Single-cell mapping of N6-methyladenosine in esophageal squamous cell carcinoma and exploration of the risk model for immune infiltration

BackgroundN6-methyladenosine (m6A) modification is the most common RNA modification, but its potential role in the development of esophageal cancer and its specific mechanisms still need to be further investigated.MethodsBulk RNA-seq of 174 patients with esophageal squamous carcinoma from the TCGA-ESCC cohort, GSE53625, and single-cell sequencing data from patients with esophageal squamous carcinoma from GSE188900 were included in this study. Single-cell analysis of scRNA-seq data from GSE188900 of 4 esophageal squamous carcinoma samples and calculation of PROGENy scores. Demonstrate the scoring of tumor-associated pathways for different cell populations. Cell Chat was calculated for cell populations. thereafter, m6A-related differential genes were sought and risk models were constructed to analyze the relevant biological functions and impact pathways of potential m6A genes and their impact on immune infiltration and tumor treatment sensitivity in ESCC was investigated.ResultsBy umap downscaling analysis, ESCC single-cell data were labelled into clusters of seven immune cell classes. Cellchat analysis showed that the network interactions of four signaling pathways, MIF, AFF, FN1 and CD99, all showed different cell type interactions. The prognostic risk model constructed by screening for m6A-related differential genes was of significant value in the prognostic stratification of ESCC patients and had a significant impact on immune infiltration and chemotherapy sensitivity in ESCC patients.ConclusionIn our study, we explored a blueprint for the distribution of single cells in ESCC based on m6A methylation and constructed a risk model for immune infiltration analysis and tumor efficacy stratification in ESCC on this basis. This may provide important potential guidance for revealing the role of m6A in immune escape and treatment resistance in esophageal cancer

Cuproptosis/ferroptosis-related gene signature is correlated with immune infiltration and predict the prognosis for patients with breast cancer

Background: Breast invasive carcinoma (BRCA) is a malignant tumor with high morbidity and mortality, and the prognosis is still unsatisfactory. Both ferroptosis and cuproptosis are apoptosis-independent cell deaths caused by the imbalance of corresponding metal components in cells and can affect the proliferation rate of cancer cells. The aim in this study was to develop a prognostic model of cuproptosis/ferroptosis-related genes (CFRGs) to predict survival in BRCA patients.Methods: Transcriptomic and clinical data for breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Cuproptosis and ferroptosis scores were determined for the BRCA samples from the TCGA cohort using Gene Set Variation Analysis (GSVA), followed by weighted gene coexpression network analysis (WGCNA) to screen out the CFRGs. The intersection of the differentially expressed genes grouped by high and low was determined using X-tile. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) were used in the TGCA cohort to identify the CFRG-related signature. In addition, the relationship between risk scores and immune infiltration levels was investigated using various algorithms, and model genes were analyzed in terms of single-cell sequencing. Finally, the expression of the signature genes was validated with quantitative real-time PCR (qRT‒PCR) and immunohistochemistry (IHC).Results: A total of 5 CFRGs (ANKRD52, HOXC10, KNOP1, SGPP1, TRIM45) were identified and were used to construct proportional hazards regression models. The high-risk groups in the training and validation sets had significantly worse survival rates. Tumor mutational burden (TMB) was positively correlated with the risk score. Conversely, Tumor Immune Dysfunction and Exclusion (TIDE) and tumor purity were inversely associated with risk scores. In addition, the infiltration degree of antitumor immune cells and the expression of immune checkpoints were lower in the high-risk group. In addition, risk scores and mTOR, Hif-1, ErbB, MAPK, PI3K/AKT, TGF-β and other pathway signals were correlated with progression.Conclusion: We can accurately predict the survival of patients through the constructed CFRG-related prognostic model. In addition, we can also predict patient immunotherapy and immune cell infiltration

Development of Omni InDel and supporting database for maize

Insertions–deletions (InDels) are the second most abundant molecular marker in the genome and have been widely used in molecular biology research along with simple sequence repeats (SSR) and single-nucleotide polymorphisms (SNP). However, InDel variant mining and marker development usually focuses on a single type of dimorphic InDel, which does not reflect the overall InDel diversity across the genome. Here, we developed Omni InDels for maize, soybean, and rice based on sequencing data and genome assembly that included InDel variants with base lengths from 1 bp to several Mb, and we conducted a detailed classification of Omni InDels. Moreover, we screened a set of InDels that are easily detected and typed (Perfect InDels) from the Omni InDels, verified the site authenticity using 3,587 germplasm resources from 11 groups, and analyzed the germplasm resources. Furthermore, we developed a Multi-InDel set based on the Omni InDels; each Multi-InDel contains multiple InDels, which greatly increases site polymorphism, they can be detected in multiple platforms such as fluorescent capillary electrophoresis and sequencing. Finally, we developed an online database website to make Omni InDels easy to use and share and developed a visual browsing function called “Variant viewer” for all Omni InDel sites to better display the variant distribution

Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma

BackgroundCuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients.MethodsRNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB).ResultsWe have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group

Responses of Rare and Abundant Bacterial Communities to Synergistic Phosphate Fertilization and <i>Trichoderma</i> Inoculation Meant to Improve Alfalfa Yields

In the field of agro-grassland, the synergism of phosphate (P) fertilization and Trichoderma inoculation in alfalfa production and the underlying mechanism of rare and abundant microbes that regulate rhizosphere soil processes in various environments are key but rarely studied topics. Here, we conducted field research through the inoculation/noninoculation of Trichoderma and five levels of phosphorus fertilizer to explore the biological relationships of rhizosphere soil properties, rare and abundant taxa, and alfalfa yields. Our results demonstrate that using Trichoderma inoculation and 15 g/m2 of phosphorus fertilizer exhibited optimal alfalfa yield compared with other treatments. alfalfa yields significantly (R2 = 0.33; p ANOSIM = 0.900; p = 0.001) and abundant (RANOSIM = 0.769; p = 0.001) bacterial communities were significantly different under Trichoderma inoculation and P fertilization in a nonmetric multidimensional scaling (NMDS) analysis. Furthermore, different ecological processes dominated the rare and abundant bacterial community assembly. PLS-PM analysis showed that Trichoderma inoculation positively regulated the abundant bacteria community and P fertilization regimes manipulated the rare bacteria community, synergistically contributing to alfalfa yields. Overall, this article believes that inoculation with Trichoderma and appropriate application of phosphorus fertilizer can significantly increase alfalfa yield and affect soil enzyme activity, and the rhizosphere soil abundant and rare bacterial community characteristics have different responses to the synergistic effect of Trichoderma and phosphorus fertilizer. Our research emphasizes the fundamental role of abundant and rare microbes in maintaining crop production using Trichoderma inoculation and P fertilization. Therefore, distinguishing rare and abundant species is beneficial to comprehensively understanding microbial-driven processes and providing theoretical support for maintaining ecosystem productivity

Protective Effects of Astragaloside IV against LPS-Induced Endometritis in Mice through Inhibiting Activation of the NF-κB, p38 and JNK Signaling Pathways

Endometritis, inflammation of the endometrium, is a common reproductive obstacle disease that can lead to infertility in female animals. Astragaloside IV (AS IV), one of the major and active components of the Astragalus membranaceus (Fisch.) Bunge, is known for its anti-inflammatory effects. In the present study, the effects and mechanisms of AS IV on lipopolysaccharide (LPS)-induced endometritis were investigated using a mouse model. Female mice were prepared with AS IV (0.01 mg/g) by gavage for six days before being stimulated with LPS. The results showed that the histopathological changes, levels of inflammatory cytokines (IL-1&#946; and TNF-&#945;), concentration of NO, and myeloperoxidase (MPO) activity in LPS-induced uteri were attenuated significantly by pretreatment with AS IV. Furthermore, LPS-induced activations of NF-&#954;B, p38, and JNK signal pathways were suppressed by pretreatment with AS IV. In conclusion, the data provided new evidence that AS IV effectively attenuates LPS-induced endometritis through inhibition of TLR4-mediated NF-&#954;B, p38, and JNK signaling pathways, implying that AS IV might become a promising potential anti-inflammatory agent for endometritis and other inflammatory diseases

Epidemiological and clinical characteristics of psittacosis among cases with complicated or atypical pulmonary infection using metagenomic next-generation sequencing: a multi-center observational study in China

