Metabolic Interactions Research Articles

Saccharomyces boulardii enhances anti-inflammatory effectors and AhR activation via metabolic interactions in probiotic communities.

Metabolic exchanges between strains in gut microbial communities shape their composition and interactions with the host. This study investigates the metabolic synergy between potential probiotic bacteria and Saccharomyces boulardii, aiming to enhance anti-inflammatory effects within a multi-species probiotic community. By screening a collection of 85 potential probiotic bacterial strains, we identified two strains that demonstrated a synergistic relationship with S. boulardii in pairwise co-cultivation. Furthermore, we computationally predicted cooperative communities with symbiotic relationships between S. boulardii and these bacteria. Experimental validation of 28 communities highlighted the role of S. boulardii as a key player in microbial communities, significantly boosting the community's cell number and production of anti-inflammatory effectors, thereby affirming its essential role in improving symbiotic dynamics. Based on our observation, one defined community significantly activated the aryl hydrocarbon receptor-a key regulator of immune response-280-fold more effectively than the community without S. boulardii. This study underscores the potential of microbial communities for the design of more effective probiotic formulations.

Metabolic interactions of host-gut microbiota: New possibilities for the precise diagnosis and therapeutic discovery of gastrointestinal cancer in the future-a review.

Gastrointestinal (GI) cancer continues to pose a significant global health challenge. Recent advances in our understanding of the complex relationship between the host and gut microbiota have shed light on the critical role of metabolic interactions in the pathogenesis and progression of GI cancer. In this study, we examined how microbiota interact with the host to influence signalling pathways that impact the formation of GI tumours. Additionally, we investigated the potential therapeutic approach of manipulating GI microbiota for use in clinical settings. Revealing the complex molecular exchanges between the host and gut microbiota facilitates a deeper understanding of the underlying mechanisms that drive cancer development. Metabolic interactions hold promise for the identification of microbial signatures or metabolic pathways associated with specific stages of cancer. Hence, this study provides potential strategies for the diagnosis, treatment and management of GI cancers to improve patient outcomes.

Molecular patterns of microbial and metabolic interactions in septic patients with persistent lymphopenia.

Persistent lymphopenia can be regarded as an important index of acquired immune dysfunction in sepsis. Whether the specific immune factor changes in septic patients with lymphopenia and the correlation to gut microbiota and metabolites remain unclear. This single-center prospective observation conducted lymphocyte subgroup analysis of blood samples and 16S rRNA gene amplicons sequencing and untargeted metabolomics analysis of fecal samples from 36 subjects with the persistent (≥3d) (n=21) and non-persistent lymphopenia (<3d) (n=15). The persistent lymphopenia showed higher the 28d mortality and 90d mortality, while significantly lower CD3+T/LY, CD3+T cells, CD3+CD4+T cells, CD3+CD8+T cells, Th1 cells, Th2 cells, CD45RA+Treg cells. The 16S rRNA results showed that Staphylococcus, Peptostreptococcus, Bulleidia, Leuconostoc were significant enriched in the persistent lymphopenia. The metabolomics analysis showed that α-Ketoisovaleric acid was increased and 7-DHCA, α-MCA, β-MCA, HCA, LCA-3S, CA, UCA and Citramalic acid were decreased in the persistent lymphopenia. In the process of interaction between host receptors and gut microbiota in patients with persistent lymphopenia sepsis, with a significant reduction in gut microbiota diversity and bile acid metabolites. That can affect various inflammatory pathways of gut immune cells, causing immune dysfunction in the body, which may be one of the main causes of death.

