ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and intellectual developmental disorder (IDD), are highly prevalent and lack effective treatments, posing significant health challenges. These disorders are frequently comorbid with disruptions in sleep rhythms, and sleep-related indicators are often used to assess disease severity and treatment efficacy. Recent evidence has highlighted the crucial roles of circadian rhythm disturbances and circadian clock gene mutations in the pathogenesis of NDDs. This review focuses on the mechanisms by which circadian rhythm disruptions and circadian clock gene mutations contribute to cognitive, behavioral, and emotional disorders associated with NDDs, particularly through the dysregulation of dopamine system. Additionally, we discussed the potential of targeting the circadian system as novel therapeutic strategies for the treatment of NDDs.
Humans ; Neurodevelopmental Disorders/genetics* ; Attention Deficit Disorder with Hyperactivity/genetics* ; Circadian Rhythm/genetics* ; Autism Spectrum Disorder/genetics* ; Mutation ; Intellectual Disability/genetics* ; Circadian Clocks/physiology* ; Dopamine/metabolism*

OBJECTIVES: Multiple myeloma (MM) is a highly heterogeneous hematologic malignancy, with disease progression driven by cytogenetic abnormalities and a complex bone marrow microenvironment. This study aims to construct a prognostic model for MM based on transcriptomic data and lipid metabolism related genes (LRGs), and to identify potential drug targets for high-risk patients to support clinical decision-making. METHODS: In this study, 2 transcriptomic datasets covering 985 newly diagnosed MM patients were retrieved from the Gene Expression Omnibus (GEO) database. Univariate Cox regression and 101 machine learning algorithms were used for gene selection. An LRG-based prognostic model was constructed using Stepwise Cox (both directions) and random survival forest (RSF) algorithms. The association between the prognostic score and clinical events was evaluated, and model performance was assessed using time-dependent receiver operating characteristic (ROC) curves and the C-index. The added predictive value of combining prognostic scores with clinical variables and staging systems was also analyzed. Differentially expressed genes between high- and low-risk groups were identified using limma and clusterProfiler and subjected to pathway enrichment analysis. Drug sensitivity analysis was conducted using the Genomics of Drug Sensitivity in Cancer (GDSC) database and oncoPredict to identify potential therapeutic targets for high-risk patients. The functional role of key LRGs in the model was validated via in vitro cell experiments. RESULTS: An LRG-based prognostic model (LRG17) was successfully developed using transcriptomic data and machine learning. The model demonstrated robust predictive performance, with area under the curve (AUC) values of 0.962, 0.912, and 0.842 for 3-, 5-, and 7-year survival, respectively. Patients were stratified into high- and low-risk groups, with high-risk patients showing significantly shorter overall survival (OS) and event-free survival (EFS) (both P<0.001) and worse clinical profiles (e.g., lower albumin, higher β2-microglobulin and lactate dehydrogenase levels). Enrichment analysis revealed that high-risk patients were significantly enriched for pathways related to chromosome segregation and mitosis, whereas low-risk patients were enriched for immune response and immune cell activation pathways. Drug screening suggested that AURKA inhibitor BMS-754807 and FGFR3 inhibitor I-BET-762 may be more effective in high-risk patients. Functional assays demonstrated that silencing of key LRG PLA2G4A significantly inhibited cell viability and induced apoptosis. CONCLUSIONS LRGs serve as promising biomarkers for prognosis prediction and risk stratification in MM. The overexpression of chromosomal instability-related and high-risk genetic event-associated genes in high-risk patients may explain their poorer outcomes. Given the observed resistance to bortezomib and lenalidomide in high-risk patients, combination therapies involving BMS-754807 or I-BET-762 may represent effective alternatives.
Humans ; Multiple Myeloma/mortality* ; Prognosis ; Lipid Metabolism/genetics* ; Transcriptome ; Machine Learning ; Male ; Female ; Gene Expression Profiling ; Algorithms

