Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 25 appraisals
Biological psychiatry
Systematic Review and Mega-Analysis of the Peripheral Blood Transcriptome in Depression Implicates Dysregulation of Lymphoid Cells and Histones
BACKGROUND: Depression has been associated with transcriptomic changes in peripheral blood. However, the contribution of specific immune cell subsets or pathways remains unclear, and findings have been variable across previous studies, which have not tended to account for sample cellular composition. METHODS: We performed a systematic review of peripheral blood transcriptome studies in depression. For the 5 datasets meeting criteria (total N = 6011), we performed harmonized reprocessing and cell composition-adjusted differential gene and transcript analyses, followed by a bias- and inflation-adjusted weighted z score mega-analysis. We investigated the biological pathways and cell subsets implicated by the results. We also performed a sex-stratified gene network mega-analysis using consensus weighted gene coexpression network analysis (WGCNA). RESULTS: Few genes showed robust differential gene expression (DGE) in depression. Depression was reproducibly associated with decreases in replication-dependent histones and with a decrease in oxidative phosphorylation pathways in females only. Cell source analyses implicated lymphoid cells (T cells and natural killer cells) as likely contributors to the depression differential expression signature. WGCNA mega-analysis revealed multiple consensus modules associated with depression, with a PUF60-related module upregulated in both female and male depression in sex-stratified analyses. Two genes predicted to be causally relevant to depression by transcriptome-wide association studies (GPX4 and GYPE) showed significant DGE. CONCLUSIONS: These results are convergent with immunogenetic evidence implicating lymphoid cell dysregulation in depression, while also highlighting histone alterations as a key molecular signature in depression. They also indicate the importance of large-scale datasets for biomarker discovery in the context of heterogeneous disorders such as depression.
16 June 2026
Read appraisal →Journal of medicinal chemistry
MEG-mod: A Multiview Enhanced Graph Neural Network for Knockdown Efficiency Prediction of Chemically Modified siRNA
Chemical modification is essential for improving the stability and knockdown efficiency of siRNAs. However, the combinatorial complexity of modification types and positions makes rational design difficult. Here, we propose MEG-mod, a deep learning framework for predicting the knockdown efficiency of chemically modified siRNAs. By integrating a literature-reading agent, manual curation, and public database resources, we construct an expanded data set. MEG-mod jointly models sequence context, physicochemical properties, chemical modification features, and duplex structural relationships. A structure-aware Transformer-based graph neural network captures base-pairing and adjacency dependencies within double-stranded siRNAs, while a modification-base fusion module models context-dependent modification effects. MEG-mod outperforms existing methods, achieving a pearson correlation coefficient of 0.9171. The attention mechanism enables interpretable analysis of key modification positions and modification types, and provides recommendations consistent with reported experimental designs. MEG-mod is available as a public web server (https://lmmd.ecust.edu.cn/megmod/), providing a practical tool for prediction and candidate prioritization of chemically modified siRNAs.
13 June 2026
Read appraisal →Translational stroke research
AI-Guided Patient-Derived Endothelial Modeling Identifies a Reversible Moyamoya-Specific miRNA Deficiency Corrected by Stem Cell-Extracellular Vesicles
Moyamoya disease (MMD) is a progressive cerebrovascular disorder characterized by abnormal vascular remodeling and impaired angiogenesis. We profiled plasma extracellular vesicle (EV)-derived microRNAs (miRNAs) from MMD patients and healthy controls and generated patient-specific induced pluripotent stem cell-derived endothelial cells (iPSC-ECs) to investigate disease-associated molecular alterations and therapeutic reversibility. Both plasma EVs and iPSC-ECs from MMD patients showed significant depletion of angiogenesis-related miRNAs. Small RNA sequencing data revealed mesenchymal stem cell-derived EVs (MSC-EVs) contain a miRNA repertoire that substantially overlaps with miRNAs deficient in MMD. Treatment of MMD iPSC-ECs with MSC-EVs restored the majority of the depleted miRNAs to normal levels, normalized predicted target mRNA expression, and rescued angiogenic function in a dose-dependent manner, as demonstrated by tube formation assays. To improve identification of biologically relevant miRNA-mRNA interactions, we developed an artificial intelligence-based framework integrating experimentally validated databases, endothelial-focused literature mining, and large language model (LLM)-assisted mechanistic scoring. Incorporation of LLM-derived contextual features modestly improved prediction performance compared with conventional sequence-based models (baseline AUROC ~ 0.51; LLM-enhanced AUROC ~ 0.72), suggesting that literature-informed biological context provides meaningful signal beyond structural target features alone. Furthermore, our findings demonstrate that MSC-EV therapy can substantially reverse molecular and functional endothelial deficits. Integrated profiling and AI-driven interaction mapping together establish a hypothesis-generating framework ("Exo-Courier") for biologically informed prioritization of EV-based therapeutic targets in vascular disorders.
