Neurodegenerative diseases are influenced by environmental factors such as exposure to toxins including the cyanotoxin β-N-methylamino-L-alanine (BMAA) that can bioaccumulate in common food sources such as fish, mussels and crabs. Accurate and precise analytical methods are needed to detect and quantify BMAA to minimize human health risks. The objective of this review is to provide a comprehensive overview of the methods used for BMAA analysis from 2003 to 2019 and to evaluate the reported performance characteristics for each method to determine the consensus data for each analytical approach and different sample matrices. Detailed searches of the database Web of Science™ (WoS) were performed between August 21^st, 2018 and April 5^th, 2019. Eligible studies included analytical methods for the detection and quantification of BMAA in cyanobacteria and bioaccumulated BMAA in higher trophic levels, in phytoplankton and zooplankton and in human tissues and fluids. This systematic review has limitations in that only the English language literature is included and it did not include standard operating protocols nor any method validation data that have not been made public. We identified 148 eligible studies, of which a positive result for BMAA in one or more samples analyzed was reported in 84% (125 out of 148) of total studies, 57% of HILIC studies, 92% of RPLC studies and 71% of other studies. The largest discrepancy between different methods arose from the analysis of cyanobacteria samples, where BMAA was detected in 95% of RPLC studies but only in 25% of HILIC studies. Without sufficient published validation of each method's performance characteristics, it is difficult to establish each method as fit for purpose for each sample matrix. The importance of establishing methods as appropriate for their intended use is evidenced by the inconsistent reporting of BMAA across environmental samples, despite its prevalence in diverse ecosystems and food webs.

Metabolomics is a mainstream approach for investigating the metabolic underpinnings of complex biological phenomena and is increasingly being applied to large-scale studies involving hundreds or thousands of samples. Although metabolomics methods are robust in smaller-scale studies, they can be challenging to apply to larger cohorts due to the inherent variability of liquid chromatography mass spectrometry (LC-MS). Much of this difficulty results from the time-dependent changes in the LC-MS system, which affects both the qualitative and quantitative performances of the instrument. Herein, we introduce an analytical strategy for addressing this problem in large-scale microbial studies. Our approach quantifies microbial boundary fluxes using two zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) columns that are plumbed to enable offline column equilibration. Using this strategy, we show that over 397 common metabolites can be resolved in 4.5 min per sample and that metabolites can be quantified with a median coefficient of variation of 0.127 across 1100 technical replicates. We illustrate the utility of this strategy via an analysis of 960 strains of Staphylococcus aureus isolated from bloodstream infections. These data capture the diversity of metabolic phenotypes observed in clinical isolates and provide an example of how large-scale investigations can leverage our novel analytical strategy.

The use of live biotherapeutic products (LBPs), including single strains of beneficial probiotic bacteria or consortiums, is gaining traction as a viable option to treat inflammatory-mediated diseases like inflammatory bowel disease (IBD). However, LBPs’ persistence in the intestine is heterogeneous since many beneficial bacteria lack mechanisms to tolerate the inflammation and the oxidative stress associated with IBD. We rationalized that optimizing LBPs with enhanced colonization and persistence in the inflamed intestine would help beneficial bacteria increase their bioavailability and sustain their beneficial responses. Our lab developed two bioengineered LBPs (SBT001/BioPersist and SBT002/BioColoniz) modified to enhance colonization or persistence in the inflamed intestine. In this study, we examined colon-derived metabolites via ultra-high performance liquid chromatography-mass spectrometry in colitic mice treated with either BioPersist or BioColoniz as compared to their unmodified parent strains (Escherichia coli Nissle 1917 [EcN] and Lactobacillus reuteri, respectively) or to each other. BioPersist administration resulted in lowered concentrations of inflammatory prostaglandins, decreased stress hormones such as adrenaline and corticosterone, increased serotonin, and decreased bile acid in comparison to EcN. In comparison to BioColoniz, BioPersist increased serotonin and antioxidant production, limited bile acid accumulation, and enhanced tissue restoration via activated purine and pyrimidine metabolism. These data generated several novel hypotheses for the beneficial roles that LBPs may play during colitis.

Lake Winnipeg in Manitoba, Canada is heavily impacted by harmful algal blooms that contain non-protein amino acids (NPAAs) produced by cyanobacteria: N-(2-aminoethyl)glycine (AEG), β-aminomethyl-L-alanine (BAMA), β-N-methylamino-L-alanine (BMAA), and 2,4-diaminobutyric acid (DAB). Our objective was to investigate the impact of microbial diversity on NPAA production by cyanobacteria using semi-purified crude cyanobacterial cultures established from field samples collected by the Lake Winnipeg Research Consortium between 2016 and 2021. NPAAs were detected and quantified by ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) using validated analytical methods, while Shannon and Simpson alpha diversity scores were determined from 16S rRNA metagenomic sequences. Alpha diversity in isolate cultures was significantly decreased compared to crude cyanobacterial cultures (p < 0.001), indicating successful semi-purification. BMAA and AEG concentrations were higher in crude compared to isolate cultures (p < 0.0001), and AEG concentrations were correlated to the alpha diversity in cultures (r = 0.554; p < 0.0001). BAMA concentrations were increased in isolate cultures (p < 0.05), while DAB concentrations were similar in crude and isolate cultures. These results demonstrate that microbial community complexity impacts NPAA production by cyanobacteria and related organisms.

Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms. Metabolic network modeling is commonly used to frame metabolomics data in the context of a broader biological system. However, network modeling of poorly characterized nonmodel organisms remains challenging due to gene homology mismatches which lead to network architecture errors. To address this, we developed the Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that uses empirical metabolomics data to refine metabolic networks. MINNO allows users to create, modify, and interact with metabolic pathway visualizations for thousands of organisms, in both individual and multispecies contexts. Herein, we illustrate the use of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme and relapsing fever diseases. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for threeBorrelia species. Using these empirically refined networks, we were able to metabolically differentiate these species via their nucleotide metabolism, which cannot be predicted from genomic networks. Additionally, using MINNO, we identified 18 missing reactions from the KEGG database, of which nine were supported by the primary literature. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study nonmodel organisms.