Harnessing landrace diversity empowers wheat breeding.

Harnessing genetic diversity in major staple crops through the development of new breeding capabilities is essential to ensure food security1. Here we examined the genetic and phenotypic diversity of the A.E. Watkins landrace collection2 of bread wheat (Triticum aestivum), a major global cereal, through whole-genome re-sequencing (827 Watkins landraces and 208 modern cultivars) and in-depth field evaluation spanning a decade. We discovered that modern cultivars are derived from just two of the seven ancestral groups of wheat and maintain very long-range haplotype integrity. The remaining five groups represent untapped genetic sources, providing access to landrace-specific alleles and haplotypes for breeding. Linkage disequilibrium (LD) based haplotypes and association genetics analyses link Watkins genomes to the thousands of high-resolution quantitative trait loci (QTL), and significant marker-trait associations identified. Using these structured germplasm, genotyping and informatics resources, we revealed many Watkins-unique beneficial haplotypes that can confer superior traits in modern wheat. Furthermore, we assessed the phenotypic effects of 44,338 Watkins-unique haplotypes, introgressed from 143 prioritised QTL in the context of modern cultivars, bridging the gap between landrace diversity and current breeding. This study establishes a framework for systematically utilising genetic diversity in crop improvement to achieve sustainable food security.

Deep Incomplete Multiview Clustering via Local and Global Pseudo-Label Propagation.

Since the rapid progress in multimedia and sensor technologies, multiview clustering (MVC) has become a prominent research area within machine learning and data mining, experiencing significant advancements over recent decades. MVC is distinguished from single-view clustering by its ability to integrate complementary information from multiple distinct data perspectives and enhance clustering performance. However, the efficacy of MVC methods is predicated on the availability of complete views for all samples-an assumption that frequently fails in practical scenarios where data views are often incomplete. To surmount this challenge, various approaches to incomplete MVC (IMVC) have been proposed, with deep neural networks emerging as a favored technique for their representation learning ability. Despite their promise, previous methods commonly adopt sample-level (e.g., features) or affinity-level (e.g., graphs) guidance, neglecting the discriminative label-level guidance (i.e., pseudo-labels). In this work, we propose a novel deep IMVC method termed pseudo-label propagation for deep IMVC (PLP-IMVC), which integrates high-quality pseudo-labels from the complete subset of incomplete data with deep label propagation networks to obtain improved clustering results. In particular, we first design a local model (PLP-L) that leverages pseudo-labels to their fullest extent. Then, we propose a global model (PLP-G) that exploits manifold regularization to mitigate the label noises, promote view-level information fusion, and learn discriminative unified representations. Experimental results across eight public benchmark datasets and three evaluation metrics prove our method's efficacy, demonstrating superior performance compared to 18 advanced baseline methods.

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes.

BACKGROUND: Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. METHODS: Transcriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the "Enrichr" platform was utilized to identify potential drugs for MDD. RESULTS: Totally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. CONCLUSION: Our work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

The miR6445-NAC029 module regulates drought tolerance by regulating the expression of glutathione S-transferase U23 and reactive oxygen species scavenging in Populus.

MicroRNAs are essential in plant development and stress resistance, but their specific roles in drought stress require further investigation. Here, we have uncovered that a Populus-specific microRNAs (miRNA), miR6445, targeting NAC (NAM, ATAF, and CUC) family genes, is involved in regulating drought tolerance of poplar. The expression level of miR6445 was significantly upregulated under drought stress; concomitantly, seven targeted NAC genes showed significant downregulation. Silencing the expression of miR6445 by short tandem target mimic technology significantly decreased the drought tolerance in poplar. Furthermore, 5' RACE experiments confirmed that miR6445 directly targeted NAC029. The overexpression lines of PtrNAC029 (OE-NAC029) showed increased sensitivity to drought compared with knockout lines (Crispr-NAC029), consistent with the drought-sensitive phenotype observed in miR6445-silenced strains. PtrNAC029 was further verified to directly bind to the promoters of glutathione S-transferase U23 (GSTU23) and inhibit its expression. Both Crispr-NAC029 and PtrGSTU23 overexpressing plants showed higher levels of PtrGSTU23 transcript and GST activity while accumulating less reactive oxygen species (ROS). Moreover, poplars overexpressing GSTU23 demonstrated enhanced drought tolerance. Taken together, our research reveals the crucial role of the miR6445-NAC029-GSTU23 module in enhancing poplar drought tolerance by regulating ROS homeostasis. This finding provides new molecular targets for improving the drought resistance of trees.

Development of a next generation SNP genotyping array for wheat.

High-throughput genotyping arrays have provided a cost-effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384-well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the 'Triticum aestivum Next Generation' array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins 'Core Collection'. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype-optimized novel SNPs and legacy cross-platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome-wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available.

Unveiling protein corona composition: predicting with resampling embedding and machine learning.

Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration. When nanoparticles (NPs) enter the living system, they quickly interact with proteins in the body fluid, forming the protein corona (PC). The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs. However, achieving accurate predictions remains a significant challenge. Although several machine learning (ML) models like Random Forest (RF) have been used for PC prediction, they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution. In this study, resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data. Various ML models were evaluated, and RF model was finally used for prediction, and good correlation coefficient (R2) and root-mean-square deviation (RMSE) values were obtained. Our ablation experiments demonstrated that the proposed method achieved an R2 of 0.68, indicating an improvement of approximately 10%, and an RMSE of 0.90, representing a reduction of approximately 10%. Furthermore, through the verification of label-free quantification of four NPs: hydroxyapatite (HA), titanium dioxide (TiO2), silicon dioxide (SiO2) and silver (Ag), and we achieved a prediction performance with an R2 value >0.70 using Random Oversampling. Additionally, the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration, PDI and surface modification.

Nlrc3 signaling is indispensable for hematopoietic stem cell emergence via Notch signaling in vertebrates.

Hematopoietic stem and progenitor cells generate all the lineages of blood cells throughout the lifespan of vertebrates. The emergence of hematopoietic stem and progenitor cells is finely tuned by a variety of signaling pathways. Previous studies have revealed the roles of pattern-recognition receptors such as Toll-like receptors and RIG-I-like receptors in hematopoiesis. In this study, we find that Nlrc3, a nucleotide-binding domain leucine-rich repeat containing family gene, is highly expressed in hematopoietic differentiation stages in vivo and vitro and is required in hematopoiesis in zebrafish. Mechanistically, nlrc3 activates the Notch pathway and the downstream gene of Notch hey1. Furthermore, NF-kB signaling acts upstream of nlrc3 to enhance its transcriptional activity. Finally, we find that Nlrc3 signaling is conserved in the regulation of murine embryonic hematopoiesis. Taken together, our findings uncover an indispensable role of Nlrc3 signaling in hematopoietic stem and progenitor cell emergence and provide insights into inflammation-related hematopoietic ontogeny and the in vitro expansion of hematopoietic stem and progenitor cells.

HALD, a human aging and longevity knowledge graph for precision gerontology and geroscience analyses.

Human aging is a natural and inevitable biological process that leads to an increased risk of aging-related diseases. Developing anti-aging therapies for aging-related diseases requires a comprehensive understanding of the mechanisms and effects of aging and longevity from a multi-modal and multi-faceted perspective. However, most of the relevant knowledge is scattered in the biomedical literature, the volume of which reached 36 million in PubMed. Here, we presented HALD, a text mining-based human aging and longevity dataset of the biomedical knowledge graph from all published literature related to human aging and longevity in PubMed. HALD integrated multiple state-of-the-art natural language processing (NLP) techniques to improve the accuracy and coverage of the knowledge graph for precision gerontology and geroscience analyses. Up to September 2023, HALD had contained 12,227 entities in 10 types (gene, RNA, protein, carbohydrate, lipid, peptide, pharmaceutical preparations, toxin, mutation, and disease), 115,522 relations, 1,855 aging biomarkers, and 525 longevity biomarkers from 339,918 biomedical articles in PubMed. HALD is available at https://bis.zju.edu.cn/hald .

The geological factors affecting gas content and permeability of coal seam and reservoir characteristics in Wenjiaba block, Guizhou province.

The gas content and permeability of coal reservoirs are the main factors affecting the productivity of coalbed methane. To explore the law of gas content and permeability of coal reservoirs in the Zhijin area of Guizhou, taking No.16, No.27 and No.30 coal seams in Wenjiaba mining area of Guizhou as the engineering background, based on the relevant data of coalbed methane exploration in Wenjiaba block, the geological structure, coal seam thickness, coal quality characteristics,coal seam gas content and permeability of the area were studied utilizing geological exploration, analysis of coal components and methane adsorption test. The results show that the average thickness of coal seams in this area is between 1.32 and 1.85 m; the average buried depth of the coal seam is in the range of 301.3-384.2 m; the gas content of No.16 and No.27 coal seams is higher in the syncline core. The gas content of the No.30 coal seam forms a gas-rich center in the south of the mining area. The buried depth and gas content of coal seams in the study area show a strong positive correlation. Under the same pressure conditions, the adsorption capacity of dry ash-free basis is significantly higher than that of air-dried coal. The permeability decreases exponentially with the horizontal maximum principal stress and the horizontal minimum principal stress. The horizontal maximum primary stress and the flat minimum prominent stress increase with the increase of the buried depth of the coal seam. The permeability and coal seam burial depth decrease exponentially. This work can provide engineering reference and theoretical support for selecting high-yield target areas for CBM enrichment in the block.

SP1 transcriptionally upregulates the expression of APOC3 in KGN cells to promote disease progression by regulating TLR2/NF-κB signalling pathway.

INTRODUCTION: Apolipoprotein C3 (APOC3) is known for its important functions in metabolism-related diseases. However, the function and molecular mechanism of APOC3 in polycystic ovarian syndrome (PCOS) have not been reported. MATERIAL AND METHODS: Quantitative polymerase chain reaction and western blot assays were used to detect the expression of APOC3 in KGN cells. Small interference APOC3 (siAPOC3) was applied to reduce APOC3 expression, and the proliferation ability of human granulosa cell line (KGN cells) was measured by cell counting kit-8 and colony formation assays. The protein levels of key genes related to apoptosis were detected by western blot assay. The transcriptional regulator of APOC3 was predicted by the UCSC and PROMO website, and verified by dual luciferase assay. siAPOC3 and pcDNA3.1-specific protein 1 (SP1) vector were co-transfected into KGN cells to detect the function of SP1 and APOC3 in KGN cells. RESULTS: APOC3 was overexpressed in KGN cells, and siAPOC3 transfection significantly reduced the growth ability of KGN cells and increased the apoptosis ability of KGN cells. SP1 directly bound to the promoter of APOC3 and transcriptional regulated APOC3 expression. Overexpression of SP1 increased the growth ability of KGN cells and decreased the apoptosis ability of KGN cells, which were reversed after siAPOC3 transfection. The increased levels of toll-like receptor 2 (TLR2) and p65 phosphorylation (p-P65) nuclear factor kappa B (NF-κB) caused by SP1 overexpression were inhibited by siAPOC3 transfection. APOC3, transcriptionally regulated by SP1, promoted the growth of KGN cells, and inhibited the apoptosis by regulating TLR2/NF-κB signalling pathway.

α-Synuclein induces deficiency in clathrin-mediated endocytosis through inhibiting synaptojanin1 expression.

Parkinson's disease (PD) is an age-related chronic neurological disorder, mainly characterized by the pathological feature of α-synuclein (α-syn) aggregation, with the exact disease pathogenesis unclear. During the onset and progression of PD, synaptic dysfunction, including dysregulation of axonal transport, impaired exocytosis, and endocytosis are identified as crucial events of PD pathogenesis. It has been reported that over-expression of α-syn impairs clathrin-mediated endocytosis (CME) in the synapses. However, the underlying mechanisms still needs to be explored. In this study, we investigated the molecular events underlying the synaptic dysfunction caused by over-expression of wild-type human α-syn and its mutant form, involving series of proteins participating in CME. We found that excessive human α-syn causes impaired fission and uncoating of clathrin-coated vesicles during synaptic vesicle recycling, leading to reduced clustering of synaptic vesicles near the active zone and increased size of plasma membrane and number of endocytic intermediates. Furthermore, over-expressed human α-syn induced changes of CME-associated proteins, among which synaptojanin1 (SYNJ1) showed significant reduction in various brain regions. Over-expression of SYNJ1 in primary hippocampal neurons from α-syn transgenic mice recovered the synaptic vesicle density, clustering and endocytosis. Using fluorescence-conjugated transferrin, we demonstrated that SYNJ1 re-boosted the CME activity by restoring the phosphatidylinositol-4,5-bisphosphate homeostasis. Our data suggested that over-expression of α-syn disrupts synaptic function through interfering with vesicle recycling, which could be alleviated by re-availing of SYNJ1. Our study unrevealed a molecular mechanism of the synaptic dysfunction in PD pathogenesis and provided a potential therapeutic target for treating PD.

Causal associations of Sjögren's syndrome with cancers: a two-sample Mendelian randomization study.

BACKGROUND: Several observational studies have explored the associations between Sjögren's syndrome (SS) and certain cancers. Nevertheless, the causal relationships remain unclear. Mendelian randomization (MR) method was used to investigate the causality between SS and different types of cancers. METHODS: We conducted the two-sample Mendelian randomization with the public genome-wide association studies (GWASs) summary statistics in European population to evaluate the causality between SS and nine types of cancers. The sample size varies from 1080 to 372,373. The inverse variance weighted (IVW) method was used to estimate the causal effects. A Bonferroni-corrected threshold of P < 0.0031 was considered significant, and P value between 0.0031 and 0.05 was considered to be suggestive of an association. Sensitivity analysis was performed to validate the causality. Moreover, additional analysis was used to assess the associations between SS and well-accepted risk factors of cancers. RESULTS: After correcting the heterogeneity and horizontal pleiotropy, the results indicated that patients with SS were significantly associated with an increased risk of lymphomas (odds ratio [OR] = 1.0010, 95% confidence interval [CI]: 1.0005-1.0015, P = 0.0002) and reduced risks of prostate cancer (OR = 0.9972, 95% CI: 0.9960-0.9985, P = 2.45 × 10-5) and endometrial cancer (OR = 0.9414, 95% CI: 0.9158-0.9676, P = 1.65 × 10-5). Suggestive associations were found in liver and bile duct cancer (OR = 0.9999, 95% CI: 0.9997-1.0000, P = 0.0291) and cancer of urinary tract (OR = 0.9996, 95% CI: 0.9992-1.0000, P = 0.0281). No causal effect of SS on other cancer types was detected. Additional MR analysis indicated that causal effects between SS and cancers were not mediated by the well-accepted risk factors of cancers. No evidence of the causal relationship was observed for cancers on SS. CONCLUSIONS: SS had significant causal relationships with lymphomas, prostate cancer, and endometrial cancer, and suggestive evidence of association was found in liver and bile duct cancer and cancer of urinary tract, indicating that SS may play a vital role in the incidence of these malignancies.

A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts.

BACKGROUND: High throughput gene expression profiling is a valuable tool in providing insight into the molecular mechanism of human diseases. Hypoxia- and lactate metabolism-related genes (HLMRGs) are fundamentally dysregulated in sepsis and have great predictive potential. Therefore, we attempted to build an HLMRG signature to predict the prognosis of patients with sepsis. METHODS: Three publicly available transcriptomic profiles of peripheral blood mononuclear cells from patients with sepsis (GSE65682, E-MTAB-4421 and E-MTAB-4451, total n = 850) were included in this study. An HLMRG signature was created by employing Cox regression and least absolute shrinkage and selection operator estimation. The CIBERSORT method was used to analyze the abundances of 22 immune cell subtypes based on transcriptomic data. Metascape was used to investigate pathways related to the HLMRG signature. RESULTS: We developed a prognostic signature based on five HLMRGs (ERO1L, SIAH2, TGFA, TGFBI, and THBS1). This classifier successfully discriminated patients with disparate 28-day mortality in the discovery cohort (GSE65682, n = 479), and consistent results were observed in the validation cohort (E-MTAB-4421 plus E-MTAB-4451, n = 371). Estimation of immune infiltration revealed significant associations between the risk score and a subset of immune cells. Enrichment analysis revealed that pathways related to antimicrobial immune responses, leukocyte activation, and cell adhesion and migration were significantly associated with the HLMRG signature. CONCLUSIONS: Identification of a prognostic signature suggests the critical role of hypoxia and lactate metabolism in the pathophysiology of sepsis. The HLMRG signature can be used as an efficient tool for the risk stratification of patients with sepsis.

Influences of Evolution of Pore Structures in Tectonic Coal under Acidization on Methane Desorption.

Experiments on corrosion reactions of pulverized coal with monomeric and polymeric (mixed) acid solutions reveal that monomeric acids are listed in a descending order as HF, HCl, and CH3COOH according to their corrosion effects on tectonic coal collected in Faer Coal Mine (Liupanshui City, Guizhou Province, China). In addition, the optimal mixing ratio of mixed acids is 6% HCl + 6% HF + 3% CH3COOH + 2% KCl. The mineral grains filled in pores in coal samples treated with mixed acid solutions are dissolved, so the porosity increases. The volumes of transition pores and mesopores are obviously affected by acidization, and some transition pores are transformed into mesopores and macropores to form dissolved pores. At the same time, inkbottle-shaped pores reduce, while slit pores or open pores increase. The coal samples after acidization show a higher aromatization degree and an increased relative content of oxygen-containing functional groups, with a generally lower hydroxyl content, so the methane (CH4) adsorption capacity of coal declines, which promotes CH4 desorption. The control effect of pore structures after acidization reactions on CH4 desorption was revealed from perspectives of the diffusion coefficient (Kn), adsorption volume (ω), average pore-throat ratio (PT), and average sinuosity (τav). That is, CH4 molecules in tectonic coal after acidization turn from Knudsen diffusion to transitional diffusion, the adsorption volume of CH4 molecules shrinks, the average pore-throat ratio decreases, and the average sinuosity reduces, which promotes CH4 desorption from tectonic coal.

Improving Interfacial Adhesion of Graphite/Epoxy Composites by Surface Functionalization.

Graphite/epoxy resin (G/EP) composites are extensively utilized in bipolar plates for fuel cells owing to their outstanding electrical and mechanical properties. However, the mechanical strength of these composites declines notably due to the inadequate bonding interface between graphite and epoxy resin. To address this issue, we used molecular dynamics (MD) simulations to study the influence of graphite surface functionalization on the interfacial structures of composites. The results of this study revealed that the functionalization of the graphite surface led to an increase in the interface thickness of the composite. This phenomenon can be attributed to the interdiffusion and hydrogen bond formation between functionalized graphite and epoxy molecular chains. And all four types of functional groups demonstrated a promoting effect on the adsorption process. Additionally, the adsorption and contact angle results provided further evidence that the adsorption rate of graphite to the epoxy resin significantly improved after functionalization. These findings contribute to a more comprehensive understanding of the microscopic process of forming interfaces in G/EP composites. In addition, these insights provide valuable guidance for improving the interface bonding of composite bipolar plates, which can ultimately increase their mechanical strength.

Aspartyl tRNA-synthetase (AspRS) gene family enhances drought tolerance in poplar through BABA-PtrIBIs-PtrVOZ signaling module.

BACKGROUND: Drought stress is a prevalent abiotic stress that significantly hinders the growth and development of plants. According to studies, ß-aminobutyric acid (BABA) can influence the ABA pathway through the AtIBI1 receptor gene to enhance cold resistance in Arabidopsis. However, the Aspartate tRNA-synthetase (AspRS) gene family, which acts as the receptor for BABA, has not yet been investigated in poplar. Particularly, it is uncertain how the AspRS gene family (PtrIBIs)r can resist drought stress after administering various concentrations of BABA to poplar. RESULTS: In this study, we have identified 12 AspRS family genes and noted that poplar acquired four PtrIBI pairs through whole genome duplication (WGD). We conducted cis-action element analysis and found a significant number of stress-related action elements on different PtrIBI genes promoters. The expression of most PtrIBI genes was up-regulated under beetle and mechanical damage stresses, indicating their potential role in responding to leaf damage stress. Our results suggest that a 50 mM BABA treatment can alleviate the damage caused by drought stress in plants. Additionally, via transcriptome sequencing, we observed that the partial up-regulation of BABA receptor genes, PtrIBI2/4/6/8/11, in poplars after drought treatment. We hypothesize that poplar responds to drought stress through the BABA-PtrIBIs-PtrVOZ coordinated ABA signaling pathway. Our research provides molecular evidence for understanding how plants respond to drought stress through external application of BABA. CONCLUSIONS: In summary, our study conducted genome-wide analysis of the AspRS family of P. trichocarpa and identified 12 PtrIBI genes. We utilized genomics and bioinformatics to determine various characteristics of PtrIBIs such as chromosomal localization, evolutionary tree, gene structure, gene doubling, promoter cis-elements, and expression profiles. Our study found that certain PtrIBI genes are regulated by drought, beetle, and mechanical damage implying their crucial role in enhancing poplar stress tolerance. Additionally, we observed that external application of low concentrations of BABA increased plant drought resistance under drought stress. Through the BABA-PtrIBIs-PtrVOZ signaling module, poplar plants were able to transduce ABA signaling and regulate their response to drought stress. These results suggest that the PtrIBI genes in poplar have the potential to improve drought tolerance in plants through the topical application of low concentrations of BABA.

Inhibitory Effects of Corydalis saxicola Bunting Total Alkaloids on Macrophage Pyroptosis.

This study investigated the effect of Corydalis saxicola Bunting total alkaloids (CSBTA) on pyroptosis in macrophages (Mϕ). In the Mϕ pyroptosis model, an inverted fluorescence microscope was used to assess cell pyroptosis, while a scanning electron microscope was used to observe morphological changes in Mϕ. NLR family pyrin domain-containing 3 (NLRP3), caspase-1, and gasdermin D (GSDMD) expression levels were detected by polymerase chain reaction and western blotting, whereas interleukin-1 (IL-1) and interleukin-18 (IL-18) expression levels were measured by an enzyme-linked immunosorbent assay. After pretreatment with CSBTA or the caspase-1 inhibitor, acetyl-tyrosyl-valyl-alanyl-aspartyl-chloromethylketone (Ac-YVAD-cmk), it was discovered that NLRP3, caspase-1, and GSDMD expressions were significantly reduced at both the mRNA and protein levels, as were IL-1 and IL-18 levels. The inhibitory effects of CSBTA and Ac-YVAD-cmk did not differ significantly. These findings indicate that CSBTA blocks Porphyromonas gingivalis-lipopolysaccharide-induced Mϕ pyroptosis.

Integrated bioinformatics analysis and experimental validation reveal ISG20 as a novel prognostic indicator expressed on M2 macrophage in glioma.

BACKGROUND: Glioma is the most common malignant primary brain tumor and is characterized by a poor prognosis and limited therapeutic options. ISG20 expression is induced by interferons or double-stranded RNA and is associated with poor prognosis in several malignant tumors. Nevertheless, the expression of ISG20 in gliomas, its impact on patient prognosis, and its role in the tumor immune microenvironment have not been fully elucidated. METHODS: Using bioinformatics, we comprehensively illustrated the potential function of ISG20, its predictive value in stratifying clinical prognosis, and its association with immunological characteristics in gliomas. We also confirmed the expression pattern of ISG20 in glioma patient samples by immunohistochemistry and immunofluorescence staining. RESULTS: ISG20 mRNA expression was higher in glioma tissues than in normal tissues. Data-driven results showed that a high level of ISG20 expression predicted an unfavorable clinical outcome in glioma patients, and revealed that ISG20 was possibly expressed on tumor-associated macrophages and was significantly associated with immune regulatory processes, as evidenced by its positive correlation with the infiltration of regulatory immune cells (e.g., M2 macrophages and regulatory T cells), expression of immune checkpoint molecules, and effectiveness of immune checkpoint blockade therapy. Furthermore, immunohistochemistry staining confirmed the enhanced expression of ISG20 in glioma tissues with a higher WHO grade, and immunofluorescence assay verified its cellular localization on M2 macrophages. CONCLUSIONS: ISG20 is expressed on M2 macrophages, and can serve as a novel indicator for predicting the malignant phenotype and clinical prognosis in glioma patients.

Novel models by machine learning to predict prognosis of breast cancer brain metastases.

