Early telomerase inactivation accelerates aging independently of telomere length.

Telomerase is required for long-term telomere maintenance and protection. Using single budding yeast mother cell analyses we found that, even early after telomerase inactivation (ETI), yeast mother cells show transient DNA damage response (DDR) episodes, stochastically altered cell-cycle dynamics, and accelerated mother cell aging. The acceleration of ETI mother cell aging was not explained by increased reactive oxygen species (ROS), Sir protein perturbation, or deprotected telomeres. ETI phenotypes occurred well before the population senescence caused late after telomerase inactivation (LTI). They were morphologically distinct from LTI senescence, were genetically uncoupled from telomere length, and were rescued by elevating dNTP pools. Our combined genetic and single-cell analyses show that, well before critical telomere shortening, telomerase is continuously required to respond to transient DNA replication stress in mother cells and that a lack of telomerase accelerates otherwise normal aging.

The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma.

Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. However, it still lacked a robust ensemble learning model with high accuracy to pick out patients with high risks. To achieve this, we proposed a novel genetic algorithm-aided three-stage ensemble learning method (3S score) for survival prediction. During the process of constructing the 3S score, double training sets were used to avoid over-fitting; the gene-pairing method was applied to reduce batch effect; a genetic algorithm was employed to select the best basic learner combination. When used to predict the survival state of glioma patients, this model achieved the highest C-index (0.697) as well as area under the receiver operating characteristic curve (ROC-AUCs) (first year = 0.705, third year = 0.825 and fifth year = 0.839) in the combined test set (n = 1191), compared with 12 other baseline models. Furthermore, the 3S score can distinguish survival significantly in eight cohorts among the total of nine independent test cohorts (P < 0.05), achieving significant improvement of ROC-AUCs. Notably, ablation experiments demonstrated that the gene-pairing method, double training sets and genetic algorithm make sure the robustness and effectiveness of the 3S score. The performance exploration on pan-cancer showed that the 3S score has excellent ability on survival prediction in five kinds of cancers, which was verified by Cox regression, survival curves and ROC curves together. To enable its clinical adoption, we implemented the 3S score and other two clinical factors as an easy-to-use web tool for risk scoring and therapy stratification in glioma patients.

Identification of Anoikis-Related Subgroups and Prognosis Model in Liver Hepatocellular Carcinoma.

Resistance to anoikis is a key characteristic of many cancer cells, promoting cell survival. However, the mechanism of anoikis in hepatocellular carcinoma (HCC) remains unknown. In this study, we applied differentially expressed overlapping anoikis-related genes to classify The Cancer Genome Atlas (TCGA) samples using an unsupervised cluster algorithm. Then, we employed weighted gene coexpression network analysis (WGCNA) to identify highly correlated genes and constructed a prognostic risk model based on univariate Cox proportional hazards regression. This model was validated using external datasets from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Finally, we used a CIBERSORT algorithm to investigate the correlation between risk score and immune infiltration. Our results showed that the TCGA cohorts could be divided into two subgroups, with subgroup A having a lower survival probability. Five genes (BAK1, SPP1, BSG, PBK and DAP3) were identified as anoikis-related prognostic genes. Moreover, the prognostic risk model effectively predicted overall survival, which was validated using ICGC and GEO datasets. In addition, there was a strong correlation between infiltrating immune cells and prognostic genes and risk score. In conclusion, we identified anoikis-related subgroups and prognostic genes in HCC, which could be significant for understanding the molecular mechanisms and treatment of HCC.

Robust prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer.

