which contained genes previously associated with BMI, demonstrated the positive relationship between functional clustering, increased enrichment for novel associations, and previously detected significant loci. Specifically, The KEGG Toll-like receptor-signaling pathway and the Signal Transduction KE ERK1/ERK2 MAPK pathway shared seven genes, including the gene NFKB1. SNPs near NFKB1 were previously associated with BMI, and we found it was one of the most highly connected genes in the protein-protein interaction cluster Some limitations should be noted when interpreting our results. First, although our analysis indicates statistically enriched association of SNPs within multiple pathways, and determined that SNPs within the examined pathways explained a significant proportion of the heritability for BMI, we were unable to determine the contribution of individual pathway gene-sets to BMI heritability, or whether specific gene-sets explained a greater than expected proportion of the heritability due to lack of power. Future studies may be able to increase power by applying a method recently developed by Jian Yang et al. that estimates the combined effect of multiple SNPs on the heritability of a trait using only summary statistics In summary, we examined summary statistics from a meta-analysis of 123,865 subjects performed by the GIANT Consortium, and a sample of 8,632 subjects to assess independent replication of pathways identified as having significant enrichment of association. Six pathways contained significant enrichment for associations with BMI after correcting for multiple testing. The Reactome regulation of ornithine decarboxylase pathway, the KEGG lysosome pathway, and the Reactome stabilization of P53 pathway are novel pathway associations with physiological effects that are relevant to BMI. These results demonstrate that whole-genome pathway analysis can detect significantly enriched pathways that do not contain specific candidate genes or individually significant SNPs. Our results also provide further evidence for the highly polygenic structure of BMI, and identify the relative contribution of SNPs within pathway gene-sets to BMI heritability. We demonstrate how network-based approaches that combine the results of pathway analysis with protein-protein interaction information can be used to gain a better understanding of the biological connections that influence BMI. Intriguingly, we show significant convergence of key genes and biological functions being broadly involved in regulation of growth and metabolism through the application of different methods of genetic analysis. This may be of significant diagnostic and therapeutic importance. More conclusive interpretation of individual loci will require more focused regional analysis, such as sequencing. For further investigation of these pathways in independent datasets, we propose testing a model that includes investigation of the effects of rare-variants and other genetic models . In combination with targeted DNA sequencing studies, this may reveal the impact of discrete molecular pathways on risk for many forms of pathology, including obesity, multiple forms of cancer, cardiovascular disease, and other serious health problems. Further functional work is required in particular to investigate the role of adipogenic differentiation between white and brown fat cells, up regulation of white fat cells, and increased cell growth/decreased apoptosis, given the growing convergence across studies of metabolic regulation on these mechanisms.http://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files) The discovery set in our analysis was composed of publicly available summary statistics from a meta-analysis of 46 GWAS of BMI performed by the GIANT Consortium, a total sample of 123,865 individuals of European ancestry  SNP Health Association Resource (SHARe), the GENEVA Genes and Environment Initiatives in Type 2 Diabetes , and the Coronary Artery Risk Development in Young Adults (CARDIA) Study – Gene Environment Association Studies Initiative (GENEVA), as available through NCBI’s database of Genotypes and Phenotypes (dbGaP). Information on genotypes (Affymetrix 6.0), phenotypes, and environmental variables from 8,632 individuals was used from across all three studies  and the risk factors that predict progression to overt cardiovascular disease The GENEVA Type 2 Diabetes (NHS and HPFS studies) are prospective cohort studies of type 2 diabetes, body mass index, and several related phenotypes in 121,700 female registered nurses between the ages of 30–55 years at baseline in 1976, and 51,529 male health professionals between the ages of 40–75 years at baseline in 1986 respectively The CARDIA study is a prospective, multi-center investigation of the natural history and etiology of cardiovascular disease between the ages of 18 to 30 at the time of initial examination The first stage of data cleaning involved using PLINK, the whole genome association analysis toolset, in combination with R statistical computing software, to perform quality control procedures on all