Medicinal Plants Approach for Diabetes Mellitus-A Computational Model

The multidimensional metabolic syndrome that includes diabetes mellitus poses a serious threat to world health. There is an increasing interest in researching herbal remedies for their possible therapeutic advantages, even as traditional allopathic treatments continue to be widely used. This work throws light on the multiple ways of metabolism and biochemical interactions of medicinal plants in the control of glucose level, highlighting their crucial role in the process. The work clarifies several herbal extracts' efficacy and safety profiles, such as Aloe vera, Garlic, Gurmar, Bitter Melon, Neem, Tulsi, and through a thorough literature review and empirical evidence. These plants, which are abundant in bioactive substances like tannins, flavonoids, and alkaloids, show promise in treating insulin resistance, improving pancreatic function, and controlling blood sugar level. A further assessment of the rising risk associated with diabetes mellitus is discussed, and a differential equation model for diabetes mellitus is developed to minimize the complications. When using medicinal plants to treat diabetes, several factors are considered, including blood sugar level, sugar intake activity, and plasma insulin concentrations. The stability criterion for the mathematical model is examined through the system of differential equations. A representation highlighting the medicinal plants that can aid individuals with diabetes mellitus is provided. The blood sugar level, insulin generalization variable and plasma insulin concentration have all been measured at different points in time. Aloe vera, Gurmar, Garlic, Tulsi, Bitter Melon and Neem are among the medicinal plants selected for their demonstrated anti-hyperglycemic properties due to their easy availability in India. Mathematical solutions were calculated for every plant and proved to be steady.

Bacterial community dynamics as a result of growth-yield trade-off and multispecies metabolic interactions toward understanding the gut biofilm niche

Bacterial communities are ubiquitous, found in natural ecosystems, such as soil, and within living organisms, like the human microbiome. The dynamics of these communities in diverse environments depend on factors such as spatial features of the microbial niche, biochemical kinetics, and interactions among bacteria. Moreover, in many systems, bacterial communities are influenced by multiple physical mechanisms, such as mass transport and detachment forces. One example is gut mucosal communities, where dense, closely packed communities develop under the concurrent influence of nutrient transport from the lumen and fluid-mediated detachment of bacteria. In this study, we model a mucosal niche through a coupled agent-based and finite-volume modeling approach. This methodology enables us to model bacterial interactions affected by nutrient release from various sources while adjusting individual bacterial kinetics. We explored how the dispersion and abundance of bacteria are influenced by biochemical kinetics in different types of metabolic interactions, with a particular focus on the trade-off between growth rate and yield. Our findings demonstrate that in competitive scenarios, higher growth rates result in a larger share of the niche space. In contrast, growth yield plays a critical role in neutralism, commensalism, and mutualism interactions. When bacteria are introduced sequentially, they cause distinct spatiotemporal effects, such as deeper niche colonization in commensalism and mutualism scenarios driven by species intermixing effects, which are enhanced by high growth yields. Moreover, sub-ecosystem interactions dictate the dynamics of three-species communities, sometimes yielding unexpected outcomes. Competitive, fast-growing bacteria demonstrate robust colonization abilities, yet they face challenges in displacing established mutualistic systems. Bacteria that develop a cooperative relationship with existing species typically obtain niche residence, regardless of their growth rates, although higher growth yields significantly enhance their abundance. Our results underscore the importance of bacterial niche dynamics in shaping community properties and succession, highlighting a new approach to manipulating microbial systems.

Sulcardine, an investigational multichannel blocker for atrial fibrillation with uniquely intense JTp inhibition to enhance safety