Immune infiltration of ovarian cancer (OV) is a critical factor in determining patient's prognosis. Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, 46 genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on 3 immune genes and 3 types of clinical information. The IGCI score has been verified by K-M curves, ROC curves and C-index on test set. In test set, IGCI score (C-index = 0.630) is significantly better than AJCC stage (C-index = 0.541, p < 0.05) and CIN25 (C-index = 0.571, p < 0.05). In addition, we identified key mutations to analyse prognosis of patients and the process related to immunity. Chi-squared tests revealed that 6 mutations are significantly (p < 0.05) related to immune infiltration: BRCA1, ZNF462, VWF, RBAK, RB1 and ADGRV1. According to mutation survival analysis, we found 5 key mutations significantly related to patient prognosis (p < 0.05): CSMD3, FLG2, HMCN1, TOP2A and TRRAP. RB1 and CSMD3 mutations had small p-value (p < 0.1) in both chi-squared tests and survival analysis. The drug sensitivity analysis of key mutation showed when RB1 mutation occurs, the efficacy of six anti-tumour drugs has changed significantly (p < 0.05).

Single-layered phase-change metasurfaces achieving efficient wavefront manipulation and reversible chiral transmission.

Efficient control of the phase and polarization of light is of significant importance in modern optics and photonics. However, traditional methods are often accompanied with cascaded and bulky designs that cannot fulfill the ongoing demand for further integrations. Here, a single-layered metasurface composed of nonvolatile phase-change material Ge2Sb2Se4Te1 (GSST) is proposed with tunable spin-orbit interactions in subwavelength scale. According to the spin-dependent destructive or constructive interference, asymmetric transmission for circularly polarized incidence (extinction ratio > 8:1) can be achieved when GSST is in an amorphous state. Moreover, when GSST changes to crystalline state, reversed chiral transmission (extinction ratio > 12:1) can be observed due to the existence of intrinsic chirality. In addition, as the average cross-polarized transmitted amplitude is larger than 85%, arbitrary wavefront manipulations can be achieved in both states simultaneously based on the theory of Pancharatnam-Berry phase. As a proof of concept, several functional metasurface devices are designed and characterized to further demonstrate the validation of our design methodology. It is believed that these multifunctional devices with ultrahigh compactness are promising for various applications including chiroptical spectroscopy, EM communication, chiral imaging, and information encryption.

Halomonas spp., as chassis for low-cost production of chemicals.

Halomonas spp. are the well-studied platform organisms or chassis for next-generation industrial biotechnology (NGIB) due to their contamination-resistant nature combined with their fast growth property. Several Halomonas spp. have been studied regarding their genomic information and molecular engineering approaches. Halomonas spp., especially Halomonas bluephagenesis, have been engineered to produce various biopolyesters such as polyhydroxyalkanoates (PHA), proteins including surfactants and enzymes, small molecular compounds including amino acids and derivates, as well as organic acids. This paper reviews all the progress reported in the last 10 years regarding this robust microbial cell factory. KEY POINTS: • Halomonas spp. are robust chassis for low-cost production of chemicals • Genomic information of some Halomonas spp. has been revealed • Molecular tools and approaches for Halomonas spp. have been developed • Halomonas spp. are becoming more and more important for biotechnology.

Expression and Prognostic Characteristics of Ferroptosis-Related Genes in Colon Cancer.

Ferroptosis is a new type of programmed cell death, which occurs with iron dependence. Previous studies have showed that ferroptosis plays an important regulatory role in the occurrence and development of tumors. Colon cancer is one of the major morbidities and causes of mortality in the world. This study used RNA-seq and colon cancer clinical data to explore the relationship between ferroptosis-related genes and colon cancer. Based on the fifteen prognostic ferroptosis-related genes, two molecular subgroups of colon cancer were identified. Surprisingly, we also found cluster2 was characterized by lower mutation burden and expression of checkpoint genes, better survival, and higher expression of NOX1. Moreover, cluster2 has fewer BRAF mutations. We also found the expression of NOX1 is related to the status of BRAF. Finally, using 15 ferroptosis-related genes from The Cancer Genome Atlas cohort, we constructed a prognosis model, and this model may be used to predict the prognosis of patients in clinics.

Prediction and Optimization of NaV1.7 Sodium Channel Inhibitors Based on Machine Learning and Simulated Annealing.