three samples included in the replication set separately nd degree relatives, were excluded from the sample. Visual inspection of Multidimensional scaling (MDS) plots was used to remove outliers with respect to ancestry. Markers were excluded if (1) genotyping rates were less than 95%, (2) minor allele frequencies were less than 0.01, and (3) if p-values from the Hardy-Weinberg Equilibrium (HWE) test were less than 1×10−4. We also removed individuals who had missing values for any covariates or phenotypic data. This resulted in a total of 8,632 unrelated European ancestry individuals that met all cleaning thresholds across all samples included in the replication set. The physical positions of all SNPs were updated to ensure concordance across datasets and compatibility with pathway annotation using the hg18 assembly of the human genome. We then extracted the 463,139 SNPs that were common to both the discovery set and each of the replication sub-sets to minimize any differences between samples used in the analysis.Subjects were excluded if genotyping rates were less than 95%. Individuals were also excluded if the predicted sex based on X-chromosome genotypes did not match the recorded sex. Subjects who were outliers with respect to estimated heterozygosity, those greater than 3 standard deviations from the mean, were excluded. All close relatives of individual subjects, based on mean identity-by-descent  values indicating relatedness of less than 2A log-transform of BMI was performed to adjust for BMI not being normally distributed, −8 to account for multiple testing A genome-wide association analysis was performed on all SNPs using the residualized BMI phenotype as the target outcome. Using the PLINK software package (v1.07) with the linear models option, a linear regression test was performed on all quality controlled SNP data using 8,632 individuals genotyped at 463,139 loci We used Interval Based Enrichment Analysis (INRICH) to identify pathways that were significantly enriched for SNP associations at four commonly used cutoffs of the top 0.5%, 1.0%, 5.0%, and 10.0% of SNP associations from the 463,137 SNPs included in our analysis Gene set annotation was downloaded from the Molecular Signatures Data-base (MSigDB version 3.7), for 880 canonical pathways Analysis using INRICH involves three stages: (1) generate interval data based on patterns of linkage disequilibrium to construct highly independent regions of association; (2) identify nominal enrichment using an interval-based permutation strategy; and (3) perform a second round of permutation to correct for multiple testing at the pathway level clump-p1 = threshold; clump-p2 = 1; clump-r2 = 0.2; clump-kb = 250). The values selected match those from previous studies that identified LD-independent associated regions using PLINK’s LD clumping option when examining the same p-value cutoff thresholds that we used A list of LD-independent associated genomic regions was generated for the replication set using the observed SNP associations and patterns of linkage-disequilibrium present in the data. In the discovery set, LD-independent associated genomic regions were identified using summary-level statistics from the BMI meta-analysis performed by GIANT in combination with a reference panel to estimate patterns of LD. HapMap Phase 2 European-American was used as a reference panel, the same reference used by GIANT to perform imputation on the original data The GCTA software package was used to generate all SNP based heritability estimates using linear mixed models y is a vector of phenotype values, b is a vector of fixed effects of the overall mean, X is an incidence matrix for the fixed effects that relates these effects to individuals, g is a vector of random additive genetic effects based on aggregate SNP information, and e is a vector of random error effects. Phenotype variance estimates were estimated by the following formula:Where σ2g, and error variance is σ2e, A is the genetic relationship matrix estimated using SNPs, and I is an identity matrix. Variances were estimated using GCTA’s restricted maximum likelihood (REML) option, and then converted to heritability estimates Additive genetic variance captured by SNPs is We determine if gene-sets were enriched for their relative contribution to the heritability of BMI by examining if a greater proportion of the heritability was explained than expected based on the proportion of the genome represented Figure S1KEGG Fc epsilon RI signaling pathway intervals threshold top 10%.(DOC)Click here for additional data file.Figure S2KEGG Lysosome pathway top 0.5% intervals.(DOC)Click here for additional data file.Figure S3KEGG Lysosome pathway top 1% intervals.(DOC)Click here for additional data file.Figure S4KEGG Toll-like receptor-signaling pathway top 5% intervals.(DOC)Click here for additional data file.Figure S5The Reactome regulation of ornithine decarboxylase pathway top 5% intervals.(DOC)Click here for additional data file.Figure S6Reactome stabilization of P53 pathway top 5% intervals.