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Background: The accelerating pace of biomedical publication has made it impractical to manually, systematically identify papers containing specific information and extract this information. This is especially challenging when the information itself resides beyond titles or abstracts. For emerging science, with a limited set of known papers of interest and an incomplete information model, this is of pressing concern. A timely example in retrospect is the identification of immune signatures (coherent sets of biomarkers) driving differential SARS-CoV-2 infection outcomes. Implementation: We built a classifier to identify papers containing domain-specific information from the document embeddings of the title and abstract. To train this classifier with limited data, we developed an iterative process leveraging pre-trained SPECTER document embeddings, SVM classifiers and web-enabled expert review to iteratively augment the training set. This training set was then used to create a classifier to identify papers containing domain-specific information. Finally, information was extracted from these papers through a semi-automated system that directly solicited the paper authors to respond via a web-based form. Results: We demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. The type of immune signature (e.g., gene expression vs. other types of profiling) was also identified with a positive predictive value of 74%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate. Conclusions: Our results demonstrate the efficacy of using a SVM classifier with document embeddings of the title and abstract, to retrieve papers with domain-specific information, even when that information is rarely present in the abstract. Targeted author engagement based on classifier predictions offers a promising pathway to build a semi-structured representation of such information. Through this approach, partially automated literature mining can help rapidly create semi-structured knowledge repositories for automatic analysis of emerging health threats. [ABSTRACT FROM AUTHOR]

Background In social groups, dominant individuals may socially inhibit reproduction of subordinates using aggressive interactions or, in the case of highly eusocial insects, pheromonal communication. It has been hypothesized these two modes of reproductive inhibition utilize conserved pathways. Here, we use a comparative framework to investigate the chemical and genomic underpinnings of reproductive dominance in the primitively eusocial wasp Polistes metricus. Our goals were to first characterize transcriptomic and chemical correlates of reproductive dominance and second, to test whether dominance-associated mechanisms in paper wasps overlapped with aggression or pheromone-related gene expression patterns in other species. To explore whether conserved molecular pathways relate to dominance, we compared wasp transcriptomic data to previous studies of gene expression associated with pheromonal communication and queen-worker differences in honey bees, and aggressive behavior in bees, Drosophila, and mice. Results By examining dominant and subordinate females from queen and worker castes in early and late season colonies, we found that cuticular hydrocarbon profiles and genome-wide patterns of brain gene expression were primarily associated with season/social environment rather than dominance status. In contrast, gene expression patterns in the ovaries were associated primarily with caste and ovary activation. Comparative analyses suggest genes identified as differentially expressed in wasp brains are not related to queen pheromonal communication or caste in bees, but were significantly more likely to be associated with aggression in other insects (bees, flies), and even a mammal (mice). Conclusions This study provides the first comprehensive chemical and molecular analysis of reproductive dominance in paper wasps. We found little evidence for a chemical basis for reproductive dominance in P. metricus, and our transcriptomic analyses suggest that different pathways regulate dominance in paper wasps and pheromone response in bees. Furthermore, there was a substantial impact of season/social environment on gene expression patterns, indicating the important role of external cues in shaping the molecular processes regulating behavior. Interestingly, genes associated with dominance in wasps were also associated with aggressive behavior in bees, solitary insects and mammals. Thus, genes involved in social regulation of reproduction in Polistes may have conserved functions associated with aggression in insects and other taxa. [ABSTRACT FROM AUTHOR]

Background: The analysis of large-scale gene expression data is a fundamental approach to functional genomics and the identification of potential drug targets. Results derived from such studies cannot be trusted unless they are adequately designed and reported. The purpose of this study is to assess current practices on the reporting of experimental design and statistical analyses in gene expression-based studies. Methods: We reviewed hundreds of MEDLINE-indexed papers involving gene expression data analysis, which were published between 2003 and 2005. These papers were examined on the basis of their reporting of several factors, such as sample size, statistical power and software availability. Results: Among the examined papers, we concentrated on 293 papers consisting of applications and new methodologies. These papers did not report approaches to sample size and statistical power estimation. Explicit statements on data transformation and descriptions of the normalisation techniques applied prior to data analyses (e.g. classification) were not reported in 57 (37.5%) and 104 (68.4%) of the methodology papers respectively. With regard to papers presenting biomedical-relevant applications, 41(29.1 %) of these papers did not report on data normalisation and 83 (58.9%) did not describe the normalisation technique applied. Clustering-based analysis, the t-test and ANOVA represent the most widely applied techniques in microarray data analysis. But remarkably, only 5 (3.5%) of the application papers included statements or references to assumption about variance homogeneity for the application of the t-test and ANOVA. There is still a need to promote the reporting of software packages applied or their availability. Conclusion: Recently-published gene expression data analysis studies may lack key information required for properly assessing their design quality and potential impact. There is a need for more rigorous reporting of important experimental factors such as statistical power and sample size, as well as the correct description and justification of statistical methods applied. This paper highlights the importance of defining a minimum set of information required for reporting on statistical design and analysis of expression data. By improving practices of statistical analysis reporting, the scientific community can facilitate quality assurance and peer-review processes, as well as the reproducibility of results. [ABSTRACT FROM AUTHOR]

The article presents papers from the Asia Pacific Bioinformatics Network (APBioNet) Eighth International Conference on Bioinformatics (InCoB2009) in Singapore from September 7-11, 2009 including "Genome-Wide Analysis of Alternative Splicing in Cow: Implications in Bovine as a Model for Human Diseases," "MapNext: A Software Tool for Spliced and Unspliced Alignments and SNP Detection of Short Sequence Reads," and "Measuring Similarity Between Gene Expression Profiles: A Bayesian Approach."

Background: Lung cancer is one of the cancers with the highest mortality rate in China. With the rapid development of high-throughput sequencing technology and the research and application of deep learning methods in recent years, deep neural networks based on gene expression have become a hot research direction in lung cancer diagnosis in recent years, which provide an effective way of early diagnosis for lung cancer. Thus, building a deep neural network model is of great significance for the early diagnosis of lung cancer. However, the main challenges in mining gene expression datasets are the curse of dimensionality and imbalanced data. The existing methods proposed by some researchers can't address the problems of high-dimensionality and imbalanced data, because of the overwhelming number of variables measured (genes) versus the small number of samples, which result in poor performance in early diagnosis for lung cancer. Method: Given the disadvantages of gene expression data sets with small datasets, high-dimensionality and imbalanced data, this paper proposes a gene selection method based on KL divergence, which selects some genes with higher KL divergence as model features. Then build a deep neural network model using Focal Loss as loss function, at the same time, we use k-fold cross validation method to verify and select the best model, we set the value of k is five in this paper. Result: The deep learning model method based on KL divergence gene selection proposed in this paper has an AUC of 0.99 on the validation set. The generalization performance of model is high. Conclusion: The deep neural network model based on KL divergence gene selection proposed in this paper is proved to be an accurate and effective method for lung cancer prediction. [ABSTRACT FROM AUTHOR]

Understanding biological mechanisms is fundamental for improving animal production and health to meet the growing demand for high-quality protein. As an emerging biotechnology, single-cell transcriptomics has been gradually applied in diverse aspects of animal research, offering an effective method to study the gene expression of high-throughput single cells of different tissues/organs in animals. In an unprecedented manner, researchers have identified cell types/subtypes and their marker genes, inferred cellular fate trajectories, and revealed cell‒cell interactions in animals using single-cell transcriptomics. In this paper, we introduce the development of single-cell technology and review the processes, advancements, and applications of single-cell transcriptomics in animal research. We summarize recent efforts using single-cell transcriptomics to obtain a more profound understanding of animal nutrition and health, reproductive performance, genetics, and disease models in different livestock species. Moreover, the practical experience accumulated based on a large number of cases is highlighted to provide a reference for determining key factors (e.g., sample size, cell clustering, and cell type annotation) in single-cell transcriptomics analysis. We also discuss the limitations and outlook of single-cell transcriptomics in the current stage. This paper describes the comprehensive progress of single-cell transcriptomics in animal research, offering novel insights and sustainable advancements in agricultural productivity and animal health. [ABSTRACT FROM AUTHOR]

