On the illusion of auxotrophy: met15Δ yeast cells can grow on inorganic sulfur, thanks to the previously uncharacterized homocysteine synthase Yll058w.

Organisms must either synthesize or assimilate essential organic compounds to survive. The homocysteine synthase Met15 has been considered essential for inorganic sulfur assimilation in yeast since its discovery in the 1970s. As a result, MET15 has served as a genetic marker for hundreds of experiments that play a foundational role in eukaryote genetics and systems biology. Nevertheless, we demonstrate here through structural and evolutionary modeling, in vitro kinetic assays, and genetic complementation, that an alternative homocysteine synthase encoded by the previously uncharacterized gene YLL058W enables cells lacking Met15 to assimilate enough inorganic sulfur for survival and proliferation. These cells however fail to grow in patches or liquid cultures unless provided with exogenous methionine or other organosulfurs. We show that this growth failure, which has historically justified the status of MET15 as a classic auxotrophic marker, is largely explained by toxic accumulation of the gas hydrogen sulfide because of a metabolic bottleneck. When patched or cultured with a hydrogen sulfide chelator, and when propagated as colony grids, cells without Met15 assimilate inorganic sulfur and grow, and cells with Met15 achieve even higher yields. Thus, Met15 is not essential for inorganic sulfur assimilation in yeast. Instead, MET15 is the first example of a yeast gene whose loss conditionally prevents growth in a manner that depends on local gas exchange. Our results have broad implications for investigations of sulfur metabolism, including studies of stress response, methionine restriction, and aging. More generally, our findings illustrate how unappreciated experimental variables can obfuscate biological discovery.

The convergent application of metabolites from Avena sativa and gut microbiota to ameliorate non-alcoholic fatty liver disease: a network pharmacology study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a serious public health issue globally, currently, the treatment of NAFLD lies still in the labyrinth. In the inchoate stage, the combinatorial application of food regimen and favorable gut microbiota (GM) are considered as an alternative therapeutic. Accordingly, we integrated secondary metabolites (SMs) from GM and Avena sativa (AS) known as potent dietary grain to identify the combinatorial efficacy through network pharmacology. METHODS: We browsed the SMs of AS via Natural Product Activity & Species Source (NPASS) database and SMs of GM were retrieved by gutMGene database. Then, specific intersecting targets were identified from targets related to SMs of AS and GM. The final targets were selected on NAFLD-related targets, which was considered as crucial targets. The protein-protein interaction (PPI) networks and bubble chart analysis to identify a hub target and a key signaling pathway were conducted, respectively. In parallel, we analyzed the relationship of GM or AS─a key signaling pathway─targets─SMs (GASTM) by merging the five components via RPackage. We identified key SMs on a key signaling pathway via molecular docking assay (MDA). Finally, the identified key SMs were verified the physicochemical properties and toxicity in silico platform. RESULTS: The final 16 targets were regarded as critical proteins against NAFLD, and Vascular Endothelial Growth Factor A (VEGFA) was a key target in PPI network analysis. The PI3K-Akt signaling pathway was the uppermost mechanism associated with VEGFA as an antagonistic mode. GASTM networks represented 122 nodes (60 GM, AS, PI3K-Akt signaling pathway, 4 targets, and 56 SMs) and 154 edges. The VEGFA-myricetin, or quercetin, GSK3B-myricetin, IL2-diosgenin complexes formed the most stable conformation, the three ligands were derived from GM. Conversely, NR4A1-vestitol formed stable conformation with the highest affinity, and the vestitol was obtained from AS. The given four SMs were no hurdles to develop into drugs devoid of its toxicity. CONCLUSION: In conclusion, we show that combinatorial application of AS and GM might be exerted to the potent synergistic effects against NAFLD, dampening PI3K-Akt signaling pathway. This work provides the importance of dietary strategy and beneficial GM on NAFLD, a data mining basis for further explicating the SMs and pharmacological mechanisms of combinatorial application (AS and GM) against NAFLD.

Porous Polyethylene Supports in Reinforcement of Multiblock Hydrocarbon Ionomers for Proton Exchange Membranes.

