Staff, Nathan P

Importance The etiology of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, is unknown. However, neuroinflammation and complement activation may play a role in disease progression. Objective To determine the effects of zilucoplan, an inhibitor of complement C5, in individuals with ALS. Design, Setting, and Participants Zilucoplan was tested as regimen A of the HEALEY ALS Platform Trial, a phase 2 to 3 multicenter, randomized, double-blind, placebo-controlled perpetual platform clinical trial with sharing of trial infrastructure and placebo data across multiple regimens. Regimen A was conducted from August 17, 2020, to May 4, 2022. A total of 162 participants were randomized to receive zilucoplan (122 [75.3%]) or regimen-specific placebo (40 [24.7%]). An additional 124 concurrently randomized participants were randomized to receive placebo in other regimens. Interventions Eligible participants were randomized in a 3:1 ratio to receive zilucoplan or matching placebo within strata of edaravone and/or riluzole use for a planned duration of 24 weeks. Active drug (zilucoplan, 0.3 mg/kg) and placebo were provided for daily subcutaneous dosing. Main Outcomes and Measures The primary end point was change in disease severity from baseline through 24 weeks as measured by the Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised (ALSFRS-R) total score and survival, analyzed using a bayesian shared-parameter model and reported as disease rate ratio (DRR; <1 indicating treatment benefit). The study included prespecified rules for early stopping for futility. Outcome analyses were performed in the full analysis set comparing the zilucoplan group with the total shared placebo group (n = 164). Results Among the 162 participants who were randomized (mean [SD] age, 59.6 [11.3]; 99 [61.1%] male), 115 (71.0%) completed the trial. The estimated DRR common to ALSFRS-R and survival was 1.08 (95% credible interval, 0.87-1.31; posterior probability of superiority, 0.24). The trial was stopped early for futility. No unexpected treatment-related risks were identified. Conclusions and Relevance In this randomized clinical trial of zilucoplan in ALS, treatment did not alter disease progression. The adaptive platform design of the HEALEY ALS Platform Trial made it possible to test a new investigational product with efficient use of time and resources. Trial Registration ClinicalTrials.gov Identifier: NCT04297683

Abstract Nerve conduction F-wave studies contain crucial information about subclinical motor dysfunction that can be used to diagnose patients with amyotrophic lateral sclerosis (ALS). However, F-wave responses are highly variable in morphology, making waveform interpretation challenging. Artificial intelligence techniques can extract time–frequency features to provide new insights into ALS diagnosis and prognosis. A retrospective analysis was performed on F-wave responses from 46 802 patients. Discrete wavelet transforms were applied to time-series waveform responses after stimulating ulnar, median, fibular and tibial nerves. Wavelet coefficient statistics, onset age, sex and body mass index were features for training a Gradient Boosting Machine model on 40 095 patients (5329 diagnosed with motor neuron disease). Model performance was tested on responses from 689 ALS patients meeting Gold Coast criteria and 689 age- and sex-matched controls. An exploratory analysis examined model performance on cohorts of patients with inclusion body myositis, cervical radiculopathy, lumbar radiculopathy or peripheral neuropathy, which can mimic ALS symptoms. Factors affecting survival were estimated through Cox proportional hazards regression. The model trained using wavelet features on the full waveform had 90% recall, 87% precision and 88% accuracy. Similar model performance was measured using features from only the M-wave or F-wave. Classification probabilities for ALS patients were statistically different from the diagnoses mimicking ALS symptoms (P < 0.001, ANOVA, Tukey’s post hoc test). Higher model classification probabilities of ALS, older age at onset, and family history of ALS alone or with frontotemporal dementia were factors decreasing survival. Longer diagnostic delay and upper limb onset site were factors increasing survival. Model scores two standard deviations below the mean had 4 months increased survival (two standard deviations below had 3 months decreased survival). Artificial intelligence techniques extracted important information from F-wave responses to estimate a patient’s likelihood of ALS and their survival risks. Although the model can make predictions at a specific decision threshold as presented here, the true strength of such a model lies in its ability to provide probabilities about whether a patient is likely to have ALS in comparison to other mimicking diagnoses, such as inclusion body myositis, cervical or lumbar radiculopathy or peripheral neuropathy. These probabilities provide clinicians with additional information that they can use to make the final diagnosis with greater confidence and precision. Integrating such a model into the clinical workflow could help clinicians to diagnose ALS sooner and manage treatment based on estimated survival, which might improve outcomes and the quality of life of patients.

Abstract Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analyses. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein–protein interaction analysis, and major histocompatibility complex (MHC)–peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical post-processing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface.