Abstract Background Sulcardine is a multi-ion channel blocker for treating atrial fibrillation (AF) with similar inhibitory potency on peak/late sodium, L-type calcium, and rapidly activating delayed-rectifier potassium (IKr) currents. The therapeutic effect of inhibiting IKr includes QT prolongation (accompanied by a proportional J to T peak [JTp] interval prolongation), which, when excessive, may cause malignant arrhythmias. As previously reported, sulcardine strongly mitigates the JTp increase that may contribute to excessive QT prolongation by inhibiting late sodium and L-type calcium currents, an intrinsic mechanism to protect from excessive IKr block. Purpose We explored the JTp mechanism, pharmacokinetics (PK), electrocardiogram (ECG) changes, safety, and drug-drug interaction (DDI) of a single sulcardine (as HBI-3000) intravenous (IV) infusion in healthy subjects. Methods An open-label Phase 1 study evaluated the PK interaction between sulcardine, dosed as a 350 mg 30-minute infusion of the sulfate salt HBI-3000, and a cytochrome P450 CYP2D6 enzyme inhibitor (paroxetine). Triplicate ECGs were extracted from continuous 12-lead Holter ECG recordings at baseline and 10 time points thereafter. Mean baseline-subtracted and heart rate-corrected QTcF (dQTcF), JTpc (dJTpc), and other ECG changes were calculated at each time point. Left ventricular ejection fraction (LVEF) was assessed at baseline, 30-, and 120-minutes post-infusion start by transthoracic echocardiography (TTE). Results 39 subjects received HBI-3000, 33 in the paired PK population. Expected ECG parameter changes proportional to sulcardine plasma concentrations were observed in the HBI-3000-alone portion of the study, including modest QTcF prolongation (Fig 1, blue slope, confidence band) and JTpc inhibition (Fig 1, red). The usual JTpc increase associated with IKr blockers, even those with mitigating channel block (such as the late sodium current), was not only reduced but actually reversed. We calculated the dQTcF change that would have been observed in the absence of JTpc reduction by sulcardine (Fig 1, green). Sulcardine reduced the QTcF increase that might have occurred by about 21 msec at the highest concentrations, and this mitigating effect was observed at all concentrations. No clinically significant safety findings or arrhythmias were observed. The most frequent TEAEs were nausea and headache (n=2 each [5.1%]). No LVEF changes were clinically significant. Sulcardine had minimal metabolic interactions. Conclusion Acute IV HBI-3000 administration resulted in no clinically significant safety findings, LVEF depression, or DDI with CYP2D6. Sulcardine facilitates QT prolongation, which has a therapeutic benefit in terminating AF, with simultaneous uncoupling and reversing of the JTpc interval increase, a potential mechanism for reducing proarrhythmic risk due to excessive QT prolongation.Figure 1.ECG Intervals vs Sulcardine Conc

Understanding disease-associated metabolic changes in human colon epithelial cells using i ColonEpithelium metabolic reconstruction.

The colon epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colon epithelium, our present study focuses on the generation of the first cell-type specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium. GEMs are powerful tools for exploring reactions and metabolites at systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. The iColonEpithelium is also capable of performing metabolic tasks specific to the cell type. A unique transport reaction compartment has been included to allow simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease. We integrated single-cell RNA sequencing data from Crohn's Diseases (CD) and ulcerative colitis (UC) samples with the iColonEpithelium metabolic network to predict metabolic signatures of colonocytes between CD and UC compared to healthy samples. We identified reactions in nucleotide interconversion, fatty acid synthesis and tryptophan metabolism were differentially regulated in CD and UC conditions, which were in accordance with experimental results. The iColonEpithelium metabolic network can be used to identify mechanisms at the cellular level, and our network has the potential to be integrated with gut microbiome models to explore the metabolic interactions between host and gut microbiota under various conditions.

Integration of transcriptomics and machine learning for insights into breast cancer: exploring lipid metabolism and immune interactions

BackgroundBreast cancer (BRCA) represents a substantial global health challenge marked by inadequate early detection rates. The complex interplay between the tumor immune microenvironment and fatty acid metabolism in BRCA requires further investigation to elucidate the specific role of lipid metabolism in this disease.MethodsWe systematically integrated nine machine learning algorithms into 184 unique combinations to develop a consensus model for lipid metabolism-related prognostic genes (LMPGS). Additionally, transcriptomics analysis provided a comprehensive understanding of this prognostic signature. Using the ESTIMATE method, we evaluated immune infiltration among different risk subgroups and assessed their responsiveness to immunotherapy. Tailored treatments were screened for specific risk subgroups. Finally, we verified the expression of key genes through in vitro experiments.ResultsWe identified 259 differentially expressed genes (DEGs) related to lipid metabolism through analysis of the cancer genome atlas program (TCGA) database. Subsequently, via univariate Cox regression analysis and C-index analysis, we developed an optimal machine learning algorithm to construct a 21-gene LMPGS model. We used optimal cutoff values to divide the lipid metabolism prognostic gene scores into two groups according to high and low scores. Our study revealed distinct biological functions and mutation landscapes between high-scoring and low-scoring patients. The low-scoring group presented a greater immune score, whereas the high-scoring group presented enhanced responses to both immunotherapy and chemotherapy drugs. Single-cell analysis highlighted significant upregulation of CPNE3 in epithelial cells. Moreover, by employing molecular docking, we identified niclosamide as a potential targeted therapeutic drug. Finally, our experiments demonstrated high expression of MTMR9 and CPNE3 in BRCA and their significant correlation with prognosis.ConclusionBy employing bioinformatics and diverse machine learning algorithms, we successfully identified genes associated with lipid metabolism in BRCA and uncovered potential therapeutic agents, thereby offering novel insights into the mechanisms and treatment strategies for BRCA.

