Screening and validation of atherosclerosis PAN-apoptotic immune-related genes based on single-cell sequencing.

Background: Carotid atherosclerosis (CAS) is a complication of atherosclerosis (AS). PAN-optosome is an inflammatory programmed cell death pathway event regulated by the PAN-optosome complex. CAS's PAN-optosome-related genes (PORGs) have yet to be studied. Hence, screening the PAN-optosome-related diagnostic genes for treating CAS was vital. Methods: We introduced transcriptome data to screen out differentially expressed genes (DEGs) in CAS. Subsequently, WGCNA analysis was utilized to mine module genes about PANoptosis score. We performed differential expression analysis (CAS samples vs. standard samples) to obtain CAS-related differentially expressed genes at the single-cell level. Venn diagram was executed to identify PAN-optosome-related differential genes (POR-DEGs) associated with CAS. Further, LASSO regression and RF algorithm were implemented to were executed to build a diagnostic model. We additionally performed immune infiltration and gene set enrichment analysis (GSEA) based on diagnostic genes. We verified the accuracy of the model genes by single-cell nuclear sequencing and RT-qPCR validation of clinical samples, as well as in vitro cellular experiments. Results: We identified 785 DEGs associated with CAS. Then, 4296 module genes about PANoptosis score were obtained. We obtained the 7365 and 1631 CAS-related DEGs at the single-cell level, respectively. 67 POR-DEGs were retained Venn diagram. Subsequently, 4 PAN-optosome-related diagnostic genes (CNTN4, FILIP1, PHGDH, and TFPI2) were identified via machine learning. Cellular function tests on four genes showed that these genes have essential roles in maintaining arterial cell viability and resisting cellular senescence. Conclusion: We obtained four PANoptosis-related diagnostic genes (CNTN4, FILIP1, PHGDH, and TFPI2) associated with CAS, laying a theoretical foundation for treating CAS.

Phosphorus-solubilizing bacteria improve the growth of Nicotiana benthamiana on lunar regolith simulant by dissociating insoluble inorganic phosphorus.

In-situ utilization of lunar soil resources will effectively improve the self-sufficiency of bioregenerative life support systems for future lunar bases. Therefore, we have explored the microbiological method to transform lunar soil into a substrate for plant cultivation. In this study, five species of phosphorus-solubilizing bacteria are used as test strains, and a 21-day bio-improving experiment with another 24-day Nicotiana benthamiana cultivation experiment are carried out on lunar regolith simulant. We have observed that the phosphorus-solublizing bacteria Bacillus mucilaginosus, Bacillus megaterium, and Pseudomonas fluorescens can tolerate the lunar regolith simulant conditions and dissociate the insoluble phosphorus from the regolith simulant. The phosphorus-solubilizing bacteria treatment improves the available phosphorus content of the regolith simulant, promoting the growth of Nicotiana benthamiana. Here we demonstrate that the phosphorus-solubilizing bacteria can effectively improve the fertility of lunar regolith simulant, making it a good cultivation substrate for higher plants. The results can lay a technical foundation for plant cultivation based on lunar regolith resources in future lunar bases.

EMNPD: a comprehensive endophytic microorganism natural products database for prompt the discovery of new bioactive substances.

The discovery and utilization of natural products derived from endophytic microorganisms have garnered significant attention in pharmaceutical research. While remarkable progress has been made in this field each year, the absence of dedicated open-access databases for endophytic microorganism natural products research is evident. To address the increasing demand for mining and sharing of data resources related to endophytic microorganism natural products, this study introduces EMNPD, a comprehensive endophytic microorganism natural products database comprising manually curated data. Currently, EMNPD offers 6632 natural products from 1017 endophytic microorganisms, targeting 1286 entities (including 94 proteins, 282 cell lines, and 910 species) with 91 diverse bioactivities. It encompasses the physico-chemical properties of natural products, ADMET information, quantitative activity data with their potency, natural products contents with diverse fermentation conditions, systematic taxonomy, and links to various well-established databases. EMNPD aims to function as an open-access knowledge repository for the study of endophytic microorganisms and their natural products, thereby facilitating drug discovery research and exploration of bioactive substances. The database can be accessed at http://emnpd.idrblab.cn/ without the need for registration, enabling researchers to freely download the data. EMNPD is expected to become a valuable resource in the field of endophytic microorganism natural products and contribute to future drug development endeavors.