Background: Medulloblastoma is one of the common primary central nervous system (CNS) malignancies in pediatric patients. The main treatment is surgical resection preceded and/or followed by chemoradiotherapy. However, their serious side effects necessitate a better understanding of medulloblastoma biology to develop novel therapeutic options. Main body: Circular RNA (circRNA) and long non-coding RNA (lncRNA) regulate gene expression via microRNA (miRNA) pathways. Although growing evidence has highlighted the significance of circRNA and lncRNA-associated competing endogenous RNA (ceRNA) networks in cancers, no study has comprehensively investigated them in medulloblastoma. For this aim, the Web of Science, PubMed, Scopus, and Embase were systematically searched to obtain the relevant papers published before 16 September 2023, adhering to the PRISMA-ScR statement. HOTAIR, NEAT1, linc-NeD125, HHIP-AS1, CRNDE, and TP73-AS1 are the oncogenic lncRNAs, and Nkx2-2as is a tumor-suppressive lncRNA that develop lncRNA-associated ceRNA networks in medulloblastoma. CircSKA3 and circRNA_103128 are upregulated oncogenic circRNAs that develop circRNA-associated ceRNA networks in medulloblastoma. Conclusion: In summary, this study has provided an overview of the existing evidence on circRNA and lncRNA-associated ceRNA networks and their impact on miRNA and mRNA expression involved in various signaling pathways of medulloblastoma. Suppressing the oncogenic ceRNA networks and augmenting tumor-suppressive ceRNA networks can provide ample opportunities for medulloblastoma treatment. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan–Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer. [ABSTRACT FROM AUTHOR]

Background: Cerebral ischemia/reperfusion injury is a common secondary effect of cardiac arrest which is largely responsible for postresuscitative mortality. Therefore development of therapies which restore and protect the brain function after cardiac arrest is essential. Methylene blue (MB) has been experimentally proven neuroprotective in a porcine model of global ischemia-reperfusion in experimental cardiac arrest. However, no comprehensive analyses have been conducted at gene expression level. Methods: Pigs underwent either untreated cardiac arrest (CA) or CA with subsequent cardiopulmonary resuscitation (CPR) accompanied with an infusion of saline or an infusion of saline with MB. Genome-wide transcriptional profiling using the Affymetrix porcine microarray was performed to 1) gain understanding of delayed neuronal death initiation in porcine brain during ischemia and after 30, 60 and 180 min following reperfusion, and 2) identify the mechanisms behind the neuroprotective effect of MB after ischemic injury (at 30, 60 and 180 min). Results: Our results show that restoration of spontaneous circulation (ROSC) induces major transcriptional changes related to stress response, inflammation, apoptosis and even cytoprotection. In contrast, the untreated ischemic and anoxic insult affected only few genes mainly involved in intra-/extracellular ionic balance. Furthermore, our data show that the neuroprotective role of MB is diverse and fulfilled by regulation of the expression of soluble guanylate cyclase and biological processes accountable for inhibition of apoptosis, modulation of stress response, neurogenesis and neuroprotection. Conclusions: Our results support that MB could be a valuable intervention and should be investigated as a therapeutic agent against neural damage associated with I/R injury induced by cardiac arrest. [ABSTRACT FROM AUTHOR]

Background: Stage 4 neuroblastoma (NBL), a solid tumor of childhood, has a poor prognosis. Despite intensive molecular genetic studies, no targetable gene abnormalities have been identified. Stage 4S NBL has a characteristic of spontaneous regression, and elucidation of the mechanistic differences between stages 4 and 4S may improve treatment. Conventional NBL studies have mainly focused on the detection of abnormalities in individual genes and have rarely examined abnormalities in gene networks. While the gene coexpression network is expected to contribute to the detection of network abnormalities, the fragility of the network due to data noise and the extraction of arbitrary topological structures for the high-dimensional network are issues. Results: The present paper concerns the classification method of stages 4 and 4S NBL patients using highly accurate gene coexpression network analysis based on RNA-sequencing data of transcription factors (TFs). In particular, after applying a noise reduction method RECODE, generalized topological overlapping measure (GTOM), which weighs the connections of nodes in the network structure, succeeded in extracting a cluster of TFs that showed high classification performance for stages 4 and 4S. In addition, we investigated how these clusters correspond to clinical information and to TFs which control the normal adrenal tissue and NBL characters. Conclusions: A clustering method is presented for finding intermediate-scale clusters of TFs that give considerable separation performance for distinguishing between stages 4 and 4S. It is suggested that this method is useful as a way to extract factors that contribute to the separation of groups from multiple pieces of information such as gene expression levels. [ABSTRACT FROM AUTHOR]

The correction notice addresses errors in figures in the original article "Pancreatic cancer-initiating cell exosome message transfer into noncancer-initiating cells: the importance of CD44v6 in reprogramming." The corrections made to Figures 3i, 4g, and 7f do not impact the overall results or conclusions of the paper. The corrected figures provide accurate representations of the data presented in the study, focusing on the effects of CIC-TEX treatment on RTK expression, downstream signaling molecules, and apoptosis resistance in pancreatic cancer cells. The study highlights the importance of CD44v6 in reprogramming and the potential implications for adjuvant pancreatic cancer therapy. [Extracted from the article]

In the past two decades, genomics has advanced significantly, with single-cell RNA-sequencing (scRNA-seq) marking a pivotal milestone. ScRNA-seq provides unparalleled insights into cellular diversity and has spurred diverse studies across multiple conditions and samples, resulting in an influx of complex multidimensional genomics data. This highlights the need for robust methodologies capable of handling the complexity and multidimensionality of such genomics data. Furthermore, single-cell data grapples with sparsity due to issues like low capture efficiency and dropout effects. Tensor factorizations (TF) have emerged as powerful tools to unravel the complex patterns from multi-dimensional genomics data. Classic TF methods, based on maximum likelihood estimation, struggle with zero-inflated count data, while the inherent stochasticity in TFs further complicates result interpretation and reproducibility. Our paper introduces Zero Inflated Poisson Tensor Factorization (ZIPTF), a novel method for high-dimensional zero-inflated count data factorization. We also present Consensus-ZIPTF (C-ZIPTF), merging ZIPTF with a consensus-based approach to address stochasticity. We evaluate our proposed methods on synthetic zero-inflated count data, simulated scRNA-seq data, and real multi-sample multi-condition scRNA-seq datasets. ZIPTF consistently outperforms baseline matrix and tensor factorization methods, displaying enhanced reconstruction accuracy for zero-inflated data. When dealing with high probabilities of excess zeros, ZIPTF achieves up to 2.4 × better accuracy. Moreover, C-ZIPTF notably enhances the factorization's consistency. When tested on synthetic and real scRNA-seq data, ZIPTF and C-ZIPTF consistently uncover known and biologically meaningful gene expression programs. Access our data and code at: https://github.com/klarman-cell-observatory/scBTF and https://github.com/klarman-cell-observatory/scbtf_experiments. [ABSTRACT FROM AUTHOR]

The article offers information on curation of information from bioscience literature into biological knowledge databases which is a is a crucial way of capturing experimental information in a computable form. It is mentioned that the first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating.

Background: Cancer subtype classification is helpful for personalized cancer treatment. Although, some approaches have been developed to classifying caner subtype based on high dimensional gene expression data, it is difficult to obtain satisfactory classification results. Meanwhile, some cancers have been well studied and classified to some subtypes, which are adopt by most researchers. Hence, this priori knowledge is significant for further identifying new meaningful subtypes. Results: In this paper, we present a combined parallel random forest and autoencoder approach for cancer subtype identification based on high dimensional gene expression data, ForestSubtype. ForestSubtype first adopts the parallel RF and the priori knowledge of cancer subtype to train a module and extract significant candidate features. Second, ForestSubtype uses a random forest as the base module and ten parallel random forests to compute each feature weight and rank them separately. Then, the intersection of the features with the larger weights output by the ten parallel random forests is taken as our subsequent candidate features. Third, ForestSubtype uses an autoencoder to condenses the selected features into a two-dimensional data. Fourth, ForestSubtype utilizes k-means++ to obtain new cancer subtype identification results. In this paper, the breast cancer gene expression data obtained from The Cancer Genome Atlas are used for training and validation, and an independent breast cancer dataset from the Molecular Taxonomy of Breast Cancer International Consortium is used for testing. Additionally, we use two other cancer datasets for validating the generalizability of ForestSubtype. ForestSubtype outperforms the other two methods in terms of the distribution of clusters, internal and external metric results. The open-source code is available at https://github.com/lffyd/ForestSubtype. Conclusions: Our work shows that the combination of high-dimensional gene expression data and parallel random forests and autoencoder, guided by a priori knowledge, can identify new subtypes more effectively than existing methods of cancer subtype classification. [ABSTRACT FROM AUTHOR]

