Genetic causality and metabolite pathway identifying the relationship of blood metabolites and psoriasis.

BACKGROUND: Psoriasis is a chronic inflammatory disease that causes significant disability. However, little is known about the underlying metabolic mechanisms of psoriasis. Our study aims to investigate the causality of 975 blood metabolites with the risk of psoriasis. MATERIALS AND METHODS: We mainly applied genetic analysis to explore the possible associations between 975 blood metabolites and psoriasis. The inverse variance weighted (IVW) method was used as the primary analysis to assess the possible association of blood metabolites with psoriasis. Moreover, generalized summary-data-based Mendelian randomization (GSMR) was used as a supplementary analysis. In addition, linkage disequilibrium score regression (LDSC) was used to investigate their genetic correction further. Metabolic pathway analysis of the most suggested metabolites was also performed using MetaboAnalyst 5.0. RESULTS: In our primary analysis, 17 metabolites, including unsaturated fatty acids, phospholipids, and triglycerides traits, were selected as potential factors in psoriasis, with odd ratios (OR) ranging from 0.986 to 1.01. The GSMR method confirmed the above results (ß = 0.001, p < 0.05). LDSC analysis mainly suggested the genetic correlation of psoriasis with genetic correlations (rg) from 0.088 to 0.155. Based on the selected metabolites, metabolic pathway analysis suggested seven metabolic pathways including ketone body that may be prominent pathways for metabolites in psoriasis. CONCLUSION: Our study supports the causal role of unsaturated fatty acid properties and lipid traits with psoriasis. These properties may be regulated by the ketone body metabolic pathway.

Lipid and sugar metabolism play an essential role in pollen development and male sterility: a case analysis in Brassica napus.

AIMS: The genic male sterility (GMS) system is an important strategy for generating heterosis in plants. To better understand the essential role of lipid and sugar metabolism and to identify additional candidates for pollen development and male sterility, transcriptome and metabolome analysis of a GMS line of 1205AB in B. napus was used as a case study. DATA RESOURCES GENERATED: To characterize the GMS system, the transcriptome and metabolome profiles were generated for 24 samples and 48 samples of 1205AB in B. napus, respectively. Transcriptome analysis yielded a total of 156.52 Gb of clean data and revealed the expression levels of 109,541 genes and 8,501 novel genes. In addition, a total of 1,353 metabolites were detected in the metabolomic analysis, including 784 in positive ion mode and 569 in negative ion mode. KEY RESULTS: A total of 15,635 differentially expressed genes (DEGs) and 83 differential metabolites (DMs) were identified from different comparison groups, most of which were involved in lipid and sugar metabolism. The combination of transcriptome and metabolome analysis revealed 49 orthologous GMS genes related to lipid metabolism and 46 orthologous GMS genes related to sugar metabolism, as well as 45 novel genes. UTILITY OF THE RESOURCE: The transcriptome and metabolome profiles and their analysis provide useful reference data for the future discovery of additional GMS genes and the development of more robust male sterility breeding systems for use in the production of plant hybrids.

Integration of targeted metabolome and transcript profiling of Pseudomonas syringae-triggered changes in defence-related phytochemicals in oat plants.

