Perfil do ambiente intestinal humano sob condições fisiológicas

Nature volume 617, páginas 581–591 (2023) Citar este artigo

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A estrutura espaço-temporal do microbioma humano1,2, proteoma3 e metaboloma4,5 reflete e determina a fisiologia intestinal regional e pode ter implicações para doenças6. No entanto, pouco se sabe sobre a distribuição de microorganismos, seu ambiente e sua atividade bioquímica no intestino devido à dependência de amostras de fezes e acesso limitado a apenas algumas regiões do intestino usando endoscopia em indivíduos em jejum ou sedados7. Para resolver essas deficiências, desenvolvemos um dispositivo ingerível que coleta amostras de várias regiões do trato intestinal humano durante a digestão normal. A coleta de 240 amostras intestinais de 15 indivíduos saudáveis usando o dispositivo e subsequentes análises multiômicas identificaram diferenças significativas entre bactérias, fagos, proteínas hospedeiras e metabólitos nos intestinos versus fezes. Certos táxons microbianos foram diferencialmente enriquecidos e a indução de profagos foi mais prevalente nos intestinos do que nas fezes. O proteoma do hospedeiro e os perfis de ácidos biliares variaram ao longo dos intestinos e foram altamente distintos daqueles das fezes. Correlações entre gradientes nas concentrações de ácidos biliares e abundância microbiana previram espécies que alteraram o pool de ácidos biliares através da desconjugação. Além disso, as concentrações de ácidos biliares conjugados microbianamente exibiram tendências dependentes de aminoácidos que não eram aparentes nas fezes. No geral, o perfil longitudinal não invasivo de microorganismos, proteínas e ácidos biliares ao longo do trato intestinal sob condições fisiológicas pode ajudar a elucidar os papéis do microbioma e metaboloma intestinal na fisiologia e doença humanas.

O trato intestinal humano abriga a grande maioria dos microorganismos que residem em ou sobre nossos corpos1; seu conteúdo genético e capacidade de transformação bioquímica são centenas de vezes maiores do que aqueles codificados pelo genoma humano8. Os seres humanos dependem de seus microrganismos intestinais para digestão de alimentos, regulação do sistema imunológico e proteção contra patógenos, entre outras funções críticas1. Um aspecto importante, mas frequentemente negligenciado, do intestino é a heterogeneidade regional e como ela afeta a fisiologia local9. Devido às dificuldades de acesso e amostragem do trato intestinal, as fezes têm sido a principal fonte de informação para estudos do microbioma intestinal humano10. No entanto, as fezes refletem produtos residuais e efluentes a jusante, dentro dos quais a variação regional é perdida. Por exemplo, os principais metabólitos, como os ácidos biliares, são alterados a montante por transformações microbianas e, então, substancialmente absorvidos pelo hospedeiro antes da excreção4. As regiões do intestino distais ao estômago (duodeno, jejuno, íleo e cólon) diferem marcadamente na disponibilidade de nutrientes, pH, pressão parcial de oxigênio, estrutura da mucosa e taxa de fluxo7. Como resultado, comunidades microbianas distintas com funções especializadas, metabolomas, nichos imunológicos e proteomas estão presentes em cada região intestinal3,4,11. Assim, uma compreensão mais profunda de como os microorganismos intestinais afetam a fisiologia humana e vice-versa requer amostragem local do microbioma intestinal e seu ambiente químico em estados naturais e imperturbáveis.

Historicamente, a amostragem do trato intestinal humano sem perturbação ou contaminação tem sido um desafio10. Recentemente, descobrimos uma variabilidade regional substancial na composição da microbiota em escalas espaciais de apenas alguns centímetros ao longo dos intestinos de doadores de órgãos falecidos2. No entanto, os doadores de órgãos geralmente são tratados com antibióticos e, mesmo nos casos em que o trato intestinal foi amostrado imediatamente após a interrupção do suporte de vida, o intestino é frequentemente isquêmico ou necrótico. A amostragem duodenal de indivíduos vivos usando endoscopia digestiva alta tem uma alta probabilidade de contaminação inadvertida de conteúdos orais, esofágicos ou gástricos. O acesso endoscópico ao jejuno médio requer um procedimento de aproximadamente 2 horas envolvendo anestesia geral ou sedação, realizado em jejum12,13. Alternativamente, um estoma da exteriorização do íleo através da parede abdominal pode fornecer amostras intestinais, mas esse procedimento é invasivo e reflete anatomia e fisiologia alteradas do intestino, em um único local14. Apesar dos efeitos importantes no microbioma e nas propriedades de sinalização dos ácidos biliares, os estudos sobre sua diversidade e concentrações químicas se basearam em medições não representativas da pequena porcentagem de ácidos biliares nas fezes ou da fração de uma porcentagem no sangue. Dispositivos ingeríveis desenvolvidos anteriormente para amostragem do trato intestinal humano têm limitações importantes, como eletrônica complexa15, tamanho grande que corre o risco de retenção do dispositivo15 ou volume de amostragem insuficiente para análises multiômicas16. Perfis de pH, peristaltismo, dieta, fisiologia, distúrbios gastrointestinais e principais metabólitos, como ácidos biliares17 diferem acentuadamente entre humanos e animais18, tornando os estudos humanos mais relevantes para a fisiologia e doenças humanas.

