Epimedii Folium decoction ameliorates osteoporosis in mice through NLRP3/caspase-1/IL-1ß signalling pathway and gut-bone axis.

AIM OF THE STUDY: This study aimed to determine the effect of Epimedium brevicornu Maxim. (EF) on osteoporosis (OP) and its underlying molecular mechanisms, and to explore the existence of the "Gut-Bone Axis". MATERIAL AND METHODS: The impact of EF decoction (EFD) on OP was evaluated using istopathological examination and biochemical assays. Targeted metabolomics was employed to identify key molecules and explore their molecular mechanisms. Alterations in the gut microbiota (GM) were evaluated by 16S rRNA gene sequencing. The role of the GM was clarified using an antibiotic cocktail and faecal microbiota transplantation. RESULTS: EFD significantly increased the weight (14.06%), femur length (4.34%), abdominal fat weight (61.14%), uterine weight (69.86%), and insulin-like growth factor 1 (IGF-1) levels (59.48%), while reducing serum type I collagen cross-linked carboxy-terminal peptide (CTX-I) levels (15.02%) in osteoporotic mice. The mechanism of action may involve the regulation of the NLRP3/cleaved caspase-1/IL-1ß signalling pathway in improving intestinal tight junction proteins and bone metabolism. Additionally, EFD modulated the abundance of related GM communities, such as Lactobacillus, Coriobacteriaceae, bacteria of family S24-7, Clostridiales, and Prevotella, and increased propionate and butyrate levels. Antibiotic-induced dysbiosis of gut bacteria disrupted OP regulation of bone metabolism, which was restored by the recovery of GM. CONCLUSIONS: Our study is the first to demonstrate that EFD works in an OP mouse model by utilising GM and butyric acid. Thus, EF shows promise as a potential remedy for OP in the future.

Single-cell phylotranscriptomics of developmental and cell type evolution.

Single-cell phylotranscriptomics is an emerging tool to reveal the molecular and cellular mechanisms of evolution. We summarize its utility in studying the hourglass pattern of ontogenetic evolution and for understanding the evolutionary history of cell types. The developmental hourglass model suggests that the mid-embryonic stage is the most conserved period of development across species, which is supported by morphological and molecular studies. Single-cell phylotranscriptomic analysis has revealed previously underappreciated heterogeneity in transcriptome ages among lineages and cell types throughout development, and has identified the lineages and tissues that drive the whole-organism hourglass pattern. Single-cell transcriptome age analyses also provide important insights into the origin of germ layers, the different selective forces on tissues during adaptation, and the evolutionary relationships between cell types.

Young duplicate genes show developmental stage- and cell type-specific expression and function in Caenorhabditis elegans.

Gene duplication produces the material that fuels evolutionary innovation. The "out-of-testis" hypothesis suggests that sperm competition creates selective pressure encouraging the emergence of new genes in male germline, but the somatic expression and function of the newly evolved genes are not well understood. We systematically mapped the expression of young duplicate genes throughout development in Caenorhabditis elegans using both whole-organism and single-cell transcriptomic data. Based on the expression dynamics across developmental stages, young duplicate genes fall into three clusters that are preferentially expressed in early embryos, mid-stage embryos, and late-stage larvae. Early embryonic genes are involved in protein degradation and develop essentiality comparable to the genomic average. In mid-to-late embryos and L4-stage larvae, young genes are enriched in intestine, epidermal cells, coelomocytes, and amphid chemosensory neurons. Their molecular functions and inducible expression indicate potential roles in innate immune response and chemosensory perceptions, which may contribute to adaptation outside of the sperm.

NADH-dependent formate dehydrogenase mutants for efficient carbon dioxide fixation.