Loss-of-function variants in CSDE1 have been strongly linked to neuropsychiatric disorders, yet the precise role of CSDE1 in neurogenesis remains elusive. In this study, we demonstrate that knockout of Csde1 during cortical development in mice results in impaired neural progenitor proliferation, leading to abnormal cortical lamination and embryonic lethality. Transcriptomic analysis revealed that Csde1 upregulates the transcription of genes involved in the cell cycle network. Applying a dual thymidine-labelling approach, we further revealed prolonged cell cycle durations of neuronal progenitors in Csde1-knockout mice, with a notable extension of the G1 phase. Intersection with CLIP-seq data demonstrated that Csde1 binds to the 3' untranslated region (UTR) of mRNA transcripts encoding cell cycle genes. Particularly, we uncovered that Csde1 directly binds to the 3' UTR of mRNA transcripts encoding Cdk6, a pivotal gene in regulating the transition from the G1 to S phases of the cell cycle, thereby maintaining its stability. Collectively, this study elucidates Csde1 as a novel regulator of Cdk6, sheds new light on its critical roles in orchestrating brain development, and underscores how mutations in Csde1 may contribute to the pathogenesis of neuropsychiatric disorders.
Animals ; Neurogenesis/genetics* ; Cell Cycle/genetics* ; Mice, Knockout ; Mice ; Neural Stem Cells/metabolism* ; DNA-Binding Proteins/metabolism* ; Cyclin-Dependent Kinase 6/genetics* ; Cell Proliferation ; 3' Untranslated Regions ; Cerebral Cortex/embryology* ; RNA-Binding Proteins ; Mice, Inbred C57BL

Ferroptosis is a novel type of iron-dependent cell death driven by lipid peroxidation. More and more evidence shows that ferroptosis is related to various pathological conditions, such as neurodegenerative diseases, diabetic nephropathy, and cancer. Ferroptosis driven by lipid peroxidation may promote or inhibit the occurrence and development of these diseases. The intracellular antioxidant system plays an important role in resisting ferroptosis by inhibiting lipid peroxidation. The key pathways of ferroptosis include the amino acid metabolism pathway with SLC7A11-GPX4 as the key molecule, the iron metabolism pathway with ferritin or transferrin as the main component, and the lipid metabolism pathway. The occurrence of ferroptosis is regulated by intracellular proteins, which undergo various post-translational modifications, including ubiquitination. The ubiquitin-proteasome system (UPS) is one of the main degradation systems in cells. It catalyzes the ubiquitin molecule to label the protein and then the proteasome recognizes and degrades the target protein. UPS promotes ferroptosis by promoting the degradation of key ferroptosis molecules (such as SLC7A11, GPX4, and GSH) and antioxidant systems (such as NRF2). UPS can also inhibit ferroptosis by promoting the degradation of related molecules in the lipid metabolism pathway (such as ACLS4 and ALOX15). In this review, we summarize the latest research progress of ubiquitination modification in the regulation of ferroptosis, generalize the published studies on the regulation of ferroptosis by E3 ubiquitin ligase and deubiquitination, and sum up the targets of ubiquitin ligase and deubiquitination regulating ferroptosis, which is helpful to identify new prognostic indicators in human diseases and provide potential therapeutic strategies for these diseases.

Circular RNAs (circRNAs) are a kind of non-coding RNA (ncRNA) with covalent closed-loop structure. They have attracted more and more attention because of their high stability, evolutionary conservatism, and tissue expression specificity. It has shown that circRNAs are involved in the development of a variety of diseases including malignant tumors recently. Nasopharyngeal carcinoma (NPC) is a malignant tumor that occurs in the nasopharynx and has a unique ethnic and geographical distribution in South China and Southeast Asia. Epstein-Barr virus (EBV) infection is closely related to the development of NPC. Radiotherapy and chemotherapy are the mainstays of treatment for NPC. But tumor recurrence or distant metastasis is the leading cause of death in patients with NPC. Several studies have shown that circRNAs, as gene expression regulators, play an important role in NPC and affect the progression of NPC. This review mainly summarized the research status of abnormally expressed circRNAs in NPC and EBV-encoded circRNAs. We also discussed the possibility of circRNAs as a therapeutic target, diagnostic and prognostic marker for NPC.

The study was carried out to understand the changes in the ethical cognition status of laboratory animals and the effectiveness of laboratory animal ethics education among medical students in Xiangya School of Medicine of Central South University (CSU), and provide new enlightenment for further strengthening the ethical education of laboratory animals. In the study, the same self-compiled questionnaire was used to investigate the ethical cognition of experimental animals among medical students in Xiangya School of Medicine of CSU in 2011 and 2021, and 359 and 363 questionnaires were collected respectively. Through comparative analysis of the questionnaire results before and after ten years, it was found that medical students’ animal experiment operation and attitudes towards laboratory animals, cognition of experimental animal ethics knowledge and their attitude to animal experiment ethics education were significantly improved. It showed that the state of experimental animal ethics cognition among medical students in Xiangya School of Medicine of CSU had improved significantly in recent 10 years, but the cognition of experimental animal ethics knowledge was higher than the actual behavior of caring for experimental animals, and there was the phenomenon of "separation of knowledge and action". The ethics education of experimental animals needs to pay more attention to the development of students’ behavior of caring for experimental animals.

