Search

Aging is associated with substantial physiological changes and constitutes a major risk factor for neurological disorders including dementia. Alterations in gene expression upon aging have been extensively studied; however, an in-depth characterization of post-transcriptional regulatory events remains elusive. Here, we profiled the age-related changes of the transcriptome and translatome in the female mouse hippocampus by RNA sequencing of total RNA and polysome preparations at four ages (3-, 6-, 12-, 20-month-old); and we implemented a variety of bioinformatics approaches to unravel alterations in transcript abundance, alternative splicing, and polyadenylation site selection. We observed mostly wellcoordinated transcriptome and translatome expression signatures across age including upregulation of transcripts related to immune system processes and neuroinflammation, though transcripts encoding ribonucleoproteins or associated with mitochondrial functions, calcium signaling and the cell-cycle displayed substantial discordant profiles, suggesting translational control associated with age-related deficits in hippocampal-dependent behavior. By contrast, alternative splicing was less preserved, increased with age and was associated with distinct functionally-related transcripts encoding proteins acting at synapses/dendrites, RNA-binding proteins; thereby predicting regulatory roles for RBM3 and CIRBP. Only minor changes in polyadenylation site selection were identified, indicating pivotal 3'-end selection in young adults compared to older groups. Overall, our study provides a comprehensive resource of age-associated post-transcriptional regulatory events in the mouse hippocampus, enabling further examination of the molecular features underlying age-associated neurological diseases. [ABSTRACT FROM AUTHOR]

Background: Spinal cord injury (SCI) is a nervous system disease leading to motor and sensory dysfunction below the injury level, and can result in paralysis. MicroRNAs (miRNAs) play a key role in SCI treatment, and related research provides insights for SCI diagnosis and treatment. Bibliometrics is an important tool for literature statistics and evaluation, objectively summarizing multidimensional information. This study comprehensively overviews the field through bibliometric analysis of miRNA and SCI research, providing contemporary resources for future collaboration and clinical treatment. Materials and methods: In this study, we searched the Web of Science Core Collection (WOSCC) database. After careful screening and data import, we extracted annual publications, citation counts, countries, institutions, authors, journals, highly cited articles, co-cited articles, keywords, and H-index. Bibliometrics and visualization analyses employed VOSviewer, CiteSpace, the R package "bibliometrix," and online analytic platforms. Using Arrowsmith, 1 we determined miRNA-SCI relationships and discussed potential miRNA mechanisms in SCI. Results: From 2008 to 2024, the number of related papers increased annually, reaching 754. The number of yearly publications remained high and entered a period of rapid development. Researchers from 50 countries/regions, 802 institutions, 278 journals, and 3,867 authors participated in the field. Currently, China has advantages in the number of national papers, citations, institutions, and authors. However, it is necessary to strengthen cooperation among different authors, institutions, and countries to promote the production of important academic achievements. The research in the field currently focuses on nerve injury, apoptosis, and gene expression. Future research directions mainly involve molecular mechanisms, clinical trials, exosomes, and inflammatory reactions. Conclusion: Overall, this study comprehensively analyzes the research status and frontier of miRNAs in SCI. A systematic summary provides a complete and intuitive understanding of the relationship between SCI and miRNAs. The presented findings establish a basis for future research and clinical application in this field. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a severe neurodegenerative disease for which there is currently no effective treatment. Mild cognitive impairment (MCI) is an early disease that may progress to AD. The effective diagnosis of AD and MCI in the early stage has important clinical significance. Methods: To this end, this paper proposed a hypergraph-based netNMF (HGnetNMF) algorithm for integrating structural magnetic resonance imaging (sMRI) of AD and MCI with corresponding gene expression profiles. Results: Hypergraph regularization assumes that regions of interest (ROIs) and genes were located on a non-linear low-dimensional manifold and can capture the inherent prevalence of two modalities of data and mined high-order correlation features of the two data. Further, this paper used the HG-netNMF algorithm to construct a brain structure connection network and a protein interaction network (PPI) with potential role relationships, mine the risk (ROI) and key genes of both, and conduct a series of bioinformatics analyses. Conclusion: Finally, this paper used the risk ROI and key genes of the AD and MCI groups to construct diagnostic models. The AUC of the AD group and MCI group were 0.8 and 0.797, respectively. [ABSTRACT FROM AUTHOR]

This document is an editorial published in the journal Frontiers in Plant Science. It discusses the topic of plant adaptation and resistance to cold temperature stress. The editorial highlights the importance of understanding the mechanisms behind plant responses to cold stress, as it can significantly impact crop productivity and survival. The document also provides a brief summary of six research papers included in the Research Topic, which cover various aspects of plant adaptation to cold stress. The findings presented in these papers contribute to our understanding of plant responses to environmental stresses and can inform efforts to enhance crop resilience in the face of changing climatic conditions. [Extracted from the article]

The epigenetic machinery has received extensive attention due to its involvement in plant growth, development, and adaptation to environmental changes. Recent studies often highlight the epigenetic regulatory network by discussing various epigenetic mutants across various plant species. However, a systemic understanding of essential epigenetic regulatory mechanisms remains limited due to a lack of representative mutants involved in multiple biological processes. Colorless Non-ripening (Cnr), a spontaneous epimutant isolated from a commercial population, was initially characterized for its role in fruit ripening regulation. Cnr fruits exhibit an immature phenotype with yellow skin, attributed to hypermethylation of the SQUAMOSA PROMOTER BINDING PROTEIN-LIKECNR (SlSPL-CNR) promoter, resulting in the repression of gene expression. In addition to DNA methylation, this process also involves histone modification and microRNA, integrating multiple epigenetic regulatory factors. Interestingly, knockout mutants of SlSPL-CNR display phenotypical distinctions from Cnr in fruit ripening, indicating complex genetic and epigenetic control over the nonripening phenotype in Cnr fruits. Accumulating evidence suggests that Cnr epimutation is pleiotropic, participating in various biological processes such as Cd stress, Fe deficiency, vivipary, and cell death. Therefore, the Cnr epimutant serve as an excellent model for unveiling how epigenetic mechanisms are involved in diverse biological processes. This review paper focuses on recent research advances regarding the Cnr epimutant, delving into its complex genetic and epigenetic regulatory mechanisms, with the aim of enhancing our understanding and facilitating the development of high-quality, high-yield crops through epigenetic modification. [ABSTRACT FROM AUTHOR]

The Frontiers Media family has over 200 journals, which are each headed by usually one Field Chief Editor, and several thousand specialty sections, which are each headed by one or more Specialty Chief Editors. The year 2021 was the 10th anniversary of the founding of the Frontiers in Genetics journal and the Frontiers in Toxicogenomics specialty section of this journal. In 2021, we also announce one of the newest of the Frontiers journals-Frontiers in Toxicology which is part of the Frontiers Media family of journals but independent of Frontiers in Genetics. Dr. Ruden is the founding, and currently sole, Specialty Chief Editor of Frontiers in Toxicogenomics and one of 9 Specialty Chief Editors of Frontiers in Toxicology. The new Field Chief editor of Frontiers in Toxicology will be Dr. Bengt Fadeel of the Karolinska Institute in Sweden. As of 2021, Frontiers in Toxicogenomics has published over 138 articles and has over 370 Editors including 90 Associate Editors and 280 Review Editors. The Frontiers in Genetics impact factor was initially approximately 2.5 when it was first listed in PubMed in 2015 and has risen steadily to its current value of 4.8, which is typical for the majority of the over 200 Frontiers journals that have established impact factors. In this overviewof the first decade of Frontiers in Toxicogenomics, we discuss the top 5 articles with the highest Scopus citations, which were all written in the first few years of the journal. The article with the highest number of citations, with 353 Scopus over 600 Google Scholar citations, and the highest average number of citations (67) that steadily increased from 10 citations in 2013 to 119 citations in 2021, was written in 2012 by Dr. Ruden's laboratory and titled, "Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift." The five most influential authors who published in the journal in the past 10 years based on Scopus citations of a particular paper are Dr. Ruden's laboratory, with 353 Scopus citations for the SnpSift paper mentioned above; Drs. Brock Christensen and Carmen J.Marsit, with 86 Scopus citations for their review, "Epigenomics in environmental health"; Dr. Michael Aschner and colleagues, with 61 Scopus citations for their paper "Genetic factors and manganese-induced neurotoxicity"; and Dr. Sandra C. dos Santos and colleagues, with 59 Scopus citations for their paper, "Yeast toxicogenomics: genome-wide responses to chemical stresses with impact in environmental health, pharmacology, and biotechnology." While the top 5 papers were published in the early years of the journal, we will also discuss a more recent article published in 2018 on a comparison of RNA-seq and microarray methods by Dr. Michael Liguori's laboratory, "Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies," that far exceeds the number of downloads and views of all the other articles published in the first 10 years of the journal and will likely be a top cited paper in the second decade highlights of this journal. Finally, we discuss where the Frontiers in Toxicogenomics specialty journal and the Frontiers in Toxicology journalwill go to advance the field of toxicogenomics, and more generally, toxicology, in the future. [ABSTRACT FROM AUTHOR]

