Category Archives: Human Genetics

Untargeted plasma metabolites in Dutch cohorts

In this study, we examined plasma metabolomes in 1,679 fasting plasma samples from 1,368 individuals from two LLD5 sub-cohorts (LLD1 and LLD2) and the GoNL6 cohort (Extended Data Fig. 1 and Supplementary Table 1). The LLD1 cohort was the discovery cohort, with information about genetics, diet and the gut microbiome available for 1,054 participants. Moreover, 311 LLD1 subjects were followed up 4years later (LLD1 follow-up). We also included two independent replication cohorts: 237 LLD2 participants for whom we had genetic and dietary data and 77 GoNL participants for whom only genetic data were available (Extended Data Fig. 1 and Supplementary Table 1). Untargeted metabolomics profiling was done using flow-injection time-of-flight mass spectrometry (FI-MS)10,11, which yielded plasma levels of 1,183 metabolites (Supplementary Table 2). These metabolites covered a wide range of lipids, organic acids, phenylpropanoids, benzenoids and other metabolites (Extended Data Fig. 2a). As we observed weak (absolute rSpearman<0.2) correlations among the 1,183 metabolites (Extended Data Fig. 2b), data reduction was not required and, consequently, all metabolites were subjected to subsequent analyses. We validated the identification and quantification of some metabolites (for example, bile acids, creatinine, lactate, phenylalanine and isoleucine) by comparing their abundance levels from FI-MS with those previously determined by liquid chromatography with tandem mass spectrometry (LC-MS/MS)12 or NMR13 (rSpearman>0.62; Extended Data Fig. 2c,d).

To compare the relative importance of diet, genetics and the gut microbiome in explaining inter-individual plasma metabolome variability, we calculated the proportion of variance explained by these three factors for the whole plasma metabolome profile and for the individual metabolites separately. We have detailed information on 78 dietary habits (Supplementary Table 3), 5.3million human genetic variants and the abundances of 156 species and 343 MetaCyc pathways for each individual of the LLD1 cohort. Diet, genetics and the gut microbiome could explain 9.3, 3.3 and 12.8%, respectively, of inter-individual variations in the whole plasma metabolome, without adjusting for covariates (see the Methods section Distance matrix-based variance estimation; false discovery rate (FDR)<0.05; Fig. 1a and Supplementary Table 4), whereas intrinsic factors (age, sex and body mass index (BMI)) and smoking collectively explained 4.9% of the variance. Together, these factors explain 25.1% of the variance in the plasma metabolome (Fig. 1a).

a, Inter-individual variation in the whole plasma metabolome explained by the indicated factors, estimated using the PERMANOVA method. All, all of the indicated factors combined; smk, smoking status. b, Venn diagram indicating the number of metabolites whose inter-individual variation was significantly explained by diet, genetics or the gut microbiome, as estimated using the linear regression method (FDRF-test<0.05). c, Inter-individual variations in metabolites explained by diet, genetics or the gut microbiome, as estimated using the linear regression method (the lasso regression method was applied for feature selection) with a significant estimated adjusted r2>5% (FDRF-test<0.05). The blue bars represent dietary contributions to metabolite variations, the yellow bars indicate genetic contributions and the orange bars indicate microbial contributions. The other colors indicate the metabolic categories of metabolites (see legend). The yaxis indicates the proportion of variation explained. TMAO, trimethylamine N-oxide.

Next, we tested for pairwise associations between each metabolite and the dietary variables, genetic variants and microbial taxa. We observed 2,854 associations with dietary habits (Supplementary Table 5), 48 associations with 40 unique genetic variants (metabolite quantitative trait loci (mQTLs); Supplementary Table 6), 1,373 associations with gut bacterial species (Supplementary Table 7) and 2,839 associations with bacterial MetaCyc pathways (Supplementary Table 8) (see the Methods sections Associations with dietary habits, QTL mapping and Microbiome-wide associations). In total, 769 metabolites were significantly associated with at least one factor (Fig. 1b and Supplementary Tables 58). We then performed interaction analysis to assess the role of dietmicrobiome, geneticsmicrobiome and dietgenetics interactions in regulating the human metabolome using an interaction term in the linear model (see the Methods section Interaction analysis). Among these, 185 metabolites were associated with multiple factors and seven were affected by either geneticsmicrobiome, geneticsdiet or dietmicrobiome interactions (Supplementary Table 9).

As interactions were limited, we further assessed the proportion of variance of each metabolite that was explained by these factors using an additive model with the least absolute shrinkage and selection operator (lasso) method (see the Methods section Estimating the variance of individual metabolites). In general, the inter-individual variations in 733 metabolites could be explained by at least one of the three factors (FDRF-test<0.05; Supplementary Table 10). In detail, dietary habits contributed 0.435% of the variance in 684 metabolites; microbial abundances contributed 0.725% of the variance in 193 metabolites; and genetic variants contributed 328% of the variance in 44 metabolites (adjusted r2; FDRF-test<0.05; Supplementary Table 10). We also estimated the explained variance of metabolites using Elastic Net14, which is designed for highly correlated features, and found that the estimated explained variances were comparable between linear regression and the Elastic Net regression (Supplementary Fig. 1).

We further compared the variance explained by each type of factor (diet, genetics or the microbiome) and assigned the dominant factor for each metabolite if one factor explained more variance than the other two. Inter-individual variations in 610 metabolites were mostly explained by diet, 85 were explained by the gut microbiome and 38 were explained by genetics (Supplementary Table 10). Hereafter, we refer to these as diet-dominant, microbiome-dominant and genetics-dominant metabolites, respectively. The dominant factors of metabolites highlight their origin. For instance, ten out of the 21 diet-dominant metabolites for which diet explained >20% of the variance (FDRF-test<0.05; Supplementary Table 10) were food components based on their annotation in the Human Metabolome Database (HMDB)15. Similarly, of the 85 microbiome-dominant metabolites, 23 were annotated in the HMDB as microbiome-related metabolites (including 15 uremic toxins). Furthermore, out of the 38 genetics-dominant metabolites, ten were lipid species and eight were amino acids. Taken together, our analysis highlights that one factoreither dietary, genetic or microbialcan have a dominant effect over the other two in explaining the variances of plasma metabolites, with diet or the microbiome being particularly dominant. However, we also found that the variances in 185 metabolites were significantly attributable to more than one factor (Supplementary Table 10), including six metabolites associated with both genetics and the microbiome and 153 metabolites associated with both diet and the microbiome. For example, genetics and the microbiome explained 4 and 5%, respectively, of the variance in plasma 5-carboxy--chromanol (Fig. 1c)a dehydrogenated carboxylate product of 5-hydroxy--tocopherol16 that may reduce cancer and cardiovascular risk17. Another example is hippuric acida uremic toxin that can be produced by bacterial conversion of dietary proteins18, with 13% of its variance explained by diet and 13% explained by the microbiome (Fig. 1c).