(DOC)Click here for additional data file.Figure S7Signal Transduction ERK1/ERK2 MAPK top 5% intervals.(DOC)Click here for additional data file.Figure S8Replication Set Manhattan Plot.(DOC)Click here for additional data file.Figure S9Replication Set Quantile-Quantile Plot.(DOC)Click here for additional data file.Table S1INRICH Results for Discovery Set cutoff top 10%.(DOC)Click here for additional data file.Table S2INRICH Results for Discovery Set cutoff top 5%.(DOC)Click here for additional data file.Table S3INRICH Results for Discovery Set cutoff top 1%.(DOC)Click here for additional data file.Table S4INRICH Results for Discovery Set cutoff top 0.5%.(DOC)Click here for additional data file.Table S5INRICH Results for Replication Set cutoff top 10%.(DOC)Click here for additional data file.Table S6INRICH Results for Replication Set cutoff top 5%.(DOC)Click here for additional data file.Table S7INRICH Results for Replication Set cutoff top 1%.(DOC)Click here for additional data file.Table S8INRICH Results for Replication Set cutoff top 0.5%.(DOC)Click here for additional data file.Table S9(XLS)Click here for additional data file.Table S10Excess of heritability beyond what is expected based on the proportion of the genome represented for nominally significant pathways at each threshold of significance examined.(DOC)Click here for additional data file.Table S11Replication set population statistics.(DOC)Click here for additional data file.


Colletotrichum lindemuthianum in which nuclei were labelled with either a green or red fluorescent protein in order to microscopically monitor the fates of nuclei and heterokaryotic cells following hyphal fusion. As opposed to fusion of hyphae in mature colonies that resulted in cell death within 3 h, fusions by conidial anastomosis tubes (CAT) between two incompatible strains during colony initiation did not induce the vegetative incompatibility response. Instead, fused conidia and germlings survived and formed heterokaryotic colonies that in turn produced uninucleate conidia that germinated to form colonies with phenotypic features different to those of either parental strain. Our results demonstrate that the vegetative incompatibility response is suppressed during colony initiation in C. lindemuthianum. Thus, CAT fusion may allow asexual fungi to increase their genetic diversity, and to acquire new pathogenic traits.It has been hypothesized that horizontal gene/chromosome transfer and parasexual recombination following hyphal fusion between different strains may contribute to the emergence of wide genetic variability in plant pathogenic and other fungi. However, the significance of vegetative (heterokaryon) incompatibility responses, which commonly result in cell death, in preventing these processes is not known. In this study, we have assessed this issue following different types of hyphal fusion during colony initiation and in the mature colony. We used vegetatively compatible and incompatible strains of the common bean pathogen  Colletotrichum lindemuthianum is an important pathogenic fungus that causes anthracnose on Phaseolus vulgaris, the common bean. Although effective control of this disease is an urgent issue for improving bean cultivation, this has proven difficult due to the large degree of pathogenic, and presumably genetic, variation observed between different C. lindemuthianum strains C. lindemuthianum, which facilitates meiotic genetic recombination, is rarely observed in nature Fungal plant pathogens exhibit a high degree of genetic variability but how this is acquired in the apparent absence of sexual reproduction is not well understood. One possible source of new genetic variability is vegetative hyphal fusion allowing horizontal gene or chromosome transfer that may be followed by parasexual (i.e. non-meiotic) recombination het or vic) loci then they are vegetatively incompatible and the fusion commonly results in rapid death of the heterokaryotic cell formed C. lindemuthianum is considerable and that numerous vegetative compatibility groups are present Colletotrichum species is still very limited Non-self fusion between mature hyphae of genetically distinct filamentous fungi results in the formation of heterokaryotic cells in which nuclei with different genotypes share the same cytoplasm. However, if the fused cells have different alleles at their heterokaryon or vegetative incompatibility , a specialized hypha or cell protrusion involved in somatic cell fusion during colony initiation, was first described in C. lindemuthianum, we generated strains in which nuclei were labelled with either a green fluorescent protein (GFP) or red fluorescent protein (RFP). The dynamics and fates of the different types of labelled nuclei from vegetatively incompatible strains of C. lindemuthianum were monitored following vegetative hyphal fusion between mature colonies and CAT fusion during colony initiation. We show that hyphal fusion in mature colonies between two incompatible strains leads to rapid cell death, while heterokaryon incompatibility is suppressed following CAT fusion during colony initiation. Heterokaryotic