High expression of NXPH4 correlates with poor prognosis, metabolic reprogramming, and immune infiltration in colon adenocarcinoma.

Background: Colon adenocarcinoma (COAD) is a prevalent gastrointestinal malignant disease with high mortality rate, and identification of novel prognostic biomarkers and therapeutic targets is urgently needed. Although neurexophilin 4 (NXPH4) has been investigated in several tumors, its role in COAD remains unclear. The aim of this study was to explore the prognostic value and potential functions of NXPH4 in COAD. Methods: The expression of NXPH4 in COAD were analyzed using The Cancer Genome Atlas (TCGA) and datasets from the Gene Expression Omnibus (GEO) database. The prognostic value of NXPH4 was determined using Kaplan-Meier analysis and Cox regression analysis. To investigate the possible mechanism underlying the role of NXPH4 in COAD, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were employed. The correlation between NXPH4 expression and immune cell infiltration levels was examined thorough single-sample gene set enrichment analysis (ssGSEA). Furthermore, the competing endogenous RNA (ceRNA) regulatory network that may be involved in NXPH4 in COAD was predicted and constructed through a variety of databases. Results: NXPH4 expression was significantly higher in COAD tissue compared with normal colon tissues. Meanwhile, high expression of NXPH4 was associated with poor prognosis in COAD patients. GO-KEGG and GSEA analyses indicated that NXPH4 was associated with glycolysis and hypoxia pathway, and may promote COAD progression and metastasis by modulating metabolic reprogramming. ssGSEA analysis demonstrated that NXPH4 expression also associated with immune infiltration. Furthermore, we identified various microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) as upstream regulators of NXPH4 in COAD. Conclusions: The present study revealed that high expression of NXPH4 is associated with tumor progression, metabolic reprogramming, and immune infiltration. These findings suggest that NXPH4 could serve as a reliable prognostic biomarker and a promising therapeutic target in COAD.

The Probiotic Kluyveromyces lactis JSA 18 Alleviates Obesity and Hyperlipidemia in High-Fat Diet C57BL/6J Mice.

Obesity poses a significant threat to various health conditions such as heart diseases, diabetes, high blood pressure, and heart attack, with the gut microbiota playing a crucial role in maintaining the body's energy balance. We identified a novel probiotic fungal strain, Kluyveromyces lactis JSA 18 (K. lactis), which was isolated from yak milk and was found to possess anti-obesity properties. Additionally, Lactobacillus plantarum CGMCC 8198 (LP8198) from our previous study was also included to evaluate its anti-obesity properties. The findings indicated that K. lactis caused a notable reduction in weight gain, liver and fat indexes, and hyperlipidemia in mice fed a high-fat diet (HFD). Administering K. lactis and LP8198 to mice on a high-fat diet resulted in a reduction of serum triglyceride levels. Furthermore, the supplements reduced ALT and AST activity, and inhibited the production of inflammatory cytokines such as TNF-α and IL-1ß. In addition, lipid metabolism was enhanced by the downregulation of ACC1, PPAR-γ, SREBP-1, and Fasn. Moreover, this study found that K. lactis and LP8198 have little effect on gut bacteria. Additionally, K. lactis partially influenced intestinal fungi, while LP8198 had a minor influence on gut mycobiota. The main goal of this research was to show how effective K. lactis can be as a probiotic in combating obesity.

Microbial chassis design and engineering for production of gamma-aminobutyric acid.

Gamma-aminobutyric acid (GABA) is a non-protein amino acid which is widely applied in agriculture and pharmaceutical additive industries. GABA is synthesized from glutamate through irreversible α-decarboxylation by glutamate decarboxylase. Recently, microbial synthesis has become an inevitable trend to produce GABA due to its sustainable characteristics. Therefore, reasonable microbial platform design and metabolic engineering strategies for improving production of GABA are arousing a considerable attraction. The strategies concentrate on microbial platform optimization, fermentation process optimization, rational metabolic engineering as key metabolic pathway modification, promoter optimization, site-directed mutagenesis, modular transporter engineering, and dynamic switch systems application. In this review, the microbial producers for GABA were summarized, including lactic acid bacteria, Corynebacterium glutamicum, and Escherichia coli, as well as the efficient strategies for optimizing them to improve the production of GABA.