Temporal changes in plasma metabolites can reflect changes in an individuals diet, gut microbiome and health status. When assessing the plasma metabolome in the 311 LLD1 follow-up samples, we indeed observed a significant shift in the plasma metabolome, with a significant difference in the second principal component (PPC1 paired Wilcoxon=0.1 and PPC2 paired Wilcoxon=1.3105; Fig. 2a). Baseline genetics, diet and microbiome, together with age, sex and BMI, could explain 59.4% of the variance in the follow-up plasma metabolome (PPERMANOVA=0.004) (Supplementary Fig. 2). We also observed that temporal stability can vary substantially between different metabolites (see the Methods section Temporal consistency of individual metabolites; Supplementary Table 11). Previously, we had assessed the changes in the gut microbiome in the LLD1 follow-up cohort and linked these to changes in the plasma metabolome7. Here, we further checked the temporal variability of the plasma metabolome and assessed the stability of diet-, microbiome- and genetics-dominant metabolites over time. Interestingly, the temporal correlation of the microbiome-dominant metabolites was similar to that of the genetics-dominant metabolites (PWilcoxon=0.51; Fig. 2b), whereas the temporal correlation between diet-dominant metabolites was significantly lower than between microbiome- and genetics-dominant metabolites (PWilcoxon<3.4105; Fig. 2b). However, the dominant dietary, microbial and genetic factors identified at baseline also explained similar variance in metabolic levels in the follow-up samples (Extended Data Fig. 3 and Supplementary Table 10). Our data also revealed a positive correlation between stability and the amount of variance that could be explained: the more variance explained, the more stable a metabolite is over time (Fig. 2c). For a few metabolites, we could not replicate the variance explained at baseline at the second time point, and these metabolites also showed weak or no correlation in their abundances between the two time points. For example, N-acetylgalactosamine showed very weak correlation between the two time points (r=0.13; P=0.02), and its genetic association was not replicated at the second time point.

a, Principal component analysis of metabolite levels at two time points (Euclidean dissimilarity). The green dots indicate baseline samples and the orange dots indicate follow-up samples (n=311 biologically independent samples). The KruskalWallis test (two sided) was used to check differences between baseline and follow-up. b, Temporal stability of metabolites stratified by the dominantly associated factor for each metabolite. The Wilcoxon test (two sided) was used to check the differences between groups. Each dot represents one metabolite. The yaxis indicates the Spearman correlation coefficient of abundances of each metabolite between two time points (n=311 biologically independent samples). In a and b, the box plots show the median and first and third quartiles (25th and 75th percentiles) of the first and second principal components (a) or correlation coefficients (b); the upper and lower whiskers extend to the largest and smallest value no further than 1.5 the interquartile range (IQR), respectively; and outliers are plotted individually. c, Correlation between metabolite stability and the metabolite variance explained by diet (left), genetics (middle) and the microbiome (right). The xaxis indicates the inter-individual variation explained by each factor and the yaxis indicates the Spearman correlation coefficient (two sided) of abundances of each metabolite between the two time points. The dashed white lines show the best fit and the gray shading represents the 95% confidence interval (CI) (n=311 biologically independent samples).

Having established the variances in metabolites explained by diet, genetics and the gut microbiome and the dominant factors that explained most of this variance, we focused on detailing specific associations and on the potential implications of our findings for assessing diet quality and improving our understanding of the genetic risk of complex diseases and the interaction and causality relationships among diet, the microbiome, genetics and metabolism.

We observed 2,854 significant associations (FDRSpearman<0.05) between 74 dietary factors and 726 metabolites (Fig. 3a and Supplementary Table 5; see the Methods section Lifelines diet quality score prediction). Associations with food-specific metabolites can, in theory, be used to verify food questionnaire data. For instance, the strongest association we observed was between quinic acid levels and coffee intake (rSpearman=0.54; P=1.61080; Fig. 3b). Quinic acid is found in a wide variety of different plants but has a particularly high concentration in coffee. Another example is 2,6-dimethoxy-4-propylphenol, which was strongly associated with fish intake (rSpearman=0.53; P=1.51076; Fig. 3c). This association is expected as this compound is particularly present in smoked fish according to HMDB annotation15. In addition, we also detected associations between dietary factors and metabolic biomarkers of some diseases. For example, 1-methylhistidine is a biomarker for cardiometabolic diseases including heart failure19 that is enriched in meat, and we observed significant associations between 1-methylhistidine and meat (rSpearman=0.12; P=7.2105) and fish intake (rSpearman=0.11; P=3.1104) as well as a lower level of 1-methylhistidine in vegetarians (rSpearman=0.15; P=9.7107; Fig. 3d).

a, Summary of the associations between diet and metabolites. The bars represent dietary habits, with the bar order sorted by the number of significant associations. Association directions are colored differently: orange indicates a positive association, whereas blue indicates a negative association. The length of each bar indicates the number of significant associations at FDR<0.05 (Spearman; two sided). b, Association between plasma quinic acid levels and coffee intake. The x and yaxes indicate residuals of coffee intake and the metabolic abundance after correcting for covariates, respectively (n=1,054 biologically independent samples). c, Association between plasma 2,6-dimethoxy-4-propylphenol levels and fish intake frequency (n=1,054 biologically independent samples). The x and yaxes refer to residuals of fish intake and metabolic abundance after correcting for covariates, respectively. d, Differential plasma levels of 1-methylhistidine between vegetarians and non-vegetarians (n=1,054 biologically independent samples). The yaxis indicates normalized residuals of metabolic abundance. The Pvalue from the Wilcoxon test (two sided) is shown. The box plots show the median and first and third quartiles (25th and 75th percentiles) of the metabolite levels. The upper and lower whiskers extend to the largest and smallest value no further than 1.5 the IQR, respectively. Outliers are plotted individually. e, Association between the diet quality score predicted by the plasma metabolome (yaxis) and the diet quality score assessed by the FFQ (xaxis) (n=237 biologically independent samples). In b, c and e, each gray dot represents one sample, the dark gray dashed line shows the linear regression line and the gray shading represents the 95% CI. In b and c, the association strength was assessed using Spearman correlation (two sided; the correlation coefficient and Pvalue are reported) and in e, the prediction performance was assessed with linear regression (F-test; two sided; the adjusted r2 value and Pvalue are reported).

Given the relationship between diet, metabolism and human health, we wondered whether the plasma metabolome could predict diet quality. For each of the Lifelines participants, we constructed a Lifelines Diet Score based on food frequency questionnaire (FFQ) data that reflected the relative diet quality based on dietdisease relationships8. To build a metabolic model to predict an individuals diet quality, we used LLD1 as the training set and LLD2 as the validation set. The resulting metabolic model included 76 metabolites, 51 of which were dominantly associated with diet. The diet score predicted by metabolites showed a significant association with the real diet score assessed by the FFQ in the validation set (r2adjusted=0.27; PF-test=3.5105; Fig. 3e). We also tested four other dietary scores (the Alternate Mediterranean Diet Score20, Healthy Eating Index (HEI)21, Protein Score22 and Modified Mediterranean Diet Score23) and found that the HEI predicted by plasma metabolites was also significantly associated with the FFQ-based HEI (r2adjusted=0.23; PF-test=6.5105; Supplementary Table 12).

Genetic associations of plasma metabolites may provide functional insights into the etiologies of complex diseases. After correcting for the first two genetic principal components, age, sex, BMI, smoking, 78 dietary habits, 40 diseases and 44 medications, QTL mapping in LLD1 identified 48 study-wide, independent genetic associations between 44 metabolites and 40 single-nucleotide polymorphisms (SNPs) (PSpearman<4.21011; clumping r2=0.05; clumping window=500kilobases (kb); Fig. 4a and Supplementary Table 6). All 48 genetic associations were replicated in either LLD1 follow-up or the two independent replication datasets (LLD2 and GoNL; Supplementary Fig. 3 and Supplementary Table 6). We also assessed the impact of physical activity, as assessed by questionnaires24, on the genetics association of metabolism, but found its influence to be negligible (Supplementary Fig. 4). Functional mapping and annotation (FUMA) of genome-wide association studies (GWAS)25 analysis revealed that the identified mQTLs were enriched in genes expressed in the liver and kidney (Extended Data Fig. 4) and related to metabolic phenotypes (Supplementary Table 6).

a, Manhattan plot showing 48 independent mQTLs identified linking 44 metabolites and 40 genetic variants with P<4.21011 (Spearman; two sided). Representative genes for the SNPs with significant mQTLs are labeled. b, Association between a tag SNP (rs1495741) of the NAT2 gene and plasma AFMU levels. c, Association between a SNP (rs13100173) within the HYAL3 gene and plasma levels of N-acetylgalactosamine-4-sulfate. d, Association between a tag SNP (rs17789626) of the SCLT1 gene and plasma mizoribine levels. e, Differences in coffee intake between participants with different genotypes at rs1495741. f, Correlations between coffee intake and AFMU in participants with different genotypes at rs1495741. g, Differences in bacterial fatty acid -oxidation pathway abundance in participants with different genotypes at rs67981690. h, Correlations between bacterial fatty acid -oxidation pathway abundance and 5-carboxy--chromanol in participants with different genotypes at rs67981690. In be and g, the xaxis indicates the genotype of the corresponding SNP and the yaxis indicates normalized residuals of the corresponding metabolic abundance (n=927 biologically independent samples). Each dot represents one sample. The box plots show the median and first and third quartiles (25th and 75th percentiles) of the metabolite levels. The upper and lower whiskers extend to the largest and smallest value no further than 1.5 the IQR, respectively. Outliers are plotted individually. The association strength is shown by the Spearman correlation coefficient and corresponding Pvalue (two sided). In f and h, the xaxis indicates the normalized abundance of coffee intake (f) or the bacterial fatty acid -oxidation pathway (h) and the yaxis indicates the normalized residuals of the corresponding metabolic abundance. Each dot represents one sample (n=927 biologically independent samples). The lines indicate linear regressions for each genotype group separately. Areas with light gray shading indicate the 95% CI of the linear regression lines. The association strength per genotype is shown by the Spearman correlation and the corresponding Pvalue (two sided).

