Pickaxe: a Python library for the prediction of novel metabolic reactions.

BACKGROUND: Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. RESULTS: Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. CONCLUSION: Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

Computational Metabolomics to Elucidate Molecular Signaling and Regulatory Mechanisms Associated with Biostimulant-Mediated Growth Promotion and Abiotic Stress Tolerance in Crop Plants.

Biostimulants show potentials as sustainable strategies for improved crop development and stress resilience. However, the cellular and molecular mechanisms, in particular the signaling and regulatory events, governing the agronomically observed positive effects of biostimulants on plants remain enigmatic, thus hampering novel formulation and exploration of biostimulants. Metabolomics offers opportunities to elucidate metabolic and regulatory processes that define biostimulant-induced changes in the plant's biochemistry and physiology, thus contributing to decode the modes of action of biostimulants. Here, we describe an application of metabolomics to elucidate biostimulant effects on crop plants. Using the case study of a humic substance (HS)-based biostimulant applied on maize plants, under normal and nutrient-starved stress conditions, this chapter proposes key methodological guidance and considerations of computational metabolomics approach to investigate metabolic and regulatory reconfiguration and networks underlying biostimulant-induced physiological changes in plants. Computational metabolome mining tools, in the Global Natural Products Social Molecular Networking (GNPS) ecosystem, are highlighted as well as metabolic pathway and network analysis for biological interpretation of the data.

Plant and microbial sciences as key drivers in the development of metabolomics research.

This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.

Effect of antiretroviral therapy on decreasing arterial stiffness, metabolic profile, vascular and systemic inflammatory cytokines in treatment-naïve HIV: A one-year prospective study.

INTRODUCTION: Cardiovascular disease is a major cause of death among people living with HIV (PLH). Non-treated PLH show increased levels of inflammation and biomarkers of vascular activation, and arterial stiffness as a prognostic cardiovascular disease risk factor. We investigated the effect of one year of ART on treatment-naïve HIV(+) individuals on arterial stiffness and inflammatory and vascular cytokines. METHODS: We cross-sectionally compared aortic stiffness via tonometry, inflammatory, and vascular serum cytokines on treatment-naïve (n = 20) and HIV (-) (n = 9) matched by age, sex, metabolic profile, and Framingham score. We subsequently followed young, treatment-naïve individuals after 1-year of ART and compared aortic stiffness, metabolic profile, and inflammatory and vascular serum biomarkers to baseline. Inflammatory biomarkers included: hs-CRP, D-Dimer, SAA, sCD163s, MCP-1, IL-8, IL-18, MRP8/14. Vascular cytokines included: myoglobin, NGAL, MPO, Cystatin C, ICAM-1, VCAM-1, and MMP9. RESULTS: Treatment-naïve individuals were 34.8 years old, mostly males (95%), and with high smoking prevalence (70%). Baseline T CD4+ was 512±324 cells/mcL. cfPWV was similar between HIV(-) and treatment-naïve (6.8 vs 7.3 m/s; p = 0.16) but significantly decreased after ART (-0.52 m/s; 95% CI -0.87 to -0.16; p0.006). Almost all the determined cytokines were significantly higher compared to controls, except for MCP-1, myoglobin, NGAL, cystatin C, and MMP-9. At follow-up, only total cholesterol and triglycerides increased and all inflammatory cytokines significantly decreased. Regarding vascular cytokines, MPO, ICAM-1, and VCAM-1 showed a reduction. D-Dimer tended to decrease (p = 0.06) and hs-CRP did not show a significant reduction (p = 0.17). CONCLUSION: One year of ART had a positive effect on reducing inflammatory and vascular cytokines and arterial stiffness.

Identification of key genes involved in secondary metabolite biosynthesis in Digitalis purpurea.