Nanotechnology is rapidly growing with nanoparticles produced and utilized in a wide range of commercial products worldwide. Among the different types of nanomaterials produced, silver nanoparticles (AgNPs) occupy a predominant position and they are used in electronics, clothing, food industry, cosmetics and medical devices. Nanosilver has also showed excellent performance in antibacterial application. Nowadays, the increasing use of AgNPs has put the evidence on their possible toxicity to the human health and the impact on the environment. This paper focus on adverse effects of AgNPs in adult of Danio rerio. Fishes exposed to increasing concentrations (8, 45, and 70µg/l) silver nanoparticles (AgNPs, 25nm in average diameter) and after treatment for 30 days, was quickly euthanized in MS-222. We have evaluated bioaccumulation of AgNPs using ICP-MS and analyzed histological changes, biomarkers of oxidative damage and gene expression in the gut, liver and gills tissues of AgNPs-treated zebrafish. The histological analysis showed lesions of secondary lamellae of the gills with different degrees of toxicity such as hyperplasia, lamellar fusion, subepithelial edema, and even in some cases telangiectasia. Huge necrosis of the intestinal villi was found in the gut. No lesion was detected in the liver. The analysis revealed a high expression of metallothioneins 1 (MTs 1) in animals exposed to AgNPs compared to the control group. The ICP-MS analysis shows that the amount of particles absorbed in all treated samples is almost the same. We can affirm that AgNPs toxicity linked more to their size and state of aggregation than to their concentrations. Silver nanoparticles can damage gills and gut because they are able to pass through the mucosal barrier thanks to their small size. The damage is still reversible because it is not documented injury to the basal membrane. [ABSTRACT FROM AUTHOR]

In the early 1900s, mutation breeding to select varieties with desirable traits using spontaneous mutation was actively conducted around the world, including Japan. In rice, the number of fixed mutations per generation was estimated to be 1.38-2.25. Although this low mutation rate was a major problem for breeding in those days, in the modern era with the development of next-generation sequencing (NGS) technology, it was conversely considered to be an advantage for efficient gene identification. In this paper, we proposed an in silico approach using NGS to compare the whole genome sequence of a spontaneous mutant with that of a closely related strain with a nearly identical genome, to find polymorphisms that differ between them, and to identify the causal gene by predicting the functional variation of the gene caused by the polymorphism. Using this approach, we found four causal genes for the dwarf mutation, the round shape grain mutation and the awnless mutation. Three of these genes were the same as those previously reported, but one was a novel gene involved in awn formation. The novel gene was isolated from Bozu-Aikoku, a mutant of Aikoku with the awnless trait, in which nine polymorphisms were predicted to alter gene function by their whole-genome comparison. Based on the information on gene function and tissue-specific expression patterns of these candidate genes, Os03g0115700/LOC_Os03g02460, annotated as a shortchain dehydrogenase/reductase SDR family protein, is most likely to be involved in the awnless mutation. Indeed, complementation tests by transformation showed that it is involved in awn formation. Thus, this method is an effective way to accelerate genome breeding of various crop species by enabling the identification of useful genes that can be used for crop breeding with minimal effort for NGS analysis. [ABSTRACT FROM AUTHOR]

This article discusses the use of single-cell analysis in understanding autoimmune diseases. Traditional methods can obscure the contributions of individual cell types, while single-cell analysis allows for precise identification of specific cell subsets and rare cells with pathogenic potential. By examining the transcriptome, proteome, and epigenome of individual cells, researchers can uncover distinct gene expression patterns and regulatory mechanisms specific to certain cell types or disease states. The article presents 15 papers exploring various aspects of autoimmune diseases using single-cell analysis, including sequencing methods, genetic and epigenetic contributions, molecular mechanisms, biomarkers, and therapeutic implications. The studies focus on autoimmune disorders affecting the skin, rheumatoid arthritis, anti-N-methyl-D-aspartate receptor encephalitis, and hypertension. Single-cell RNA sequencing is used to analyze gene expression patterns and identify potential biomarkers and therapeutic targets. The findings highlight the complexity of these diseases and suggest new strategies for diagnosis and treatment, contributing to the advancement of personalized medicine in autoimmune disease research and potentially improving patient outcomes. [Extracted from the article]

A challenge for central nervous system (CNS) tissue analysis in neuroscience research has been the difficulty to codetect and colocalize gene and protein expression in the same tissue. Given the importance of identifying gene expression relative to proteins of interest, for example, cell-type specific markers, we aimed to develop a protocol to optimize their codetection. RNAscope fluorescent in situ hybridization (FISH) combined with immunohistochemistry (IHC) in fixed (CNS) tissue sections allows for reliable quantification of gene transcripts of interest within IHC-labeled cells. This paper describes a new method for simultaneous visualization of FISH and IHC in thicker (14-µm), fixed tissue samples, using spinal cord sections. This method's effectiveness is shown by the cell-type-specific quantification of two genes, namely the proinflammatory cytokine interleukin-1beta (IL-1b) and the inflammasome NLR family pyrin domain containing 3 (NLRP3). These genes are challenging to measure accurately using immunohistochemistry (IHC) due to the nonspecificity of available antibodies and the hard-todistinguish, dot-like visualizations of the labeled proteins within the tissue. These measurements were carried out in spinal cord sections after unilateral chronic constriction injury of the sciatic nerve to induce neuroinflammation in the spinal cord. RNAscope is used to label transcripts of genes of interest and IHC is used to label cell-type specific antigens (IBA1 for microglia, NeuN for neurons). This combination allowed for labeled RNA transcripts to be quantified within celltype specific boundaries using confocal microscopy and standard image analysis methods. This method makes it easy to answer empirical questions that are intractable with standard IHC or in situ hybridization alone. The method, which has been optimized for spinal cord tissue and to minimize tissue preparation time and costs, is described in detail from tissue collection to image analysis. Further, the relative expression changes in inflammatory genes NLRP3 and IL-1b in spinal cord microglia vs. neurons of somatotopically relevant laminae are described for the first time. [ABSTRACT FROM AUTHOR]

The given document is a list of various scientific articles and research papers related to topics such as Martian mineralogy, the habitability of Mars and Europa, the survival of bacteria in Martian conditions, the adaptation mechanisms of halophilic archaea, and the analysis of gene expression in different organisms. The articles cover a range of subjects including astrobiology, RNA sequencing, osmotic stress, and the effects of environmental factors on microbial life. The document provides a comprehensive collection of scientific literature that can be useful for library patrons conducting research on these specific topics. [Extracted from the article]

Single-Cell Sequencing (SCS) technology plays an important role in the field of Mesenchymal Stem Cells (MSCs) research. This paper comprehensively describes the application of SCS technology in the field of MSCs research, including (1) SCS enables more precise MSCs characterization and biomarker definition. (2) SCS reveals the prevalent gene expression heterogeneity among different subclusters within MSCs, which contributes to a more comprehensive understanding of MSCs function and diversity in developmental, regenerative, and pathological contexts. (3) SCS provides insights into the dynamic transcriptional changes experienced by MSCs during differentiation and the complex web of important signaling pathways and regulatory factors controlling key processes within MSCs, including proliferation, differentiation and regulation, and interactions mechanisms. (4) The analytical methods underpinning SCS data are rapidly evolving and converging with the field of histological research to systematically deconstruct the functions and mechanisms of MSCs. This review provides new perspectives for unraveling the biological properties, heterogeneity, differentiation potential, biological functions, and clinical potential of MSCs at the single-cell level. [ABSTRACT FROM AUTHOR]