MAIN CONCLUSION: A gene-to-metabolite approach afforded new insights regarding defence mechanisms in oat plants that can be incorporated into plant breeding programmes for the selection of markers and genes related to disease resistance. Monitoring metabolite levels and changes therein can complement and corroborate transcriptome (mRNA) data on plant-pathogen interactions, thus revealing mechanisms involved in pathogen attack and host defence. A multi-omics approach thus adds new layers of information such as identifying metabolites with antimicrobial properties, elucidating metabolomic profiles of infected and non-infected plants, and reveals pathogenic requirements for infection and colonisation. In this study, two oat cultivars (Dunnart and SWK001) were inoculated with Pseudomonas syringae pathovars, pathogenic and non-pathogenic on oat. Following inoculation, metabolites were extracted with methanol from leaf tissues at 2, 4 and 6 days post-infection and analysed by multiple reaction monitoring (MRM) on a triple quadrupole mass spectrometer system. Relatedly, mRNA was isolated at the same time points, and the cDNA analysed by quantitative PCR (RT-qPCR) for expression levels of selected gene transcripts associated with avenanthramide (Avn) biosynthesis. The targeted amino acids, hydroxycinnamic acids and Avns were successfully quantified. Distinct cultivar-specific differences in the metabolite responses were observed in response to pathogenic and non-pathogenic strains. Trends in aromatic amino acids and hydroxycinnamic acids seem to indicate stronger activation and flux through these pathways in Dunnart as compared to SWK001. A positive correlation between hydroxycinnamoyl-CoA:hydroxyanthranilate N-hydroxycinnamoyl transferase (HHT) gene expression and the abundance of Avn A in both cultivars was documented. However, transcript profiling of selected genes involved in Avn synthesis did not reveal a clear pattern to distinguish between the tolerant and susceptible cultivars.

Integrated Analysis of Transcriptome and Metabolome Reveals Differential Responses to Alternaria brassicicola Infection in Cabbage (Brassica oleracea var. capitata).

Black spot, caused by Alternaria brassicicola (Ab), poses a serious threat to crucifer production, and knowledge of how plants respond to Ab infection is essential for black spot management. In the current study, combined transcriptomic and metabolic analysis was employed to investigate the response to Ab infection in two cabbage (Brassica oleracea var. capitata) genotypes, Bo257 (resistant to Ab) and Bo190 (susceptible to Ab). A total of 1100 and 7490 differentially expressed genes were identified in Bo257 (R_mock vs. R_Ab) and Bo190 (S_mock vs. S_Ab), respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that "metabolic pathways", "biosynthesis of secondary metabolites", and "glucosinolate biosynthesis" were the top three enriched KEGG pathways in Bo257, while "metabolic pathways", "biosynthesis of secondary metabolites", and "carbon metabolism" were the top three enriched KEGG pathways in Bo190. Further analysis showed that genes involved in extracellular reactive oxygen species (ROS) production, jasmonic acid signaling pathway, and indolic glucosinolate biosynthesis pathway were differentially expressed in response to Ab infection. Notably, when infected with Ab, genes involved in extracellular ROS production were largely unchanged in Bo257, whereas most of these genes were upregulated in Bo190. Metabolic profiling revealed 24 and 56 differentially accumulated metabolites in Bo257 and Bo190, respectively, with the majority being primary metabolites. Further analysis revealed that dramatic accumulation of succinate was observed in Bo257 and Bo190, which may provide energy for resistance responses against Ab infection via the tricarboxylic acid cycle pathway. Collectively, this study provides comprehensive insights into the Ab-cabbage interactions and helps uncover targets for breeding Ab-resistant varieties in cabbage.

Protocol for genome-wide association study of human blood metabolites.

Genetic variations influence the levels of blood metabolites. We present analytical pipelines for assessing genetic influences on human blood metabolites. We describe steps for the normalization of metabolome data, genome-wide association studies, and the identification of metabolite quantitative trait loci (mQTLs). We then detail procedures for functional enrichment analysis of mQTLs. This protocol could be applicable to other quantitative traits, such as clinical measurements or proteome data. For complete details on the use and execution of this protocol, please refer to Iwasaki et al.1.

Transcriptomic and metabolomic analyses reveal CmMYB308 as a key regulator in the pink flower color variation of 'Dante Purple' chrysanthemum.