Background: Depression is a mental disorder characterized by a pervasive low mood and loss of pleasure or interest in usual activities, and often results in cognitive dysfunction. The disturbance of cognitive processes associated with depression, especially the impairment of learning and memory, exacerbates illness and increases recurrence of depression. XingPiJieYu (XPJY) is one of the most widely clinical formulas of traditional Chinese medicine (TCM) and can improve the symptoms of depression, including learning and memory. However, its regulatory effects haven't been comprehensively studied so far. Recently, some animal tests have indicated that the cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA)-cAMP response element-binding protein (CREB)-brain derived neurotrophic factor (BDNF) signaling pathway in hippocampus is closely related to depression and the pathogenesis of cognitive function impairments. The present study was performed to investigate the effect and mechanism of XPJY on depression and learning and memory in animal model. Materials: The rat model of depression was established by chronic unpredictable stress (CUS) for 21 days. The rats were randomly divided into six groups: control group, CUS group, CUS + XPJY (1.4 g/kg, 0.7 g/kg and 0.35 g/kg) groups, and CUS + sertraline (10 mg/kg) group. The sucrose preference, open field exploration and Morris water maze (MWM) were tested. The expression of cAMP, CREB, PKA and BDNF protein in hippocampus was examined with Elisa and Western Blot. The mRNA level of CREB and BDNF in hippocampus was measured with PCR. Results: The results demonstrated that rats subjected to CUS exhibited decreases in sucrose preference, total ambulation, percentage of central ambulation, rearing in the open field test and spatial performance in the MWM. CUS reduced the expression of cAMP, PKA, CREB and BDNF in hippocampus of model rats. These effects could be reversed by XPJY. Conclusion: The results indicated that XPJY can improve depression and related learning and memory and the effect of XPJY is partly exerted through the cAMP-PKA-CREB-BDNF signaling pathway. [ABSTRACT FROM AUTHOR]

Objective: Alternative polyadenylation (APA) is a co-transcriptional process that leads to isoform diversity in the 3' ends of mRNAs. APA is known to occur during differentiation, and its dysregulation is observed in diseases like cancer and autoimmune disorders. It has been previously reported that differentiation of 3T3-L1 cells to adipocytes leads to an overall lengthening of mRNAs, but the proteins involved in this regulation have not been identified. The expression levels of subunits of the cleavage and polyadenylation (C/P) complex can regulate the choice of poly(A) site, which in turn can affect different cellular activities. In this paper, we studied the change in levels of C/P proteins during 3T3-L1 differentiation. Results: We observed that while the RNA expression of these proteins is unchanged during differentiation, the protein levels of some subunits do change, including a decrease in levels of CPSF73, the nuclease that cuts at the poly(A) site. However, overexpression of CPSF73 alone does not affect the efficiency and rate of differentiation. [ABSTRACT FROM AUTHOR]

Background: Dauricine (DAU), a benzyl tetrahydroisoquinoline alkaloid isolated from the root of Menispermum dauricum DC, exhibits promising anti-Alzheimer's disease (AD) effects, but its underlying mechanisms remain inadequately investigated. This paper aims to identify potential targets and molecular mechanisms of DAU in AD treatment. Methods: Network pharmacology and molecular docking simulation method were used to screen and focus core targets. Various transgenic Caenorhabditis elegans models were chosen to validate the anti-AD efficacy and mechanism of DAU. Results: There are 66 potential DAU-AD target intersections identified from 100 DAU and 3036 AD-related targets. Subsequent protein-protein interaction (PPI) network analysis identified 16 core targets of DAU for anti-AD. PIK3CA, AKT1 and mTOR were predicted to be the central targets with the best connectivity through the analysis of "compound-target-biological process-pathway network". Molecular docking revealed strong binding affinities between DAU and PIK3CA, AKT1, and mTOR. In vivo experiments demonstrated that DAU effectively reduced paralysis in AD nematodes caused by Aβ aggregation toxicity, downregulated expression of PIK3CA, AKT1, and mTOR homologues (age-1, akt-1, let-363), and upregulated expression of autophagy genes and the marker protein LGG-1. Simultaneously, DAU increased lysosomal content and enhanced degradation of the autophagy-related substrate protein P62. Thioflavin T（Th-T）staining experiment revealed that DAU decreased Aβ accumulation in AD nematodes. Further experiments also confirmed DAU's protein scavenging activity in polyglutamine (polyQ) aggregation nematodes. Conclusion: Collectively, the mechanism of DAU against AD may be related to the activation of the autophagy-lysosomal protein clearance pathway, which contributes to the decrease of Aβ aggregation and the restoration of protein homeostasis. [ABSTRACT FROM AUTHOR]

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces "GENESO," a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called "Symmetrical Occlusion (SO)". It collaborates with the baseline LSTM network, mimicking the "gain of function" and "loss of function" of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test. [ABSTRACT FROM AUTHOR]

Background: Excessive inflammation is a major cause of implant failure. The surface morphology, hydrophilicity, and loading of biomaterials are major properties modulating anti-inflammatory macrophage activation. This paper investigates the regulatory effects of modifying the surface of Titanium dioxide nanotubes (TNTs) with graphene oxide (GO) on the polarization of mouse monocyte macrophages (RAW264.7). Methods: TNT was produced by the anodic oxidation of titanium. GO was subsequently electrodeposited on the TNT to obtain a TNT–GO composite. The samples were characterised through scanning electron microscopy (SEM), Raman spectroscopy, and X-ray diffraction. RAW264.7 cells were separately seeded onto the surface of three groups of samples: pure Ti, TNT, and TNT–GO. Under the condition of lipopolysaccharide stimulation, the influence of the sample surfaces on the gene expression profiles was investigated through RNA sequence analysis. In addition, cell spreading was observed through SEM, cell adhesion and proliferation were analysed using the CCK8 assay, and the expression of inflammation-related factors was investigated by ELISA and cellular immunofluorescence staining. The production of reactive oxygen species (ROS) in the RAW264.7 cells on the surface of the three groups was detected via immunofluorescence staining. Results: The CCK8 results indicated that the adhesion and proliferation of the RAW264.7 cells were reduced on the TNT and TNT–GO surfaces. ELISA results revealed significant differences in the pro-inflammatory factors tumour necrosis factor-α and interleukin-6 secretion among the three groups at 24 h (p < 0.05). The secretion of pro-inflammatory factors significantly reduced and the expression of anti-inflammatory factor IL-10 increased on the TNT and TNT–GO surfaces. The RNA sequencing, ELISA, and cell immunofluorescence staining test results suggested that the inflammatory response of M1 polarization was reduced and the M2 polarization of macrophages was induced on the TNT–GO surface, which may be attributed to the reduction in ROS production. Conclusions: Under lipopolysaccharide stimulation, the inflammatory response of the RAW264.7 cells was reduced and the M2 polarization of macrophages was promoted on the TNT–GO surface, which may be caused by the reduced ROS production. Consequently, the designed TNT–GO material is promising for implants owing to its excellent inflammation regulation ability. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "c-Myc-activated long non-coding RNA LINC01050 promotes gastric cancer growth and metastasis by sponging miR-7161-3p to regulate SPZ1 expression." The authors of the article identified a minor error in one of the images used in the publication. Specifically, the incorrect representative image was used for invasion transwell assays of KATO III cells transfected with si-LINC01050#1. The correction does not affect the final conclusions of the paper. [Extracted from the article]

Gene therapy aims to modify or manipulate gene expression and change the biological characteristics of living cells to achieve the purpose of treating diseases. The safe, efficient, and stable expression of exogenous genes in cells is crucial for the success of gene therapy, which is closely related to the vectors used in gene therapy. Currently, gene therapy vectors are mainly divided into two categories: viral vectors and non-viral vectors. Viral vectors are widely used due to the advantages of persistent and stable expression, high transfection efficiency, but they also have certain issues such as infectivity, high immunological rejection, randomness of insertion mutation, carcinogenicity, and limited vector capacity. Non-viral vectors have the advantages of non-infectivity, controllable chemical structure, and unlimited vector capacity, but the transfection efficiency is low. With the rapid development of nanotechnology, the unique physicochemical properties of nanomaterials have attracted increasing attention in the field of drug and gene delivery. Among many nanomaterials, iron-based nanomaterials have attracted much attention due to their superior physicochemical properties, such as Fenton reaction, magnetic resonance imaging, magnetothermal therapy, photothermal therapy, gene delivery, magnetically-assisted drug delivery, cell and tissue targeting, and so on. In this paper, the research progress of iron-based nanomaterials in gene delivery and tumor gene therapy is reviewed, and the future application direction of iron-based nanomaterials is further prospected. [ABSTRACT FROM AUTHOR]