Bioconversion of CO2 to high-valuable products is a globally pursued sustainable technology for carbon neutrality. However, low CO2 activation with formate dehydrogenase (FDH) remains a major challenge for further upcycling due to the poor CO2 affinity, reduction activity and stability of currently used FDHs. Here, we present two recombined mutants, ΔFDHPa48 and ΔFDHPa4814, which exhibit high CO2 reduction activity and antioxidative activity. Compared to FDHPa, the reduction activity of ΔFDHPa48 was increased up to 743 % and the yield in the reduction of CO2 to methanol was increased by 3.16-fold. Molecular dynamics identified that increasing the width of the substrate pocket of ΔFDHPa48 could improve the enzyme reduction activity. Meanwhile, the enhanced rigidity of C-terminal residues effectively protected the active center. These results fundamentally advanced our understanding of the CO2 activation process and efficient FDH for enzymatic CO2 activation and conversion.

Beam quality control technology for solid-state lasers based on thermal effects.

Thermal effects under high-power pumping significantly limit the laser beam quality. To address this, we developed an M2 simulation algorithm based on ray trajectory simulation and established a corresponding experimental platform. This approach optimized the M2 factor of pulsed lasers to 2.2 and output power of 25.9 W under a repetition rate of 10 kHz. The results revealed that under specific conditions, thermal effects, typically considered detrimental to beam quality, could significantly enhance it. Compared to other methods necessitating additional optical components, our strategy offers a streamlined and straightforward solution for beam quality control under high-power pumping conditions.

[Advances in methods and applications of single-cell Hi-C data analysis].

Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.

Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment.

INTRODUCTION: The COVID-19 patients in the convalescent stage noticeably have pulmonary diffusing capacity impairment (PDCI). The pulmonary diffusing capacity is a frequently-used indicator of the COVID-19 survivors' prognosis of pulmonary function, but the current studies focusing on prediction of the pulmonary diffusing capacity of these people are limited. The aim of this study was to develop and validate a machine learning (ML) model for predicting PDCI in the COVID-19 patients using routinely available clinical data, thus assisting the clinical diagnosis. METHODS: Collected from a follow-up study from August to September 2021 of 221 hospitalized survivors of COVID-19 18 months after discharge from Wuhan, including the demographic characteristics and clinical examination, the data in this study were randomly separated into a training (80%) data set and a validation (20%) data set. Six popular machine learning models were developed to predict the pulmonary diffusing capacity of patients infected with COVID-19 in the recovery stage. The performance indicators of the model included area under the curve (AUC), Accuracy, Recall, Precision, Positive Predictive Value(PPV), Negative Predictive Value (NPV) and F1. The model with the optimum performance was defined as the optimal model, which was further employed in the interpretability analysis. The MAHAKIL method was utilized to balance the data and optimize the balance of sample distribution, while the RFECV method for feature selection was utilized to select combined features more favorable to machine learning. RESULTS: A total of 221 COVID-19 survivors were recruited in this study after discharge from hospitals in Wuhan. Of these participants, 117 (52.94%) were female, with a median age of 58.2 years (standard deviation (SD) = 12). After feature selection, 31 of the 37 clinical factors were finally selected for use in constructing the model. Among the six tested ML models, the best performance was accomplished in the XGBoost model, with an AUC of 0.755 and an accuracy of 78.01% after experimental verification. The SHAPELY Additive explanations (SHAP) summary analysis exhibited that hemoglobin (Hb), maximal voluntary ventilation (MVV), severity of illness, platelet (PLT), Uric Acid (UA) and blood urea nitrogen (BUN) were the top six most important factors affecting the XGBoost model decision-making. CONCLUSION: The XGBoost model reported here showed a good prognostic prediction ability for PDCI of COVID-19 survivors during the recovery period. Among the interpretation methods based on the importance of SHAP values, Hb and MVV contributed the most to the prediction of PDCI outcomes of COVID-19 survivors in the recovery period.

Fluorescence coupling strategies in fluorescence-activated droplet sorting (FADS) for ultrahigh-throughput screening of enzymes, metabolites, and antibodies.

