Integrative analysis of exosomal ncRNAs and their regulatory networks in liver cancer progression

Background

Hepatocellular carcinoma (HCC) is a significant global health challenge with complex molecular underpinnings. Recent advancements in understanding the role of non-coding RNAs (ncRNAs) and exosomes in cancer biology have opened new avenues for research into potential diagnostic and therapeutic strategies.

Methods

This study utilized a comprehensive approach to analyze gene expression patterns and regulatory networks in HCC. We integrated RNA sequencing data gathered from both tissue samples and exosomes. The WGCNA and limma R packages were employed to construct co-expression networks and identify differentially expressed ncRNAs, including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs).

Results

Our analysis demonstrated distinct expression profiles of various ncRNAs in HCC, revealing their intricate interactions with cancer-related genes. Key findings include the identification of a network of microRNAs that interact with selected lncRNAs and their potential roles as biomarkers. Moreover, exosomal RNA was shown to effectively reflect tissue-specific gene expression changes.

Conclusions

The results of this study highlight the significance of exosomal ncRNAs in the progression of liver cancer, suggesting their potential as both diagnostic biomarkers and therapeutic targets. Future research should focus on the functional implications of these ncRNAs to further elucidate their roles in HCC and explore their applications in clinical settings.

Thrombospondin-1 binds to integrin β3 to inhibit vascular calcification through suppression of NF-κB pathway

Vascular calcification is an important risk factor related to all-cause mortality of cardiovascular events in patients with chronic kidney disease (CKD). Vascular extracellular matrix (ECM) proteins have been demonstrated to regulate vascular calcification. ECM protein thrombospondin 1 (THBS1/TSP-1) plays a critical role in the regulation of vascular diseases. However, whether THBS1 is involved in vascular calcification in CKD patients remains unclear. In this study, RNA sequencing datasets from the Gene Expression Omnibus (GEO) database GSE146638 showed that THBS1 was upregulated in the aortas of CKD rats. Enzyme-linked immunosorbent assay (elisa) revealed that serum THBS1 levels were increased in CKD patients with thoracic calcification. Western blotting and immunofluorescence analysis showed that THBS1 expression was increased in calcified vascular smooth muscle cells (VSMCs) and arteries. THBS1 knockdown exacerbated rat VSMC calcification induced by high phosphorus and calcium, as shown by Alizarin red staining and calcium content assays. Conversely, THBS1 overexpression attenuated VSMC calcification and abdominal aortic calcification in rats with CKD. Moreover, addition of recombinant THBS1 protein inhibited calcification of VSMCS and human arterial rings. Smooth muscle cell-specific knockout of THBS1 mice treated with vitamin D3 displayed aggravated aortic calcification. Mechanistically, the protein-protein interaction database STRING (http://string-db.org/) analysis and coimmunoprecipitation assays revealed THBS1 bound to integrin β3. Reduction of integrin β3 levels abrogated the protective effect of THBS1 on vascular calcification. RNA-seq analysis revealed that THBS1 overexpression modulated the nuclear factor-kappa B (NF-κB) signaling pathway. Of note, the inhibitory effect of THBS1 overexpression on the NF-κB signal was abolished by knockdown of integrin β3. In conclusion, THBS1 interacts with integrin β3 to inhibit vascular calcification through suppression of NF-κB signal, suggesting a promising therapeutic target for vascular calcification in CKD. © 2025 The Pathological Society of Great Britain and Ireland.

The prediction of RNA-small molecule binding sites in RNA structures based on geometric deep learning

Biological interactions between RNA and small-molecule ligands play a crucial role in determining the specific functions of RNA, such as catalysis and folding, and are essential for guiding drug design in the medical field. Accurately predicting the binding sites of ligands within RNA structures is therefore of significant importance. To address this challenge, we introduced a computational approach named RLBSIF (RNA-Ligand Binding Surface Interaction Fingerprints) based on geometric deep learning. This model utilizes surface geometric features, including shape index and distance-dependent curvature, combined with chemical features represented by atomic charge, to comprehensively characterize RNA-ligand interactions through MaSIF-based surface interaction fingerprints. Additionally, we employ the ResNet18 network to analyze these fingerprints for identifying ligand binding pockets. Trained on 440 binding pockets, RLBSIF achieves an overall pocket-level classification accuracy of 90 %. Through a full-space enumeration method, it can predict binding sites at nucleotide resolution. In two independent tests, RLBSIF outperformed competing models, demonstrating its efficacy in accurately identifying binding sites within complex molecular structures. This method shows promise for drug design and biological product development, providing valuable insights into RNA-ligand interactions and facilitating the design of novel therapeutic interventions. For access to the related source code, please visit RLBSIF on GitHub (https://github.com/ZUSTSTTLAB/RLBSIF).

Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models

RNAs are emerging as promising therapeutic targets, yet identifying small molecules that bind to them remains a significant challenge in drug discovery. This underscores the crucial role of computational modeling in predicting RNA-small molecule binding sites. However, accurate and efficient computational methods for identifying these interactions are still lacking. Recently, advances in large language models (LLMs), previously successful in DNA and protein research, have spurred the development of RNA-specific LLMs. These models leverage vast unlabeled RNA sequences to autonomously learn semantic representations with the goal of enhancing downstream tasks, particularly those constrained by limited annotated data. Here, we develop RNABind, an embedding-informed geometric deep learning framework to detect RNA-small molecule binding sites from RNA structures. RNABind integrates RNA LLMs into advanced geometric deep learning networks, which encodes both RNA sequence and structure information. To evaluate RNABind, we first compile the largest RNA-small molecule interaction dataset from the entire multi-chain complex structure instead of single-chain RNAs. Extensive experiments demonstrate that RNABind outperforms existing state-of-the-art methods. Besides, we conduct an extensive experimental evaluation of eight pre-trained RNA LLMs, assessing their performance on the binding site prediction task within a unified experimental protocol. In summary, RNABind provides a powerful tool on exploring RNA-small molecule binding site prediction, which paves the way for future innovations in the RNA-targeted drug discovery.

Self-supervised learning for graph-structured data in healthcare applications: A comprehensive review

The increasing complexity and interconnectedness of healthcare data present numerous opportunities to improve prediction, diagnosis, and treatment. Graph-structured data, which represents entities and their relationships, is well-suited for modeling these complex connections. However, effectively utilizing this data often requires strong and efficient learning algorithms, especially when dealing with limited labeled data. Self-supervised learning (SSL) has emerged as a powerful paradigm for leveraging unlabeled data to learn effective representations. This paper presents a comprehensive review of SSL approaches specifically designed for graph-structured data in healthcare applications. We explore the challenges and opportunities associated with healthcare data and assess the effectiveness of SSL techniques in real-world healthcare applications. Our discussion encompasses various healthcare settings, such as disease prediction, medical image analysis, and drug discovery. We critically evaluate the performance of different SSL methods across these tasks, highlighting their strengths, limitations, and potential future research directions. To the best of our knowledge, this is the first comprehensive review of SSL applied to graph data in healthcare, providing valuable guidance for researchers and practitioners looking to leverage these techniques to enhance outcomes and drive progress in the field.

Sex-biased gene expression precedes sexual dimorphism in the agonadal annelid Platynereis dumerilii

Gametogenesis is the process by which germ cells differentiate into mature sperm and oocytes - cells that are essential for sexual reproduction. The sex-specific molecular programs that drive spermatogenesis and oogenesis can also serve as sex identification markers. Platynereis dumerilii is a research organism that has been studied in many areas of developmental biology. However, investigations often disregard sex, as P. dumerilii juveniles lack sexual dimorphism. The molecular mechanisms of gametogenesis in the segmented worm P. dumerilii are also largely unknown. In this study, we used RNA sequencing to investigate the transcriptomic profiles of gametogenesis in P. dumerilii juveniles. Our analysis revealed that sex-biased gene expression becomes increasingly pronounced during the advanced developmental stages, as worms approach maturation. We identified conserved genes associated with spermatogenesis, such as dmrt1, and with oogenesis, such as the previously unidentified gene psmt. Additionally, putative long non-coding RNAs were upregulated in both male and female gametogenic programs. This study provides a foundational resource for germ cell research in P. dumerilii and markers for sex identification, and offers comparative data to enhance our understanding of the evolution of gametogenesis mechanisms across species.

