Природный метаболизм, том 5, страницы 777–788 (2023 г.) Процитировать эту статью
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Большая часть переработки пищи человека происходит в тонком кишечнике. Метаболиты в тонком кишечнике образуются в результате выделений хозяина, а также проглатываемого экспосома1 и микробных трансформаций. Здесь мы исследуем пространственно-временные изменения содержимого просвета верхних отделов кишечника во время обычного ежедневного пищеварения у 15 здоровых участников мужского и женского пола. Для этого мы используем неинвазивное проглатываемое устройство для сбора и анализа 274 образцов кишечника и 60 соответствующих гомогенатов стула, сочетая пять масс-спектрометрических анализов2,3 и секвенирование 16S рРНК. Мы идентифицируем 1909 метаболитов, включая сульфонолипиды и липидные эфиры жирных кислот и гидроксижирных кислот (FAHFA). Мы наблюдаем, что метаболомы стула и кишечника резко различаются. Пищевые метаболиты демонстрируют тенденции изменения диетических биомаркеров, неожиданное увеличение содержания дикарбоновых кислот в кишечном тракте и положительную связь между люминальными кетокислотами и потреблением фруктов. Наибольшие межиндивидуальные различия обусловлены метаболитами, полученными из рациона питания, и микробно-связанными метаболитами. Примечательно, что у двух человек, которые принимали антибиотики в течение 6 месяцев до отбора проб, наблюдаются большие различия в уровнях биоактивных жирных кислот жирных кислот, сульфонолипидов и других микробно-родственных метаболитов. На основании межиндивидуальных вариаций мы идентифицируем виды Blautia как кандидаты на участие в метаболизме FAHFA. В заключение, неинвазивный отбор проб in vivo тонкой и восходящей ободочной кишки человека в физиологических условиях выявляет связь между диетой, хозяином и микробным метаболизмом.
Мы стремились всесторонне изучить метаболомические различия между образцами просвета верхних отделов кишечника 15 здоровых людей, чтобы лучше понять степень пространственных и временных изменений и оценить перспективы интеграции данных метаболома и микробиома. В сопутствующей публикации4 мы используем эти устройства для изучения изменений в составе микробиоты кишечника, индукции профагов, протеома хозяина и микробной модификации желчных кислот. Добровольцы проглатывали комплекты из четырех устройств для отбора проб за один момент времени. Эти проглатываемые устройства для отбора проб представляли собой сжатую сборную камеру, закрытую односторонним клапаном в капсуле с pH-чувствительным покрытием. Четыре типа устройств различались только кишечнорастворимым покрытием, которое растворялось при pH 5,5 (тип 1), pH 6 (тип 2) и pH 7,5 (типы 3 и 4) (рис. 1а). Толщина и чувствительность покрытия к pH позволяли брать образцы из определенных участков кишечного тракта после опорожнения желудка. Устройства не содержали никакой электроники, кроме чипа пассивной радиочастотной идентификации, предназначенного для отслеживания. После растворения покрытий эластичный сборный пузырь расширялся и собирал до 400 мкл содержимого просвета путем вакуумного отсасывания. Односторонний клапан предотвратил потерю образца и загрязнение последующими жидкостями. Образцы стула были заморожены при температуре -20 °C, и все устройства были извлечены из стула перед анализом. Жидкое содержимое извлекали из устройств с помощью игл для подкожных инъекций. Аликвоты необработанного образца использовались для анализа микробиома 16S рибосомальной РНК, а супернатанты из центрифугированных образцов использовались для метаболомных исследований. Здесь мы проводим тщательный анализ метаболома в тех же образцах, сообщая о метаболитах, никогда ранее не обнаруживаемых в образцах человека, ключевых биомаркерах диеты и сравнивая химические профили между участниками и внутри них (дополнительные таблицы 1 и 2).
а. Дизайн исследования для исследования верхних отделов кишечного тракта. Для отбора проб из проксимальных и дистальных отделов верхних отделов кишечника использовали четыре типа устройств для отбора проб кишечника. Пятнадцать участников-людей проглотили не менее 16 устройств в течение 2 дней после обеда и после ужина после первоначального теста в первый день. Устройства были извлечены и проанализированы с помощью целевых и нецелевых методов ЖХ-МС/МС и ГХ-МС. б — Идентифицированные метаболиты в пяти метаболомных анализах, использованных для анализа образцов. Фракции химических классов включены на основе автоматизированной химической классификации ClassyFire. c. Значимость различий между отделами верхних отделов кишечного тракта рассчитывали с использованием LMM. Горизонтальная пунктирная линия представляет порог значимости P <0,05 (n = 1182 метаболита). Круги указывают на незначимость, а ромбовидные формы указывают на значимость (P <0,05) после коррекции FDR. В этот анализ были включены только метаболиты, обнаруженные в> 50% образцов кишечника (n = 1182). Коэффициент размера эффекта представляет собой наклон, оцениваемый с помощью LMM, при этом положительный (отрицательный) коэффициент указывает на более высокие (более низкие) уровни в дистальном отделе по сравнению с проксимальным верхним отделом кишечника. Вертикальные штрихпунктирные линии – коэффициент величины эффекта ±0,2.