Open chromatin regions are the genomic regions associated with basic cellular physiological activities, while chromatin accessibility is reported to affect gene expressions and functions. A basic computational problem is to efficiently estimate open chromatin regions, which could facilitate both genomic and epigenetic studies. Currently, ATAC-seq and cfDNA-seq (plasma cell-free DNA sequencing) are two popular strategies to detect OCRs. As cfDNA-seq can obtain more biomarkers in one round of sequencing, it is considered more effective and convenient. However, in processing cfDNA-seq data, due to the dynamically variable chromatin accessibility, it is quite difficult to obtain the training data with pure OCRs or non-OCRs, and leads to a noise problem for either feature-based approaches or learning-based approaches. In this paper, we propose a learning-based OCR estimation approach with a noise-tolerance design. The proposed approach, named OCRFinder, incorporates the ideas of ensemble learning framework and semi-supervised strategy to avoid potential overfitting of noisy labels, which are the false positives on OCRs and non-OCRs. Compared to different noise control strategies and state-of-the-art approaches, OCRFinder achieved higher accuracies and sensitivities in the experiments. In addition, OCRFinder also has an excellent performance in ATAC-seq or DNase-seq comparison experiments. [ABSTRACT FROM AUTHOR]

Arabidopsis is used as a model species in numerous physiological and genetic studies. Most of them employ parafilm-wrapped sterile culture. Here we demonstrate that this method is prone to potential artifacts and can lead to erroneous conclusions. We compared the effect of different sealing methods including air-permeable paper tape and traditional parafilm on Arabidopsis seedling growth, root development and gene expression network. Although seedlings grown in Petri dishes after 1 week sealed with paper tape showed a similar growth phenotype to that of parafilm-sealed seedlings, more than 700 differentially expressed genes (DEG) were found, including stress and nutrition-responsive genes. In addition, more H2O2 was accumulated in the tissues of parafilm-sealed plants. After 14 days of growth, paper tape-sealed plants grew much better than parafilm-sealed ones and accumulated higher chlorophyll content, with 490 DEGs found. After 3 weeks of growth, paper tape-sealed plants had higher chlorophyll and better growth compared to parafilm-sealed ones; and only 10 DEGs were found at this stage. Thus, the obvious phenotype observed at the latter stage was a result of differential gene expression at earlier time points, mostly of defense, abiotic stress, nutrition, and phytohormone-responsive genes. More O2 content was detected inside paper tape-sealed Petri dishes at early growth stage (7 days), and distinct difference in the CO2 content was observed between parafilm-sealed and paper tape-sealed Petri dishes. Furthermore, the carbon source also influenced seedlings growth with different sealing methods. In conclusion, conventional sealing using parafilm was not the optimal choice, most likely because of the limited gas exchange and a consequent stress caused to plants. [ABSTRACT FROM AUTHOR]

Gene clustering is one of the important techniques to identify co-expressed gene groups from gene expression data, which provides a powerful tool for investigating functional relationships of genes in biological process. Self-training is a kind of important semi-supervised learning method and has exhibited good performance on gene clustering problem. However, the self-training process inevitably suffers from mislabeling, the accumulation of which will lead to the degradation of semi-supervised learning performance of gene expression data. To solve the problem, this paper proposes a self-training subspace clustering algorithm based on adaptive confidence for gene expression data (SSCAC), which combines the low-rank representation of gene expression data and adaptive adjustment of label confidence to better guide the partition of unlabeled data. The superiority of the proposed SSCAC algorithm is mainly reflected in the following aspects. 1) In order to improve the discriminative property of gene expression data, the low-rank representation with distance penalty is used to mine the potential subspace structure of data. 2) Considering the problem of mislabeling in self-training, a semi-supervised clustering objective function with label confidence is proposed, and a self-training subspace clustering framework is constructed on this basis. 3) In order to mitigate the negative impact of mislabeled data, an adaptive adjustment strategy based on gravitational search algorithm is proposed for label confidence. Compared with a variety of state-of-the-art unsupervised and semi-supervised learning algorithms, the SSCAC algorithm has demonstrated its superiority through extensive experiments on two benchmark gene expression datasets. [ABSTRACT FROM AUTHOR]

Premature ovarian failure (POF), or premature ovarian insufficiency (POI), is a multifactorial and heterogeneous disease characterized by amenorrhea, decreased estrogen levels and increased female gonadotropin levels. The incidence of POF is increasing annually, and POF has become one of the main causes of infertility in women of childbearing age. The etiology and pathogenesis of POF are complex and have not yet been clearly elucidated. In addition to genetic factors, an increasing number of studies have revealed that epigenetic changes play an important role in the occurrence and development of POF. However, we found that very few papers have summarized epigenetic variations in POF, and a systematic analysis of this topic is therefore necessary. In this article, by reviewing and analyzing the most relevant literature in this research field, we expound on the relationship between DNA methylation, histone modification and non-coding RNA expression and the development of POF. We also analyzed how environmental factors affect POF through epigenetic modulation. Additionally, we discuss potential epigenetic biomarkers and epigenetic treatment targets for POF. We anticipate that our paper may provide new therapeutic clues for improving ovarian function and maintaining fertility in POF patients. [ABSTRACT FROM AUTHOR]

Polysaccharides and saponins are the main active components of Polygonati Rhizoma. Studying the molecular mechanism of their synthesis pathway is helpful in improving the content of active components at the molecular level. At present, transcriptome analysis of three Polygonatum species (Polygonatum sibiricum Red., Polygonatum cyrtonema Hua, Polygonatum kingianum Coll. et Hemsl.) has been reported, but no comparative study has been found on the transcriptome data of the three species. Transcriptome sequencing was performed on the rhizomes of three Polygonatum species based on highthroughput sequencing technology, and all transcripts were assembled. A total of 168,108 unigenes were generated after the removal of redundancy, of which 121,642 were annotated in seven databases. Through differential analysis and expression analysis of key enzyme genes in the synthesis pathway of three Polygonatum polysaccharides and steroidal saponins, 135 differentially expressed genes encoding 18 enzymes and 128 differentially expressed genes encoding 28 enzymes were identified, respectively. Numerous transcription factors are involved in the carbohydrate synthesis pathway. Quantitative realtime PCR was used to further verify the gene expression level. In this paper, we present a public transcriptome dataset of three medicinal plants of the genus Polygonatum, and analyze the key enzyme genes of polysaccharide and steroidal saponins synthesis pathway, which lays a foundation for improving the active component content of Polygonati Rhizoma by molecular means. [ABSTRACT FROM AUTHOR]

Certain viruses called tumor viruses or oncoviruses are capable to change the gene expression pattern of distinct human or animal cell types in tissue culture, resulting in uncontrolled proliferation as well as a change in the social behavior of the infected cells: the oncovirus-transformed, immortalized cells are capable to form malignant neoplasms in suitable animal models. At present, seven human viruses are categorized as causative agents of distinct human malignancies. The genomes of human tumor viruses, typically encode viral oncoproteins and non-translated viral RNAs that affect the gene expression pattern of their target cells or induce genetic and epigenetic alterations contributing to oncogenesis. Recently, the application of chromatin conformation capture technologies and threedimensional (3D) molecular imaging techniques revealed how the gene products or genomes of certain human tumor viruses interact with and induce alterations in the 3D host genome structure. This Mini Review aims to cover selected aspects of these developments. The papers, discussed briefly, describe how insertion of a novel viral binding site for the 3D genome organizer cellular protein CCCTC-binding factor (CTCF) into the DNA of T cells infected by human T-cell lymphotropic virus type 1 (HTLV-1) may contribute to lymphomagenesis, as well as how integration of high risk human papillomavirus genome into the host cell DNA may facilitate cervical carcinogenesis. Recent results regarding the interactions of cellular genomes with the episomal, chromatinized DNA genomes of oncogenic human herpesvirus, Epstein-Barr virus (EBV) will also be summarized, similarly to available data regarding contacts formed by episomal or integrated hepatitis B virus (HBV) DNA with host chromatin. Finally, a putative mechanism of hepatitis C virus (HCV) induced chromatin alterations will be presented, which may solve the riddle, how a cytoplasmic RNA virus without a viral oncogene could induce malingnant transfrormation of hepatocytes. [ABSTRACT FROM AUTHOR]