The prediction of RNA-small-molecule ligand binding affinity based on geometric deep learning

Small molecule-targeted RNA is an emerging technology that plays a pivotal role in drug discovery and inhibitor design, with widespread applications in disease treatment. Consequently, predicting RNA-small-molecule ligand interactions is crucial. With advancements in computer science and the availability of extensive biological data, deep learning methods have shown great promise in this area, particularly in efficiently predicting RNA-small molecule binding sites. However, few computational methods have been developed to predict RNA-small molecule binding affinities. Meanwhile, most of these approaches rely primarily on sequence or structural representations. Molecular surface information, vital for RNA and small molecule interactions, has been largely overlooked. To address these gaps, we propose a geometric deep learning method for predicting RNA-small molecule binding affinity, named RNA-ligand Surface Interaction Fingerprinting (RLASIF). In this study, we create RNA-ligand interaction fingerprints from the geometrical and chemical features present on molecular surface to characterize binding affinity. RLASIF outperformed other computational methods across ten different test sets from PDBbind NL2020. Compared to the second-best method, our approach improves performance by 10.01 %, 6.67 %, 2.01 % and 1.70 % on four evaluation metrics, indicating its effectiveness in capturing key features influencing RNA-ligand binding strength. Additionally, RLASIF holds potential for virtual screening of potential ligands for RNA and predicting small molecule binding nucleotides within RNA structures.

Targeting the gut microbiota-inflammation-brain axis as a potential therapeutic strategy for psychiatric disorders: A Mendelian randomization analysis

BACKGROUND

Extensive research indicates a link between gut microbiota dysbiosis and psychiatric disorders. However, the causal relationships between gut microbiota and different types of psychiatric disorders, as well as whether inflammatory factors mediate these relationships, remain unclear.

METHODS

We utilized summary statistics from the largest genome-wide association studies to date for gut microbiota (n = 18,340 in MiBioGen consortium), circulating inflammatory factors (n = 8293 for 41 factors and n = 14,824 for 91 factors in GWAS catalog), and six major psychiatric disorders from the Psychiatric Genomics Consortium (PGC): attention deficit hyperactivity disorder (ADHD, n = 38,691), anxiety disorder (ANX, n = 2248), bipolar disorder (BIP, n = 41,917), anorexia nervosa (AN, n = 16,992), schizophrenia (SCZ, n = 36,989), and autism spectrum disorder (ASD, n = 18,381). We conducted bidirectional Mendelian randomization (MR) analysis to explore the causal relationships between gut microbiota and psychiatric disorders. Additionally, we performed two-step MR and multivariable MR (MVMR) analyses to identify potential mediating inflammatory factors.

RESULTS

We found significant causal relationships between 11 gut microbiota and ADHD, 2 gut microbiota and ANX, 11 gut microbiota and BIP, 8 gut microbiota and AN, 15 gut microbiota and SCZ, and 5 gut microbiota and ASD. There were 16 positive and 15 negative causal effects between inflammatory factors and psychiatric disorders. Furthermore, MVMR analysis results indicated that the correlation between genus Roseburia and ADHD was mediated by MCSF, with a mediation proportion of 3.3 %; the correlation between genus Erysipelotrichaceae UCG003 and BIP was mediated by GDNF, with a mediation proportion of 3.7 %; and the correlation between family Prevotellaceae and SCZ was mediated by CD40, with a mediation proportion of 8.2 %.

CONCLUSIONS

The MR analysis results supported causal relationships between gut microbiota and six psychiatric disorders, as well as the potential mediating role of inflammatory factors. This study highlights the potential role of the gut microbiota-inflammation-brain axis in psychiatric disorders.

Study on the mechanism of BGN in progression and metastasis of ccRCC

PURPOSE

To investigate the role of Biglycan(BGN) in the progression and metastasis of clear cell renal cell carcinoma(ccRCC).

METHODS

Based on multiple public databases, we investigated the expression level of BGN in ccRCC, its clinical significance, and its association with immune cells. Real-time fluorescence quantitative polymerase chain reaction(PCR) was employed to validate BGN expression in tumor and adjacent normal tissues from ten patients. We utilized RNA sequencing results for further analysis, including differential gene analysis, GO-KEGG analysis, and GSEA analysis, to identify the signaling pathways through which BGN exerts its effects. BGN knockdown cells(786-0 and Caki-1) were generated through lentiviral transfection to examine the impact of BGN on ccRCC. Cell proliferation, migration, and invasion were assessed using CCK8, colony formation, wound healing, Transwell migration, and invasion assays, respectively.

RESULTS

Our findings from database analysis and PCR revealed a significant upregulation of BGN expression in kidney cancer tissues compared to normal tissues. Further analysis demonstrated a correlation between high BGN expression and ccRCC progression and immune infiltration. In vitro experiments confirmed that BGN silencing effectively inhibited cell proliferation, migration, and invasion of ccRCC. Mechanistically, these effects may be mediated through the MAPK signaling pathway.

CONCLUSION

BGN potentially plays a pivotal role in the progression and metastasis of ccRCC, possibly acting through the MAPK signaling pathway. Therefore, BGN holds promise as a potential therapeutic target for ccRCC.

Sensing ceramides by CYSLTR2 and P2RY6 to aggravate atherosclerosis

Recent evidence has shown that increased levels of circulating long-chain ceramides predict atherosclerotic cardiovascular disease independently of cholesterol1,2. Although targeting ceramide signalling may provide therapeutic benefits beyond the treatment of hypercholesterolaemia, the underlying mechanism by which circulating ceramides aggravate atherosclerotic cardiovascular disease remains elusive. Here we examine whether circulating long-chain ceramides activate membrane G-protein-coupled receptors to exacerbate atherosclerosis. We perform a systematic screen that combines G-protein-signalling quantification, bioinformatic analysis of G-protein-coupled receptor expression and functional examination of NLRP3 inflammasome activation. The results suggest that CYSLTR2 and P2RY6 are potential endogenous receptors of C16:0 ceramide-induced inflammasome activation in both endothelial cells and macrophages. Inhibition of CYSLTR2 and P2RY6 genetically or pharmacologically alleviates ceramide-induced atherosclerosis aggravation. Moreover, increased ceramide levels correlate with the severity of coronary artery disease in patients with varying degrees of renal impairment. Notably, CYSLTR2 and P2RY6 deficiency mitigates chronic-kidney-disease-aggravated atherosclerosis in mice without affecting cholesterol or ceramide levels. Structural analyses of ceramide-CYSLTR2-Gq complexes reveal that both C16:0 and C20:0 ceramides bind in an inclined channel-like ligand-binding pocket on CYSLTR2. We further reveal an unconventional mechanism underlying ceramide-induced CYSLTR2 activation and the CYSLTR2-Gq interface. Overall, our study provides structural and molecular mechanisms of how long-chain ceramides initiate transmembrane Gq and inflammasome signalling through direct binding to CYSLTR2 and P2RY6 receptors. Therefore, blocking these signals may provide a new therapeutic potential to treat atherosclerosis-related diseases.

Advances in molecular mechanisms and therapeutic strategies for central nervous system diseases based on gut microbiota imbalance

BACKGROUD

Central nervous system (CNS) diseases pose a serious threat to human health, but the regulatory mechanisms and therapeutic strategies of CNS diseases need to be further explored. It has been demonstrated that the gut microbiota (GM) is closely related to CNS disease. GM structure disorders, abnormal microbial metabolites, intestinal barrier destruction and elevated inflammation exist in patients with CNS diseases and promote the development of CNS diseases. More importantly, GM remodeling alleviates CNS pathology to some extent.

AIM OF REVIEW

Here, we have summarized the regulatory mechanism of the GM in CNS diseases and the potential treatment strategies for CNS repair based on GM regulation, aiming to provide safer and more effective strategies for CNS repair from the perspective of GM regulation.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The abundance and composition of GM is closely associated with the CNS diseases. On the basis of in-depth analysis of GM changes in mice with CNS disease, as well as the changes in its metabolites, therapeutic strategies, such as probiotics, prebiotics, and FMT, may be used to regulate GM balance and affect its microbial metabolites, thereby promoting the recovery of CNS diseases.

Comprehensive transcriptome analysis reveals lncRNA-mRNA interactions and immune response mechanisms in Mytilus coruscus upon Vibrio alginolyticus infection

Long non-coding RNAs (lncRNAs) play diverse biological roles within cells. Despite not encoding proteins, they are crucial in regulating gene expression, chromatin structure and function, cell differentiation and development, and the occurrence of diseases. Vibrio alginolyticus (V. alginolyticus) is a common bacterium found in marine environments that poses a threat to shellfish by infecting them through filtration feeding. Research has demonstrated the substantial involvement of lncRNAs in the immune response of shellfish. However, the specific mechanism by which lncRNAs participate in the immune regulatory process following infection of Mytilus coruscus (M. coruscus) with V. alginolyticus has not been investigated. Therefore, the transcription profiles of lncRNAs in M. coruscus hemocytes were investigated. A grand total of 48,246 lncRNAs were detected, with 2421 genes that exhibited and 717 lncRNAs that had differential expression. To gain a better understanding of the potential roles of the differentially expressed lncRNAs (DE-lncRNAs), GO and KEGG pathway analyses were performed on their target mRNAs, suggesting that lncRNAs have the ability to control gene expression levels and consequently influence immune-related pathways, hence regulating the immune response in M. coruscus. Additionally, a total of 138 lncRNA-mRNA pairs were identified through the calculation of co-expression relationships between DE-lncRNAs and immune-related DE-mRNAs. These findings provide new insights into the role of lncRNAs in the immune response of M. coruscus and offer important resources for further investigation into the role of lncRNAs in M. coruscus pathogen infection.

