Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk.

The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.

CYTOSOLIC INVERTASE2 regulates flowering and reactive oxygen species-triggered programmed cell death in tomato.

Cytosolic invertase (CIN) in plants hydrolyzes sucrose into fructose and glucose, influencing flowering time and organ development. However, the underlying molecular mechanisms remain elusive. Through expressional, genetic, and histological analyses, we identified a substantially role of SlCIN2 (localized in mitochondria) in regulating flowering and pollen development in tomato (Solanum lycopersicum). The overexpression of SlCIN2 resulted in increased hexose accumulation and decreased sucrose and starch content. Our findings indicated that SlCIN2 interacts with Sucrose transporter2 (SlSUT2) to inhibit the sucrose transport activity of SlSUT2, thereby suppressing sucrose content in flower buds and delaying flowering. We found that higher levels of glucose in SlCIN2-overexpressing anthers result in the accumulation of abscisic acid (ABA) and reactive oxygen species (ROS), thereby disrupting programmed cell death (PCD) in anthers and delaying the end of tapetal degradation. Exogenous sucrose partially restored fertility in SlCIN2-overexpressing plants. This study revealed the mechanism by which SlCIN2 regulates pollen development and demonstrated a strategy for creating sugar-regulated gene male sterility lines for tomato hybrid seed production.

Clinical Value of Three-Dimensional Transvaginal Ultrasound in Diagnosis of Endometrial Receptivity and Ovarian Function in Patients with Infertility.

Objective: A case-control study was conducted to explore the clinical value of three-dimensional transvaginal ultrasound in the diagnosis of endometrial receptivity (ER) and ovarian function in patients with infertility. Methods: A total of 308 infertile women treated in our hospital from March 2020 to June 2021 were enrolled as the observation group, and another 300 women of childbearing age who underwent physical examination in the same period were enrolled as the control group. The clinical value of three-dimensional transvaginal ultrasound in ER in patients with infertility was analyzed by comparing the classification of endometrial and subendometrial blood perfusion, endometrial AUC value and Pi value, and subendometrial AUC value and Pi value. According to the number of oocytes obtained, the patients were assigned into the normal response group (182 cases, ≥5 oocytes) and the low response group (126 cases, <5 oocytes). The levels of some hormones, such as follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), and FSH/LH, were measured. Transvaginal ultrasonography was performed to detect ovarian volume (OV), antral follicle count (AFC), and peak flow rate of the ovarian interstitial artery (PSV). The peak of end-diastolic flow velocity (EDV) and other indexes were analyzed. The correlation between FSH level, FSH/LH, and ultrasound indexes was analyzed, and the ROC curve was established to analyze the value of transvaginal Doppler ultrasound in evaluating ovarian reserve function and predicting ovulation. Results: There were significant differences in late proliferation type I and type III, ovulatory type II and type III (P < 0.05). There exhibited no significant difference in late proliferation type II, ovulation stage type I, and implantation window stage type I, type II, and type III (P > 0.05). Regarding the endometrial AUC and Pi values, the endometrial AUC and Pi values in the observation group were lower compared to the control group during late proliferation and ovulation (P < 0.05). There exhibited no significant difference in AUC and Pi (P > 0.05). Regarding the subintimal AUC and Pi values, the subintimal AUC and Pi values in the observation group were higher compared to the control group during late proliferation and ovulation (P < 0.05). There exhibited no significant difference in AUC and Pi during the implantation window (P > 0.05). There exhibited no significant difference in menarche age, age, body mass index, and menstrual cycle between the normal response group and the low response group (P > 0.05). The levels of EDV, OV, AFC, and PSV in the normal response group were higher compared to the low response group (P < 0.01). Compared with the low response group, the levels of FSH and FSH/LH in the normal response group were lower, but the levels of LH and E2 in the normal response group were higher (P < 0.05). The results of correlation analysis of FSH, FSH/LH, and ultrasound parameters between the normal response group and the low response group indicated that FSH was negatively correlated with E2, EDV, OV, AFC, and PSV in 308 infertile women (r = -0.817, -0.846, -0.707, -0.845, -0.911, P < 0.01), but it was positively correlated with FSH/LH (r = 0.714, P < 0.01). The ultrasound parameters of ovarian reserve function in the normal response group and the low response group were compared with the indexes that predicted ovulation. The results of ROC curve analysis indicated that the cutoff values of EDV, OV, AFC, and PSV were 4.141, 3.726, 4.106, and 13.944, respectively, the specificity of each index was higher than 90.00%, and the sensitivity was higher than 80.00% except PSV. Conclusion: Transvaginal ultrasound can not only accurately evaluate the ER of infertile women but also directly observe follicular development and monitor ovulation, which is of high value in evaluating ovarian reserve function and predicting ovulation.