12 June 2026
Read appraisal →Bioorganic chemistry
In silico discovery and biological evaluation of a novel selective HDAC8 inhibitor featuring 1H-pyrazolo[3,4-b]pyridine scaffold
Histone deacetylase 8 (HDAC8), a pivotal epigenetic regulator orchestrating chromatin remodeling and transcriptional control, has emerged as a compelling anticancer target owing to its frequent dysregulation in malignancies. The development of isoform-selective inhibitors, however, confronts substantial challenges because of the pronounced structural conservation across HDAC isoforms. This study developed a high-efficiency drug screening pipeline primarily leveraging artificial learning (AI) models and molecular simulation techniques, and further evaluated the biological activity of computationally prioritized compounds through enzymatic inhibition assays and cellular experiments. The structurally novel compound Cmpd.16 was successfully identified, exhibiting promising inhibitory activity against HDAC8 (IC50 = 0.16 ± 0.01 μM) and moderately inhibited HCT116 cell proliferation mainly through the induction of apoptosis and G2/M phase cell cycle arrest. Subsequently, molecular dynamics simulations were employed to investigate the binding conformation of Cmpd.16 within the active pocket, and the key residues identified through free energy decomposition further elucidated the drug-target binding mode. Collectively, Cmpd.16, featuring 1H-pyrazolo[3,4-b]pyridine scaffold, was discovered via an efficient screening workflow, offering valuable insights to guide the rational design of HDAC8-targeted agents.
7 June 2026
Read appraisal →Molecular biology reports
The role of non-coding RNAs in the coronary no-reflow phenomenon: a systematic review
Coronary no-reflow poses a challenge in interventional cardiology. Increasing attention has been directed toward the potential role of non-coding RNAs (ncRNAs) in predicting coronary no-reflow (CNR), with no prior systematic review available to date. We aimed to systematically review the available evidence on the potential clinical utility of ncRNAs in CNR and/or microvascular obstruction (MVO). Embase, MEDLINE Ultimate, PubMed, Scopus, and Web of Science were searched, last on February 9, 2026. Studies enrolling adult patients with CNR and/or MVO that evaluated the expression of ncRNAs or investigated genetic variants within ncRNA-related pathways were included. Risk of bias was not assessed. Upregulation of miR-1, miR-7a/b, miR-98-5p, miR-133a, miR-133b, miR-208a, miR-224-3p, miR-660-5p, long non-coding RNA (lncRNA) TUG1, lncRNA MALAT1, and downregulation of miR-30e, miR-126, miR-155, and miR-542-3p were reported in patients with STEMI with CNR and/or MVO compared with those without CNR/MVO. Almost all ncRNAs were reported by a single study, with the exception of miR-30e, which was consistently reported in three independent studies. Several ncRNAs may demonstrate good performance in identifying patients at risk of CNR and/or MVO occurrence. Nevertheless, the available evidence is scarce and limited by the low robustness of the reported findings. Circulating ncRNAs are promising biomarkers for CNR. Nevertheless, several limitations persist, highlighting the need for further studies with standardized methodology to validate the clinical utility of ncRNAs.
28 May 2026
Read appraisal →Neuroscience
Therapeutic efficacy of engineered exosomes in Alzheimer's disease: A systematic review and meta-analysis of preclinical animal models
Engineered exosomes are modified extracellular vesicles designed to enhance targeting and cargo delivery, and they have been proposed as a therapeutic strategy for Alzheimer's disease. We systematically reviewed preclinical animal studies evaluating engineered exosomes, synthesized evidence from comparisons with disease models and with natural exosomes, and reported the study in accordance with the PRISMA 2020 checklist. Outcomes included spatial learning and memory assessed by the Morris water maze, amyloid beta pathology, tau phosphorylation, and neuroinflammatory markers. Random effects meta-analyses suggested that engineered exosomes improved Morris water maze performance and reduced amyloid beta burden and pro-inflammatory cytokines compared with natural exosomes, whereas evidence regarding tau phosphorylation was limited and largely qualitative, and the overall certainty of evidence was low to very low. These findings support further investigation of engineered exosomes, but conclusions should be interpreted cautiously until confirmed by rigorously designed and blinded preclinical studies and clinical trials with standardized protocols.
25 May 2026
Read appraisal →Molecular cell
Rapid customization of base editors via machine learning-powered combinatorial mutagenesis
Being able to control the complementarity and hindrance between target DNA and base editor proteins enables precise, bystander-free editing. Here, we combined combinatorial mutagenesis with machine learning to analyze and engineer these interactions at scale. By profiling DNA motif preferences across 160,000 evoAPOBEC1 and 64 million TadA variants in human cells, we used as little as 0.004% of the mutational landscape to make predictions. This identified variants with motif-specific activity and eliminated residual adenine editing in cytosine base editors. In correcting >800 disease-associated mutations, our variants outperformed previous versions in precluding unintended edits at purine motifs, achieving undetectable bystander edits in 50% of cases. Additionally, a pre-trained, structure-based deep learning model predicted functional TadA variants with 63% success across 2026 variants spanning 26 amino acid sites, without experimental data and in a single prediction round. These approaches streamline the re-engineering of base editors for enhanced precision tailored to specific targets.