Mixed and membrane-separated culturing of synthetic cyanobacteria-yeast consortia reveals metabolic cross-talk mimicking natural cyanolichens

Metabolite exchange mediates crucial interactions in microbial communities, significantly impacting global carbon and nitrogen cycling. Understanding these chemically-mediated interactions is essential for elucidating natural community functions and developing engineered synthetic communities. This study investigated membrane-separated bioreactors (mBRs) as a novel tool to identify transient metabolites and their producers/consumers in mixed microbial communities. We compared three co-culture methods (direct mixed, 2-chamber mBR, and 3-chamber mBR) to grow a synthetic binary community of the cyanobacterium Synechococcus elongatus PCC 7942 and the fungus Rhodotorula toruloides NBRC 0880, as well as axenic S. elongatus. Despite not being natural lichen constituents, these organisms exhibited interactions resembling those in cyanolichens. S. elongatus fixed CO2 into sugars as the primary shared metabolite, while R. toruloides secreted various biochemicals, predominantly sugar alcohols, mirroring the metabolite exchange observed in natural lichens. The mBR systems successfully captured metabolite gradients and revealed rapidly consumed compounds, including TCA cycle intermediates and amino acids. Our approach demonstrated that the 2-chamber mBR optimally balanced metabolite exchange and growth dynamics. This study provides insights into cross-species metabolic interactions and presents a valuable tool for investigating and engineering synthetic microbial communities with potential applications in biotechnology and environmental science.

Microbial metabolomics in acute myeloid leukemia: From pathogenesis to treatment.

Acute myeloid leukemia (AML), the most common leukemia in adults, is a biologically heterogeneous disease arising from clonally proliferating hematopoietic stem cells. Increased appreciation of novel genetic methods has improved the understanding of AML biology. Recently, the emerging field of metabolomics has indicated qualitative and quantitative alterations in metabolic profiles in AML pathogenesis, progression and treatment. Multiple metabolic and molecular pathways regulate human metabolism and host-microbiome interactions may significantly affect this biochemical machinery. Microbiota have been found to play a significant role in hematopoietic function, metabolism and immunity, contributing to AML occurrence. A large number of studies have highlighted the importance of the composition and diversity of the gut microbiota (GM) in response to treatment and prognosis in AML. Moreover, strong evidence emphasizes the detrimental link between dysbiosis and infectious complications, a leading cause of morbidity and mortality for patients with AML. Several microbiota-related mechanisms have been linked to particular changes in host physiology so far, and microbial-derived metabolites belong to one of the most important. Circulating in the body, they modulate human conditions both locally and systemically. The extensive and diverse repertoire of bacterial metabolic functions plays a critical role in numerous processes, including leukemogenesis. Integrative analysis of microbiome and metabolome data is a promising avenue for better understanding the complex relationship between the microbiota, biochemical alterations and AML pathogenesis to effectively prevent, treat and mitigate its outcomes. This review concentrates on the pathologic roles and therapeutic implications of microbe-derived metabolites in AML settings.

Construction of a synthetic anaerobic dechlorination microbiome to degrade chlorinated ethenes by application of metabolic interactions principle

