Challenges in Bioinformatics Workflows for Processing Microbiome Omics Data at Scale

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.

Metabolomic, photoprotective, and photosynthetic acclimatory responses to post-flowering drought in sorghum

Climate change is globally affecting rainfall patterns, necessitating the improvement of drought tolerance in crops. Sorghum bicolor is a drought-tolerant cereal capable of producing high yields under water scarcity conditions. Functional stay-green sorghum genotypes can maintain green leaf area and efficient grain filling in terminal post-flowering water deprivation, a period of ~10 weeks. To obtain molecular insights into these characteristics, two drought-tolerant genotypes, BTx642 and RTx430, were grown in control and terminal post-flowering drought field plots in the Central Valley of California. Photosynthetic, photoprotective, water dynamics, and biomass traits were quantified and correlated with metabolomic data collected from leaves, stems, and roots at multiple timepoints during drought. Physiological and metabolomic data was then compared to longitudinal RNA sequencing data collected from these two genotypes. The metabolic response to drought highlights the uniqueness of the post-flowering drought acclimation relative to pre-flowering drought. The functional stay-green genotype BTx642 specifically induced photoprotective responses in post-flowering drought supporting a putative role for photoprotection in the molecular basis of the functional stay-green trait. Specific genes are highlighted that may contribute to post-flowering drought tolerance and that can be targeted in crops to maximize yields under limited water input conditions.

Transcriptomic, Metabolomic and Ionomic Analyses Reveal Early Modulation of Leaf Mineral Content in Brassica napus under Mild or Severe Drought

While it is generally acknowledged that drought is one of the main abiotic factors affecting plant growth, how mineral nutrition is specifically and negatively affected by water deficit has received very little attention, other than being analyzed as a consequence of reduced growth. Therefore, Brassica napus plants were subjected to a gradual onset of water deficits (mild, severe, or severe extended), and leaves were analyzed at the ionomic, transcriptomic and metabolic levels. The number of Differentially Expressed Genes (DEGs) and of the most differentially accumulated metabolites increased from mild (525 DEGs, 57 metabolites) to severe (5454 DEGs, 78 metabolites) and severe extended (9346 DEGs, 95 metabolites) water deficit. Gene ontology enrichment analysis of the 11,747 DEGs identified revealed that ion transport was one of the most significant processes affected, even under mild water deficit, and this was also confirmed by the shift in ionomic composition (mostly micronutrients with a strong decrease in Mo, Fe, Zn, and Mn in leaves) that occurred well before growth reduction. The metabolomic data and most of the transcriptomic data suggested that well-known early leaf responses to drought such as phytohormone metabolism (ABA and JA), proline accumulation, and oxidative stress defense were induced later than repression of genes related to nutrient transport.

Gut Metabolites Predict Clostridioides Difficile Recurrence

Background Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the U.S., with recurrence rates >15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Moreover, previous studies have not shown that microbial abundances in the gut measured by 16S rRNA amplicon sequencing alone can accurately predict CDI recurrence. Results We conducted a prospective, longitudinal study of 53 non-immunocompromised participants with primary CDI. Stool sample collection began pre-CDI antibiotic treatment at the time of diagnosis, and continued up to eight weeks post-antibiotic treatment, with weekly or twice weekly collections. Samples were analyzed using: (1) 16S rRNA amplicon sequencing, (2) liquid chromatography/mass-spectrometry metabolomics measuring 1387 annotated metabolites, and (3) short-chain fatty acid profiling. The amplicon sequencing data showed significantly delayed recovery of microbial diversity in recurrent participants, and depletion of key anaerobic taxa at multiple time-points, including Clostridium cluster XIVa and IV taxa. The metabolomic data also showed delayed recovery in recurrent participants, and moreover mapped to pathways suggesting distinct functional abnormalities in the microbiome or host, such as decreased microbial deconjugation activity, lowered levels of endocannabinoids, and elevated markers of host cell damage. Further, using predictive statistical/machine learning models, we demonstrated that the metabolomic data, but not the other data sources, can accurately predict future recurrence at one week (AUC 0.77 [0.71, 0.86; 95% interval]) and two weeks (AUC 0.77 [0.69, 0.85; 95% interval]) post-treatment for primary CDI. Conclusions The prospective, longitudinal and multi-omic nature of our CDI recurrence study allowed us to uncover previously unrecognized dynamics in the microbiome and host presaging recurrence, and, in particular, to elucidate changes in the understudied gut metabolome. Moreover, we demonstrated that a small set of metabolites can accurately predict future recurrence. Our findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence.