The medicinal plant Digitalis purpurea produces cardiac glycosides that are useful in the pharmaceutical industry. These bioactive compounds are in high demand due to ethnobotany's application to therapeutic procedures. Recent studies have investigated the role of integrative analysis of multi-omics data in understanding cellular metabolic status through systems metabolic engineering approach, as well as its application to genetically engineering metabolic pathways. In spite of numerous omics experiments, most molecular mechanisms involved in metabolic pathways biosynthesis in D. purpurea remain unclear. Using R Package Weighted Gene Co-expression Network Analysis, co-expression analysis was performed on the transcriptome and metabolome data. As a result of our study, we identified transcription factors, transcriptional regulators, protein kinases, transporters, non-coding RNAs, and hub genes that are involved in the production of secondary metabolites. Since jasmonates are involved in the biosynthesis of cardiac glycosides, the candidate genes for Scarecrow-Like Protein 14 (SCL14), Delta24-sterol reductase (DWF1), HYDRA1 (HYD1), and Jasmonate-ZIM domain3 (JAZ3) were validated under methyl jasmonate treatment (MeJA, 100 µM). Despite early induction of JAZ3, which affected downstream genes, it was dramatically suppressed after 48 hours. SCL14, which targets DWF1, and HYD1, which induces cholesterol and cardiac glycoside biosynthesis, were both promoted. The correlation between key genes and main metabolites and validation of expression patterns provide a unique insight into the biosynthesis mechanisms of cardiac glycosides in D. purpurea.

Transcriptome and Metabolome Reveal the Molecular Mechanism of Barley Genotypes Underlying the Response to Low Nitrogen and Resupply.

Nitrogen is one of the most important mineral elements for plant growth and development. Excessive nitrogen application not only pollutes the environment, but also reduces the quality of crops. However, are few studies on the mechanism of barley tolerance to low nitrogen at both the transcriptome and metabolomics levels. In this study, the nitrogen-efficient genotype (W26) and the nitrogen-sensitive genotype (W20) of barley were treated with low nitrogen (LN) for 3 days and 18 days, then treated with resupplied nitrogen (RN) from 18 to 21 days. Later, the biomass and the nitrogen content were measured, and RNA-seq and metabolites were analyzed. The nitrogen use efficiency (NUE) of W26 and W20 treated with LN for 21 days was estimated by nitrogen content and dry weight, and the values were 87.54% and 61.74%, respectively. It turned out to have a significant difference in the two genotypes under the LN condition. According to the transcriptome analysis, 7926 differentially expressed genes (DEGs) and 7537 DEGs were identified in the leaves of W26 and W20, respectively, and 6579 DEGs and 7128 DEGs were found in the roots of W26 and W20, respectively. After analysis of the metabolites, 458 differentially expressed metabolites (DAMs) and 425 DAMs were found in the leaves of W26 and W20, respectively, and 486 DAMs and 368 DAMs were found in the roots of W26 and W20, respectively. According to the KEGG joint analysis of DEGs and DAMs, it was discovered that glutathione (GSH) metabolism was the pathway of significant enrichment in the leaves of both W26 and W20. In this study, the metabolic pathways of nitrogen metabolism and GSH metabolism of barley under nitrogen were constructed based on the related DAMs and DEGs. In leaves, GSH, amino acids, and amides were the main identified DAMs, while in roots, GSH, amino acids, and phenylpropanes were mainly found DAMs. Finally, some nitrogen-efficient candidate genes and metabolites were selected based on the results of this study. The responses of W26 and W20 to low nitrogen stress were significantly different at the transcriptional and metabolic levels. The candidate genes that have been screened will be verified in future. These data not only provide new insights into how barley responds to LN, but also provide new directions for studying the molecular mechanisms of barley under abiotic stress.

Combined Metabolome and Transcriptome Analysis Elucidates Sugar Accumulation in Wucai (Brassica campestris L.).