Coffin-Siris syndrome (CSS) is a rare autosomal dominant inheritance disorder characterized by distinctive facial features, hypoplasia of the distal phalanx or nail of the fifth and additional digits, developmental or cognitive delay of varying degree, hypotonia, hirsutism/hypertrichosis, sparse scalp hair and varying kind of congenital anomalies. CSS can easily be misdiagnosed as other syndromes or disorders with a similar clinical picture because of their genetic and phenotypic heterogeneity. We describde the genotype-phenotype correlation of one patient from a healthy Chinese family with a novel genotype underlying CSS, who was first diagnosed in the ophthalmology department as early-onset high myopia (eoHM). Comprehensive ophthalmic tests as well as other systemic examinations were performed on participants to confirm the phenotype. The genotype was identified using whole exome sequencing, and further verified the results among other family members by Sanger sequencing. Real-time quantitative PCR (RT-qPCR) technology was used to detect the relative mRNA expression levels of candidate genes between proband and normal family members. The pathogenicity of the identified variant was determined by The American College of Medical Genetics and Genomics (ACMG) guidelines. STRING protein-protein interactions (PPIs) network analysis was used to detect the interaction of candidate gene-related proteins with high myopia gene-related proteins. The patient had excessive eoHM, cone-rod dystrophy, coarse face, excessive hair growth on the face, sparse scalp hair, developmental delay, intellectual disability, moderate hearing loss, dental hypoplasia, patent foramen ovale, chronic non-atrophic gastritis, bilateral renal cysts, cisterna magna, and emotional outbursts with aggression. The genetic assessment revealed that the patient carries a de novo heterozygous frameshift insertion variant in the ARID1B c.3981dup (p.Glu1328ArgfsTer5), which are strongly associated with the typical clinical features of CSS patients. The test results of RT-qPCR showed that mRNA expression of the ARID1B gene in the proband was approximately 30% lower than that of the normal control in the family, suggesting that the variant had an impact on the gene function at the level of mRNA expression. The variant was pathogenic as assessed by ACMG guidelines. Analysis of protein interactions in the STRING online database revealed that the ARID1A protein interacts with the high myopia gene-related proteins FGFR3, ASXL1, ERBB3, and SOX4, whereas the ARID1A protein antagonizes the ARID1B protein. Therefore, in this paper, we are the first to report a de novo heterozygous frameshift insertion variant in the ARID1B gene causing CSS with excessive eoHM. Our study extends the genotypic and phenotypic spectrums for ARID1B-CSS and supplies evidence of significant association of eoHM with variant in ARID1B gene. As CSS has high genetic and phenotypic heterogeneity, our findings highlight the importance of molecular genetic testing and an interdisciplinary clinical diagnostic workup to avoid misdiagnosis as some disorders with similar manifestations of CSS. [ABSTRACT FROM AUTHOR]

XTH genes are key genes that regulate the hydrolysis and recombination of XG components and plays role in the structure and composition of plant cell walls. Therefore, clarifying the changes that occur in XTHs during plant defense against abiotic stresses is informative for the study of the plant stress regulatory mechanism mediated by plant cell wall signals. XTH proteins in Arabidopsis thaliana was selected as the seed sequences in combination with its protein structural domains, 80 members of the BnXTH gene family were jointly identified from the whole genome of the Brassica napus ZS11, and analyzed for their encoded protein physicochemical properties, phylogenetic relationships, covariance relationships, and interoperating miRNAs. Based on the transcriptome data, the expression patterns of BnXTHs were analyzed in response to different abiotic stress treatments. The relative expression levels of some BnXTH genes under Al, alkali, salt, and drought treatments after 0, 6, 12 and 24 h were analyzed by using qRT-PCR to explore their roles in abiotic stress tolerance in B. napus. BnXTHs showed different expression patterns in response to different abiotic stress signals, indicating that the response mechanisms of oilseed rape against different abiotic stresses are also different. This paper provides a theoretical basis for clarifying the function and molecular genetic mechanism of the BnXTH gene family in abiotic stress tolerance in rapeseed. [ABSTRACT FROM AUTHOR]

Background: Multiple studies have demonstrated a negative correlation between gene expression and positioning of genes at the nuclear envelope (NE) lined by nuclear lamina, but the exact relationship remains unclear, especially in light of the highly stochastic, transient nature of the gene association with the NE. Results: In this paper, we ask whether there is a causal, systematic, genome-wide relationship between the expression levels of the groups of genes in topologically associating domains (TADs) of Drosophila nuclei and the probabilities of TADs to be found at the NE. To investigate the nature of this possible relationship, we combine a coarse-grained dynamic model of the entire Drosophila nucleus with genome-wide gene expression data; we analyze the TAD averaged transcription levels of genes against the probabilities of individual TADs to be in contact with the NE in the control and lamins-depleted nuclei. Our findings demonstrate that, within the statistical error margin, the stochastic positioning of Drosophila melanogaster TADs at the NE does not, by itself, systematically affect the mean level of gene expression in these TADs, while the expected negative correlation is confirmed. The correlation is weak and disappears completely for TADs not containing lamina-associated domains (LADs) or TADs containing LADs, considered separately. Verifiable hypotheses regarding the underlying mechanism for the presence of the correlation without causality are discussed. These include the possibility that the epigenetic marks and affinity to the NE of a TAD are determined by various non-mutually exclusive mechanisms and remain relatively stable during interphase. Conclusions: At the level of TADs, the probability of chromatin being in contact with the nuclear envelope has no systematic, causal effect on the transcription level in Drosophila. The conclusion is reached by combining model-derived time-evolution of TAD locations within the nucleus with their experimental gene expression levels. [ABSTRACT FROM AUTHOR]

Finding out how diet impacts health and metabolism while concentrating on the functional qualities and bioactive components of food is the crucial scientific objective of nutritional research. The complex relationship between metabolism and nutrition could be investigated with cutting-edge "omics" and bioinformatics techniques. This review paper provides an overview of the use of omics technologies in nutritional research, with a particular emphasis on the new applications of transcriptomics, proteomics, metabolomics, and genomes in functional and biological activity research on ruminant livestock and products in the tropical regions. A wealth of knowledge has been uncovered regarding the regulation and use of numerous physiological and pathological processes by gene, mRNA, protein, and metabolite expressions under various physiological situations and guidelines. In particular, the components of meat and milk were assessed using omics research utilizing the various methods of transcriptomics, proteomics, metabolomics, and genomes. The goal of this review is to use omics technologies—which have been steadily gaining popularity as technological tools—to develop new nutritional, genetic, and leadership strategies to improve animal products and their quality control. We also present an overview of the new applications of omics technologies in cattle production and employ nutriomics and foodomics technologies to investigate the microbes in the rumen ecology. Thus, the application of state-of-the-art omics technology may aid in our understanding of how species and/or breeds adapt, and the sustainability of tropical animal production, in the long run, is becoming increasingly important as a means of mitigating the consequences of climate change. [ABSTRACT FROM AUTHOR]

Hormonal necrosis of the femoral head is caused by long-term use of glucocorticoids and other causes of abnormal bone metabolism, lipid metabolism imbalance and blood microcirculation disorders in the femoral head, resulting in bone trabecular fracture, bone tissue necrosis collapse, and hip dysfunction. It is the most common type of non-traumatic necrosis of the femoral head, and its pathogenesis is complex, while impaired blood circulation is considered to be the key to its occurrence. There are a large number of microvessels in the femoral head, among which H-type vessels play a decisive role in the "angiogenesis and osteogenesis coupling", and thus have an important impact on the occurrence and development of femoral head necrosis. Glucocorticoids can cause blood flow injury of the femoral head mainly through coagulation dysfunction, endothelial dysfunction and impaired angiogenesis. Glucocorticoids may inhibit the formation of H-type vessels by reducing the expression of HIF-1α, PDGF-BB, VGEF and other factors, thus causing damage to the "angiogenesis-osteogenesis coupling" and reducing the ability of necrosis reconstruction and repair of the femoral head. Leads to the occurrence of hormonal femoral head necrosis. Therefore, this paper reviewed the progress in the study of the mechanism of hormone-induced femoral head necrosis based on microvascular blood flow at home and abroad, hoping to provide new ideas for the study of the mechanism of femoral head necrosis and provide references for clinical treatment of femoral head necrosis. [ABSTRACT FROM AUTHOR]

