Amyloid-Journal of Protein Folding Disorders最新文献

Background: The amyloid-β peptide 42 (Aβ42) forms fibrillar aggregates that are a hallmark of Alzheimer's disease. While recent therapeutic strategies targeting Aβ42 fibrils and oligomers have shown promise, safer and more effective interventions are still needed. Noninvasive brain stimulation (NIBS) techniques such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) have emerged as potential complementary approaches, yet the molecular mechanisms by which electric fields influence amyloid aggregation remain poorly understood.

Methods: We performed atomistic molecular dynamics simulations to investigate the response of Aβ42 fibrils to static electric fields of increasing strength. Simulations were based on an ex vivo fibril structure with reconstructed N-terminal regions, and different structural restraint conditions were used to disentangle surface and core effects.

Results: Electric fields perturb the disordered N-terminal 'fuzzy coat', altering its conformational dynamics and weakening its interactions with the fibril core, thereby modifying the fibril surface properties. Simulations with unrestrained fibril ends further reveal increased fluctuations in core residues, indicating field-induced destabilization that may hinder elongation.

Conclusions: These findings provide molecular-level insight into how static electric fields can modulate amyloid fibril formation and propagation, offering a possible mechanistic basis for the effects of tDCS and related brain stimulation techniques.

Background: Atrial fibrillation (AF) is common and poorly tolerated in cardiac amyloidosis (CA) patients. No current tool assesses AF risk in this population.

Methods: We enrolled patients with light chain (AL) or transthyretin (ATTR) CA, with no prior history of AF at diagnosis between January 2015 and September 2023 across three university hospitals. Clinical, biochemical and electrocardiographic parameters were retrospectively collected and their predictive value for AF was assessed.

Results: A total of 169 patients were included (56.2% wild-type (wt) ATTR, 35.5% AL and 8.3% variant (v) ATTR). Over a median follow-up of 21 months, 55 patients (33%) presented a first episode of AF. Five independent predictors for AF were identified using multivariate logistic regression model: obstructive sleep apnea, hypertension, CA subtype, P wave duration ≥120 ms, 1st degree atrioventricular (AV) block. Based on these variables, the Amy-Lyon AF score (range 0-46) was developed to estimate AF risk (area under the receiver operating characteristic (ROC) curve (AUC) 0.768 [0.685-0.852], p < .001). At two years of follow-up, the incidence of AF reached 82.7% in patients with a score >28. In 80 patients, the AUC of the Amy-Lyon AF score was higher than that of left atrial strain.

Conclusions: Five simple and readily available predictors may stratify the risk of AF in CA.

Background: In hereditary transthyretin (ATTRv) amyloidosis, axonal neuropathy is the main pathologic basis of polyneuropathy. However, whether nerve conduction slowing resembling chronic inflammatory demyelinating polyneuropathy (CIDP), observed in some patients, reflects primary demyelination or secondary changes related to axonal damage remains unclear.

Methods: Electrophysiologic findings were compared between 96 patients with V30M ATTRv amyloidosis from nonendemic areas and 175 patients with nutritional or alcoholic axonal neuropathies. Myelinated fiber density was assessed in sural nerve biopsy specimens from 66 and 133 patients, respectively.

Results: Motor nerve conduction velocity (MCV) was reduced and distal motor latency (DML) was prolonged in both groups compared with normal controls, with more marked changes in ATTRv amyloidosis. Compound muscle action potential (CMAP) amplitude and myelinated fiber density were significantly lower in ATTRv amyloidosis than in nutritional/alcoholic neuropathies (p < 0.001). Conduction slowing fulfilling EAN/PNS CIDP criteria was observed only in patients with severe axonal loss. After adjusting for age, disease duration, and CMAP amplitude, only MCV slowing and DML prolongation in median nerve remained more pronounced in ATTRv amyloidosis.

Conclusions: CIDP-like conduction slowing in ATTRv amyloidosis largely represents secondary changes caused by axonal degeneration, although additional mechanisms may contribute to slowing in the median nerve.

Aims: We sought to investigate the prevalence of Transthyretin amyloid (ATTR) deposits in the carpal ligament of patients undergoing surgery for carpal tunnel syndrome (CTS) and the frequency of concomitant ATTR-cardiomyopathy (CM) in a prospective study with long-term follow-up.

Methods and results: Our prospective exploratory study enrolled 551 patients treated for CTS. All resection specimens of carpal ligament underwent histopathological evaluation. Patients with confirmed amyloid deposits were referred to cardiac evaluation, including cardiac MRI (CMR) scans, as well as follow-up examinations. Amyloid deposits were found in 52 (9%) specimens (1 light chain amyloid, 51 ATTR). Among patients with positive biopsy ATTR-CM was diagnosed in 4 (8%) patients, 1 patient was diagnosed at initial evaluation, and 3 patients were diagnosed during the follow-up. All patients had ATTRwt at an early stage of disease. Extracellular volume (ECV) measured by CMR was significantly higher in amyloid-positive CTS patients compared to healthy controls (30 ± 2 vs. 24 ± 1%, p < .01).