Wucai (Brassica campestris L.) is a leafy vegetable that originated in China, its soluble sugars accumulate significantly to improve taste quality during maturation, and it is widely accepted by consumers. In this study, we investigated the soluble sugar content at different developmental stages. Two periods including 34 days after planting (DAP) and 46 DAP, which represent the period prior to and after sugar accumulation, respectively, were selected for metabolomic and transcriptomic profiling. Differentially accumulated metabolites (DAMs) were mainly enriched in the pentose phosphate pathway, galactose metabolism, glycolysis/gluconeogenesis, starch and sucrose metabolism, and fructose and mannose metabolism. By orthogonal projection to latent structures-discriminant s-plot (OPLS-DA S-plot) and MetaboAnalyst analyses, D-galactose and ß-D-glucose were identified as the major components of sugar accumulation in wucai. Combined with the transcriptome, the pathway of sugar accumulation and the interact network between 26 DEGs and the two sugars were mapped. CWINV4, CEL1, BGLU16, and BraA03g023380.3C had positive correlations with the accumulation of sugar accumulation in wucai. The lower expression of BraA06g003260.3C, BraA08g002960.3C, BraA05g019040.3C, and BraA05g027230.3C promoted sugar accumulation during the ripening of wucai. These findings provide insights into the mechanisms underlying sugar accumulation during commodity maturity, providing a basis for the breeding of sugar-rich wucai cultivars.

Participants in the Trans-Antarctic Winter Traverse Expedition Showed Increased Bacterial Load and Diversity in Saliva but Maintained Individual Differences within Stool Microbiota and Across Metabolite Fingerprints.

Understanding the impact of long-term physiological and environmental stress on the human microbiota and metabolome may be important for the success of space flight. This work is logistically difficult and has a limited number of available participants. Terrestrial analogies present important opportunities to understand changes in the microbiota and metabolome and how this may impact participant health and fitness. Here, we present work from one such analogy: the Transarctic Winter Traverse expedition, which we believe is the first assessment of the microbiota and metabolome from different bodily locations during prolonged environmental and physiological stress. Bacterial load and diversity were significantly higher during the expedition when compared with baseline levels (p < 0.001) in saliva but not stool, and only a single operational taxonomic unit assigned to the Ruminococcaceae family shows significantly altered levels in stool (p < 0.001). Metabolite fingerprints show the maintenance of individual differences across saliva, stool, and plasma samples when analysed using flow infusion electrospray mass spectrometry and Fourier transform infrared spectroscopy. Significant activity-associated changes in terms of both bacterial diversity and load are seen in saliva but not in stool, and participant differences in metabolite fingerprints persist across all three sample types.

Biocontrol endophytes Bacillus subtilis R31 influence the quality, transcriptome and metabolome of sweet corn.

During colonization of soil and plants, biocontrol bacteria can effectively regulate the physiological metabolism of plants and induce disease resistance. To illustrate the influence of Bacillus subtilis R31 on the quality, transcriptome and metabolome of sweet corn, field studies were conducted at a corn experimental base in Zhuhai City. The results show that, after application of B. subtilis R31, sweet corn was more fruitful, with a 18.3 cm ear length, 5.0 cm ear diameter, 0.4 bald head, 403.9 g fresh weight of single bud, 272.0 g net weight of single ear, and 16.5 kernels sweetness. Combined transcriptomic and metabolomic analyses indicate that differentially expressed genes related to plant-pathogen interactions, MAPK signaling pathway-plant, phenylpropanoid biosynthesis, and flavonoid biosynthesis were significantly enriched. Moreover, the 110 upregulated DAMs were mainly involved in the flavonoid biosynthesis and flavone and flavonol biosynthesis pathways. Our study provides a foundation for investigating the molecular mechanisms by which biocontrol bacteria enhance crop nutrition and taste through biological means or genetic engineering at the molecular level.

Chronological age-related metabolome responses in the dinoflagellate Karenia mikimotoi, can predict future bloom demise.

Karenia mikimotoi is a common harmful algal bloom (HAB)-forming dinoflagellate and has caused severe financial loss in aquaculture. There are limited metabolomic studies on dinoflagellate biology. Here, we examined alterations in metabolic profiles over the growth curve of K. mikimotoi under nitrogen or phosphorus deficiency and further explored a key criterion for the diagnosis of late stationary phase to identify when the dinoflagellate cells will enter bloom demise. The results demonstrate the differential expression of metabolites for coping with chronological aging or nutrient deprivation. Furthermore, an increase in the glucose to glycine ratio in the late stationary phase was indicative of dinoflagellate cells entering bloom demise; this was also detected in the cultured diatom, Chaetoceros tenuissimus, indicating that this may be the general criterion for phytoplankton species. Our findings provide insights regarding chronological aging and the criterion for the prediction of phytoplankton bloom demise.