Terpene volatiles mediates the chemical basis of blueberry aroma and consumer acceptability

Among the main features treasured by blueberry consumers, flavor is the most important. Human perception of food flavors can primarily be divided into two main sensory inputs, taste and aroma. Through retronasal olfaction, a group of metabolites called volatile organic compounds (VOCs) emitted from the fruit are able to produce the sensation of aroma, creating the myriad of flavors experienced during our life. In blueberry, breeders have noticed some genotypes with unique floral and sweet flavor notes that, ultimately, enhance human aroma perception. Despite the importance, both the understanding of which chemicals are mediating this variation across phenotypes and the potential impact on consumer acceptability remains largely unknown. In this study we dissected the main components underlying blueberry aroma and associated it with consumer predilections by paring metabolomics with sensory analysis. Our contribution in this study is four-fold: (i) first, based on a representative blueberry germplasm cultivated at the University of Florida, we differentiated genotypes with floral and sweet aromatic notes and confirmed that such unique characteristics are preferred by consumers; (ii) at the chemical level, we showed that a group of eight terpene volatiles constitute the primary metabolic group associated with aroma sensation; (iii) we demonstrated that aromatic genotypes can be classified using information from a group of a few key volatiles; and finally, (iv) we combined pedigree and metabolomic information in a single predictive framework and showed the importance of metabolomic data for flavor-assisted selection. For the blueberry community, our findings open new venues to explore flavor. Broadly, we present an emerging view about flavor and provide a detailed blueprint of how this target could be addressed in fruits and vegetables.

In vitro co-metabolism of epigallocatechin-3-gallate (EGCG) by the mucin-degrading bacterium Akkermansia muciniphila

Akkermansia muciniphila is a Gram-negative bacterium that resides within the gut mucus layer, and plays an important role in promoting gut barrier integrity, modulating the immune response and inhibiting gut inflammation. Growth stimulation of A. muciniphila by polyphenols including epigallocatechin-3-gallate (EGCG) from difference sources is well-documented. However, no published in vitro culture data on utilization of polyphenols by A. muciniphila are available, and the mechanism of growth-stimulating prebiotic effect of polyphenols on it remains unclear. Here in vitro culture studies have been carried out on the metabolism of EGCG by A. muciniphila in the presence of either mucin or glucose. We found that A. muciniphila did not metabolize EGCG alone but could co-metabolize it together with both these substrates in the presence of mineral salts and amino acids for mucin and protein sources for glucose. Our metabolomic data show that A. muciniphila converts EGCG to gallic acid, epigallocatechin, and (-)-epicatechin through ester hydrolysis. The (-)-epicatechin formed is then further converted to hydroxyhydroquinone. Co-metabolism of A. muciniphila of EGCG together with either mucin or glucose promoted substantially its growth, which serves as a further demonstration of the growth-promoting effect of polyphenols on A. muciniphila and provides an important addition to the currently available proposed mechanisms of polyphenolic prebiotic effects on A. muciniphila.

