Microcystis spp. are renowned for producing the hepatotoxin microcystin in freshwater cyanobacterial harmful algal blooms around the world, threatening drinking water supplies and public and environmental health. However, Microcystis genomes also harbor numerous biosynthetic gene clusters (BGCs) encoding the biosynthesis of other secondary metabolites, including many with toxic properties. Most of these BGCs are uncharacterized and currently lack links to biosynthesis products. However, recent field studies show that many of these BGCs are abundant and transcriptionally active in natural communities, suggesting potentially important yet unknown roles in bloom ecology and water quality. Here, we analyzed 21 xenic Microcystis cultures isolated from western Lake Erie to investigate the diversity of the biosynthetic potential of this genus. Through metabologenomic and in silico approaches, we show that these Microcystis strains contain variable BGCs, previously observed in natural populations, and encode distinct metabolomes across cultures. Additionally, we find that the majority of metabolites and gene clusters are uncharacterized, highlighting our limited understanding of the chemical repertoire of Microcystis spp. Due to the complex metabolomes observed in culture, which contain a wealth of diverse congeners as well as unknown metabolites, these results underscore the need to deeply explore and identify secondary metabolites produced by Microcystis beyond microcystins to assess their impacts on human and environmental health.IMPORTANCEThe genus Microcystis forms dense cyanobacterial harmful algal blooms (cyanoHABs) and can produce the toxin microcystin, which has been responsible for drinking water crises around the world. While microcystins are of great concern, Microcystis also produces an abundance of other secondary metabolites that may be of interest due to their potential for toxicity, ecological importance, or pharmaceutical applications. In this study, we combine genomic and metabolomic approaches to study the genes responsible for the biosynthesis of secondary metabolites as well as the chemical diversity of produced metabolites in Microcystis strains from the Western Lake Erie Culture Collection. This unique collection comprises Microcystis strains that were directly isolated from western Lake Erie, which experiences substantial cyanoHAB events annually and has had negative impacts on drinking water, tourism, and industry.

In this editorial I comment on the article \"Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer\" published in the recent issue of the World Journal of Clinical Cases 2023 November 6; 11 (31): 7593-7609. Almost all living forms are able to manufacture particular chemicals-metabolites that enable them to differentiate themselves from one another and to overcome the unique obstacles they encounter in their natural habitats. Numerous methods for chemical warfare, communication, nutrition acquisition, and stress prevention are made possible by these specialized metabolites. Metabolomics is a popular technique for collecting direct measurements of metabolic activity from many biological systems. However, confusing metabolite identification is a typical issue, and biochemical interpretation is frequently constrained by imprecise and erroneous genome-based estimates of enzyme activity. Metabolite annotation and gene integration uses a biochemical reaction network to obtain a metabolite-gene association so called metabologenomics. This network uses an approach that emphasizes metabolite-gene consensus via biochemical processes. Combining metabolomics and genomics data is beneficial. Furthermore, computer networking proposes that using metabolomics data may improve annotations in sequenced species and provide testable hypotheses for specific biochemical processes.

UNASSIGNED: Hepatocellular carcinoma (HCC) is an aggressive malignancy with steadily increasing incidence rates worldwide and poor therapeutic outcomes. Studies show that metabolic reprogramming plays a key role in tumor genesis and progression. In this study, we analyzed the metabolic heterogeneity of epithelial cells in the HCC and screened for potential biomarkers.
UNASSIGNED: The hepatic single-cell RNA sequencing (scRNA-seq) datasets of HCC patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Based on data intergration and measurement of differences among groups, the metabolic epithelial cell subpopulations were identified. The single-cell metabolic pathway was analyzed and the myeloid subpopulations were identified. Cell-cell interaction analysis and single-cell proliferation analysis were performed. The gene expression profiles of HCC patients were obtained from the GSE14520 dataset of GEO and TCGA-LIHC cohort of the UCSC Xena website. Immune analysis was performed. The differentially expressed genes (DEGs) were identified and functionally annotated. Tumor tissues from HCC patients were probed with anti-ALDOA, anti-CD68, anti-CD163, anti-CD4 and anti-FOXP3 antibodies. Results We analyzed the scRNA-seq data from 48 HCC patients and 14 healthy controls. The epithelial cells were significantly enriched in HCC patients compared to the controls (p = 0.011). The epithelial cells from HCC patients were classified into two metabolism-related subpopulations (MRSs) - pertaining to amino acid metabolism (MRS1) and glycolysis (MRS2). Depending on the abundance of these metabolic subpopulations, the HCC patients were also classified into the MRS1 and MRS2 subtype distinct prognoses and immune infiltration. The MRS2 group had significantly worse clinical outcomes and more inflamed tumor microenvironment (TME), as well as a stronger crosstalk between MRS2 cells and immune subpopulations that resulted in an immunosuppressive TME. We also detected high expression levels of ALDOA in the MRS2 cells and HCC tissues. In the clinical cohort, HCC patients with higher ALDOA expression showed greater enrichment of immunosuppressive cells including M2 macrophages and T regulatory cells.
UNASSIGNED: The glycolytic subtype of HCC cells with high ALDOA expression is associated with an immunosuppressive TME and predicts worse clinical outcomes, providing new insights into the metabolism and prognosis of HCC.

