Facemask acne attenuation through modulation of indirect microbiome interactions.

During the COVID-19 pandemic, facemasks played a pivotal role in preventing person-person droplet transmission of viral particles. However, prolonged facemask wearing causes skin irritations colloquially referred to as 'maskne' (mask + acne), which manifests as acne and contact dermatitis and is mostly caused by pathogenic skin microbes. Previous studies revealed that the putative causal microbes were anaerobic bacteria, but the pathogenesis of facemask-associated skin conditions remains poorly defined. We therefore characterized the role of the facemask-associated skin microbiota in the development of maskne using culture-dependent and -independent methodologies. Metagenomic analysis revealed that the majority of the facemask microbiota were anaerobic bacteria that originated from the skin rather than saliva. Previous work demonstrated direct interaction between pathogenic bacteria and antagonistic strains in the microbiome. We expanded this analysis to include indirect interaction between pathogenic bacteria and other indigenous bacteria classified as either 'pathogen helper (PH)' or 'pathogen inhibitor (PIn)' strains. In vitro screening of bacteria isolated from facemasks identified both strains that antagonized and promoted pathogen growth. These data were validated using a mouse skin infection model, where we observed attenuation of symptoms following pathogen infection. Moreover, the inhibitor of pathogen helper (IPH) strain, which did not directly attenuate pathogen growth in vitro and in vivo, functioned to suppress symptom development and pathogen growth indirectly through PH inhibitory antibacterial products such as phenyl lactic acid. Taken together, our study is the first to define a mechanism by which indirect microbiota interactions under facemasks can control symptoms of maskne by suppressing a skin pathogen.

Genome-wide characterization and expression profiling of E2F/DP gene family members in response to abiotic stress in tomato (Solanum lycopersicum L.).

BACKGROUND: E2F/DP (Eukaryotic 2 transcription factor/dimerization partner) family proteins play an essential function in the cell cycle development of higher organisms. E2F/DP family genes have been reported only in a few plant species. However, comprehensive genome-wide characterization analysis of the E2F/DP gene family of Solanum lycopersicum has not been reported so far. RESULTS: This study identified eight nonredundant SlE2F/DP genes that were classified into seven groups in the phylogenetic analysis. All eight genes had a single E2F-TDP domain and few genes had additional domains. Two segmental duplication gene pairs were observed within tomato, in addition to cis-regulatory elements, miRNA target sites and phosphorylation sites which play an important role in plant development and stress response in tomato. To explore the three-dimensional (3D) models and gene ontology (GO) annotations of SlE2F/DP proteins, we pointed to their putative transporter activity and their interaction with several putative ligands. The localization of SlE2F/DP-GFP fused proteins in the nucleus and endoplasmic reticulum suggested that they may act in other biological functions. Expression studies revealed the differential expression pattern of most of the SlE2F/DP genes in various organs. Moreover, the expression of E2F/DP genes against abiotic stress, particularly SlE2F/DP2 and/or SlE2F/DP7, was upregulated in response to heat, salt, cold and ABA treatment. Furthermore, the co-expression analysis of SlE2F/DP genes with multiple metabolic pathways was co-expressed with defence genes, transcription factors and so on, suggested their crucial role in various biological processes. CONCLUSIONS: Overall, our findings provide a way to understand the structure and function of SlE2F/DP genes; it might be helpful to improve fruit development and tolerance against abiotic stress through marker-assisted selection or transgenic approaches.

Noninvasive ROS imaging and drug delivery monitoring in the tumor microenvironment.

Reactive oxygen species (ROS) that are overproduced in certain tumors can be considered an indicator of oxidative stress levels in the tissue. Here, we report a magnetic resonance imaging (MRI)-based probe capable of detecting ROS levels in the tumor microenvironment (TME) using ROS-responsive manganese ion (Mn2+)-chelated, biotinylated bilirubin nanoparticles (Mn@bt-BRNPs). These nanoparticles are disrupted in the presence of ROS, resulting in the release of free Mn2+, which induces T1-weighted MRI signal enhancement. Mn@BRNPs show more rapid and greater MRI signal enhancement in high ROS-producing A549 lung carcinoma cells compared with low ROS-producing DU145 prostate cancer cells. A pseudo three-compartment model devised for the ROS-reactive MRI probe enables mapping of the distribution and concentration of ROS within the tumor. Furthermore, doxorubicin-loaded, cancer-targeting ligand biotin-conjugated Dox/Mn@bt-BRNPs show considerable accumulation in A549 tumors and also effectively inhibit tumor growth without causing body weight loss, suggesting their usefulness as a new theranostic agent. Collectively, these findings suggest that Mn@bt-BRNPs could be used as an imaging probe capable of detecting ROS levels and monitoring drug delivery in the TME with potential applicability to other inflammatory diseases.