Small molecule metabolites: discovery of biomarkers and therapeutic targets.

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Effect of environmental variance-based resilience selection on the gut metabolome of rabbits.

BACKGROUND: Gut metabolites are key actors in host-microbiota crosstalk with effect on health. The study of the gut metabolome is an emerging topic in livestock, which can help understand its effect on key traits such as animal resilience and welfare. Animal resilience has now become a major trait of interest because of the high demand for more sustainable production. Composition of the gut microbiome can reveal mechanisms that underlie animal resilience because of its influence on host immunity. Environmental variance (VE), specifically the residual variance, is one measure of resilience. The aim of this study was to identify gut metabolites that underlie differences in the resilience potential of animals originating from a divergent selection for VE of litter size (LS). We performed an untargeted gut metabolome analysis in two divergent rabbit populations for low (n = 13) and high (n = 13) VE of LS. Partial least square-discriminant analysis was undertaken, and Bayesian statistics were computed to determine dissimilarities in the gut metabolites between these two rabbit populations. RESULTS: We identified 15 metabolites that discriminate rabbits from the divergent populations with a prediction performance of 99.2% and 90.4% for the resilient and non-resilient populations, respectively. These metabolites were suggested to be biomarkers of animal resilience as they were the most reliable. Among these, five that derived from the microbiota metabolism (3-(4-hydroxyphenyl)lactate, 5-aminovalerate, and equol, N6-acetyllysine, and serine), were suggested to be indicators of dissimilarities in the microbiome composition between the rabbit populations. The abundances of acylcarnitines and metabolites derived from the phenylalanine, tyrosine, and tryptophan metabolism were low in the resilient population and these pathways can, therefore impact the inflammatory response and health status of animals. CONCLUSIONS: This is the first study to identify gut metabolites that could act as potential resilience biomarkers. The results support differences in resilience between the two studied rabbit populations that were generated by selection for VE of LS. Furthermore, selection for VE of LS modified the gut metabolome, which could be another factor that modulates animal resilience. Further studies are needed to determine the causal role of these metabolites in health and disease.

Comprehensive analysis of metabolome and transcriptome reveals the mechanism of color formation in different leave of Loropetalum Chinense var. Rubrum.

BACKGROUND: Loropetalum chinense var. rubrum (L. chinense var. rubrum) is a precious, coloured-leaf native ornamental plant in the Hunan Province. We found an L. chinense var. rubrum tree with three different leaf colours: GL (green leaf), ML (mosaic leaf), and PL (purple leaf). The mechanism of leaf coloration in this plant is still unclear. Therefore, this study aimed to identify the metabolites and genes involved in determining the colour composition of L. chinense var. rubrum leaves, using phenotypic/anatomic observations, pigment content detection, and comparative metabolomics and transcriptomics. RESULTS: We observed that the mesophyll cells in PL were purple, while those in GL were green and those in ML were a mix of purple-green. The contents of chlorophyll a, b, carotenoids, and total chlorophyll in PL and ML were significantly lower than those in GL. While the anthocyanin content in PL and ML was significantly higher than that in GL. The metabolomics results showed the differences in the content of cyanidin 3-O-glucoside, delphinidin 3-O-glucoside, cyanidin 3,5-O-diglucoside, pelargonidin, and petunidin 3,5-diglucoside in ML, GL, and PL were significant. Considering that the change trend of anthocyanin content change was consistent with the leaf colour difference, we speculated that these compounds might influence the colour of L. chinense var. rubrum leaves. Using transcriptomics, we finally identified nine differentially expressed structural genes (one ANR (ANR1217); four CYP75As (CYP75A1815, CYP75A2846, CYP75A2909, and CYP75A1716); four UFGTs (UFGT1876, UFGT1649, UFGT1839, and UFGT3273) and nine transcription factors (two MYBs (MYB1057 and MYB1211), one MADS-box (MADS1235), two AP2-likes (AP2-like1779 and AP2-like2234), one bZIP (bZIP3720), two WD40s (WD2173 and WD1867) and one bHLH (bHLH1631) that might be related to flavonoid biosynthesis and then impacted the appearance of colour in L. chinense var. rubrum leaves. CONCLUSION: This study revealed potential molecular mechanisms associated with leaf coloration in L. chinense var. rubrum by analyzing differential metabolites and genes related to the anthocyanin biosynthesis pathway. It also provided a reference for research on leaf colour variation in other ornamental plants.