Although the NaV1.7 sodium channel is a promising drug target for pain, traditional screening strategies for discovery of NaV1.7 inhibitors are very painstaking and time-consuming. Herein, we aimed to build machine learning models for screening and design of potent and effective NaV1.7 sodium channel inhibitors. We customized the imbalanced data set from ChEMBL and BindingDB to train and filter the best classification model. Then, the whole-cell voltage-clamp was employed to validate the inhibitors. We assembled a molecular group optimization method by combining the Grammar Variational Autoencoder, classification model, and simulated annealing. We found that the RF-CDK model (random forest + CDK fingerprint) performs best in the imbalanced data set. Of the three compounds that may have inhibitory effects, nortriptyline has been experimentally verified. In the molecule optimization process, 40 molecules located in the applicability domain of RF-CDK were used as a starting point, among which 34 molecules evolved to molecules with greater molecular scores (MS). The molecule with the highest MS was derived from CHEMBL2325245. The model and method we developed for NaV1.7 inhibitors are also applicable to other targets.

Identification of Key Genes Related to Lung Squamous Cell Carcinoma Using Bioinformatics Analysis.

Lung squamous cell carcinoma (LUSC) is often diagnosed at the advanced stage with poor prognosis. The mechanisms of its pathogenesis and prognosis require urgent elucidation. This study was performed to screen potential biomarkers related to the occurrence, development and prognosis of LUSC to reveal unknown physiological and pathological processes. Using bioinformatics analysis, the lung squamous cell carcinoma microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs). Furthermore, PPI and WGCNA network analysis were integrated to identify the key genes closely related to the process of LUSC development. In addition, survival analysis was performed to achieve a prognostic model that accomplished good prediction accuracy. Three hundred and thirty-seven up-regulated and 119 down-regulated genes were identified, in which four genes have been found to play vital roles in LUSC development, namely CCNA2, AURKA, AURKB, and FEN1. The prognostic model contained 5 genes, which were all detrimental to prognosis. The AUC of the established prognostic model for predicting the survival of patients at 1, 3, and 5 years was 0.692, 0.722, and 0.651 in the test data, respectively. In conclusion, this study identified several biomarkers of significant interest for additional investigation of the therapies and methods of prognosis of lung squamous cell carcinoma.

Multiple-Purpose Connectivity Map Analysis Reveals the Benefits of Esculetin to Hyperuricemia and Renal Fibrosis.

Hyperuricemia (HUA) is a risk factor for chronic kidney disease (CKD). Serum uric acid (SUA) levels in CKD stage 3-4 patients closely correlate with hyperuricemic nephropathy (HN) morbidity. New uric acid (UA)-lowering strategies are required to prevent CKD. The multiple-purpose connectivity map (CMAP) was used to discover potential molecules against HUA and renal fibrosis. We used HUA and unilateral ureteral occlusion (UUO) model mice to verify renoprotective effects of molecules and explore related mechanisms. In vitro experiments were performed in HepG2 and NRK-52E cells induced by UA. Esculetin was the top scoring compound and lowered serum uric acid (SUA) levels with dual functions on UA excretion. Esculetin exerted these effects by inhibiting expression and activity of xanthine oxidase (XO) in liver, and modulating UA transporters in kidney. The mechanism by which esculetin suppressed XO was related to inhibiting the nuclear translocation of hexokinase 2 (HK2). Esculetin was anti-fibrotic in HUA and UUO mice through inhibiting TGF-ß1-activated profibrotic signals. The renoprotection effects of esculetin in HUA mice were associated with lower SUA, alleviation of oxidative stress, and inhibition of fibrosis. Esculetin is a candidate urate-lowering drug with renoprotective activity and the ability to inhibit XO, promote excretion of UA, protect oxidative stress injury, and reduce renal fibrosis.

Genome-scale fluxes predicted under the guidance of enzyme abundance using a novel hyper-cube shrink algorithm.

