Metabolic Interactions Research Articles

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

BackgroundPersistent 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. MethodsThis 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). ResultsThe 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. ConclusionIn 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.

Network structure and fluctuation data improve inference of metabolic interaction strengths with the inverse Jacobian

Based on high-throughput metabolomics data, the recently introduced inverse differential Jacobian algorithm can infer regulatory factors and molecular causality within metabolic networks close to steady-state. However, these studies assumed perturbations acting independently on each metabolite, corresponding to metabolic system fluctuations. In contrast, emerging evidence puts forward internal network fluctuations, particularly from gene expression fluctuations, leading to correlated perturbations on metabolites. Here, we propose a novel approach that exploits these correlations to quantify relevant metabolic interactions. By integrating enzyme-related fluctuations in the construction of an appropriate fluctuation matrix, we are able to exploit the underlying reaction network structure for the inverse Jacobian algorithm. We applied this approach to a model-based artificial dataset for validation, and to an experimental breast cancer dataset with two different cell lines. By highlighting metabolic interactions with significantly changed interaction strengths, the inverse Jacobian approach identified critical dynamic regulation points which are confirming previous breast cancer studies.

Synthetic Ecosystems: From the Test Tube to the Biosphere.

The study of ecosystems, both natural and artificial, has historically been mediated by population dynamics theories. In this framework, quantifying population numbers and related variables (associated with metabolism or biological-environmental interactions) plays a central role in measuring and predicting system-level properties. As we move toward advanced technological engineering of cells and organisms, the possibility of bioengineering ecosystems (from the gut microbiome to wildlands) opens several questions that will require quantitative models to find answers. Here, we present a comprehensive survey of quantitative modeling approaches for managing three kinds of synthetic ecosystems, sharing the presence of engineered strains. These include test tube examples of ecosystems hosting a relatively low number of interacting species, mesoscale closed ecosystems (or ecospheres), and macro-scale, engineered ecosystems. The potential outcomes of synthetic ecosystem designs and their limits will be relevant to different disciplines, including biomedical engineering, astrobiology, space exploration and fighting climate change impacts on endangered ecosystems. We propose a space of possible ecosystems that captures this broad range of scenarios and a tentative roadmap for open problems and further exploration.

Uncovering metabolic signatures in cancer-derived exosomes: LC-MS/MS and NMR profiling.

Understanding the intricate interplay between cancer metabolism and intercellular communication within the tumour microenvironment (TME) is crucial for advancing cancer diagnostics and therapeutics. In this study, we investigate the metabolites present in exosomes derived from three distinct cancer cell lines: pancreatic cancer (MiaPaCa-2), lung cancer (A549), and glioma (C6). Exosomes were isolated using ultrafiltration and characterized using a combination of techniques including nanoparticle tracking analysis (NTA), electron microscopy (EM), western blotting (WB) and Fourier-transform infrared (FTIR) spectroscopy. Leveraging state-of-the-art metabolomics techniques, including untargeted LC-MS/MS and NMR analyses, we elucidated the metabolic signatures encapsulated within cancer-derived exosomes. Notably, our investigation represents the first exploration of exosomal metabolites from pancreatic and glioma cells, addressing a significant gap in current knowledge. Furthermore, our study investigates the correlation between metabolites derived from different cancer cells, shedding light on potential metabolic interactions within the TME. Through comprehensive analyses, this study provides insights into dysregulated metabolic pathways driving cancer progression and offers novel perspectives on the diagnostic and therapeutic utility of exosomal metabolites. Importantly, common metabolites identified among cancer types suggest potential markers detectable by multiple techniques, enhancing their clinical applicability.

OsbHLH5 Synergically Regulates Phenolamide and Diterpenoid Phytoalexins Involved in the Defense of Rice Against Pathogens.