Cohort profile: Celiac disease genomic, environmental, microbiome and metabolome study; a prospective longitudinal birth cohort study of children at-risk for celiac disease.

The Celiac Disease Genomic, Environmental, Microbiome and Metabolomic (CDGEMM) study is an international prospective birth cohort in children at-risk of developing celiac disease (CD). The CDGEMM study has been designed to take a multi-omic approach to predicting CD onset in at-risk individuals. Participants are required to have a first-degree family member with biopsy diagnosed CD and must be enrolled prior to the introduction of solid food. Participation involves providing blood and stool samples longitudinally over a period of five years as well as answering questionnaires related to the participant, their family, and environment. Recruitment and data collection have been ongoing since 2014. As of 2022 we have a total of 554 participants and the average age of the cohort is 56.4 months. A total of 54 participants have developed positive antibodies for CD and 31 have confirmed CD. Approximately 80% of the 54 participants with CD have developed it by 3 years of age. To date we have identified several microbial strains, pathways, and metabolites occurring in increased abundance and detected before CD onset, which have previously been linked to autoimmune and inflammatory conditions while others occurred in decreased abundance before CD onset and are known to have anti-inflammatory effects. Our ongoing analysis includes expanding our metagenomic and metabolomic analyses, evaluating environmental risk factors linked to CD onset, and mechanistic studies investigating how alterations in the microbiome and metabolites may protect against or contribute to CD development.

Large-scale metabolome analysis reveals dynamic changes of metabolites during foxtail millet grain filling.

Compared with traditional staple crops, foxtail millet grain is rich in nutrition and beneficial to human health. Foxtail millet is also tolerance to various abiotic stresses, including drought, making it a good plant for growing in barren land. The study on the composition of metabolites and its dynamics changes during grain development is helpful to understand the process of foxtail millet grain formation. In our study, metabolic and transcriptional analysis were used to uncover the metabolic processes that could influence grain filling in foxtail millet. A total of 2104 known metabolites, belonging to 14 categories, were identified during grain filling. Functional analysis of DAMs and DEGs revealed a stage-specific metabolic properties in foxtail millet grain filling. Some important metabolic processes, such as flavonoid biosynthesis, glutathione metabolism, linoleic acid metabolism, starch and sucrose metabolism and valine, leucine and isoleucine biosynthesis were co-mapped for DEGs and DAMs. Thus, we constructed a gene-metabolite regulatory network of these metabolic pathways to explain their potential functions during grain filling. Our study showed the important metabolic processes during grain filling and focused on the dynamic changes of related metabolites and genes at different stages, which provided a reference for us to better understand and improve foxtail millet grain development and yield.

Comparative proteome and volatile metabolome analysis of Aspergillus oryzae 3.042 and Aspergillus sojae 3.495 during koji fermentation.