Abstract Introduction/Aims Biomarkers have shown promise in amyotrophic lateral sclerosis (ALS) research, but the quest for reliable biomarkers remains active. This study evaluates the effect of debamestrocel on cerebrospinal fluid (CSF) biomarkers, an exploratory endpoint. Methods A total of 196 participants randomly received debamestrocel or placebo. Seven CSF samples were to be collected from all participants. Forty‐five biomarkers were analyzed in the overall study and by two subgroups characterized by the ALS Functional Rating Scale‐Revised (ALSFRS‐R). A prespecified model was employed to predict clinical outcomes leveraging biomarkers and disease characteristics. Causal inference was used to analyze relationships between neurofilament light chain (NfL) and ALSFRS‐R. Results We observed significant changes with debamestrocel in 64% of the biomarkers studied, spanning pathways implicated in ALS pathology (63% neuroinflammation, 50% neurodegeneration, and 89% neuroprotection). Biomarker changes with debamestrocel show biological activity in trial participants, including those with advanced ALS. CSF biomarkers were predictive of clinical outcomes in debamestrocel‐treated participants (baseline NfL, baseline latency‐associated peptide/transforming growth factor beta1 [LAP/TGFβ1], change galectin‐1, all p < .01), with baseline NfL and LAP/TGFβ1 remaining ( p < .05) when disease characteristics ( p < .005) were incorporated. Change from baseline to the last measurement showed debamestrocel‐driven reductions in NfL were associated with less decline in ALSFRS‐R. Debamestrocel significantly reduced NfL from baseline compared with placebo (11% vs. 1.6%, p = .037). Discussion Following debamestrocel treatment, many biomarkers showed increases (anti‐inflammatory/neuroprotective) or decreases (inflammatory/neurodegenerative) suggesting a possible treatment effect. Neuroinflammatory and neuroprotective biomarkers were predictive of clinical response, suggesting a potential multimodal mechanism of action. These results offer preliminary insights that need to be confirmed.

Functional loss of TDP-43, an RNA binding protein genetically and pathologically linked to amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), leads to the inclusion of cryptic exons in hundreds of transcripts during disease. Cryptic exons can promote the degradation of affected transcripts, deleteriously altering cellular function through loss-of-function mechanisms. Here, we show that mRNA transcripts harboring cryptic exons generated de novo proteins in TDP-43–depleted human iPSC–derived neurons in vitro, and de novo peptides were found in cerebrospinal fluid (CSF) samples from patients with ALS or FTD. Using coordinated transcriptomic and proteomic studies of TDP-43–depleted human iPSC–derived neurons, we identified 65 peptides that mapped to 12 cryptic exons. Cryptic exons identified in TDP-43–depleted human iPSC–derived neurons were predictive of cryptic exons expressed in postmortem brain tissue from patients with TDP-43 proteinopathy. These cryptic exons produced transcript variants that generated de novo proteins. We found that the inclusion of cryptic peptide sequences in proteins altered their interactions with other proteins, thereby likely altering their function. Last, we showed that 18 de novo peptides across 13 genes were present in CSF samples from patients with ALS/FTD spectrum disorders. The demonstration of cryptic exon translation suggests new mechanisms for ALS/FTD pathophysiology downstream of TDP-43 dysfunction and may provide a potential strategy to assay TDP-43 function in patient CSF.

AbstractIntroduction/AimsFemoral neuropathies can cause severe, prolonged debility, yet there have been few clinical and electrodiagnostic (EDx) studies addressing this condition. The aim of this study was to better understand the etiologies, EDx features, and clinical course of femoral neuropathy.MethodsWe identified patients evaluated at Mayo Clinic Rochester between January 1, 1999 and July 31, 2019, with possible new femoral neuropathy ascertained via International Classification of Diseases‐versions 9 and 10 diagnosis codes presenting within 6 months of symptom onset.ResultsA retrospective review of 1084 records was performed and we ultimately identified 159 patients with isolated femoral neuropathy for inclusion. The most common femoral neuropathy etiologies were compressive (40%), perioperative stretch (35%), and inflammatory (6%). Presenting symptoms included weakness (96%), sensory loss (73%), and pain (53%). Presenting motor physical exam findings demonstrated moderate weakness (34%) or no activation (25%) of knee extension and mild (32%) or moderate (35%) weakness of hip flexion. Seventy‐two percent of patients underwent EDx testing, including 22 with femoral motor nerve conduction studies. Treatment often involved physical therapy (89%) and was otherwise etiology‐specific. In patients with follow‐up data available (n = 154), 83% had subjective clinical improvement at follow‐up with a mean time to initial improvement of 3.3 months and mean time to recovery at final follow‐up of 14.8 months. Only 48% of patients had nearly complete or complete recovery.DiscussionIn our cohort, the most common etiologies of femoral neuropathy were compression or perioperative stretch with high initial morbidity. Although motor recovery is common, improvement is often prolonged and incomplete.