A chitosan/alginate coated nano-liposome to improve intestinal absorption of curcumin for oral administration.

Attempts to improve low absorption and rapid metabolic conversion of curcumin were made by developing curcumin-loaded bilayer nanoliposomes coated with chitosan and alginate for intestinal-specific drug delivery. A curcumin-loaded nano-liposome was prepared with optimized formulations with phosphatidylcholine, curcumin, chitosan, and alginate. The particle size of the optimized formulation was approximately 400 nm, and the encapsulation efficiency was more than 99%. In the in vitro release study, curcumin release from the curcumin-loaded nanoliposome with double layers of chitosan/alginate (CNL-CH/AL) was suppressed in the simulated gastric fluid (SGF, pH 1.2) and enhanced in the simulated intestinal fluid (SIF, pH 6.8). In the in vivo pharmacokinetic study in rats, the CNL-CH/AL-treated group showed a prolonged absorption pattern of curcumin and the area under the plasma concentration-time curve from 0 to 24 h (AUC0-24) was improved 109-fold compared to the control group treated with a curcumin solution without a nanocarrier.

Longitudinal Magnetic Resonance Imaging with ROS-Responsive Bilirubin Nanoparticles Enables Monitoring of Nonalcoholic Steatohepatitis Progression to Cirrhosis.

Despite the vital importance of monitoring the progression of nonalcoholic fatty liver disease (NAFLD) and its progressive form, nonalcoholic steatohepatitis (NASH), an efficient imaging modality that is readily available at hospitals is currently lacking. Here, a new magnetic-resonance-imaging (MRI)-based imaging modality is presented that allows for efficient and longitudinal monitoring of NAFLD and NASH progression. The imaging modality uses manganese-ion (Mn2+)-chelated bilirubin nanoparticles (Mn@BRNPs) as a reactive-oxygen-species (ROS)-responsive MRI imaging probe. Longitudinal T1-weighted MR imaging of NASH model mice is performed after injecting Mn@BRNPs intravenously. The MR signal enhancement in the liver relative to muscle gradually increases up to 8 weeks of NASH progression, but decreases significantly as NASH progresses to the cirrhosis-like stage at weeks 10 and 12. A new dual input pseudo-three-compartment model is developed to provide information on NASH stage with a single MRI scan. It is also demonstrated that the ROS-responsive Mn@BRNPs can be used to monitor the efficacy of potential anti-NASH drugs with conventional MRI. The findings suggest that the ROS-responsive Mn@BRNPs have the potential to serve as an efficient MRI contrast for monitoring NASH progression and its transition to the cirrhosis-like stage.

Glycocalyx-Mimicking Nanoparticles with Differential Organ Selectivity for Drug Delivery and Therapy.

Organ-selective drug delivery is expected to maximize the efficacy of various therapeutic modalities while minimizing their systemic toxicity. Lipid nanoparticles and polymersomes can direct the organ-selective delivery of mRNAs or gene editing machineries, but their delivery is limited to mostly liver, spleen, and lung. A platform that enables delivery to these and other target organs is urgently needed. Here, a library of glycocalyx-mimicking nanoparticles (GlyNPs) comprising five randomly combined sugar moieties is generated, and direct in vivo library screening is used to identify GlyNPs with preferential biodistribution in liver, spleen, lung, kidneys, heart, and brain. Each organ-targeting GlyNP hit show cellular tropism within the organ. Liver, kidney, and spleen-targeting GlyNP hits equipped with therapeutics effectively can alleviate the symptoms of acetaminophen-induced liver injury, cisplatin-induced kidney injury, and immune thrombocytopenia in mice, respectively. Furthermore, the differential organ targeting of GlyNP hits is influenced not by the protein corona but by the sugar moieties displayed on their surface. It is envisioned that the GlyNP-based platform may enable the organ- and cell-targeted delivery of therapeutic cargoes.