KLF9, Epigenetic Silenced by DNMT1, Promotes ERK-Mediated Ferroptosis of Osteoarthritic Chondrocytes Through Transcriptionally Regulating CYP1B1

Ferroptosis plays a crucial role in the pathogenesis of osteoarthritis (OA), and inhibition of chondrocyte ferroptosis effectively alleviates OA progression. Krüppel-like factor 9 (KLF9) is demonstrated to be upregulated in OA, but its molecular mechanism remains unclear. The study aimed to investigate the role of KLF9 in OA progression. Primary chondrocytes were treated with IL-1β to establish an OA cell model, and showed that KLF9 was highly expressed in IL-1β-incubated chondrocytes. Knockdown of KLF9 alleviated IL-1β-induced chondrocyte degeneration. In addition, chondrocytes treated with IL-1β showed a decreased methylation proportion in the KLF9 gene promoter. DNA methyltransferase 1 (DNMT1) directly bound to the KLF9 promoter, and overexpression of DNMT1 inhibited KLF9 expression by promoting its promoter methylation in chondrocytes. Subsequently, KLF9 shRNA and pcDNA-CYP1B1 were individually or altogether transfected into chondrocytes. KLF9 shRNA inhibited Cytochrome P450 1B1 (CYP1B1) expression in chondrocytes, and pcDNA-CYP1B1 abrogated the inhibitory effect of KLF9 shRNA on IL-1β-induced chondrocyte ferroptosis. Moreover, Ferrostatin-1 (Fer-1, an inhibitor of ferroptosis) reversed the promotion of pcDNA-CYP1B1 on IL-1β-induced chondrocyte ferroptosis. Finally, in vivo experiments showed that KLF9 shRNA significantly suppressed the cartilage tissue damage, ferroptosis, and the IHC scores of KLF9 and CYP1B1 in rats. In conclusion, our results suggested that KLF9, epigenetic silenced by DNMT1, promoted extracellular signal-regulated kinase (ERK)-mediated ferroptosis of OA chondrocytes through transcriptionally regulating CYP1B1. Thus, KLF9 is expected to be a new target for the treatment of OA.

Hypothesizing mechanistic links between microbes and disease using knowledge graphs

Knowledge graphs have been a useful tool for many biomedical applications because of their effective representation of biological concepts. Plentiful evidence exists linking the gut microbiome to disease in a correlative context, but uncovering the mechanistic explanation for those associations remains a challenge. Here we demonstrate the potential of knowledge graphs to hypothesize plausible mechanistic accounts of host-microbe interactions in disease. We have constructed a knowledge graph of linked microbes, genes and metabolites called MGMLink, and, using a shortest path or template-based search through the graph and a novel path-prioritization methodology based on the structure of the knowledge graph, we show that this knowledge supports inference of mechanistic hypotheses that explain observed relationships between microbes and disease phenotypes. We discuss specific applications of this methodology in inflammatory bowel disease and Parkinson's disease. This approach enables mechanistic hypotheses surrounding the complex interactions between gut microbes and disease to be generated in a scalable and comprehensive manner.

Quinoa Saponin Ameliorates Lipopolysaccharide-Induced Behavioral Disorders in Mice by Inhibiting Neuroinflammation, Modulating Gut Microbiota, and Counterbalancing Intestinal Inflammation

Triterpenoids derived from plants are a promising class of natural antidepressants. This research focused on the therapeutic potential of quinoa saponin (QS) in alleviating lipopolysaccharide (LPS)-induced anxiety and depressive-like behaviors in mice. The most abundant saponin fraction, QS-3, was isolated from QS extracts, and its major saponin components and chemical structure were elucidated. Six pentacyclic triterpene saponins and three tetracyclic triterpene saponins were identified in QS-3, with phytolaccagenin and oleanolic acid being the dominant sapogenins. In vivo studies demonstrated that QS significantly mitigated LPS-induced anxiety and depressive-like behaviors in mice, enhanced the levels of neurotrophic proteins, key synaptic proteins, and neurotransmitters, and restored synaptic function and neuronal damage. Furthermore, QS inhibited neuroinflammation by curtailing the activity of the TLR4/MyD88/NF-κB pathway and modulating microglial phenotypes. Notably, QS also ameliorated colonic inflammation by promoting gut microbiota homeostasis and increasing short-chain fatty acids (SCFAs) production, which contributed to the improvement of anxiety and depressive behaviors in mice. Our findings suggest that QS holds potential as a natural dietary supplement for the treatment and prevention of anxiety and depression, possibly through its modulation of gut-brain axis dynamics and suppression of the activation of the TLR4/MyD88/NF-κB pathway.

Characterization of lncRNAs contributing to drug resistance in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is the second leading cause of death among women with gynecological cancers, particularly in high-income countries. Despite significant advancements in molecular oncology and an initially positive response to primary chemotherapy, the development of drug resistance remains a major challenge in the effective management of EOC. Consequently, there is an urgent need for innovative biological markers that can enable early diagnosis and provide more accurate predictions of recurrence risk in ovarian cancer patients. This study investigated the expression profiles of seven specific long noncoding RNAs (lncRNAs)-SNHG7, TUG1, XIST1, PRLB, TLR8-AS1, ZFAS1, and PVT1-associated with epithelial ovarian cancer and their relationship with drug resistance. To achieve this, drug-resistant subtypes of aggressive EOC cell lines, including carboplatin/paclitaxel-resistant OVCAR3 and SKOV3 lines, were developed. The expression profiles of the selected lncRNAs were quantitatively analyzed using RT-qPCR across various ovarian cancer cell lines and in serum samples from 25 patients before chemotherapy, six months after treatment, and 23 healthy controls. The findings revealed that the target lncRNAs were significantly upregulated under drug-resistant conditions and in post-chemotherapy serum samples, suggesting their involvement in a complex regulatory network. These results highlight the critical roles of lncRNAs in the progression and treatment response of EOC, positioning them as potential therapeutic targets and biomarkers for early diagnosis and treatment stratification. Identifying reliable lncRNA biomarkers could enable the early detection of patients at risk for developing drug resistance, thereby facilitating personalized treatment strategies to improve patient outcomes and survival rates.

Truncated Arctangent Rank Minimization and Double-Strategy Neighborhood Constraint Graph Inference for Drug-Disease Association Prediction

Accurately identifying new therapeutic uses for drugs is essential to advancing pharmaceutical research and development. Graph inference techniques have shown great promise in predicting drug-disease associations, offering both high convergence accuracy and efficiency. However, most existing methods fail to sufficiently address the issue of numerous missing information in drug-disease association networks. Moreover, existing methods are often constrained by local or single-directional reasoning. To overcome these limitations, we propose a novel approach, truncated arctangent rank minimization and double-strategy neighborhood constraint graph inference (TARMDNGI), for drug-disease association prediction. First, we calculate Gaussian kernel and Laplace kernel similarities for both drugs and diseases, which are then integrated using nonlinear fusion techniques. We introduce a new matrix completion technique, referred to as TARM. TARM takes the adjacency matrix of drug-disease heterogeneous networks as the target matrix and enhances the robustness and formability of the edges of DDA networks by truncated arctangent rank minimization. Additionally, we propose a double-strategy neighborhood constrained graph inference method to predict drug-disease associations. This technique focuses on the neighboring nodes of drugs and diseases, filtering out potential noise from more distant nodes. Furthermore, the DNGI method employs both top-down and bottom-up strategies to infer associations using the entire drug-disease heterogeneous network. The synergy of the dual strategies can enhance the comprehensive processing of complex structures and cross-domain associations in heterogeneous graphs, ensuring that the rich information in the network is fully utilized. Experimental results consistently demonstrate that TARMDNGI outperforms state-of-the-art models across two drug-disease datasets, one lncRNA-disease dataset, and one microbe-disease dataset.

