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The paper mulberry is a commonly found tree species with a long history of cultivation. It also serves as a crucial case study for understanding how woody plants adapt to low temperatures. Under cold treatment, we observed a substantial number of alternative splicing (AS) genes, showcasing the intricate landscape of AS events. We have detected all seven types of AS events, with the alternative 3′ splice site (A3) having the most. We observed that many genes that underwent differential AS were significantly enriched in starch and sucrose metabolism and circadian rhythm pathways. Moreover, a considerable proportion of differentially spliced genes (DSGs) also showed differential expression, with 20.38% and 25.65% under 12 h and 24 h cold treatments, respectively. This suggests a coordinated regulation between gene AS and expression, playing a pivotal role in the paper mulberry's adaptation to cold stress. We further investigated the regulatory mechanisms of AS, identifying 41 serine/arginine-rich (SR) splicing factors, among which 11 showed differential expression under cold treatment, while 29 underwent alternative splicing. Additionally, genes undergoing AS displayed significantly higher DNA methylation levels under cold stress, while normal splicing (non-AS) genes exhibited relatively lower methylation levels. These findings suggest that methylation may play an important role in governing gene AS. Finally, our research will provide useful information on the role of AS in the cold acclimation tolerance of the paper mulberry. [ABSTRACT FROM AUTHOR]

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]

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]

This document is a summary of a special issue of the International Journal of Molecular Sciences titled "The Molecular and Cellular Pathophysiologic Mechanisms Underlying Ocular Diseases and Emerging Therapies." The special issue focuses on the prevalence and rising global public health concerns of visual impairment and ophthalmic diseases. It aims to enhance researchers' and eyecare professionals' understanding of the risk factors, biomarkers, and cellular and molecular mechanisms underlying various eye diseases. The special issue includes eight papers, including original research papers, reviews, and a case report, covering topics such as diabetic retinopathy, retinal diseases, tear biomarkers, and uveal melanoma. The papers provide new advancements in the molecular and cellular pathogenesis of eye diseases and the development of preventive measures and emerging therapies. The document concludes by expressing hope that the special issue will inspire further research and understanding of eye diseases. [Extracted from the article]

This document discusses the use of omics analysis, such as genomics, transcriptomics, proteomics, and metabolomics, in plant research to uncover the physiological mechanisms of plant development, stress responses, and key trait formation. The document includes ten papers that cover topics such as hormone effects on cutting rooting in woody plants, gene expression in sandalwood and maize, volatile aroma formation in pears, and the transcriptional regulation of miRNAs in plants. The research papers highlight the importance of combining omics and physiological analyses to enhance plant conservation and utilization. Two review papers provide an overview of phosphate-binding loop guanosine triphosphatases (P-loop GTPases) and the transcriptional regulation of miRNAs in plants. The authors emphasize the need for further research using innovative omics techniques to expand our knowledge of plant physiology. [Extracted from the article]

Simple Summary: Neurofibromatosis Type 1 is an inherited tumour predisposition syndrome with a varied clinical phenotype. Long-term monitoring through imaging is inconsistent and varies in high- and low-income countries. Implementation of a clinical practice guideline through a multidisciplinary clinic is instrumental to the care of adult Neurofibromatosis Type 1 patients. This systematic review aims to evaluate the association between a country's socioeconomic status and diagnostic modalities and strategies used for adult Neurofibromatosis Type 1 patients. Our results show multiple imaging modalities are used in high-income countries; however, there is limited use in low-income countries. The two most common diagnostic modalities used in developed countries are WB MRI and FDG PET/CT. Background: Neurofibromatosis Type 1 is an autosomal dominant tumour-predisposition condition commonly diagnosed in childhood and fully penetrant by adulthood. Long-term monitoring through imaging is inconsistent and varies between high- and low-income countries. Implementation of a clinical practice guideline through a multidisciplinary clinic is instrumental to the care of adult Neurofibromatosis Type 1 patients. We aim to systematically review international diagnostic modalities and strategies to evaluate any association between a country's socioeconomic status and diagnostic modalities or strategies used for Neurofibromatosis Type 1 patients. Methods: We searched PubMed, Embase, Web of Science, and Cochrane. Relevant clinical information on the surveillance of adult Neurofibromatosis Type 1 patients worldwide was reviewed, extracted, and synthesised. Results: We identified 51 papers reporting on 7724 individuals. Multiple imaging modalities are actively employed in high-income and upper-middle-income countries for surveying adult Neurofibromatosis Type 1 patients. We did not find any relevant papers from low- and middle-income countries. Conclusions: This systematic review suggests that there is robust data on diagnostic modalities for adult Neurofibromatosis Type 1 patients in high-income countries, but not for low- and middle-income countries. There is a lack of data on consolidated diagnostic strategies from both high- and low-income countries. Efforts should be made to publish data on usual clinical practice in low- and middle-income countries to develop clinical practice guidelines describing best medical practice to fit a local context. [ABSTRACT FROM AUTHOR]