Nanotechnology is rapidly growing with nanoparticles produced and utilized in a wide range of commercial products worldwide. Among the different types of nanomaterials produced, silver nanoparticles (AgNPs) occupy a predominant position and they are used in electronics, clothing, food industry, cosmetics and medical devices. Nanosilver has also showed excellent performance in antibacterial application. Nowadays, the increasing use of AgNPs has put the evidence on their possible toxicity to the human health and the impact on the environment. This paper focus on adverse effects of AgNPs in adult of Danio rerio. Fishes exposed to increasing concentrations (8, 45, and 70 μg/l) silver nanoparticles (AgNPs, 25nm in average diameter) and after treatment for 30 days, was quickly euthanized in MS-222. We have evaluated bioaccumulation of AgNPs using ICP-MS and analyzed histological changes, biomarkers of oxidative damage and gene expression in the gut, liver and gills tissues of AgNPs-treated zebrafish. The histological analysis showed lesions of secondary lamellae of the gills with different degrees of toxicity such as hyperplasia, lamellar fusion, subepithelial edema, and even in some cases telangiectasia. Huge necrosis of the intestinal villi was found in the gut. No lesion was detected in the liver. The analysis revealed a high expression of metallothioneins 1 (MTs 1) in animals exposed to AgNPs compared to the control group. The ICP-MS analysis shows that the amount of particles absorbed in all treated samples is almost the same. We can affirm that AgNPs toxicity linked more to their size and state of aggregation than to their concentrations. Silver nanoparticles can damage gills and gut because they are able to pass through the mucosal barrier thanks to their small size. The damage is still reversible because it is not documented injury to the basal membrane. [ABSTRACT FROM AUTHOR]

Background: The diagnostic capabilities of exosomes in the field of reproductive biomedicine have attracted much attention. The aim of this scientometric study was to statistically and qualitatively assess the knowledge structure, hot issues, and research trends of papers about exosomes in the field of reproduction using visualization methods. Methods: The Web of Science Core Collection was searched for studies on exosomes in the field of reproduction. We performed bibliometric and visual analyses using VOSviewer, CiteSpace, and Microsoft Excel. Results: After database search, 1,011 articles were included, with number of studies being published every year continually increasing. These publications came from 61 nations or regions, with the US having the highest number. The University of Queensland was the main institution in which the research was conducted. The journal Placenta contained the highest number studies. There were 5,247 authors in total. Carlos Salomon had the highest number of papers with co-citations. Exosomes, extracellular vesicles, pregnancy, microRNAs, preeclampsia, placenta, microvesicles, gene expression, biomarkers, and first trimester were the most frequently used terms. Conclusion: Exosome research is booming in reproductive biomedicine. Future studies will likely focus on exosomes as biomarkers in gamete formation and fertilization, pregnancy, and cancers associated with reproduction. In addition to focusing on fundamental research, we should concentrate on the application of the results and the investigation of exosomes in infertile patients. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, which suffers from the lack of diagnosis and treatment methods, and many patients cannot be diagnosed at first time. Gasdermin D (GSDMD) is involved in inflammatory reactions and pyroptosis and is considered a potential therapeutic target. This paper's aim is to elucidate the expression of GSDMD in clear cell renal cell carcinoma and its value for treatment and prognosis, as well as its impact on the biological function of clear cell renal cell carcinoma. Method: The Cancer Genome Atlas (TCGA) database was used to compare the expression of GSDMD in tumor and normal tissues, analyze its correlation with cancer stage and overall survival time, and establish receiver operating characteristic (ROC) curve, which was confirmed by the Gene Expression Omnibus (GEO) database and immunohistochemical staining of clinical samples and PCR and Western blotting (WB) of cell lines. The relationship between GSDMD and patient prognosis and staging was analyzed using TCGA database and validated using clinical sample data. Differentially expressed genes (DEGs) and epithelial--mesenchymal transition (EMT)-related genes of GSDMD were screened by TCGA database. Protein--protein interaction (PPI) of GSDMD was constructed by GeneMANIA and STRING, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were analyzed by the Metascape database. Then, R software was used to analyze the immune cell infiltration, immune microenvironment score, and tumor mutational burden (TMB) analysis of GSDMD high- and low-expression groups in TCGA database. GSDMD lentivirus was used to transfect 769-P cells to construct stable upregulated and downregulated transfected cell lines. PCR was used to verify the expression differences of differentially expressed genes between the highand low-expression groups of GSDMD; then, MTT, flow apoptosis, and Transwell were used to detect the proliferation, apoptosis, invasion, and migration of the transfected cells. Results: The results of bioinformatics analysis showed that the expression of GSDMD in clear cell renal cell carcinoma was significantly correlated with patient stage and overall survival, and the tumor with high expression of GSDMD had a worse stage and overall survival. GSDMD has some significance in the diagnosis of ccRCC. The results of EMT correlation analysis and enrichment analysis showed that GSDMD was correlated with genes and pathways related to invasion and metastasis of renal cell carcinoma. The subsequent immune cell infiltration analysis showed that there were many differences in the infiltration of immune cells between the high- and low-expression groups of GSDMD, such as naive B cells. The immune microenvironment score showed that the highexpression group had a lower proportion of stromal cells than the local expression group but had a higher proportion of immune cells. Through TMB, it was shown that the high-expression group had a higher mutation. The expression of GSDMD in renal cell carcinoma by immunohistochemistry and in vitro cell experiments was confirmed. According to the prognostic information of clinical patients, it was found that GSDMD was significantly correlated with TNM stage, Fuhrman grade, lymph node metastasis, gender, and smoking or not, and the prognosis of patients with high expression of GSDMD was worse. After that, we constructed stable transfection cell lines with high expression and knockdown through lentivirus transfection and verified the expression amount of differentially expressed genes by PCR, which is consistent with the results of TCGA database. Then, we confirmed that GSDMD is related to proliferation, invasion, migration, and apoptosis of ccRCC by MTT, flow apoptosis, and Transwell assay. The low expression of GSDMD inhibits the proliferation, invasion, and migration of tumors and enhances apoptosis and vice versa. Therefore, GSDMD can be used as a potential biological marker for the diagnosis and prognosis of ccRCC. [ABSTRACT FROM AUTHOR]

Footpad dermatitis (FPD) is a type of skin inflammation that causes necrotic lesions on the plantar surface of the footpads in commercial poultry, with significant animal welfare, and economic implications. To identify biomarkers for FPD development and wound healing, a battery cage trial was conducted in which a paper sheet was put on the bottom of cages to hold feces to induce FPD of broilers. Day-of-hatch Ross 308 male broiler chicks were fed a corn-soybean meal diet and assigned to 3 treatments with 8 cages per treatment and 11 birds per cage. Cages without paper sheets were used as a negative control (NEG). Cages with paper sheets during the entire growth period (d 0-30) were used as a positive control (POS) to continually induce FPD. Cages with paper sheets during d 0-13 and without paper sheets during d 14-30 were used to examine the dynamic of FPD development and lesion wound healing (LWH). Footpad lesions were scored to grade (G) 1-5 with no lesion in G1 and most severe lesion in G5. Covering with paper sheets in POS and LWH induced 99% incidence of G3 footpads on d 13. Removing paper sheets from LWH healed footpad lesions by d 30. One representative bird, with lesions most close to pen average lesion score, was chosen to collect footpad skin samples for biomarker analysis. Total collagen protein and mRNA levels of tenascin X (TNX), type I α1 collagen (COL1A1), type III α1 collagen (COL3A1), tissue inhibitor of metalloproteinase 3 (TIMP3), and integrin α1 (ITGA1) mRNA levels were decreased (P < 0.05), while mRNA levels of tenascin C (TNC), tumor necrosis factor (TNF) α, Toll-like receptor (TLR) 4 and vascular endothelial growth factor (VEGF), IL-1β, and the ratio of MMP2 to all TIMP were increased (P < 0.03) in G3 footpads in POS and LWH compared to G1 footpads in NEG on d 14. These parameters continued to worsen with development ofmore severe lesions in POS. After paper sheets were removed (i.e., LWH), levels of these parameters gradually or rapidly returned to levels measured in NEG. Regression analysis indicated significant quadratic changes of these parameters to footpad lesion scores. In summary, these biomarkers were interrelated with dynamic changes of footpad lesion scores, suggesting they may be used as potential biomarkers for footpad lesion development and wound healing process. [ABSTRACT FROM AUTHOR]