Bioaugmentation is a bioremediation approach to treat groundwater contaminated with chlorinated ethenes, but currently it faces challenges such as poor microbiome stability and effectiveness, due to blind species integration and metabolic inhibition. The objective of this study was to create a controllable and functionally stable microbial community for dichlorination application. For this, we utilized targeted screening to identify dechlorinating bacteria from contaminated groundwater, that in combination would form an anaerobic dechlorination microbial community with stabilizing metabolic interactions between the constituents. The results showed that two organohalide-respiring bacterial (OHRB) species were isolated, and these were identified as Enterobacter bugandensis X4 and Enterobacter sichuanensis C4. Upon co-cultivation with lactic acid as the carbon source, the strains demonstrated metabolic interactions and synergistic dehalogenation ability towards trichloroethene (TCE). It was further demonstrated that the functional microbiome constructed with the strains was stable over time and exhibited a robust TCE degradation rate of 80.85% at 13.13 mg/L TCE within 10 days. Additionally, the complete conversion of TCE was achieved through microbiome bioaugmentation, this augmented microbiome increased the degradation rate towards 52.55 mg/L TCE by approximately 30% within 6 days. Additionally, bioaugmentation stimulated the growth of indigenous OHRB, such as Dehalobacter and Desulfovibrio. It also promoted a positive succession of the microbial community. These findings offer valuable insights into the microbial remediation of chlorinated ethenes-contaminated groundwater and offers novel ideas for the construction of an artificial functional microbiome.

Clotrimazole-induced shifts in vaginal bacteriome and lipid metabolism: insights into recovery mechanisms in vulvovaginal candidiasis.

Vulvovaginal candidiasis (VVC) is a prevalent condition affecting a significant proportion of women worldwide, with recurrent episodes leading to detrimental effects on quality of life. While treatment with clotrimazole is common, the specific alterations it evokes in the vaginal bacteriome and metabolome were previously underexplored. In this prospective study, we enrolled reproductive-age women diagnosed with single VVC and conducted comprehensive analyses of vaginal fungi, bacteriome, and metabolome before and after local clotrimazole treatment. We observed a significant reduction in Candida albicans and notable improvements in vaginal cleanliness. Advanced sequencing revealed substantial shifts in the vaginal bacteriome, with an increase in Lactobacillus-dominant communities post-treatment. Our findings identified 17 differentially abundant bacterial species, including notable decreases in pathogenic anaerobes such as Gardnerella vaginalis, Dialister micraerophilus, and Aerococcus christensenii, suggesting a restoration of a healthier microbial balance. Furthermore, metabolomic analysis revealed significant changes in 230 metabolites, particularly within lipid metabolism pathways, with marked downregulation of lipid-related compounds linked to inflammation. Correlation studies indicated a strong interplay between lipid metabolites and specific bacterial species, emphasizing the influence of clotrimazole treatment on microbial and metabolic interactions. Importantly, predictive models using microbiota and metabolite signatures demonstrated high accuracy in distinguishing pre- and post-treatment states. This research highlights clotrimazole's dual role in effectively clearing Candida infection and promoting a healthier vaginal microenvironment, paving the way for novel microbial and metabolomic-based diagnostic approaches to enhance VVC management and understand its underlying mechanisms.

A metabolic modeling-based framework for predicting trophic dependencies in native rhizobiomes of crop plants.

The exchange of metabolites (i.e., metabolic interactions) between bacteria in the rhizosphere determines various plant-associated functions. Systematically understanding the metabolic interactions in the rhizosphere, as well as in other types of microbial communities, would open the door to the optimization of specific predefined functions of interest, and therefore to the harnessing of the functionality of various types of microbiomes. However, mechanistic knowledge regarding the gathering and interpretation of these interactions is limited. Here, we present a framework utilizing genomics and constraint-based modeling approaches, aiming to interpret the hierarchical trophic interactions in the soil environment. 243 genome scale metabolic models of bacteria associated with a specific disease-suppressive vs disease-conducive apple rhizospheres were drafted based on genome-resolved metagenomes, comprising an in silico native microbial community. Iteratively simulating microbial community members' growth in a metabolomics-based apple root-like environment produced novel data on potential trophic successions, used to form a network of communal trophic dependencies. Network-based analyses have characterized interactions associated with beneficial vs non-beneficial microbiome functioning, pinpointing specific compounds and microbial species as potential disease supporting and suppressing agents. This framework provides a means for capturing trophic interactions and formulating a range of testable hypotheses regarding the metabolic capabilities of microbial communities within their natural environment. Essentially, it can be applied to different environments and biological landscapes, elucidating the conditions for the targeted manipulation of various microbiomes, and the execution of countless predefined functions.

Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters.