Conclusion: Histopathological evaluation of resected carpal ligament should be considered in patients at least older than 70 years. Even though absolute numbers were low, cardiac screening of patients with evidence of amyloid deposits in in carpal tunnel tissues might facilitate early diagnosis of ATTR-CM in some patients.

Background: Acquired transthyretin (ATTR) amyloidosis is increasingly reported among domino liver transplantation (DLT) recipients who receive livers from patients with hereditary variant TTR (ATTRv) amyloidosis. However, its long-term outcomes and the effects of disease-modifying drugs remain unclear.

Methods: We retrospectively analyzed 30 DLT recipients who received liver grafts from ATTRv amyloidosis patients. Longitudinal evaluations included clinical scores, nerve conduction studies (NCS),¹²³I-metaiodobenzylguanidine scintigraphy, echocardiography, electrocardiography,^99mTc-pyrophosphate scintigraphy, and serum biomarkers.

Results: Overall survival at 1, 3, and 10 years after DLT was 86.7%, 76.7%, and 57.7%, respectively. Amyloid deposition occurred in 17 recipients, with a median time from DLT to deposition of 7.7 years (range, 3.1-9.0 years). Among the 12 patients followed for >3 years after amyloid detection, tafamidis clinically stabilized neuropathy in most cases, however, NCSs revealed progressive subclinical axonal degeneration. Two patients experienced clinically significant neuropathy progression during tafamidis treatment, which was stabilized after switching to siRNA therapy. Clinically significant cardiac amyloidosis developed in only one patient.

Conclusions: Acquired ATTR amyloidosis frequently develops in long-term DLT recipients. Although tafamidis stabilizes clinical manifestations in most patients, it may not completely prevent disease progression in some cases. Further long-term evaluation is needed to determine optimal treatment strategies, including siRNA therapy.

Background: Congo red (CR) staining of surrogate tissues, such as the abdominal fat pad (FP) or bone marrow (BM), provides a minimally invasive approach to diagnosing systemic amyloidosis. This study evaluated the diagnostic yield of surrogate tissue biopsies across amyloidosis classes.

Methods: We retrospectively analyzed 4,027 patients with systemic amyloidosis (1968-2023) who underwent CR staining of FP aspirates (n = 3,873) and/or BM core biopsies (n = 2,598). Detection rates were compared by amyloidosis class and biopsy site.

Results: AL amyloidosis had the highest CR positivity rates (FP: 73%; BM: 53%), whereas ATTRwt amyloidosis had the lowest (FP: 22%; BM: 26%). CR positivity in BM was not exclusive to AL amyloidosis; 74 CR-positive BM biopsies were in non-AL cases. Among 2,213 patients with AL amyloidosis who underwent both FP and BM sampling, combined testing increased detection to 85% (40% positive in both; 32% FP-only; 13% BM-only). Higher CR positivity grades in FP aspirates correlated with shorter survival in AL amyloidosis but not in other amyloidosis classes.

Conclusions: The diagnostic yield of CR-stained surrogate tissue biopsies varies by amyloidosis class and biopsy site. In AL amyloidosis, combining FP and BM sampling enhances detection, and FP CR positivity grade offers prognostic insight.

Background: Predicting amyloidogenic risk of immunoglobulin light chains in amyloid light-chain (AL) amyloidosis is a major challenge, and existing computational models fail to generalize to new patient data.

Methods: We developed ALyzer3D.AI, a multi-modal deep learning architecture that integrates evolutionary features from the ESM-2 Protein Language Model, structural metrics from ColabFold and engineered biophysical features. Generalizability was evaluated on published and independent datasets, and model interpretability was assessed using IMGT-aligned SHapley Additive exPlanations (SHAP).

Results: While existing models dropped in performance on new data (accuracy 0.42-0.56), ALyzer3D.AI maintained an accuracy of 0.65 when trained and tested on the same datasets. A final model built on the full combined cohort of 5261 sequences achieved stable performance across repeated splits, with independent test AUC of 0.86 and accuracy of 0.84 at an optimized decision threshold. Performance arose from synergistic integration of PLM, structural and scalar features. PLM attributions were broadly distributed and slightly enriched in framework regions, whereas structural SHAP values were concentrated in CDRs and FR4. The most informative positions had intermediate sequence entropy.

Conclusions: ALyzer3D.AI provides a robust, interpretable tool for predicting light-chain amyloidogenicity that generalizes better to independent datasets than existing methods. It is publicly available via https://colab.research.google.com/github/pcmay/ALyzer3D.AI/blob/main/ALyzer3DAI.ipynb.