Hydrocarbon (HC)-based block copolymers have been recognized as promising candidates for proton exchange membranes (PEMs) due to their distinct hydrophilic-hydrophobic separation, which results in improved proton transport compared to that of random copolymers. However, most PEMs derived from HC-based ionomers, including block copolymers, encounter challenges related to durability in electrochemical cells due to their low mechanical and chemical properties. One method for reinforcing HC-based ionomers involves incorporating the ionomers into commercially available low surface tension PTFE porous substrates. Nevertheless, the high interfacial energy between the hydrocarbon-based ionomer solution and PTFE remains a challenge in this reinforcement process, which necessitates the application of surface energy treatment to PTFE. Here, multiblock sulfonated poly(arylene ether sulfone) (SPAES) ionomers are being reinforced using untreated PE on the surface, and this is compared to reinforcement using surface-treated porous PTFE. The PE support layer exhibits a lower surface energy barrier compared to the surface-treated PTFE layer for the infiltration of the multiblock SPAES solution. This is characterized by the absence of noticeable voids, high translucency, gas impermeability, and a physical and chemical stability. By utilizing a high surface tension PE support with a comparable value to the multiblock SPAES, effective reinforcement of the multiblock SPAES ionomers is achieved for a PEM, which is potentially applicable to various hydrogen energy-based electrochemical cells.

Hydrogen-rich gas production from disposable COVID-19 mask by steam gasification.

Globally, the demand for masks has increased due to the COVID-19 pandemic, resulting in 490,201 tons of waste masks disposed of per month. Since masks are used in places with a high risk of virus infection, waste masks retain the risk of virus contamination. In this study, a 1 kg/h lab-scale (diameter: 0.114 m, height: 1 m) bubbling fluidized bed gasifier was used for steam gasification (temperature: 800 °C, steam/carbon (S/C) ratio: 1.5) of waste masks. The use of a downstream reactor with activated carbon (AC) for tar cracking and the enhancement of hydrogen production was examined. Steam gasification with AC produces syngas with H2, CO, CH4, and CO2 content of 38.89, 6.40, 21.69, and 7.34 vol%, respectively. The lower heating value of the product gas was 29.66 MJ/Nm3 and the cold gas efficiency was 74.55 %. This study showed that steam gasification can be used for the utilization of waste masks and the production of hydrogen-rich gas for further applications.

Microbiome and Metabolomics in Liver Cancer: Scientific Technology.

Primary liver cancer is a heterogeneous disease. Liver cancer metabolism includes both the reprogramming of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment and fluctuations in regular tissue metabolism. Currently, metabolomics and metabolite profiling in liver cirrhosis, liver cancer, and hepatocellular carcinoma (HCC) have been in the spotlight in terms of cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecules, chemicals, and metabolites. Metabolomics technologies can provide critical information about the liver cancer state. Here, we review how liver cirrhosis, liver cancer, and HCC therapies interact with metabolism at the cellular and systemic levels. An overview of liver metabolomics is provided, with a focus on currently available technologies and how they have been used in clinical and translational research. We also list scalable methods, including chemometrics, followed by pathway processing in liver cancer. We conclude that important drivers of metabolomics science and scientific technologies are novel therapeutic tools and liver cancer biomarker analysis.

The Link between Gut Microbiota and Hepatic Encephalopathy.

Hepatic encephalopathy (HE) is a serious complication of cirrhosis that causes neuropsychiatric problems, such as cognitive dysfunction and movement disorders. The link between the microbiota and the host plays a key role in the pathogenesis of HE. The link between the gut microbiome and disease can be positively utilized not only in the diagnosis area of HE but also in the treatment area. Probiotics and prebiotics aim to resolve gut dysbiosis and increase beneficial microbial taxa, while fecal microbiota transplantation aims to address gut dysbiosis through transplantation (FMT) of the gut microbiome from healthy donors. Antibiotics, such as rifaximin, aim to improve cognitive function and hyperammonemia by targeting harmful taxa. Current treatment regimens for HE have achieved some success in treatment by targeting the gut microbiota, however, are still accompanied by limitations and problems. A focused approach should be placed on the establishment of personalized trial designs and therapies for the improvement of future care. This narrative review identifies factors negatively influencing the gut-hepatic-brain axis leading to HE in cirrhosis and explores their relationship with the gut microbiome. We also focused on the evaluation of reported clinical studies on the management and improvement of HE patients with a particular focus on microbiome-targeted therapy.