The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory. The metabolic model encompassed three major microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB). Our findings revealed a shift from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increased, along with a decline in MFC performance. The mmGEM accurately predicted microbial relative abundance at low OLRs (L-OLRs) and adaptation to high OLRs (H-OLRs). The simulations revealed constraints on SOB growth under H-OLRs due to reduced sulfate-sulfide (S) cycling and acetate cross-feeding with SRB. More cross-fed metabolites from SRB were diverted to MET, facilitating their competitive dominance. Assessing cross-feeding dynamics under varying OLRs enabled the execution of practical scenario-based simulations to explore the potential impact of elevated acidity levels on SOB growth and MFC performance. This work highlights the role of metabolic cross-feeding in shaping microbial community structure in response to high OLRs. The insights gained will inform the development of effective strategies for implementing MFC technology in real-world industrial environments.

Neodymium-Facilitated Visualization of Extreme Phosphate Accumulation in Fibroblast Filopodia: Implications for Intercellular and Cell–Matrix Interactions

A comprehensive understanding of intercellular and cell–matrix interactions is essential for advancing our knowledge of cell biology. Existing techniques, such as fluorescence microscopy and electron microscopy, face limitations in resolution and sample preparation. Supravital lanthanoid staining provides new opportunities for detailed visualization of cellular metabolism and intercellular interactions. This study aims to describe the structure, elemental chemical, and probable origin of zones of extreme lanthanoid (neodymium) accumulation that form during preparation for scanning electron microscopy (SEM) analysis in corneal fibroblasts filopodia. The results identified three morphological patterns of neodymium staining in fibroblast filopodia, each exhibiting asymmetric staining within a thin, sharp, and extremely bright barrier zone, located perpendicular to the filopodia axis. Semi-quantitative chemical analyses showed neodymium-labeled non-linear phosphorus distribution within filopodia, potentially indicating varying phosphate anion concentrations and extreme phosphate accumulation at a physical or physicochemical barrier. Phosphorus zones labeled with neodymium did not correspond to mitochondrial clusters. During apoptosis, the number of filopodia with extreme and asymmetric phosphorus accumulation increases. Supravital lanthanoid staining coupled with SEM allows detailed visualization of intercellular and cell–matrix interactions with high contrast and resolution. These results enhance our understanding of phosphate anion accumulation and transfer mechanisms in cells under normal conditions and during apoptosis.

Carotenoid-carbohydrate crosstalk: evidence for genetic and physiological interactions in storage tissues across crop species.

Carotenoids play essential roles in photosynthesis, photoprotection, and human health. Efforts to increase carotenoid content in several staple crops have been successful through both conventional selection and genetic engineering methods. Interestingly, in some cases, altering carotenoid content has had unexpected effects on other aspects of plant metabolism, impacting traits like sugar content, dry matter percentage, fatty acid content, stress tolerance, and phytohormone concentrations. Studies across several diverse crop species have identified negative correlations between carotenoid and starch contents, as well as positive correlations between carotenoids and soluble sugars. Collectively, these reports suggest a metabolic interaction between carotenoids and carbohydrates. We synthesize evidence pointing to four hypothesized mechanisms: (1) direct competition for precursors; (2) physical interactions in plastids; (3) influences of sugar or apocarotenoid signaling networks; and (4) nonmechanistic population or statistical sources of correlations. Though the carotenoid biosynthesis pathway is well understood, the regulation and interactions of carotenoids, especially in nonphotosynthetic tissues, remain unclear. This topic represents an underexplored interplay between primary and secondary metabolism where further research is needed.

Glucagon, Metabolic Dysfunction-Associated Steatotic Liver Disease and Amino Acids in Humans and Animals without Diabetes Mellitus—An Evidence Map