Gene expression is highly impacted by the environment and can be reflective of past events that affected developmental processes. It is therefore expected that gene expression can serve as a signal of a current or future phenotypic traits. In this paper we identify sets of genes, which we call Prognostic Transcriptomic Biomarkers (PTBs), that can predict firmness in Malus domestica (apple) fruits. In apples, all individuals of a cultivar are clones, and differences in fruit quality are due to the environment. The apples transcriptome responds to these differences in environment, which makes PTBs an attractive predictor of future fruit quality. PTBs have the potential to enhance supply chain efficiency, reduce crop loss, and provide higher and more consistent quality for consumers. However, several questions must be addressed. In this paper we answer the question of which of two common modeling approaches, Random Forest or ElasticNet, outperforms the other. We answer if PTBs with few genes are efficient at predicting traits. This is important because we need few genes to perform qPCR, and we answer the question if qPCR is a cost-effective assay as input for PTBs modeled using high-throughput RNA-seq. To do this, we conducted a pilot study using fruit texture in the 'Gala' variety of apples across several postharvest storage regiments. Fruit texture in 'Gala' apples is highly controllable by post-harvest treatments and is therefore a good candidate to explore the use of PTBs. We find that the RandomForest model is more consistent than an ElasticNet model and is predictive of firmness (r2 = 0.78) with as few as 15 genes. We also show that qPCR is reasonably consistent with RNA-seq in a follow up experiment. Results are promising for PTBs, yet more work is needed to ensure that PTBs are robust across various environmental conditions and storage treatments. [ABSTRACT FROM AUTHOR]

Down syndrome is a well-studied aneuploidy condition in humans, which is associated with various disease phenotypes including cardiovascular, neurological, haematological and immunological disease processes. This review paper aims to discuss the research conducted on gene expression studies during fetal development. A descriptive review was conducted, encompassing all papers published on the PubMed database between September 1960 and September 2022. We found that in amniotic fluid, certain genes such as COL6A1 and DSCR1 were found to be affected, resulting in phenotypical craniofacial changes. Additionally, other genes such as GSTT1, CLIC6, ITGB2, C21orf67, C21orf86 and RUNX1 were also identified to be affected in the amniotic fluid. In the placenta, dysregulation of genes like MEST, SNF1LK and LOX was observed, which in turn affected nervous system development. In the brain, dysregulation of genes DYRK1A, DNMT3L, DNMT3B, TBX1, olig2 and AQP4 has been shown to contribute to intellectual disability. In the cardiac tissues, dysregulated expression of genes GART, ETS2 and ERG was found to cause abnormalities. Furthermore, dysregulation of XIST, RUNX1, SON, ERG and STAT1 was observed, contributing to myeloproliferative disorders. Understanding the differential expression of genes provides insights into the genetic consequences of DS. A better understanding of these processes could potentially pave the way for the development of genetic and pharmacological therapies. [ABSTRACT FROM AUTHOR]

Invasive paper wasps such as Polistes dominula are a major pest and problem for biodiversity around the globe. Safe and highly targeted methods for the control of these and other social wasp populations are needed. We attempted to identify potentially-lethal gene targets that could be used on adult paper wasps in a gene silencing or RNA interference (RNAi) approach. Double-stranded RNA (dsRNA) was designed to target genes for which silencing has proven lethal in other insects. dsRNA was provided either orally to foragers or directly injected into the wasps. We also provided the dsRNA unprotected or protected from degradation by gut nucleases in two different forms (lipofectamine and carbon quantum dots). The effects of oral delivery of 22 different gene targets to forager wasps was evaluated. The expression of five different genes was successfully reduced following dsRNA ingestion or injection. These gene targets included the FACT complex subunit spt16 (DRE4) and RNA-binding protein fusilli (FUSILLI), both of which have been previously shown to have potential as lethal targets for pest control in other insects. However, we found no evidence of significant increases in adult wasp mortality following ingestion or injection of dsRNA for these genes when compared with control treatments in our experiments. The methods we used to protect the dsRNA from digestive degradation altered gene expression but similarly did not influence wasp mortality. Our results indicate that while many of the same gene targets can be silenced and induce mortality in other insects, dsRNA and RNAi approaches may not be useful for paper wasp control. [ABSTRACT FROM AUTHOR]

They describe the structure of the MRN Complex and its function in double-strand break (DSB) repair and analyze the different germline alterations of the MRN complex genes in autosomal recessive syndromes, as well as the association between heterozygous germline alterations in these genes and cancer predisposition. In the rapidly evolving landscape of molecular genetics and genomics, this Special Issue brings together a collection of insightful review articles that delve into the forefront of scientific exploration. The authors also propose a possible explanation of the discrepancies in the published results of the effects of the OXTR gene polymorphisms and methylation on different diseases [[2]]. [Extracted from the article]

Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners. [ABSTRACT FROM AUTHOR]

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]

The penalized Lasso Cox proportional hazards model has been widely used to identify prognosis biomarkers in high-dimension settings. However, this method tends to select many false positives, affecting its interpretability. In order to improve the reproducibility, we develop a knockoff procedure that consists on wrapping the Lasso Cox model with the model-X knockoff, resulting in a powerful tool for variable selection that allows for the control of the false discovery rate in the presence of finite sample guarantees. In this paper, we propose a novel approach to sample valid knockoffs for ordinal and continuous variables whose distributions can be skewed or heavy-tailed, which employs a Latent Mixed Gaussian Copula model to account for the dependence structure between the variables, leading to what we call the Latent Gaussian Copula Knockoff (LGCK) procedure. We then combine the LGCK method with the Lasso coefficient difference (LCD) statistic as the importance metric. To our knowledge, our proposal is the first knockoff framework for jointly considering ordinal and continuous data in a non-Gaussian setting and a survival context. We illustrate the proposed methodology's effectiveness by applying it to a real lung cancer gene expression dataset. [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]

Clostridioides difficile infections (CDIs) continue to be a persistent healthcare concern despite newer antibiotic treatments, enhanced infection control practices, and preventive strategies focused on restoring the protective intestinal microbial barrier. Recent strides in gene sequencing research have identified many genes regulating diverse virulence factors for CDIs. These genes may be over- or under-expressed when triggered by various environmental and nutritional factors. The aims of this paper are to review the important genes involved in C. difficile pathogenesis and to identify modifiable environmental, nutritional, and other factors that may trigger the expression of these genes and thus offer new strategies to prevent CDIs. [ABSTRACT FROM AUTHOR]

Pregnancy-related complications (PRC) impact maternal and fetal morbidity and mortality and place a huge burden on healthcare systems. Thus, effective diagnostic screening strategies are crucial. Currently, national and international guidelines define patients at low risk of PRC exclusively based on their history, thus excluding the possibility of identifying patients with de novo risk (patients without a history of disease), which represents most women. In this setting, previous studies have underlined the potential contribution of non-coding RNAs (ncRNAs) to detect patients at risk of PRC. However, placenta biopsies or cord blood samples are required, which are not simple procedures. Our review explores the potential of ncRNAs in biofluids (fluids that are excreted, secreted, or developed because of a physiological or pathological process) as biomarkers for identifying patients with low-risk pregnancies. Beyond the regulatory roles of ncRNAs in placental development and vascular remodeling, we investigated their specific expressions in biofluids to determine favorable pregnancy outcomes as well as the most frequent pathologies of pregnant women. We report distinct ncRNA panels associated with PRC based on omics technologies and subsequently define patients at low risk. We present a comprehensive analysis of ncRNA expression in biofluids, including those using next-generation sequencing, shedding light on their predictive value in clinical practice. In conclusion, this paper underscores the emerging significance of ncRNAs in biofluids as promising biomarkers for risk stratification in PRC. The investigation of ncRNA expression patterns and their potential clinical applications is of diagnostic, prognostic, and theragnostic value and paves the way for innovative approaches to improve prenatal care and maternal and fetal outcomes. [ABSTRACT FROM AUTHOR]

Non-coding RNAs have been described as crucial regulators of gene expression and guards of cellular homeostasis. Some recent papers focused on vault RNAs, one of the classes of non-coding RNA, and their role in cell proliferation, tumorigenesis, apoptosis, cancer response to therapy, and autophagy, which makes them potential therapy targets in oncology. In the human genome, four vault RNA paralogues can be distinguished. They are associated with vault complexes, considered the largest ribonucleoprotein complexes. The protein part of these complexes consists of a major vault protein (MVP) and two minor vault proteins (vPARP and TEP1). The name of the complex, as well as vault RNA, comes from the hollow barrel-shaped structure that resembles a vault. Their sequence and structure are highly evolutionarily conserved and show many similarities in comparison with different species, but vault RNAs have various roles. Vaults were discovered in 1986, and their functions remained unclear for many years. Although not much is known about their contribution to cell metabolism, it has become clear that vault RNAs are involved in various processes and pathways associated with cancer progression and modulating cell functioning in normal and pathological stages. In this review, we discuss known functions of human vault RNAs in the context of cellular metabolism, emphasizing processes related to cancer and cancer therapy efficacy. [ABSTRACT FROM AUTHOR]

In biological systems, impulses are often used to model mutations in gene expression. In order to describe the instantaneity of the impulse, this paper employs a hybrid‐index model of impulsive Boolean networks with state‐triggered impulses. Based on this model, the time‐domain output controllability is investigated. Using the quotient mapping method, the hybrid‐domain solutions of this model are mapped to the time‐domain solutions, mathematically. Then a necessary and sufficient condition for output controllability in the time domain is presented. Finally, an example is given to demonstrate the correctness and effectiveness of the obtained results. [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]

