EMBO Molecular Medicine最新文献

Salmonella Typhi secretes typhoid toxin that activates cellular DNA damage responses (DDR) during acute typhoid fever. Human infection challenge studies revealed that the toxin suppresses bacteraemia via unknown mechanisms. Using quantitative proteomic analysis on the plasma of bacteraemic participants, we demonstrate that wild-type toxigenic Salmonella induced secretion of lysozyme (LYZ) and apolipoprotein C3 (APOC3). Recombinant typhoid toxin or Salmonella infection recapitulated LYZ and APOC3 secretion in cultured cells, which involved ATM/ATR-dependent DDRs and confirmed observations in typhoid fever. LYZ caused spheroplast formation, inhibited the Salmonella type 3 secretion system, and intracellular infections. LYZ expression was regulated by p53 in a cell type-specific manner and driven by mitochondrial oxidative stress that caused nuclear DDRs and p53-mediated senescence responses. Addition of LYZ inhibited oxidative DNA damage and resulting senescence responses caused by typhoid toxin. Our findings may indicate that toxin-induced DDRs elicit antimicrobial responses, which suppress Salmonella bacteraemia during typhoid fever.

The biomechanical signature is directly correlated with the progression of disease in multiple soft tissues. However, their variations and roles, particularly during the initiation period of the disease, remain unclear. Here, we report that PM2.5 exposure induces corneal biomechanical cues alterations prior to corneal injury, as evidenced by increased corneal hysteresis in humans, thickened corneal thickness in rats, and enhanced tensile stress and cortical stiffness in HCECs. Specifically, intracellular PAI-2 is identified as a crucial mediator of the biomechanical responses in HCECs. It modulates PM2.5-induced autophagy and inflammation through a PAI-2/myosin II/F-actin/YAP-positive feedback loop, which ultimately drives HCEC injury. Furthermore, extracellular secretory PAI-2 levels in tears reflect PM2.5-related corneal damage in real time, making it a specific biomarker for the early diagnosis when combined with biomechanical cues. Early intervention for PM2.5-induced ocular damage could be achieved by developing an LNP-siPAI-2 ocular local delivery system targeting intracellular PAI-2. Overall, we propose that biomechanical cues in conjunction with specific biomarkers may serve as targets for the early diagnosis and intervention of soft tissue diseases.

Spinal muscular atrophy (SMA) results from SMN1 gene loss-of-function (LOF), with disease severity directly linked to the level of remaining SMN protein. Nusinersen, risdiplam, and onasemnogene abeparvovec are revolutionary treatments but should ideally be implemented before clinical symptoms appear. Because of this, prenatal and newborn screenings are increasingly used to identify common SMN1 variants and patients requiring therapy. However, for novel variants, clinicians lack robust analytic tools to predict pathogenicity before irreversible damage occurs. To address this gap, we deployed a zebrafish model presenting smn1-LOF, exhibiting progressive motor defects and death by only six days of age. We evaluated two SMN1-variants of uncertain significance (VUS) identified in newborn infants awaiting definite diagnosis and treatment recommendations. We demonstrated that while known pathogenic variants did not change the disease course, wild-type SMN1 and both infants variants rescued SMA hallmarks in zebrafish, demonstrating the relevance of this approach for VUS-testing within a crucial timeframe for patients. Extending the assay to known SMN1-hypomorphs showed partial rescue, weaker than wild-type or VUS, demonstrating that this approach can also discriminate partial-LOF effects. Both VUS were resolved to be non-pathogenic, and the therapeutic costs of >US$2 million per child were avoided. Beyond SMA, this study provides robust proof-of-principle that the zebrafish represents a powerful translational tool for VUS-analysis, and that such approaches should be considered in clinical settings for supporting diagnosis and treatment decisions.

Recent advancements in artificial intelligence (AI) have revolutionized the application of electrocardiography (ECG) in cardiovascular diagnostics. This review highlights the transformative impact of AI on traditional ECG analysis, detailing how deep learning algorithms are overcoming the limitations of human interpretation and conventional diagnostic criteria. Historically, ECG interpretation has relied on well-established, physiologically-based criteria. The advancement of AI-ECG is marked by its capacity to process complex high-dimensional data directly from raw signals, revealing patterns often missed by conventional methods. Notably, AI models have identified signs of asymptomatic low ejection fraction and paroxysmal atrial fibrillation during normal sinus rhythm, enabling earlier clinical intervention. In addition to improved diagnostic utility, AI-ECG offers promising applications in risk stratification and community screening. Several randomized controlled trials (RCTs) have shown that integrating AI into clinical workflows not only reduces critical intervention times but also identifies patients at elevated risk of adverse outcomes. Future directions involve integrating additional clinical data sources, improving model interpretability through explainable AI, and developing unified platforms to manage outputs from multiple models.

Brain amyloid-β (Aβ) pathology is a core feature of Alzheimer disease (AD) and can be quantified using positron emission tomography (PET). Cerebrospinal fluid (CSF) and plasma biomarkers detect abnormal Aβ, but it is unclear to what degree they can predict quantitative Aβ-PET. We explored plasma and CSF biomarkers in relation to Aβ-PET in the BioFINDER-2 study (N = 1053), and the BioFINDER-1 study (N = 238). We developed a machine learning pipeline to predict Aβ-PET using CSF and plasma measures. The best models achieved R² = 0.79. Plasma P-tau217 and CSF Aβ42/Aβ40 contributed the most. CSF Aβ42/Aβ40 contributed most to identify Aβ-positivity, while continuous Aβ-PET load within the positive range was best predicted by plasma P-tau217. Models using only plasma measures approached performance of CSF models. Altered metabolism of soluble Aβ may be highly associated with presence of Aβ plaques, while soluble P-tau217 levels may continue to change during build-up of Aβ pathology.