Fluorescence-activated droplet sorting (FADS) has emerged as a powerful tool for ultrahigh-throughput screening of enzymes, metabolites, and antibodies. Fluorescence coupling strategies (FCSs) are key to the development of new FADS methods through their coupling of analyte properties such as concentration, activities, and affinity with fluorescence signals. Over the last decade, a series of FCSs have been developed, greatly expanding applications of FADS. Here, we review recent advances in FCS for different analyte types, providing a critical comparison of the available FCSs and further classification into four categories according to their principles. We also summarize successful FADS applications employing FCSs in enzymes, metabolites, and antibodies. Further, we outline possible future developments in this area.

A 3-D Chromosome Structure Reconstruction Method With High Resolution Hi-C Data Using Nonlinear Dimensionality Reduction and Divide-and-Conquer Strategy.

Chromosomes are fundamental components of genetic material, and their structural characteristics play an essential role in the regulation of gene expression. The advent of high-resolution Hi-C data has enabled scientists to explore the three-dimensional structure of chromosomes. However, most of the currently available methods for reconstructing chromosome structures are unable to achieve high resolutions, such as 5 Kilobase (KB). In this study, we present NeRV-3D, an innovative method that utilizes a nonlinear dimensionality reduction visualization algorithm to reconstruct 3D chromosome structures at low resolutions. Additionally, we introduce NeRV-3D-DC, which employs a divide-and-conquer technique to reconstruct and visualize 3D chromosome structures at high resolutions. Our results demonstrate that both NeRV-3D and NeRV-3D-DC outperform existing methods in terms of 3D visualization effects and evaluation metrics on simulated and actual Hi-C datasets. The implementation of NeRV-3D-DC can be found at https://github.com/ghaiyan/ NeRV-3D-DC.

Transcriptome age of individual cell types in Caenorhabditis elegans.

The phylotranscriptomic analysis of development in several species revealed the expression of older and more conserved genes in midembryonic stages and younger and more divergent genes in early and late embryonic stages, which supported the hourglass mode of development. However, previous work only studied the transcriptome age of whole embryos or embryonic sublineages, leaving the cellular basis of the hourglass pattern and the variation of transcriptome ages among cell types unexplored. By analyzing both bulk and single-cell transcriptomic data, we studied the transcriptome age of the nematode Caenorhabditis elegans throughout development. Using the bulk RNA-seq data, we identified the morphogenesis phase in midembryonic development as the phylotypic stage with the oldest transcriptome and confirmed the results using whole-embryo transcriptome assembled from single-cell RNA-seq data. The variation in transcriptome ages among individual cell types remained small in early and midembryonic development and grew bigger in late embryonic and larval stages as cells and tissues differentiate. Lineages that give rise to certain tissues (e.g., hypodermis and some neurons) but not all recapitulated the hourglass pattern across development at the single-cell transcriptome level. Further analysis of the variation in transcriptome ages among the 128 neuron types in C. elegans nervous system found that a group of chemosensory neurons and their downstream interneurons expressed very young transcriptomes and may contribute to adaptation in recent evolution. Finally, the variation in transcriptome age among the neuron types, as well as the age of their cell fate regulators, led us to hypothesize the evolutionary history of some neuron types.

All-in-One Digital Microfluidics System for Molecular Diagnosis with Loop-Mediated Isothermal Amplification.

In this study, an "all-in-one" digital microfluidics (DMF) system was developed for automatic and rapid molecular diagnosis and integrated with magnetic bead-based nucleic acid extraction, loop-mediated isothermal amplification (LAMP), and real-time optical signal monitoring. First, we performed on- and off-chip comparison experiments for the magnetic bead nucleic acid extraction module and LAMP amplification function. The extraction efficiency for the on-chip test was comparable to that of conventional off-chip methods. The processing time for the automatic on-chip workflow was only 23 min, which was less than that of the conventional methods of 28 min 45 s. Meanwhile, the number of samples used in on-chip experiments was significantly smaller than that used in off-chip experiments; only 5 µL of E. coli samples was required for nucleic acid extraction, and 1 µL of the nucleic acid template was needed for the amplification reaction. In addition, we selected SARS-CoV-2 nucleic acid reference materials for the nucleic acid detection experiment, demonstrating a limit of detection of 10 copies/µL. The proposed "all-in-one" DMF system provides an on-site "sample to answer" time of approximately 60 min, which can be a powerful tool for point-of-care molecular diagnostics.