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNA-mediated regulation at the protein level. In this paper, the MoPC computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPC returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases. [ABSTRACT FROM AUTHOR]

China leads the world in freshwater pearl production, an industry in which the triangle sail mussel (Sinohyriopsis cumingii) plays a pivotal role. In this paper, we report a high-quality chromosome-level genome assembly of S. cumingii with a size of 2.90 Gb—the largest yet reported among bivalves—and 89.92% anchorage onto 19 linkage groups. The assembled genome has 37,696 protein-coding genes and 50.86% repeat elements. A comparative genomic analysis revealed expansions of 752 gene families, mostly associated with biomineralization, and 237 genes under strong positive selection. Notably, the fibrillin gene family exhibited gene family expansion and positive selection simultaneously, and it also exhibited multiple high expressions after mantle implantation by transcriptome analysis. Furthermore, RNA silencing and an in vitro calcium carbonate crystallization assay highlighted the pivotal role played by one fibrillin gene in calcium carbonate deposition and aragonite transformation. This study provides a valuable genomic resource and offers new insights into the mechanism of pearl biomineralization. [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]

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]

Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII. [ABSTRACT FROM AUTHOR]

Simple Summary: The cornerstone of successful treatment of hematologic diseases, including multiple myeloma (MM), is early diagnosis and establishing a prognosis for the patient. Therefore, it is necessary to identify sensitive and specific markers for this disease. Non-coding RNAs including miRNAs are increasingly being recognized as potential diagnostic, predictive, and prognostic markers. These molecules are non-coding, single-stranded RNAs that regulate the expression of many target genes involved in key biological processes, such as cell proliferation, differentiation, and apoptosis. This paper aims to identify the role of non-coding RNAs including miRNAs as potential markers for diagnosis and prognosis in patients with MM. Multiple myeloma (MM) is the second most common hematologic malignancy in the world and accounts for 15% of primary hemocytopathies, with an ever-increasing number of new cases. It is asymptomatic in 30% of instances; hence, the determination of highly sensitive and specific markers is necessary to make a proper diagnosis. In the last 20 years, miRNAs, involved in regulating the expression of genes responsible for cell proliferation and differentiation, including tumor cells, have been identified as potential diagnostic and prognostic markers. The main aim of the following review was to outline the role of miRNAs in the diagnosis and prognosis of MM, considering their role in the pathogenesis of the disease and identifying their target genes and pathways. For this purpose, publications dating from 2013–2023 have been reviewed. Based on the available data, it is concluded that non-coding RNAs including miRNAs could be potential markers in MM. Furthermore, they may serve as therapeutic targets for certain drugs. [ABSTRACT FROM AUTHOR]

Flatfish undergo a remarkable metamorphosis from symmetrical pelagic larvae to fully asymmetrical benthic juveniles. The most distinctive features of this transformation is the migration of one eye. The molecular role of thyroid hormone in the metamorphosis process in flatfishes is well established. However, the regulatory network that facilitates eye movement remains enigmatic. This paper presents a morphological investigation of the metamorphic process in turbot eyes, using advanced imaging techniques and a global view of gene expression. The study covers migrant and non-migrant eyes and aims to identify the genes that are active during ocular migration. Our transcriptomic analysis shows a significant up-regulation of immune-related genes. The analysis of eye-specific genes reveals distinct patterns during the metamorphic process. Myosin is highlighted in the non-migrant eye, while ependymin is highlighted in the migrant eye, possibly involved in optic nerve regeneration. Furthermore, a potential association between the alx3 gene and cranial restructuring has been identified. Additionally, it confirmed simultaneous adaptation to low light in both eyes, as described by changes in opsins expression during the metamorphic process. The study also revealed that ocular migration activates systems asynchronously in both eyes, providing insight into multifaceted reorganization processes during metamorphosis of flatfish. Flatfish eye migration in metamorphosis study shows up-regulated immune genes. The process involves roles of myosin/ependymin, the link of alx3's to cranial restructuring, changes in opsins adaptation to low light and an asynchronous system activation. [ABSTRACT FROM AUTHOR]