Animals ; Deer ; Antlers ; Stem Cells

OBJECTIVE: To explore the genetic basis for 7 families with gonadal mosaicism for Duchenne muscular dystrophy (DMD). METHODS: For the 7 families presented at the CITIC Xiangya Reproductive and Genetic Hospital from September 2014 to March 2022, clinical data were collected. Preimplantation genetic testing for monogenic disorders (PGT-M) was carried out for the mother of the proband from family 6. Peripheral venous blood samples of the probands, their mothers and other patients from the families, amniotic fluid samples from families 1 ~ 4 and biopsied cells of embryos cultured in vitro from family 6 were collected for the extraction of genomic DNA. Multiplex ligation-dependent probe amplification (MLPA) was carried out for the DMD gene, and short tandem repeat (STR)/single nucleotide polymorphism (SNP)-based haplotypes were constructed for the probands, other patients, fetuses and embryos. RESULTS: The results of MLPA showed that the probands and the fetuses/probands' brothers in families 1 ~ 4, 5, 7 had carried the same DMD gene variants, whilst the probands' mothers were all normal. The proband in family 6 carried the same DMD gene variant with only 1 embryo (9 in total) cultured in vitro, and the DMD gene of the proband's mother and the fetus obtained through the PGT-M were normal. STR-based haplotype analysis showed that the probands and the fetuses/probands' brothers in families 1 ~ 3 and 5 have inherited the same maternal X chromosome. SNP-based haplotype analysis showed that the proband from family 6 has inherited the same maternal X chromosome with only 1 embryo (9 in total) cultured in vitro. The fetuses in families 1 and 6 (via PGT-M) were both confirmed to be healthy by follow up, whilst the mothers from families 2 and 3 had chosen induced labor. CONCLUSION Haplotype analysis based on STR/SNP is an effective method for judging gonad mosaicism. Gonad mosaicisms should be suspected for women who have given births to children with DMD gene variants but with a normal peripheral blood genotype. Prenatal diagnosis and reproductive intervention may be adapted to reduce the births of further affected children in such families.
Male ; Pregnancy ; Child ; Humans ; Female ; Muscular Dystrophy, Duchenne/diagnosis* ; Dystrophin/genetics* ; Mosaicism ; Exons ; Prenatal Diagnosis/methods* ; Nucleotides

OBJECTIVES: To investigate the prevalence rate of non-alcoholic fatty liver disease (NAFLD) in overweight/obese children who visit a hospital, and to explore the influencing factors of NAFLD, in order to provide a basis for the prevention of NAFLD in overweight/obese children. METHODS: Overweight/obese children who visited Hunan Children's Hospital from June 2019 to September 2021 were recruited. The prevalence rate of NAFLD was examined. Logistic regression analysis was used to explore the factors influencing the development of NAFLD [non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH)]. Receiver operating characteristic curve analysis was used to evaluate the predictive value of the influencing factors for NAFL and NASH. RESULTS: A total of 844 overweight/obese children aged 6-17 years were enrolled. The prevalence rate of NAFLD in overweight/obese children was 38.2% (322/844), among which the prevalence rates of NAFL and NASH were 28.8% (243/844) and 9.4% (79/844), respectively. Multivariate logistic regression analysis showed that the increase of waist-to-hip ratio (WHR) and low high-density lipoprotein cholesterol (HDL-C) were associated with the development of NAFL and NASH (P<0.05). The receiver operating characteristic curve analysis showed that the combined measurement of WHR and HDL-C had a predictive value for NAFL (area under the curve: 0.653, 95%CI: 0.613-0.694), and for NASH (area under the curve: 0.771, 95%CI: 0.723-0.819). CONCLUSIONS The prevalence rate of NAFLD in overweight/obese children who visit a hospital is high. WHR and HDL-C are associated with the development of NAFLD and the combined measurement of WHR and HDL-C has a certain value for predicating the development of NAFLD.
Child ; Humans ; Cholesterol, HDL ; Cross-Sectional Studies ; Non-alcoholic Fatty Liver Disease/complications* ; Overweight/complications* ; Pediatric Obesity/epidemiology* ; Prevalence ; Adolescent