Krüppel-like factor 9 alleviates Alzheimer's disease via IDE-mediated Aβ degradation

The deposition of β-amyloid (Aβ) in the brain is a crucial factor in the pathogenesis of Alzheimer's disease (AD). Insulin-degrading enzyme (IDE) plays a critical role in the balance between Aβ production and degradation. However, the regulatory mechanisms of IDE are not yet fully understood. Therefore, uncovering additional IDE regulatory mechanisms will help elucidate the pathogenesis of AD and identify key therapeutic targets for this disease. This study revealed that global Krüppel-like factor 9-mutant (Klf9-/-) mice exhibited impaired cognitive function. Additionally, we found that Klf9 expression in hippocampal tissue was reduced in APPswe/PS1dE9 (APP/PS1) mice. This study also showed that Klf9 stimulates IDE expression and promotes the Aβ degradation process by directly binding to IDE and activating its transcription. Silencing IDE blocked the Klf9-induced Aβ degradation process. We stereotactically injected an adeno-associated virus to selectively overexpress IDE (AAV-IDE) in the hippocampal neurons of Klf9-/- mice and found that the overexpression of IDE in hippocampal neurons ameliorated cognitive deficits and reduced the Aβ content in Klf9-/- mice. Additionally, we also stereotactically injected AAV-Klf9 into the hippocampal neurons of APP/PS1 mice and found that overexpression of Klf9 in hippocampal neurons ameliorated cognitive deficits and reduced Aβ levels in APP/PS1 mice. These findings suggest that downregulation of Klf9 may be a key factor in AD progression, as it reduces Aβ clearance by decreasing IDE expression. Overexpression or activation of Klf9 may be a potential strategy for preventing the pathogenesis of AD.

Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning

Recent studies indicate that microorganisms are crucial for maintaining human health. Dysbiosis, or an imbalance in these microbial communities, is strongly linked to a variety of human diseases. Therefore, understanding the impact of microbes on disease is essential. The DuGEL model leverages the strengths of graph convolutional neural network (GCN) and graph attention network (GAT), ensuring that both local and global relationships within the microbe-disease association network are captured. The integration of the Long Short-Term Memory Network (LSTM) further enhances the model's ability to understand sequential dependencies in the feature representations. This comprehensive approach allows DuGEL to achieve a high level of accuracy in predicting potential microbe-disease associations, making it a valuable tool for biomedical research and the discovery of new therapeutic targets. By combining advanced graph-based and sequence-based learning techniques, DuGEL addresses the limitations of existing methods and provides a robust framework for the prediction of microbe-disease associations. To evaluate the performance of DuGEL, we conducted comprehensive comparative experiments and case studies based on two databases, HMDAD, and Disbiome to demonstrate that DuGEL can effectively predict potential microbe-disease associations.

Linking dietary fiber to human malady through cumulative profiling of microbiota disturbance

Dietary fiber influences the composition and metabolic activity of microbial communities, impacting disease development. Current understanding of the intricate fiber-microbe-disease tripartite relationship remains fragmented and elusive, urging a systematic investigation. Here, we focused on microbiota disturbance as a robust index to mitigate various confounding factors and developed the Bio-taxonomic Hierarchy Weighted Aggregation (BHWA) algorithm to integrate multi-taxonomy microbiota disturbance data, thereby illuminating the complex relationships among dietary fiber, microbiota, and disease. By leveraging microbiota disturbance similarities, we (1) classified 32 types of dietary fibers into six functional subgroups, revealing correlations with fiber solubility; (2) established associations among 161 diseases, uncovering shared microbiota disturbance patterns that explain disease co-occurrence (e.g., type II diabetes and kidney diseases) and distinct microbiota patterns that discern symptomatically similar diseases (e.g., inflammatory bowel disease and irritable bowel syndrome); (3) designed a body-site-specific microbiota disturbance scoring scheme, computing a disturbance score (DS) for each disease and highlighting the pronounced capacity of Crohn's disease to disturb gut microbiota (DS = 14.01) in contrast with food allergy's minimal capacity (DS = 0.74); (4) identified 1659 fiber-disease associations, predicting the potential of dietary fiber to modulate specific microbiota changes associated with diseases of interest; (5) established murine models of inflammatory bowel disease to validate the preventive and therapeutic effects of arabinoxylan that notably perturbed the Bacteroidetes and Firmicutes phyla, as well as the Bacteroidetes and Lactobacillus genera, aligning with our model predictions. To enhance data accessibility and facilitate targeted dietary intervention development, we launched an interactive webtool-mDiFiBank at https://mdifibank.org.cn/.

Transcriptional modulation in Mediterranean Mussel Mytilus galloprovincialis following exposure to four pharmaceuticals widely distributed in coastal areas

Ecotoxicological risk and the mode of action of human drugs on non-target marine animals remain unclear, keeping a gap of knowledge on risks related to ecosystem disruption and chemical contamination of food chains. Understanding these impacts is critical to developing proper waste management practices and regulatory frameworks to prevent long-term environmental and human health problems. This study investigates the impacts of Gemfibrozil, Metformin, Ramipril, and Venlafaxine, individually and combined on Mytilus galloprovincialis over 30 days and assesses persistent effects post-recovery using RNA-seq and 16S rRNA microbiota profiling. All pharmaceuticals caused few changes in the microbiota while gene expression analyses highlighted drug-specific alterations. Gemfibrozil exposure led to alterations in lipid and fatty acid metabolism, suggesting a similar mode of action to that observed in target species. Metformin significantly impacted the mussels' energy metabolism, with disruptions in specific genes and pathways potentially related to glucose uptake and insulin signaling. Metformin was also the treatment leading to the most significant changes in predicted functional profiles of the microbiota, suggesting that it may influence the microbiota's potential to interact with host glucose metabolism. Ramipril exposure resulted in the up-regulation of stress response and cell cycle regulation pathways and Venlafaxine induced changes in serotonin and synapse pathways, indicating potential similarities in mechanisms of action with target species. Mixture of the four pharmaceuticals severely impacted mussel physiology, including impairment of oxidative phosphorylation and compensatory activation of several pathways involved in energy metabolism. Despite recovery after depuration, changes in stress and energy related metabolism pathways suggests potential persistent effects from combined pharmaceutical exposure. Notably, the up-regulation of mTOR1 signaling in all treatments after 30 days underscores its key role in coordinating bivalve stress responses. The Transcriptomic Hazard Index (THI) calculated for each treatment indicates major/severe hazards after exposure that decreased to slight/moderate hazards after depuration.

Comparison of Gut Microbiota in Two Different Maternal Exposure Models of Autism Spectrum Disorder in Mice

Background

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders with unknown etiology and unclear pathogenesis. Although construction of animal models of ASD using chemical exposure during pregnancy is a mature technique, the gut microbiota of these exposure models induced using different chemicals in mice have not been compared.

Methods

To compare the effects of exposure to different chemicals during pregnancy on the composition of gut microbiota in offspring, we treated Institute of Cancer Research (ICR) mice with lipopolysaccharide (LPS) and valproic acid (VPA) during pregnancy to construct different offspring ASD mouse models. After successful model construction, the gut microbiota of these models were studied.

Results

After adjusting for the random effects of the litter, the two groups showed a significant reduction in social time (social deficits) and an increase in self-grooming behaviors (repetitive and stereotyped behaviors). Gut microbiota analysis revealed significant changes, mostly a decrease, in the abundance of four phyla, 52 genera, and 41 species in the two types of ASD models. Several different gut microbes could be related to the development of ASD.

Conclusions

Chemicals exposure during pregnancy induces ASD-related behavioral abnormalities in offspring mice. Importantly, exposure to different chemicals during pregnancy produces varying degrees of effects on gut microbiota composition in offspring ASD models. This finding can provide a reference for studies on the etiology and pathogenesis of ASD.

Combined analysis of bulk, single-cell RNA sequencing, and spatial transcriptomics reveals the expression patterns of lipid metabolism and ferroptosis in the immune microenvironment of metabolic-associated fatty liver disease

AIMS

This study aims to identify key biomarkers associated with ferroptosis and lipid metabolism and investigate their roles in the progression of metabolic dysfunction-associated fatty liver disease (MAFLD). It further explores interactions between these biomarkers and the immune-infiltration environment, shedding light on how ferroptosis and lipid metabolism influence immune dynamics in MAFLD.

MAIN METHODS

Single-cell RNA sequencing data from liver samples were analyzed to evaluate expression variations related to ferroptosis and lipid metabolism in MAFLD patients. Gene scores were assessed to explore their impact on the immune microenvironment, particularly hepatocyte-macrophage communication. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to Bulk-RNA-Seq data to identify gene clusters associated with ferroptosis and lipid metabolism. The analyses were integrated into a risk assessment system and predictive model, with validation conducted through in vivo experiments.

KEY FINDINGS

Integration of single-cell and WGCNA data identified 11 key genes linked to ferroptosis and lipid metabolism (e.g., IER5L, SOCS2, KLF9), significantly influencing the liver's immune microenvironment. The risk assessment system and predictive model achieved an AUC of 0.92 and revealed distinct immune and biological characteristics in MAFLD patients across risk levels. The expression patterns and biological roles of these genes were confirmed in in vivo studies.

SIGNIFICANCE

This study establishes a strong link between ferroptosis- and lipid metabolism-related gene expression and MAFLD's complexity. It provides novel insights into disease mechanisms, supporting personalized prognosis and targeted therapeutic strategies for MAFLD patients.