KEY MESSAGE: CmMYB308 was identified as a key regulator in chrysanthemum flower color variation from purple to pink by conducting transcriptome and metabolome analysis. CmMYB308 can inhibit anthocyanin biosynthesis by suppressing the expression of CmPAL, CmC4H, and Cm4CL. Flower color variation is a widespread natural occurrence that plays a significant role in floral breeding. We discovered a variation in the flower of the chrysanthemum cultivar 'Dante Purple' (abbreviated as 'DP'), where the flower color shifted from purple to pink. We successfully propagated these pink flowers through tissue culture and designated them as DPM. By conducting transcriptome and metabolome analysis, we identified a reduction in the expression of critical genes involved in anthocyanin biosynthesis-CmPAL, CmC4H, and Cm4CL-in the DPM. This downregulation led to an accumulation of phenylalanine and cinnamic acid within the general phenylpropanoid pathway (GPP), which prevented their conversion into cyanidin and cyanidin 3-glucoside. As a result, the flowers turned pink. Additional transformation and biochemical experiments confirmed that the upregulation of CmMYB308 gene expression in the DPM directly suppressed CmPAL-1 and CmC4H genes, which indirectly affected Cm4CL-3 expression and ultimately inhibited anthocyanin biosynthesis in the DPM. This study offers a preliminary insight into the molecular mechanism underlying chrysanthemum flower color mutation, paving the way for genetic improvements in chrysanthemum flower color breeding.

Transcriptome and Metabolome Reveal Key Genes from the Plant Hormone Signal Transduction Pathway Regulating Plant Height and Leaf Size in Capsicum baccatum.

Plant structure-related agronomic traits like plant height and leaf size are critical for growth, development, and crop yield. Defining the types of genes involved in regulating plant structure size is essential for the molecular-assisted breeding of peppers. This research conducted comparative transcriptome analyses using Capsicum baccatum germplasm HNUCB0112 and HNUCB0222 and their F2 generation as materials. A total of 6574 differentially expressed genes (DEGs) were detected, which contain 379 differentially expressed transcription factors, mainly including transcription factor families such as TCP, WRKY, AUX/IAA, and MYB. Seven classes of DEGs were annotated in the plant hormone signal transduction pathway, including indole acetic acid (IAA), gibberellin (GA), cytokinin (CK), abscisic acid (ABA), jasmonic acid (JA), ethylene (ET), and salicylic acid (SA). The 26 modules were obtained by WGCNA analysis, and the MEpink module was positively correlated with plant height and leaf size, and hub genes associated with plant height and leaf size were anticipated. Differential genes were verified by qRT-PCR, which was consistent with the RNA-Seq results, demonstrating the accuracy of the sequencing results. These results enhance our understanding of the developmental regulatory networks governing pepper key traits like plant height and leaf size and offer new information for future research on the pepper plant architecture system.

Combined full-length transcriptomic and metabolomic analysis reveals the molecular mechanisms underlying nutrients and taste components development in Primulina juliae.

BACKGROUND: Primulina juliae has recently emerged as a novel functional vegetable, boasting a significant biomass and high calcium content. Various breeding strategies have been employed to the domestication of P. juliae. However, the absence of genome and transcriptome information has hindered the research of mechanisms governing the taste and nutrients in this plant. In this study, we conducted a comprehensive analysis, combining the full-length transcriptomics and metabolomics, to unveil the molecular mechanisms responsible for the development of nutrients and taste components in P. juliae. RESULTS: We obtain a high-quality reference transcriptome of P. juliae by combing the PacBio Iso-seq and Illumina sequencing technologies. A total of 58,536 cluster consensus sequences were obtained, including 28,168 complete protein coding transcripts and 8,021 Long Non-coding RNAs. Significant differences were observed in the composition and content of compounds related to nutrients and taste, particularly flavonoids, during the leaf development. Our results showed a decrease in the content of most flavonoids as leaves develop. Malate and succinate accumulated with leaf development, while some sugar metabolites were decreased. Furthermore, we identified the different accumulation of amino acids and fatty acids, which are associated with taste traits. Moreover, our transcriptomic analysis provided a molecular basis for understanding the metabolic variations during leaf development. We identified 4,689 differentially expressed genes in the two developmental stages, and through a comprehensive transcriptome and metabolome analysis, we discovered the key structure genes and transcription factors involved in the pathways. CONCLUSIONS: This study provides a high-quality reference transcriptome and reveals molecular mechanisms associated with the development of nutrients and taste components in P. juliae. These findings will enhance our understanding of the breeding and utilization of P. juliae as a vegetable.

Transcriptomic and metabolomic profiling provide insight into the role of sugars and hormones in leaf senescence of Pinellia ternata.

