Генетические исследования парных метаболомов выявляют ферментативные и транспортные процессы на границе раздела плазмы и мочи.

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Почки работают на границе плазмы и мочи, очищая молекулярные отходы, сохраняя при этом ценные растворенные вещества. Генетические исследования парных метаболомов плазмы и мочи могут выявить основные процессы. Мы провели полногеномные исследования 1916 метаболитов плазмы и мочи и обнаружили 1299 значимых ассоциаций. Ассоциации с 40% вовлеченных метаболитов были бы упущены при исследовании только плазмы. Мы обнаружили специфические для мочи результаты, которые предоставляют информацию о реабсорбции метаболитов в почках, таких как аквапорин (AQP)-7-опосредованный транспорт глицерина, а также различные метаболомические следы экспрессируемых почками белков в плазме и моче, которые соответствуют их локализации и функции. , включая транспортеры NaDC3 (SLC13A3) и ASBT (SLC10A2). Общие генетические детерминанты 7073 комбинаций метаболитов и заболеваний представляют собой ресурс для лучшего понимания метаболических заболеваний и выявленных связей дипептидазы 1 с циркулирующими пищеварительными ферментами и гипертонией. Расширение генетических исследований метаболома за пределы плазмы дает уникальное понимание процессов на границе разделов тела.

Почки человека очищают низкомолекулярные отходы из плазмы, сохраняя при этом ценные растворенные вещества, такие как аминокислоты, для поддержания метаболического гомеостаза. После клубочковой фильтрации плазмы в ультрафильтрат первичной мочи ее состав модифицируется в высокоскоординированном процессе по ходу нефрона. Сотни узкоспециализированных транспортных белков перемещают растворенные вещества через мембраны клеток, выстилающих нефрон, для реабсорбции важных молекул, одновременно активно выводя токсичные или ненужные молекулы1. Многие из этих транспортных белков, а также ферменты, ответственные за образование или расщепление транспортируемых метаболитов, были идентифицированы в результате изучения моногенных заболеваний человека. Они представляют собой привлекательные мишени для лекарств не только для лечения заболеваний почек, но и метаболических заболеваний, примером чего являются ингибиторы транспортеров SGLT2 и URAT1 (ссылки 2,3). Однако многие транспортеры и ферменты, а также их субстраты и продукты in vivo еще предстоит охарактеризовать. Мы предположили, что объединение информации, полученной в результате генетических исследований человека, с метаболомами плазмы и мочи позволит по-новому взглянуть на роль этих белков в здоровье и заболеваниях.

Генетические эффекты на уровни метаболитов в моче могут отражать системные процессы, такие как генотип-зависимое поглощение метаболитов в кишечнике или реакции печеночной трансформации, которые обнаруживаются в моче из-за фильтрации соответствующих метаболитов из плазмы. Они также могут отражать специфичные для почек процессы, например, активную продукцию, обратный захват или секрецию малых молекул клетками, выстилающими нефрон. Исследования с парными измерениями метаболитов в плазме и моче могут помочь различить эти процессы.

Здесь мы изучаем различия и сходства в отношении генетического влияния на метаболомы, полученные из двух «матриц», плазмы и мочи, чтобы проверить гипотезу, которая оба предоставляют дополнительную информацию. Путем систематической интеграции общегеномной генетической информации с парными измерениями метаболитов в плазме и моче у 5023 участников немецкого исследования хронической болезни почек (GCKD) мы выявляем основные системные, а также специфичные для почек процессы. Мы обнаружили 1299 значимых ассоциаций по всему геному и показали, что при изучении только плазмы можно было бы пропустить ассоциации почти с 40% метаболитов. Мы подчеркиваем примеры специфичных для мочи ассоциаций, следов, которые экспрессируемые почками транспортеры оставляют в плазме и метаболомах мочи, а также ранее неописанной системной роли фермента, обогащенного почками. Это исследование создает богатый ресурс для будущей экспериментальной проверки еще не охарактеризованных ферментативных и транспортных процессов, которые могут представлять собой молекулярную связь между генетическими вариантами и человеческими особенностями и заболеваниями.

0.8). In summary, discovery GWAS of the plasma and urine metabolomes identified a wealth of significantly associated loci, the basis for subsequent characterizations./p> 0.8), gray labels indicate genetic regions identified in both plasma and urine without intermatrix colocalization, and red or blue labels indicate genetic regions exclusively identified in plasma or urine, respectively. The number of plasma and urine mQTLs annotated to a gene is given in parentheses (plasma, urine). The pie chart reflects the proportions of the 282 unique genes that were annotated as enzymes and transporters. Official gene symbols for PYCRL and ERO1L are PYCR3 and ERO1A, respectively./p>5 colocalizing regions are color coded and labeled. For the three other groups, all genes assigned to >50 colocalizing metabolite regions are color coded and labeled./p> 0.8; Methods) involving 1,162 mQTLs. Colocalizing associations were divided into four groups (Supplementary Table 10): those where the same genetic signal affected different metabolites in the same matrix ((1) ‘intraplasma’, n = 3,189; (2) ‘intraurine’, n = 3,155), the same metabolite in both plasma and urine ((3) ‘intermatrix, same metabolite’, n = 204) and different metabolites in plasma and urine ((4) ‘intermatrix, different metabolite’, n = 4,048)./p>50% of the 3,155 intraurine colocalizations (Fig. 3c). This is consistent with FADS1 encoding a central enzyme in polyunsaturated fatty acid metabolism17 and the predominance of these lipid metabolites in plasma and with NAT8 encoding an N-acetyltransferase highly expressed in the kidney that generates water-soluble molecules for excretion18 and the abundance of N-acetylated metabolites in urine. Similarly, the organic anion transporter encoded by SLCO1B1 and the solute transporters encoded by the SLC17A family show high and specific expression in liver and kidney, respectively, where they transport dozens of physiological and pharmacological substrates19,20./p>