Rice (Oryza sativa) produces phenolamides and diterpenoids as major phytoalexins. Although the biosynthetic pathways of phenolamides and diterpenoids in plants have been revealed, knowledge of their accumulation regulatory mechanisms remains limited, and, in particular, no co-regulatory factor has been identified to date. Here, using a combined co-expression and evolutionary analysis, we identified the basic helix-loop-helix (bHLH) transcription factor OsbHLH5 as a positive bifunctional regulator of phenolamide and diterpenoid biosynthesis in rice. Metabolomic analysis revealed that OsbHLH5 significantly increased the content of phenolamides (such as feruloyl tryptamine (Fer-Trm) and p-coumaroyl tyramine (Cou-Tyr)) and diterpenoid phytoalexins (such as momilactones A, momilactones B) in the overexpression lines, while their content was reduced in the OsbHLH5 knockout lines. Gene expression and dual-luciferase assays revealed that OsbHLH5 activates phenolamide biosynthetic genes (including putrescine hydroxycinnamoyltransferase 3 (OsPHT3), tyramine hydroxycinnamoyltransferases 1/2 (OsTHT1/2), and tryptamine benzoyltransferase 2 (OsTBT2)) as well as diterpenoid biosynthetic genes (including copalyl diphosphate synthase 4 (OsCPS4) and kaurene synthase-like 4/7/10/11 (OsKSL4/7/10/11)). Furthermore, we have demonstrated that OsbHLH5 is induced by jasmonic acid (JA), while pathogen inoculation assays indicated that the overexpression of OsbHLH5 in transgenic rice plants leads to enhanced resistance to Xanthomonas oryzae pv. oryzae (Xoo). Overall, we have identified a positive regulator of phenolamide and diterpenoid biosynthesis and have demonstrated that biotic stress induces phytoalexin accumulation partly in an OsbHLH5-dependent manner, providing new insights into the metabolic interactions involved in pathogen response and offering valuable gene resources for the development, through genetic improvement, of new rice varieties that are resistant to diseases.

Understanding Antibiotic Influence on Medical Test Outcomes: A Systematic Review

Objective: This systematic review examines the effects of antibiotic administration on the accuracy of medical test outcomes, aiming to provide healthcare professionals with insights into the diagnostic challenges posed by antibiotic interference. Methods: A comprehensive literature search was conducted across databases including PubMed, Scopus, and Web of Science, covering studies published between 2016 and 2024. Studies were selected based on their focus on antibiotic interactions with diagnostic tests in clinical settings, with strict inclusion and exclusion criteria applied. Data were extracted from relevant studies, focusing on specific medical tests affected, types of antibiotics involved, and outcomes observed. Quality assessment was conducted using PRISMA guidelines to ensure the reliability of included studies. Results: The review identified multiple instances where antibiotics influenced the accuracy of key diagnostic tests. For example, antibiotics commonly impacted blood culture results, leading to false negatives in detecting infections. Urinalysis and inflammatory marker tests were also affected, often resulting in reduced sensitivity and specificity. Liver function and renal function tests showed variability in results due to certain antibiotics, potentially complicating patient assessment. Mechanistic insights suggest that antibiotic effects on bacterial growth, immune modulation, and metabolic interactions are primary contributors to altered diagnostic results. Conclusion: Antibiotic administration can significantly interfere with various diagnostic tests, posing risks to accurate diagnosis and timely intervention. These findings highlight the importance of obtaining a thorough medication history and considering potential test interference in clinical decision-making. Further research is recommended to develop guidelines for minimizing diagnostic inaccuracies due to antibiotic influence.

TMIC-52. DECODING METABOLIC INTERACTIONS IN GLIOBLASTOMA TUMOR MICROENVIRONMENT THROUGH COMPREHENSIVE SPATIAL TRANSCRIPTOMIC AND PROTEOMIC ANALYSIS

Abstract INTRODUCTION Within the GBM microenvironment, distinct micro-niches arise from independent cancer cell lineages with unique metabolic needs, characterized as Fast Cycling Cells (FCCs) and Slow Cycling Cells (SCCs). FCCs preferentially utilize aerobic glycolysis for energy, whereas treatment-resistant SCCs depend on lipid metabolism. Our study decodes the molecular and spatial heterogeneity within the GBM microenvironment, focusing on the dichotomy of immune infiltrates and the metabolic interactions between SCCs and the immune compartment in human GBM patients. METHODS We performed spatial transcriptomics and proteomics combined with geospatially resolved single-cell neighborhood analysis of IHC-stained GBM tissues using CellProfiler and the SNAQ algorithm that we developed to investigate the specific immune microenvironment of SCCs and FCCs. Holotomography and fluorescence-based time-lapse imaging coupled with flow cytometry were used to study lipid transfer between immune cells and GBM cells. Finally, the effects of pharmacological and genetic targeting of lipid transfer were investigated both in vitro and in vivo. RESULTS Our study establishes a connection between spatial metabolic heterogeneity and immune diversity, highlighting how specific tumor cell lineages correlate with characteristic immune micro-niches. In particular, SCCs exploit the metabolic properties of recruited immunosuppressive myeloid-derived cells, which metabolically support tumor cells by facilitating lipid transport and supporting SCC metabolism. Our results show that inhibiting lipid transfer affect SCCs, remodels the immune microenvironment, delays tumor growth and improves disease outcome. Our study also demonstrates a correlation between predicted sensitivity to lipid-modifying drugs, such as statins, and the SCC phenotype. This correlation could enable stratification and selection of patients most likely to benefit from such treatments. CONCLUSIONS This study establishes critical connections between the TME, cellular metabolism, anti-tumor immunity, and treatment vulnerabilities. Our research provides novel insights into how metabolic exchanges contribute to tumor progression and suggests that targeting metabolic interactions could be an effective therapeutic strategy for GBM.