RNA sequence analysis landscape: A comprehensive review of task types, databases, datasets, word embedding methods, and language models

Deciphering information of RNA sequences reveals their diverse roles in living organisms, including gene regulation and protein synthesis. Aberrations in RNA sequence such as dysregulation and mutations can drive a diverse spectrum of diseases including cancers, genetic disorders, and neurodegenerative conditions. Furthermore, researchers are harnessing RNA's therapeutic potential for transforming traditional treatment paradigms into personalized therapies through the development of RNA-based drugs and gene therapies. To gain insights of biological functions and to detect diseases at early stages and develop potent therapeutics, researchers are performing diverse types RNA sequence analysis tasks. RNA sequence analysis through conventional wet-lab methods is expensive, time-consuming and error prone. To enable large-scale RNA sequence analysis, empowerment of wet-lab experimental methods with Artificial Intelligence (AI) applications necessitates scientists to have a comprehensive knowledge of both DNA and AI fields. While molecular biologists encounter challenges in understanding AI methods, computer scientists often lack basic foundations of RNA sequence analysis tasks. Considering the absence of a comprehensive literature that bridges this research gap and promotes the development of AI-driven RNA sequence analysis applications, the contributions of this manuscript are manifold: It equips AI researchers with biological foundations of 47 distinct RNA sequence analysis tasks. It sets a stage for development of benchmark datasets related to 47 distinct RNA sequence analysis tasks by facilitating cruxes of 64 different biological databases. It presents word embeddings and language models applications across 47 distinct RNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 58 word embeddings and 70 language models based predictive pipelines performance values as well as top performing traditional sequence encoding based predictors and their performances across 47 RNA sequence analysis tasks.

Maize transcriptome profiling reveals low temperatures affect photosynthesis during the emergence stage

Introduction

Earlier sowing is a promising strategy of ensuring sufficiently high maize yields in the face of negative environmental factors caused by climate change. However, it leads to the low temperature exposure of maize plants during emergence, warranting a better understanding of their response and acclimation to suboptimal temperatures.

Materials and Methods

To achieve this goal, whole transcriptome sequencing was performed on two maize inbred lines - tolerant/susceptible to low temperatures, at the 5-day-old seedling stage. Sampling was performed after 6h and 24h of treatment (10/8°C). The data was filtered, mapped, and the identified mRNAs, lncRNAs, and circRNAs were quantified. Expression patterns of the RNAs, as well as the interactions between them, were analyzed to reveal the ones important for low-temperature response.

Results and Discussion

Genes involved in different steps of photosynthesis were downregulated in both genotypes: psa, psb, lhc, and cab genes important for photosystem I and II functioning, as well as rca, prk, rbcx1 genes necessary for the Calvin cycle. The difference in low-temperature tolerance between genotypes appeared to arise from their ability to mitigate damage caused by photoinhibition: ctpa2, grx, elip, UF3GT genes showed higher expression in the tolerant genotype. Certain identified lncRNAs also targeted these genes, creating an interaction network induced by the treatment (XLOC_016169-rca; XLOC_002167-XLOC_006091-elip2). These findings shed light on the potential mechanisms of low-temperature acclimation during emergence and lay the groundwork for subsequent analyses across diverse maize genotypes and developmental stages. As such, it offers valuable guidance for future research directions in the molecular breeding of low-temperature tolerant maize.

RNA-Seq transcriptomic landscape profiling of spontaneously hypertensive rats in youth treated with a ARNI versus ARB

Angiotensin receptor-neprilysin inhibitor (ARNI) and angiotensin II receptor blockers (ARB) are antihypertension medications that improve cardiac remodeling and protect the heart. However, at the early stage of hypertension, it is still unclear how these two drugs affect the transcriptomic profile of multiple organs in hypertensive rats and the transcriptomic differences between them. We performed RNA sequencing to define the RNA expressing profiles of the eight tissues (atrium, ventricle, aorta, kidney, brain, lung, white fat, and brown fat) in spontaneously hypertensive rats (SHRs) and SHRs treated with ARNI or ARB. Acquired data was processed and analyzed by computational analyses (i.e., clustering, DEG, functional association, WGCNA, and protein-protein interaction (PPI) network analysis). We discovered that various tissues produced significant transcriptomic changes at the early stage of hypertension. The transcriptomic differences are notably in brown fat, kidney, lung, and brain tissues between ARNI and ARB treatment. Meanwhile, ARNI or ARB treatment can reverse the dysregulated expression genes related to the metabolism process, especially in brown fat, lung, and kidney tissues under hypertension. The current study has presented a comprehensive rat RNA-Seq transcriptomic landscape in SHRs and compared the transcriptome differences between ARNI and ARB treatment as a biomedical research resource for further study.

Plasma proteome fingerprint in kidney diseases

Introduction

Kidney diseases pose a serious healthcare problem because of their high prevalence, worsening of patients' quality of life, and high mortality. Patients with kidney diseases are often asymptomatic until disease progression starts. Expensive renal replacement therapy options, such as dialysis or kidney transplant, are required for end-stage kidney disease. Early diagnosis of kidney pathology is crucial for slowing down or curbing further damage. This study aimed to analyze the features of the protein composition of blood plasma in patients with the most common kidney pathologies: kidney calculus, kidney cyst, and kidney cancer.

Methods

The study involved 75 subjects. Proteins associated with kidney pathologies (CFB, SERPINA3, HPX, HRG, SERPING1, HBB, ORM2, and CP) were proposed. These proteins are important participants of complement and coagulation cascade activation and lipid metabolism.

Results

The revealed phosphorylated proteoforms (CFB, C4A/C4B, F2, APOB, TTR, and NRAP) were identified. For them, modification sites were mapped on 3D protein models, and the potential role in formation of complexes with native partner proteins was assessed.

Discussion

The study demonstrates that the selected kidney pathologies have a similar proteomic profile, and patients can be classified into kidney pathology groups with an accuracy of (70-80)%.

Inhibition of aortic CX3CR1+ macrophages mitigates thoracic aortic aneurysm progression in Marfan syndrome in mice

The pathogenesis of thoracic aortic aneurysm (TAA) in Marfan syndrome (MFS) is generally attributed to vascular smooth muscle cell (VSMC) pathologies. However, the role of immune cell-mediated inflammation remains elusive. Single-cell RNA sequencing identified a subset of CX3CR1+ macrophages mainly located in the intima in the aortic roots and ascending aortas of Fbn1C1041G/+ mice, further validated in MFS patients. Specific elimination of CX3CR1+ cells by diphtheria toxin in Cx3cr1-CreERT2iDTRF/+Fbn1C1041G/+ mice efficiently ameliorated TAA progression. Administering the monoclonal antibodies to respectively neutralize TNF-α and IGF1 produced by CX3CR1+ cells from MFS patients greatly suppressed the cocultured MFS patient-specific induced pluripotent stem cell-derived VSMC inflammation. BM transplantation and parabiosis revealed that CX3CR1+ macrophages are mainly originated from BM-derived monocytes. Targeting TNF-α and IGF1 in CX3CR1+ macrophages via shRNA lentivirus transduction in BM cells efficiently suppressed TAA development in BM-transplanted Fbn1C1041G/+ mice. Application of the CCR2 antagonist RS504393 to inhibit monocyte infiltration markedly reduced the accumulation of CX3CR1+ macrophages and subsequently alleviated TAA progression in Fbn1C1041G/+ mice. In summary, CX3CR1+ macrophages mainly located in aortic intima mediate TAA formation by paracrinally causing VSMC inflammation, and targeting them offers a potential antiinflammatory therapeutic strategy for MFS-related TAA.

Advances and Mechanisms of RNA-Ligand Interaction Predictions

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA's specific recognition of other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA-ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA-ligand complexes by examining RNA's sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA-ligand interaction prediction tools.

Neighborhood Topology-Aware Knowledge Graph Learning and Microbial Preference Inferring for Drug-Microbe Association Prediction

The human microbiota may influence the effectiveness of drug therapy by activating or inactivating the pharmacological properties of drugs. Computational methods have demonstrated their ability to screen reliable microbe-drug associations and uncover the mechanism by which drugs exert their functions. However, the previous prediction methods failed to completely exploit the neighborhood topologies of the microbe and drug entities and the diverse correlations between the microbe-drug entity pair and the other entities. In addition, they ignored the case that a microbe prefers to associate with its own specific drugs. A novel prediction method, PCMDA, was proposed by learning the neighborhood topologies of entities, inferring the association preferences, and integrating the features of each entity pair based on multiple biological premises. First, a knowledge graph consisting of microbe, disease, and drug entities is established to help the subsequent integration of the topological structure of entities and the similarity, interaction, and association relationship between any two entities. We generate various topological embeddings for each microbe (or drug) entity through random walks with neighborhood restarts on the microbe-disease-drug knowledge graph. Distance-level attention is designed to adaptively fuse neighborhood topologies covering multiple ranges. Second, the topological embeddings of entities imply the latent topological relationships between entities, while the relational embeddings of entities are derived from the semantics of connections among the entities. The topological structure and relational semantics of entities are fused by a designed knowledge graph learning module based on multilayer perceptron networks. Third, considering the preference that each microbe tends to especially associate with a group of drugs, information-level attention is designed to integrate the dependency between microbial preference and the candidate drug. Finally, a dual-gated network is established to encode the features of a microbe-drug entity pair from multiple biological perspectives. The comparative experiments with seven state-of-the-art methods demonstrate PCMDA's superior performance for microbe-drug association prediction. The case studies on three drugs and the recall rate evaluation for the top-ranked candidates indicate that PCMDA has the capability of discovering reliable candidate microbes associated with a drug. The datasets and source codes are freely available at https://github.com/pingxuan-hlju/pcmda.