Exploring the effects of the dietary fiber compound mediated by a longevity dietary pattern on antioxidation, characteristic bacterial genera, and metabolites based on fecal metabolomics.

BACKGROUND: Age-related dysbiosis of the microbiota has been linked to various negative health outcomes. This study aims to investigate the effects of a newly discovered dietary fiber compound (DFC) on aging, intestinal microbiota, and related metabolic processes. The DFC was identified through in vitro fermentation screening experiments, and its dosage and composition were determined based on a longevity dietary pattern. METHODS: Aged SPF C57BL/6 J mice (65 weeks old) and young mice (8 weeks old) were divided into three groups: a subgroup without dietary fiber (NDF), a low DFC dose subgroup (LDF, 10% DFC), and a high DFC dose subgroup (HDF, 20% DFC). The total antioxidant capacity (T-AOC), total superoxide dismutase (T-SOD) activity, malondialdehyde (MDA) content, and glutathione peroxidase (GSH-Px) activity in liver and serum samples of the mice were measured according to the manufacturer's protocol. The expression levels of characteristic bacterial genera and fecal metabolite concentrations in mice were determined using quantitative real-time PCR (qPCR) and nuclear magnetic resonance hydrogen spectroscopy (1H NMR). Metabolomics analysis was further conducted to identify biological functions and potential pathways related to aging. RESULTS: After an 8-weeks dietary intervention, DFC supplementation significantly attenuated age-related weight loss, organ degeneration, and oxidative stress. And promoted the growth of Lactobacillus and Bifidobacterium and inhibited the growth of Escherichia coli (E. coli) and Bacteroides (p < 0.05) in the intestinal tracts of aged mice. Metabolomic analysis identified glycolipid and amino acid metabolic pathway biomarkers associated with aging that were differentially regulated by DFC consumption. Correlation analysis between the identified microbial flora and the biomarkers revealed potential mechanistic links between altered microbial composition and metabolic activity with aging markers. CONCLUSIONS: In conclusion, this study revealed an important mechanism by which DFC consumption impacts healthspan and longevity, shedding light on optimizing dietary fiber or developing fiber-based interventions to improve human health.

CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions.

Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the exploration of data-driven deep learning technologies as a viable, efficient, and accurate alternative. Nonetheless, most current deep learning-based methods regarded a pair of proteins to be predicted for possible interaction as two separate entities when extracting PPI features, thus neglecting the knowledge sharing among the collaborative protein and the target protein. Aiming at the above issue, a collaborative learning framework CollaPPI was proposed in this study, where two kinds of collaboration, i.e., protein-level collaboration and task-level collaboration, were incorporated to achieve not only the knowledge-sharing between a pair of proteins, but also the complementation of such shared knowledge between biological domains closely related to PPI (i.e., protein function, and subcellular location). Evaluation results demonstrated that CollaPPI obtained superior performance compared to state-of-the-art methods on two PPI benchmarks. Besides, evaluation results of CollaPPI on the additional PPI type prediction task further proved its excellent generalization ability.

HiSIF-DTA: A Hierarchical Semantic Information Fusion Framework for Drug-Target Affinity Prediction.

Accurately identifying drug-target affinity (DTA) plays a significant role in promoting drug discovery and has attracted increasing attention in recent years. Exploring appropriate protein representation methods and increasing the abundance of protein information is critical in enhancing the accuracy of DTA prediction. Recently, numerous deep learning-based models have been proposed to utilize the sequential or structural features of target proteins. However, these models capture only the low-order semantics that exist in a single protein, while the high-order semantics abundant in biological networks are largely ignored. In this article, we propose HiSIF-DTA'a hierarchical semantic information fusion framework for DTA prediction. In this framework, a hierarchical protein graph is constructed that includes not only contact maps as low-order structural semantics but also protein-rotein interaction (PPI) networks as high-order functional semantics. Particularly, two distinct hierarchical fusion strategies (i.e., Top-down and Bottom-Up) are designed to integrate the different protein semantics, therefore contributing to a richer protein representation. Comprehensive experimental results demonstrate that HiSIF-DTA outperforms current state-of-the-art methods for prediction on the benchmark datasets of the DTA task. Further validation on binary tasks and visualization analysis demonstrates the generalization and interpretation abilities of the proposed method.