Microbial metabolism disrupts cytokine activity to impact host immune response

Host-pathogen interactions are shaped by the metabolic status of both the host and pathogen. The host must regulate metabolism to fuel the immune response, while the pathogen must extract metabolic resources from the host to enable its own survival. In this study, we focus on the metabolic interactions of Mycobacterium abscessus with Drosophila melanogaster. We identify MAB_1132c as an asparagine transporter required for pathogenicity in M. abscessus. We show that this requirement is specifically associated with damage to the host: flies infected with MAB_1132c knockout bacteria, or with wild-type bacteria grown in asparagine-restricted conditions, are longer lived without showing a significant change in bacterial load. This is associated with a reduction in the host innate immune response, demonstrated by the decreased transcription of antimicrobial peptides as well as a significant reduction in the ability of the infection to disrupt systemic insulin signaling. Much of the increase in host survival during infection with asparagine-limited M. abscessus can be attributed to alterations in unpaired cytokine signaling. This demonstrates that asparagine transport in M. abscessus prior to infection is not required for replicative fitness in vivo but does significantly influence the interaction with the host immune responses.

Gut Microbiome-Host Genetics Co-Evolution Shapes Adiposity by Modulating Energy and Lipid Metabolism in Selectively Bred Broiler Chickens.

Optimizing fat deposition is crucial for improving chicken production and meat quality. This study investigated the interactive roles of host genetics and gut microbiome in regulating abdominal fat deposition in selectively bred broiler chicken lines. We compared the gut microbiome composition and host whole-genome profiles between fat-line and lean-line broiler chickens that had been selectively bred for divergent abdominal fat levels over 15 generations. Despite identical dietary and environmental conditions, the two chicken lines exhibited significant differences in their gut microbiota. Lean-line broiler chickens exhibited an increased abundance of intestinal Lactobacillus and a decreased presence of potentially pathogenic species, such as Campylobacter coli, Corynebacterium casei, and Enterococcus faecalis. These microbial alterations were accompanied by shifts in the functional metagenome, with enrichment in pathways involved in energy metabolism and nutrient utilization in the lean-line chickens. Notably, the selective breeding process also led to genomic variations in the lean broilers, with single nucleotide polymorphisms predominantly observed in genes related to energy and lipid metabolism. Our findings suggest that the host-microbiome interactions play a key role in the divergent abdominal fat deposition phenotypes observed in these selectively bred chicken lines. The co-evolution of the gut microbiome and host genetics highlights the importance of considering both factors to optimize poultry production efficiency and meat quality. This study offers new insights into the intricate gut-genome interactions in chicken fat metabolism, paving the way for more effective breeding and microbiome-based strategies to manage adiposity in poultry.

Metabolic interactions of limosilactobacillus reuteri ZJ625 and ligilactobacillus salivarius ZJ614 in Co-culture: implications for multi-strain probiotics.

Limosilactobacillus reuteri ZJ625 and Ligilactobacillus salivarius ZJ614 are potential probiotic bacteria. The mechanisms of enhanced benefits by muti-strain probiotics are yet fully understood. We elucidated the influence of co-culturing on the metabolite profiles of L. reuteri ZJ625 and L. salivarius ZJ614 to decipher the impacts of co-culturing on metabolic interactions between the strains. L. reuteri ZJ625 and L. salivarius ZJ614 were grown in single and co-cultures in defined media. Bacterial cell metabolites were extracted at the mid-stationary growth phase and analysed using two-dimensional gas-column time-of-flight-mass spectrometry (GCxGC- TOFMS). Mass-spectral data was preprocessed and analysed using unsupervised and supervised methods based on the group allocations. A total of 1387 metabolites were identified, with 18.31% significant metabolites (p<0.05) and 10.17% differential metabolites (p<0.05, VIP>1). The differential metabolites identified include arabinofuranose, methyl-galactoside, N-acetyl-glutamic acid, phosphoric acid, and decanoic acid. The metabolites impacted carbohydrate and amino-sugar metabolism. co-culturing of L. reuteri ZJ625 and L. salivarius ZJ614 influenced the metabolite profiles of the strains and impacted metabolic/biosynthetic pathways, indicating cell-to-cell interactions between the strains.

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.