The most prevalent mental illness worldwide and the main contributor to suicide and disability is major depressive disorder. Major depressive disorder is now diagnosed and treated based on the patient's statement of symptoms, mental status tests, and clinical behavioral observations. The central element of this review is the increased need for an accurate diagnostic method. In this context, the present research aims to investigate the potential role of two non-coding RNA species (microRNA and long non-coding RNA) in peripheral blood samples and brain tissue biopsy from patients with major depressive disorder. This study reviewed the literature on microRNA and long non-coding RNA expression in blood and brain tissue samples in human and animal depression models by retrieving relevant papers using the PubMed database. The results reveal significant variations in microRNA and long non-coding RNA levels in depressed patients, making it a crucial diagnostic tool that predicts treatment outcomes. It can help track severe cases and adjust therapy dosages based on treatment responses. In conclusion, microRNAs and long non-coding RNAs are pertinent biomarkers that can be added to the diagnostic test panel for major depressive disorder. Both microRNAs and non-coding RNAs can also be used as a tool to track patient progress during therapy and to assist the attending physician in tracking the molecular development of the disease. [ABSTRACT FROM AUTHOR]

Simple Summary: Yaks are a major economic source for people in the Tibetan Plateau region. Yaks are rich in nutrients, but their low fat content is not conducive to the large-scale promotion of yak meat, so the study of the mechanism of yak lipid deposition is beneficial to the marketing of yak meat. In this paper, the results of transcriptome sequencing analysis of yak liver showed that it could be determined that the expression levels of genes associated with partial lipid deposition were significantly up-regulated during yak fattening. In addition, this study found that the tenderness of yak meat improved during this process. Fattening significantly affects fat deposition in yaks, which may be realized through its effects on lipid metabolic pathways. Therefore, studying the mechanism of lipid deposition in yaks and fattening yaks will improve the quality of yak meat. Yak meat is nutritionally superior to beef cattle but has a low fat content and is slow-growing. The liver plays a crucial role in lipid metabolism, and in order to determine whether different feeding modes affect lipid metabolism in yaks and how it is regulated, we employed RNA sequencing (RNA-seq) technology to analyze the genome-wide differential gene expression in the liver of yaks maintained under different raising systems. A total of 1663 differentially expressed genes (DEGs) were identified (|log2FC| ≥ 0 and p-value ≤ 0.05), including 698 down-regulated and 965 up-regulated genes. According to gene ontology (GO) and KEGG enrichment analyses, these DEGs were significantly enriched in 13 GO terms and 26 pathways (p < 0.05). Some DEGs were enriched in fatty acid degradation, PPAR, PI3K-Akt, and ECM receptor pathways, which are associated with lipid metabolism. A total of 16 genes are well known to be related to lipid metabolism (e.g., APOA1, FABP1, EHHADH, FADS2, SLC27A5, ACADM, CPT1B, ACOX2, HMGCS2, PLIN5, ACAA1, IGF1, FGFR4, ALDH9A1, ECHS1, LAMA2). A total of 11 of the above genes were significantly enriched in the PPAR signaling pathway. The reliability of the transcriptomic data was verified using qRT-PCR. Our findings provide new insights into the mechanisms regulating yak meat quality. It shows that fattening improves the expression of genes that regulate lipid deposition in yaks and enhances meat quality. This finding will contribute to a better understanding of the various factors that determine yak meat quality and help develop strategies to improve yield and quality. [ABSTRACT FROM AUTHOR]

Currently, only a limited set of molecular traits are utilized to direct treatment for metastatic CRC (mCRC). The molecular classification of CRC depicts tumor heterogeneity based on gene expression patterns and aids in comprehending the biological characteristics of tumor formation, growth and prognosis. Additionally, it assists physicians in tailoring the therapeutic approach. Microsatellite instability (MSI-H)/deficient mismatch repair proteins (MMRd) status has become a ubiquitous biomarker in solid tumors, caused by mutations or methylation of genes and, in turn, the accumulation of mutations and antigens that subsequently induce an immune response. Immune checkpoint inhibitors (ICI) have recently received approval for the treatment of mCRC with MSI-H/MMRd status. However, certain individuals experience either initial or acquired resistance. The tumor-programmed cell death ligand 1 (PD-L1) has been linked to the ability of CRC to evade the immune system and promote its growth. Through comprehensive research conducted via the PUBMED database, the objectives of this paper were to review the molecular characteristics linked to tumor response in metastatic CRC in light of improved patients' outcomes following ICI therapies as seen in clinical trials and to identify particular microRNAs that can modulate the expression of specific oncoproteins, such as PD-L1, and disrupt the mechanisms that allow the immune system to be evaded. [ABSTRACT FROM AUTHOR]

During its first decade of life, Physiological Reports has become a home for well‐conceived and rigorously performed exercise physiology and metabolism studies. The breadth of research within this area is impressive, covering exercise‐induced increases in skeletal muscle gene expression to the effects of exercise on the gut microbiome. The purpose of the current review is to highlight some of the impactful exercise physiology and metabolism papers published in the journal and to look ahead to what areas exercise physiology publications might address in the next 10 years. [ABSTRACT FROM AUTHOR]