23 May 2026
Read appraisal →Apoptosis : an international journal on programmed cell death
Single-cell profiling and machine learning identify cuproptosis-related fibroblast subpopulations and fibrogenesis modulator AEBP1 in endometriosis
Endometriosis is characterized by progressive fibrosis and limited therapeutic options. Cuproptosis, a copper-dependent form of regulated cell death, has been implicated in multiple pathological conditions, but its relevance to fibroblast-mediated fibrotic progression in endometriosis remains unclear. Single-cell RNA sequencing data from normal, eutopic, and ectopic endometrial tissues were analyzed to assess cuproptosis-related gene (CRG) activity and fibroblast heterogeneity. Pseudotime analysis, cell-cell communication analysis and high-dimensional weighted gene co-expression network analysis were performed to identify disease-associated fibroblast states and candidate fibrosis-related genes. Machine learning approaches were applied to prioritize candidate hub genes. Functional validation was conducted in endometrial stromal cells, and a mouse model of endometriosis was used to assess the effects of tetrathiomolybdate (TTM), a copper chelator. Elevated CRG activity was enriched in a distinct fibroblast subpopulation with profibrotic transcriptional features. Network and machine learning analyses consistently prioritized AEBP1 as a candidate fibroblast-associated hub gene linked to cuproptosis-related signatures. In vitro, CuCl2 plus elesclomol treatment was associated with increased AEBP1 and fibrosis-related marker expression, accompanied by changes in β-catenin pathway-related proteins, whereas FDX1 or AEBP1 knockdown attenuated these effects. In vivo, TTM treatment reduced lesion burden, fibrotic marker expression and collagen deposition in ectopic lesions. Cuproptosis-related molecular alterations are associated with fibroblast activation and fibrotic progression in endometriosis. Targeting copper metabolism may have therapeutic potential in limiting lesion fibrosis.
19 May 2026
Read appraisal →Journal of molecular neuroscience : MN
Transcriptomic Analysis and Multiple Machine Learning Approaches Identify ZDHHC20 and Its Highly Correlated Gene AK5 as Biomarkers in Multiple System Atrophy
Multiple system atrophy (MSA) is a fatal neurodegenerative disorder lacking effective diagnostic tools. While protein palmitoylation is crucial for neuronal function, its specific role in MSA pathogenesis remains unexplored. We integrated bulk and single-nucleus RNA sequencing (snRNA-seq) data from postmortem MSA brain tissues. Eight machine learning algorithms were utilized to screen palmitoylation-related genes. Downstream analyses, including functional enrichment, cellular deconvolution, and pseudotime trajectory inference, were then conducted. ZDHHC20 and its highly correlated gene, AK5, were identified as hub genes. Both demonstrated significant downregulation in MSA, particularly within the cerebellar white matter. Functional enrichment analysis linked this expression pattern to mitochondrial dysfunction and impaired energy metabolism. Furthermore, snRNA-seq revealed that ZDHHC20 and AK5 are predominantly expressed in oligodendrocytes and exhibit lower expression levels along the developmental trajectory in MSA compared to healthy controls. ZDHHC20 and AK5 represent promising biomarkers for MSA. These findings provide new insights into the diagnosis and treatment of MSA.
18 May 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Circulating Tumor DNA Dynamics and Clinical Outcomes in Patients with Advanced Colorectal Cancer Treated with Cetuximab-Based Induction and Maintenance Treatment
PURPOSE: We investigated whether circulating tumor DNA (ctDNA) changes may be useful to assess clinical outcomes in patients with metastatic colorectal cancer (mCRC) randomized in the TIME-PRODIGE-28 trial comparing biweekly maintenance with cetuximab alone with observation after 4-month fluorouracil, folinic acid, and irinotecan (FOLFIRI) plus cetuximab induction chemotherapy. EXPERIMENTAL DESIGN: ctDNA samples were collected at four time points from baseline until disease progression during the first chemotherapy-free interval and analyzed using next-generation sequencing and methylation marker approaches. Progression-free survival (PFS) and overall survival (OS) from randomization were analyzed according to ctDNA kinetics and EGFR-MAPK pathway alterations. RESULTS: Among 139 randomized patients, 104 (74.8%) had paired samples available. Patients with negative baseline ctDNA remaining negative after 4-month induction chemotherapy had significantly longer PFS from randomization (9.6 months) as compared with patients with a ctDNA decrease of ≥80% (3.4 months) or a ctDNA decrease of <80% (2.1 months; P = 0.013). Patients with EGFR-MAPK pathway alterations identified either in tissue or baseline ctDNA had worse PFS and OS from randomization. Acquired alterations found in 17 of 63 (26.9%) patients at disease progression during the first chemotherapy-free interval were associated with worse OS from reintroduction of the full induction chemotherapy (14.9 vs. 19.4 months; P = 0.025). CONCLUSIONS: Our findings show the prognostic impact of both ctDNA kinetics and EGFR-MAPK pathway alteration dynamics following induction chemotherapy with FOLFIRI-cetuximab in patients with mCRC. Prospective studies evaluating ctDNA-guided treatment strategies are needed to validate the clinical utility of ctDNA monitoring to improve patient selection for first-line treatment de-escalation and anti-EGFR-based maintenance regimens, including treatment adaptation over time.