Improve L-isoleucine production in Corynebacterium glutamicum WM001 by destructing the biosynthesis of trehalose dicorynomycolate.

Trehalose dicorynomycolates are structurally important constituents of the cell envelope in Corynebacterium glutamicum. The genes treS, treY, otsA, mytA and mytB are necessary for the biosynthesis of trehalose dicorynomycolates. In this study, the effect of biosynthesis of trehalose dicorynomycolates on L-isoleucine production in C. glutamicum has been investigated by deleting the genes treS, treY, otsA, mytA, and mytB in the L-isoleucine producing C. glutamicum WM001. L-isoleucine production was slightly improved in the mutants ΔtreY, ΔotsA, and ΔtreYA, and not improved in the single deletion mutant ΔtreS , but significantly improved in the triple deletion mutant ΔtreSYA. Deletion of mytA or mytB in ΔtreSYA could further improve L-isoleucine production. However, deletion of both mytA and mytB in ΔtreSYA significantly decreased L-isoleucine production. The final L-isoleucine producing C. glutamicum WL001 was constructed by deletion of treS, treY, otsA, and mytB, insertion of lrp, and replacement of the native promoter of ilvA with the L-isoleucine sensitive promoter PbrnFE7. WL001 grew worse than the control WM001, but produced 36.1% more L-isoleucine after 72 h shake flask cultivation than WM001.

Predicting Drug-Target Affinity by Learning Protein Knowledge From Biological Networks.

Predicting drug-target affinity (DTA) is a crucial step in the process of drug discovery. Efficient and accurate prediction of DTA would greatly reduce the time and economic cost of new drug development, which has encouraged the emergence of a large number of deep learning-based DTA prediction methods. In terms of the representation of target proteins, current methods can be classified into 1D sequence- and 2D-protein graph-based methods. However, both two approaches focused only on the inherent properties of the target protein, but neglected the broad prior knowledge regarding protein interactions that have been clearly elucidated in past decades. Aiming at the above issue, this work presents an end-to-end DTA prediction method named MSF-DTA (Multi-Source Feature Fusion-based Drug-Target Affinity). The contributions can be summarized as follows. First, MSF-DTA adopts a novel "neighboring feature"-based protein representation. Instead of utilizing only the inherent features of a target protein, MSF-DTA gathers additional information for the target protein from its biologically related "neighboring" proteins in PPI (i.e., protein-protein interaction) and SSN (i.e., sequence similarity) networks to get prior knowledge. Second, the representation was learned using an advanced graph pre-training framework, VGAE, which could not only gather node features but also learn topological connections, therefore contributing to a richer protein representation and benefiting the downstream DTA prediction task. This study provides new perspective for the DTA prediction task, and evaluation results demonstrated that MSF-DTA obtained superior performances compared to current state-of-the-art methods.

CRK41 Modulates Microtubule Depolymerization in Response to Salt Stress in Arabidopsis.

The pivotal role of cysteine-rich receptor-like kinases (CRKs) in modulating growth, development, and responses to stress has been widely acknowledged in Arabidopsis. However, the function and regulation of CRK41 has remained unclear. In this study, we demonstrate that CRK41 is critical for modulating microtubule depolymerization in response to salt stress. The crk41 mutant exhibited increased tolerance, while overexpression of CRK41 led to hypersensitivity to salt. Further analysis revealed that CRK41 interacts directly with the MAP kinase3 (MPK3), but not with MPK6. Inactivation of either MPK3 or MPK6 could abrogate the salt tolerance of the crk41 mutant. Upon NaCl treatment, microtubule depolymerization was heightened in the crk41 mutant, yet alleviated in the crk41mpk3 and crk41mpk6 double mutants, indicating that CRK41 suppresses MAPK-mediated microtubule depolymerizations. Collectively, these results reveal that CRK41 plays a crucial role in regulating microtubule depolymerization triggered by salt stress through coordination with MPK3/MPK6 signalling pathways, which are key factors in maintaining microtubule stability and conferring salt stress resistance in plants.

Knockdown SENP1 Suppressed the Angiogenic Potential of Mesenchymal Stem Cells by Impacting CXCR4-Regulated MRTF-A SUMOylation and CCN1 Expression.

