A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations

Background Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links. Results In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites “robustly well-predicted”. To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite’s level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis. Conclusions Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies.

Antibiotic Biosynthesis Pathways from Endophytic Streptomyces SUK 48 through Metabolomics and Genomics Approaches

Streptomyces sp. has been known to be a major antibiotic producer since the 1940s. As the number of cases related to resistance pathogens infection increases yearly, discovering the biosynthesis pathways of antibiotic has become important. In this study, we present the streamline of a project report summary; the genome data and metabolome data of newly isolated Streptomyces SUK 48 strain are also analyzed. The antibacterial activity of its crude extract is also determined. To obtain genome data, the genomic DNA of SUK 48 was extracted using a commercial kit (Promega) and sent for sequencing (Pac Biosciences technology platform, Menlo Park, CA, USA). The raw data were assembled and polished using Hierarchical Genome Assembly Process 4.0 (HGAP 4.0). The assembled data were structurally predicted using tRNAscan-SE and rnammer. Then, the data were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) database and antiSMASH analysis. Meanwhile, the metabolite profile of SUK 48 was determined using liquid chromatography-mass spectrophotometry (LC-MS) for both negative and positive modes. The results showed that the presence of kanamycin and gentamicin, as well as the other 11 antibiotics. Nevertheless, the biosynthesis pathways of aurantioclavine were also found. The cytotoxicity activity showed IC50 value was at 0.35 ± 1.35 mg/mL on the cell viability of HEK 293. In conclusion, Streptomyces sp. SUK 48 has proven to be a non-toxic antibiotic producer such as auranticlavine and gentamicin.

Integrated omics endotyping of infants with respiratory syncytial virus bronchiolitis and risk of childhood asthma

Respiratory syncytial virus (RSV) bronchiolitis is not only the leading cause of hospitalization in U.S. infants, but also a major risk factor for asthma development. While emerging evidence suggests clinical heterogeneity within RSV bronchiolitis, little is known about its biologically-distinct endotypes. Here, we integrated clinical, virus, airway microbiome (species-level), transcriptome, and metabolome data of 221 infants hospitalized with RSV bronchiolitis in a multicentre prospective cohort study. We identified four biologically- and clinically-meaningful endotypes: A) clinicalclassicmicrobiomeM. nonliquefaciensinflammationIFN-intermediate, B) clinicalatopicmicrobiomeS. pneumoniae/M. catarrhalisinflammationIFN-high, C) clinicalseveremicrobiomemixedinflammationIFN-low, and D) clinicalnon-atopicmicrobiomeM.catarrhalisinflammationIL-6. Particularly, compared with endotype A infants, endotype B infants—who are characterized by a high proportion of IgE sensitization and rhinovirus coinfection, S. pneumoniae/M. catarrhalis codominance, and high IFN-α and -γ response—had a significantly higher risk for developing asthma (9% vs. 38%; OR, 6.00: 95%CI, 2.08–21.9; P = 0.002). Our findings provide an evidence base for the early identification of high-risk children during a critical period of airway development.

Metabolome and Transcriptome Integration Reveals Insights Into Flavor Formation of ‘Crimson’ Watermelon Flesh During Fruit Development

Metabolites have been reported as the main factor that influences the fruit flavor of watermelon. But the comprehensive study on the dynamics of metabolites during the development of watermelon fruit is not up-to-date. In this study, metabolome and transcriptome datasets of ‘Crimson’ watermelon fruit at four key developmental stages were generated. A total of 517 metabolites were detected by ultrahigh-performance liquid chromatography–electrospray ionization–tandem mass spectrometry and gas chromatography–solid-phase microextraction–mass spectrometry. Meanwhile, by K-means clustering analysis, the total differentially expressed genes were clustered in six classes. Integrating transcriptome and metabolome data revealed similar expression trends of sugars and genes involved in the glycolytic pathway, providing molecular insights into the formation of taste during fruit development. Furthermore, through coexpression analysis, we identified five differentially expressed ADH (alcohol dehydrogenase) genes (Cla97C01G013600, Cla97C05G089700, Cla97C01G001290, Cla97C05G095170, and Cla97C06G118330), which were found to be closely related to C9 alcohols/aldehydes, providing information for the formation of fruit aroma. Our findings establish a metabolic profile during watermelon fruit development and provide insights into flavor formation.