Aspergillus oryzae 3.042 and Aspergillus sojae 3.495 are crucial starters for fermented soybean foods since their abundant secreted enzymes. This study aimed to compare the differences in protein secretion between A. oryzae 3.042 and A. sojae 3.495 during the soy sauce koji fermentation and the effect on volatile metabolites to understand the fermentation characteristics of the strains better. Label-free proteomics detected 210 differentially expressed proteins (DEPs) enriched in amino acid metabolism and protein folding, sorting and degradation pathways. Subsequently, extracellular enzyme analysis showed that three peptidases, including peptide hydrolase, dipeptidyl aminopeptidase, and peptidase S41, were up-regulated in A. sojae 3.495. Seven carbohydrases, including α-galactosidase, endo-arabinase, ß-glucosidase, α-galactosidase, α-glucuronidase, arabinan-endo 1,5-α-l-arabinase, and endo-1,4-ß-xylanase were up-regulated in A. oryzae 3.042, contributing to the difference in enzyme activity. Significantly different extracellular enzymes influenced the content of volatile alcohols, aldehydes and esters such as (R, R)-2,3-butanediol, 1-hexanol, hexanal, decanal, ethyl l-lactate and methyl myristate in both strains, which affected the type of aroma of koji. Overall, this study revealed the differences in molecular mechanisms between A. oryzae 3.042 and A. sojae 3.495 under solid-state fermentation, providing a reference for targeted enhancement strains.

Evaluating response mechanisms of soil microbiomes and metabolomes to Bt toxin additions.

The accumulation and persistence of Bt toxins in soils from Bt plants and Bt biopesticides may result in environmental hazards such as adverse impacts on soil microorganisms. However, the dynamic relationships among exogenous Bt toxins, soil characteristics, and soil microorganisms are not well understood. Cry1Ab is one of the most commonly used Bt toxins and was added to soils in this study to evaluate subsequent changes in soil physiochemical properties, microbial taxa, microbial functional genes, and metabolites profiles via 16S rRNA gene pyrosequencing, high-throughput qPCR, metagenomic shotgun sequencing, and untargeted metabolomics. Higher additions of Bt toxins led to higher concentrations of soil organic matter (SOM), ammonium (NH+4-N), and nitrite (NO2--N) compared against controls without addition after 100 days of soil incubation. High-throughput qPCR analysis and shotgun metagenomic sequencing analysis revealed that the 500 ng/g Bt toxin addition significantly affected profiles of soil microbial functional genes involved in soil carbon (C), nitrogen (N), and phosphorus (P) cycling after 100 days of incubation. Furthermore, combined metagenomic and metabolomic analyses indicated that the 500 ng/g Bt toxin addition significantly altered low molecular weight metabolite profiles of soils. Importantly, some of these altered metabolites are involved in soil nutrient cycling, and robust associations were identified among differentially abundant metabolites and microorganisms due to Bt toxin addition treatments. Taken together, these results suggest that higher levels of Bt toxin addition can alter soil nutrients, probably by affecting the activities of Bt toxin-degrading microorganisms. These dynamics would then activate other microorganisms involved in nutrient cycling, finally leading to broad changes in metabolite profiles. Notably, the addition of Bt toxins did not cause the accumulation of potential microbial pathogens in soils, nor did it adversely affect the diversity and stability of microbial communities. This study provides new insights into the putative mechanistic associations among Bt toxins, soil characteristics, and microorganisms, providing new understanding into the ecological impacts of Bt toxins on soil ecosystems.

System network analysis of Rosmarinus officinalis transcriptome and metabolome-Key genes in biosynthesis of secondary metabolites.

Medicinal plants contain valuable compounds that have attracted worldwide interest for their use in the production of natural drugs. The presence of compounds such as rosmarinic acid, carnosic acid, and carnosol in Rosmarinus officinalis has made it a plant with unique therapeutic effects. The identification and regulation of the biosynthetic pathways and genes will enable the large-scale production of these compounds. Hence, we studied the correlation between the genes involved in biosynthesis of the secondary metabolites in R. officinalis using proteomics and metabolomics data by WGCNA. We identified three modules as having the highest potential for the metabolite engineering. Moreover, the hub genes highly connected to particular modules, TFs, PKs, and transporters were identified. The TFs of MYB, C3H, HB, and C2H2 were the most likely candidates associated with the target metabolic pathways. The results indicated that the hub genes including Copalyl diphosphate synthase (CDS), Phenylalanine ammonia lyase (PAL), Cineole synthase (CIN), Rosmarinic acid synthase (RAS), Tyrosine aminotransferase (TAT), Cinnamate 4-hydroxylase (C4H), and MYB58 are responsible for biosynthesis of important secondary metabolites. Thus, we confirmed these results using qRT-PCR after treating R. officinalis seedlings with methyl jasmonate. These candidate genes may be employed for genetic and metabolic engineering research to increase R. officinalis metabolite production.