mycelia formed from CAT fusion survived with altered phenotypic traits.In order to follow up on earlier observations of heterokaryon formation in sgfp gene fused to N. crassa histone H1, and contains the hph gene which confers hygromycin B resistance. Plasmid pGR02 carries the RFP gene for a dimeric version of dsRed (‘tdimer2(12)’; F. graminearum histone H4-2, and contains the ble gene which confers phleomycin resistance. Wild type strains LV115, LV51 and LV77 were transformed with these plasmids. The transformation efficiency of C. lindemuthianum varied between strains and plasmids used. The highest transformation efficiency (1.67 transformants µg−1 DNA) was obtained with LV115 with pMF357. Transformation efficiency was lower (0.2 transformants µg−1 DNA) when LV51 was transformed with pGR02. Despite numerous attempts, we were unable to transform LV77 with these plasmids. We refer to the LV115 transformant with H1-GFP as tFI01 and LV51 transformant with RFP-H4 as tFI04  nuclear migration occurred , 15 min 2) and incompatible combinations (25 fusions per mm2). Non-self fusions were rare, however, especially between the incompatible strains in which the amount of non-self fusion was less than 5% of all hyphal fusion observed . These results indicate that vegetative hyphal fusion between incompatible strains is restricted and leads to rapid cell death within a few hours.We then quantified the amount of fusion between hyphae of the same colony (self fusion) and between different colonies (non-self fusion) in the region of contact in both the compatible and incompatible combinations. Self fusion was relatively common in both the compatible . However, a χ2 test comparing the proportions of self and non-self fusions between hyphae in mature colonies and CAT fusion in the incompatible combination showed that there was significant difference between them (P<0.01). This implies that incompatible hyphal fusion occurs more commonly during colony initiation than in mature colonies.We next investigated the amount of CAT fusion in both compatible and incompatible combinations , and theThe fate of fused conidia or conidial germlings following CAT fusion between the incompatible strains (tFI01 and tFI04) was further investigated using confocal live-cell imaging. CAT fusion between two uninucleate conidia from incompatible strains was typically followed by mitosis , but notSubsequent to heterokaryon formation following CAT fusion, the formation of yellow nuclei (which resulted from the co-existence of H1-GFP and RFP-H4 within single nuclei), was sometimes observed. After 72 h of incubation, yellow nuclei were observed in 27% of the heterokaryotic cells generated by CAT fusion (n = 60). There seemed to be two distinct mechanisms by which these yellow nuclei were formed: (1) by a fluorescent fusion protein encoded by one of the nuclei being transferred into a nucleus labelled with the other fluorescent protein; and (2) by the fusion of red and green fluorescing nuclei . 70% of Heterokaryotic colonies originating from CAT fusion were grown on double-selective medium for 7–15 days and imaged by confocal microscopy. Although hyphae with both green and red nuclei were observed , the dis3S, M3S with Hyg, M3S with Phleo, and M3S with both antibiotics), and counted the number of colonies formed  between the number of conidia formed on each medium  , were inoculated onto PDA containing both antibiotics, no colonies were formed. This clearly indicated that the antibiotics do not allow ‘background’ growth of any non-resistant colonies. In contrast, we observed colonies growing  on PDA cs formed . Since oh medium . This stWe sought further confirmation of the idea that heterokaryon formation following CAT fusion can lead to emergence of strains with different genetic traits. We performed pathogenicity tests on differential bean cultivars and the Pérola cultivar . Single C. lindemuthianum that are vegetatively incompatible always results in rapid cell death of the fused cells. However, the remarkable finding that we have consistently demonstrated in this study is that heterokaryotic cells formed following CAT fusion between vegetatively incompatible strains survive and stable heterokaryons are maintained during the early stages of colony development following CAT fusion. This represents an escape from the normal incompatibility response resulting in cell death that we showed to occur in mature colonies of C. lindemuthianum. Thus, our results indicate that vegetative incompatibility can be suppressed during colony initiation and that CAT fusion may facilitate horizontal gene/chromosome transfer and non-meiotic recombination of genetic information from two incompatible strains to promote expansion of genetic diversity. Suppression of the vegetative incompatibility response following CAT fusion in C. lindemuthianum has been previously proposed C. lindemuthianum. These findings challenge the previous idea that genetic exchange between two incompatible strains will be invariably prevented following vegetative fusion Accumulating evidence has