KEY MESSAGE: The interaction network and pathway map uncover the potential crosstalk between sugar and hormone metabolisms as a possible reason for leaf senescence in P. ternata. Pinellia ternata, an environmentally sensitive medicinal plant, undergoes leaf senescence twice a year, affecting its development and yield. Understanding the potential mechanism that delays leaf senescence could theoretically decrease yield losses. In this study, a typical senescent population model was constructed, and an integrated analysis of transcriptomic and metabolomic profiles of P. ternata was conducted using two early leaf senescence populations and two stay-green populations. The result showed that two key gene modules were associated with leaf senescence which were mainly enriched in sugar and hormone signaling pathways, respectively. A network constructed by unigenes and metabolisms related to the obtained two pathways revealed that several compounds such as D-arabitol and 2MeScZR have a higher significance ranking. In addition, a total of 130 hub genes in this network were categorized into 3 classes based on connectivity. Among them, 34 hub genes were further analyzed through a pathway map, the potential crosstalk between sugar and hormone metabolisms might be an underlying reason of leaf senescence in P. ternata. These findings address the knowledge gap regarding leaf senescence in P. ternata, providing candidate germplasms for molecular breeding and laying theoretical basis for the realization of finely regulated cultivation in future.

Metabolome and transcriptome integration reveals insights into petals coloration mechanism of three species in Sect. Chrysantha chang.

Background: Sect. Chrysantha Chang, belonging to the Camellia genus, is one of the rare and precious ornamental plants distinguished by a distinctive array of yellow-toned petals. However, the variation mechanisms of petal color in Sect. Chrysantha Chang remains largely unclear. Methods: We conducted an integrated analysis of metabolome and transcriptome to reveal petal coloration mechanism in three species, which have different yellow tones petals, including C. chuongtsoensis (CZ, golden yellow), C. achrysantha (ZD, light yellow), and C. parvipetala (XB, milk white). Results: A total of 356 flavonoid metabolites were detected, and 295 differential metabolites were screened. The contents of 74 differential metabolites showed an upward trend and 19 metabolites showed a downward trend, among which 11 metabolites were annotated to the KEGG pathway database. We speculated that 10 metabolites were closely related to the deepening of the yellowness. Transcriptome analysis indicated that there were 2,948, 14,018 and 13,366 differentially expressed genes (DEGs) between CZ vs. ZD, CZ vs. XB and ZD vs. XB, respectively. Six key structural genes (CcCHI, CcFLS, CcDFR1, CcDFR2, CcDFR3, and CcCYP75B1) and five candidate transcription factors (MYB22, MYB28, MYB17, EREBP9, and EREBP13) were involved in the regulation of flavonoid metabolites. The findings indicate that flavonoid compounds influence the color intensity of yellow-toned petals in Sect. Chrysantha Chang. Our results provide a new perspective on the molecular mechanisms underlying flower color variation and present potential candidate genes for Camellia breeding.

Constructing the metabolic network of wheat kernels based on structure-guided chemical modification and multi-omics data.

Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities, although it often proves to be challenging and labor-intensive, particularly with non-model organisms. In this study, we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat. This construction results in a comprehensive structure-guided network, including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database. Using a combination of gene annotation, reaction classification, structure similarity, and correlations from transcriptome and metabolome analysis, a total of 229 potential genes related to these reactions are identified within this network. To validate the network, the functionality of a hydroxycinnamoyltransferase (TraesCS3D01G314900) for the synthesis of polyphenols and a rhamnosyltransferase (TraesCS2D01G078700) for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests, respectively. Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.

Gastrointestinal Characteristics of Constipation from the Perspectives of Microbiome and Metabolome.