Motivation: Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. There are numerous normalizationmethods available in the literature. Based on our experience, in the context of oscillatory systems, such as cell-cycle, circadian clock, etc., the choice of the normalization method may substantially impact the determination of a gene to be rhythmic. Thus rhythmicity of a gene can purely be an artifact of how the data were normalized. Since the determination of rhythmic genes is an important component of modern toxicological and pharmacological studies, it is important to determine truly rhythmic genes that are robust to the choice of a normalization method. Results: In this paper we introduce a rhythmicitymeasure and a bootstrap methodology to detect rhythmic genes in an oscillatory system. Although the proposed methodology can be used for any high-throughput gene expression data, in this paper we illustrate the proposed methodology using several publicly available circadian clock microarray gene-expression datasets.We demonstrate that the choice of normalization method has very little effect on the proposed methodology. Specifically, for any pair of normalization methods considered in this paper, the resulting values of the rhythmicity measure are highly correlated. Thus it suggests that the proposed measure is robust to the choice of a normalization method. Consequently, the rhythmicity of a gene is potentially not a mere artifact of the normalization method used. Lastly, as demonstrated in the paper, the proposed bootstrap methodology can also be used for simulating data for genes participating in an oscillatory system using a reference dataset. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of the cancer deaths. Therefore, predicting the survival status of lung cancer patients is of great value. However, the existing methods mainly depend on statistical machine learning (ML) algorithms. Moreover, they are not appropriate for high-dimensionality genomics data, and deep learning (DL), with strong high-dimensional data learning capability, can be used to predict lung cancer survival using genomics data. The Cancer Genome Atlas (TCGA) is a great database that contains many kinds of genomics data for 33 cancer types. With this enormous amount of data, researchers can analyze key factors related to cancer therapy. This paper proposes a novel method to predict lung cancer long-term survival using gene expression data from TCGA. Firstly, we select the most relevant genes to the target problem by the supervised feature selection method called mutual information selector. Secondly, we propose a method to convert gene expression data into two kinds of images with KEGG BRITE and KEGG Pathway data incorporated, so that we could make good use of the convolutional neural network (CNN) model to learn high-level features. Afterwards, we design a CNN-based DL model and added two kinds of clinical data to improve the performance, so that we finally got a multimodal DL model. The generalized experiments results indicated that our method performed much better than the ML models and unimodal DL models. Furthermore, we conduct survival analysis and observe that our model could better divide the samples into high-risk and low-risk groups. [ABSTRACT FROM AUTHOR]

Background: Sjögren's syndrome (SS) is a systemic autoimmune disease that affects about 0.04-0.1% of the general population. SS diagnosis depends on symptoms, clinical signs, autoimmune serology, and even invasive histopathological examination. This study explored biomarkers for SS diagnosis. Methods: We downloaded three datasets of SS patients' and healthy pepole's whole blood (GSE51092, GSE66795, and GSE140161) from the Gene Expression Omnibus (GEO) database. We used machine learning algorithm to mine possible diagnostic biomarkers for SS patients. Additionally, we assessed the biomarkers' diagnostic value using the receiver operating characteristic (ROC) curve. Moreover, we confirmed the expression of the biomarkers through the reverse transcription quantitative polymerase chain reaction (RT-qPCR) using our own Chinese cohort. Eventually, the proportions of 22 immune cells in SS patients were calculated by CIBERSORT, and connections between the expression of the biomarkers and immune cell ratios were studied. Results: We obtained 43 DEGs that were mainly involved in immune-related pathways. Next, 11 candidate biomarkers were selected and validated by the validation cohort data set. Besides, the area under curves (AUC) of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF in the discovery and validation datasets were 0.903 and 0.877, respectively. Subsequently, eight genes, including HES4, IFI27, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2,were selected as prospective biomarkers and verified by RT-qPCR. Finally, we revealed the most relevant immune cells with the expression of HES4, IFI27, LY6E, OTOF, TTC21A, XAF1, and ZCCHC2. Conclusion: In this paper, we identified seven key biomarkers that have potential value for diagnosing Chinese SS patients. [ABSTRACT FROM AUTHOR]

Osteosarcoma is a primary malignant tumor found mainly in teenagers and young adults. Patients have very little long-term survival. MYC controls tumor initiation and progression by regulating the expression of its target genes; thus, constructing a risk signature of osteosarcoma MYC target gene set will benefit the evaluation of both treatment and prognosis. In this paper, we used GEO data to download the ChIP-seq data of MYC to obtain the MYC target gene. Then, a risk signature consisting of 10 MYC target genes was developed using Cox regression analysis. The signature indicates that patients in the high-risk group performed poorly. After that, we verified it in the GSE21257 dataset. In addition, the difference in tumor immune function among the low- and high-risk populations was compared by single sample gene enrichment analysis. Immunotherapy and prediction of response to the anticancer drug have shown that the risk signature of the MYC target gene set was positively correlated with immune checkpoint response and drug sensitivity. Functional analysis has demonstrated that these genes are enriched in malignant tumors. Finally, STX10 was selected for functional experimentation. STX10 silence has limited osteosarcoma cell migration, invasion, and proliferation. Therefore, these findings indicated that the MYC target gene set risk signature could be used as a potential therapeutic target and prognostic indicator in patients with osteosarcoma. [ABSTRACT FROM AUTHOR]

Primary complement system (C) deficiencies are rare but notably associated with an increased risk of infections, autoimmunity, or immune disorders. Patients with terminal pathway C-deficiency have a 1,000-to 10,000-fold-higher risk of Neisseria meningitidis infections and should be therefore promptly identified to minimize the likelihood of further infections and to favor vaccination. In this paper, we performed a systematic review about clinical and genetic patterns of C7 deficiency starting from the case of a ten-year old boy infected by Neisseria meningitidis B and with clinical presentation suggestive of reduced C activity. Functional assay via Wieslab ELISA Kit confirmed a reduction in total C activity of the classical (0.6% activity), lectin (0.2% activity) and alternative (0.1% activity) pathways. Western blot analysis revealed the absence of C7 in patient serum. Sanger sequencing of genomic DNA extracted from peripheral blood of the patient allowed the identification of two pathogenetic variants in the C7 gene: the already well-characterized missense mutation G379R and a novel heterozygous deletion of three nucleotides located at the 3'UTR (c.*99_*101delTCT). This mutation resulted in an instability of the mRNA; thus, only the allele containing the missense mutation was expressed, making the proband a functional hemizygote for the expression of the mutated C7 allele. [ABSTRACT FROM AUTHOR]

Background: Corona Virus Disease 2019 (COVID-19) and Osteoarthritis (OA) are diseases that seriously affect the physical and mental health and life quality of patients, particularly elderly patients. However, the association between COVID-19 and osteoarthritis at the genetic level has not been investigated. This study is intended to analyze the pathogenesis shared by OA and COVID-19 and to identify drugs that could be used to treat SARS-CoV-2-infected OA patients. Methods: The four datasets of OA and COVID-19 (GSE114007, GSE55235, GSE147507, and GSE17111) used for the analysis in this paper were obtained from the GEO database. Common genes of OA and COVID-19 were identified through Weighted Gene Co-Expression Network Analysis (WGCNA) and differential gene expression analysis. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen key genes, which were analyzed for expression patterns by single-cell analysis. Finally, drug prediction and molecular docking were carried out using the Drug Signatures Database (DSigDB) and AutoDockTools Results: Firstly, WGCNA identified a total of 26 genes common between OA and COVID-19, and functional analysis of the common genes revealed the common pathological processes and molecular changes between OA and COVID-19 are mainly related to immune dysfunction. In addition, we screened 3 key genes, DDIT3, MAFF, and PNRC1, and uncovered that key genes are possibly involved in the pathogenesis of OA and COVID-19 through high expression in neutrophils. Finally, we established a regulatory network of common genes between OA and COVID-19, and the free energy of binding estimation was used to identify suitable medicines for the treatment of OA patients infected with SARS-CoV-2. Conclusion: In the present study, we succeeded in identifying 3 key genes, DDIT3, MAFF, and PNRC1, which are possibly involved in the development of both OA and COVID-19 and have high diagnostic value for OA and COVID-19. In addition, niclosamide, ciclopirox, and ticlopidine were found to be potentially useful for the treatment of OA patients infected with SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) has developed into one of the most lethal, aggressive, and malignant cancers worldwide. Although HCC treatment has improved in recent years, the incidence and lethality of HCC continue to increase yearly. Therefore, an in-depth study of the pathogenesis of HCC and the search formore reliable therapeutic targets are crucial to improving the survival quality of HCC patients. Currently, miRNAs have become one of the hotspots in life science research, which are widely present in living organisms and are non-coding RNAs involved in regulating gene expression. MiRNAs exert their biological roles by suppressing the expression of downstream genes and are engaged in various HCC-related processes, including proliferation, apoptosis, invasion, and metastasis. In addition, the expression status of miRNAs is related to the drug resistance mechanism of HCC, which has important implications for the systemic treatment of HCC. This paper reviews the regulatory role of miRNAs in the pathogenesis of HCC and the clinical applications of miRNAs in HCC in recent years. [ABSTRACT FROM AUTHOR]