16 May 2026
Read appraisal →Annals of human biology
Machine learning combined with WGCNA reveals potential calcium uptake-associated biomarkers in Parkinson's disease
BACKGROUND: Parkinson's disease (PD) is the second most common neurodegenerative disease, characterised by progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Although previous studies have suggested a correlation between calcium signal disruption and the development of PD, the role of genes associated with store-operated calcium entry (SOCE) in PD remains unclear. AIM: To combine machine learning methods with Weighted Gene Co-expression Network Analysis (WGCNA) to systematically screen and identify SOCE-associated candidate genes and evaluate their potential for assisting in discrimination. SUBJECTS AND METHODS: This study is based on the GSE6613 dataset, using ssGSEA and Weighted Gene Co-expression Network Analysis (WGCNA) to screen for co-expression modules related to SOCE, and combining LASSO, SVM-RFE, and random forest algorithm to identify feature genes. Subsequently, validation was conducted in GSE20163 and GSE22491, and in vitro experiments were carried out in 6-OHDA and MPP+ models of dopaminergic neurons derived from LUHMES. RESULTS: A total of 5 SOCE-associated feature genes (LPCAT3, CLCNKB, TXLNA, etc.) were identified, and their combined model showed moderate discriminative ability in external validation. Immune analysis showed immune dysfunction in PD patients, and in vitro experiments also observed consistent expression trends with patient samples. CONCLUSIONS: In summary, this study identified potential PD feature genes associated with SOCE, providing clues for further exploration of the molecular mechanisms of SOCE-associated calcium signalling in PD.
15 May 2026
Read appraisal →Endocrine-related cancer
Systematic review of the molecular basis for cavernous sinus invasion in somatotropinomas
Somatotropinomas are a subtype of pituitary adenomas that have a particular predilection to invade the cavernous sinus. The objective of this systematic review was to examine the evidence regarding the molecular basis for cavernous sinus invasion in somatotropinomas. This review was conducted in accordance with the 2020 PRISMA guidelines on 13 April 2025. Inclusion criteria were reports of associations between somatotropinoma molecular changes and cavernous sinus invasion in adult patients. Title/abstract screening and full-text screening were performed, with studies assessed for risk of bias using the Newcastle-Ottawa scale. A total of 43 studies were identified, encompassing 1,824 patients (724 invasive tumours). Overall, 33 studies identified molecules that were upregulated in invasive tumours and 20 studies identified molecules that were downregulated. Few studies incorporated modern proteomic or transcriptomic techniques. Risk of bias was low with a mean Newcastle-Ottawa scale score of 7.0 (±0.6). Molecules associated with invasion were related to epithelial-mesenchymal transition (E-cadherin, ESRP1, fascin-1, and MMP-9), cellular proliferation (PTTG, AIP, TCERG1, EIF2β, E2F1, Notch 2, STAT3, ARRB1, TGFB1, SMAD3, SOX9, SGK1, MAGEA6 DLL3, EGFL7, and pEGFR), hormonal signalling (GNAS, DRD5, DRD1, sst5TMD4, SSTR2, and SSTR5), and tumour angiogenesis (VEGF and Drp1). These molecular variations present a possible explanation for the proclivity of somatotropinomas for the cavernous sinus. Identified molecules represent options for novel targeted therapies or biomarkers that could inform prognostication. Modern proteomic and transcriptomic techniques and larger somatotropinoma datasets are required to further elucidate the molecular pathways responsible for cavernous sinus invasion in somatotropinomas.