Influential articles in autism and gut microbiota: bibliometric profile and research trends

Objective

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Increasing evidence suggests that it is potentially related to gut microbiota, but no prior bibliometric analysis has been performed to explore the most influential works in the relationships between ASD and gut microbiota. In this study, we conducted an in-depth analysis of the most-cited articles in this field, aiming to provide insights to the existing body of research and guide future directions.

Methods

A search strategy was constructed and conducted in the Web of Science database to identify the 100 most-cited papers in ASD and gut microbiota. The Biblioshiny package in R was used to analyze and visualize the relevant information, including citation counts, country distributions, authors, journals, and thematic analysis. Correlation and comparison analyses were performed using SPSS software.

Results

The top 100 influential manuscripts were published between 2000 and 2021, with a total citation of 40,662. The average number of citations annually increased over the years and was significantly correlated to the year of publication (r = 0.481, p < 0.01, Spearman's rho test). The United States was involved in the highest number of publications (n = 42). The number of publications in the journal was not significantly related to the journal's latest impact factor (r = 0.016, p > 0.05, Spearman's rho test). Co-occurrence network and thematic analysis identified several important areas, such as microbial metabolites of short-chain fatty acids and overlaps with irritable bowel syndrome.

Conclusion

This bibliometric analysis provides the key information of the most influential studies in the area of ASD and gut microbiota, and suggests the hot topics and future directions. The findings of this study can serve as a valuable reference for researchers and policymakers, guiding the development and implementation of the scientific research strategies in this area.

The Gut Microbiota-Related Antihyperglycemic Effect of Metformin

It is critical to sustain the diversity of the microbiota to maintain host homeostasis and health. Growing evidence indicates that changes in gut microbial biodiversity may be associated with the development of several pathologies, including type 2 diabetes mellitus (T2DM). Metformin is still the first-line drug for treatment of T2DM unless there are contra-indications. The drug primarily inhibits hepatic gluconeogenesis and increases the sensitivity of target cells (hepatocytes, adipocytes and myocytes) to insulin; however, increasing evidence suggests that it may also influence the gut. As T2DM patients exhibit gut dysbiosis, the intestinal microbiome has gained interest as a key target for metabolic diseases. Interestingly, changes in the gut microbiome were also observed in T2DM patients treated with metformin compared to those who were not. Therefore, the aim of this review is to present the current state of knowledge regarding the association of the gut microbiome with the antihyperglycemic effect of metformin. Numerous studies indicate that the reduction in glucose concentration observed in T2DM patients treated with metformin is due in part to changes in the biodiversity of the gut microbiota. These changes contribute to improved intestinal barrier integrity, increased production of short-chain fatty acids (SCFAs), regulation of bile acid metabolism, and enhanced glucose absorption. Therefore, in addition to the well-recognized reduction of gluconeogenesis, metformin also appears to exert its glucose-lowering effect by influencing gut microbiome biodiversity. However, we are only beginning to understand how metformin acts on specific microorganisms in the intestine, and further research is needed to understand its role in regulating glucose metabolism, including the impact of this remarkable drug on specific microorganisms in the gut.

MicrobiomeNet: exploring microbial associations and metabolic profiles for mechanistic insights

The growing volumes of microbiome studies over the past decade have revealed a wide repertoire of microbial associations under diverse conditions. Microbes produce small molecules to interact with each other as well as to modulate their environments. Their metabolic profiles hold the key to understanding these association patterns for translational applications. Based on this concept, we developed MicrobiomeNet, a comprehensive database that integrates microbial associations with their metabolic profiles for mechanistic insights. It currently contains a total of ∼5.8 million known microbial associations, coupled with >12 400 genome-scale metabolic models (GEMs) covering ∼6000 microbial species. Users can intuitively explore microbial associations and compare their corresponding metabolic profiles. Our case studies show that MicrobiomeNet can provide mechanistic insights that are consistent with the literature. MicrobiomeNet is freely available at https://www.microbiomenet.com/.

Mice expressing the autism-associated neuroligin-3 R451C variant exhibit increased mucus density and altered distributions of intestinal microbiota

The intestinal mucus layer protects the host from invading pathogens and is essential for maintaining a healthy mucosal microbial community. Alterations in the mucus layer and composition of mucus-residing microbiota in people diagnosed with autism may contribute to dysbiosis and gastrointestinal dysfunction. Although microbial dysbiosis based on sequencing data is frequently reported in autism, spatial profiling of microbes adjacent to the mucosa is needed to identify changes in bacterial subtypes in close contact with host tissues. Here, we analysed the spatial distribution of the mucin-2 protein using immunofluorescence as well as total bacteria, Bacteroidetes, Firmicutes phyla, and Akkermansia muciniphila using fluorescent in situ hybridization in mice expressing the autism-associated R451C variant in the Neuroligin-3 gene. We show that the Neuroligin-3 R451C variant increases mucus density adjacent to the distal ileal epithelium in mice. The relative density of total bacteria, Firmicutes, and A. muciniphila was increased whereas the density of Bacteroidetes was decreased closer to the epithelium in Neuroligin-3R451C mice. In summary, the autism-associated R451C variant in the Neuroligin-3 gene increases mucus density adjacent to the epithelium and alters microbial spatial distribution in the mouse distal ileum.

HIF-2α/LPCAT1 orchestrates the reprogramming of lipid metabolism in ccRCC by modulating the FBXW7-mediated ubiquitination of ACLY

The current research revealed a strong link between lipid reprogramming and dysregulated lipid metabolism to the genesis and development of clear cell renal cell carcinoma (ccRCC). Pathologically, ccRCC exhibits a high concentration of lipid droplets within the cytoplasm. HIF-2α expression has previously been demonstrated to be elevated in ccRCC caused by mutations in the von Hippel-Lindau (VHL) gene, which plays a vital role in the development of renal cell carcinoma. Nevertheless, the mechanisms by which HIF-2α influences lipid metabolism reprogramming are unknown. Our investigation demonstrated that HIF-2α directly binds to the promoter region of LPCAT1, promoting its transcription. RNA-seq and lipidomics mass spectrometry studies showed that knocking down LPCAT1 significantly reduced triglyceride production. Research suggests that KD-LPCAT1 involves activation of the NF-κB signaling pathway, which activates F-Box/WD Repeat-Containing Protein 7 (FBXW7). FBXW7, an E3 ubiquitin ligase involved in lipid metabolism, interacts with ATP Citrate Lyase (ACLY) to promote its degradation, lowering fatty acid production and contributing to the lipid content reduction.

Leupaxin promotes hepatic gluconeogenesis and glucose metabolism by coactivation with hepatic nuclear factor 4α

BACKGROUND

As the primary source of glucose during fasting, hepatic gluconeogenesis is rigorously regulated to maintain euglycemia. Abnormal gluconeogenesis in the liver can lead to hyperglycemia, a key diagnostic marker and the primary pathological contributor to type 2 diabetes (T2D) and metabolic disorders. Hepatic nuclear factor-4 (HNF4α) is an important regulator of gluconeogenesis. In this study, we identify leupaxin (LPXN) as a novel coactivator for HNF4α. Although previous studies have shown that LPXN is highly correlated with cancer types such as B-cell differentiation and hepatocellular carcinoma progression, the role of LPXN in gluconeogenesis remains unknown.

METHODS

We initially used protein pull-down assays, mass spectrometry and luciferase assays to identify the coactivator that interacts with HNF4α in gluconeogenesis. We further leveraged cell cultures and mouse models to validate the functional importance of molecular pathway during gluconeogenesis by using adenovirus-mediated overexpression and adeno-associated virus shRNA-mediated knockdown both in vivo and ex vivo, such as in ob/db/DIO mice, HepG2 and primary hepatocytes. Following, we used CUT&Tag and chip qPCR to identify the LPXN-mediated mechanisms underlying the observed abnormal gluconeogenesis. Additionally, we assessed the translational relevance of our findings using human liver tissues from both healthy donors and patients with obesity/type 2 diabetes.