Improved thermostability of proteinase K and recognizing the synergistic effect of Rosetta and FoldX approaches.

Proteinase K (PRK) is a proteolytic enzyme that has been widely used in industrial applications. However, poor stability has severely limited the uses of PRK. In this work, we used two structure-guided rational design methods, Rosetta and FoldX, to modify PRK thermostability. Fifty-two single amino acid conversion mutants were constructed based on software predictions of residues that could affect protein stability. Experimental characterization revealed that 46% (21 mutants) exhibited enhanced thermostability. The top four variants, D260V, T4Y, S216Q, and S219Q, showed improved half-lives at 69°C by 12.4-, 2.6-, 2.3-, and 2.2-fold that of the parent enzyme, respectively. We also found that selecting mutations predicted by both methods could increase the predictive accuracy over that of either method alone, with 73% of the shared predicted mutations resulting in higher thermostability. In addition to providing promising new variants of PRK in industrial applications, our findings also show that combining these programs may synergistically improve their predictive accuracy.

Genome-wide screen identifies curli amyloid fibril as a bacterial component promoting host neurodegeneration.

Growing evidence indicates that gut microbiota play a critical role in regulating the progression of neurodegenerative diseases such as Parkinson's disease. The molecular mechanism underlying such microbe-host interaction is unclear. In this study, by feeding Caenorhabditis elegans expressing human α-syn with Escherichia coli knockout mutants, we conducted a genome-wide screen to identify bacterial genes that promote host neurodegeneration. The screen yielded 38 genes that fall into several genetic pathways including curli formation, lipopolysaccharide assembly, and adenosylcobalamin synthesis among others. We then focused on the curli amyloid fibril and found that genetically deleting or pharmacologically inhibiting the curli major subunit CsgA in E. coli reduced α-syn-induced neuronal death, restored mitochondrial health, and improved neuronal functions. CsgA secreted by the bacteria colocalized with α-syn inside neurons and promoted α-syn aggregation through cross-seeding. Similarly, curli also promoted neurodegeneration in C. elegans models of Alzheimer's disease, amyotrophic lateral sclerosis, and Huntington's disease and in human neuroblastoma cells.

Large genetic diversity and strong positive selection in F-box and GPCR genes among the wild isolates of Caenorhabditis elegans.

The F-box and chemosensory GPCR (csGPCR) gene families are greatly expanded in nematodes, including the model organism Caenorhabditis elegans, compared to insects and vertebrates. However, the intraspecific evolution of these two gene families in nematodes remain unexamined. In this study, we analyzed the genomic sequences of 330 recently sequenced wild isolates of C. elegans using a range of population genetics approaches. We found that F-box and csGPCR genes, especially the Srw family csGPCRs, showed much more diversity than other gene families. Population structure analysis and phylogenetic analysis divided the wild strains into eight non-Hawaiian and three Hawaiian subpopulations. Some Hawaiian strains appeared to be more ancestral than all other strains. F-box and csGPCR genes maintained a great amount of the ancestral variants in the Hawaiian subpopulation and their divergence among the non-Hawaiian subpopulations contributed significantly to population structure. F-box genes are mostly located at the chromosomal arms and high recombination rate correlates with their large polymorphism. Moreover, using both neutrality tests and Extended Haplotype Homozygosity analysis, we identified signatures of strong positive selection in the F-box and csGPCR genes among the wild isolates, especially in the non-Hawaiian population. Accumulation of high-frequency derived alleles in these genes was found in non-Hawaiian population, leading to divergence from the ancestral genotype. In summary, we found that F-box and csGPCR genes harbour a large pool of natural variants, which may be subjected to positive selection. These variants are mostly mapped to the substrate-recognition domains of F-box proteins and the extracellular and intracellular regions of csGPCRs, possibly resulting in advantages during adaptation by affecting protein degradation and the sensing of environmental cues, respectively.