The strongest association we found was between the caffeine metabolite 5-acetylamino-6-formylamino-3-methyluracil (AFMU) and SNP rs1495741 near the N-acetyltransferase 2 (NAT2) gene (rSpearman=0.52; P=1.71066; Fig. 4b), which showed strong linkage disequilibrium (r2=0.98) with a SNP, rs35246381, that was recently reported to be associated with urinary AFMU26. AFMU is a direct product of NAT2 activity and has been associated with bladder cancer risk27. Interestingly, the plasma level of AFMU was associated not only with coffee intake (rSpearman=0.29; P=9.21022; Supplementary Table 5) and the genotype of rs1495741, but also with their interactions (Supplementary Table 9). Individuals with a homologous AA genotype had a similar level of coffee intake, but their correlation between coffee intake and plasma AFMU level was significantly lower compared with individuals with GG and GA genotypes (Fig. 4e,f).

Pleotropic mQTL effects were also observed at several loci, including SLCO1B1, FADS2, KLKB1 and PYROXD2 (Supplementary Table 6). For example, three associations (related to three metabolites, two of them lipids) were observed for two SNPs (rs67981690 and rs4149067; linkage disequilibrium r2=0.72 in Northern Europeans from Utah) in SLCO1B1, which encodes the solute carrier organic anion transporter family member 1B1. Expression of the SLCO1B1 protein is specific to the liver, where this transporter is involved in the transport of various endogenous compounds and drugs, including statins28, from blood into the liver. The SLCO1B1 locus has also been linked to plasma levels of fatty acids and to statin-induced myopathy29. Furthermore, we detected a geneticsmicrobiome interaction between rs67981690 and microbial fatty acid oxidation pathways in regulating plasma levels of 5-carboxy--chromanol (P=1.5103), where the association of the bacterial fatty acid oxidation pathway with plasma levels of 5-carboxy--chromanol was dependent on the genotype of rs67981690 (Fig. 4g,h).

To identify novel mQTLs, we performed a systematic search of all published mQTL studies from 2008 onwards (Supplementary Table 13). This approach identified three novel mQTLs in our datasets (Supplementary Table 13) that were either not located close to previously reported mQTLs (distance>1,000kb) or not in linkage disequilibrium (r2<0.05). The first two novel SNPsrs13100173 at HYAL3 and rs11741352 at ARSBwere associated with N-acetylgalactosamine-4-sulfate (Fig. 4c,d), which is associated with mucopolysaccharidosis30. Interestingly, N-acetylgalactosamine-4-sulfate can bind to HYAL proteins (HYAL1, HYAL2, HYAL3 and HYAL4), suggesting that mQTLs can also pinpoint potential metaboliteprotein interactions. The third novel mQTL was rs17789626 at SCLT1, which was associated with mizoribinea compound used to treat nephrotic syndrome31.

We established 4,212 associations between 208 metabolites and 314 microbial factors (114 species and 200 MetaCyc pathways) (FDRLLD1<0.05; PLLD1 follow-up<0.05; Supplementary Tables 7 and 8). Interestingly, many of the metabolites that were associated with microbial species and MetaCyc pathways are also known to be gut microbiome related based on their HMDB annotations15. For instance, we observed 919 associations with 25 uremic toxins, 142 associations with thiamine (vitamin B1) and 117 associations with five phytoestrogens (FDR<0.05; Supplementary Tables 7 and 8). Uremic toxins and thiamine have been shown to be related to various diseases, including chronic kidney disease and cardiovascular diseases32,33. Phytoestrogens are a class of plant-derived polyphenolic compounds that can be transformed by gut microbiota into metabolites that promote the hosts metabolism and immune system33,34.

To assess whether gut microbiome composition causally contributes to plasma metabolite levels, we carried out bi-directional MR analyses (see the Methods section Bi-directional MR analysis). Here, we focused on the 37 microbial features that were associated with at least three independent genetic variants at P<1105 and with 45 metabolites (Supplementary Table 14). At FDR<0.05 (corresponding to P=2103 obtained from the inverse variance weighted (IVW) test)35, we observed four potential causal relationships at baseline that could also be found in the follow-up in the microbiomes to metabolites direction (Fig. 5ad and Supplementary Tables 15 and 16) but not in the opposite direction (Supplementary Table 17), and these outcomes were maintained following weighted median testing (P<0.03; Supplementary Fig. 5). To ensure that the data followed MR assumptions, we performed several sensitivity analyses, including checking for horizontal pleiotropy (MR-Egger36 intercept P>0.05; Supplementary Table 15) and heterogeneity (Cochrans Q test P>0.05; Supplementary Table 15) and leave-one-out analysis (Extended Data Fig. 5). We did not use causal estimates derived using the MR-Egger method to filter the results, as its power to detect causality is known to be low36. These sensitivity checks further confirmed the reliability of these four MR causal estimates.

a, Analysis of the association between adenosylcobalamin biosynthesis pathway abundance and 5-hydroxytryptophol levels. b, Glycogen biosynthesis pathway abundance versus 5-sulfo-1,3-benzenedicarboxylic acid levels. c, E. rectale abundance versus hydrogen sulfite levels. d, Veillonella parvula abundance versus 2,3-dehydrosilybin levels. In the top panels of ad, the xaxis shows the SNP exposure effect, and the yaxis shows the SNP outcome effect and each dot represents a SNP. Error bars represent the s.e. of each effect size. The bottom panels of ad, show the MR effect size (center dot) and 95% CI for the baseline (blue) and follow-up (green) datasets of the LLD1 cohort, estimated with the IVW MR approach (two sided) (n=927 biologically independent samples at baseline and n=311 biologically independent samples at follow-up).

We further found that increased abundance of microbial adenosylcobalamin biosynthesis (coenzyme B12) was associated with reduced plasma levels of 5-hydroxytryptophol (Fig. 5a)a uremic toxin related to Parkinsons disease37. We also found that plasma hydrogen sulfite levels were related to Eubacterium rectale (Fig. 5c)a core gut commensal species38 that is highly prevalent (presence rate=97%) and abundant (mean abundance=8.5%) in both our cohorts and in other populations39,40,41. As a strict anaerobe, E. rectale promotes the hosts intestinal health by producing butyrate and other short-chain fatty acids from non-digestible fibers42, and a reduced abundance of this species has been observed in subjects with inflammatory bowel disease39,43 and colorectal cancer44 compared with healthy controls. As a toxin, hydrogen sulfite interferes with the nervous system, cardiovascular functions, inflammatory processes and the gastrointestinal and renal system45. Our results thus reveal a potential new beneficial effect of E. rectale.