RESULTS

We found that LPXN interacts with HNF4α to participate in gluconeogenesis. Knockdown of LPXN expression in the liver effectively enhanced glucose metabolism, while its overexpression in the liver effectively inhibited it. Mechanistically, LPXN could translocate into the nucleus and was essential for regulating gluconeogenesis by binding to the PEPCK promoter, which controlled the expression of an enzyme involved in gluconeogenesis, mainly through the Gcg-cAMP-PKA pathway. Additionally, LPXN expression was found to be increased in the livers of patients with steatosis and diabetes, supporting a pathological role of LPXN.

CONCLUSIONS

Taken together, our study provides evidence that LPXN plays a critical role in modulating hepatic gluconeogenesis, thereby reinforcing the fact that targeting LPXN may be a potential approach for the treatment of diabetes and metabolic disorders.

The miR3367-lncRNA67-GhCYP724B module regulates male sterility by modulating brassinosteroid biosynthesis and interacting with Aorf27 in Gossypium hirsutum

Cytoplasmic male sterile (CMS) lines play a crucial role in utilization of heterosis in crop plants. However, the mechanism underlying the manipulation of male sterility in cotton by long non-coding RNA (lncRNA) and brassinosteroids (BRs) remains elusive. Here, using an integrative approach combining lncRNA transcriptomic profiles with virus-induced gene silencing experiments, we identify a flower bud-specific lncRNA in the maintainer line 2074B, lncRNA67, negatively modulating with male sterility in upland cotton (Gossypium hirsutum). lncRNA67 positively regulates cytochrome P274B (GhCYP724B), which acted as an eTM (endogenous target mimic) for miR3367. The suppression of GhCYP724B induced symptoms of BR deficiency and male semi-sterility in upland cotton as well as in tobacco, which resulted from a reduction in the endogenous BR contents. GhCYP724B regulates BRs synthesis by interacting with GhDIM and GhCYP90B, two BRs biosynthesis proteins. Additionally, GhCYP724B suppressed a unique chimeric open reading frame (Aorf27) in 2074A mitochondrial genome. Ectopic expression of Aorf27 in yeast inhibited cellular growth, and over expression of Aorf27 in tobacco showed male sterility. Overall, the results proved that the miR3367-lncRNA67-GhCYP724B module positively regulates male sterility by modulating BRs biosynthesis. The findings uncovered the function of lncRNA67-GhCYP724B in male sterility, providing a new mechanism for understanding male sterility in upland cotton.

Modeling ncRNA Synergistic Regulation in Cancer

Cancer seriously threatens human life and health, and the structure and function of genes within cancer cells have changed relative to normal cells. Essentially, cancer is a polygenic disorder, and the core of its occurrence and development is caused by polygenic synergy. Non-coding RNAs (ncRNAs) act as regulators to modulate gene expression levels, and they provide theoretical basis and new technology for the diagnosis and preventive treatment of cancer. However, the study of ncRNA regulation and its role as biomarkers in cancer remain largely unearthed. Driven by multi-omics data, an abundance of computational methods, tools, and databases have been developed for predicting ncRNA-cancer association/causality, inferring ncRNA regulation, and modeling ncRNA synergistic regulation. This chapter aims to provide a comprehensive perspective of modeling ncRNA synergistic regulation. Since the ncRNAs involved in cancer contribute to modeling cancer-associated ncRNA synergistic regulation, we first review the databases and tools of cancer-related ncRNAs. Then we investigate the existing tools or methods for modeling ncRNA-directed and ncRNA-mediated regulation. In addition, we survey the available computational tools or methods for modeling ncRNA synergistic regulation, including synergistic interaction and synergistic competition. Finally, we discuss the future directions and challenges in modeling ncRNA synergistic regulation.

Ferroptosis-Related Transcriptional Level Changes and the Role of CIRBP in Glioblastoma Cells Ferroptosis

BACKGROUND/OBJECTIVE

We aimed to elucidate the roles of ferroptosis-associated differentially expressed genes (DEGs) in glioblastoma and provide a comprehensive resource for researchers in the field of glioblastoma cell ferroptosis.

METHODS

We used RNA sequencing to identify the DEGs associated with erastin-induced ferroptosis in glioblastoma cells. We further unraveled the biological functions and clinical implications of cold-inducible RNA-binding protein (CIRBP) in the context of glioblastoma by using a multifaceted approach, encompassing gene expression profiling, survival analysis, and functional assays to elucidate its role in glioblastoma cell mortality and its potential influence on patient prognosis.

RESULTS

We identified and validated the gene encoding CIRBP, the expression of which is altered during glioblastoma ferroptosis. Our findings highlight the relationship between CIRBP expression and ferroptosis in glioblastoma cells. We demonstrated that CIRBP modulates key aspects of cell death, thereby altering the sensitivity of glioblastoma cells to erastin-induced ferroptosis. A prognostic model, constructed based on CIRBP expression levels, revealed an association between lower CIRBP levels and poorer prognosis in glioma patients; this finding was corroborated by our comprehensive in vitro and in vivo assays that highlighted the impact of modulating CIRBP expression on glioblastoma cell viability and ferroptotic response.

CONCLUSION

Our research unravels the complex molecular dynamics of ferroptosis in glioblastoma and underscores CIRBP as a potential biomarker and therapeutic target. This improved understanding of the role of CIRBP in ferroptosis paves the way for more precise and efficacious treatments for glioblastoma, potentially improving patient outcomes.

Gut Dysbiosis and Adult Atopic Dermatitis: A Systematic Review

Background/Objectives: Research on the relationship between gut microbiota (GM) and atopic dermatitis (AD) has seen a growing interest in recent years. The aim of this systematic review was to determine whether differences exist between the GM of adults with AD and that of healthy adults (gut dysbiosis). Methods: We conducted a systematic review based on the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed using PubMed, EMBASE, and Web of Science. Observational and interventional studies were analyzed. Results: Although the studies showed heterogeneous results, some distinguishing characteristics were found in the intestinal microbial composition of adults with dermatitis. Even though no significant differences in diversity were found between healthy and affected adults, certain microorganisms, such as Bacteroidales, Enterobacteriaceae, and Clostridium (perfringens), were more characteristic of the fecal microbiota in adults with AD. Healthy individuals exhibited lower abundances of aerobic bacteria and higher abundances of short-chain fatty acid-producing species and polyamines. Clinical trials showed that the consumption of probiotics (Bifidobacterium and/or Lactobacillus), fecal microbiota transplants, and balneotherapy modified the fecal microbiota composition of participants and were associated with significant improvements in disease management. Conclusions: In anticipation of forthcoming clinical trials, it is essential to conduct meta-analyses that comprehensively evaluate the effectiveness and safety of interventions designed to modify intestinal flora in the context of AD. Preliminary evidence suggests that certain interventions may enhance adult AD management.

Identification of crucial LncRNAs associated with testicular development and LOC108635509 as a potential regulator in black goat spermatogenesis

The establishment and maintenance of spermatogenesis is a complex process involving a vast of regulatory pathways. There is growing evidence revealing that long noncoding RNAs (lncRNA) play important roles in regulating testicular development and spermatogenesis in a stage-specific way. However, our understanding of how lncRNA regulates testicular development and spermatogenesis in black goats is quite limited. In the current study, we screened the transcriptomes (lncRNA and mRNA) of testicular from Guangxi black goats before puberty (3 days old, D3; 30 days old, D30), puberty (90 days old, D90) and postpuberty (180 days old, D180), in order to identify the lncRNA interaction with mRNAs contributes to goat spermatogenesis. The RNA-sequencing (RNA-seq) analysis showed that there were 1211, 12,180, 834 differential lncRNAs and 1196, 8838,269 differential mRNAs at the ages of D30 vs. D3, D90 vs. D30, and D180 vs. D90. The lncRNAs showed the most significantly changes from D30 to D90, which indicated that D90 was a key node of lncRNAs participated in the regulation of testicular development and spermatogenesis in black goat. According to functional enrichment analysis of GO and KEGG, we found that differentially expressed lncRNAs (DE lncRNAs) and their target genes regulated spermatogenesis through signal pathways including MAPK, Ras, and PI3K-Akt. Using cis- and trans-acting, 39 DE lncRNAs-targeted genes were found to be enriched for male reproduction. Of these, LOC108635509, which specific expressed in testis and upregulated the expression levels at D90, was found participated in the regulation of testicular development through promoting the proliferation of Sertoli cells (SCs). Overall, this study provides new insight into the regulatory mechanisms that support spermatogenesis and testicular development in black goats.

Predicting microbe-disease associations via graph neural network and contrastive learning

In the contemporary field of life sciences, researchers have gradually recognized the critical role of microbes in maintaining human health. However, traditional biological experimental methods for validating the association between microbes and diseases are both time-consuming and costly. Therefore, developing effective computational methods to predict potential associations between microbes and diseases is an important and urgent task. In this study, we propose a novel computational framework, called GCATCMDA, for forecasting potential associations between microbes and diseases. Firstly, we construct Gaussian kernel similarity networks for microbes and diseases using known microbe-disease association data. Then, we design a feature encoder that combines graph convolutional network and graph attention mechanism to learn the node features of networks, and propose a feature dual-fusion module to effectively integrate node features from each layer's output. Next, we apply the feature encoder separately to the microbe similarity network, disease similarity network, and microbe-disease association network, and enhance the consistency of features for the same nodes across different association networks through contrastive learning. Finally, we pass the microbe and disease features into an inner product decoder to obtain the association scores between them. Experimental results demonstrate that the GCATCMDA model achieves superior predictive performance compared to previous methods. Furthermore, case studies confirm that GCATCMDA is an effective tool for predicting microbe-disease associations in real situations.

Opportunities and challenges of foodborne polyphenols applied to anti-aging health foods

Abstract

With the increasing proportion of the global aging population, aging mechanisms and anti-aging strategies become hot topics. Nonetheless, the safety of non-natural anti-aging active molecule and the changes in physiological function that occur during aging have not been clarified. There is therefore a need to develop safer pharmaceutical interventions for anti-aging. Numerous types of research have shown that food-derived biomolecules are of great interest due to their unique contribution to anti-aging safety issues and the prevention of degenerative diseases. Among these, polyphenolic organic compounds are widely used in anti-aging research for their ability to mitigate the physiological functional changes that occur during aging. The mechanisms include the free radical theory, immune aging theory, cellular autophagy theory, epigenetic modification theory, gut microbial effects on aging theory, telomere shortening theory, etc. This review elucidates the mechanisms underlying the anti-aging effects of polyphenols found in food-derived bioactive molecules, while also addressing the challenges associated with anti-aging pharmaceuticals. The review concludes by offering insights into the current landscape of anti-aging active molecule research, aiming to serve as a valuable resource for further scholarly inquiry.

Graphical abstract

AI-integrated network for RNA complex structure and dynamic prediction

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.

SARS-CoV-2 disrupts host gene networks: Unveiling key hub genes as potential therapeutic targets for COVID-19 management

PURPOSE

Although the end of COVID-19 as a public health emergency was declared on May 2023, still new cases of the infection are reported and the risk remains of new variants emerging that may cause new surges in cases and deaths. While clinical symptoms have been rapidly defined worldwide, the basic body responses and pathogenetic mechanisms acting in patients with SARS-CoV-2 infection over time until recovery or death require further investigation. The understanding of the molecular mechanisms underlying the development and course of the disease is essential in designing effective preventive and therapeutic approaches, and ultimately reducing mortality and disease spreading.

METHODS

The current investigation aimed to identify the key genes engaged in SARS-CoV-2 infection. To achieve this goal high-throughput RNA sequencing of peripheral blood samples collected from healthy donors and COVID-19 patients was performed. The resulting sequence data were processed using a wide range of bioinformatics tools to obtain detailed modifications within five transcriptomic phenomena: expression of genes and long non-coding RNAs, alternative splicing, allel-specific expression and circRNA production. The in silico procedure was completed with a functional analysis of the identified alterations.

RESULTS

The transcriptomic analysis revealed that SARS-CoV-2 has a significant impact on multiple genes encoding ribosomal proteins (RPs). Results show that these genes differ not only in terms of expression but also manifest biases in alternative splicing and ASE ratios. The integrated functional analysis exposed that RPs mostly affected pathways and processes related to infection-COVID-19 and NOD-like receptor signaling pathway, SARS-CoV-2-host interactions and response to the virus. Furthermore, our results linked the multiple intronic ASE variants and exonic circular RNA differentiations with SARS-CoV-2 infection, suggesting that these molecular events play a crucial role in mRNA maturation and transcription during COVID-19 disease.

CONCLUSIONS

By elucidating the genetic mechanisms induced by the virus, the current research provides significant information that can be employed to create new targeted therapeutic strategies for future research and treatment related to COVID-19. Moreover, the findings highlight potentially promising therapeutic biomarkers for early risk assessment of critically ill patients.

TRIP13: A promising cancer immunotherapy target

The tumor microenvironment (TME) facilitates tumor development through intricate intercellular signaling, thereby supporting tumor growth and suppressing the immune response. Thyroid hormone receptor interactor 13 (TRIP13), an AAA+ ATPase, modulates the conformation of client macromolecules, consequently influencing cellular signaling pathways. TRIP13 has been implicated in processes such as proliferation, invasion, migration, and metastasis during tumor progression. Recent studies have revealed that TRIP13 also plays a role in immune response suppression within the TME. Thus, inhibiting these functions of TRIP13 could potentially enhance immune responses and improve the efficacy of immune checkpoint inhibition. This review summarizes the recent research progress of TRIP13 and discusses the potential of targeting TRIP13 to improve immune-based therapies for patients with cancer.

Transcriptome Analysis Reveals the lncRNA-mRNA Co-expression Network Regulating the Aestivation of Sea Cucumber

LncRNAs are long non-coding RNAs that are widely recognized as crucial regulators of gene expression and metabolic control, involved in numerous dormancy-related processes. Aestivation is a common hypometabolism strategy of sea cucumber (Apostichopus japonicus) in response to high-temperature conditions and is typically characterized by the degradation of the intestine and respiratory tree. Although the aestivation process has been extensively studied in sea cucumbers, the role of lncRNAs in the context of aestivation states remains a conspicuous knowledge gap. Here, we identified and characterized 14,711 lncRNAs in A. japonicus and analyzed their differential expression patterns during the aestivation process in the intestine and respiratory tree. The results revealed the physiological differences, especially the metabolic processes, between the intestine and respiratory tree during the aestivation. The co-expression network of lncRNA-mRNA suggested the dominant role of lncRNA in regulating the differential response of the intestine and respiratory trees. Differentially co-expressed factors were significantly enriched in the deep-aestivation stage-specific modules. Conserved co-expressed factors included several transcription factors known to be involved in rhythm regulation, such as Klf2 and Egr1. Furthermore, a specific trans-acting lncRNA (lncrna.1393.1) was identified as a potential regulator of Klf2 and Egr1. Overall, the systematic identification, characterization, and expression analysis of lncRNAs in A. japonicus enhanced our knowledge of long non-coding regulation of aestivation in sea cucumber and provided new clues for understanding the common "toolkit" of dormancy regulatory mechanisms.

PhyImpute and UniFracImpute: two imputation approaches incorporating phylogeny information for microbial count data

Sequencing-based microbial count data analysis is a challenging task due to the presence of numerous non-biological zeros, which can impede downstream analysis. To tackle this issue, we introduce two novel approaches, PhyImpute and UniFracImpute, which leverage similar microbial samples to identify and impute non-biological zeros in microbial count data. Our proposed methods utilize the probability of non-biological zeros and phylogenetic trees to estimate sample-to-sample similarity, thus addressing this challenge. To evaluate the performance of our proposed methods, we conduct experiments using both simulated and real microbial data. The results demonstrate that PhyImpute and UniFracImpute outperform existing methods in recovering the zeros and empowering downstream analyses such as differential abundance analysis, and disease status classification.

De novo structural variants in autism spectrum disorder disrupt distal regulatory interactions of neuronal genes

Three-dimensional genome organization plays a critical role in gene regulation, and disruptions can lead to developmental disorders by altering the contact between genes and their distal regulatory elements. Structural variants (SVs) can disturb local genome organization, such as the merging of topologically associating domains upon boundary deletion. Testing large numbers of SVs experimentally for their effects on chromatin structure and gene expression is time and cost prohibitive. To address this, we propose a computational approach to predict SV impacts on genome folding, which can help prioritize causal hypotheses for functional testing. We developed a weighted scoring method that measures chromatin contact changes specifically affecting regions of interest, such as regulatory elements or promoters, and implemented it in the SuPreMo-Akita software (Gjoni and Pollard 2024). With this tool, we ranked hundreds of de novo SVs (dnSVs) from autism spectrum disorder (ASD) individuals and their unaffected siblings based on predicted disruptions to nearby neuronal regulatory interactions. This revealed that putative cis-regulatory element interactions (CREints) are more disrupted by dnSVs from ASD probands versus unaffected siblings. We prioritized candidate variants that disrupt ASD CREints and validated our top-ranked locus using isogenic excitatory neurons with and without the dnSV, confirming accurate predictions of disrupted chromatin contacts. This study establishes disrupted genome folding as a potential genetic mechanism in ASD and provides a general strategy for prioritizing variants predicted to disrupt regulatory interactions across tissues.