Prepregnancy Body Mass Index and Lipoprotein Fractions are Associated with Changes in Women's Serum Metabolome from Late Pregnancy to the First Months of Postpartum.

BACKGROUND: Pregnancy and postpartum are periods of intense changes in women's metabolism. The knowledge of the metabolites and maternal factors underlying these changes is limited. OBJECTIVES: We aimed to investigate the maternal factors that could influence serum metabolome changes from late pregnancy to the first months of postpartum. METHODS: Sixty-eight healthy women from a Brazilian prospective cohort were included. Maternal blood and general characteristics were collected during pregnancy (28-35 wk) and postpartum (27-45 d). A targeted metabolomics approach was applied to quantify 132 serum metabolites, including amino acids, biogenic amines, acylcarnitines, lysophosphatidylcholines (LPC), diacyl phosphatidylcholines (PC), alkyl:acyl phosphatidylcholines (PC-O), sphingomyelins with (SM) and without hydroxylation [SM(OH)], and hexoses. Metabolome changes from pregnancy to postpartum were measured as log2 fold change (log2FC), and simple linear regressions were employed to evaluate associations between maternal variables and metabolite log2FC. Multiple comparison-adjusted P values of < 0.05 were considered significant. RESULTS: Of 132 metabolites quantified in serum, 90 changed from pregnancy to postpartum. Most metabolites belonging to PC and PC-O classes decreased, whereas most LPC, acylcarnitines, biogenic amines, and a few amino acids increased in postpartum. Maternal prepregnancy body mass index (ppBMI) showed positive associations with leucine and proline. A clear opposite change pattern was observed for most metabolites across ppBMI categories. Few phosphatidylcholines were decreased in women with normal ppBMI, while an increase was observed in women with obesity. Similarly, women with high postpartum levels of total cholesterol, LDL cholesterol, and non-HDL cholesterol showed increased sphingomyelins, whereas a decrease was observed for women with lower levels of those lipoproteins. CONCLUSIONS: The results revealed several maternal serum metabolomic changes from pregnancy to postpartum, and the maternal ppBMI and plasma lipoproteins were associated with these changes. We highlight the importance of the nutritional care of women prepregnancy to improve their metabolic risk profile.

Oxidative stress, dysfunctional energy metabolism, and destabilizing neurotransmitters altered the cerebral metabolic profile in a rat model of simulated heliox saturation diving to 4.0 MPa.

The main objective of the present study was to determine metabolic profile changes in the brains of rats after simulated heliox saturated diving (HSD) to 400 meters of sea water compared to the blank controls. Alterations in the polar metabolome in the rat brain due to HSD were investigated in cortex, hippocampus, and striatum tissue samples by applying an NMR-based metabolomic approach coupled with biochemical detection in the cortex. The reduction in glutathione and taurine levels may hypothetically boost antioxidant defenses during saturation diving, which was also proven by the increased malondialdehyde level, the decreased superoxide dismutase, and the decreased glutathione peroxidase in the cortex. The concomitant decrease in aerobic metabolic pathways and anaerobic metabolic pathways comprised downregulated energy metabolism, which was also proven by the biochemical quantification of the metabolic enzymes Na-K ATPase and LDH in cerebral cortex tissue. The significant metabolic abnormalities of amino acid neurotransmitters, such as GABA, glycine, and aspartate, decreased aromatic amino acids, including tyrosine and phenylalanine, both of which are involved in the metabolism of dopamine and noradrenaline, which are downregulated in the cortex. Particularly, a decline in the level of N-acetyl aspartate is associated with neuronal damage. In summary, hyperbaric decompression of a 400 msw HSD affected the brain metabolome in a rat model, potentially including a broad range of disturbing amino acid homeostasis, metabolites related to oxidative stress and energy metabolism, and destabilizing neurotransmitter components. These disturbances may contribute to the neurochemical and neurological phenotypes of HSD.