Background: Integrating multi-omics data is fast becoming a powerful approach for predicting disease progression and treatment outcomes. In light of that, we introduce a modified version of the NetRank algorithm, a network-based algorithm for biomarker discovery that incorporates the protein associations, co-expressions, and functions with its phenotypic association to differentiate different types of cancer. NetRank is introduced here as a robust feature selection method for biomarker selection in cancer prediction. We assess the robustness and suitability of the RNA gene expression data through scanning genomic data for 19 cancer types with more than 3000 patients from The Cancer Genome Atlas (TCGA). Results: The results of evaluating different cancer type profiles from the TCGA data demonstrate the strength of our approach to identifying interpretable biomarker signatures for cancer outcome prediction. NetRank's biomarkers segregate most cancer types with an area under the curve (AUC) above 90% using compact signatures. Conclusion: In this paper we provide a fast and efficient implementation of NetRank, with a case study from The Cancer Genome Atlas, to assess the performance. We incorporated complete functionality for pre and post-processing for RNA-seq gene expression data with functions for building protein-protein interaction networks. The source code of NetRank is freely available (at github.com/Alfatlawi/Omics-NetRank) with an installable R library. We also deliver a comprehensive practical user manual with examples and data attached to this paper. [ABSTRACT FROM AUTHOR]

Background: There are a number of dietary components that may prove useful in the prevention and treatment of cancer. In some cultures, fenugreek seeds are used to treat cancer. The current study focuses on the anticancer properties and proteomic profiles of fenugreek seeds, and is prompted by the clinical profile of a case of primary CNS T cell lymphoma that responded to fenugreek treatment and resulted in tumor regression. Method: Various normal and cancer cell lines were exposed to fenugreek extract at differing concentrations (100 µg/ml, 200 µg/ml and 300 µg/ml) and at different time points (0, 24, 48, 72 and 96 hrs). Protein fingerprints of fenugreek grain/seed types, obtained from four different geographical regions, were analyzed by proteomic expression profiles. Results: We observed selective cytotoxic effects of fenugreek extract in vitro to a panel of cancer cell lines, including T-cell lymphoma. Additionally, the cluster analysis of proteomics data showed that the protein profile of the particular fenugreek used by the patient is significantly different from three other regional subtypes of fenugreek extract. Conclusion: The in vitro effect of fenugreek as a substance with significant cytotoxicity to cancer cells points to the potential usefulness of fenugreek in the prevention and treatment of cancer. [ABSTRACT FROM AUTHOR]

Background Research on Genomic medicine has suggested that the exposure of patients to early life risk factors may induce the development of chronic diseases in adulthood, as the presence of premature risk factors can influence gene expression. The large number of scientific papers published in this research area makes it difficult for the healthcare professional to keep up with individual results and to establish association between them. Therefore, in our work we aim at building a computational system that will offer an innovative approach that alerts health professionals about human development problems such as cardiovascular disease, obesity and type 2 diabetes. Methods We built a computational system called Chronic Illness Surveillance System (CISS), which retrieves scientific studies that establish associations (conceptual relationships) between chronic diseases (cardiovascular diseases, diabetes and obesity) and the risk factors described on clinical records. To evaluate our approach, we submitted ten queries to CISS and to three other search engines (GoogleTM, Google ScholarTMand Pubmedr)- the queries were composed of terms and expressions from a list of risk factors provided by specialists. Results CISS retrieved a higher number of closely related (+) and somewhat related (+/-) documents, and a smaller number of unrelated (-) and almost unrelated (-/+) documents, in comparison with the three other systems. The results from the Friedman's test carried out with the post-hoc Holm procedure (95% confidence) for our system (control) versus the results for the three other engines indicate that our system had the best performance in three of the categories (+), (-) and (+/-).This is an important result, since these are the most relevant categories for our users. Conclusion Our system should be able to assist researchers and health professionals in finding out relationships between potential risk factors and chronic diseases in scientific papers. [ABSTRACT FROM AUTHOR]

Backgrounds: It has been observed that high levels of enhancer of zeste homolog 2 (EZH2) expression are associated with unsatisfactory prognoses and can be found in a wide range of malignancies. However, the effects of EZH2 on Lung Adenocarcinoma (LUAD) remain elusive. Through the integration of bioinformatic analyses, the present paper sought to ascertain the effects of EZH2 in LUAD. Methods: The TIMER and UALCAN databases were applied to analyze mRNA and protein expression data for EZH2 in LUAD. The result of immunohistochemistry was obtained from the HPA database, and the survival curve was drawn according to the library provided by the HPA database. The LinkedOmics database was utilized to investigate the co-expressed genes and signal transduction pathways with EZH2. Up- and down-regulated genes from The Linked Omics database were introduced to the CMap database to predict potential drug targets for LUAD using the CMap database. The association between EZH2 and cancer-infiltrating immunocytes was studied through TIMER and TISIDB. In addition, this paper explores the relationship between EZH2 mRNA expression and NSCLC OS using the Kaplan–Meier plotter database to further validate and complement the research. Furthermore, the correlation between EZH2 expression and EGFR genes, KRAS genes, BRAF genes, and smoking from the Cancer Genome Atlas (TCGA) database is analyzed. Results: In contrast to paracancer specimens, the mRNA and protein levels of EZH2 were higher in LUAD tissues. Significantly, high levels of EZH2 were associated with unsatisfactory prognoses in LUAD patients. Additionally, the coexpressed genes of EZH2 were predominantly associated with numerous cell growth-associated pathways, including the cell cycle, DNA replication, RNA transport, and the p53 signaling pathway, according to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The results of TCGA database revealed that the expression of EZH2 was lower in normal tissues than in lung cancer tissues (p < 0.05). Smoking was associated with elevated EZH2 expression (p < 0.001). EZH2 was highly expressed in lung cancers with positive KRAS expression, and the correlation was significant in lung adenocarcinoma (r = 0.3129, p < 0.001). CMap was applied to determine the top 15 positively correlated drugs/molecules and the top 15 negatively correlated drugs/molecules. MK-1775, MK-5108, fenbendazole, albendazole, BAY-K8644, evodiamine, purvalanol-a, mycophenolic-acid, PHA-793887, and cyclopamine are potential drugs for patients with lung adenocarcinoma and high EZH2 expression. Conclusions: Highly expressed EZH2 is a predictor of a suboptimal prognosis in LUAD and may serve as a prognostic marker and target gene for LUAD. The underlying cause may be associated with the synergistic effect of KRAS, immune cell infiltration, and metabolic processes. [ABSTRACT FROM AUTHOR]

Background: Northern blotting is still used as a gold standard for validation of the data obtained from high-throughput whole transcriptome-based methods. However, its disadvantages of lower sensitivity, labor-intensive operation, and higher quality of RNA required limit its utilization in a routine molecular biology laboratory to monitor gene expression at RNA level. Therefore, it is necessary to optimize the traditional Northern protocol to make the technique more applicable for standard use. Results: In this paper, we report modifications and tips used to improve the traditional Northern protocol for the detection of mRNAs in total RNA. To maximize the retention of specifically bound radiolabeled probes on the blot, posthybridization washes were performed under only with moderate-stringency until the level of radioactivity retained on the filter decreased to 20~50 counts per second, rather than normally under high and low stringency sequentially for scheduled time or under only high stringent condition. Successful detection of the low-expression gene using heterologous DNA probes in 20 µg of total RNA after a two-day exposure suggested an improvement in detection sensitivity. Quantitatively controlled posthybridization washes combined with an ethidium bromide-prestaining RNA procedure to directly visualize prestained RNA bands at any time during electrophoresis or immediately after electrophoresis, which made the progress of the Northern procedure to be monitored and evaluated step by step, thereby making the experiment reliable and controllable. We also report tips used in the modified Northern protocol, including the moderate concentration of formaldehyde in the gel, the accessory capillary setup, and the staining jar placed into an enamel square tray with a lid used for hybridization. Using our modified Northern protocol, eight rounds of rehybridization could be performed on a single blot. The modification made and tips used ensured the efficient proceeding of the experiment and the resulting good performance, but without using special reagents or equipment. Conclusions: The modified Northern protocol improved detection sensitivity and made the experiment easy, less expensive, reliable, and controllable, and can be employed in a routine molecular biology laboratory to detect low-expressed mRNAs with heterologous DNA probes in total RNA. [ABSTRACT FROM AUTHOR]