highlighted the importance of the vegetative incompatibility response in preventing establishment of a heterokaryon between two incompatible fungal isolates Time-lapse imaging of heterokaryotic cells resulting from CAT fusion revealed the formation of yellow fluorescing nuclei containing both green and red fluorescently labelled histones. The yellow nuclei seemed to be formed in two different ways , either C. lindemuthianum were unsuccessful in convincingly showing the presence of diploid nuclei We also obtained evidence of non-meiotic genetic recombination occurring following CAT fusion because we were able to grow colonies exhibiting resistance against both Hyg and Phleo from uninucleate conidia harvested from heterokaryotic colonies. As the parental strains did not form any colonies on plates with both antibiotics, these putative recombinant or hybrid colonies are not merely background survivors and are thus likely to be carrying the both resistance genes. The ratio of uninucleate to binucleate conidia and the number of colonies formed on plates with or without the antibiotics provided strong evidence that the colonies resistant to both antibiotics arose from uninucleate conidia. This further supports the occurrence of fusion between two nuclei of different genotypes and the resultant formation of diploid nuclei in the heterokaryotic colonies. Since our persistent attempts to determine the ploidy of conidia by flow cytometry were unsuccessful (data not shown), it is not yet clear whether the heterokaryotic colonies form diploid, haploid or aneuploid nuclei. It has been suggested that if a diploid nucleus is formed during the vegetative stage, somatic crossing over or non-disjunction can sometimes occur to result in haploidization, with resultant conidia either having diploid nuclei homozygous for certain genes or having haploid nuclei with new combinations of genes phleo marker, but not the hph marker, in the genomes of putative recombinant or hybrid colonies by Southern analyses (data not shown). As mentioned above, the putative recombinant/hybrid colonies do not seem to be merely background survivors and are thus likely carrying at least a small amount of the hygromycin phosphotransferase. This apparent contradiction might be explained by observations from F. oxysporum where only a certain lineage-specific (LS) chromosome can be stably maintained after horizontal gene transfer hph selectable marker could be only transferred between two incompatible biotypes of C. gloesporioides when integrated in a supernumerary chromosome (2-Mb) but not when integrated in other chromosomes hph has been integrated may not be maintained stably after hyphal fusion. It is possible that the diploid nuclei lose some of their chromosomes, and that the hph-marked chromosome is maintained at levels that were below the detection limit for the Southern assay. Nevertheless, pathogenicity tests clearly demonstrated that the putative recombinants or hybrids have different pathogenic traits from the parental strains. Two strains used as parental strains showed differences for two virulence factors (see 0) and México 222 (26) cultivars, and tFI04, race 72, is pathogenic to México 222 (26) and Cornell 49242 (23). The pathogenicity test showed that we obtained one strain (R2) classified as race 8 that was only pathogenic to the Cornell 49242 cultivar (23). The virulence factor to México 222 (26) cultivar was lost in all the recombinants. Thus, heterokaryon formation as a consequence of CAT fusion at least leads to the generation of new strains with distinct phenotypes, which may be achieved by the generation of new genotypes.Disappointingly, despite numerous attempts, we have so far been able to detect only the tors see . The strF. oxysporum may thus explain the apparent polyphyletic origins of host specialization and rapid emergence of new pathogenic lineages F. oxysporumAlthough the importance of non-meiotic recombination for generating genetic variability has long been recognized, most studies have not investigated mechanisms by which heterokaryotic cells are formed prior to recombination. With genome sequencing of several organisms, it became possible to identify common sequences in different species, which suggests horizontal gene transfer between their ancestors C. lindemuthianumFusarium oxysporumCAT fusion may account for horizontal gene and chromosome transfer between otherwise incompatible strains Colletotrichum lindemuthianum strains used in this study were derived from the culture collection of the Department of Biology, Universidade Federal de Lavras  from Fusarium graminearum PH1 under the control of the Neurospora crassa ccg-1 promoter, with ApaI and XbaI. The purified restriction fragment was ligated into ApaI- and XbaI-digested pBC-phleo ble gene under the control of the Aspergillus nidulans gpdA promoter to confer phleomycin (Phleo) resistance. Plasmid pMF357 carries the histone H1-sgfp fusion gene under the control of the Neurospora ccg-1 promoter, and the hph gene to confer hygromycin (Hyg) resistance Escherichia coli strain DH5α