Advancements in single-cell sequencing research have revolutionized our understanding of cellular heterogeneity and functional diversity through the analysis of single-cell transcriptomes and genomes. A crucial step in single-cell RNA sequencing (scRNA-seq) analysis is identifying cell types. However, scRNAseq data are often high dimensional and sparse, and manual cell type identification can be time-consuming, subjective, and lack reproducibility. Consequently, analyzing scRNA-seq data remains a computational challenge. With the increasing availability of well-annotated scRNA-seq datasets, advanced methods are emerging to aid in cell type identification by leveraging this information. Deep learning neural networks have great potential for analyzing single-cell data. This paper proposes MulCNN, a multi-level convolutional neural network that uses a unique cell type-specific gene expression feature extraction method. This method extracts critical features through multi-scale convolution while filtering noise. Extensive testing using datasets from various species and comparisons with popular classification methods show that MulCNN has outstanding performance and offers a new and scalable direction for scRNAseq analysis. [ABSTRACT FROM AUTHOR]

Introduction: The majority of peppers in the US for fresh market and processing are handpicked, and harvesting can account for 20-50% of production costs. Innovation in mechanical harvesting would increase availability; lower the costs of local, healthy vegetable products; and perhaps improve food safety and expand markets. Most processed peppers require removal of pedicels (stem and calyx) from the fruit, but lack of an efficient mechanical process for this operation has hindered adoption of mechanical harvest. In this paper, we present characterization and advancements in breeding green chile peppers for mechanical harvesting. Specifically, we describe inheritance and expression of an easy-destemming trait derived from the landrace UCD-14 that facilitates machine harvest of green chiles. Methods: A torque gauge was used for measuring bending forces similar to those of a harvester and applied to two biparental populations segregating for destemming force and rate. Genotyping by sequencing was used to generate genetic maps for quantitative trait locus (QTL) analyses. Results: A major destemming QTL was found on chromosome 10 across populations and environments. Eight additional population and/or environment-specific QTL were also identified. Chromosome 10 QTL markers were used to help introgress the destemming trait into jalapeño-type peppers. Low destemming force lines combined with improvements in transplant production enabled mechanical harvest of destemmed fruit at a rate of 41% versus 2% with a commercial jalapeńo hybrid. Staining for the presence of lignin at the pedicel/fruit boundary indicated the presence of an abscission zone and homologs of genes known to affect organ abscission were found under several QTL, suggesting that the easydestemming trait may be due to the presence and activation of a pedicel/fruit abscission zone. Conclusion: Presented here are tools to measure the easy-destemming trait, its physiological basis, possible molecular pathways, and expression of the trait in various genetic backgrounds. Mechanical harvest of destemmed mature green chile fruits was achieved by combining easy-destemming with transplant management. [ABSTRACT FROM AUTHOR]

N-4 cytidine acetylation (ac4C) is an epitranscriptome modification catalyzed by N-acetyltransferase 10 (NAT10) and is essential for cellular mRNA stability, rRNA biosynthesis, cell proliferation, and epithelial-mesenchymal transition (EMT). Numerous studies have confirmed the inextricable link between NAT10 and the clinical characteristics of malignancies. It is unclear, however, how NAT10 might affect pancreatic ductal adenocarcinoma. We downloaded pancreatic ductal adenocarcinoma patients from the TCGA database. We obtained the corresponding clinical data for data analysis, model construction, differential gene expression analysis, and the GEO database for external validation. We screened the published papers for NAT10-mediated ac4C modifications in 2156 genes. We confirmed that the expression levels and genomic mutation rates of NAT10 differed significantly between cancer and normal tissues. Additionally, we constructed a NAT10 prognostic model and examined immune infiltration and altered biological pathways across the models. The NAT10 isoforms identified in this study can effectively predict clinical outcomes in pancreatic ductal adenocarcinoma. Furthermore, our study showed that elevated levels of NAT10 expression correlated with gemcitabine resistance, that aberrant NAT10 expression may promote the angiogenic capacity of pancreatic ductal adenocarcinoma through activation of the TGF-b pathway, which in turn promotes distal metastasis of pancreatic ductal adenocarcinoma, and that NAT10 knockdown significantly inhibited the migration and clonogenic capacity of pancreatic ductal adenocarcinoma cells. In conclusion, we proposed a predictive model based on NAT10 expression levels, a non-invasive predictive approach for genomic profiling, which showed satisfactory and effective performance in predicting patients' survival outcomes and treatment response. Medicine and electronics will be combined in more interdisciplinary areas in the future. [ABSTRACT FROM AUTHOR]

Biofilm formation of Vibrio parahaemolyticus enhanced its tolerance to the environment, but caused many serious problems to food safety and human health. In this paper, the effects of copper and carbenicillin (CARB) stress on the formation of the biofilms of V. parahaemolyticus organisms were studied, and RNA sequencing technology was used to compare the differences in transcriptome profiles of the biofilm-related genes of V. parahaemolyticus organisms under different sub-inhibitory stresses. The results proved that V. parahaemolyticus had a large growth difference under the two stresses, copper and CARB at 1/2 minimal inhibitory concentration (MIC), and it could form a stable biofilm under both stress conditions. The amount of biofilm formed under CARB stress was significantly higher than that of copper stress (p < 0.05). Based on the analysis of transcriptome sequencing results 323, 1,550, and 1,296 significantly differential expressed genes were identified in the three treatment groups namely 1/2 MIC CARB, Cu2+, and Cu2++CARB. Through COG annotation, KEGG metabolic pathway analysis and gene expression analysis related to biofilm formation, the functional pathways of transcriptome changes affecting V. parahaemolyticus were different in the three treatment groups, and the CARB treatment group was significantly different from the other two groups. These differences indicated that the ABC transport system, two-component system and quorum sensing were all involved in the biofilm formation of the V. parahaemolytic by regulating flagellar motility, extracellular polysaccharides and extracellular polymer synthesis. Exploring the effects of different stress conditions on the transcriptome of V. parahaemolyticus could provide a basis for future research on the complex network system that regulates the formation of bacterial biofilms. [ABSTRACT FROM AUTHOR]

Enhancers play a crucial role in controlling gene transcription and expression. Therefore, bioinformatics puts many emphases on predicting enhancers and their strength. It is vital to create quick and accurate calculating techniques because conventional biomedical tests take too long time and are too expensive. This paper proposed a new predictor called iEnhancer-DCSV built on a modified densely connected convolutional network (DenseNet) and an improved convolutional block attention module (CBAM). Coding was performed using one-hot and nucleotide chemical property (NCP). DenseNet was used to extract advanced features from raw coding. The channel attention and spatial attention modules were used to evaluate the significance of the advanced features and then input into a fully connected neural network to yield the prediction probabilities. Finally, ensemble learning was employed on the final categorization findings via voting. According to the experimental results on the test set, the first layer of enhancer recognition achieved an accuracy of 78.95%, and the Matthews correlation coefficient value was 0.5809. The second layer of enhancer strength prediction achieved an accuracy of 80.70%, and the Matthews correlation coefficient value was 0.6609. The iEnhancer-DCSV method can be found at https://github.com/leirufeng/iEnhancer-DCSV. It is easy to obtain the desired results without using the complex mathematical formulas involved. [ABSTRACT FROM AUTHOR]