Transcriptome and Metabolome-based Analysis of Anthocyanidin Biosynthesis in Purple Pepper

In order to clarify the profile of gene expression and metabolites for color formation and the molecular mechanism of anthocyanidin accumulation in purple pepper fruits, we analyzed the anthocyanidin metabolome data of the fruits of 2 purple pepper lines and 1 green pepper line and detected a total of 5 anthocyanidin-like metabolites, of which delphin chloride was unique to purple pepper fruits and 3 other anthocyanidin-like substances shared the metabolic pathway ko00942 and were up-regulated. Based on the transcriptome data, three pathways (ko00360, ko00400, and ko00941) related to anthocyanidin metabolism were identified through KEGG analysis. Three enzymes (DFR, ANS, and UFGT) and three transcription factors (MYB, BHLH, and WD40) in the purple pepper anthocyanidin biosynthetic pathway were up-regulated. We proposed a model to explain the regulation of pepper anthocyanidin biosynthesis: MYB, BHLH, and WD40 formed a ternary complex and bound to the specific cis-acting elements in the promoter region of the structural genes related to anthocyanidin biosynthesis to directly regulate their transcription, which resulted in the accumulation of a large amount of anthocyanidin metabolites including delphinidin 3-O-glucoside, delphinidin 3-O-rutinoside, and delphin chloride, giving color to pepper fruits. This study clarified the metabolic pathways and key genes affecting the color of purple pepper fruits and provided new insights into the synthesis and accumulation of anthocyanidins in pepper fruits.

Next-Generation Mass Spectrometry Metabolomics Revives the Functional Analysis of Plant Metabolic Diversity

The remarkable diversity of specialized metabolites produced by plants has inspired several decades of research and nucleated a long list of theories to guide empirical ecological studies. However, analytical constraints and the lack of untargeted processing workflows have long precluded comprehensive metabolite profiling and, consequently, the collection of the critical currencies to test theory predictions for the ecological functions of plant metabolic diversity. Developments in mass spectrometry (MS) metabolomics have revolutionized the large-scale inventory and annotation of chemicals from biospecimens. Hence, the next generation of MS metabolomics propelled by new bioinformatics developments provides a long-awaited framework to revisit metabolism-centered ecological questions, much like the advances in next-generation sequencing of the last two decades impacted all research horizons in genomics. Here, we review advances in plant (computational) metabolomics to foster hypothesis formulation from complex metabolome data. Additionally, we reflect on how next-generation metabolomics could reinvigorate the testing of long-standing theories on plant metabolic diversity. Expected final online publication date for the Annual Review of Plant Biology, Volume 72 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Integrated Metabolomics and Transcriptomics Analysis of Monolayer and Neurospheres from Established Glioblastoma Cell Lines

Altered metabolic processes contribute to carcinogenesis by modulating proliferation, survival and differentiation. Tumours are composed of different cell populations, with cancer stem-like cells being one of the most prominent examples. This specific pool of cells is thought to be responsible for cancer growth and recurrence and plays a particularly relevant role in glioblastoma (GBM), the most lethal form of primary brain tumours. Here, we have analysed the transcriptome and metabolome of an established GBM cell line (U87) and a patient-derived GBM stem-like cell line (NCH644) exposed to neurosphere or monolayer culture conditions. By integrating transcriptome and metabolome data, we identified key metabolic pathways and gene signatures that are associated with stem-like and differentiated states in GBM cells, and demonstrated that neurospheres and monolayer cells differ substantially in their metabolism and gene regulation. Furthermore, arginine biosynthesis was identified as the most significantly regulated pathway in neurospheres, although individual nodes of this pathway were distinctly regulated in the two cellular systems. Neurosphere conditions, as opposed to monolayer conditions, cause a transcriptomic and metabolic rewiring that may be crucial for the regulation of stem-like features, where arginine biosynthesis may be a key metabolic pathway. Additionally, TCGA data from GBM patients showed significant regulation of specific components of the arginine biosynthesis pathway, providing further evidence for the importance of this metabolic pathway in GBM.