BACKGROUND: Constipation is one of the most common gastrointestinal complaints. Yet, the underlying mechanisms of constipation remain to be explored deeply. Integration of microbiome and metabolome is powerful and promising to demonstrate characteristics of constipation. AIM OF STUDY: This study aimed to characterize intestinal microbiome and metabolome of constipation. In addition, this study revealed the correlations among behaviors, intestinal microbiota, and metabolites interrupted by constipation. METHODS: Firstly, the constipation model was successfully applied. At the macro level, the ability of learning, memory, locomotor activity, and the defecation index of rats with constipation-like phenotype were characterized. At the micro-level, 16S rRNA sequencing was applied to analyze the intestinal microbiota in rats with constipation-like phenotype. 1H nuclear magnetic resonance (NMR)-based metabolomics was employed to investigate the metabolic phenotype of constipation. In addition, we constructed a correlation network, intuitively showing the correlations among behaviors, intestinal microbiota, and metabolites. RESULTS: Constipation significantly attenuated the locomotor activity, memory recognition, and frequency of defecation of rats, while increased the time of defecation. Constipation significantly changed the diversity of intestinal microbial communities, which correspondingly involved in 5 functional pathways. Besides, 28 fecal metabolites were found to be associated with constipation, among which 14 metabolites were further screened that can be used to diagnose constipation. On top of this, associated networks intuitively showed the correlations among behaviors, intestinal microbiota, and metabolites. CONCLUSIONS: The current findings are significant in terms of not only laying a foundation for understanding characteristics of constipation, but also providing accurate diagnosis and treatments of constipation clinically.

Analysis of alterations in serum vitamins and correlations with gut microbiome, microbial metabolomics in patients with sepsis.

BACKGROUND: Vitamins are essential micronutrients that play key roles in many biological pathways associated with sepsis. The gut microbiome plays a pivotal role in the progression of sepsis and may contribute to the onset of multi-organ dysfunction syndrome (MODS). The aim of this study was to investigate the changes in serum vitamins, and their correlation with intestinal flora and metabolomic profiles in patients with sepsis. METHODS: The serum levels of vitamins were determined by Ultra Performance Liquid Chromatography (UPLC). 16S rRNA gene sequencing and Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) targeted metabolomics were used for microbiome and metabolome analysis. RESULTS: In the training cohort: After univariate, multivariate (OPLS-DA) and Spearman analyses, it was concluded that vitamin levels of 25 (OH) VD3 and (VD2 + VD3), as well as vitamins A and B9, differed significantly among healthy controls (HC), non-septic critical patients (NS), and sepsis patients (SS) (P < 0.05). The validation cohort confirmed the differential vitamin findings from the training cohort. Moreover, analyses of gut flora and metabolites in septic patients and healthy individuals revealed differential flora, metabolites, and metabolic pathways that were linked to alterations in serum vitamin levels. We found for the first time that vitamin B9 was negatively correlated with g_Sellimonas. CONCLUSION: Sepsis patients exhibited significantly lower levels of 25 (OH) VD3 and (VD2 + VD3), vitamins A and B9, which hold potential as predictive markers for sepsis prognosis. The changes in these vitamins may be associated with inflammatory factors, oxidative stress, and changes in gut flora.

Genetic investigation into the broad health implications of caffeine: evidence from phenome-wide, proteome-wide and metabolome-wide Mendelian randomization.

BACKGROUND: Caffeine is one of the most utilized drugs in the world, yet its clinical effects are not fully understood. Circulating caffeine levels are influenced by the interplay between consumption behaviour and metabolism. This study aimed to investigate the effects of circulating caffeine levels by considering genetically predicted variation in caffeine metabolism. METHODS: Leveraging genetic variants related to caffeine metabolism that affect its circulating levels, we investigated the clinical effects of plasma caffeine in a phenome-wide association study (PheWAS). We validated novel findings using a two-sample Mendelian randomization framework and explored the potential mechanisms underlying these effects in proteome-wide and metabolome-wide Mendelian randomization. RESULTS: Higher levels of genetically predicted circulating caffeine among caffeine consumers were associated with a lower risk of obesity (odds ratio (OR) per standard deviation increase in caffeine = 0.97, 95% confidence interval (CI) CI: 0.95-0.98, p = 2.47 × 10-4), osteoarthrosis (OR = 0.97, 95% CI: 0.96-0.98, P=1.10 × 10-8) and osteoarthritis (OR: 0.97, 95% CI: 0.96 to 0.98, P = 1.09 × 10-6). Approximately one third of the protective effect of plasma caffeine on osteoarthritis risk was estimated to be mediated through lower bodyweight. Proteomic and metabolomic perturbations indicated lower chronic inflammation, improved lipid profiles, and altered protein and glycogen metabolism as potential biological mechanisms underlying these effects. CONCLUSIONS: We report novel evidence suggesting that long-term increases in circulating caffeine may reduce bodyweight and the risk of osteoarthrosis and osteoarthritis. We confirm prior genetic evidence of a protective effect of plasma caffeine on risk of overweight and obesity. Further clinical study is warranted to understand the translational relevance of these findings before clinical practice or lifestyle interventions related to caffeine consumption are introduced.