Evaluation and optimization of sequence-based gene regulatory deep learning models.

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study.

BACKGROUND: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. METHODS: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO2/FIO2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine). The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley's additive explanations (SHAP). RESULTS: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756-0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626-0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. CONCLUSION: Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources.

Carbon source-dependent control of bacterial growth is fundamental to bacterial physiology and survival. However, pinpointing the metabolic steps important for cell growth is challenging due to the complexity of cellular networks. Here, the elastic net model and multilayer perception model that integrated genome-wide gene-deletion data and simulated flux distributions were constructed to identify metabolic reactions beneficial or detrimental to Escherichia coli grown on 30 different carbon sources. Both models outperformed traditional in silico methods by identifying not just essential reactions but also nonessential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosynthetic pathways generally promote growth across various carbon sources, whereas the impact of energy-generating pathways varies with the carbon source. Intriguing predictions were experimentally validated for findings beyond experimental training data and the impact of various carbon sources on the glyoxylate shunt, pyruvate dehydrogenase reaction, and redundant purine biosynthesis reactions. These highlight the practical significance and predictive power of the models for understanding and engineering microbial metabolism.

Genome-scale metabolic network model and phenome of solvent-tolerant Pseudomonas putida S12.

BACKGROUND: Pseudomonas putida S12 is a gram-negative bacterium renowned for its high tolerance to organic solvents and metabolic versatility, making it attractive for various applications, including bioremediation and the production of aromatic compounds, bioplastics, biofuels, and value-added compounds. However, a metabolic model of S12 has yet to be developed. RESULTS: In this study, we present a comprehensive and highly curated genome-scale metabolic network model of S12 (iSH1474), containing 1,474 genes, 1,436 unique metabolites, and 2,938 metabolic reactions. The model was constructed by leveraging existing metabolic models and conducting comparative analyses of genomes and phenomes. Approximately 2,000 different phenotypes were measured for S12 and its closely related KT2440 strain under various nutritional and environmental conditions. These phenotypic data, combined with the reported experimental data, were used to refine and validate the reconstruction. Model predictions quantitatively agreed well with in vivo flux measurements and the batch cultivation of S12, which demonstrated that iSH1474 accurately represents the metabolic capabilities of S12. Furthermore, the model was simulated to investigate the maximum theoretical metabolic capacity of S12 growing on toxic organic solvents. CONCLUSIONS: iSH1474 represents a significant advancement in our understanding of the cellular metabolism of P. putida S12. The combined results of metabolic simulation and comparative genome and phenome analyses identified the genetic and metabolic determinants of the characteristic phenotypes of S12. This study could accelerate the development of this versatile organism as an efficient cell factory for various biotechnological applications.

SiSe2 for Superior Sulfide Solid Electrolytes and Li-Ion Batteries.

Among the various existing layered compounds, silicon diselenide (SiSe2) possesses diverse chemical and physical properties, owing to its large interlayer spacing and interesting atomic arrangements. Despite the unique properties of layered SiSe2, it has not yet been used in energy applications. Herein, we introduce the synthesis of layered SiSe2 through a facile solid-state synthetic route and demonstrate its versatility as a sulfide solid electrolyte (SE) additive for all-solid-state batteries (ASSBs) and as an anode material for Li-ion batteries (LIBs). Li-argyrodites with various compositions substituted with SiSe2 are synthesized and evaluated as sulfide SEs for ASSBs. SiSe2-substituted Li-argyrodites exhibit high ionic conductivities, low activation energies, and high air stabilities. In addition, when using a sulfide SE, the ASSB full cell exhibits a high discharge/charge capacity of 202/169 mAh g-1 with a high initial Coulombic efficiency (ICE) of 83.7% and stable capacity retention at 1C after 100 cycles. Furthermore, the Li-storage properties of SiSe2 as an anode material for LIBs are evaluated, and its Li-pathway mechanism is explored by using various cutting-edge ex situ analytical tools. Moreover, the SiSe2 nanocomposite anode exhibits a high Li- insertion/extraction capacity of 950/775 mAh g-1, a high ICE of 81.6%, a fast rate capability, and stable capacity retention after 300 cycles. Accordingly, layered SiSe2 and its versatile applications as a sulfide SE additive for ASSBs and an anode material for LIBs are promising candidates in energy storage applications as well as myriad other applications.