0.75 between devices and stool that were significantly differentially abundant (n = 28 ASVs across n = 268 analysed samples; limma-voom was used to calculate differential expression after size factors were estimated and normalized using DESeq2; P < 0.05, Benjamini–Hochberg correction)./p> 0.75) in intestinal samples than in stool (Fig. 1f). The Romboutsia genus was recently named following isolation of a species from rat ileal digesta26, in line with this genus having a niche in the small intestine./p>40% (Fig. 2c). Consequently, individual intestinal samples contained communities with lower alpha diversity relative to the intra-individual diversity represented by all samples from a device of a certain type or by all samples from devices swallowed at the same time (Fig. 2d and Extended Data Fig. 3b,c). Thus, much of the higher variability across intestinal samples relative to stool is probably due to the dynamic and heterogeneous nature of the microbiota along the intestinal tract./p>75% complete and <25% contamination) from these data (Methods and Supplementary Table 3), which enabled taxonomic identification for read-mapping applications. On the basis of the established role of the gut microbiota in carbohydrate degradation and its links to health and disease27, we first focused on carbohydrate active enzyme (CAZyme) gene abundance in each region. The percentage of reads that mapped to CAZymes in devices exhibited greater variance than in stool (Extended Data Fig. 5a,b). Within devices, CAZyme gene abundance was positively correlated with the relative abundance of five ASVs: two unnamed Bacteroides species, two Bacteroides vulgatus strains and Parabacteroides merdae (P < 0.001, Benjamini–Hochberg corrected; Extended Data Fig. 5c). The B. vulgatus strains exhibited the highest slope and strongest correlation (Spearman's ρ = 0.77 and 0.75). By contrast, in stool, despite a correlation between the abundance of CAZyme genes and the Bacteroidaceae family (Extended Data Fig. 5d), there were no ASVs whose abundance correlated with CAZyme gene abundance, probably because of the greater evenness of the taxa observed in stool compared with intestinal samples (Fig. 2c)./p>95%; Supplementary Table 2) from this library showed that the 35 members of the Bacteroidetes phylum typically contained more CAZyme genes than members of other phyla (Extended Data Fig. 5e). The dataset included ten Parabacteroides strains (eight Parabacteroides distasonis and two P. merdae). Each CAZyme gene was annotated with a CAZyme enzyme class and family to give a putative functional category. The CAZymes detected in the P. merdae strains were assigned to a mean of 107.5 unique CAZyme functional categories out of a mean of 237.5 CAZymes, and P. distasonis enzymes were assigned to 95 unique CAZyme functional categories out of a mean of 237.5 CAZymes; thus, P. distasonis strains appear to contain greater redundancy than P. merdae strains (Supplementary Table 4). Furthermore, P. merdae strains contained seven additional unique CAZyme functional categories in the glycoside hydrolase family and five additional unique polysaccharide lyase functional categories compared with P. distasonis strains (Supplementary Table 4). We also investigated five strains of B. vulgatus: each possessed 301 or 302 CAZyme genes representing 131 unique functional categories, more than in any other non-Bacteroides isolate (Extended Data Fig. 5e and Supplementary Table 4). However, B. vulgatus was the Bacteroides species with the fewest CAZyme genes (Extended Data Fig. 5e and Supplementary Table 4), indicating that factors other than CAZyme abundance influence the dominance of B. vulgatus over other Bacteroides species in the intestines. These differences in CAZyme gene abundance and functional categories are an important consideration for how diet drives the growth of certain bacteria in the gastrointestinal tract and for which by-products of carbohydrate degradation may be available to the host./p>50% completeness, of which 629 were integrated prophages (Methods). Of these vOTUs, 83% (1,343/1,607) were present in both stool and intestinal samples (Fig. 3a), indicating that the intestines and stool have similar viromes. The abundance of these vOTUs as determined by read mapping was generally correlated between intestinal and stool samples (Extended Data Fig. 6a), although the intestinal samples had higher viral read mapping fractions (Extended Data Fig. 6b), perhaps owing to lower bacterial densities1. Viromes were more similar between stool and intestinal samples from the same participant (Jaccard distance of 0.40 ± 0.14, mean ± s.d.) than between stool (0.58 ± 0.09) or intestinal (0.62 ± 0.10) samples from different participants (P < 10−10 in both cases, two-tailed Student's t-test), and PCoA of the viromes (Fig. 3b) showed similar clustering as with the microbiota (Fig. 1e)./p> 1 and P < 0.05 are coloured on the basis of sample type and enrichment. c, PCA of normalized human protein abundance shows separation between intestinal and stool samples (n = 212 and 56, respectively). d, Human proteome composition varies significantly more between intestinal samples (n = 212) than between stool samples (n = 56), both within (top) and across (bottom) participants. Top, each circle is the median Pearson correlation coefficient of all sample pairs for a given participant. Bottom, each circle is the median of all correlation coefficients between all pairs of samples from any two participants (n = 105 for each intestinal and stool sample). ****P ≤ 0.0001, Bonferroni-corrected two-tailed Wilcoxon rank-sum test. e, PCA from c highlighting the clustering of intestinal and stool samples from participant 15 (n = 15 and 4, respectively). f, Canberra distance between microbiota compositions was higher in samples with less similar human proteomes for all sample pairs of a given type (n = 20,706 pairwise comparisons for devices, n = 1,485 pairwise comparisons for stool)./p>90% and contamination of <10%, dereplicated to 99% average nucleotide identity (ANI)) and searched for the canonical BSH gene in each using a hidden Markov model. We found putative BSH genes in A. hadrus (7 of 8 MAGs) and A. putredinis (4 of 4 MAGs), in accordance with previous literature38. By contrast, none of the 12 F. prausnitzii MAGs nor the 3 B. wadsworthia MAGs contained any putative BSH genes, suggesting that these taxa may use glycine and taurine25 generated by other microbial deconjugation reactions./p>50 µl of intestinal fluids and were subjected to DNA extraction and 16S rRNA gene and metagenomic sequencing; the remainder sampled <50 µl or were filled with gas, most likely from the colon./p>50 µl, DNA was extracted using a Microbial DNA extraction kit (Qiagen)43 and 50 µl from a device, 200 µl of saliva or 100 mg of stool./p>2,500 reads were retained for analyses. We obtained sufficient sequencing reads from 210 samples, which were the focus of subsequent analyses, along with sequencing data from 29 saliva and 58 stool samples (one participant provided only one saliva sample, and one stool sample had insufficient sequencing reads; Extended Data Fig. 2a)./p>75% completeness and <25% contamination were dereplicated at 99% ANI (strain level) with dRep (v.3.0.0)58, resulting in 696 representative MAGs across all samples. GTDB-Tk was used to assign taxonomy59. Default parameters were used for all computational tools./p>90% coverage and dereplicated to create a curated database of AMR genes. Metagenomic reads for each sample were mapped against this database to calculate the percentage of reads mapped./p>1 kb in length were analysed using VirSorter2 (ref. 65), DeepVirFinder66 and VIBRANT67. Contigs identified as viral by at least one algorithm (VirSorter2 score ≥0.9, or DeepVirFinder score ≥0.9 and P < 0.05, or VIBRANT score of medium quality or higher) were clustered using an ANI cut-off of 0.95 and coverage cut-off of 85%. The quality of the clustered contigs was analysed using CheckV68, which also classified viral contigs as prophages if they contained both viral and bacterial regions./p>2 and the prophage region has >50% coverage./p> 0.995 for all bile acids), and the model was applied to all samples and blanks to calculate concentrations. The average concentration reported for method blanks was subtracted from sample concentrations. Because multiple dilutions were analysed for each sample, the measurement closest to the centre of the standard curve (750 ng ml–1) was used. Zero values were imputed with a concentration value between 0.001 and 0.1 ng ml–1. Concentrations were reported as ng ml–1 for intestinal sample liquid supernatant and ng g–1 for wet stool. In all, 218 device samples and 57 stool samples passed quality control and were used for analyses (Extended Data Fig. 2a)./p>