Abstract Background Chlamydia psittaci (C. psittaci) causes parrot fever in humans. Development of metagenomic next-generation sequencing (mNGS) enables the identification of C. psittaci. Methods This study aimed to determine the epidemiological and clinical characteristics of parrot fever cases in China. A multi-center observational study was conducted in 44 tertiary and secondary hospitals across 14 provinces and municipalities between April 2019 and October 2021. Results A total of 4545 patients with complicated or atypical pulmonary infection were included in the study, among which the prevalence of C. psittaci was determined to be 2.1% using mNGS. The prevalence of C. psittaci was further determined across demographic groups and types of specimens. It was significantly higher in patients with senior age (2.6% in those > 50 years), winter-spring (3.6%; particularly in December, January, and February), and southwestern (3.4%) and central and southern China (2.7%) (each P  0.05). Conclusion Parrot fever remains low in patients with complicated or atypical pulmonary infection. It is likely to occur in winter-spring and southwestern region in China. BALF may be the optimal specimen in the application of mNGS. Co-infection of multiple microorganisms should be further considered

Trichoderma Biofertilizer Links to Altered Soil Chemistry, Altered Microbial Communities, and Improved Grassland Biomass

In grasslands, forage and livestock production results in soil nutrient deficits as grasslands typically receive no nutrient inputs, leading to a loss of grassland biomass. The application of mature compost has been shown to effectively increase grassland nutrient availability. However, research on fertilization regime influence and potential microbial ecological regulation mechanisms are rarely conducted in grassland soil. We conducted a two-year experiment in meadow steppe grasslands, focusing on above- and belowground consequences of organic or Trichoderma biofertilizer applications and potential soil microbial ecological mechanisms underlying soil chemistry and microbial community responses. Grassland biomass significantly (p = 0.019) increased following amendment with 9,000 kg ha−1 of Trichoderma biofertilizer (composted cattle manure + inoculum) compared with other assessed organic or biofertilizer rates, except for BOF3000 (fertilized with 3,000 kg ha−1 biofertilizer). This rate of Trichoderma biofertilizer treatment increased soil antifungal compounds that may suppress pathogenic fungi, potentially partially responsible for improved grassland biomass. Nonmetric multidimensional scaling (NMDS) revealed soil chemistry and fungal communities were all separated by different fertilization regime. Trichoderma biofertilizer (9,000 kg ha−1) increased relative abundances of Archaeorhizomyces and Trichoderma while decreasing Ophiosphaerella. Trichoderma can improve grassland biomass, while Ophiosphaerella has the opposite effect as it may secrete metabolites causing grass necrosis. Correlations between soil properties and microbial genera showed plant-available phosphorus may influence grassland biomass by increasing Archaeorhizomyces and Trichoderma while reducing Ophiosphaerella. According to our structural equation modeling (SEM), Trichoderma abundance was the primary contributor to aboveground grassland biomass. Our results suggest Trichoderma biofertilizer could be an important tool for management of soils and ultimately grassland plant biomass

DataSheet_1_Single-cell mapping of N6-methyladenosine in esophageal squamous cell carcinoma and exploration of the risk model for immune infiltration.zip

BackgroundN6-methyladenosine (m6A) modification is the most common RNA modification, but its potential role in the development of esophageal cancer and its specific mechanisms still need to be further investigated.MethodsBulk RNA-seq of 174 patients with esophageal squamous carcinoma from the TCGA-ESCC cohort, GSE53625, and single-cell sequencing data from patients with esophageal squamous carcinoma from GSE188900 were included in this study. Single-cell analysis of scRNA-seq data from GSE188900 of 4 esophageal squamous carcinoma samples and calculation of PROGENy scores. Demonstrate the scoring of tumor-associated pathways for different cell populations. Cell Chat was calculated for cell populations. thereafter, m6A-related differential genes were sought and risk models were constructed to analyze the relevant biological functions and impact pathways of potential m6A genes and their impact on immune infiltration and tumor treatment sensitivity in ESCC was investigated.ResultsBy umap downscaling analysis, ESCC single-cell data were labelled into clusters of seven immune cell classes. Cellchat analysis showed that the network interactions of four signaling pathways, MIF, AFF, FN1 and CD99, all showed different cell type interactions. The prognostic risk model constructed by screening for m6A-related differential genes was of significant value in the prognostic stratification of ESCC patients and had a significant impact on immune infiltration and chemotherapy sensitivity in ESCC patients.ConclusionIn our study, we explored a blueprint for the distribution of single cells in ESCC based on m6A methylation and constructed a risk model for immune infiltration analysis and tumor efficacy stratification in ESCC on this basis. This may provide important potential guidance for revealing the role of m6A in immune escape and treatment resistance in esophageal cancer. </p