13 May 2026
Read appraisal →Journal of chemical information and modeling
MechMemDyn: Coupling Frustration Analysis with Membrane Dynamics to Target the TREM2-DAP12 Complex Interface
The transmembrane complex formed by TREM2 (Triggering Receptor Expressed on Myeloid cells 2) and DAP12 (DNAX-activating protein of 12 kDa) constitutes a pivotal therapeutic target for Alzheimer's disease. However, the intrinsic plasticity of its interface poses a formidable challenge for structure-based discovery of protein-protein interaction stabilizers (PPI stabilizers). Conventional approaches, ranging from static molecular docking to geometric deep learning, fail to capture the subtle interfacial energetics required for stabilizing this assembly, often leading to erroneous activity predictions. Here, we present MechMemDyn, a novel predictive framework that uniquely integrates protein frustration analysis with membrane-embedded molecular dynamics (MD) simulations. To our knowledge, this is the first systematic application of frustration analysis to rationalize PPI-stabilizing activity and guide ligand design. By mapping the local frustration landscape, we identify critical, minimally frustrated contact networks at the protein interface, which are essential for stabilizing the PPI. We demonstrate that the ability of a ligand to dampen distance fluctuations within these key networks, a metric rooted in thermodynamic rigor, correlates strongly with experimental potency. This method outperforms conventional static docking, AI-driven dynamic docking approaches, and standard molecular simulations, providing a more accurate and reproducible basis for cross-ligand comparison. This work not only resolves the intractability of the TREM2-DAP12 complex but also establishes a physics-driven paradigm for targeting dynamic transmembrane interfaces via frustration-optimized PPI stabilizers.
13 May 2026
Read appraisal →Journal of chemical information and modeling
Examining Genetic Variants Associated with FOXP1 Syndrome through Molecular Dynamics of Its DNA-Binding Domain and Self-Organizing Maps
Genetic mutations in the transcription factor FOXP1 (forkhead box protein P1) cause an autosomal dominant neurodevelopmental disorder called FOXP1 syndrome. To understand the structural impact of pathogenic variants associated with FOXP1 syndrome, we investigated the conformational changes resulting from six distinct missense variants in FOXP1 by combining molecular dynamics simulations, molecular docking, and machine learning via self-organizing maps. Our results reveal different conformational landscapes mapped by the FOXP1 variants and reduced interactions with the DNA for mutations residing in helix H3 of the DNA-binding domain. These analyses offer a framework for assessing the structural impact of missense variants implicated in the FOXP1 syndrome, highlighting the importance of structural inferences in interpreting genetic variants.
13 May 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Phase I Study of Mosperafenib, a Novel Paradox Breaker B-Raf Proto-Oncogene Serine/Threonine Kinase (BRAF) Inhibitor, in Patients With BRAF V600-Mutant Solid Tumors
PURPOSE: BRAF V600 mutations are tumor-agnostic oncogenic drivers whose targeted inhibition is challenged by therapeutic resistance. Mosperafenib is a novel paradox breaker and brain-penetrant BRAF inhibitor (BRAFi), tested in this study for safety, maximum tolerated dose (MTD), pharmacokinetics (PK), and preliminary clinical activity. PATIENTS AND METHODS: This phase Ia/b study was conducted in patients with advanced solid tumors harboring a BRAF V600 mutation (ISRCTN13713551). The modified continuous reassessment method guided dose escalation of mosperafenib, which was given orally up to 3,600 mg once daily in 28-day cycles. The primary objective was to estimate the MTD and/or recommended phase II dose(s). RESULTS: Eighty patients (60% BRAFi-exposed)-63 with colorectal cancer (CRC), 13 with melanoma, and 4 with other solid tumors-received ≥1 dose of mosperafenib as a single agent for a median of 3.7 months (range, 0.2-28.6). Two dose-limiting grade 3 toxicities of rash and rash maculopapular were reported. MTD was not reached. Grade 3 to 4 treatment-related adverse events (TRAEs) occurred in 13 patients (16.3%); no grade 5 TRAEs events were reported. Two patients (2.5%) discontinued study treatment due to TRAEs. There were no reports of palmar-plantar erythrodysesthesia or keratoacanthoma. Linear and time-independent PK was demonstrated across the tested dose range, achieving exposure levels with sustained PK-derived pERK inhibition ≥90%. An exposure-response relationship was observed. Overall response rate was 24.2%, including two complete responses and 14 partial responses. Median progression-free survival was 6.4 months in patients with CRC (200 mg once daily-1200 mg three times a day) and 3.5 months in patients with melanoma (200 mg once daily-800 mg twice a day). CONCLUSION: Mosperafenib demonstrated a favorable safety profile, sustained target inhibition, and early signs of clinically meaningful single-agent activity in BRAFi-naïve and BRAFi-exposed patients.
13 May 2026
Read appraisal →FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Lysosomal Membrane Proteins: Key Regulators of Glucose Metabolism and Its Associated Diseases
Glucose homeostasis, which is critical for maintaining energy supply and health, involves glycogen metabolism, glycolysis, and gluconeogenesis. Lysosomal membrane proteins (LMPs) play core roles in regulating these processes. However, no focused systematic review of the topic has been reported. This review provides an in-depth analysis of the central roles of LMPs in glucose metabolism, focusing on the regulation of glucose homeostasis and their potential effects on metabolic diseases through the regulation of autophagy, signaling networks, specialized transporter functions, and other relevant mechanisms. In general, LMPs play core roles in lysosomal biosynthesis, and an in-depth study of their relationship with glucose metabolism could significantly highlight the important contribution of lysosomes in the development of related diseases.