Cancer is a leading cause of death globally. The majority of cancer cases are only diagnosed in the late stages of cancer due to the use of conventional methods. This reduces the chance of survival for cancer patients. Therefore, early detection consequently followed by early diagnoses are important tasks in cancer research. Gene expression microarray technology has been applied to detect and diagnose most types of cancers in their early stages and has gained encouraging results. In this paper, we address the problem of classifying cancer based on gene expression for handling the class imbalance problem and the curse of dimensionality. The oversampling technique is utilized to overcome this problem by adding synthetic samples. Another common issue related to the gene expression dataset addressed in this paper is the curse of dimensionality. This problem is addressed by applying chi-square and information gain feature selection techniques. After applying these techniques individually, we proposed a method to select the most significant genes by combining those two techniques (CHiS and IG). We investigated the effect of these techniques individually and in combination. Four benchmarking biomedical datasets (Leukemia-subtypes, Leukemia-ALLAML, Colon, and CuMiDa) were used. The experimental results reveal that the oversampling techniques improve the results in most cases. Additionally, the performance of the proposed feature selection technique outperforms individual techniques in nearly all cases. In addition, this study provides an empirical study for evaluating several oversampling techniques along with ensemble-based learning. The experimental results also reveal that SVM-SMOTE, along with the random forests classifier, achieved the highest results, with a reporting accuracy of 100%. The obtained results surpass the findings in the existing literature as well. [ABSTRACT FROM AUTHOR]

Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the world. Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector. In quantitative genetics prediction of genetic values is a central issue. Wheat is an allohexaploid (AABBDD) with three distinct genomes. The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat, Therefore, genome sequence approaches based on techniques of Artificial Intelligence (AI) are necessary. This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources andmanaging genetic variation in their breeding program, as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints. In this paper, the hyperparameters of the network are optimized in the CNN to decrease the requirement formanual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks (CNN). [ABSTRACT FROM AUTHOR]

Simple Summary: This paper delves into the issue of metastatic endometrial cancer (EC), which significantly impacts treatment success and overall survival rates. The study explores various factors contributing to metastasis, including the molecular profile of EC, hormone activity (like estrogen and prolactin), and pro-inflammatory adipocytokines. It also investigates how altered microRNA expression affects gene regulation linked to the dissemination of the cancer. The paper highlights the importance of imaging techniques, particularly transvaginal ultrasound with tumor-free distance (uTFD), in detecting metastases. Additionally, it discusses how diagnostic and therapeutic methods can influence the spread of EC, with hysteroscopy potentially increasing risk in advanced stages and laparoscopic hysterectomy being safer if performed with care. This research could improve our understanding of EC and guide better diagnostic and treatment strategies. The presence of metastatic endometrial cancer (EC) is a key problem in treatment failure associated with reduced overall survival rates. The most common metastatic location is the pelvic lymph nodes, and the least common is the brain. The presence of metastasis depends on many factors, including the molecular profile of cancer (according to the TCGA—Genome Atlas), the activity of certain hormones (estrogen, prolactin), and pro-inflammatory adipocytokines. Additionally, an altered expression of microRNAs affecting the regulation of numerous genes is also related to the spread of cancer. This paper also discusses the value of imaging methods in detecting metastases; the primary role is attributed to the standard transvaginal USG with the tumor-free distance (uTFD) option. The influence of diagnostic and therapeutic methods on EC spread is also described. Hysteroscopy, according to the analysis discussed above, may increase the risk of metastases through a fluid medium, mainly performed in advanced stages of EC. According to another analysis, laparoscopic hysterectomy performed with particular attention to avoiding risky procedures (trocar flushing, tissue traumatization, preserving a margin of normal tissue) was not found to increase the risk of EC dissemination. [ABSTRACT FROM AUTHOR]

Enhancers are located upstream or downstream of key deoxyribonucleic acid (DNA) sequences in genes and can adjust the transcription activity of neighboring genes. Identifying enhancers and determining their functions are important for understanding gene regulatory networks and expression regulatory mechanisms. However, traditional enhancer recognition relies on manual feature engineering, which is time-consuming and labor-intensive, making it difficult to perform large-scale recognition analysis. In addition, if the original dataset is too small, there is a risk of overfitting. In recent years, emerging methods, such as deep learning, have provided new insights for enhancing identification. However, these methods also present certain challenges. Deep learning models typically require a large amount of high-quality data, and data acquisition demands considerable time and resources. To address these challenges, in this paper, we propose a data-augmentation method based on generative adversarial networks to solve the problem of small datasets. Moreover, we used regularization methods such as weight decay to improve the generalizability of the model and alleviate overfitting. The Transformer encoder was used as the main component to capture the complex relationships and dependencies in enhancer sequences. The encoding layer was designed based on the principle of k-mers to preserve more information from the original DNA sequence. Compared with existing methods, the proposed approach made significant progress in enhancing the accuracy and strength of enhancer identification and prediction, demonstrating the effectiveness of the proposed method. This paper provides valuable insights for enhancer analysis and is of great significance for understanding gene regulatory mechanisms and studying disease correlations. [ABSTRACT FROM AUTHOR]