Introduction: The intracellular Ca2+ sensor stromal interaction molecule 1 (STIM1) is thought to play a critical role in enamel development, as its mutations cause Amelogenesis Imperfecta (AI). We recently established an ameloblast-specific (AmelX-iCre) Stim1 conditional deletion mouse model to investigate the role of STIM1 in controlling ameloblast function and differentiation in vivo (Stim1 cKO). Our pilot data (Said et al., J. Dent. Res., 2019, 98, 1002–1010) support our hypothesis for a broad role of Stim1 in amelogenesis. This paper aims to provide an in-depth characterization of the enamel phenotype observed in our Stim1 cKO model. Methods: We crossed AmelX-iCre mice with Stim1-floxed animals to develop ameloblast-specific Stim1 cKO mice. Scanning electron microscopy, energy dispersive spectroscopy, and micro- CT were used to study the enamel phenotype. RNAseq and RT-qPCR were utilized to evaluate changes in the gene expression of several key ameloblast genes.Immunohistochemistry was used to detect the amelogenin, matrix metalloprotease 20 and kallikrein 4 proteins in ameloblasts. Results: Stim1 cKO animals exhibited a hypomineralized AI phenotype, with reduced enamel volume, diminished mineral density, and lower calcium content. The mutant enamel phenotype was more severe in older Stim1 cKO mice compared to younger ones and changes in enamel volume and mineral content were more pronounced in incisors compared to molars. Exploratory RNAseq analysis of incisors’ ameloblasts suggested that ablation of Stim1 altered the expression levels of several genes encoding enamel matrix proteins which were confirmed by subsequent RT-qPCR. On the other hand, RT-qPCR analysis of molars’ ameloblasts showed non-significant differences in the expression levels of enamel matrix genes between control and Stim1-deficient cells. Moreover, gene expression analysis of incisors’ and molars’ ameloblasts showed that Stim1 ablation caused changes in the expression levels of several genes associated with calcium transport and mitochondrial kinetics. Conclusions: Collectively, these findings suggest that the loss of Stim1 in ameloblasts may impact enamel mineralization and ameloblast gene expression. [ABSTRACT FROM AUTHOR]

The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal Frontiers in Cellular & Infection Microbiology. It discusses the issue of antibiotic resistance and the development of new antimicrobial drugs. The editorial highlights the challenges posed by multi-drug resistant (MDR) and extensively drug-resistant (XDR) strains of bacteria, as well as the various mechanisms by which pathogens acquire resistance to antibiotics. The goal of the research presented in this editorial is to advance our understanding of antibiotic resistance and to explore new possibilities for combating it. The editorial also provides a brief overview of the research articles and review articles published in the journal on this topic. [Extracted from the article]

Introduction: In December 2019, a novel epidemic of coronavirus pneumonia (COVID-19) was reported, and population-based studies had shown that cancer was a risk factor for death from COVID-19 infection. However, the molecular mechanism between COVID-19 and cancer remains indistinct. In this paper, we analyzed the nucleic acid sensor (DDX58) of SARS-CoV-2 virus, which is a significant gene related to virus infection. For purpose of clarifying the characteristics of DDX58 expression in malignant tumors, this study began to systematically analyze the DDX58 expression profile in the entire cancer type spectrum. Methods: Using TCGA pan-cancer database and related data resources, we analyzed the expression, survival analysis, methylation expression, mutation status, microsatellite instability (MSI), immune related microenvironment, gene related network, function and drug sensitivity of DDX58. Results: The expression level of DDX58 mRNA in most cancers was higher than the expression level in normal tissues. Through TIMER algorithm mining, we found that DDX58 expression was closely related to various levels of immune infiltration in pan-cancer. The promoter methylation level of DDX58 was significantly increased in multiple cancers. In addition, abnormal expression of DDX58 was related to MSI and TMB in multiple cancers, and the most common type of genomic mutation was "mutation." In the protein-protein interaction (PPI) network, we found that type I interferon, phagocytosis, ubiquitinase, and tumor pathways were significantly enriched. Finally, according to the expression of DDX58 indicated potential sensitive drugs such as Cediranib, VE-821, Itraconazole, JNJ-42756493, IWR-1, and Linsitinib. Discussion: In conclusion, we had gained new insights into how DDX58 might contribute to tumor development, and DDX58 could be used as an immunerelated biomarker and as a potential immunotherapeutic target for COVID-19 infected cancer patients. [ABSTRACT FROM AUTHOR]

As the third most common cancer and the second leading cause of cancer death worldwide, colorectal cancer (CRC) poses a serious threat to people's health. In recent years, circRNA has been widely reported as a new biomarker in CRC, but a comprehensive summary and analysis is lacking. This study aims to evaluate the diagnostic, therapeutic and prognostic significance of circRNAs in CRC by systematically analysing their expression patterns, biological functions and clinical significance in CRC. The literature on circRNA in CRCwas searched in the PubMed database and included for analysis after screening according to strict inclusion and exclusion criteria. The UALCAN online tool was used to obtain host gene expression data. The miRTargetLink 2.0 was used to predict target genes for miRNAs action in CRC patients. Cytoscape was used to construct circRNA-miRNA-mRNA interaction networks. From the 236 included papers, we identified 217 circRNAs and their associated 108 host genes and 145miRNAs.Among the 145miRNAs, 27 miRNAs had no corresponding target genes. After prediction of target genes and differential analysis, a total of 25 target genes were obtained and a circRNA-miRNA-mRNA interaction network was constructed. Among the 217 circRNAs, 74 were associated with diagnosis, 160 with treatment and 51 with prognosis. And 154 of themfunction as oncogenes while 58 as tumour suppressor genes. In addition, these circRNAs include 32 exosomal circRNAs, which have unique advantages as biomarkers. In total, we summarize and analyze the expression patterns, biological functions and clinical significance of circRNAs in CRC. In addition, we constructed some new circRNA-miRNA-mRNA regulatory axes based on themiRNAs sponged by circRNAs. [ABSTRACT FROM AUTHOR]

Background: A recent paper has revealed a novel cell death pathway, cuproptosis, a programmed cell death based on copper. This study aimed to evaluate the pancancer genomics and clinical association of cuproptosis and copper metabolismrelated cell death genes, including SLC25A3, SLC25A37, SLC31A1, FDX1, DLAT, LIAS, ATP7A, ATP7B, COX17, SCO1, SCO2, COX11, and COX19. Methods: By mining multi-omics profiling data, we performed a comprehensive and systematic characterization of cuproptosis genes across more than 9,000 samples of over 30 types of cancer. Results: ATP7B and ATP7A were the two most frequently mutated copper cell death genes in cancer. UCEC and SKCM were the two cancer types that have the highest mutation rates while the mutation of LIAS was associated with worse survival of BRCA. Brain cancer was potentially affected by copper cell death because of the difference in copper cell death gene expression among subtypes and stages. On the contrary, KIRC might have a lower cuproptosis activity because of the decrease in copper cell death gene expression. In lung cancer and kidney cancer, most of the cancer–noncancer expression patterns of copper cell death genes were consistent between mRNA and protein levels. Some of the cuproptosis gene expression was associated with the survival of LGG, KIRC, and ACC. The top five expression-copy numbers correlating cancer types were BRCA, OV, LUSC, HNSC, BLCA, and LUAD. Generally, the copy number variations of these genes in KIRC, UCEC, and LGG were associated with survival. The expression of DLAT, LIAS, and ATP7B was negatively correlated with the methylation in most of the cancer types. The copper cell death genes regulating miRNA and pathway regulation networks were constructed. The copper cell death genes were correlated with immune cell infiltration levels of multiple immune cells. These genes were correlated with the sensitivity of cancer cells to multiple drugs. Conclusion: Copper cell death genes are potentially involved in many cancer types and can be developed as candidates for cancer diagnosis, prognosis, and therapeutic biomarkers. [ABSTRACT FROM AUTHOR]

Keywords: genomics; bioinformactics; systems biology; plant science; gene expression; special issue EN genomics bioinformactics systems biology plant science gene expression special issue 1 3 3 07/31/20 20200728 NES 200728 This Research Topic presents the studies in the field of computational genomics. These papers were discussed at BGRS\SB-2018 (Bioinformatics of Genome Regulation and Structure Systems Biology) multi-conference, along with the hybrid wet-lab/computational genetics studies focused on genome-wide gene expression regulation. In this Research Topic, we arranged the papers by areas of applications - clinical bioinformatics and human genome studies are followed by the plant genetics and then by systems biology applications. Genomics, bioinformactics, systems biology, plant science, gene expression, special issue. [Extracted from the article]