The angiogenic potential of mesenchymal stem cells (MSCs) is critical for adult vascular regeneration and repair, which is regulated by various growth factors and cytokines. In the current study, we report that knockdown SUMO-specific peptidase 1 (SENP1) stimulated the SUMOylation of MRTF-A and prevented its translocation into the nucleus, leading to downregulation of the cytokine and angiogenic factor CCN1, which significantly impacted MSC-mediated angiogenesis and cell migration. Further studies showed that SENP1 knockdown also suppressed the expression of a chemokine receptor CXCR4, and overexpression of CXCR4 could partially abrogate MRTF-A SUMOylation and reestablish the CCN1 level. Mutation analysis confirmed that SUMOylation occurred on three lysine residues (Lys-499, Lys-576, and Lys-624) of MRTF-A. In addition, SENP1 knockdown abolished the synergistic co-activation of CCN1 between MRTF-A and histone acetyltransferase p300 by suppressing acetylation on histone3K9, histone3K14, and histone4. These results revealed an important signaling pathway to regulate MSC differentiation and angiogenesis by MRTF-A SUMOylation involving cytokine/chemokine activities mediated by CCN1 and CXCR4, which may potentially impact a variety of cellular processes such as revascularization, wound healing, and progression of cancer.

Mesenchymal stem cell suppresses the efficacy of CAR-T toward killing lymphoma cells by modulating the microenvironment through stanniocalcin-1.

Stem cells play critical roles both in the development of cancer and therapy resistance. Although mesenchymal stem cells (MSCs) can actively migrate to tumor sites, their impact on chimeric antigen receptor modified T cell (CAR-T) immunotherapy has been little addressed. Using an in vitro cell co-culture model including lymphoma cells and macrophages, here we report that CAR-T cell-mediated cytotoxicity was significantly inhibited in the presence of MSCs. MSCs caused an increase of CD4+ T cells and Treg cells but a decrease of CD8+ T cells. In addition, MSCs stimulated the expression of indoleamine 2,3-dioxygenase and programmed cell death-ligand 1 which contributes to the immune-suppressive function of tumors. Moreover, MSCs suppressed key components of the NLRP3 inflammasome by modulating mitochondrial reactive oxygen species release. Interestingly, all these suppressive events hindering CAR-T efficacy could be abrogated if the stanniocalcin-1 (STC1) gene, which encodes the glycoprotein hormone STC-1, was knockdown in MSC. Using xenograft mice, we confirmed that CAR-T function could also be inhibited by MSC in vivo, and STC1 played a critical role. These data revealed a novel function of MSC and STC-1 in suppressing CAR-T efficacy, which should be considered in cancer therapy and may also have potential applications in controlling the toxicity arising from the excessive immune response.

Immunotherapy is a type of cancer treatment that helps the immune system fight cancer. For example, chimeric antigen receptor T cell (CAR-T) therapy is used to target several types of blood cancer. It works by reprogramming patients' immune cells to target specific tumor cells. In blood cancers, CAR-T therapy works very well, but it can cause extreme responses from the patient's immune system, which can be life threatening. In solid tumors, CAR-T therapy is much less successful because the tumors secrete molecules into the space surrounding them, which weaken the immune processes that attack cancerous cells. Stem cells are the master cells of the body. Originating in the bone marrow, they can repair and regenerate the body's cells. Cancer stem cells play a role in resistance to CAR-T therapy, due ­ in part ­ to their ability to renew themselves, but the role of another type of stem cell, called mesenchymal stem cells, was less clear. Mesenchymal stem cells develop into tissues that line organs and blood vessels. Although it is known that mesenchymal stem cells are present in most cancers and play a role in shaping and influencing the space around tumors, their impact on CAR-T therapy has not been studied in depth. To find out more, Zhang et al. looked at the influence of a protein, called staniocalcin-1 (STC1), on CAR-T therapy, by studying cells grown in the laboratory and human tumor cells that had been implanted in mice. Zhang et al. found that mesenchymal stem cells reduce the ability of CAR-T therapy to destroy cancer cells and that they needed STC1 to do this successfully. They also increased the expression of molecules that dampen the immune system, and suppressed molecules called inflammasomes, which are an important part of the way the immune system detects disease. Moreover, reducing the amount of STC1 that mesenchymal stem cells expressed restored the effectivity of CAR-T therapy. This study increases our understanding of the way that mesenchymal stem cells affect CAR-T therapy. It has the potential to open up a new way of improving the efficiency of this treatment and of reducing the harmful side effects that it can cause.