Motivation: One of the long-expected goals of genome-scale metabolic modelling is to evaluate the influence of the perturbed enzymes on flux distribution. Both ordinary differential equation (ODE) models and constraint-based models, like Flux balance analysis (FBA), lack the capacity to perform metabolic control analysis (MCA) for large-scale networks. Results: In this study, we developed a hyper-cube shrink algorithm (HCSA) to incorporate the enzymatic properties into the FBA model by introducing a pseudo reaction V constrained by enzymatic parameters. Our algorithm uses the enzymatic information quantitatively rather than qualitatively. We first demonstrate the concept by applying HCSA to a simple three-node network, whereby we obtained a good correlation between flux and enzyme abundance. We then validate its prediction by comparison with ODE and with a synthetic network producing voilacein and analogues in Saccharomyces cerevisiae. We show that HCSA can mimic the state-state results of ODE. Finally, we show its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast. We show the ability of HCSA to operate without biomass flux and perform MCA to determine rate-limiting reactions. Availability and implementation: Algorithm was implemented by Matlab and C ++. The code is available at https://github.com/kekegg/HCSA. Contact: xiezhengwei@hsc.pku.edu.cn or qi@pku.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

Numerical simulation research of catenary tip-insulator-metal structure for nano-lithography.

Catenary optics has attracted much interest due to its unique properties in wave-front manipulation, field enhancement, and dispersion engineering. In this paper, the applications of catenary optics in the near-field lithography are studied. The catenary shaped nanostructures and tip-insulator-metal (TIM) structures are simultaneously utilized to increase the contrast ratio of the focal plane and to give rise to a sharp focusing focal spot with high intensity. Moreover, the full width at half-maximum of the focal spot maintains well below the diffraction limit. The proposed catenary TIM structure may improve the quality of near-field lithography and find applications in super-resolution near-field direct writing nano-lithography.

Ganoderma Lucidum Polysaccharide Peptide Alleviates Hepatoteatosis via Modulating Bile Acid Metabolism Dependent on FXR-SHP/FGF.

BACKGROUND/AIMS: Non-alcoholic fatty liver disease (NAFLD) encompasses a series of pathologic changes ranging from steatosis to steatohepatitis, which may progress to cirrhosis and hepatocellular carcinoma. The purpose of this study was to determine whether ganoderma lucidum polysaccharide peptide (GLPP) has therapeutic effect on NAFLD. METHODS: Ob/ ob mouse model and ApoC3 transgenic mouse model were used for exploring the effect of GLPP on NAFLD. Key metabolic pathways and enzymes were identified by metabolomics combining with KEGG and PIUmet analyses and key enzymes were detected by Western blot. Hepatosteatosis models of HepG2 cells and primary hepatocytes were used to further confirm the therapeutic effect of GLPP on NAFLD. RESULTS: GLPP administrated for a month alleviated hepatosteatosis, dyslipidemia, liver dysfunction and liver insulin resistance. Pathways of glycerophospholipid metabolism, fatty acid metabolism and primary bile acid biosynthesis were involved in the therapeutic effect of GLPP on NAFLD. Detection of key enzymes revealed that GLPP reversed low expression of CYP7A1, CYP8B1, FXR, SHP and high expression of FGFR4 in ob/ob mice and ApoC3 mice. Besides, GLPP inhibited fatty acid synthesis by reducing the expression of SREBP1c, FAS and ACC via a FXR-SHP dependent mechanism. Additionally, GLPP reduced the accumulation of lipid droplets and the content of TG in HepG2 cells and primary hepatocytes induced by oleic acid and palmitic acid. CONCLUSION: GLPP significantly improves NAFLD via regulating bile acid synthesis dependent on FXR-SHP/FGF pathway, which finally inhibits fatty acid synthesis, indicating that GLPP might be developed as a therapeutic drug for NAFLD.

Metasurface with multi-sized structure for multi-band coherent perfect absorption.