Symbiotic probiotic communities with multiple targets successfully combat obesity in high-fat-diet-fed mice

ABSTRACT Probiotics hold great potential for treating metabolic diseases such as obesity. Given the complex and multifactorial nature of these diseases, research on probiotic combination with multiple targets has become popular. Here, we choose four obesity-related targets to perform high-throughput screening, including pancreatic lipase activity, bile salt hydrolase activity, glucagon-like peptide-1 secretion and adipocyte differentiation. Then, we obtained 649 multi-strain combinations with the requirement that each must cover all these targets in principle. After in vitro co-culture and in vivo co-colonization experiments, only four (<0.7%) combinations were selected as symbiotic probiotic communities (SPCs). Next, genome-scale metabolic model analysis revealed that these SPCs showed lower metabolic resource overlap and higher metabolic interaction potential involving amino acid metabolism (Ammonium, L−Lysine, etc.) and energy metabolism (Phosphate, etc.). Further animal experiments demonstrated that all SPCs exhibited a good safety profile and excellent effects in improving obesity and associated glucose metabolism disruptions and depression-like behaviors in high-fat-diet-fed mice. This anti-obesity improvement was achieved through reduced cholesterol level, fat accumulation and inhibited adipocyte differentiation. Taken together, our study provides a new perspective for designing multi-strain combinations, which may facilitate greater therapeutic effect on obesity and other complex diseases in the future.

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.

Formate-facilitated recovery of wastewater anammox granular sludge from oxygen inhibition: Effects and mechanisms

Oxygen leakage due to poor reactor sealing or accidental aeration can have detrimental impacts on the performance of wastewater anammox processes. Strategies of restoring functionality of anammox consortia are scarce, and providing appropriate chemical reducing reagents may help reduce oxidative damage. This study characterized the responses of anammox granular sludge (AnGS) to oxygen inhibition, and identified formate as an effective promoter capable of facilitating activity recovery, while other organics (acetate, glucose, methanol) were less effective. Upon 12 h of air exposure (DO: 1.2 ± 0.1 mg/L), the total nitrogen removal efficiency of a lab-scale AnGS reactor decreased from 85.15 % to 45.46 %. Compared with natural recovery (without chemical addition), potassium formate at 10 mg COD/L for 3 days significantly increased the total nitrogen removal efficiency, both in the short-term (66.68 % vs 57.09 %, 3 days post inhibition) and long-term (82.34 % vs 72.88 %, 30 days post inhibition). By examining sludge morphology, metagenome and transcriptome, the following potential mechanisms were proposed: 1) rapidly decreasing intracellular ROS; 2) enhancing nitrite turnover; 3) maintaining granular sludge integrity; 4) facilitating interspecies metabolic interactions. The ability of formate to facilitate sludge recovery from oxygen inhibition also implies its potential in mitigating other oxidative stresses in wastewater anammox reactors.

Dietary omega-3 polyunsaturated fatty acids reduce cytochrome c oxidase in brain white matter and sensorimotor regions while increasing functional interactions between neural systems related to escape behavior in postpartum rats.

Previously, we showed that omega-3 polyunsaturated fatty acid n-3 (PUFA) supplementation improved the performance of postpartum rats in the shuttle box escape test (SBET). The brains of these rats were used in the current study which examined brain cytochrome c oxidase (CCO) activity in white matter bundles and 39 regions spanning sensorimotor, limbic, and cognitive areas to determine the effects of n-3 PUFAs on neural metabolic capacity and network interactions. We found that n-3 PUFA supplementation decreased CCO activity in white matter bundles, deep and superficial areas within the inferior colliculus, the anterior and barrel field regions of the primary somatic sensorimotor cortex, the secondary somatic sensorimotor cortex, the lateral, anterior regions of the secondary visual cortex and the ventral posterior nucleus of the thalamus, and the medial nucleus of the amygdala. Structural equation modeling revealed that animals consuming diets without n-3 PUFAs exhibited fewer inter-regional interactions when compared to those fed diets with n-3 PUFAs. Without n-3 PUFAs, inter-regional interactions were observed between the posterior cingulate cortex and amygdala as well as among amygdala subregions. With n-3 PUFAs, more inter-regional interactions were observed, particularly between regions associated with fear memory processing and escape. Correlations between regional CCO activity and SBET behavior were observed in rats lacking dietary n-3 PUFAs but not in those supplemented with these nutrients. In conclusion, consumption of n-3 PUFAs results in reduced CCO activity in white matter bundles and sensorimotor regions, reflecting more efficient neurotransmission, and an increase in inter-regional interactions, facilitating escape from footshock.

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.

Breast 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. We 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. We 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. By 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.