Engineering CAR-NK cells targeting CD33 with concomitant extracellular secretion of anti-CD16 antibody revealed superior antitumor effects toward myeloid leukemia.

Acute myeloid leukemia (AML) is a common form of acute leukemia, and the currently available treatments are unsatisfactory. In the present study, we report an immune cell therapeutic strategy that employed genetically modified bifunctional CAR-NK cells. These cells combined the efficient targeting of AML cells by the CD33 molecule with the concomitant stimulation of NK cell-mediated cytotoxicity via the expression and extracellular secretion of anti-CD16 antibody (B16) that binds back to the FC receptor of NK cells. Compared to CAR-NK cells that target CD33 only, the bifunctional CD33/B16 CAR-NK cells showed superior killing efficiency toward AML cells in vitro. The increase in efficiency was approximately four-fold, as determined based on the number of cells needed to achieve 80% killing activity. An in vivo study using a xenograft model also revealed the effective clearance of leukemic cells and much longer survival, with no relapse or death for at least 60 days. In addition, the safety of CAR-NK cells did not change with additional expression of B16, as determined by the release of cytokines. These data revealed the development of a promising CAR-NK approach for the treatment of patients with AML, which may improve CAR-NK-based treatment strategy in general and may potentially be used to treat other tumors as well.

In silico assessment of pharmacotherapy for carbon monoxide induced arrhythmias in healthy and failing human hearts.

Background: Carbon monoxide (CO) is gaining increased attention in air pollution-induced arrhythmias. The severe cardiotoxic consequences of CO urgently require effective pharmacotherapy to treat it. However, existing evidence demonstrates that CO can induce arrhythmias by directly affecting multiple ion channels, which is a pathway distinct from heart ischemia and has received less concern in clinical treatment. Objective: To evaluate the efficacy of some common clinical antiarrhythmic drugs for CO-induced arrhythmias, and to propose a potential pharmacotherapy for CO-induced arrhythmias through the virtual pathological cell and tissue models. Methods: Two pathological models describing CO effects on healthy and failing hearts were constructed as control baseline models. After this, we first assessed the efficacy of some common antiarrhythmic drugs like ranolazine, amiodarone, nifedipine, etc., by incorporating their ion channel-level effects into the cell model. Cellular biomarkers like action potential duration and tissue-level biomarkers such as the QT interval from pseudo-ECGs were obtained to assess the drug efficacy. In addition, we also evaluated multiple specific I Kr activators in a similar way to multi-channel blocking drugs, as the I Kr activator showed great potency in dealing with CO-induced pathological changes. Results: Simulation results showed that the tested seven antiarrhythmic drugs failed to rescue the heart from CO-induced arrhythmias in terms of the action potential and the ECG manifestation. Some of them even worsened the condition of arrhythmogenesis. In contrast, I Kr activators like HW-0168 effectively alleviated the proarrhythmic effects of CO. Conclusion: Current antiarrhythmic drugs including the ranolazine suggested in previous studies did not achieve therapeutic effects for the cardiotoxicity of CO, and we showed that the specific I Kr activator is a promising pharmacotherapy for the treatment of CO-induced arrhythmias.

Ameliorative Effect of Chitosan Oligosaccharides on Hepatic Encephalopathy by Reshaping Gut Microbiota and Gut-Liver Axis.

This study investigated the influence of chitosan oligosaccharides (COSs) on a thioacetamide-induced hepatic encephalopathy (HE) Wistar rat model. COS treatment statistically reduced the false neurotransmitters and blood ammonia in HE rats, along with the suppression of oxidative stress and inflammation. The disbalanced gut microbiota was detected in HE rats by 16S rDNA sequencing, but the abundance alterations of some intestinal bacteria at either the phylum or genus level were at least partly restored by COS treatment. According to metabolomics analysis of rat feces, six metabolism pathways with the greatest response to HE were screened, several of which were remarkably reversed by COS. The altered metabolites might serve as a bridge for the alleviated HE rats treated with COS and the enhanced intestinal bacterial structure. This study provides novel guidance to develop novel food or dietary supplements to improve HE diseases due to the potential beneficial effect of COS on gut-liver axis.