While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like over-dispersion, zero-inflation, high gene-gene correlation, and large data volume with many features pose challenges for most existing feature selection methods. In this paper, we present a feature selection method based on neural network (scFSNN) to solve classification problem for the scRNA-seq data. scFSNN is an embedded method that can automatically select features (genes) during model training, control the false discovery rate of selected features and adaptively determine the number of features to be eliminated. Extensive simulation and real data studies demonstrate its excellent feature selection ability and predictive performance. [ABSTRACT FROM AUTHOR]

Background: Wheat (Triticum aestivum L.) is one of the most widely grown and vital cereal crops, containing a high percentage of basic nutrients such as carbohydrates and proteins. Drought stress is one of the most significant limitations on wheat productivity. Due to climate change influences plant development and growth, physiological processes, grain quality, and yield. Drought stress has elicited a wide range of plant responses, namely physiological and molecular adaptations. Biopriming is one of the recent attempts to combat drought stress. Mitigating the harmful impact of abiotic stresses on crops by deploying extreme-habitat-adapted symbiotic microbes. The purpose of this study was to see how biopriming Triticum aestivum grains affected the effects of inoculating endophytic fungi Aspergillus fumigatus ON307213 isolated from stressed wheat plants in four model agricultural plants (Gemmiza-7, Sids-1, Sakha8, and Giza 168). And its viability in reducing drought stress through the use of phenotypic parameters such as root and shoot fresh and dry weight, shoot and root length, and so on. On a biochemical and physiological level, enzymatic parameters such as catalase and superoxidase dismutase are used. Total phenolics, flavonoids, and photosynthetic pigments are non-enzymatic parameters. Making use of molecular techniques such as reverse transcriptase polymerase chain reaction (RT-PCR). Results: It has been found that using Aspergillus fumigatus as a biological biopriming tool can positively impact wheat plants experiencing drought stress. The total biomass of stressed wheat plants that had been bio-primed rose by more than 40% as compared to wheat plants that had not been bio-primed. A. fumigatus biopriming either increased or decreased the amount of enzymatic and non-enzymatic substances on biochemical scales, aside from the noticeable increase in photosynthetic pigment that occurs in plants that have been bio-primed and stressed. Drought-resistant genes show a biopriming influence in gene expression. Conclusions: This is the first paper to describe the practicality of a. fumigatus biopriming and its effect on minimizing the degrading effects of drought through water limitation. It suggests the potential applications of arid habitat-adapted endophytes in agricultural systems. [ABSTRACT FROM AUTHOR]

Bone metastasis is considered as a considerable challenge for breast cancer patients. Various in vitro and in vivo models have been developed to examine this occurrence. In vitro models are employed to simulate the intricate tumor microenvironment, investigate the interplay between cells and their adjacent microenvironment, and evaluate the effectiveness of therapeutic interventions for tumors. The endeavor to replicate the latency period of bone metastasis in animal models has presented a challenge, primarily due to the necessity of primary tumor removal and the presence of multiple potential metastatic sites. The utilization of novel bone metastasis models, including three-dimensional (3D) models, has been proposed as a promising approach to overcome the constraints associated with conventional 2D and animal models. However, existing 3D models are limited by various factors, such as irregular cellular proliferation, autofluorescence, and changes in genetic and epigenetic expression. The imperative for the advancement of future applications of 3D models lies in their standardization and automation. The utilization of artificial intelligence exhibits the capability to predict cellular behavior through the examination of substrate materials' chemical composition, geometry, and mechanical performance. The implementation of these algorithms possesses the capability to predict the progression and proliferation of cancer. This paper reviewed the mechanisms of bone metastasis following primary breast cancer. Current models of breast cancer bone metastasis, along with their challenges, as well as the future perspectives of using these models for translational drug development, were discussed. [ABSTRACT FROM AUTHOR]

Background: Trypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presents a growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is delayed due to a lack of functional information on many of the pathogen's proteins. Accordingly, this paper presents a computational framework for prioritizing drug targets within the editosome, a vital molecular machinery responsible for mitochondrial RNA processing in T. brucei. Importantly, this framework may eliminate the need for prior gene or protein characterization, potentially accelerating drug discovery efforts. Results: By integrating protein-protein interaction (PPI) network analysis, PPI structural modeling, and residue interaction network (RIN) analysis, we quantitatively ranked and identified top hub editosome proteins, their key interaction interfaces, and hotspot residues. Our findings were cross-validated and further prioritized by incorporating them into gene set analysis and differential expression analysis of existing quantitative proteomics data across various life stages of T. brucei. In doing so, we highlighted PPIs such as KREL2-KREPA1, RESC2-RESC1, RESC12A-RESC13, and RESC10-RESC6 as top candidates for further investigation. This includes examining their interfaces and hotspot residues, which could guide drug candidate selection and functional studies. Conclusion: RNA editing offers promise for target-based drug discovery, particularly with proteins and interfaces that play central roles in the pathogen's life cycle. This study introduces an integrative drug target identification workflow combining information from the PPI network, PPI 3D structure, and reside-level information of their interface which can be applicable to diverse pathogens. In the case of T. brucei, via this pipeline, the present study suggested potential drug targets with residue-resolution from RNA editing machinery. However, experimental validation is needed to fully realize its potential in advancing urgently needed antiparasitic drug development. [ABSTRACT FROM AUTHOR]

Background: Major depressive disorder (MDD) is a common mental illness that affects millions of people worldwide and imposes a heavy burden on individuals, families and society. Previous studies on MDD predominantly focused on neurons and employed bulk homogenates of brain tissues. This paper aims to decipher the relationship between oligodendrocyte lineage (OL) development and MDD at the single-cell resolution level. Methods: Here, we present the use of a guided regularized random forest (GRRF) algorithm to explore single-nucleus RNA sequencing profiles (GSE144136) of the OL at four developmental stages, which contains dorsolateral prefrontal cortex of 17 healthy controls (HC) and 17 MDD cases, generated by Nagy C et al. We prioritized and ordered differentially expressed genes (DEGs) based on Nagy et al., which could predominantly discriminate cells in the four developmental stages and two adjacent developmental stages of the OL. We further screened top-ranked genes that distinguished between HC and MDD in four developmental stages. Moreover, we estimated the performance of the GRRF model via the area under the curve value. Additionally, we validated the pivotal candidate gene Malat1 in animal models. Results: We found that, among the four developmental stages, the onset development of OL (OPC2) possesses the best predictive power for distinguishing HC and MDD, and long noncoding RNA MALAT1 has top-ranked importance value in candidate genes of four developmental stages. In addition, results of fluorescence in situ hybridization assay showed that Malat1 plays a critical role in the occurrence of depression. Conclusions: Our work elucidates the mechanism of MDD from the perspective of OL development at the single-cell resolution level and provides novel insight into the occurrence of depression. [ABSTRACT FROM AUTHOR]

Liver cancer is one of the most common cancers in the world and a primary cause of cancer-related death. In recent years, despite the great development of diagnostic methods and targeted therapies for liver cancer, the incidence and mortality of liver cancer are still on the rise. As a universal post-transcriptional modification, N6-methyladenosine (m6A) modification accomplishes a dynamic and reversible m6A modification process, which is executed by three types of regulators, methyltransferases (called writers), demethylases (called erasers) and m6A-binding proteins (called readers). Many studies have shown that m6A RNA methylation has an important impact on RNA metabolism, whereas its regulation exception is bound up with the occurrence of human malignant tumors. Aberrant methylation of m6A RNA and the expression of related regulatory factors may be of the essence in the pathogenesis and progression of liver cancer, yet the precise molecular mechanism remains unclear. In this paper, we review the current research situations of m6A methylation in liver cancer. Among the rest, we detail the mechanism by which methyltransferases, demethylases and m6A binding proteins regulate the occurrence and development of liver cancer by modifying mRNA. As well as the potential effect of m6A regulators in hepatocarcinogenesis and progression. New ideas and approaches will be given to the prevention and treatment of liver cancer through the following relevant research results. [ABSTRACT FROM AUTHOR]