Introduction: Health systems are confronted with not only the growing worldwide childhood obesity epidemic but also associated comorbidities. These subsequently cause variations in distinct metabolic pathways, leading to metabolic dysfunction-associated steatotic liver disease (MASLD). The aim of this evidence map is to systematically evaluate the evidence and to identify research gaps on glucagon-induced amino acid (AA) turnover and its metabolic interaction with MASLD. Methodology: A systematic literature search was conducted up to April 2023 in three electronic databases. Studies were required to include at least two of the main research areas, glucagon, AA metabolism and MASLD. Two independent reviewers screened titles and abstracts according to prespecified eligibility criteria, as well as full-text articles. Results are summarized in tables stratified by human and animal studies and study population age. Results: Thirty-four references were ultimately included. The publication years dated back to 1965 showed a great increase from 2012 to 2023. In total, there were 19 animal studies and 15 human studies. Among the human studies, except for two studies in adolescents, all the studies were conducted in adults. In human studies, the methods used to evaluate metabolic changes differed among hyperinsulinemic-euglycemic clamp and oral glucose tolerance tests. Thirteen studies focused on the metabolic effects of MASLD, while only two studies explored the interaction between MASLD, glucagon and AA metabolism in humans. The other 19 studies focused on metabolomics, beta cell function or just one topic of a research area and not on interactions between one another. Conclusion: Research on the interaction between MASLD, glucagon and AA metabolism in humans is sparse and complete lacking in pediatrics. Furthermore, longitudinal studies with a focus on hyperglucagonemia independent of diabetes but related to MASLD present an unambiguous research gap.

A metagenomic approach to demystify the anaerobic digestion black box and achieve higher biogas yield: a review.

The increasing reliance on fossil fuels and the growing accumulation of organic waste necessitates the exploration of sustainable energy alternatives. Anaerobic digestion (AD) presents one such solution by utilizing secondary biomass to produce biogas while reducing greenhouse gas emissions. Given the crucial role of microbial activity in anaerobic digestion, a deeper understanding of the microbial community is essential for optimizing biogas production. While metagenomics has emerged as a valuable tool for unravelling microbial composition and providing insights into the functional potential in biodigestion, it falls short of interpreting the functional and metabolic interactions, limiting a comprehensive understanding of individual roles in the community. This emphasizes the significance of expanding the scope of metagenomics through innovative tools that highlight the often-overlooked, yet crucial, role of microbiota in biomass digestion. These tools can more accurately elucidate microbial ecological fitness, shared metabolic pathways, and interspecies interactions. By addressing current limitations and integrating metagenomics with other omics approaches, more accurate predictive techniques can be developed, facilitating informed decision-making to optimize AD processes and enhance biogas yields, thereby contributing to a more sustainable future.

Investigation of ponatinib metabolism and drug-drug interactions with lycopene and shikonin in vitro and invivo

Ponatinib is approved for use in patients with chronic myeloid leukemia (CML) who are resistant to or intolerant to prior tyrosine kinase inhibitor (TKI) therapy. Given that ponatinib can induce significant cardiotoxicity when taken, and that most Chinese medicines have cardioprotective effects, it is possible to administer them in combination in clinic to alleviate adverse effects. The quantitative determination of ponatinib and its metabolite N-desmethyl ponatinib was optimized and fully verified by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). And the drug-drug interactions (DDI) of ponatinib with lycopene and shikonin, both in vivo and in vitro, were studied. The results of bioanalytical methodology showed that ponatinib and N-desmethyl ponatinib had good linearity in plasma samples, and their selectivity, accuracy, precision, stability, matrix effect and recovery were all satisfied with the need of quantitative analysis of samples. In animal experiments, compared with the control group, lycopene and shikonin significantly changed the pharmacokinetic parameters of ponatinib, including AUC(0-t), AUC(0-∞) and CLz/F, while having no effect on the pharmacokinetic parameters of N-desmethyl ponatinib. In vitro interaction studies indicated that lycopene showed mixed inhibition mechanism on ponatinib metabolism in both rat liver microsomes (RLM) and human liver microsomes (HLM). And, shikonin displayed mixed inhibition mechanism in RLM and competitive inhibition mechanism in HLM, respectively. In summary, the UPLC-MS/MS method can accurately and sensitively quantify ponatinib and N-desmethyl ponatinib, and provide further reference for clinical drug combination between ponatinib and lycopene or shikonin.

Reply to the Letter to the Editor Concerning Metabolic and Pharma-cological Interactions of 131I-MIBG.

Reply to the Letter to the Editor Concerning Metabolic and Pharma-cological Interactions of 131I-MIBG.