and purified with Qiagen DNA miniprep columns .Plasmid pGR02 was constructed by digesting pLC11, which contains tdimerRed, RFP;  fused toC. lindemuthianum strains  at a concentration of 107 protoplasts in 100 µl were combined with 3–5 µg of plasmid DNA (either pMF357 or pGR02) in a 50-ml polypropylene centrifuge tube. The mixture was incubated on ice for 20 min, 1 ml of PEG  was added and the mixture incubated at room temperature for 20 min. Regeneration agar  was added. Accurate pH adjustment was important for selection on Phleo containing medium. Transformants were selected based on resistance to antibiotics (50 µg ml−1 Hyg or 25 µg ml−1 Phleo). After transformation, Petri dishes were incubated at 22°C, and after 7 days Hyg- or Phleo-resistant colonies were transferred to PDA containing the same amounts of antibiotics. Conidia from these transformants were spread onto PDA containing the respective antibiotic and after 48 h, single Hyg- or Phleo-resistant germlings were recovered from each plate and transferred to PDA+Hyg or PDA+Phleo. Single conidia that expressed H1-GFP or RFP-H4 were selected under a fluorescence stereomicroscope  with filter sets appropriate for dsRED  and GFP . The strain that carries pMF357 and expresses H1-GFP was designated tFI01, while the strain that carries pGR02 and expresses RFP-H4 was designated tFI04.Conidia were harvested from 10–15 days-old cultures of the  strains , which wWe used an inverted Nikon TE2000E microscope equipped with 40× (1.3 N.A.) oil immersion and 60× (1.2 N.A.) water immersion plan apo objectives for routine light microscopy. Confocal laser scanning microscopy was performed with a BioRad Radiance 2100 system equipped with blue diode and argon ion lasers mounted on a Nikon TE 2000 U Eclipse inverted microscope. GFP and RFP were imaged simultaneously by exciting with the 488 and 543 nm laser lines and fluorescence detection was at 500/30 nm (for GFP) and >560 nm (for RFP). Confocal images were captured with the Lasersharp software . Images were transferred into Imaris (version 4.1), Image J (version 1.42q) or Paintshop Pro software (version 7.0) for further processing.Heterokaryon compatibility between strains was investigated by a conventional confrontation assay (10 replicates), in which blocks of agar containing the mycelium were placed 4–5 cm away from each other on PDA in Petri dishes, and the presence of hyphae in the contact area was assessed macroscopically. To better observe hyphal fusion between colonies, a sheet of sterile cellophane was placed on PDA between the two colonies, and after mycelial growth had taken place the cellophane along with mycelia was mounted on a cover glass and imaged by confocal microscopy (see previous section). The number of vegetative hyphal fusion events in five 195×195 µm squares was quantified (n = 10–12).6 spores per ml. For CAT fusion, equal proportions of conidial suspensions from the tFI01 and either tFI04 or LV77 strains were mixed and incubated in the dark at 22°C for 48 h. For imaging samples and for the quantification of CAT fusion, 200 µl drops of conidial suspension were place in an eight-well slide culture chamber . For time lapse imaging of nuclear dynamics in conidia and conidial germlings, we used the inverted agar block method Conidia were collected from 13–17-day-old cultures, suspended in water, and their concentration adjusted to 10−1 were plated on: (i) medium without antibiotics; (ii) medium with Hyg; (iii) medium with Phleo; (iv) medium with both antibiotics (Hyg and Phleo). After 7–10 days, we counted colonies that had grown on each plate. The putative recombinants/hybrids that had grown on double-selective media (PDA+Hyg+Phleo) were purified by isolation of single conidia. To count nuclei, conidia from heterokaryotic colonies were suspended in PBS at a concentration of 106 conidia ml−1 and their nuclei stained with 300 nM DAPI  from a 30 mM stock in ethanol.Conidia were collected from the four different heterokaryotic colonies grown on PDA plates containing both Hyg and Phleo. The conidia were suspended in water and 100 µl of diluted conidial suspensions each containing 200 conidia.ml6 spores ml−1) that were harvested from 15–20 day-old cultures, and the trays were placed in a moist chamber at 20±2°C with a photoperiod of 12 h dark/12 h light. Seedlings were incubated for 10 days, after which symptoms of infected plants were evaluated on a scale from 1 to 9, in which scores of 1–3 represent resistant plants and scores 4–9 represent susceptible plants Transformants tFI01 and tFI04 and some of the putative recombinants or hybrids were used to inoculate twelve different bean cultivars 2) test. The number of colonies originating from conidia derived from heterokaryotic colonies grown on plates with different combinations of antibiotics was compared by an analysis of variance (ANOVA) using the statistical program MSTAT-C 1.0 .Proportions of self and non-self fusion were compared between compatible and incompatible combinations, involving hyphal fusion in mature colonies and CAT fusion, using the Chi-square (χ