Microbiome-Based Metabolic Therapeutic Approaches in Alcoholic Liver Disease.

Alcohol consumption is a global healthcare problem. Chronic alcohol consumption generates a wide spectrum of hepatic lesions, the most characteristic of which are steatosis, hepatitis, fibrosis, and cirrhosis. Alcoholic liver diseases (ALD) refer to liver damage and metabolomic changes caused by excessive alcohol intake. ALD present several clinical stages of severity found in liver metabolisms. With increased alcohol consumption, the gut microbiome promotes a leaky gut, metabolic dysfunction, oxidative stress, liver inflammation, and hepatocellular injury. Much attention has focused on ALD, such as alcoholic fatty liver (AFL), alcoholic steatohepatitis (ASH), alcoholic cirrhosis (AC), hepatocellular carcinoma (HCC), a partnership that reflects the metabolomic significance. Here, we report on the global function of inflammation, inhibition, oxidative stress, and reactive oxygen species (ROS) mechanisms in the liver biology framework. In this tutorial review, we hypothetically revisit therapeutic gut microbiota-derived alcoholic oxidative stress, liver inflammation, inflammatory cytokines, and metabolic regulation. We summarize the perspective of microbial therapy of genes, gut microbes, and metabolic role in ALD. The end stage is liver transplantation or death. This review may inspire a summary of the gut microbial genes, critical inflammatory molecules, oxidative stress, and metabolic routes, which will offer future promising therapeutic compounds in ALD.

Alcohol-Treated Porous PTFE Substrate for the Penetration of PTFE-Incompatible Hydrocarbon-Based Ionomer Solutions.

For a mechanically tough proton exchange membrane, a composite membrane incorporated with a porous polymer substrate is of great interest to suppress the ionomer swelling and to improve the dimensional stability and mechanical strength of the ionomers. For the composite membranes, good impregnation of substrate-incompatible ionomer solution into the substrate pores still remains one of the challenges to be solved. Here, we demonstrated a facile process (surface treatment with solvents compatible with both substrate and the ionomer solution) for the fabrication of the composite membranes using polytetrafluoroethylene (PTFE) as a porous substrate and poly(arylene ether sulfone) (SPAES) as a hydrocarbon-based (HC) ionomer. Appropriate solvents for the surface treatment were sought through the contact angle measurement, and it was found that alcohol solvents effectively tuned the surface property of PTFE pores to facilitate the penetration of the SPAES/N-methyl-2-pyrrolidone (NMP) solution into ∼300 nm pores of the substrate. Using this simple alcohol treatment, the SPAES/NMP contact angle was reduced in half, and we could fabricate the mechanically tough PTFE/HC composite membranes, which were apparently translucent and microscopically almost void-free composite membranes.

The Comprehensive "Omics" Approach from Metabolomics to Advanced Omics for Development of Immune Checkpoint Inhibitors: Potential Strategies for Next Generation of Cancer Immunotherapy.

In the past decade, immunotherapies have been emerging as an effective way to treat cancer. Among several categories of immunotherapies, immune checkpoint inhibitors (ICIs) are the most well-known and widely used options for cancer treatment. Although several studies continue, this treatment option has yet to be developed into a precise application in the clinical setting. Recently, omics as a high-throughput technique for understanding the genome, transcriptome, proteome, and metabolome has revolutionized medical research and led to integrative interpretation to advance our understanding of biological systems. Advanced omics techniques, such as multi-omics, single-cell omics, and typical omics approaches, have been adopted to investigate various cancer immunotherapies. In this review, we highlight metabolomic studies regarding the development of ICIs involved in the discovery of targets or mechanisms of action and assessment of clinical outcomes, including drug response and resistance and propose biomarkers. Furthermore, we also discuss the genomics, proteomics, and advanced omics studies providing insights and comprehensive or novel approaches for ICI development. The overview of ICI studies suggests potential strategies for the development of other cancer immunotherapies using omics techniques in future studies.

Recent Advances of Microbiome-Associated Metabolomics Profiling in Liver Disease: Principles, Mechanisms, and Applications.