To further investigate the metabolic potential of individual bacterial species, we applied newly developed pipelines to identify microbial primary metabolic gene clusters (gutSMASH pathways)46 and microbial genomic structural variants (SVs)47. These two tools profile microbial genomic entities that are implicated in metabolic functions. By associating 1,183 metabolites with 3,075 gutSMASH pathways and 6,044 SVs (1,782 variable SVs (vSVs) and 4,262 deletion SVs (dSVs); see Methods), we observed 23,662 associations with gutSMASH pathways and 790 associations with bacterial SVs (FDRLLD1<0.05; PLLD1 follow-up<0.05; Supplementary Tables 1820). These associations connect the genetically encoded functions of microbes with metabolites, thereby providing putative mechanistic information underlying the functional output of the gut microbiome. In one example, we observed that the microbial uremic toxin biosynthesis pathways, including the glycine cleavage pathway (in Olsenella and Clostridium species) and the hydroxybenzoate-to-phenol pathway (in Clostridium species) responsible for hippuric acid and phenol sulfate biosynthesis, were associated with the hippuric acid (Olsenella species: rSpearman=0.15; P=9.3107; Clostridium species: rSpearman=0.18; P=5.9109) and phenol sulfate (rSpearman=0.17; P=4.2108; Extended Data Fig. 6a) levels measured in plasma, respectively (FDRLLD1<0.05 and PLLD1 follow-up<0.05; Extended Data Fig. 6b).

Next, we carried out a mediation analysis to investigate the links between diet, the microbiome and metabolites. For 675 microbial features that were associated with both dietary habits and metabolites (FDR<0.05), we applied bi-directional mediation analysis to evaluate the effects of microbiome and metabolites for diet (see the Methods section Bi-directional mediation analysis). This approach established 146 mediation linkages: 133 for the dietary impact on the microbiome through metabolites and 13 for the dietary impact on metabolites through the microbiome (FDRmediation<0.05 and Pinverse-mediation>0.05; Fig. 6a,b and Supplementary Table 21). Most of these linkages were related to the impact of coffee and alcohol on microbial metabolic functionalities (Fig. 6a).

a, Parallel coordinates chart showing the 133 mediation effects of plasma metabolites that were significant at FDR<0.05. Shown are dietary habits (left), plasma metabolites (middle) and microbial factors (right). The curved lines connecting the panels indicate the mediation effects, with colors corresponding to different metabolites. freq., frequency; PFOR, pyruvate:ferredoxin oxidoreductase; OD, oxidative decarboxylation; HGD, 2-hydroxyglutaryl-CoA dehydratase; TPP, thiamine pyrophosphate. b, Parallel coordinates chart showing the 13 mediation effects of the microbiome that were significant at FDR<0.05. Shown are dietary habits (left), microbial factors (middle) and plasma metabolites (right). For the microbial factors column, number ranges represent the genomic location of microbial structure variations (SVs) in kilobyte unit, and colons represent the detailed annotation of certain gutSMASH pathway. c, Analysis of the effect of coffee intake on the abundance of M. smithii as mediated by hippuric acid. d, Analysis of the effect of beer intake on the C. methylpentosum Rnf complex pathway as mediated by hulupinic acid. e, Analysis of the effect of fruit intake on urolithin B in plasma as mediated by a vSV in Ruminococcus species (300305kb). In ce, the gray lines indicate the associations between the two factors, with corresponding Spearman coefficients and Pvalues (two sided). Direct mediation is shown by a red arrow and reverse mediation is shown by a blue arrow. Corresponding Pvalues from mediation analysis (two sided) are shown. inv., inverse; mdei., mediation.

Coffee contains various phenolic compounds that can be converted to hippuric acid by colonic microflora48. Hippuric acid is an acyl glycine that is associated with phenylketonuria, propionic acidemia and tyrosinemia49. We observed that hippuric acid can mediate the impact of drinking coffee on Methanobrevibacter smithii abundance (Pmediation=2.21016; Fig. 6c). We also observed that hulupinic acid, which is commonly detected in alcoholic drinks, can mediate the impact of beer consumption on the Clostridium methylpentosum ferredoxin:NAD+ oxidoreductase (Rnf) complex (Pmediation=2.21016; Fig. 6d)an important membrane protein in driving the ATP synthesis essential for all bacterial metabolic activities50.

Of the dietary impacts on metabolites through the microbiome (Fig. 6b and Supplementary Table 21), one interesting example is a Ruminococcus species vSV (300305kb) that encodes an ATPase responsible for transmembrane transport of various substrates51. This Ruminococcus species vSV mediated the effect of fruit consumption on plasma levels of urolithin B (Pmediation=2.21016; Fig. 6e). Urolithin B is a gut microbiota metabolite that protects against myocardial ischemia/reperfusion injury via the p62/Keap1/Nrf2 signaling pathway52. Taken together, our data provide potential mechanistic underpinnings for dietmetabolite and dietmicrobiome relationships.

Original post:
Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome - Nature.com

The Steering Committee for Human Rights in the fields of Biomedicine and Health (CDBIO)* has achieved the final step of the re-examination process of Article 13 of the Convention on Human Rights and Biomedicine (Oviedo Convention) with the adoption of the clarifications on the scope of the provisions with regard to research and the purposes limitation provided for any intervention on the human genome.

In June 2021, as a first conclusion, the Committee had agreed that taking into account the technical and scientific aspects of theses developments, as well as the ethical issues they raise, it considered that the conditions were not met for a modification of the provisions of Article 13. However, it agreed on the need to provide clarifications, in particular on the terms preventive, diagnostic and therapeutic and to avoid misinterpretation of the applicability of this provision to research.

These clarifications were adopted by the CDBIO at its 1st plenary meeting (31 May 3 June 2022) and presented to the Committee of Ministers on 27 September 2022.

In this video, Anne Forus, Chair, and Pete Mills, member, of the CDBIO Drafting group on genome editing present the context, the content and the importance of these clarifications.

Context

This re-examination process of Article 13 was undertaken within the framework of the Strategic Action Plan on Human Rights and Technologies (2020 2025), as part of the actions planned under its Governance pilar and the specific objective of embedding human rights in the development of technologies which have an application in the field of biomedicine.

As underlined by the DH-BIO in November 2018, ethics and human rights must guide any use of genome editing technologies in human beings in accordance with the Convention on Human Rights and Biomedicine (the Oviedo Convention, 1997) - the only international legally binding instrument addressing human rights in the biomedical field which provides a unique reference framework to that end. The Oviedo Convention represents the outcome of an in-depth discussion at European level, on developments in the biomedical field, including in the field of genetics.

Article 13 of the Convention addresses these concerns about genetic enhancement or germline genetic engineering by limiting the purposes of any intervention on the human genome, including in the field of research, to prevention, diagnosis or therapy. Furthermore, it prohibits any intervention with the aim of introducing a modification in the genome of any descendants. This Article was guided by the acknowledgement of the positive perspectives of genetic modification with the development of knowledge of the human genome; but also by the greater possibility to intervene on and control genetic characteristics of human beings, raising concern about possible misuse and abuses.

More information:

* In January 2022, the CDBIO took over the responsibility of the Committee on Bioethics (DH-BIO) as the committee responsible for the conduct of the intergovernmental work on human rights in the fields of biomedicine and health. The CDBIO is also advising and providing expertise to the Committee of Ministers of the Council of Europe on all questions within its field of competence.

Read the original here:
Genome editing technologies: final conclusions of the re-examination of Article 13 of the Oviedo Convention - Council of Europe

image:This figure shows the 23 biobanks across four continents that have joined GBMI as of April 2022, bringing the total number of samples with matched health data and genotypes to more than 2.2 million. Biobanks are colored based on the sample recruiting strategies. view more

Credit: Zhou et al./Cell Genomics

Human genetic discoveries have historically focused on individuals of European descent, so how well these findings transfer to other non-European populations has remained an open question. A collaborative network of 23 biobanks from 4 continents holding genomic data for over 2 million consenting individuals is now revealing the gaps caused by this lack of diversity, such as missed mutations that cause genetic diseases. The first studies from the Global Biobank Meta-analysis Initiative (GBMI), published October 12 in the journal Cell Genomics, offer guidance on how and why to make genome-wide association studies (GWASs) more representative.

The aims of the GBMI are to increase the power to discover genetic variation associated with phenotypes for GWAS analyses, increase replication power, and determine more accurate polygenic risk scores, says Cell Genomics Editor-in-Chief Laura Zahn. Their work is helping to provide new insights into the underlying biology of human diseases and traits.