12,000 unknown chromatographic features were reliably detected above the level of method blanks (Supplementary Table 2). Using ClassyFire software7, structurally annotated metabolites fell into 61 chemical subclasses (Supplementary Table 1). Two untargeted high-resolution liquid chromatography (LC) MS/MS assays focusing on hydrophilic and lipophilic metabolites yielded most of the annotated compounds, with 1,612 identifications. Untargeted gas chromatography (GC)–MS added 119 primary metabolites, supplemented by targeting six short-chain fatty acids (SCFAs) and a targeted LC–MS/MS assay for 17 bile acids (Fig. 1b). QC analysis of total metabolic variance revealed separation of stool and intestinal samples, with strong clustering of pooled quality control samples (Extended Data Fig. 1b)./p>50% of device samples. Of these, 630 (54%) were significantly different in the proximal compared to distal upper intestine (false discovery rate (FDR) P < 0.05; LMM) (Fig. 1c and Supplementary Table 4), with 473 metabolites at higher levels in the proximal compared to distal upper intestine and 157 compounds at lower levels in the proximal compared to distal upper intestine (Fig. 1c). Known microbially generated chemicals including SCFAs8,9, secondary bile acids10 and some microbially conjugated bile acids11,12, increased from the proximal to distal upper intestine (Extended Data Table 1 and Fig. 1c). Of the 11 detected acetylated amino acids, 7 increased from the proximal to distal upper intestine (raw P < 0.05; LMM) (Extended Data Table 1 and Fig. 1c). We also examined the 12,346 chemically unannotated metabolite signals, restricting our attention to 9,317 signals that were detected in >50% of intestinal samples (Supplementary File 1). Overall, 3,594 (38%) features were significantly different between the proximal and distal upper intestine, with 1,937 features at higher levels in the proximal compared to distal upper intestine and 1,657 features at lower levels in the proximal compared to distal upper intestine (FDR P < 0.05; LMM) (Extended Data Fig. 4)./p>100 times more abundant on average in the intestine compared to stool. These metabolites consisted of glycinated lipids, sugars, plant natural products, carnitines, microbially conjugated bile acids and S-succinylcysteine (Supplementary Table 6). Peptides were also generally at much lower levels in stool samples compared to intestinal samples, especially when compared to the proximal intestine (Extended Data Fig. 2). We also identified >100 metabolites that were >100 times more abundant in stool compared to intestinal samples (Supplementary Table 6); these metabolites were mostly polar lipids such as phosphatidylethanolamines, phosphatidylinositols and phosphatidylglycerols, as well as specific FAHFAs. The high abundance of membrane lipids in stool samples is likely due to the high amount of bacterial cell material in stool compared to luminal samples from the upper intestine./p>50% of intestinal samples were included in this analysis. Effect size coefficient is the slope estimate calculated by LMM, with positive (negative) coefficient meaning the metabolite was higher (lower) after food consumption. Vertical dashed-dotted lines are ±0.2 effect size coefficient. c, Chemical enrichment statistics (ChemRICH) analysis revealed significant chemical classes after fruit consumption visualized by separating classes by chemical lipophilicity (logP) and chemical class significance level of −log10(P). Red circles indicate that the chemical class increased after fruit consumption and blue circle indicates that the chemical class decreased after fruit consumption. Circle size indicates the size of the chemical class. e, Theophylline and theobromine levels are strongly associated with caffeine levels. Circles represent measured levels in each sample for which both metabolites were detected. f, Chemical diagram of caffeine and known metabolic pathways with structures of detected metabolites and Spearman rank correlation coefficient (rs) for each structure (P < 1.0 × 10−13 for all metabolites; n = 1,182 metabolites)./p>70% of all significantly different metabolites in five participants and >40% for another seven participants (Extended Data Fig. 7a). For metabolites that differentiated sampling time points, sugars (organooxygen compounds) were enriched in 13 of 15 participants (Extended Data Fig. 7b). Similarly, more significantly different imidazopyrimidines, indoles and isoflavonoids were found to distinguish sampling time points than intestinal regions (Extended Data Fig. 7). These classes signify dietary metabolites that were different due to variation between food types ingested during different meals, but were not as useful for differentiating between intestinal regions./p>50% of annotated metabolites exhibited significantly different levels between proximal and distal locations. An important goal for future investigation is to characterize the effect of antibiotics on intestinal sulfonolipid-, stercobilin- and long-chain AAHFA-producing bacteria and the consequences of such disruptions on health and disease. The