Gut-Flora-Dependent Metabolite Trimethylamine-N-Oxide Promotes Atherosclerosis-Associated Inflammation Responses by Indirect ROS Stimulation and Signaling Involving AMPK and SIRT1.

Trimethylamine-N-oxide (TMAO), a gut-microbiota-dependent metabolite after ingesting dietary choline, has been identified as a novel risk factor for atherosclerosis through inducing vascular inflammation. However, the underlying molecular mechanism is poorly understood. Using an in vitro vascular cellular model, we found that the TMAO-induced inflammation responses were correlated with an elevation of ROS levels and downregulation of SIRT1 expression in VSMCs and HUVECs. The overexpression of SIRT1 could abrogate both the stimulation of ROS and inflammation. Further studies revealed that AMPK was also suppressed by TMAO and was a mediator upstream of SIRT1. Activation of AMPK by AICAR could reduce TMAO-induced ROS and inflammation. Moreover, the GSH precursor NAC could attenuate TMAO-induced inflammation. In vivo studies with mice models also showed that choline-induced production of TMAO and the associated glycolipid metabolic changes leading to atherosclerosis could be relieved by NAC and a probiotic LP8198. Collectively, the present study revealed an unrecognized mechanistic link between TMAO and atherosclerosis risk, and probiotics ameliorated TMAO-induced atherosclerosis through affecting the gut microbiota. Consistent with previous studies, our data confirmed that TMAO could stimulate inflammation by modulating cellular ROS levels. However, this was not due to direct cytotoxicity but through complex signaling pathways involving AMPK and SIRT1.

Enhancing Protein Function Prediction Performance by Utilizing AlphaFold-Predicted Protein Structures.

The structure of a protein is of great importance in determining its functionality, and this characteristic can be leveraged to train data-driven prediction models. However, the limited number of available protein structures severely limits the performance of these models. AlphaFold2 and its open-source data set of predicted protein structures have provided a promising solution to this problem, and these predicted structures are expected to benefit the model performance by increasing the number of training samples. In this work, we constructed a new data set that acted as a benchmark and implemented a state-of-the-art structure-based approach for determining whether the performance of the function prediction model can be improved by putting additional AlphaFold-predicted structures into the training set and further compared the performance differences between two models separately trained with real structures only and AlphaFold-predicted structures only. Experimental results indicated that structure-based protein function prediction models could benefit from virtual training data consisting of AlphaFold-predicted structures. First, model performances were improved in all three categories of Gene Ontology terms (GO terms) after adding predicted structures as training samples. Second, the model trained only on AlphaFold-predicted virtual samples achieved comparable performances to the model based on experimentally solved real structures, suggesting that predicted structures were almost equally effective in predicting protein functionality.

Mechanistic Insights Into Inflammation-Induced Arrhythmias: A Simulation Study.

Cardiovascular diseases are the primary cause of death of humans, and among these, ventricular arrhythmias are the most common cause of death. There is plausible evidence implicating inflammation in the etiology of ventricular fibrillation (VF). In the case of systemic inflammation caused by an overactive immune response, the induced inflammatory cytokines directly affect the function of ion channels in cardiomyocytes, leading to a prolonged action potential duration (APD). However, the mechanistic links between inflammatory cytokine-induced molecular and cellular influences and inflammation-associated ventricular arrhythmias need to be elucidated. The present study aimed to determine the potential impact of systemic inflammation on ventricular electrophysiology by means of multiscale virtual heart models. The experimental data on the ionic current of three major cytokines [i.e., tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1ß), and interleukin-6 (IL-6)] were incorporated into the cell model, and the effects of each cytokine and their combined effect on the cell action potential (AP) were evaluated. Moreover, the integral effect of these cytokines on the conduction of excitation waves was also investigated in a tissue model. The simulation results suggested that inflammatory cytokines significantly prolonged APD, enhanced the transmural and regional repolarization heterogeneities that predispose to arrhythmias, and reduced the adaptability of ventricular tissue to fast heart rates. In addition, simulated pseudo-ECGs showed a prolonged QT interval-a manifestation consistent with clinical observations. In summary, the present study provides new insights into ventricular arrhythmias associated with inflammation.