Cell Genomics features seven initial studies from the GBMI:

1. GWASs in different biobanks worldwide can be successfully integrated

Utilizing most of the biobanks represented in GBMI, researchers generated GWASs that identified 317 known and 183 new genes associated with 14 diseases, from asthma and gout to certain cancers. The pilot studies also reflected consistent results despite differences among biobanks, encouraging the sharing and integration of their unique genomic data, thus making it possible to conduct some of the largest GWAS analyses of certain diseases to date.

Zhou et al.: Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease. https://www.cell.com/cell-genomics/fulltext/S2666-979X(22)00141-0

2. Looking across ancestries can identify more drug targets for genetic diseases

Genetic tools provide a cost-effective way to understand whether drug targets for genetic diseases may have similar or different effects across ancestries. In this study, researchers used biobank samples to screen about 1,300 proteins, each measured in populations of African and European ancestry, for their role in 8 complex diseases. They identified 45 proteins that could potentially be involved in both ancestries and 7 pairs with specific effects in the two ancestries separately, with 16 of these prioritized for investigation in future drug trials.

Zhao et al.: Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases. https://www.cell.com/cell-genomics/fulltext/S2666-979X(22)00144-6

3. Introducing a drug discovery framework for cross-population GWAS meta-analyses

GWASs have the potential to identify and evaluate drug candidates and drug targets. This research team created guidelines that utilizes three techniques for in-depth, genomics-driven drug discovery that work across populations. They applied this framework to 13 common diseases to nominate promising drug candidates targeting the genes involved in the coagulation process for a certain type of blood clot as well as in immune signaling pathways for gout.

Namba and Konuma et al.: A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis. https://www.cell.com/cell-genomics/fulltext/S2666-979X(22)00139-2

4. Forty years of genetic data comes with advantages

Since 1984, around 229,000 people from Trndelag County, Norway, have taken part in the Trndelag Health Study (HUNT), providing health records and biological samples with nearly 40 years of follow-up. Of the HUNT participants, approximately 88,000 individuals have provided genetic data, which have been used to generate insights into the mechanism of cardiovascular, metabolic, osteoporotic, and liver-related diseases. This resource acts as inspiration to conduct similar longitudinal studies across more diverse populations.

Brumptom, Graham, and Surakka et al.: The HUNT study: A population-based cohort for genetic research. https://www.cell.com/cell-genomics/fulltext/S2666-979X(22)00142-2

5. New opportunities to combine data to study rare diseases

By combining data from 13 biobanks around the globe, this research team performed a multi-ancestry GWAS to look at thousands of patients with idiopathic pulmonary fibrosis (IPF), a rare disease characterized by lung tissue scarring. The researchers identified seven new gene markers linked to IPF, including those involved in lung function and COVID-19 response, as well as sex-specific effects. Only one of these gene markers would have been identified had the analysis been limited to European ancestry individuals.

Partanen et al.: Leveraging global multi-ancestry meta-analysis in the study of idiopathic pulmoary fibrosis genetics. https://www.cell.com/cell-genomics/fulltext/S2666-979X(22)00126-4

6. Overcoming statistical challenges studying ancestry-specific genetic associations

Transcriptome-wide association studies (TWASs) boost detection power and provide biological context to genetic associations by integrating genetic variant-to-trait associations with predictive models of gene expression. In this paper, researchers highlight practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. This provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.

Bhattacharya and Hirbo et al.: Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative. https://www.cell.com/cell-genomics/fulltext/S2666-979X(22)00125-2

7. The Taiwan Biobank offers East Asian population diversity in genetics research

The Taiwan Biobank is an ongoing prospective population study of over 150,000 people of predominantly Han Chinese ancestry. Through physical examinations and biological samples, researchers are tracing more than 1,000 genetic traits, as well as lifestyle traits and environmental factors, that are more specific to populations in East and Southeast Asia. Their membership in the GMBI is an example of the population diversity possible with a global genetics research effort.

Feng et al.: Taiwan Biobank: A rich biomedical research database of the Taiwanese population. https://www.cell.com/cell-genomics/fulltext/S2666-979X(22)00146-X

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Funding information and declarations of interest can be found in the manuscripts.

Cell Genomics (@CellGenomics), is a new gold open access journal from Cell Press publishing multidisciplinary research at the forefront of genetics and genomics. The journal aims to bring together diverse communities to advance genomics and its impact on biomedical science, precision medicine, and global and ecological health. Visit https://www.cell.com/cell-genomics/home. To receive Cell Press media alerts, please contact press@cell.com.

Meta-analysis

Human tissue samples

Global Biobank Meta-analysis Initiative: powering genetic discovery across human diseases

12-Oct-2022

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Original post:
Global Biobank Meta-analysis Initiative making genome-wide association studies more diverse and representative - EurekAlert

Very sick babies and children will be diagnosed and start treatment more quickly thanks to a revolutionary new genetic testing service being launched by the NHS.

Doctors will gain vital insights within as little as two days into what illnesses more than 1,000 newborns and infants a year in England have from the rapid analysis of blood tests.

Until now, when doctors suspected a genetic disorder, such tests have sometimes taken weeks as they had to be done in a sequential order to rule out other possible diagnoses, delaying treatment.

NHS England bosses say the service could save the lives of thousands of seriously ill children over time and will usher in a new era of genomic medicine.

The clinical scientists, genetic technologists and bioinformaticians will carry out much faster processing of DNA samples, including saliva and other tissue samples as well as blood. They will share their findings with medical teams and patients families.

They will undertake whole genome sequencing in a quest to identify changes in the childs DNA and so diagnose conditions such as cancer and rare genetic disorders.

While such testing is available in parts of other countries such as the US and Australia, NHS England said that its new service will be the first in the world to cover an entire country. Wales also has a similar service but it is more limited in its scope than the new service in England.

This global first is an incredible moment for the NHS and will be revolutionary in helping us to rapidly diagnose the illnesses of thousands of seriously ill children and babies, saving countless lives in the years to come, said Amanda Pritchard, NHS Englands chief executive.

The new national rapid whole genome sequencing service will be based in Exeter as part of the NHSs existing Genomic Medicine Service which is based there. It follows a successful trial in some parts of England.

Dr Emma Baple, who is running the new service, said it will transform how rare genetic conditions are diagnosed. It is a new national test being offered with results delivered inside seven days as compared to a much longer turnaround time.

It is the only test in the NHS that looks at all 22,000 genes in the human genome and all the parts in between the genes. Eighty-five per cent of all changes that lead to disease are in the genes themselves, whilst the rest is caused by the bits of DNA in between.

Test results should be available in anything between two and seven days, depending on the complexity of the childs condition, though that should get faster as technology improves, Baple added.

We know that with prompt and accurate diagnosis conditions could be cured or better managed with the right clinical care, which would be life-altering and potentially life-saving for so many seriously unwell babies and children.

Read more from the original source:
New NHS genetic testing service could save thousands of children in England - The Guardian

File picture of a person after a Covid jab

Some people with "lucky genes" or certain DNA may get extra strong protection after Covid jabs, say scientists from University of Oxford.

The researchers found people with a version of a gene called HLA-DQB1*06 had a bigger antibody response following vaccination than others.

About 30 to 40% of the UK population have this type.

The preliminary work appears in Nature Medicine. More research is needed to confirm it.

Experts say vaccines are the best way people can protect themselves against Covid.

People are being invited for boosters this autumn to top up their immunity.

There are fears of a flu and Covid "twindemic" this winter, and officials say those who qualify for free jabs should get them.

Researchers analysed blood samples from people who took part in five different trials, including 1,600 adults who had either the Oxford-AstraZeneca or Pfizer-BioNTech vaccine as their first jab.

They found people who carried the gene variant were more likely to have higher levels of antibodies - proteins that recognise and attack coronavirus - a month after their first jab than people who had other versions of the gene.

The study also followed a group of people who had weekly Covid tests for more than a year after their first jab.