disruption of these bacteria by antibiotics may be linked to the incidence and etiology of inflammation, diabetes and inflammatory bowel disease55,60,63. Consequently, it will be important to uncover the dynamics and mechanisms of repopulation of antibiotic-treated individuals with these microbes./p>2,500) for genomic analysis. Every bowel movement during the study was immediately frozen by the participant at −20 °C. Participant 1 provided additional samples for assessment of replicability. A total of 333 intestinal and stool samples were analysed with metabolomics methods./p>2,500 reads were retained for analyses./p>0.75 and Benjamini–Hochberg-corrected P < 0.1 were considered. ChemRICH75 was used to calculate enrichment statistics. Clustering was performed using the hclust function with the metabolite Spearman rank correlation matrix calculated using the cor function in R and Euclidean distance calculated with the as.dist function in R. PLS-DA and principal-component analysis (PCA) were performed with the ropls package in R76. PLS-DA models to distinguish participant and device type were assessed by sevenfold cross validation. Using 20–1,000 random permutations of class labels performed by the ropls R package to test for overfitting, models maintained Q2Y > 0.15 and P < 0.05 (ref. 77). Untargeted LC–MS/MS (HILIC and RP ESI+/−) features were normalized to the sum of internal standards for each platform, which has been shown to be more robust than normalizations to single compounds78. This normalization was performed by dividing each LC–MS feature by the sum of internal standard peak heights for that sample78,79. GC–MS data were normalized to the summed intensity of all annotated metabolites as extensively discussed in published protocols80. This method addresses differences specific to GC methods, recently called normalization to the total useful peak area81. Pooled QC data were found in a dense cluster when compared to CapScan and stool samples (Extended Data Fig. 1). During merging of datasets, metabolites detected by multiple assays were simplified to keep only data from one instrument, with preference for retaining data from the assay with lower technical variance (% coefficient of variance of pooled QC). Metabolites that were detected only in a single assay remained in the dataset, independent of the % coefficient of variance of pooled QC (Supplementary Table 1). Log10 transformation and zero-value imputation using one-tenth of the minimum reported peak height for non-detected features was performed for each metabolite before PCA and PLS-DA./p> ± 0.2. Only features detected in >50% of intestinal samples were included in this analysis (n = 9,317 features). Effect size coefficient is the slope estimated by the LMM, with positive (negative) coefficient representing a metabolite that is higher (lower) in the distal compared to proximal upper intestine. Vertical dashed lines are ±0.20 times the effect size coefficient./p>50% of intestinal samples were included in this analysis (n = 1182 metabolites). These results were visualized by separating classes by chemical lipophilicity (logP) and chemical class significance level of -log10(p-value). Red circles indicate that the chemical class was higher in the distal compared to proximal upper intestine, and blue indicates that the chemical class was lower in the distal compared to the proximal upper intestine. Purple indicates the chemical cluster has metabolites that are significantly higher as well as metabolites that are significantly lower in the distal compared to proximal upper intestine. Circle size represents the size of the chemical class./p>50% of samples for each subject were used for this analysis (n = 1182 metabolites). Non-FDR-corrected p < 0.05 was used as a significance threshold. b, Multivariate discriminant analysis (PLS-DA) was performed to identify metabolites that were most important for distinguishing between subjects, or between regions. The 100 metabolites most important for distinguishing these groups were ranked by variable importance in projection score (VIP) and are categorized by chemical subclass. Chemical subclasses with <3 metabolites are reported as ‘Other’./p>50% of samples for each subject were used for this analysis (n = 1182 metabolites). Non-FDR-corrected p < 0.05 was used as a significance value cutoff. a, Metabolites with significantly different abundance between intestinal regions for each subject, grouped by chemical class and the proportion of each chemical class. b, Metabolites with significantly different abundance between sampling time points, grouped by chemical class and the proportion of each chemical class./p>50% of all device samples. All device samples are shown, and are organized by subject. Within the top (FAHFA) and lower (fatty acid) sections, the metabolites are ordered based on hierarchical clustering. Color bar represents metabolite abundance (peak height) or concentration (ng/mL) for bile acids. Minimum and maximum values were used to set the color scale for each metabolite (each row)./p>
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