Glycosylation Modification Enhances (2S)-Naringenin Production in Saccharomyces cerevisiae.

(2S)-Naringenin is an important flavonoid precursor, with multiple nutritional and pharmacological activities. Both (2S)-naringenin and other flavonoid production are hindered by poor water solubility and inhibited cell growth. To address this, we increased solubility and improved cell growth by partially glycosylating (2S)-naringenin to naringenin-7-O-glucoside, which facilitated increased extracellular secretion, by knocking out endogenous glycosyl hydrolase genes, EXG1 and SPR1, and expressing the glycosyltransferase gene (UGT733C6). Naringenin-7-O-glucoside synthesis was further improved by optimizing UDP-glucose and shikimate pathways. Then, hydrochloric acid was used to hydrolyze naringenin-7-O-glucoside to (2S)-naringenin outside the cell. Thus, our optimized Saccharomyces cerevisiae strain E32T19 produced 1184.1 mg/L (2S)-naringenin, a 7.9-fold increase on the starting strain. Therefore. we propose that glycosylation modification is a useful strategy for the efficient heterologous biosynthesis of (2S)-naringenin in S. cerevisiae.

Remnant Cholesterol Predicts Risk of Cardiovascular Events in Patients With Myocardial Infarction With Nonobstructive Coronary Arteries.

Background Remnant cholesterol (RC) has been reported to promote atherosclerotic cardiovascular disease. Yet little is known regarding the RC-related residual risk in patients with myocardial infarction (MI) with nonobstructive coronary arteries. Methods and Results A total of 1179 patients with MI with nonobstructive coronary arteries were enrolled and divided according to median level of RC calculated as non-high-density lipoprotein cholesterol minus low-density lipoprotein cholesterol. The primary end point was a composite of major adverse cardiovascular events (MACEs), including all-cause death, nonfatal MI, stroke, revascularization, and hospitalization for unstable angina or heart failure. Kaplan-Meier, Cox regression, and receiver-operating characteristic analyses were used. Patients with higher median level of RC had a significantly higher incidence of MACEs (16.9% versus 11.5%; P=0.009) over the median follow-up of 41.7 months. High RC levels were significantly associated with an increased risk of MACEs after adjustment for multiple clinically relevant variables (per 1 SD increase, hazard ratio, 0.61; 95% CI, 1.12-2.31; P=0.009). Elevated RC also contributed to residual risk beyond conventional lipid parameters. Moreover, RC had an area under the curve of 0.61 for MACE prediction. When adding RC to the Thrombolysis in Myocardial Infarction risk score, the combined model yielded a significant improvement in discrimination for MACEs. Conclusions Elevated RC was closely associated with poor outcomes after MI with nonobstructive coronary arteries independent of traditional risk factors, indicating the utility of RC for risk stratification and a rationale for targeted RC-lowering trials in patients with MI with nonobstructive coronary arteries.

Chromatin Regulators Ahc1p and Eaf3p Positively Influence Nitrogen Metabolism in Saccharomyces cerevisiae.

There is a complex regulatory network of nitrogen metabolism in Saccharomyces cerevisiae, and many details of this regulatory network have not been revealed. This study explored the global regulation of nitrogen metabolism in S. cerevisiae from an epigenetic perspective. Comparative transcriptome analysis of S. cerevisiae S288C treated with 30 nitrogen sources identified nine chromatin regulators (CRs) that responded significantly to different nitrogen sources. Functional analysis showed that among the CRs identified, Ahc1p and Eaf3p promoted the utilization of non-preferred nitrogen sources through global regulation of nitrogen metabolism. Ahc1p regulated nitrogen metabolism through amino acid transport, nitrogen catabolism repression (NCR), and the Ssy1p-Ptr3p-Ssy5p signaling sensor system. Eaf3p regulated nitrogen metabolism via amino acid transport and NCR. The regulatory mechanisms of the effects of Ahc1p and Eaf3p on nitrogen metabolism depended on the function of their histone acetyltransferase complex ADA and NuA4. These epigenetic findings provided new insights for a deeper understanding of the nitrogen metabolism regulatory network in S. cerevisiae.