Integrative analysis of the salt stress response in cyanobacteria

Microorganisms evolved specific acclimation strategies to thrive in environments of high or fluctuating salinities. Here, salt acclimation in the model cyanobacterium Synechocystis sp. PCC 6803 was analyzed by integrating transcriptomic, proteomic and metabolomic data. A dynamic reorganization of the transcriptome occurred during the first hours after salt shock, e.g. involving the upregulation of genes to activate compatible solute biochemistry balancing osmotic pressure. The massive accumulation of glucosylglycerol then had a measurable impact on the overall carbon and nitrogen metabolism. In addition, we observed the coordinated induction of putative regulatory RNAs and of several proteins known for their involvement in other stress responses. Overall, salt-induced changes in the proteome and transcriptome showed good correlations, especially among the stably up-regulated proteins and their transcripts. We define an extended salt stimulon comprising proteins directly or indirectly related to compatible solute metabolism, ion and water movements, and a distinct set of regulatory RNAs involved in post-transcriptional regulation. Our comprehensive data set provides the basis for engineering cyanobacterial salt tolerance and to further understand its regulation.

Metabolomic Profile at Birth, Bronchiolitis and Recurrent Wheezing: A 3-Year Prospective Study

There is growing interest for studying how early-life influences the development of respiratory diseases. Our aim was to apply metabolomic analysis to urine collected at birth, to evaluate whether there is any early metabolic signatures capable to distinguish children who will develop acute bronchiolitis and/or recurrent wheezing. Urine was collected at birth in healthy term newborns. Children were followed up to the age of 3 years and evaluated for the development of acute bronchiolitis and recurrent wheezing (≥3 episodes). Urine were analyzed through a liquid-chromatography mass-spectrometry based untargeted approach. Metabolomic data were investigated applying univariate and multivariate techniques. 205 children were included: 35 had bronchiolitis, 11 of whom had recurrent wheezing. Moreover, 13 children had recurrent wheezing not preceded by bronchiolitis. Multivariate data analysis didn’t lead to reliable classification models capable to distinguish children with and without bronchiolitis or with recurrent wheezing preceded by bronchiolitis neither by PLS for classification (PLS2C) nor by Random Forest (RF). However, a reliable signature was discovered to distinguish children who later develop recurrent wheezing not preceded by bronchiolitis, from those who do not (MCCoob = 0.45 for PLS2C and MCCoob = 0.48 for RF). In this unselected birth cohort, a well-established untargeted metabolomic approach found no biochemical-metabolic dysregulation at birth associated with the subsequent development of acute bronchiolitis or recurrent wheezing post-bronchiolitis, not supporting the hypothesis of an underlying predisposing background. On the other hand, a metabolic signature was discovered that characterizes children who develop wheezing not preceded by bronchiolitis.

Gut metabolites predict Clostridioides difficile recurrence

Background Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the U.S., with recurrence rates >15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Moreover, previous studies have not shown that microbial abundances in the gut measured by 16S rRNA amplicon sequencing alone can accurately predict CDI recurrence. Results We conducted a prospective, longitudinal study of 53 non-immunocompromised participants with primary CDI. Stool sample collection began pre-CDI antibiotic treatment at the time of diagnosis, and continued up to eight weeks post-antibiotic treatment, with weekly or twice weekly collections. Samples were analyzed using: (1) 16S rRNA amplicon sequencing, (2) liquid chromatography/mass-spectrometry metabolomics measuring 1387 annotated metabolites, and (3) short-chain fatty acid profiling. The amplicon sequencing data showed significantly delayed recovery of microbial diversity in recurrent participants, and depletion of key anaerobic taxa at multiple time-points, including Clostridium cluster XIVa and IV taxa. The metabolomic data also showed delayed recovery in recurrent participants, and moreover mapped to pathways suggesting distinct functional abnormalities in the microbiome or host, such as decreased microbial deconjugation activity, lowered levels of endocannabinoids, and elevated markers of host cell damage. Further, using predictive statistical/machine learning models, we demonstrated that the metabolomic data, but not the other data sources, can accurately predict future recurrence at one week (AUC 0.77 [0.71, 0.86; 95% interval]) and two weeks (AUC 0.77 [0.69, 0.85; 95% interval]) post-treatment for primary CDI. Conclusions The prospective, longitudinal and multi-omic nature of our CDI recurrence study allowed us to uncover previously unrecognized dynamics in the microbiome and host presaging recurrence, and, in particular, to elucidate changes in the understudied gut metabolome. Moreover, we demonstrated that a small set of metabolites can accurately predict future recurrence. Our findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence.