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Jul 07, 2023

Nature Biotechnology (2023) Cite este artigo

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As culturas bacterianas puras permanecem essenciais para estudos experimentais e mecanísticos detalhados na pesquisa de microbiomas, e os métodos tradicionais para isolar bactérias individuais de ecossistemas microbianos complexos são trabalhosos, difíceis de dimensionar e carecem de integração fenótipo-genótipo. Aqui descrevemos uma plataforma de isolamento de tensão robótica de alto rendimento de código aberto para a geração rápida de isolados sob demanda. Desenvolvemos uma abordagem de aprendizado de máquina que aproveita a morfologia da colônia e os dados genômicos para maximizar a diversidade de micróbios isolados e permitir a seleção direcionada de gêneros específicos. A aplicação desta plataforma em amostras fecais de 20 humanos produz biobancos de microbioma intestinal personalizados, totalizando 26.997 isolados que representam >80% de todos os táxons abundantes. A análise espacial em mais de 100.000 colônias capturadas visualmente revela padrões de cocrescimento entre as famílias Ruminococcaceae, Bacteroidaceae, Coriobacteriaceae e Bifidobacteriaceae que sugerem importantes interações microbianas. A análise comparativa de 1.197 genomas de alta qualidade desses biobancos mostra uma interessante evolução de linhagens intra e interpessoais, seleção e transferência horizontal de genes. Essa estrutura culturômica deve capacitar novos esforços de pesquisa para sistematizar a coleta e a análise quantitativa de fenótipos baseados em imagens com dados genômicos de alta resolução para muitos estudos emergentes de microbioma.

A metagenômica oferece a capacidade de pesquisar amplamente a composição de diversos ecossistemas microbianos, desde as comunidades do solo até o microbioma intestinal. No entanto, os micróbios precisam ser isolados e cultivados para dissecar mecanicamente seus papéis funcionais no habitat e a miríade de processos interespécies que ocorrem. Os métodos tradicionais de cultivo que dependem da colheita aleatória de colônias de 'força bruta' são tediosos e trabalhosos1,2,3,4. Métodos de isolamento baseados em diluição serial usando 96 ou 384 poços são intensivos em recursos e resultam em isolamento repetido das mesmas cepas dominantes da população5. Os sistemas microfluídicos permitem o crescimento em reatores de nanolitros, mas os isolados clonais são difíceis de extrair6,7. Dado que um microbioma típico pode conter centenas a milhares de espécies únicas exibindo uma distribuição de abundância de cauda longa8 (ou seja, poucos dominam enquanto a maioria é rara), gerar coleções de cepas abrangentes por meio de culturômica sistemática continua sendo um desafio importante e notável.

Os micróbios podem ser distinguidos com base em seus diversos fenótipos, seja por sua capacidade de crescer em determinados meios ou pelos metabólitos que produzem9,10,11,12. A seleção baseada no crescimento pode melhorar o isolamento de espécies raras, por exemplo, com meios de crescimento contendo diferentes nutrientes ou antibióticos1,2,13. Os espectros de espectrometria de massa podem ser usados ​​para diferenciar as espécies14,15, mas a abordagem é de baixo rendimento e requer processamento manual. A classificação celular ativada por imagem foi desenvolvida para isolar células eucarióticas com base em imagens multidimensionais, mas esse método requer instrumentação sofisticada e não foi implementado para bactérias16. Com avanços recentes em inteligência artificial (IA) e modelos de aprendizado profundo treinados para discernir características diferenciadas em imagens multidimensionais e dados biológicos17, o aprendizado de máquina (ML) de fluxos de dados fenotípicos e genômicos combinados está pronto para transformar a cultura microbiana de próxima geração.

Aqui descrevemos uma plataforma de isolamento e genotipagem de cepa robótica guiada por ML que permite a geração rápida e de alto rendimento de biobancos cultivados sob demanda. Este sistema usa um algoritmo baseado em imagem inteligente para aumentar a diversidade taxonômica de culturômica em comparação com um método de seleção aleatória. Demonstramos a utilidade deste sistema gerando anaerobicamente biobancos isolados personalizados para 20 participantes humanos, produzindo um total de 26.997 isolados com 1.197 rascunhos de genomas de alta qualidade, abrangendo 394 variantes de sequência amplicon 16S (ASVs). Usando as informações genômicas e morfológicas pareadas para cada isolado, treinamos um modelo de ML que pode prever a identidade taxonômica com base apenas na morfologia da colônia. A aplicação desse modelo de ML levou a uma melhoria no isolamento direcionado de micróbios de interesse. A análise de imagem em larga escala de todas as colônias cultivadas em placas de ágar revelou padrões de crescimento específicos de espécies interessantes e interações interespécies. A análise de todo o genoma de biobancos personalizados revelou variação de nível de cepa específica de pessoa e assinaturas de transferência horizontal de genes (HGT) dentro dos principais filos intestinais. Desenvolvemos ainda um banco de dados baseado na Web de acesso aberto (http://microbial-culturomics.com/) contendo dados genotípicos, morfológicos e fenotípicos pesquisáveis ​​de todos os isolados gerados por culturômica automatizada como um recurso comunitário único e em expansão para o campo do microbioma.