Integration of transcriptome and metabolome reveals the accumulation of related metabolites and gene regulation networks during quinoa seed development.

Quinoa seeds are gluten- and cholesterol-free, contain all amino acids required by the human body, have a high protein content, provide endocrine regulation, protein supplementation, and cardiovascular protection effects. However, metabolite accumulation and transcriptional regulatory networks in quinoa seed development are not well understood. Four key stages of seed development in Dianli-3260 and Dianli-557 were thus analyzed and 849 metabolites were identified, among which sugars, amino acids, and lipids were key for developmental processes, and their accumulation showed a gradual decrease. Transcriptome analysis identified 40,345 genes, of which 20,917 were differential between the M and F phases, including 8279 and 12,638 up- and down-regulated genes, respectively. Grain development processes were mainly enriched in galactose metabolism, pentose and glucuronate interconversions, the biosynthesis of amino acids, and carbon metabolism pathways, in which raffinose, phosphoenolpyruvate, series and other metabolites are significantly enriched, gene-LOC110689372, Gene-LOC110710556 and gene-LOC110714584 are significantly expressed, and these metabolites and genes play an important role in carbohydrate metabolism, lipid and Amino acid synthesis of quinoa. This study provides a theoretical basis to expand our understanding of the molecular and metabolic development of quinoa grains.

Effects of radiofrequency field from 5G communication on fecal microbiome and metabolome profiles in mice.

With the rapid development of 5G networks, the influence of the radiofrequency field (RF) generated from 5G communication equipment on human health is drawing increasing attention in public. The study aimed at assessing the effects of long-term exposure to 4.9 GHz (one of the working frequencies of 5G communication) RF field on fecal microbiome and metabolome profiles in adult male C57BL/6 mice. The animals were divided into Sham group and radiofrequency group (RF group). For RF group, the mice were whole body exposed to 4.9 GHz RF field for three weeks, 1 h/d, at average power density (PD) of 50 W/m2. After RF exposure, the mice fecal samples were collected to detect gut microorganisms and metabolites by 16S rRNA gene sequencing and LC-MS method, respectively. The results showed that intestinal microbial compositions were altered in RF group, as evidenced by reduced microbial diversity and changed microbial community distribution. Metabolomics profiling identified 258 significantly differentially abundant metabolites in RF group, 57 of which can be classified to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Besides, functional correlation analysis showed that changes in gut microbiota genera were significantly correlated with changes in fecal metabolites. In summary, the results suggested that altered gut microbiota and metabolic profile are associated with 4.9 GHz radiofrequency exposure.

Genome scale metabolic network modelling for metabolic profile predictions.

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

Categorization of the effects of E. coli LF82 and mutants lacking the chuT and shuU genes on survival, the transcriptome, and metabolome in germ-free honeybee.