3 May 2026
Read appraisal →Current allergy and asthma reports
Interception of Regulatory RNAs on TGF-β Signaling in the Pathogenesis of Idiopathic Pulmonary Fibrosis: A Systematic Review
PURPOSE OF REVIEW: Idiopathic Pulmonary Fibrosis is a type of interstitial lung disease characterized by lung scarring due to excessive extracellular matrix (ECM) deposition. The transforming growth factor-beta (TGF-β) signaling pathway is the master regulator of fibrosis, driving myofibroblast activation and differentiation, epithelial-mesenchymal transition (EMT), and inhibition of ECM degradation. While individual classes of non-coding RNAs (ncRNAs) have been studied in IPF, a comprehensive understanding of how microRNAs (miRNAs), circular RNAs (circRNAs), and long non-coding RNAs (LncRNAs) collectively regulate canonical TGF-β signaling remains lacking. Addressing this gap, this systematic review was conducted which presents current evidence on the integrated roles of ncRNAs in modulating TGF-β signaling in IPF pathogenesis. RECENT FINDINGS: A comprehensive search of PubMed and Web of Science databases (2015-2025), identified 45 eligible studies. miRNAs (19 antifibrotic, 11 profibrotic) directly targeted the core components of the TGF-β signaling pathway, whereas circRNAs (5 antifibrotic, 5 profibrotic) and LncRNAs (6 profibrotic) acted as competing endogenous RNAs and sponged their target miRNAs. Notably, all identified LncRNAs were profibrotic, amplifying the fibrotic signaling pathway. We found that ncRNAs critically fine-tune TGF-β signaling at multiple regulatory nodes, including ligand activation, receptor expression, Smad protein phosphorylation, nuclear translocation, and inhibitory feedback mechanism. The ncRNA-mediated regulatory mechanisms provide valuable insight into IPF pathogenesis and may help identify potential biomarkers and therapeutic targets. Future efforts should prioritize validating in vitro and animal model data in human samples, integrating regulatory network analysis, in vivo functional validation of RNA-based therapeutics, and exploring therapeutic delivery systems to develop an effective ncRNA-based antifibrotic therapy.
3 May 2026
Read appraisal →PloS one
Biomarkers of mitochondrial dynamics in idiopathic pulmonary fibrosis: Identification and validation through transcriptomic and single-cell analyses
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease that has increasingly been associated with dysregulated mitochondrial quality control and dynamics. However, the molecular mechanisms underlying these alterations remain incompletely understood. This study aimed to systematically identify and validate candidate biomarkers related to mitochondrial dynamics in IPF and to characterize their cell-type specificity and putative regulatory relationships. METHODS: We integrated bulk transcriptomic datasets from the Gene Expression Omnibus (GEO), single-cell RNA sequencing (scRNA-seq) data, and literature-derived mitochondrial dynamics gene sets. Candidate genes were identified through differential expression analysis and consensus clustering, followed by functional enrichment and protein-protein interaction (PPI) network analyses. A total of 101 machine-learning model combinations-including random forest, LASSO, and support vector machine-were constructed to select optimal feature genes. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and further evaluated with artificial neural network (ANN) modeling. Additional analyses included chromosomal localization, immune infiltration profiling, multilayer regulatory network construction (transcription factors, lncRNAs, circRNAs), molecular docking prediction, and single-cell expression and pseudotime trajectory analysis. Key biomarkers were further evaluated by RT-qPCR in an independent clinical cohort. RESULTS: Integrated multi-omics and machine-learning analyses identified CD247, IL7R, and RETN as candidate biomarkers related to mitochondrial dynamics-associated pathways in IPF. Across independent transcriptomic datasets, RETN was upregulated, whereas CD247 and IL7R were downregulated, and each showed diagnostic value (single-gene AUC > 0.7). The ANN model based on these genes achieved encouraging discriminative performance (training AUC = 0.91; validation AUC = 0.82), and expression differences were confirmed by RT-qPCR in a modest independent cohort. Enrichment analyses indicated convergence on spliceosome-related pathways, and regulatory-network analysis highlighted interactions involving transcription factors and non-coding RNAs, including circRNA CDR1as. Molecular docking suggested putative interactions with selected compounds. Single-cell analyses suggested that dysregulation was most evident in monocyte-associated compartments in one publicly available scRNA-seq dataset, and pseudotime analysis indicated dynamic expression patterns, with early transient increases in CD247 and IL7R and progressive elevation of RETN. CONCLUSION: Through multi-omics integration and machine-learning approaches, we identified and preliminarily validated CD247, IL7R, and RETN as candidate biomarkers related to mitochondrial dynamics-associated pathways in IPF. These findings provide transcriptomic and cell-type-specific evidence suggesting potential immune-mitochondrial associations in IPF and may inform future biomarker validation and mechanistic hypothesis generation.
26 Apr 2026
Read appraisal →Human mutation
HSPB6: A Potential Prognostic Biomarker, Inhibiting the Epithelial-Mesenchymal Transition (EMT) Process Through the PI3K/Akt Signaling Pathway Based on the Machine Learning and Experimental Validation
Bladder cancer (BC) is a prevalent malignant tumor worldwide, posing a significant public health burden and challenge to human society. Current therapeutic modalities for BC include surgical treatment, radiotherapy, chemotherapy, targeted therapy, and immunosuppressive therapy. However, almost all patients experience disease progression and ultimately succumb to BC. Our study demonstrated that elevated expression of Heat Shock Protein Beta-6 (HSPB6) correlated with higher clinical grades and stages, establishing it as an independent prognostic risk factor for BC. Enrichment analysis indicated that HSPB6 is associated with the extracellular matrix in BC. Experimental validation revealed that HSPB6 overexpression inhibits the proliferation of BC cell line T24. This effect may be achieved by inhibiting the PI3K/Akt signaling pathway, which in turn leads to inhibition of epithelial-mesenchymal transition (EMT). Furthermore, we developed a prognostic risk model that incorporated DDR2, DPYSL3, MFAP5, PDGFRB, and SPOCD1, allowing accurate prediction of patient outcomes based on immunological status. In conclusion, this study highlights that HSPB6 overexpression can restrain the proliferation of BC cells and inhibit EMT, underscoring its potential as a diagnostic marker and therapeutic target in BC.
23 Apr 2026
Read appraisal →Chemical Society reviews
Machine learning-driven molecular engineering of nucleic acids
Molecular engineering has played a pivotal role in biomedical fields, driving significant advancements in gene therapy, disease diagnosis, and biosensing. However, nucleic acid molecular engineering faces various challenges including vast design spaces, complex structure-function relationships, lengthy application validation cycles, and inefficient optimization processes. Machine learning (ML), with its superior pattern recognition, multidimensional data integration, and automated optimization capabilities, offers a unique opportunity to construct predictive models of sequence-structure-function relationships, thereby enabling a paradigm shift from empirically driven to data-driven approaches. This review systematically surveys recent progress in ML applications across three major domains: nucleic acid structure construction, performance modulation, and application expansion. It also explores core challenges such as data quality, model interpretability, and experimental validation efficiency, along with potential resolution strategies. These insights are poised to propel nucleic acid molecular engineering from static structure prediction toward dynamic behavior simulation, and from single-molecule design to complex system engineering, guiding future directions in hybrid ML-quantum models and expanded applications to non-canonical nucleic acids for transformative innovation in biomedicine, environmental monitoring, and information technology.
20 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Making invisible excited-state structures of pro-interleukin-18 visible by combining NMR and machine learning
NMR spectroscopy is a powerful tool for studies of protein dynamics over timescales extending more than twelve orders of magnitude, with motion queried at many of the backbone and sidechain positions in the molecule of interest. NMR experiments can, in principle, provide atomic resolution descriptions of excited conformational states that are sparsely populated and transiently formed-i.e., invisible, and there are examples of such structures in the literature. However, in other cases, the NMR data are not sufficient for generating structural ensembles of rare and transient higher energy conformers of the protein of interest. One example is provided by the precursor form of the proinflammatory cytokine interleukin-18, pro-IL-18, studied here. Although NMR studies show that pro-IL-18 adopts two sparsely populated (<0.5%) and transiently formed (ms lifetimes) excited-state conformations in exchange with a highly populated ground state conformer and localize regions undergoing exchange to a pair of short β-strands that are preserved in at least one of the excited states, additional structural information is not forthcoming. Here, we develop a protocol whereby the NMR data are used to select alternative conformers of pro-IL-18 from ensembles predicted by the generative machine learning model AlphaFlow that are then evaluated through further NMR experiments. The method identifies distinct conformers that correspond to the pair of excited states reported by NMR, combining experiment with computation to characterize the pro-IL-18 energy landscape in ways that are not possible with either approach alone.
20 Apr 2026
Read appraisal →Cell systems
Context-informed subgraph foundation models enable interpretable protein-function prediction
Protein-function prediction is crucial for elucidating molecular mechanisms driving biological processes and therapeutics development. Despite numerous computational tools demonstrating promising performance, they fall short when predicting rare, uncharacterized functions or indirect activities. Here, we present COSMOS, a context-aware Gene Ontology (GO) subgraph mining system for protein-function prediction. By leveraging inductive subgraph foundation models and an enriched knowledge graph of protein-GO relationships, COSMOS performs zero-shot, few-shot, and low-homology protein-function prediction. Built on 7,923,952 functional semantic relationships, COSMOS demonstrates robust capabilities to (1) generate state-of-the-art predictions for GO classes with sparse or no experimental annotations, (2) provide interpretable functional subgraphs for transparent rationale analysis, and (3) deliver complementary benefits when integrated with existing embedding-based prediction methods. We anticipate that COSMOS will serve as a complementary approach to conventional protein annotation methods and an interpretable tool for predicting protein functions within underexplored GO classes, thereby advancing genomics and therapeutic research.
18 Apr 2026
Read appraisal →Epigenetics
The role of R-loops-mediated epigenetic regulation in genome stability maintenance and disease pathogenesis: a systematic review.
This systematic review synthesizes current evidence linking R-loops - associated processes with epigenetic regulation, genome stability, and human disease, integrating findings across DNA/RNA/protein modifications and 3D genome organization, and highlighting key mechanistic and technical gaps relevant to future studies, including longer-term translational exploration. Following PRISMA guidelines (Registration No.: INPLASY202560055), PubMed and ScienceDirect databases were searched through 18 January 2026. Two independent reviewers conducted blinded screening via the Rayyan platform. From 1290 initial articles, 71 studies met inclusion criteria. Methodological rigour was assessed using a modified SYRCLE risk-of-bias tool. Thematic synthesis identifies R-loops as recurrent intermediates linking transcription, chromatin regulation, and genome maintenance; feedback-like relationships are reported in selected systems. They spatially impede DNMTs to sustain promoter hypomethylation while recruiting TET enzymes for demethylation, and RNA modifications (e.g. m5C/m6A) may tune hybrid stability and, in some contexts, influence effector recruitment. R-loops facilitate the recruitment of histone-modifying complexes (PRC1/PRC2, G9a/GLP), thereby influencing chromatin landscapes and 3D genome architecture - including associations with TAD boundary insulation and with LLPS-related repair compartments in specific contexts. Dysregulation of R-loop homeostasis links to disease-related epigenetic alterations and genome instability with distinct disease-, locus- and cell-state specificity, and its context-dependent roles in diverse disorders involve transcriptional silencing, DNA damage, replication-transcription conflicts, repair pathways, chromatin regulation and mitochondrial genome maintenance. Integrating DRIP-seq with epigenomic profiling and functional tests supports mapping of context-specific relationships.
14 Apr 2026
Read appraisal →Journal of computer-aided molecular design
Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach
Cyclin-dependent kinase 16 (CDK16), a serine/threonine protein kinase, is a critical regulator of cell cycle progression, vesicle trafficking, and apoptosis. Its dysregulation is implicated in the progression of aggressive cancers, including triple-negative breast, lung, and prostate cancer, where its overexpression correlates with poor prognosis. Despite its therapeutic promise, CDK16 remains an understudied kinase lacking selective inhibitors, underscoring the need for innovative discovery approaches. This study introduces a novel computational framework that leverages multiple docked poses to augment datasets for regression-based machine learning (ML) models targeting CDK16 inhibition. This data augmentation strategy incorporated docking scores, ligand-receptor contact fingerprints (LRCFs), and conformation-sensitive physicochemical descriptors as input features. To our knowledge, this is the first application of docked pose augmentation for a regression-based drug discovery model. A systematic evaluation of eight ML algorithms across multiple docking score consensus levels identified Gradient Boosted Trees as the optimal learner. The Genetic Function Algorithm was integrated to select a minimal set of descriptors, which improved model generalizability. The resulting validated ML-QSAR model guided the generation of a robust pharmacophore model, which was used for virtual screening of the NCI and OpnMe databases. Subsequent in vitro LanthaScreen kinase assays confirmed two novel and potent CDK16 inhibitors: compound H_28 (OpnMe code: BI-831266) with an IC50 of 3.5 µM, and H_33 (OpnMe code: BI-1282) with an IC50 of 5.8 µM. These findings highlight the efficacy of integrating docked pose augmentation with regression modeling and experimental validation, offering a robust framework for accelerating the discovery of targeted anticancer agents.
7 Apr 2026
Read appraisal →Science
Deep generative models design mRNA sequences with enhanced translational capacity and stability
Despite the success of messenger RNA (mRNA) COVID-19 vaccines, extending this modality to more diseases necessitates substantial enhancements. We present GEMORNA, a generative RNA model that uses transformer architectures tailored for mRNA coding sequences (CDSs) and untranslated regions (UTRs) to design mRNAs with enhanced expression and stability. GEMORNA-designed full-length mRNAs exhibited up to a 41-fold increase in firefly luciferase expression compared with an optimized benchmark in vitro. GEMORNA-generated therapeutic mRNAs achieved up to a 15-fold enhancement in human erythropoietin (EPO) expression and substantially elicited antibody titers of COVID vaccine in mice. Additionally, GEMORNA's versatility extends to circular RNA, substantially enhancing circular EPO expression and boosting antitumor cytotoxicity in chimeric antigen receptor T cells. These advancements highlight the vast potential of deep generative artificial intelligence for mRNA therapeutics.
1 Nov 2025
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