0.8) with rs601338, at which the minor A allele encodes the stop-gain variant p.Trp154Ter (NP_000502.4) that was associated with higher levels of only these two urine metabolites. The encoded fucosyltransferase 2 is a ubiquitously expressed enzyme that mediates the inclusion of fucose into glycans on a variety of glycolipids and glycoproteins. Individuals homozygous for p.Trp154Ter have lower risk of several infectious diseases during childhood25,26, a selective advantage. Indeed, we detected positive selection at this and other loci, including positive controls such as the LCT locus (Methods and Supplementary Table 21). The extended homozygosity of the haplotype carrying the minor, derived allele at the galactosylglycerol mQTL further supported positive selection (Fig. 5b)./p>64-fold higher urine but not plasma glycerol levels (Fig. 5g), thereby confirming a single case report through evidence from population studies./p> 0.8), with color coding representing the phenotype category. Effect directions are indicated by the line type (solid, positive association; dashed, inverse association). CNS, central nervous system; NOS, not otherwise specified./p>50% of the observed metabolite variance. Although this translates into much smaller effects on complex diseases such as hypertension, arthropathies or gallstone disease, colocalization can nominate shared pathophysiological mechanisms and inform about potential therapeutic targets, repurposing opportunities and potential side effects of approved drugs. Our study includes numerous such examples, supported by the recent launch of new drugs such as evinacumab, a monoclonal antibody targeting angiopoietin-like 3 (ANGPTL3) to treat dyslipidemia, or the SLC10A2 inhibitor odevixibat to treat cholestasis. Even if a target implicated by metabolites in our study is not desirable or amenable for therapeutic modulation, disease-associated metabolites may represent valuable intermediate biomarkers for risk prediction or response to treatment./p>60 ml min−1 per 1.73 m2 with UACR > 300 mg per g (or urinary protein/creatinine ratio > 500 mg per g) were included53. This study used biomaterials collected at the baseline visit, shipped frozen to a central biobank and stored at −80 °C54. A more detailed description of the study design, standard operating procedures and the recruited study population has been published53,55. The GCKD study was registered in the national registry for clinical studies (DRKS 00003971) and approved by local ethic committees of the participating institutions (universities or medical faculties of Aachen, Berlin, Erlangen, Freiburg, Hannover, Heidelberg, Jena, München and Würzburg)53. All participants provided written informed consent. For this project, metabolites were quantified from stored EDTA plasma and spot urine. Information on genome-wide genotypes, covariates and metabolites was available for 4,960 (plasma) and 4,912 (urine) persons./p>