The interactions between circular RNAs (circRNAs) and microRNAs (miRNAs) have been shown to alter gene expression and regulate genes on diseases. Since traditional experimental methods are time-consuming and labor-intensive, most circRNA-miRNA interactions remain largely unknown. Developing computational approaches to large-scale explore the interactions between circRNAs and miRNAs can help bridge this gap. In this paper, we proposed a graph convolutional neural network-based approach named GCNCMI to predict the potential interactions between circRNAs and miRNAs. GCNCMI first mines the potential interactions of adjacent nodes in the graph convolutional neural network and then recursively propagates interaction information on the graph convolutional layers. Finally, it unites the embedded representations generated by each layer to make the final prediction. In the five-fold cross-validation, GCNCMI achieved the highest AUC of 0.9312 and the highest AUPR of 0.9412. In addition, the case studies of two miRNAs, hsa-miR-622 and hsa-miR-149-5p, showed that our model has a good effect on predicting circRNA-miRNA interactions. The code and data are available at https://github.com/csuhjhjhj/GCNCMI. [ABSTRACT FROM AUTHOR]

Purple-stem Brassica napus (B. napus) is a phenotype with unique color because of its high anthocyanins content. Anthocyanins are naturally occurring plant pigments that have antioxidants activity and play important role in plant defense against abiotic and biotic stresses. In the present study, drought induced effects on plants were investigated in hydroponically grown seedlings of green stem (GS) and purple stem (PS) genotypes of B. napus. The results of this study showed that the major function of anthocyanins accumulation during drought was to enhance the antioxidant capability and stress tolerance in B. napus plants. Our results showed that drought significantly inhibited the plant growth in terms of decreased biomass accumulation in both genotypes, although marked decline was observed in GS genotype. The reduction in photosynthetic attributes was more noticeable in the GS genotype, whereas the PS genotype showed better performance under drought stress. Under stressful conditions, both the genotype showed excessive accumulation of reactive oxygen species (ROS) and malondialdehyde (MDA), as well as higher levels of antioxidant enzymes activities. Under drought conditions, the GS genotype showed apparent damages on chloroplast deformation like in thylakoid membrane and grana structural distortion and fewer starch grains and bigger plastoglobuli. Moreover, during drought stress, the PS genotype exhibited maximum expression levels of anthocyanins biosynthesis genes and antioxidant enzymes accompanied by higher stress tolerance relative to GS genotype. Based on these findings, it can be concluded that GS genotype found more sensitive to drought stress than the PS genotype. Furthermore this research paper also provides practical guidance for plant biologists who are developing stress-tolerant crops by using anthocyanin biosynthesis or regulatory genes. [ABSTRACT FROM AUTHOR]

Ubiquitin-Conjugating Enzyme E2 C (UBE2C) is a gene that encodes protein. Disorders associated with UBE2C include methotrexate-related lymphatic hyperplasia and complement component 7 deficiency. The encoded protein is necessary for the destruction of mitotic cell cyclins and cell cycle progression, and may be involved in cancer progression. In this paper, on the basis of public databases, we study the expression differential mechanism of gene expression of UBE2C in various tumors and the performance of prognosis, clinical features, immunity, methylation, etc. [ABSTRACT FROM AUTHOR]

In the process of growth and development in life, gene expressions that control quantitative traits will turn on or off with time. Studies of longitudinal traits are of great significance in revealing the genetic mechanism of biological development. With the development of ultra-high-density sequencing technology, the associated analysis has tremendous challenges to statistical methods. In this paper, a longitudinal functional data association test (LFDAT) method is proposed based on the function-on-function regression model. LFDAT can simultaneously treat phenotypic traits and marker information as continuum variables and analyze the association of longitudinal quantitative traits and gene regions. Simulation studies showed that: 1) LFDAT performs well for both linkage equilibrium simulation and linkage disequilibrium simulation, 2) LFDAT has better performance for gene regions (include common variants, low-frequency variants, rare variants and mixture), and 3) LFDAT can accurately identify gene switching in the growth and development stage. The longitudinal data of the Oryza sativa projected shoot area is analyzed by LFDAT. It showed that there is the advantage of quick calculations. Further, an association analysis was conducted between longitudinal traits and gene regions by integrating the micro effects of multiple related variants and using the information of the entire gene region. LFDAT provides a feasible method for studying the formation and expression of longitudinal traits. [ABSTRACT FROM AUTHOR]

Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer. [ABSTRACT FROM AUTHOR]

Growing evidence have demonstrated that many biological processes are inseparable from the participation of key proteins. In this paper, a novel iterative method called linear neighborhood similarity-based protein multifeatures fusion (LNSPF) is proposed to identify potential key proteins based on multifeature fusion. In LNSPF, an original protein-protein interaction (PPI) network will be constructed first based on known protein-protein interaction data downloaded from benchmark databases, based on which, topological features will be further extracted. Next, gene expression data of proteins will be adopted to transfer the original PPI network to a weighted PPI network based on the linear neighborhood similarity. After that, subcellular localization and homologous information of proteins will be integrated to extract functional features for proteins, and based on both functional and topological features obtained above. And then, an iterative method will be designed and carried out to predict potential key proteins. At last, for evaluating the predictive performance of LNSPF, extensive experiments have been done, and compare results between LNPSF and 15 state-of-the-art competitive methods have demonstrated that LNSPF can achieve satisfactory recognition accuracy, which is markedly better than that achieved by each competing method. [ABSTRACT FROM AUTHOR]

Discovering cancer subtypes is useful for guiding clinical treatment of multiple cancers. Progressive profile technologies for tissue have accumulated diverse types of data. Based on these types of expression data, various computational methods have been proposed to predict cancer subtypes. It is crucial to study how to better integrate these multiple profiles of data. In this paper, we collect multiple profiles of data for five cancers on The Cancer Genome Atlas (TCGA). Then, we construct three similarity kernels for all patients of the same cancer by gene expression, miRNA expression and isoform expression data. We also propose a novel unsupervised multiple kernel fusion method, Similarity Kernel Fusion (SKF), in order to integrate three similarity kernels into one combined kernel. Finally, we make use of spectral clustering on the integrated kernel to predict cancer subtypes. In the experimental results, the P -values from the Cox regression model and survival curve analysis can be used to evaluate the performance of predicted subtypes on three datasets. Our kernel fusion method, SKF, has outstanding performance compared with single kernel and other multiple kernel fusion strategies. It demonstrates that our method can accurately identify more accurate subtypes on various kinds of cancers. Our cancer subtype prediction method can identify essential genes and biomarkers for disease diagnosis and prognosis, and we also discuss the possible side effects of therapies and treatment. [ABSTRACT FROM AUTHOR]

Hyponatremia is frequently encountered in clinical practice and usually induced by renal water retention. Many medications are considered to be among the various causes of hyponatremia, because they either stimulate the release of arginine vasopressin (AVP) or potentiate its action in the kidney. Antidepressants, anticonvulsants, antipsychotics, diuretics, and cytotoxic agents are the major causes of drug-induced hyponatremia. However, studies addressing the potential of these drugs to increase AVP release from the posterior pituitary gland or enhance urine concentration through intrarenal mechanisms are lacking. We previously showed that in the absence of AVP, sertraline, carbamazepine, haloperidol, and cyclophosphamide each increased vasopressin V2 receptor (V2R) mRNA and aquaporin-2 (AQP2) protein and mRNA expression in primary cultured inner medullary collecting duct cells. The upregulation of AQP2 was blocked by the V2R antagonist tolvaptan or protein kinase A (PKA) inhibitors. These findings led us to conclude that the nephrogenic syndrome of inappropriate antidiuresis (NSIAD) is the main mechanism of drug-induced hyponatremia. Previous studies have also shown that the V2R has a role in chlorpropamide-induced hyponatremia. Several other agents, including metformin and statins, have been found to induce antidiuresis and AQP2 upregulation through various V2R-independent pathways in animal experiments but are not associated with hyponatremia despite being frequently used clinically. In brief, drug-induced hyponatremia can be largely explained by AQP2 upregulation from V2R-cAMP-PKA signaling in the absence of AVP stimulation. This paper reviews the central and nephrogenic mechanisms of drug-induced hyponatremia and discusses the importance of the canonical pathway of AQP2 upregulation in drug-induced NSIAD. [ABSTRACT FROM AUTHOR]

After carefully reading, we identified some mistakes in the odds ratio (OR) calculations in B Table 1 b "Genotypes and alleles frequencies of lncRNAs genes polymorphisms in RA patients and normal controls" [sic]. Keywords: ANRIL; lnc-DC; single-nucleotide polymorphism; rheumatoid arthritis; ZFAS1; MALAT1 EN ANRIL lnc-DC single-nucleotide polymorphism rheumatoid arthritis ZFAS1 MALAT1 1 4 4 12/13/21 20211209 NES 211209 Introduction We read the paper by Zhang et al. ([1]) with interest. [Extracted from the article]