The chemotherapeutic agent paclitaxel causes peripheral neuropathy, a dose-limiting side effect, in up to 68% of cancer patients. In this study, we investigated the impact of paclitaxel therapy on the skin of breast cancer patients with chemotherapy-induced peripheral neuropathy (CIPN), building upon previous findings in zebrafish and rodents. Comprehensive assessments, including neurological examinations and quality of life questionnaires, were conducted, followed by intraepidermal nerve fiber (IENF) density evaluations using skin punch biopsies. Additionally, RNA sequencing, immunostaining for Matrix-Metalloproteinase 13 (MMP-13), and transmission electron microscopy provided insights into molecular and ultrastructural changes in this skin. The results showed no significant difference in IENF density between the control and CIPN patients despite the presence of patient-reported CIPN symptoms. Nevertheless, the RNA sequencing and immunostaining on the skin revealed significantly upregulated MMP-13, which is known to play a key role in CIPN caused by paclitaxel therapy. Additionally, various genes involved in the regulation of the extracellular matrix, microtubules, cell cycle, and nervous system were significantly and differentially expressed. An ultrastructural examination of the skin showed changes in collagen and basement membrane structures. These findings highlight the presence of CIPN in the absence of IENF density changes and support the role of skin remodeling as a major contributor to CIPN.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with a median survival of about 3 years. An ALS multidisciplinary team can provide primary palliative care and improve outcomes and quality of life for patients. Feeding tube insertion may be considered for patients with significant weight loss, or respiratory insufficiency. While radiologically inserted gastrostomy (RIG) tube placement may be an option, further studies are required to determine its best timing and appropriateness. This study’s objectives were to evaluate the feasibility and outcomes of RIG tube placement in ALS patients over a 90-day follow-up period through the assessment and primary palliative care provided by the multidisciplinary team. This retrospective study reviewed the placement of 16 or 18 French RIG-tube without intubation or endoscopy for 36 ALS patients at a single center between April 2019 and December 2021. Measures included ALS Functional Rating Scale-Revised (ALSFRS-R) scores to determine the ALS stage. Demographic, clinical, procedural, and follow-up data were reviewed. Results showed that the RIG tube placement had a low rate of minor adverse events (11%) and no major procedure-related adverse events. The mean ALSFRS-R score at the time of procedure in subjects who died within 90 days was lower than of those alive beyond 90 days ( P = .04). This study found that RIG-tube placement is a safe and effective way to manage dysphagia in ALS patients and highlights the importance of educating members of the multidisciplinary clinic in palliative care principles to determine the appropriateness of RIG tube placement.

Abstract Objective To identify potential diagnostic and prognostic biomarkers for clinical management and clinical trials in amyotrophic lateral sclerosis. Methods We analysed proteomics data of ALS patient‐induced pluripotent stem cell‐derived motor neurons available through the AnswerALS consortium. After stratifying patients using clinical ALSFRS‐R and ALS‐CBS scales, we identified differentially expressed proteins indicative of ALS disease severity and progression rate as candidate ALS‐related and prognostic biomarkers. Pathway analysis for identified proteins was performed using STITCH. Protein sets were correlated with the effects of drugs using the Connectivity Map tool to identify compounds likely to affect similar pathways. RNAi screening was performed in a Drosophila TDP‐43 ALS model to validate pathological relevance. A statistical classification machine learning model was constructed using ridge regression that uses proteomics data to differentiate ALS patients from controls. Results We identified 76, 21, 71 and 1 candidate ALS‐related biomarkers and 22, 41, 27 and 64 candidate prognostic biomarkers from patients stratified by ALSFRS‐R baseline, ALSFRS‐R progression slope, ALS‐CBS baseline and ALS‐CBS progression slope, respectively. Nineteen proteins enhanced or suppressed pathogenic eye phenotypes in the ALS fly model. Nutraceuticals, dopamine pathway modulators, statins, anti‐inflammatories and antimicrobials were predicted starting points for drug repurposing using the connectivity map tool. Ten diagnostic biomarker proteins were predicted by machine learning to identify ALS patients with high accuracy and sensitivity. Interpretation This study showcases the powerful approach of iPSC‐motor neuron proteomics combined with machine learning and biological confirmation in the prediction of novel mechanisms and diagnostic and predictive biomarkers in ALS.