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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]

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]

Epigenetic regulation has been thoroughly investigated in recent years and has emerged as an important aspect of chronic lymphocytic leukemia (CLL) biology. Characteristic aberrant features such as methylation patterns and global DNA hypomethylation were the early findings of the research during the last decades. The investigation in this field led to the identification of a large number of genes where methylation features correlated with important clinical and laboratory parameters. Gene-specific analyses investigated methylation in the gene body enhancer regions as well as promoter regions. The findings included genes and proteins involved in key pathways that play central roles in the pathophysiology of the disease. Τhe application of these findings beyond the theoretical understanding can not only lead to the creation of prognostic and predictive models and scores but also to the design of novel therapeutic agents. The following is a review focusing on the present knowledge about single gene/gene promoter methylation or mRNA expression in CLL cases as well as records of older data that have been published in past papers. [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]

Ferroptosis has garnered attention as a potential approach to fight against cancer, which is characterized by the iron-driven buildup of lipid peroxidation. However, the robust defense mechanisms against intracellular ferroptosis pose significant challenges to its effective induction. In this paper, an effective gene delivery vehicle was developed to transport solute carrier family 7 member 11 (SLC7A11) shRNA (shSLC7A11), which downregulates the expression of the channel protein SLC7A11 and glutathione peroxidase 4 (GPX4), evoking a surge in reactive oxygen species production, iron accumulation, and lipid peroxidation in hepatocellular carcinoma (HCC) cells, and subsequently leading to ferroptosis. This delivery system is composed of an HCC-targeting lipid layer and esterase-responsive cationic polymer, a poly{N-[2-(acryloyloxy)ethyl]-N-[p-acetyloxyphenyl]-N} (PQDEA) condensed shSLC7A11 core (G−LPQDEA/shSLC7A11). After intravenous (i.v.) injection, G−LPQDEA/shSLC7A11 quickly accumulated in the tumor, retarding its growth by 77% and improving survival by two times. This study is the first to construct a gene delivery system, G−LPQDEA/shSLC7A11, that effectively inhibits HCC progression by downregulating SLC7A11 expression. This underscores its therapeutic potential as a safe and valuable candidate for clinical treatment. [ABSTRACT FROM AUTHOR]

Plants are vulnerable to many abiotic stresses, resulting in reduced plant productivity. Its adaptation to unfavorable environments relies on transmitting external stress signals into internal signaling pathways. A series of stress response mechanisms have been developed. Among them, brassinosteroids (BRs) are a class of steroid hormones that are widely involved in plant growth, development, and stress response. Via genetics, proteomics, and genomics studies, the major components of signaling and signaling pathways through a series of phosphorylation cascade reactions have been identified in model plants such as Arabidopsis. Numerous studies have shown that BRs play important roles in plant responses to drought, temperature, salt, heavy metals, and other environmental stresses. The application of BRs to improve plant stress resistance has become the focus of research in recent years, especially the regulation of stress via endogenous BRs. Therefore, this paper systematically summarizes the research progress related to endogenous BR levels and provides an overview of BR biosynthesis and the signaling pathway, as well as the function of endogenous BRs in the response to abiotic stresses. [ABSTRACT FROM AUTHOR]

Physical inactivity is considered a significant risk factor for mortality and morbidity among chronic hemodialysis (HD) patients. Therefore, physical exercise is recommended in the treatment of HD patients. Although the beneficial effects of physical exercise in HD patients are well-described in the literature, the underlying physiological mechanisms still need to be fully understood. Recently, microRNAs (miRNAs) have emerged as potential mediators of the therapeutic effects of physical exercise in healthy individuals. miRNAs are short, single-stranded, noncoding RNAs involved in gene expression regulation. Specifically, upon forming the RNA-induced silencing complex, miRNAs selectively bind to specific miRNAs within cells, reducing gene expression. miRNAs can be secreted by cells in an accessible form or enclosed within exosomes or extracellular vesicles. They can be detected in various body fluids, including serum (circulating miRNAs), facilitating the study of their diverse expression. Currently, there is no available data regarding the impact of physical exercise on the expression of miRNAs involved in osteogenic differentiation, a fundamental mechanism in the development of vascular calcification, for HD patients. Therefore, we have designed an observational and longitudinal case-control study to evaluate the expression of miR-9 and miR-30b in HD patients participating in a 3-month interdialytic physical exercise program. This paper aims to present the study protocol and review the expression of circulating miRNAs in HD patients and their modulation through physical exercise. [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]

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]