Advances in high-throughput screening of metabolic stability in liver and gut microbiota are able to identify and quantify small-molecule metabolites (metabolome) in different cellular microenvironments that are closest to their phenotypes. Metagenomics and metabolomics are largely recognized to be the "-omics" disciplines for clinical therapeutic screening. Here, metabolomics activity screening in liver disease (LD) and gut microbiomes has significantly delivered the integration of metabolomics data (i.e., a set of endogenous metabolites) with metabolic pathways in cellular environments that can be tested for biological functions (i.e., phenotypes). A growing literature in LD and gut microbiomes reports the use of metabolites as therapeutic targets or biomarkers. Although growing evidence connects liver fibrosis, cirrhosis, and hepatocellular carcinoma, the genetic and metabolic factors are still mainly unknown. Herein, we reviewed proof-of-concept mechanisms for metabolomics-based LD and gut microbiotas' role from several studies (nuclear magnetic resonance, gas/lipid chromatography, spectroscopy coupled with mass spectrometry, and capillary electrophoresis). A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to improve liver health.

Diet-Regulating Microbiota and Host Immune System in Liver Disease.

The gut microbiota has been known to modulate the immune responses in chronic liver diseases. Recent evidence suggests that effects of dietary foods on health care and human diseases are related to both the immune reaction and the microbiome. The gut-microbiome and intestinal immune system play a central role in the control of bacterial translocation-induced liver disease. Dysbiosis, small intestinal bacterial overgrowth, translocation, endotoxemia, and the direct effects of metabolites are the main events in the gut-liver axis, and immune responses act on every pathways of chronic liver disease. Microbiome-derived metabolites or bacteria themselves regulate immune cell functions such as recognition or activation of receptors, the control of gene expression by epigenetic change, activation of immune cells, and the integration of cellular metabolism. Here, we reviewed recent reports about the immunologic role of gut microbiotas in liver disease, highlighting the role of diet in chronic liver disease.

Systemic and Local Metabolic Alterations in Sleep-Deprivation-Induced Stress: A Multiplatform Mass-Spectrometry-Based Lipidomics and Metabolomics Approach.

Sleep deprivation (SD) is known to be associated with metabolic disorders and chronic diseases. Complex metabolic alterations induced by SD at omics scale and the associated biomarker candidates have been proposed. However, in vivo systemic and local metabolic shift patterns of the metabolome and lipidome in acute and chronic partial SD models remain to be elucidated. In the present study, the serum, hypothalamus, and hippocampus CA1 of sleep-deprived rats (SD rats) from acute and chronic sleep restriction models were analyzed using three different omics platforms for the discovery and mechanistic assessment of systemic and local SD-induced dysregulated metabolites. We found a similar pattern of systemic metabolome alterations between two models, for which the area under the curve (AUC) of receiver operating characteristic curves was AUC = 0.847 and 0.930 with the pseudotargeted and untargeted metabolomics approach, respectively. However, SD-induced systemic lipidome alterations were significantly different and appeared to be model-dependent (AUC = 0.374). Comprehensive pathway analysis of the altered lipidome and metabolome in the hypothalamus indicated the abnormal behavior of eight metabolic and lipid metabolic pathways. The metabolic alterations of the hippocampus CA1 was subtle in two SD models. Collectively, these results extend our understanding of the quality of sleep and suggest metabolic targets in developing diagnostic biomarkers for better SD control.

High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer.

The advancement of bioinformatics and machine learning has facilitated the discovery and validation of omics-based biomarkers. This study employed a novel approach combining multi-platform transcriptomics and cutting-edge algorithms to introduce novel signatures for accurate diagnosis of colorectal cancer (CRC). Different random forests (RF)-based feature selection methods including the area under the curve (AUC)-RF, Boruta, and Vita were used and the diagnostic performance of the proposed biosignatures was benchmarked using RF, logistic regression, naïve Bayes, and k-nearest neighbors models. All models showed satisfactory performance in which RF appeared to be the best. For instance, regarding the RF model, the following were observed: mean accuracy 0.998 (standard deviation (SD) < 0.003), mean specificity 0.999 (SD < 0.003), and mean sensitivity 0.998 (SD < 0.004). Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Some biomarkers were found to be enriched in epithelial cell signaling in Helicobacter pylori infection and inflammatory processes. The overexpression of TGFBI and S100A2 was associated with poor disease-free survival while the down-regulation of NR5A2, SLC4A4, and CD177 was linked to worse overall survival of the patients. In conclusion, novel transcriptome signatures to improve the diagnostic accuracy in CRC are introduced for further validations in various clinical settings.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

INTRODUCTION: Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients. OBJECTIVES: In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

5L-Scale Magnesio-Milling Reduction of Nanostructured SiO2 for High Capacity Silicon Anodes in Lithium-Ion Batteries.