Improving glycerol utilization during high-temperature xylitol production with Kluyveromyces marxianus using a transient clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 system.

Glycerol is an ideal co-substrate for xylitol production with Kluyveromyces marxianus. This study demonstrated that K. marxianus catabolizes glycerol through the Gut1-Gut2 pathway instead of the previously speculated NADPH-dependent Gcy1-Dak1 pathway using the transient clustered regularly interspaced short palindromic repeats/ CRISPR-associated protein 9 (CRISPR/Cas9) system. Additionally, Utr1p was demonstrated to mediate NADPH generation through NADH phosphorylation. YZB392, which was constructed by integrating Utr1 into the Ypr1 site in the strain overexpressing NcXyl1 and CiGxf1 and harboring disrupted Xyl2, exhibited enhanced glycerol utilization for xylitol production (from 2.50- to 3.30- g/L after consuming 1 g/L glycerol). Fed-batch fermentation at 42 °C with YZB392 yielded 322.07 g/L xylitol, which is the highest known xylitol titer obtained via biological method. Feeding crude glycerol, xylose mother liquor, and corn steep liquor powder into a bioreactor resulted in the production of 235.69 g/L xylitol. This study developed a platform for xylitol production from industrial by-products.

Ergosterol production at elevated temperatures by Upc2-overexpressing Kluyveromyces marxianus using Jerusalem artichoke tubers as feedstock.

Ergosterol is an important precursor in the pharmaceutical industry for the production of numerous drugs. In this study, Kluyveromyces marxianus that showed more potential for ergosterol production than some other yeasts was reported. The effects of transcription factors UPC2, MOT3, and ROX1 of K. marxianus on ergosterol synthesis were explored, and a Upc2-overexpressing strain produced 1.78 times more ergosterol (167.33 mg/L) than the wild-type strain (60.04 mg/L). A total of 239.98 mg/L ergosterol was produced when glucose was replaced with fructose to limit ethanol production. Enhanced aeration increased ergosterol titer from 63.09 mg/L to 128.46 mg/L at 42 °C. The ergosterol titer reached 304.37 mg/L in a shake flask at 37 °C, or 1124.38 and 948.32 mg/L at 37 °C and 42 °C, respectively, in a 5 L bioreactor, using Jerusalem artichoke tubers as the sole carbon source. This study establishes a platform for ergosterol biosynthesis using inexpensive materials.

Regularizing Deep Networks With Semantic Data Augmentation.

Data augmentation is widely known as a simple yet surprisingly effective technique for regularizing deep networks. Conventional data augmentation schemes, e.g., flipping, translation or rotation, are low-level, data-independent and class-agnostic operations, leading to limited diversity for augmented samples. To this end, we propose a novel semantic data augmentation algorithm to complement traditional approaches. The proposed method is inspired by the intriguing property that deep networks are effective in learning linearized features, i.e., certain directions in the deep feature space correspond to meaningful semantic transformations, e.g., changing the background or view angle of an object. Based on this observation, translating training samples along many such directions in the feature space can effectively augment the dataset for more diversity. To implement this idea, we first introduce a sampling based method to obtain semantically meaningful directions efficiently. Then, an upper bound of the expected cross-entropy (CE) loss on the augmented training set is derived by assuming the number of augmented samples goes to infinity, yielding a highly efficient algorithm. In fact, we show that the proposed implicit semantic data augmentation (ISDA) algorithm amounts to minimizing a novel robust CE loss, which adds minimal extra computational cost to a normal training procedure. In addition to supervised learning, ISDA can be applied to semi-supervised learning tasks under the consistency regularization framework, where ISDA amounts to minimizing the upper bound of the expected KL-divergence between the augmented features and the original features. Although being simple, ISDA consistently improves the generalization performance of popular deep models (e.g., ResNets and DenseNets) on a variety of datasets, i.e., CIFAR-10, CIFAR-100, SVHN, ImageNet, and Cityscapes. Code for reproducing our results is available at https://github.com/blackfeather-wang/ISDA-for-Deep-Networks.