Cystic Fibrosis (CF) is present due to mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene, the most frequent variant being p.phe508del. The CFTR protein is a chloride (Cl-) channel which is defective and almost absent of cell membranes when the p.Phe508del mutation is present. The p.Phe508del-CFTR protein is retained in the endoplasmic reticulum (ER) and together with inflammation and infection triggers the Unfolded Protein Response (UPR). During the UPR, the Activating Transcription Factor 6 (ATF6) is activated with cleavage and then decreases the expression of p.Phe508del-CFTR. We have previously shown that the inhibition of the activation of ATF6 alleviates the p.Phe508del-CFTR defects in cells overexpressing the mutated protein. In the present paper, our aim was to inhibit the cleavage of ATF6, and thus its activation in a human bronchial cell line with endogenous p.Phe508del-CFTR expression and in bronchial cells from patients, to be more relevant to CF. This was achieved by inhibiting the protease MBTP1 which is responsible for the cleavage of ATF6. We show here that this inhibition leads to increased mRNA and p.Phe508del-CFTR expression and, consequently, to increased Cl-efflux. We also explain the mechanisms linked to these increases with the modulation of genes when MBTP1 is inhibited. Indeed, RT-qPCR assays show that genes such as HSPA1B, CEBPB, VIMP, PFND2, MAPK8, XBP1, INSIG1, and CALR are modulated. In conclusion, we show that the inhibition of MBTP1 has a beneficial effect in relevant models to CF and that this is due to the modulation of genes involved in the disease. [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]

Mast cell tumors are a large group of diseases occurring in dogs, cats, mice, as well as in humans. Systemic mastocytosis (SM) is a disease involving the accumulation of mast cells in organs. KIT gene mutations are very often seen in abnormal mast cells. In SM, high KIT/CD117 expression is observed; however, there are usually no KIT gene mutations present. Mastocytoma (MCT)—a form of cutaneous neoplasm—is common in animals but quite rare in humans. KIT/CD117 receptor mutations were studied as the typical changes for human mastocytosis. In 80% of human cases, the KIT gene substitution p.D816H was present. In about 25% of MCTs, metastasis was observed. Changes in the gene expression of certain genes, such as overexpression of the DNAJ3A3 gene, promote metastasis. In contrast, the SNORD93 gene blocks the expression of metastasis genes. The panel of miR-21-5p, miR-379, and miR-885 has a good efficiency in discriminating healthy and MCT-affected dogs, as well as MCT-affected dogs with and without nodal metastasis. Further studies on the pathobiology of mast cells can lead to clinical improvements, such as better MCT diagnosis and treatment. Our paper reviews studies on the topic of mast cells, which have been carried out over the past few years. [ABSTRACT FROM AUTHOR]

DNA methylation is critically involved in the regulation of chromatin states and cell-type-specific gene expression. The exclusive expression of imprinted genes from either the maternal or the paternal allele is regulated by allele-specific DNA methylation at imprinting control regions (ICRs). Aberrant DNA hyper- or hypomethylation at the ICR1 of the H19/IGF2 imprinting locus is characteristic for the imprinting disorders Beckwith–Wiedemann syndrome (BWS) and Silver–Russell syndrome (SRS), respectively. In this paper, we performed epigenome editing to induce targeted DNA demethylation at ICR1 in HEK293 cells using dCas9-SunTag and the catalytic domain of TET1. 5-methylcytosine (5mC) levels at the target locus were reduced up to 90% and, 27 days after transient transfection, >60% demethylation was still observed. Consistent with the stable demethylation of CTCF-binding sites within the ICR1, the occupancy of the DNA methylation-sensitive insulator CTCF protein increased by >2-fold throughout the 27 days. Additionally, the H19 expression was increased by 2-fold stably, while IGF2 was repressed though only transiently. Our data illustrate the ability of epigenome editing to implement long-term changes in DNA methylation at imprinting control regions after a single transient treatment, potentially paving the way for therapeutic epigenome editing approaches in the treatment of imprinting disorders. [ABSTRACT FROM AUTHOR]

Corynebacterium glutamicum is an important industrial production strain that is widely used in amino acid fermentation, biopharmaceuticals, and other fields. It is particularly important to develop efficient genome editing methods for the targeted modification of C. glutamicum production strains. Currently, the gene editing system of C. glutamicum is inefficient and time-consuming. In this paper, we reported on a Francisella novicida (Fn) CRISPR-Cpf1-based system for genome editing. The system used linear DNA detached from the plasmid, and, with the assistance of the recombinase RecET, gene deletion was achieved by simultaneous electrotransformation of linear DNA with a plasmid carrying the FnCpf1 and crRNA expression cassette for double-strand breaks of the genome. Compared with previous all-in-one plasmids, this system reduced the time for one round of constructing recombinant plasmids and shortened the editing cycle by about 24 h. Finally, we successfully constructed an engineered strain (X−2) with high L-valine production by using the linear DNA-mediated gene deletion system. This method is of great importance for accelerating the process of metabolic engineering modification of C. glutamicum and its further application in high value-added products. [ABSTRACT FROM AUTHOR]