Nanostructured silicon (Si) is useful in many applications and has typically been synthesized by bottom-up colloid-based solution processes or top-down gas phase reactions at high temperatures. These methods, however, suffer from toxic precursors, low yields, and impractical processing conditions (i.e., high pressure). The magnesiothermic reduction of silicon oxide (SiO2) has also been introduced as an alternative method. Here, we demonstrate the reduction of SiO2 by a simple milling process using a lab-scale planetary-ball mill and industry-scale attrition-mill. Moreover, an ignition point where the reduction begins was consistently observed for the milling processes, which could be used to accurately monitor and control the reaction. The complete conversion of rice husk SiO2 to high purity Si was demonstrated, taking advantage of the rice husk's uniform nanoporosity and global availability, using a 5L-scale attrition-mill. The resulting porous Si showed excellent performance as a Li-ion battery anode, retaining 82.8% of the initial capacity of 1466 mAh g-1 after 200 cycles.

Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease.

Alcoholic-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) show a high prevalence rate worldwide. As gut microbiota represents current state of ALD and MASLD via gut-liver axis, typical characteristics of gut microbiota can be used as a potential diagnostic marker in ALD and MASLD. Machine learning (ML) algorithms improve diagnostic performance in various diseases. Using gut microbiota-based ML algorithms, we evaluated the diagnostic index for ALD and MASLD. Fecal 16S rRNA sequencing data of 263 ALD (control, elevated liver enzyme [ELE], cirrhosis, and hepatocellular carcinoma [HCC]) and 201 MASLD (control and ELE) subjects were collected. For external validation, 126 ALD and 84 MASLD subjects were recruited. Four supervised ML algorithms (support vector machine, random forest, multilevel perceptron, and convolutional neural network) were used for classification with 20, 40, 60, and 80 features, in which three nonsupervised ML algorithms (independent component analysis, principal component analysis, linear discriminant analysis, and random projection) were used for feature reduction. A total of 52 combinations of ML algorithms for each pair of subgroups were performed with 60 hyperparameter variations and Stratified ShuffleSplit tenfold cross validation. The ML models of the convolutional neural network combined with principal component analysis achieved areas under the receiver operating characteristic curve (AUCs) > 0.90. In ALD, the diagnostic AUC values of the ML strategy (vs. control) were 0.94, 0.97, and 0.96 for ELE, cirrhosis, and liver cancer, respectively. The AUC value (vs. control) for MASLD (ELE) was 0.93. In the external validation, the AUC values of ALD and MASLD (vs control) were > 0.90 and 0.88, respectively. The gut microbiota-based ML strategy can be used for the diagnosis of ALD and MASLD.ClinicalTrials.gov NCT04339725.

Mitochondrial respiratory function is preserved under cysteine starvation via glutathione catabolism in NSCLC.

Cysteine metabolism occurs across cellular compartments to support diverse biological functions and prevent the induction of ferroptosis. Though the disruption of cytosolic cysteine metabolism is implicated in this form of cell death, it is unknown whether the substantial cysteine metabolism resident within the mitochondria is similarly pertinent to ferroptosis. Here, we show that despite the rapid depletion of intracellular cysteine upon loss of extracellular cystine, cysteine-dependent synthesis of Fe-S clusters persists in the mitochondria of lung cancer cells. This promotes a retention of respiratory function and a maintenance of the mitochondrial redox state. Under these limiting conditions, we find that glutathione catabolism by CHAC1 supports the mitochondrial cysteine pool to sustain the function of the Fe-S proteins critical to oxidative metabolism. We find that disrupting Fe-S cluster synthesis under cysteine restriction protects against the induction of ferroptosis, suggesting that the preservation of mitochondrial function is antagonistic to survival under starved conditions. Overall, our findings implicate mitochondrial cysteine metabolism in the induction of ferroptosis and reveal a mechanism of mitochondrial resilience in response to nutrient stress.