The precise etiology of inflammatory bowel diseases (IBDs) remains elusive. The Escherichia coli strain LF82 (LF82) is known to be associated with IBD, and we hypothesized that this association may be related to the chuT and shuU genes. Here we constructed a germ-free (GF) honeybee model to investigate the effects of LF82 chuT and shuU genes on the honeybee intestine and their mechanisms. The chuT and shuU gene deletion strains LF82∆chuT and LF82∆shuU were generated by CRISPR-Cas9. These strains, together with nonpathogenic E. coli MG1655 (MG1655) and wildtype LF82, were allowed to colonize the guts of GF honeybees to establish single bacterial colonization models. Intestinal permeability was assessed following the administration of a sterile Brilliant Blue (FCF) solution. Comprehensive transcriptomic and metabolomic analyses of intestinal samples indicated that MG1655 had few disadvantageous effects on honeybees. Conversely, colonization with LF82 and its gene-deletion mutants provoked pronounced activation of genes associated with innate immune pathways, stimulated defensive responses, and induced expression of genes associated with inflammation, oxidative stress, and glycosaminoglycan degradation. Crucially, the LF82∆chuT and LF82∆shuU strains perturbed host heme and iron regulation, as well as tryptophan metabolism. These findings suggest that the deletion of chuT and shuU genes in E. coli LF82 may alleviate intestinal inflammation by partially modulating tryptophan catabolism. Our study proposes that targeting iron uptake mechanisms could be a potential strategy to mitigate the virulence of IBD-associated bacteria.

Comparative Metabolome and Transcriptome Analyses Reveal the Regulatory Mechanism of Purple Leafstalk Production in Taro (Colocasia esculenta L. Schott).

Taro is a plant in the Araceae family, and its leafstalk possesses significant botanical and culinary value owing to its noteworthy medicinal and nutritional attributes. Leafstalk colour is an essential attribute that significantly influences its desirability and appeal to both breeders and consumers. However, limited information is available about the underlying mechanism responsible for the taro plant's colouration. Thus, the purpose of the current study was to elucidate the information on purple leafstalks in taro through comprehensive metabolome and transcriptome analysis. In total, 187 flavonoids, including 10 anthocyanins, were identified. Among the various compounds analysed, it was observed that the concentrations of five anthocyanins (keracyanin chloride (cyanidin 3-O-rutinoside chloride), cyanidin 3-O-glucoside, tulipanin (delphinidin 3-rutinoside chloride), idaein chloride (cyanidin 3-O-galactoside), and cyanidin chloride) were found to be higher in purple taro leafstalk compared to green taro leafstalk. Furthermore, a total of 3330 differentially expressed genes (DEGs) were identified by transcriptome analysis. Subsequently, the correlation network analysis was performed to investigate the relationship between the expression levels of these differentially expressed genes and the content of anthocyanin. There were 18 DEGs encoding nine enzymes detected as the fundamental structural genes contributing to anthocyanin biosynthesis, along with seven transcription factors (3 MYB and 4 bHLH) that may be promising candidate modulators of the anthocyanin biosynthesis process in purple taro leafstalk. The findings of the current investigation not only provide a comprehensive transcriptional code, but also give information on anthocyanin metabolites as well as beneficial insights into the colour mechanism of purple taro leafstalk.

Dynamic metabolite QTL analyses provide novel biochemical insights into kernel development and nutritional quality improvement in common wheat.

Despite recent advances in crop metabolomics, the genetic control and molecular basis of the wheat kernel metabolome at different developmental stages remain largely unknown. Here, we performed widely targeted metabolite profiling of kernels from three developmental stages (grain-filling kernels [FKs], mature kernels [MKs], and germinating kernels [GKs]) using a population of 159 recombinant inbred lines. We detected 625 annotated metabolites and mapped 3173, 3143, and 2644 metabolite quantitative trait loci (mQTLs) in FKs, MKs, and GKs, respectively. Only 52 mQTLs were mapped at all three stages, indicating the high stage specificity of the wheat kernel metabolome. Four candidate genes were functionally validated by in vitro enzymatic reactions and/or transgenic approaches in wheat, three of which mediated the tricin metabolic pathway. Metabolite flux efficiencies within the tricin pathway were evaluated, and superior candidate haplotypes were identified, comprehensively delineating the tricin metabolism pathway in wheat. Finally, additional wheat metabolic pathways were re-constructed by updating them to incorporate the 177 candidate genes identified in this study. Our work provides new information on variations in the wheat kernel metabolome and important molecular resources for improvement of wheat nutritional quality.