An ultrahigh-throughput screening platform based on flow cytometric droplet sorting for mining novel enzymes from metagenomic libraries.

Uncultivable microbial communities provide enormous reservoirs of enzymes, but their experimental identification by functional metagenomics is challenging, mainly due to the difficulty of screening enormous metagenomic libraries. Here, we propose a reliable and convenient ultrahigh-throughput screening platform based on flow cytometric droplet sorting (FCDS). The FCDS platform employs water-in-oil-in-water double emulsion droplets serving as single-cell enzymatic micro-reactors and a commercially available flow cytometer, and it can efficiently isolate novel biocatalysts from metagenomic libraries by processing single cells as many as 108 per day. We demonstrated the power of this platform by screening a metagenomic library constructed from domestic running water samples. The FCDS assay screened 30 million micro-reactors in only 1 h, yielding a collection of esterase genes. Among these positive hits, Est WY was identified as a novel esterase with high catalytic efficiency and distinct evolutionary origin from other lipolytic enzymes. Our study manifests that the FCDS platform is a robust tool for functional metagenomics, with the potential to significantly improve the efficiency of exploring novel enzymes from nature.

Improved thermostability of creatinase from Alcaligenes Faecalis through non-biased phylogenetic consensus-guided mutagenesis.

BACKGROUND: Enzymatic quantification of creatinine has become an essential method for clinical evaluation of renal function. Although creatinase (CR) is frequently used for this purpose, its poor thermostability severely limits industrial applications. Herein, we report a novel creatinase from Alcaligenes faecalis (afCR) with higher catalytic activity and lower KM value, than currently used creatinases. Furthermore, we developed a non-biased phylogenetic consensus method to improve the thermostability of afCR. RESULTS: We applied a non-biased phylogenetic consensus method to identify 59 candidate consensus residues from 24 creatinase family homologs for screening afCR mutants with improved thermostability. Twenty-one amino acids of afCR were selected to mutagenesis and 11 of them exhibited improved thermostability compared to the parent enzyme (afCR-M0). Combination of single-site mutations in sequential screens resulted in a quadruple mutant D17V/T199S/L6P/T251C (M4-2) which showed ~ 1700-fold enhanced half-life at 57 °C and a 4.2 °C higher T5015 than that of afCR-M0. The mutant retained catalytic activity equivalent to afCR-M0, and thus showed strong promise for application in creatinine detection. Structural homology modeling revealed a wide range of potential molecular interactions associated with individual mutations that contributed to improving afCR thermostability. CONCLUSIONS: Results of this study clearly demonstrated that the non-biased-phylogenetic consensus design for improvement of thermostability in afCR is effective and promising in improving the thermostability of more enzymes.

Electro-acupuncture regulates glucose metabolism in chronic stress model rats.

Studies have shown that acupuncture is very effective in treating chronic stress depression. However, little is known about the therapeutic mechanism of electro-acupuncture. Metabolomics, on the other hand, is a technology that determines the metabolic changes of organisms caused by various interventions as a whole and is related to the overall effect of electro-acupuncture (EA). 1HNMR, serum sample analysis, and histopathology and molecular biology analysis were used to evaluate the effects of EA. The results show that electro-acupuncture points can regulate the heat pain threshold of chronic stress model rats and change the morphology of adrenal cortex cells Structure, and regulate the contents of corticotropin-releasing hormone, Corticosterone (CORT), glucose, alanine and valine in the samples. These findings help to clarify the therapeutic mechanism of electro-acupuncture on heterologous chronic stress model rats. The effect of electro-acupuncture on improving chronic stress is likely to be achieved by regulating glucose metabolism, which can provide a reference for clinical acupuncture treatment of chronic stress depression.