Background: We previously reported aberrant expression of the cytosolic ribosomal biogenesis factor Nop-7-associated 2 (NSA2) in diabetic nephropathy, the latter also known to involve mitochondrial dysfunction, however the connections between NSA2, mitochondria and renal disease were unclear. In the current paper, we show that NSA2 expression is co-regulated with the GTP-dependent ribosome recycling factor mitochondrial 2 (GFM2) and provide a molecular link between cytosolic and mitochondrial ribosomal biogenesis with mitochondrial dysfunction in chronic kidney disease (CKD). Methods: Human renal tubular cells (HK-2) were cultured (+/- zinc, or 5mM/20mM glucose). mRNA levels were quantified using real-time qPCR. Transcriptomics data were retrieved and analysed from Nakagawa chronic kidney disease (CKD) Dataset (GSE66494) and Kidney Precision Medicine Project (KPMP) (https://atlas.kpmp.org/). Protein levels were determined by immunofluorescence and Western blotting. Cellular respiration was measured using Agilent Seahorse XF Analyzer. Data were analysed using one-way ANOVA, Students' t-test and Pearson correlation. Results: The NSA2 gene, on human chromosome 5q13 was next to GFM2. The two genes were syntenic on opposite strands and orientation in multiple species. Their common 381 bp 5' region contained multiple transcription factor binding sites (TFBS) including the zinc-responsive transcription factor MTF1. NSA2 and GFM2 mRNAs showed a dose-dependent increase to zinc in-vitro and were highly expressed in proximal tubular cells in renal biopsies. CKD patients showed higher renal NSA2/GFM2 expression. In HK-2 cells, hyperglycaemia led to increased expression of both genes. The total cellular protein content remained unchanged, but GFM2 upregulation resulted in increased levels of several mitochondrial oxidative phosphorylation (OXPHOS) subunits. Furthermore, increased GFM2 expression, via transient transfection or hyperglycemia, correlated with decrease cellular respiration. Conclusion: The highly conserved synteny of NSA2 and GFM2, their shared 5' region, and co-expression in-vitro and in CKD, shows they are co-regulated. Increased GFM2 affects mitochondrial function with a disconnect between an increase in certain mitochondrial respiratory proteins but a decrease in cellular respiration. These data link the regulation of 2 highly conserved genes, NSA2 and GFM2, connected to ribosomes in two different cellular compartments, cytosol and mitochondria, to kidney disease and shows that their dysregulation may be involved in mitochondrial dysfunction. [ABSTRACT FROM AUTHOR]

Background: With the rapid advancement of genomic sequencing techniques, massive production of gene expression data is becoming possible, which prompts the development of precision medicine. Deep learning is a promising approach for phenotype prediction (clinical diagnosis, prognosis, and drug response) based on gene expression profile. Existing deep learning models are usually considered as black-boxes that provide accurate predictions but are not interpretable. However, accuracy and interpretation are both essential for precision medicine. In addition, most models do not integrate the knowledge of the domain. Hence, making deep learning models interpretable for medical applications using prior biological knowledge is the main focus of this paper. Results: In this paper, we propose a new self-explainable deep learning model, called Deep GONet, integrating the Gene Ontology into the hierarchical architecture of the neural network. This model is based on a fully-connected architecture constrained by the Gene Ontology annotations, such that each neuron represents a biological function. The experiments on cancer diagnosis datasets demonstrate that Deep GONet is both easily interpretable and highly performant to discriminate cancer and non-cancer samples. Conclusions: Our model provides an explanation to its predictions by identifying the most important neurons and associating them with biological functions, making the model understandable for biologists and physicians. [ABSTRACT FROM AUTHOR]

Motivation: Studies have shown that classifying cancer subtypes can provide valuable information for a range of cancer research, from aetiology and tumour biology to prognosis and personalized treatment. Current methods usually adopt gene expression data to perform cancer subtype classification. However, cancer samples are scarce, and the high-dimensional features of their gene expression data are too sparse to allow most methods to achieve desirable classification results. Results: In this paper, we propose a deep learning approach by combining a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU): our approach, DCGN, aims to achieve nonlinear dimensionality reduction and learn features to eliminate irrelevant factors in gene expression data. Specifically, DCGN first uses the synthetic minority oversampling technique algorithm to equalize data. The CNN can handle high-dimensional data without stress and extract important local features, and the BiGRU can analyse deep features and retain their important information; the DCGN captures key features by combining both neural networks to overcome the challenges of small sample sizes and sparse, high-dimensional features. In the experiments, we compared the DCGN to seven other cancer subtype classification methods using breast and bladder cancer gene expression datasets. The experimental results show that the DCGN performs better than the other seven methods and can provide more satisfactory classification results. [ABSTRACT FROM AUTHOR]

Background: Pyroptosis plays an important role in the pathological process of ischemic stroke (IS). However, the exact mechanism of pyroptosis remains unclear. This paper aims to reveal the key molecular markers associated with pyroptosis in IS. Methods: We used random forest learning, gene set variation analysis, and Pearson correlation analysis to screen for biomarkers associated with pyroptosis in IS. Middle cerebral artery occlusion/reperfusion (MCAO/R) and oxygen and glucose deprivation/reoxygenation (OGD/R) models were constructed in vitro and in vivo. Cells were transfected with an Annexin A3 silencing (si-ANXA3) plasmid to observe the effects of ANXA3 on OGD/R + lipopolysaccharides (LPS)-induced pyroptosis. qRT‒PCR and western blotting were used to detect the expression of potential biomarkers and pyroptotic pathways. Results: Samples from a total of 170 IS patients and 109 healthy individuals were obtained from 5 gene expression omnibus databases. Thirty important genes were analyzed by random forest learning from the differentially expressed genes. Then, we investigated the relationship between the above genes and the pyroptosis score, obtaining three potential biomarkers (ANXA3, ANKRD22, ADM). ANXA3 and ADM were upregulated in the MCAO/R model, and the fold difference in ANXA3 expression was greater. Pyroptosis-related factors (NLRP3, NLRC4, AIM2, GSDMD-N, caspase-8, pro-caspase-1, cleaved caspase-1, IL-1β, and IL-18) were upregulated in the MCAO/R model. Silencing ANXA3 alleviated the expression of pyroptosis-related factors (NLRC4, AIM2, GSDMD-N, caspase-8, pro-caspase-1, cleaved caspase-1, and IL-18) induced by OGD/R + LPS or MCAO/R. Conclusion: This study identified ANXA3 as a possible pyroptosis-related gene marker in IS through bioinformatics and experiments. ANXA3 could inhibit pyroptosis through the NLRC4/AIM2 axis. [ABSTRACT FROM AUTHOR]

Background: The identification of essential proteins is of great significance in biology and pathology. However, protein–protein interaction (PPI) data obtained through high-throughput technology include a high number of false positives. To overcome this limitation, numerous computational algorithms based on biological characteristics and topological features have been proposed to identify essential proteins. Results: In this paper, we propose a novel method named SESN for identifying essential proteins. It is a seed expansion method based on PPI sub-networks and multiple biological characteristics. Firstly, SESN utilizes gene expression data to construct PPI sub-networks. Secondly, seed expansion is performed simultaneously in each sub-network, and the expansion process is based on the topological features of predicted essential proteins. Thirdly, the error correction mechanism is based on multiple biological characteristics and the entire PPI network. Finally, SESN analyzes the impact of each biological characteristic, including protein complex, gene expression data, GO annotations, and subcellular localization, and adopts the biological data with the best experimental results. The output of SESN is a set of predicted essential proteins. Conclusions: The analysis of each component of SESN indicates the effectiveness of all components. We conduct comparison experiments using three datasets from two species, and the experimental results demonstrate that SESN achieves superior performance compared to other methods. [ABSTRACT FROM AUTHOR]