UNASSIGNED: Bifidobacterium longum BB536 supplementation can be used to regulate bowel movements in various people, including healthy subjects and patients with irritable bowel syndrome (IBS); however, individuals vary in their responses to B. longum BB536 treatment. One putative factor is the gut microbiota; recent studies have reported that the gut microbiota mediates the effects of diet or drugs on the host. Here, we investigated intestinal features, such as the microbiome and metabolome, related to B. longum BB536 effectiveness in increasing bowel movement frequency.
UNASSIGNED: A randomized, double-blind controlled crossover trial was conducted with 24 adults who mainly tended to be constipated. The subjects received a two-week dietary intervention consisting of B. longum BB536 in acid-resistant seamless capsules or similarly encapsulated starch powder as the placebo control. Bowel movement frequency was recorded daily, and fecal samples were collected at several time points, and analyzed by metabologenomic approach that consists of an integrated analysis of metabolome data obtained using mass spectrometry and microbiome data obtained using high-throughput sequencing. There were differences among subjects in B. longum intake-induced bowel movement frequency. The responders were predicted by machine learning based on the microbiome and metabolome features of the fecal samples collected before B. longum intake. The abundances of eight bacterial genera were significantly different between responders and nonresponders.
UNASSIGNED: Intestinal microbiome and metabolome profiles might be utilized as potential markers of improved bowel movement after B. longum BB536 supplementation. These findings have implications for the development of personalized probiotic treatments.

Streptomyces genomes harbor numerous, biosynthetic gene clusters (BGCs) encoding for drug-like compounds. While some of these BGCs readily yield expected products, many do not. Biosynthetic crypticity represents a significant hurdle to drug discovery, and the biological mechanisms that underpin it remain poorly understood. Polycyclic tetramate macrolactam (PTM) antibiotic production is widespread within the Streptomyces genus, and examples of active and cryptic PTM BGCs are known. To reveal further insights into the causes of biosynthetic crypticity, we employed a PTM-targeted comparative metabologenomics approach to analyze a panel of S. griseus clade strains that included both poor and robust PTM producers. By comparing the genomes and PTM production profiles of these strains, we systematically mapped the PTM promoter architecture within the group, revealed that these promoters are directly activated via the global regulator AdpA, and discovered that small promoter insertion-deletion lesions (indels) differentiate weaker PTM producers from stronger ones. We also revealed an unexpected link between robust PTM expression and griseorhodin pigment coproduction, with weaker S. griseus-clade PTM producers being unable to produce the latter compound. This study highlights promoter indels and biosynthetic interactions as important, genetically encoded factors that impact BGC outputs, providing mechanistic insights that will undoubtedly extend to other Streptomyces BGCs. We highlight comparative metabologenomics as a powerful approach to expose genomic features that differentiate strong, antibiotic producers from weaker ones. This should prove useful for rational discovery efforts and is orthogonal to current engineering and molecular signaling approaches now standard in the field.

Metabolic alterations have implications in a spectrum of tissue functions and disease. Aided by novel molecular biological and computational tools, our understanding of physiological and pathological processes underpinning endocrine and endocrine-related disease has significantly expanded over the last decade. Herein, we focus on novel metabolomics-related methodologies in adrenal research: in situ metabolomics by mass spectrometry imaging, steroid metabolomics by gas and liquid chromatography-mass spectrometry, energy pathway metabologenomics by liquid chromatography-mass spectrometry-based metabolomics of Krebs cycle intermediates, and cellular reprogramming to generate functional steroidogenic cells and hence to modulate the steroid metabolome. All four techniques to assess and/or modulate the metabolome in biological systems provide tremendous opportunities to manage neoplastic and non-neoplastic disease of the adrenal glands in the era of precision medicine. In this context, we discuss emerging clinical applications and/or promising metabolic-driven research towards diagnostic, prognostic, predictive and therapeutic biomarkers in tumours arising from the adrenal gland and extra-adrenal paraganglia as well as modern approaches to delineate and reprogram adrenal metabolism.

Intestinal microbiota and their metabolites are strongly associated with host physiology. Developments in DNA sequencing and mass spectrometry technologies have allowed us to obtain additional data that enhance our understanding of the interactions among microbiota, metabolites, and the host. However, the strategies used to analyze these datasets are not yet well developed. Here, we describe an original analytical strategy, metabologenomics, consisting of an integrated analysis of mass spectrometry-based metabolome data and high-throughput-sequencing-based microbiome data. Using this approach, we compared data obtained from C57BL/6J mice fed an American diet (AD), which contained higher amounts of fat and fiber, to those from mice fed control rodent diet. The feces of the AD mice contained higher amounts of butyrate and propionate, and higher relative abundances of Oscillospira and Ruminococcus. The amount of butyrate positively correlated with the abundance of these bacterial genera. Furthermore, integrated analysis of the metabolome data and the predicted metagenomic data from Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) indicated that the abundance of genes associated with butyrate metabolism positively correlated with butyrate amounts. Thus, our metabologenomic approach is expected to provide new insights and understanding of intestinal metabolic dynamics in complex microbial ecosystems.