20 times higher capacity and faster than manual colony isolation by a person. To ensure that our genomic analysis capacity matches the robotic isolation throughput, we also developed a low-cost, high-throughput sequencing pipeline that leverages liquid handling automation to generate barcoded libraries for 16S rRNA sequencing or whole-genome sequencing (WGS; Methods). The cost per isolate in this pipeline is $0.45 for colony isolation and genomic DNA (gDNA) preparation, $0.46 for 16S rRNA sequencing and $6.37 for WGS at a coverage of >60× on an Illumina HiSeq platform, which is substantially cheaper than commercial services (Supplementary Table 2)./p> 0.1% are shown and the side bar on the left represents their family-level taxonomy. ASVs found in personalized biobanks are shown as black bars in the right heatmap and uncultured ASVs not found in any biobank are highlighted. f, Correlation of average relative abundance in original feces sample and number of isolates in entire collection for ASVs. Highly abundant ASVs that are difficult to culture, that is, with fewer isolates, are highlighted. g, Average relative abundance of top abundant ASVs but with no more than 2 isolates in the entire collection. Color of bars represents family-level taxonomy./p> 0.1%) are found in the biobank. Notably, a substantial fraction of the uncultured gut bacteria belonged to the Ruminococcaceae and Lachnospiraceae families (Fig. 2e and Supplementary Table 6), which has also been previously documented as ‘unculturable’24. For each ASV, we compared the number of isolates generated in our total isolate collection versus their average abundance in the bulk feces (Fig. 2f), which appeared to be positively correlated. Still, we identified a set of abundant yet difficult-to-culture bacteria, including Faecalibacterium ASV-58, Prevatella ASV-470 and ASV-324, Oscillibacter ASV-215 and Clostridium XlVa ASV-287 (Fig. 2g). Interestingly, Faecalibacterium ASV-58, from which we obtained one isolate and performed WGS, matched with >98% genome-wide average nucleotide identity (ANI) to the metagenome-assembled genome (MAG) of Candidatus cibiobacter qucibialis. This strain in our collection was previously reported as the most abundant uncultured species in human gut25 and is highly depleted in inflammatory bowel disease (IBD) patients, as are other Faecalibacterium strains26./p>3% relative abundance on average in the bulk fecal matter, which further expands the collection of culturable gut microbiomes. Together, these results highlight cultured isolates and the remaining missing diversity based on our current media and growth conditions, and offer directions to guide future culturomics efforts focused on these ‘dark matter’ gut microbiome (Supplementary Table 6)./p> Bacteroides > Dorea), reflecting differences in their growth characteristics. On the other hand, colonies of Faecalibacterium are smaller and fainter, in line with our earlier results of their poor culturability. Furthermore, colony morphologies are significantly clustered according to their phylogeny (P = 0.008 by PERMANOVA test in Fig. 3c). For instance, most genera of Clostridia are closer to each other by morphology-based ordination (Fig. 3c). Therefore, colony morphologies may embed a substantial amount of information that could be linked to taxonomic identities./p>2 kb in length (Methods). Consistent with recent reports38,39, we observed that HGT events were strongly linked to the phylogeny of the isolates, that is, most HGT events occurred within the same phyla but were also quite prevalent across different families and between distinct species (Fig. 5c and Supplementary Table 10). Interestingly, we observed that HGTs were predominantly enriched between isolates with the same Gram staining, with Gram-negative species showing more prevalent HGTs than Gram-positive species (P = 0.0005 by Pearson's chi-squared test). This result is consistent with recent finding39 and suggests that different cell wall structures may play an important role in HGTs. Notably, HGTs between Gram-positive and -negative species were also observed in our dataset, inspiring future studies to study the effect of cell wall structures on HGTs and engineer these HGT elements into a microbiome editing tool. Next, to examine whether these HGTs occurred recently, we calculated the mean HGT frequency between all species pairs (Methods). We hypothesized that if HGTs occurred recently between two species, they would be only associated with a small proportion of isolates, resulting in a low frequency between species, while if HGTs occurred earlier and provided growth benefits, they would be enriched and vertically inherited by later generation, resulting in a high frequency. Interestingly, we found most HGT elements were frequently present across isolates (71.5% HGTs with >50% frequency), especially for ones within Bacteroidaceae species (Fig. 5c), suggesting that they occurred in the distant past and were enriched under strong selection within the gut environment./p>80% of all microbiota by abundance present. This isolate collection covers a majority of microbial diversity in the healthy gut and is one of the most extensive personalized isolate biobanks described to date. Using this resource, we demonstrated that quantitative analysis of colony morphologies can predict taxonomy, enhance the isolation of targeted genera and reveal potential interactions between microbes. Systematic analysis of genomic differences between isolates within and across people revealed interesting patterns of population selection, adaptation and HGT./p>100 isolates across all 20 individuals) were subjected to model training and testing. Considering the potential impact of antibiotics perturbation and neighbor colonies, a multilabel random forest model was trained on 70% of isolates, which was randomly sampled using 14 colony morphological features, antibiotics condition and number of nearby colonies, and the performance (precision and recall) of the model was evaluated on the remaining 30% of isolates. The procedure of model training and evaluating was bootstrapped 20 times with different randomization settings to minimize bias, and the background performance of the model was calculated by null model (prediction based on the number of isolates). To perform targeted microbial isolation, a multilabel random forest model was trained on colonies of data-rich genus (>15 isolates from the same individual) from individuals H12, H13 and H14 separately as described above. The same fecal samples were then plated out and the model was applied to the new plates after bacterial growth to screen all colonies on plates and predict colonies of targeted genus-level taxonomy. All colonies of the plates were then isolated on CAMII and subjected to 16S V4 sequencing to identify their taxonomy and evaluate the performance of targeted isolation./p> 20×; N50 > 5,000 bp; completeness > 80%; contamination < 5%) and used for downstream genomic variation and HGT analysis. To identify strain-level genomic variation of gut microbiota isolates within and between individuals, draft assemblies with the highest completeness and N50 of each species were selected as the reference genomes for reads alignment, and processed Illumina reads of isolates were aligned to reference genomes of the same species by Bowtie2 v2.3.4 (ref. 55) in paired-end mode with ‘--very-sensitive’ setting. Resulting reads alignments were then processed by SAMtools v1.9 and BCFtools v1.9 (ref. 56) with ‘--ploidy 1’ setting to call genomic variation (SNPs and Indels). Resulting variations were then subjected to quality filtering to identify ‘reliable’ genotypes (covered by ≥5 reads; with ≥0.9 haploidy) and only SNP variations with more than 90% ‘reliable’ genotypes across all isolates were used for downstream analysis. To construct SNP-based phylogeny, base profiles of isolates at SNP sites were concatenated together and UPGMA tree was then calculated by MEGA v11.0.11 with the default setting./p>95% ANI were considered to be the same species./p>20 and query coverage >50. Secretion systems were also predicted on CDS of HGT elements by EffectiveDB62 with the default setting./p>