Background: Recent studies have shown that circRNAs are involved in the pathogenesis of osteoarthritis (OA) by affecting various fundamental cellular characteristics of chondrocytes. The purpose of this paper is to investigate the role and regulatory mechanism of hsa_circ_0020014 (circ_0020014) in chondrocytes of OA. Methods: The inflammatory cytokine interleukin 1 beta (IL-1β) was used to stimulate human chondrocytes. Cell viability, proliferation, and apoptosis were evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT), 5-Ethynyl-2′-deoxyuridine (EdU), and flow cytometry assays. Several protein levels were determined by western blotting (WB). Levels of inflammatory cytokines and malondialdehyde (MDA) were determined by enzyme-linked immunosorbent assay (ELISA). Relative expression of circ_0020014 was estimated by real-time polymerase quantitative chain reaction (RT-qPCR). Bioinformatics prediction combined with dual-luciferase reporter, RIP and RNA pull-down assays were done to probe into the regulatory mechanism of circ_0020014. Results: Circ_0020014 was overexpressed in OA patient-derived articular cartilages and IL-1β-stimulated chondrocytes. Silencing of circ_0020014 lighted IL-1β-prompted chondrocyte proliferation repression, apoptosis, inflammation, and oxidative stress. Mechanically, circ_0020014 functioned as a miR-24-3p molecular sponge to regulate cathepsin B (CTSB) expression. Furthermore, miR-24-3p inhibition alleviated circ_0020014 knockdown-mediation repression of IL-1β-urged chondrocyte injury. In addition, CTSB overexpression whittled miR-24-3p upregulation-mediated suppression of IL-1β-urged chondrocyte injury. Conclusion: Our findings demonstrated that the circ_0020014/miR-24-3p/CTSB axis was associated with IL-1β-prompted chondrocyte injury, supporting the involvement of circ_0020014 in the OA pathogenesis. Highlights: A significant upregulation of circ_0020014 was observed in OA patient-derived articular cartilages. Inhibition of circ_0020014 lessened IL-1β-urged chondrocyte injury. Circ_0020014 could interact with miR-24-3p to mediate CTSB expression. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA), characterized by inflammation and articular cartilage degradation, is a prevalent arthritis among geriatric population. This paper was to scrutinize the novel mechanism of long noncoding RNA (lncRNA) NEAT1 in OA etiology. Methods: A total of 10 OA patients and 10 normal individuals was included in this study. Cell model of OA was built in human normal chondrocytes induced by lipopolysaccharide (LPS). An OA Wistar rat model was established through intra-articular injection of L-cysteine and papain mixtures (proportion at 1:2) into the right knee. Quantitative reverse transcription-polymerase chain reaction was employed to ascertain the expression levels of NEAT1, microRNA (miR)-374b-5p and post-GPI attachment to protein 1 (PGAP1), while dual-luciferase reporter experiments were used for the validation of target relationship among them. Cell cycle and apoptosis were calculated by flow cytometry analysis. CCK-8 assay was done to evaluate the proliferative potentials of chondrocytes. The levels of cell cycle-related proteins (Cyclin A1, Cyclin B1 and Cyclin D2) and pro-apoptotic proteins (Caspase3 and Caspase9) were measured by western blotting. Tumor necrosis factor-alpha (TNF-α), interleukin-1beta (IL-1β) and IL-6 levels were determined via ELISA. Hematoxylin & eosin (HE) Staining was used for pathological examination in OA rats. Results: Pronounced downregulation of NEAT1 and PGAP1 and high amounts of miR-374b-5p were identified in OA patients, LPS-induced chondrocytes and OA rats. NEAT1 targeted miR-374b-5p to control PGAP1 expression. Loss of NEAT1 or upregulation of miR-374b-5p dramatically accelerated apoptosis, led to the G1/S arrest and promoted the secretion of inflammatory cytokines in LPS-induced chondrocytes, while ectopic expression of PGAP1 exhibited the opposite influences on chondrocytes. Additionally, we further indicated that upregulation of miR-374b-5p attenuated the effects of PGAP1 overexpression on LPS-induced chondrocytes. Conclusions: Reduced NEAT1 induces the development of OA via miR-374b-5p/PGAP1 pathway. This suggests that the regulatory axis NEAT1/miR-374b-5p/PGAP1 is a novel and prospective target for OA treatment. [ABSTRACT FROM AUTHOR]

Lung cancer subtyping based on gene expression data is important for identifying patient subgroups with differing survival prognosis to facilitate customized treatment strategies for each subtype of patients. Unsupervised clustering methods are the traditional approach for clustering patients into subtypes. However, since those methods cluster patients based only on gene expression data, the resulting clusters may not always be relevant to the survival outcome of interest. In recent years, semi-supervised and supervised methods have been proposed, which leverage the survival outcome data to identify clusters more relevant to survival prognosis. This paper aims to compare the performance of different clustering methods for identifying clinically prognostic lung cancer subtypes based on two lung adenocarcinoma datasets. For each method, we clustered patients into two clusters and assessed the difference in patient survival time between clusters. Unsupervised methods were found to have large logrank p-values and no significant results in most cases. Semi-supervised and supervised methods had improved performance over unsupervised methods and very significant p-values. These results indicate that unsupervised methods are not capable of identifying clusters with significant differences in survival prognosis in most cases, while supervised and semi-supervised methods can better cluster patients into clinically useful subtypes. [ABSTRACT FROM AUTHOR]

Background: Gene co-expression networks (GCNs) are not easily comparable due to their complex structure. In this paper, we propose a tool, Juxtapose, together with similarity measures that can be utilized for comparative transcriptomics between a set of organisms. While we focus on its application to comparing co-expression networks across species in evolutionary studies, Juxtapose is also generalizable to co-expression network comparisons across tissues or conditions within the same species. Methods: A word embedding strategy commonly used in natural language processing was utilized in order to generate gene embeddings based on walks made throughout the GCNs. Juxtapose was evaluated based on its ability to embed the nodes of synthetic structures in the networks consistently while also generating biologically informative results. Evaluation of the techniques proposed in this research utilized RNA-seq datasets from GTEx, a multi-species experiment of prefrontal cortex samples from the Gene Expression Omnibus, as well as synthesized datasets. Biological evaluation was performed using gene set enrichment analysis and known gene relationships in literature. Results: We show that Juxtapose is capable of globally aligning synthesized networks as well as identifying areas that are conserved in real gene co-expression networks without reliance on external biological information. Furthermore, output from a matching algorithm that uses cosine distance between GCN embeddings is shown to be an informative measure of similarity that reflects the amount of topological similarity between networks. Conclusions: Juxtapose can be used to align GCNs without relying on known biological similarities and enables post-hoc analyses using biological parameters, such as orthology of genes, or conserved or variable pathways. Availability: A development version of the software used in this paper is available at https://github.com/klovens/juxtapose [ABSTRACT FROM AUTHOR]

Background: We currently have the ability to quantify transcript abundance of messenger RNA (mRNA), genome-wide, using microarray technologies. Analyzing genotype, phenotype and expression data from 20 pedigrees, the members of our Genetic Analysis Workshop (GAW) 19 gene expression group published 9 papers, tackling some timely and important problems and questions. To study the complexity and interrelationships of genetics and gene expression, we used established statistical tools, developed newer statistical tools, and developed and applied extensions to these tools. Methods: To study gene expression correlations in the pedigree members (without incorporating genotype or trait data into the analysis), 2 papers used principal components analysis, weighted gene coexpression network analysis, meta-analyses, gene enrichment analyses, and linear mixed models. To explore the relationship between genetics and gene expression, 2 papers studied expression quantitative trait locus allelic heterogeneity through conditional association analyses, and epistasis through interaction analyses. A third paper assessed the feasibility of applying allele-specific binding to filter potential regulatory single-nucleotide polymorphisms (SNPs). Analytic approaches included linear mixed models based on measured genotypes in pedigrees, permutation tests, and covariance kernels. To incorporate both genotype and phenotype data with gene expression, 4 groups employed linear mixed models, nonparametric weighted U statistics, structural equation modeling, Bayesian unified frameworks, and multiple regression. Results and discussion: Regarding the analysis of pedigree data, we found that gene expression is familial, indicating that at least 1 factor for pedigree membership or multiple factors for the degree of relationship should be included in analyses, and we developed a method to adjust for familiality prior to conducting weighted co-expression gene network analysis. For SNP association and conditional analyses, we found FaST-LMM (Factored Spectrally Transformed Linear Mixed Model) and SOLAR-MGA (Sequential Oligogenic Linkage Analysis Routines -Major Gene Analysis) have similar type 1 and type 2 errors and can be used almost interchangeably. To improve the power and precision of association tests, prior knowledge of DNase-I hypersensitivity sites or other relevant biological annotations can be incorporated into the analyses. On a biological level, eQTL (expression quantitative trait loci) are genetically complex, exhibiting both allelic heterogeneity and epistasis. Including both genotype and phenotype data together with measurements of gene expression was found to be generally advantageous in terms of generating improved levels of significance and in providing more interpretable biological models. Conclusions: Pedigrees can be used to conduct analyses of and enhance gene expression studies. [ABSTRACT FROM AUTHOR]