They found those who had the gene variant were less likely to experience a "breakthrough infection" over this time period, where people still got a mild Covid infection after vaccination.

Scientists acknowledge many other factors contribute to the risk of getting Covid, including age, other illnesses and people's occupations.

But they say genetics still played a significant role after accounting for these.

Dr Alexander Mentzer, NIHR academic clinical lecturer at the Wellcome Centre for Human Genetics and a lead researcher on the study, said: "We have seen a wide variation in how quickly people test positive for Covid-19 after vaccination.

Story continues

"Our findings suggest that our genetic code may influence how likely this is to happen over time.

"We hope that our findings will help us improve vaccines for the future so they not only stop us developing severe disease, but also keep us symptom-free for as long as possible."

Lead researcher Prof Julian Knight added: "From this study we have evidence that our genetic make-up is one of the reasons why we may differ from each other in our immune response following Covid-19 vaccination.

"We found that inheriting a specific variant of an HLA gene was associated with higher antibody responses, but this is only the start of the story.

"Further work is needed to better understand the clinical significance of this specific association," he added. "And more broadly what identifying this gene variant can tell us about how effective immune responses are generated, and ways to continue to improve vaccines for everyone."

The team acknowledge there is also an urgent need to understand whether the findings are applicable to more ethnically diverse populations, because different groups have different levels of the gene variant.

The rest is here:
Covid protection may be boosted by genes, study shows - Yahoo News Australia

Covina, Oct. 11, 2022 (GLOBE NEWSWIRE) -- Genomics is the study of all of persons gene. Genomics play role in health and disease. Genomics are widely used in cancer care treatment for diagnosing and treating cancer disease. Structural Genomics and Functional Genomics are two types of Genomics.Gene Therapy, Gene Discovery, Personalized Medicine, Pharmacogenetics & Targeted Therapy, Metagenomics, Mitochondrial Genomics, Pharmacogenomics are variety of applications in genomics. Metagenomics has become the important application in genomics. The newer technique genome editing is used in gene therapy. Genome editing help to introduce gene-editing tools which can able to change existing DNA in cell. Genomics are used in drug discovery due to their properties like high-throughput sequencing & characterization of expressed human genes. Genomics has allowed effective preventive measures, change in drug research strategy and development process in drug discovery due to knowledge about human genes and their functions. A complete human genome contains about 3 billion base pairs of DNA. Pharmacogenomics is the study of genes and their functions to develop safe medications which are effective and can be prescribed based on persons genetic makeup. Pharmacogenomics choose the drug and drug doses that are effective for that particular person by using genetic information about that person. Pharmacogenomics helps in improving patient safety, health care costs and drug efficiency. Single nucleotide variant (SNV) panels are used in pharmacogenetics. Genomics helps to reveal the abnormalities in genes which has drived the development and growth of different types of cancer.Study of cancer genome has improved in understanding the biology of cancer which has enabled to discover new methods for diagnosing & treating the disease. The importance of Genomics in cancer care has provided to discover new drug development and effective treatment in diagnosing and treating the disease which has driven positive impact on target market growth.

The reportGlobal Genomics in Cancer Care Market, By Type (Structural Genomics, Functional Genomics), By Application (Gene Therapy, Gene Discovery, Personalized Medicine, Pharmacogenetics & Targeted Therapy, Metagenomics, Mitochondrial Genomics, Pharmacogenomics, and Others), By End-User (Research Institute, Hospitals, Academic Research Institutes, Diagnostic Centers, and Others) andBy Region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa) - Trends, Analysis and Forecast till 2032

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Increase in cancer disease, rising emergence of clinical relievance in genomic medicine, recent advancement in genomics, newly developed technology like next-generation sequencing has given rise in use ofGenomics in Cancer Care. Wide variety of applications in Gene Therapy, Gene Discovery, Personalized Medicine, Pharmacogenetics & Targeted Therapy, Metagenomics, Mitochondrial Genomics, Pharmacogenomics has fueled the target market growth. Rising awareness in individual who are pertaining to cancer genomics, rapid growth in biotechnology industries, research institutes, diagnostic centers is expected to have positive impact on Genomics in Cancer Care market. Importance of Genomics in cancer care has enabled to provide effective treatment, new drug development, diagnosing and treating disease which has enhanced the target market growth.As a result, market competition is intensifying, and both big international corporations and start-ups are vying to establish position in the market.

Browse 60 market data tables* and 35figures* through 140 slides and in-depth TOC onGlobal Genomics in Cancer Care Market, By Type (Structural Genomics, Functional Genomics), By Application (Gene Therapy, Gene Discovery, Personalized Medicine, Pharmacogenetics & Targeted Therapy, Metagenomics, Mitochondrial Genomics, Pharmacogenomics, and Others), By End-User (Research Institute, Hospitals, Academic Research Institutes, Diagnostic Centers, and Others) andBy Region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa) - Trends, Analysis and Forecast till 2032

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GlobalGenomics in Cancer CareMarketaccounted for US$ 16.1 Bn in 2022 and is estimated to be US$ 72.61 Bn by 2032 and is anticipated to register a CAGR of 16.3%.TheGlobalGenomics in Cancer CareMarketis segmented based on Type, Application, End-User and Region.

Competitive Landscape & their strategies ofGlobalGenomics in Cancer Care Market:

The prominent players operating in theGlobalGenomics in Cancer CareMarketincludes,Pacific Biosciences Inc., Abbott Molecular Oxford Gene Technology, Roche Diagnostics, Bio-Rad Labs, Illumina Inc., Quest Diagnostics, Beckman Coulter Inc., Intrexon Bioinformatics Germany GmbH, Agilent Technologies, PerkinElmer, Danaher Corporation, Cancer Genetics Inc., Thermo Fisher Scientific Inc., and others.

The market provides detailed information regarding the industrial base, productivity, strengths, manufacturers, and recent trends which will help companies enlarge the businesses and promote financial growth. Furthermore, the report exhibits dynamic factors including segments, sub-segments, regional marketplaces, competition, dominant key players, and market forecasts. In addition, the market includes recent collaborations, mergers, acquisitions, and partnerships along with regulatory frameworks across different regions impacting the market trajectory. Recent technological advances and innovations influencing the global market are included in the report.

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Genomics in Cancer Care Market is estimated to be US$ 72.61 billion by 2032 with a CAGR of 16.3% during the forecast period 2032 - By PMI -...

Identification of DEGs after weight loss

After standardizing gene sets (Fig.1), 1011 DEGs (|logFC|>1, p<0.05) were screened out from GSE103766, GSE35411, GSE112307, GSE43471, and GSE35710 based on the above method. The results included 513 downregulated and 498 upregulated genes, as shown in the volcano plot (Fig.2 and Supplementary Table S1). The abscissa in the volcano plot is log2 (fold change) value, and the ordinate is log10 (p-value).

Box-plots of the expression profiles after consolidation and standardization. The x-axis label represents the sample symbol and the y-axis label represents gene expression values. The black line in the box-plot represents the median value of gene expression. (a) Standardization of GSE43471, (b) Standardization of GSE35411, (c) Standardization of GSE103766, (d) Standardization of GSE35710, (e) Standardization of GSE112307.

Volcano plot to identify differentially expressed genes (DEGs). (a) GSE43471, (b) GSE35710, (c) GSE35411, (d) GSE103766, (e) GSE112307. The x-axis label represents fold changes and the y-axis label represents the p-values. Red dots represent the 498 upregulated genes and green dots represent the 513 downregulated genes.

As shown in Supplementary Fig. S1, the PPI network of DEGs, based on the Search Tool for the Retrieval of Interacting Genes (STRING) database, includes 584 nodes and 1417 edges. Using the MCODE plugin in Cytoscape software, the most significant modules (score=6.667) were recognized from the PPI network as comprising 27 hub genes, including ACP5, CETP, COL1A1, COL1A2, CSF1, DNMT3B, EED, HIST1H2AI, HIST1H2BB, HIST1H2BD, HIST1H4B, HIST1H4H, HIST2H3C, HP, LCN2, LIPC, LPA, MMP2, MMP7, MMP9, MSR1, MUC1, PLA2G7, SPP1, THBS1, THBS2, and VLDLR (Table 1 and Fig.3).