BACKGROUND: Breast cancer brain metastases (BCBM) are the most fatal, with limited survival in all breast cancer distant metastases. These patients are deemed to be incurable. Thus, survival time is their foremost concern. However, there is a lack of accurate prediction models in the clinic. What's more, primary surgery for BCBM patients is still controversial. METHODS: The data used for analysis in this study was obtained from the SEER database (2010-2019). We made a COX regression analysis to identify prognostic factors of BCBM patients. Through cross-validation, we constructed XGBoost models to predict survival in patients with BCBM. Meanwhile, a BCBM cohort from our hospital was used to validate our models. We also investigated the prognosis of patients treated with surgery or not, using propensity score matching and K-M survival analysis. Our results were further validated by subgroup COX analysis in patients with different molecular subtypes. RESULTS: The XGBoost models we created had high precision and correctness, and they were the most accurate models to predict the survival of BCBM patients (6-month AUC = 0.824, 1-year AUC = 0.813, 2-year AUC = 0.800 and 3-year survival AUC = 0.803). Moreover, the models still exhibited good performance in an externally independent dataset (6-month: AUC = 0.820; 1-year: AUC = 0.732; 2-year: AUC = 0.795; 3-year: AUC = 0.936). Then we used Shiny-Web tool to make our models be easily used from website. Interestingly, we found that the BCBM patients with an annual income of over USD$70,000 had better BCSS (HR = 0.523, 95%CI 0.273-0.999, P < 0.05) than those with less than USD$40,000. The results showed that in all distant metastasis sites, only lung metastasis was an independent poor prognostic factor for patients with BCBM (OS: HR = 1.606, 95%CI 1.157-2.230, P < 0.01; BCSS: HR = 1.698, 95%CI 1.219-2.365, P < 0.01), while bone, liver, distant lymph nodes and other metastases were not. We also found that surgical treatment significantly improved both OS and BCSS in BCBM patients with the HER2 + molecular subtypes and was beneficial to OS of the HR-/HER2- subtype. In contrast, surgery could not help BCBM patients with HR + /HER2- subtype improve their prognosis (OS: HR = 0.887, 95%CI 0.608-1.293, P = 0.510; BCSS: HR = 0.909, 95%CI 0.604-1.368, P = 0.630). CONCLUSION: We analyzed the clinical features of BCBM patients and constructed 4 machine-learning prognostic models to predict their survival. Our validation results indicate that these models should be highly reproducible in patients with BCBM. We also identified potential prognostic factors for BCBM patients and suggested that primary surgery might improve the survival of BCBM patients with HER2 + and triple-negative subtypes.

Causal effects of COVID-19 on cancer risk: A Mendelian randomization study.

In contemporary literature, little attention has been paid to the association between coronavirus disease-2019 (COVID-19) and cancer risk. We performed the Mendelian randomization (MR) to investigate the causal associations between the three types of COVID-19 exposures (critically ill COVID-19, hospitalized COVID-19, and respiratory syndrome coronavirus 2 (SARS-CoV-2) infection) and 33 different types of cancers of the European population. The results of the inverse-variance-weighted model indicated that genetic liabilities to critically ill COVID-19 had suggestive causal associations with the increased risk for HER2-positive breast cancer (odds ratio [OR] = 1.0924; p-value = 0.0116), esophageal cancer (OR = 1.0004; p-value = 0.0226), colorectal cancer (OR = 1.0010; p-value = 0.0242), stomach cancer (OR = 1.2394; p-value = 0.0331), and colon cancer (OR = 1.0006; p-value = 0.0453). The genetic liabilities to hospitalized COVID-19 had suggestive causal associations with the increased risk for HER2-positive breast cancer (OR = 1.1096; p-value = 0.0458), esophageal cancer (OR = 1.0005; p-value = 0.0440) as well as stomach cancer (OR = 1.3043; p-value = 0.0476). The genetic liabilities to SARS-CoV-2 infection had suggestive causal associations with the increased risk for stomach cancer (OR = 2.8563; p-value = 0.0019) but with the decreasing risk for head and neck cancer (OR = 0.9986, p-value = 0.0426). The causal associations of the above combinations were robust through the test of heterogeneity and pleiotropy. Together, our study indicated that COVID-19 had causal effects on cancer risk.