We demonstrate that multi-band coherent perfect absorption can be achieved at infrared frequencies by a metasurface in which four-sized columnar metal patches are separated by a dielectric layer in a unit cell. The absorption bandwidth is enhanced by three times compared with single-band absorption while high absorbance is maintained. The coherent perfect absorption is polarization-independent and can be independently modulated at each resonant frequency by tuning the phase difference of two coherent incident beams. Moreover, the resonant frequency is sensitive to the radius of the columnar patch, and thus a wide coherent perfect absorption frequency range can be obtained by adjusting the radius. Through optimizing the structural parameters, nearly perfect absorption at oblique incidence for both TE and TM polarizations are achieved. The optimized metasurface can be used as a beamsplitter at oblique incidence.

Dual-band tunable perfect metamaterial absorber based on graphene.

In this paper, a dual-band perfect metamaterial absorber based on graphene is proposed in the terahertz region. The metamaterial absorber consists of two sizes of graphene disks and a gold film separated by a dielectric spacer in a unit cell. The numerical results demonstrate that the dual-band perfect absorption can be achieved by the superposition of the specific absorption peaks induced by different disks. The resonance frequency can be tuned via controlling the graphene conductivity and the sizes of the disks. The metamaterial absorber can achieve selectively frequency tunability and it can tune each resonance independently. And the dual-band absorption will not be changed when the small disks move along the diagonal within the range of our research. In addition, owing to the symmetry of the structure, the absorber is insensitive to polarization and can keep a high absorptivity with a wide angle. The flexible and simple design makes it possible for our proposed single-layer graphene absorber to be applied in many metamaterial fields, such as sensing, detecting, and cloaking objects.

Systematic analysis of asymmetric partitioning of yeast proteome between mother and daughter cells reveals "aging factors" and mechanism of lifespan asymmetry.

Budding yeast divides asymmetrically, giving rise to a mother cell that progressively ages and a daughter cell with full lifespan. It is generally assumed that mother cells retain damaged, lifespan limiting materials ("aging factors") through asymmetric division. However, the identity of these aging factors and the mechanisms through which they limit lifespan remain poorly understood. Using a flow cytometry-based, high-throughput approach, we quantified the asymmetric partitioning of the yeast proteome between mother and daughter cells during cell division, discovering 74 mother-enriched and 60 daughter-enriched proteins. While daughter-enriched proteins are biased toward those needed for bud construction and genome maintenance, mother-enriched proteins are biased towards those localized in the plasma membrane and vacuole. Deletion of 23 of the 74 mother-enriched proteins leads to lifespan extension, a fraction that is about six times that of the genes picked randomly from the genome. Among these lifespan-extending genes, three are involved in endosomal sorting/endosome to vacuole transport, and three are nitrogen source transporters. Tracking the dynamic expression of specific mother-enriched proteins revealed that their concentration steadily increases in the mother cells as they age, but is kept relatively low in the daughter cells via asymmetric distribution. Our results suggest that some mother-enriched proteins may increase to a concentration that becomes deleterious and lifespan-limiting in aged cells, possibly by upsetting homeostasis or leading to aberrant signaling. Our study provides a comprehensive resource for analyzing asymmetric cell division and aging in yeast, which should also be valuable for understanding similar phenomena in other organisms.

Utilization of gene manipulation system for advancing the biotechnological potential of halophiles: A review.

Halophiles are salt-loving microorganisms known to have their natural resistance against media contamination even when cultivated in nonsterile and continuous bioprocess system, thus acting as promising cell factories for Next Generation of Industrial Biotechnology (NGIB). NGIB - a successor to the traditional industrial biotechnology, is a more sustainable and efficient bioprocess technology while saving energy and water in a more convenient way as well as reducing the investment cost and skilled workforce requirement. Numerous studies have achieved intriguing outcomes during synthesis of different metabolite using halophiles such as polyhydroxyalkanoates (PHA), ectoine, biosurfactants, and carotenoids. Present-day development in genetic maneuverings have shown optimistic effects on the industrial applications of halophiles. However, viable and competent genetic manipulation system and gene editing tools are critical to accelerate the process of halophile engineering. With the aid of such powerful gene manipulation systems, exclusive microbial chassis are being crafted with desirable features to breed another innovative area of research such as synthetic biology. This review provides an aerial perspective on how the expansion of adaptable gene manipulation toolkits in halophiles are contributing towards biotechnological advancement, and also focusses on their subsequent application for production improvement. This current methodical and comprehensive review will definitely help the scientific fraternity to bridge the gap between challenges and opportunities in halophile engineering.