Subnetwork of 27 hub genes from the proteinprotein interaction (PPI) network. Node size and temperature color reflect the degree of connectivity (bigger node represents a higher degree and smaller node represents a lower degree; red node represents a higher degree and yellow node represents a lower degree).

An enrichment analysis bubble chart was drawn under GO level 2 classifications using Omicshare tools (Fig.4 and Supplementary Table S2). As shown in the figure, hub genes were significantly enriched in regulating plasma lipoprotein particle levels, lipid transport, extracellular matrix (ECM) organization, response to reactive oxygen species, and the oxygen-containing compound for biological process (BP). The hub genes were significantly enriched for cell composition (CC) in lipoprotein particles, extracellular regions, ECM, extracellular exosomes, and secretory granules. For molecular function (MF), the hub genes were significantly elevated in lipoprotein particle binding, glycosaminoglycan binding, ECM structural constituents, and peptidase activity.

Biological functions based on Gene Ontology (GO) analysis of obesity-related hub genes. Advanced bubble chart shows significance in GO enrichment items of hub genes in three functional groups: biological process (BP), cell composition (CC), and molecular function (MF). The x-axis label represents the gene ratio (Rich Factor) and the y-axis label represents GO terms.

KEGG pathway enrichment analysis showed that the hub genes were primarily enriched in ECMreceptor interaction, cholesterol metabolism, PI3K-Akt, IL-17, and TNF signaling pathways, endocrine resistance, and leukocyte transendothelial migration (Fig.5 and Supplementary Table S3).

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of hub genes. The x-axis label represents the gene ratio (Rich factor) and the y-axis label represents the pathway.

We converted 27 gene names of the hub genes into protein names that could be recognized through the TCMSP database using the Universal Protein Resource (Uniprot). Moreover, the hub genes can be input in the required format to identify potential herbs with anti-obesity effects from the TCMSP database. After excluding the genes that were not present in the databases or those that had no related ingredients, nine were screened for further research, namely, COL1A1, MMP2, MMP9, SPP1, DNMT3B, MMP7, CETP, COL1A2, and MUC1. These genes corresponded to 16 ingredients [(-)-epigallocatechin-3-gallate (EGCG), arachidonic acid, arctiin, baicalein, beta-carotene, capillarisin, deoxypodophyllotoxin, ellagic acid, fisetin, irisolidone, luteolin, matrine, nobiletin, quercetin, rutaecarpine, tanshinone IIa] showing adequate OB and DL values (OB30%, DL0.18) (Supplementary Table S4).

There were 254 herbs with active ingredients in the databases. The top 10 herbs were Aloe, Portulacae Herba, Mori Follum, Silybum Marianum, Phyllanthi Fructus, Pollen Typhae, Ginkgo Semen, Leonuri Herba, Eriobotryae Folium, and Litseae Fructus. These were associated with more DEGs (related genes=6) and were, therefore, selected as crucial herbs in our study and annotated using Chinese pharmaceutical properties (CMPs), including characters, tastes, and meridian tropisms (Table 2).

We screened the key ingredients in treating obesity using an Ingredients-Targets network containing 25 nodes and 27 edges (Fig.6). The nine orange nodes represent the target genes and 16 green nodes represent the active ingredients. As most genes could be linked (degree=4), quercetin and EGCG were considered the most critical components in the treatment of obesity.

Ingredients-Targets network. Nine orange nodes represent the target genes, whereas the 16 green nodes represent the active compounds. The edges represent the interaction between the compounds and targets.

As shown in Fig.7a, the Herbs-Ingredients-Targets network containing 24 nodes and 43 edges was constructed to demonstrate the relationship between them: the 10 green nodes represent the key herbs and the six yellow nodes represent the active ingredients in them; the eight blue nodes depict the target genes. By analyzing the network, Phyllanthi Fructus and Portulacae Herba were associated with the most ingredients (degree=4). Moreover, quercetin was the most frequent active ingredient (degree=23) found in all herbs. Regarding gene targets, MMP2 was targeted by most ingredients (degree=5) followed by MMP9 (degree=4). Other genes were only acted upon by one component (degree=1).

Herbs-Ingredients-Targets network (a) and Herbs-Taste-Meridian tropism (b) network. (a) Yellow nodes represent the active ingredients and the blue nodes represent the target genes. (b) Yellow nodes represent tastes and purple nodes represent meridian tropisms. In all networks, the light green nodes represent cold-cool herbs, medium green nodes represent calm herbs, and dark green nodes represent warm herbs.

We also established the Herbs-Taste-Meridian tropism network containing 24 nodes and 40 edges to clarify the distribution of CMPs (Fig.7b). Five yellow nodes represent tastes and eight purple nodes represent meridian tropisms. To indicate different characters, we presented 10 nodes of herbs having different greens (light green, medium green, and dark green). Regarding characters, cold-cool herbs like Mori Follum were the most frequent (nodes=7), followed by herbs having calm (nodes=2) and warm (nodes=1) characters. In terms of taste, herbs were mostly bitter (edges=6), followed by sweet (edges=4), acid (edges=2), symplectic (edges=2), and astringent (edges=2). Regarding meridian tropism, most herbs belonged to the liver meridian (edges=6), followed by the stomach and lung (edges=4), large intestine (edges=2), bladder (edges=2), kidney (edges=2), pericardium (edges=2), spleen (edges=1), and gallbladder (edges=1) meridians.

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Identification of hub genes and candidate herbal treatment in obesity through integrated bioinformatic analysis and reverse network pharmacology |...

When the first modern humans arose in East Africa sometime between 200,000 and 300,000 years ago, the world was very different compared to today. Perhaps the biggest difference was that we meaning people of our species, Homo sapiens were only one of several types of humans (or hominins) that simultaneously existed on Earth.

From the well-known Neanderthals and more enigmatic Denisovans in Eurasia, to the diminutive hobbit Homo floresiensis on the island of Flores in Indonesia, to Homo naledi that lived in South Africa, multiple hominins abounded.

Then, between 30,000 and 40,000 years ago, all but one type of these hominins disappeared, and for the first time we were alone.

Until recently, one of the mysteries about human history was whether our ancestors interacted and mated with these other types of humans before they went extinct. This fascinating question was the subject of great and often contentious debates among scientists for decades, because the data needed to answer this question simply didnt exist. In fact, it seemed to many that the data would never exist.

Svante Pbo, however, paid little attention to what people thought was or was not possible. His persistence in developing tools to extract, sequence and interpret ancient DNA enabled sequencing the genomes of Neanderthals, Denisovans and early modern humans who lived over 45,000 years ago.

For developing this new field of paleogenomics, Pbo was awarded the 2022 Nobel Prize in Physiology or Medicine. This honor is not only well-deserved recognition for Pbos triumphs, but also for evolutionary genomics and the insights it can contribute toward a more comprehensive understanding of human health and disease.

Genetic studies of living people over the past several decades revealed the general contours of human history. Our species arose in Africa, dispersing out from that continent around 60,000 years ago, ultimately spreading to nearly all habitable places on Earth. Other types of humans existed as modern humans migrated throughout the world, but the genetic data showed little evidence that modern humans mated with other hominins.

Over the past decade, however, the study of ancient DNA, recovered from fossils up to around 400,000 years old, has revealed startling new twists and turns in the story of human history.

For example, the Neanderthal genome provided the data necessary to definitively show that humans and Neanderthals mated. Non-African people alive today inherited about 2% of their genomes from Neanderthal ancestors, thanks to this kind of interbreeding.

In one of the biggest surprises, when Pbo and his colleagues sequenced ancient DNA obtained from a small finger bone fragment that was assumed to be Neanderthal, it turned out to be an entirely unknown type of human, now called Denisovans. Humans and Denisovans also mated, with the highest levels of Denisovan ancestry present today between 4% and 6% in individuals of Oceanic ancestry.