Associations Between Genetically Predicted Plasma N-Glycans and Prostate Cancer Risk: Analysis of Over 140,000 European Descendants.

BACKGROUND: Previous studies suggest a potential link between glycosylation and prostate cancer. To better characterize the relationship between the two, we performed a study to comprehensively evaluate the associations between genetically predicted blood plasma N-glycan levels and prostate cancer risk. METHODS: Using genetic variants associated with N-glycan levels as instruments, we evaluated the associations between levels of 138 plasma N-glycans and prostate cancer risk. We analyzed data of 79,194 cases and 61,112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL. RESULTS: We identified three N-glycans with genetically predicted levels in plasma to be associated with prostate cancer risk after Bonferroni correction. The estimated odds ratios (95% confidence intervals) were 1.29 (1.20-1.40), 0.80 (0.74-0.88), and 0.79 (0.72-0.87) for PGP18, PGP33, and PGP109, respectively, per every one standard deviation increase in genetically predicted levels of N-glycan. However, the instruments for these N-glycans only involved one to two variants. The proportions of variations that can be explained by the instruments range from 1.58% to 2.95% for these three N-glycans. CONCLUSION: We observed associations between genetically predicted levels of three N-glycans PGP18, PGP33, and PGP109 and prostate cancer risk. Given the correlated nature of the N-glycans and that many N-glycans share genetic loci, pleiotropy is a major concern. Future work is warranted to better characterize the relationship between N-glycans and prostate cancer.

GranatumX: A Community-engaging, Modularized, and Flexible Webtool for Single-cell Data Analysis.

We present GranatumX, a next-generation software environment for single-cell RNA sequencing (scRNA-seq) data analysis. GranatumX is inspired by the interactive webtool Granatum. GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named Gboxes, which wrap around bioinformatics tools written in various programming languages and on various platforms. GranatumX can be run on the cloud or private servers and generate reproducible results. It is a community-engaging, flexible, and evolving software ecosystem for scRNA-seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.

Identification of lncRNA biomarkers for lung cancer through integrative cross-platform data analyses.

This study was designed to identify lncRNA biomarker candidates using lung cancer data from RNA-Seq and microarray platforms separately.Lung cancer datasets were obtained from the Gene Expression Omnibus (GEO, n = 287) and The Cancer Genome Atlas (TCGA, n = 216) repositories, only common lncRNAs were used. Differentially expressed (DE) lncRNAs in tumors with respect to normal were selected from the Affymetrix and TCGA datasets. A training model consisting of the top 20 DE Affymetrix lncRNAs was used for validation in the TCGA and Agilent datasets. A second similar training model was generated using the TCGA dataset.First, a model using the top 20 DE lncRNAs from Affymetrix for training and validated using TCGA and Agilent, achieved high prediction accuracy for both training (98.5% AUC for Affymetrix) and validation (99.2% AUC for TCGA and 92.8% AUC for Agilent). A similar model using the top 20 DE lncRNAs from TCGA for training and validated using Affymetrix and Agilent, also achieved high prediction accuracy for both training (97.7% AUC for TCGA) and validation (96.5% AUC for Affymetrix and 80.9% AUC for Agilent). Eight lncRNAs were found to be overlapped from these two lists.

[Distribution, structure and sequence alignment, and metagenomics analysis of two nitrite reductases with NO forming].

Objective: To reflect the importance of nitrite reductase (NIR) in the environment, we studied its distribution. Methods: The sequences of NIR were searched in the sequenced genome database at NCBI based on previous reported NIR sequences. The sequence similarity was done by multiple sequence alignment, and phylogenetic relationship was evaluated via constructing the phylogenetic tree. Furthermore, their distribution in the marine metagenome was studied by metagenomics. Results: The homologues of these two enzymes were 397 and 812 strains in sequenced genome, and the proportion was 8 and 15.7 percent, respectively. Almost all of archaea containing type II NIR. They have high identity by multiple sequence alignment analysis. The cofactor, the substrate and the cooper binding sites in type II were high conserved, suggesting that these enzymes had the specific function in denitrification. Phylogenetic analysis showed the two enzymes may have the common ancestor. In marine metagenome analysis, type I and II have 6 and 35 reads per 100000 reads, respectively, the two types of NIRs have the biggest proportion at the tropical south pacific area. Conclusion: Collectively, we suggested NIR, especially type II, play a key role in bioremediation of nitrogen contamination.