Constructing a Stable Conductive Network for High-Performance Silicon-Based Anode in Lithium-Ion Batteries.

The application of carbon nanotubes to silicon nanoparticles has been used to improve the electrical conductivity of silicon-carbon anodes and prevent agglomeration of silicon nanoparticles during cycling. In this study, the composites are synthesized through an uncomplicated technique that involves the ultrasonication mixing of pyrene derivatives and carbon nanotubes and the formation of complexes with silicon nanoparticles in ultrasonic dispersion and magnetic stirring and then treated under vacuum. When the prepared composites are applied as lithium-ion battery anodes, the Si@(POH-AOCNTs) electrode displays a high reversible capacity of 3254.7 mAh g-1 at a current density of 0.1 A g-1. Furthermore, it exhibits excellent cycling stability with a specific capacity of 1195.8 mAh g-1 after 500 cycles at 1.0 A g-1. The superior electrochemical performance may be attributed to a large π-conjugated electron system of pyrene derivatives, which prompts the formation of a homogeneous CNTs conductive network and ensures the effective electron transfer, while the interaction between hydroxyl functional groups of hydroxypyrene and binder synergizes with CNTs network to further enhance the cycling stability of the composite.

Ataluren prevented bone loss induced by ovariectomy and aging in mice through the BMP-SMAD signaling pathway.

Both estrogen deficiency and aging may lead to osteoporosis. Developing novel drugs for treating osteoporosis is a popular research direction. We screened several potential therapeutic agents through a new deep learning-based efficacy prediction system (DLEPS) using transcriptional profiles for osteoporosis. DLEPS screening led to a potential novel drug examinee, ataluren, for treating osteoporosis. Ataluren significantly reversed bone loss in ovariectomized mice. Next, ataluren significantly increased human bone marrow-derived mesenchymal stem cell (hBMMSC) osteogenic differentiation without cytotoxicity, indicated by the high expression index of osteogenic differentiation genes (OCN , BGLAP, ALP, COL1A, BMP2, RUNX2). Mechanistically, ataluren exerted its function through the BMP-SMAD pathway. Furthermore, it activated SMAD phosphorylation but osteogenic differentiation was attenuated by BMP2-SMAD inhibitors or small interfering RNA of BMP2. Finally, ataluren significantly reversed bone loss in aged mice. In summary, our findings suggest that the DLEPS-screened ataluren may be a therapeutic agent against osteoporosis by aiding hBMMSC osteogenic differentiation.

Deep Learning-Predicted Dihydroartemisinin Rescues Osteoporosis by Maintaining Mesenchymal Stem Cell Stemness through Activating Histone 3 Lys 9 Acetylation.

Maintaining the stemness of bone marrow mesenchymal stem cells (BMMSCs) is crucial for bone homeostasis and regeneration. However, in vitro expansion and bone diseases impair BMMSC stemness, limiting its functionality in bone tissue engineering. Using a deep learning-based efficacy prediction system and bone tissue sequencing, we identify a natural small-molecule compound, dihydroartemisinin (DHA), that maintains BMMSC stemness and enhances bone regeneration. During long-term in vitro expansion, DHA preserves BMMSC stemness characteristics, including its self-renewal ability and unbiased differentiation. In an osteoporosis mouse model, oral administration of DHA restores the femur trabecular structure, bone density, and BMMSC stemness in situ. Mechanistically, DHA maintains BMMSC stemness by promoting histone 3 lysine 9 acetylation via GCN5 activation both in vivo and in vitro. Furthermore, the bone-targeted delivery of DHA by mesoporous silica nanoparticles improves its therapeutic efficacy in osteoporosis. Collectively, DHA could be a promising therapeutic agent for treating osteoporosis by maintaining BMMSC stemness.