Strikingly, ancient DNA from a 90,000-year-old female revealed that she had a Neanderthal mother and a Denisovan father. Although there are still many unanswered questions, the picture emerging from analyses of ancient and modern DNA is that not only did multiple hominins overlap in time and space, but that matings were relatively common.

Estimating the proportion of ancestry that modern individuals have from Neanderthals or Denisovans is certainly interesting. But ancestry proportions provide limited information about the consequences of these ancient matings.

For instance, does DNA inherited from Neanderthals and Denisovans influence biological functions that occur within our cells? Does this DNA influence traits like eye color or susceptibility to disease? Were DNA sequences from our evolutionary cousins ever beneficial, helping humans adapt to new environments?

To answer these questions, we need to identify the bits of Neanderthal and Denisovan DNA scattered throughout the genomes of modern individuals.

In 2014, my group and David Reichs group independently published the first maps of Neanderthal sequences that survive in the DNA of modern humans. Today, roughly 40% of the Neanderthal genome has been recovered not by sequencing ancient DNA recovered from a fossil, but indirectly by piecing together the Neanderthal sequences that persist in the genomes of contemporary individuals.

Similarly, in 2016 my group and David Reichs group published the first comprehensive catalogs of DNA sequences in modern individuals inherited from Denisovan ancestors. Surprisingly, when we analyzed the Denisovan sequences that persist in people today, we discovered they came from two distinct Denisovan populations, and therefore at least two separate waves of matings occurred between Denisovans and modern humans.

The analysis of Neanderthal and Denisovan DNA in modern humans reveals that some of their sequence was harmful and rapidly got purged from human genomes. In fact, the initial fraction of Neanderthal ancestry in humans who lived approximately 45,000 years ago was around 10%. That amount rapidly declined over a small number of generations to the 2% observed in contemporary individuals.

The removal of deleterious archaic sequences also created large regions of the human genome that are significantly depleted of both Neanderthal and Denisovan ancestry. These deserts of archaic hominin sequences are interesting because they may help identify genetic changes that contribute to uniquely modern human traits, such as our capacity for language, symbolic thought and culture, although there is debate about just how unique these traits are to modern humans.

In contrast, there are also sequences inherited from Neanderthals and Denisovans that were advantageous, and helped modern humans adapt to new environments as they dispersed out of Africa. Neanderthal versions of several immune-related genes have risen to high frequency in several non-African populations, which likely helped humans fend off exposure to new pathogens. Similarly, a version of the EPAS1 gene, which contributes to high-altitude adaptation in Tibetan populations, was inherited from Denisovans.

It is also becoming clear that DNA sequences inherited from Neanderthal and Denisovan ancestors contribute to the burden of disease in present day individuals. Neanderthal sequences have been shown to influence both susceptibility to and protection against severe COVID-19. Archaic hominin sequences have also been shown to influence susceptibility to depression, Type 2 diabetes and celiac disease among others. Ongoing studies will undoubtedly reveal more about how Neanderthal and Denisovan ancestry contributes to human disease.

I was a graduate student when the Human Genome Project was nearing completion a little over two decades ago. I was drawn to genetics because I found it fascinating that, by analyzing the DNA of present-day individuals, you could learn aspects about a populations history that occurred tens of thousands of years ago.

Today, I am just as fascinated by the stories contained in our DNA, and the work of Svante Pbo and his colleagues has enabled these stories to be told in a way that simply was not possible before.

Excerpt from:
Our *Homo sapiens* ancestors shared the world with Neanderthals, Denisovans and other types of humans whose DNA lives on in our genes -...

uman genetics is very interesting as it is what defines our physical characteristics, something that not long ago defined the sort of rights someone had in this world and sadly in some corners of the world still does. Nevertheless, it has been defined by scientists that people who have blue eyes may have only a single ancestor, meaning that all people who are blue-eyed could be related to one another. This may not apply to those who have a more religious belief about the human ancestral tree, but those who believe in science will find this very interesting.

If you were paying attention in science class, you would know that blue eyes are a recessive gene, meaning that you need to have two of the genes for the color to become apparent. Scientists have tracked down a genetic mutation that took place 6,000-10,000 years ago and is the cause of the eye color of all blue-eyed humans alive on the planet today. The change from brown to blue eyes is neither a good nor a bad mutation. It is one of a number of mutations, including those that affect hair colour, baldness, freckles, and beauty spots, that neither improves nor impairs a persons probability of surviving.

Professor Hans Eiberg from the Department of Cellular and Molecular Medicine was the one to discover this back in 2008. Based on this study and many others that had been previously done with a focus on the human genome, Professor Eiberg came up with an interesting conclusion stating that the human organism will keep on evolving:

it simply shows that nature is constantly shuffling the human genome, creating a genetic cocktail of human chromosomes and trying out different changes as it does so.

Originally, all our ancestors had brown eyes, but a mutation affection the OCA2 gene in our chromosomes resulted in humans being born with different colored eyes. Taking into consideration that between 8% to 10% of the global population has blue eyes, it is possible that blue-eyed people have a single ancestor. However, the question that everyone is probably asking is, who is this special ancestor?

In 2013, Peter Ralph from USC Dornsife a university inSouthern California used maths, statistics, and data analysis of genomic data to learn about human demographics and evolution. The issue is that the math does not add up if we were to consider that there were two or multiple ancestors with the same gene.

The fact that everyone has two parents means that the number of ancestors for each individual doubles every generation. By using basic mathematics, we can calculate that ten generations ago each individual had a thousand ancestors, and 20 generations ago they had a million and so on. But when we get to 40 generations ago, in the time of Charlemagne, we arrive at a trillion ancestors and that is a problem because we now have more ancestors than there were people. Thus one can deduce that a lot of those ancestors must be the same person.

It is almost impossible to deduce who is that one ancestor simply because we do not have the data necessary to point the research toward a single person, but most of the studies that have been performed point at there being only a single ancestor. Take for example the argument of Lucy who is considered by many historians to be the oldest known human ancestor. Lucy and other more recent examples come singular. Despite all the data and research, until more concrete evidence comes to light, this is still an argument to be debated from different perspectives and beliefs.

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Blue Eyed People Have a Single Ancestor | History of Yesterday - History of Yesterday

Medical and genetics experts in Poland say that a heart infection caused by a common skin bacteria could have caused the 1817 death of Tadeusz Kosciuszko, a Polish and U.S. military leader and national hero

WARSAW, Poland -- Medical and genetics experts in Poland say that a heart infection caused by a common skin bacteria could have caused the 1817 death of Tadeusz Kosciuszko, a Polish and U.S. military leader and national hero.

The experts said last month they found the genome of the Cutibacterium acne in the wax, wood and linen that had long-term contact with the tissues of Kosciuszkos heart, which has been preserved. They said it could have led to endocarditis, or inflammation inside the heart, and to his death, aged 71, in Switzerland.

The team was led by Prof. Micha Witt, head of the human genetics institute at the Polish Academy of Sciences in Poznan and Dr. Tadeusz Dobosz of the Wroclaw Medical University. They took the samples for their molecular tests from a vessel where the heart is being kept, at the Royal Castle in Warsaw.

Under some conditions, skin bacteria can attack the internal organs, including the heart, leading to very serious problems, Witt told Polish Radio Zet24.

He stressed that it's hard to say for sure what caused Kosciuszko's death but that their findings have led them to the rationally based hypothesis that it was the acne bacteria that caused the documented rapid deterioration of his health and death.

Previously, typhoid fever or pneumonia were believed to have ended Kosciuszkos life. He was said to have developed a high fever and chills after he had fallen off his horse into a cold stream.

Born in 1746 in the then-Polish-Lithuanian Commonwealth, Kosciuszko fought as colonel of the Continental Army in the 1776 American Revolutionary War. A military engineer and architect, he designed and oversaw the construction of America's fortifications, including West Point.

Back to restless Poland, in 1794 he commanded an ill-fated uprising against the Russian Empire that was annexing some of Polands lands. He spent his last years in Switzerland.

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Heart infection could be cause of death of Polish, US hero - ABC News