4,500 purified standards) was used for metabolite identification. Known metabolites reported in this study conformed to confidence level 1 (the highest confidence level of identification) of the Metabolomics Standards Initiative58,59, unless otherwise denoted with an asterisk. Additional mass spectral entries have been created for compounds of unknown structural identity (unnamed biochemicals; >2,750 in the Metabolon library), which have been identified by virtue of their recurrent nature (both chromatographic and mass spectral). Peaks were quantified using the area under the curve and normalized to correct for variation resulting from instrument interday tuning differences by the median value for each run day. Likewise, metabolites in the ARIC replication sample were also quantified with the Metabolon HD4 platform./p>50% missing data. A total of 130 plasma and 131 urine metabolites were removed, as less than 300 genotyped samples were available./p>5% of samples outlying >5 s.d.). Three plasma samples and one urine sample represented an outlier >5 s.d. along one of the first 15 principal components based on metabolites with complete information. The final dataset consisted of 1,296 plasma and 1,401 urine log2-transformed traits for subsequent GWAS. Supplementary Table 2 provides detailed annotation of the metabolites, including heritability estimates for metabolites with at least one genetic association. Over the course of this project, two formerly different urine metabolites were merged because they represented the same molecule: X-12739 and X-24527 to the glutamine conjugate of C6H10O2 (1)* and X-23667 and X-24759 to (2-butoxyethoxy)acetic acid./p> 0.8) within a window of ±500 kb around the index SNP based on genetic data from the 1000 Genome Project phase 3 version 5 of European ancestry using https://snipa.helmholtz-muenchen.de/snipa/?task=proxy_search. For each study, the best available proxy SNP in terms of maximal LD and minimal distance was selected. Summary statistics were downloaded from https://metabolomics.helmholtz-muenchen.de/gwas/index.php?task=download (Shin et al.6), http://www.hli-opendata.com/Metabolome (Long et al.7, only summary statistics with P value < 10−5), https://omicscience.org/apps/crossplatform/ (Lotta et al.8), https://pheweb.org/metsim-metab/ (Yin et al.10), https://omicscience.org/apps/mgwas/mgwas.table.php (Surendran et al.11) and http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/; accession numbers for European GWAS are GCST90199621–GCST90201020 (Chen et al.12). Hysi et al.9 shared their summary statistics upon request./p> 0.6 using GCTA-GRM71. GCTA-GREML72 was then used to estimate the proportion of variation in log2-transformed and, in the case of urine, pq-normalized metabolite levels that can be explained by the SNPs for all metabolites that gave rise to an mQTL./p> 0.8). For each mQTL, the GCTA-COJO Slct algorithm version 1.91.6 (ref. 73) was used to identify independent genome-wide significant SNPs (Pconditional < 3.9 × 10−11), using a collinearity cutoff of 0.1. For mQTL with multiple independent SNPs, approximate conditional analyses were carried out conditioning on the other independent SNPs in the region using the GCTA-COJO Cond algorithm to estimate conditional effect sizes. Statistical fine mapping was performed for all independent SNPs per mQTL. In loci with a single independent SNP, approximate Bayes factors (ABFs) were calculated from the original GWAS effect estimates using Wakefield's formula74 with a standard deviation prior of 1.33. For mQTL with multiple independent SNPs, ABFs were derived from the conditional effect estimates. The SNP's ABF was used to calculate the posterior probability for the variant driving the association signal (PPA, ‘causal variant’). Credible sets were calculated by summing the PPA across PPA-ranked variants until the cumulative PPA was >99%. log2-transformed credible set sizes were regressed on the MAFs of independent index SNPs./p>

We also performed colocalization analyses of mQTLs with disease outcomes and biomarker measurements in the UK Biobank, with two representative kidney function traits and with trans pQTLs using the precomputed pQTL data from Sun et al.79 to gain insights into clinical consequences and potential molecular mediators of mQTLs. Association summary statistics between SNPs and 30 biomarkers from the UK Biobank baseline examination, including the liver function markers AST, ALT, GGT, bilirubin and albumin, were computed using BOLT-LMM80 (application no. 20272) in the same subset of European-ancestry participants as previous studies81. Precomputed GWAS summary statistics of diseases as ascertained in the UK Biobank and analyzed using phecodes were obtained from https://www.leelabsg.org/resources (1,403 binary traits) and from https://yanglab.westlake.edu.cn/data/ukb_fastgwa/imp_binary/ (2,325 of 2,989 binary traits82; traits containing job-coding terms were excluded from the analysis). There were 816 phecodes analyzed in both, but only unique phecodes were counted for positive colocalizations. We used GWAS summary statistics of creatinine-based and cystatin C-based eGFR (eGFRcrea and eGFRcys) from Stanzick et al.1.2 million individuals. Nat. Commun. 12, 4350 (2021)." href="/articles/s41588-023-01409-8#ref-CR83" id="ref-link-section-d87044679e3267"83, who meta-analyzed kidney function GWAS from the CKDGen Consortium and the UK Biobank. The GWAS summaries were downloaded from the CKDGen data website at https://ckdgen.imbi.uni-freiburg.de. Colocalization testing between mQTL and trans pQTL was performed within a window of ±500 kb around the mQTL's index SNP when at least one trans pQTL association with P < 0.05 ÷ 409 ÷ 3,000 for plasma and P < 0.05 ÷ 410 ÷ 3,000 for urine was present within a window of ±100 kb around the index SNP. Similarly, colocalization analysis between mQTL and biomarkers, diseases and kidney function traits was performed within ±500 kb of the index SNP when there were one or more associated variants with MAF > 0.01 and P < 0.05 ÷ 409 or P < 0.05 ÷ 410, respectively, within ±100 kb of the index SNP, using the method outlined above./p> 0.01)./p>

1.2 million individuals. Nat. Commun. 12, 4350 (2021)./p>25% are labeled with the associated metabolite and most likely gene; error bars represent h2 variance) can contain interesting biological information: for example, three metabolites with larger estimated heritabilities in urine than in plasma are N-acetylated amino acids, all of which have an mQTL at NAT8. NAT8 is highly and selectively expressed in the kidney, where the encoded enzyme N-acetylates molecules to make them water soluble for subsequent excretion./p>3% are shown). The two inner bands represent the effect size of the mQTL in plasma (framed in red) and urine (framed in blue). Shades of orange indicate positive effect sizes, shades of aquamarine negative ones. The two outer bands represent the variance in metabolite levels in plasma and urine explained by the index SNP of the corresponding mQTL, where a darker shade of green corresponds to a greater explained variance./p>