Clinical experiment designed to assess clinical efficacy targeting AHR/BDNF/CASP1 in human. Primary outcome: Validate Microbiome-Gut Barrier Signatures in ALS — Experiment Design
Description
Microbiome-Gut Barrier Signatures in ALS — Experiment Design
Background and Rationale
This prospective longitudinal clinical study addresses the critical knowledge gap regarding microbiome-gut barrier dysfunction in ALS pathogenesis and progression. Growing evidence suggests that gut dysbiosis and intestinal barrier compromise may contribute to neuroinflammation and motor neuron degeneration through the gut-brain axis, but causal relationships remain unclear. The study employs a comprehensive multi-omics approach to characterize gut microbiome composition, intestinal permeability markers, and systemic inflammatory profiles in ALS patients compared to healthy controls and disease mimics. The design includes newly diagnosed ALS patients (n=150), age-matched healthy controls (n=100), and patients with other motor neuron diseases (n=50) followed for 24 months. Key measurements include 16S rRNA and shotgun metagenomic sequencing of fecal samples, serum zonulin and lipopolysaccharide levels as gut barrier markers, cytokine profiling, metabolomics analysis of short-chain fatty acids and other microbial metabolites, and comprehensive clinical assessments using ALSFRS-R scores....
Microbiome-Gut Barrier Signatures in ALS — Experiment Design
Background and Rationale
This prospective longitudinal clinical study addresses the critical knowledge gap regarding microbiome-gut barrier dysfunction in ALS pathogenesis and progression. Growing evidence suggests that gut dysbiosis and intestinal barrier compromise may contribute to neuroinflammation and motor neuron degeneration through the gut-brain axis, but causal relationships remain unclear. The study employs a comprehensive multi-omics approach to characterize gut microbiome composition, intestinal permeability markers, and systemic inflammatory profiles in ALS patients compared to healthy controls and disease mimics. The design includes newly diagnosed ALS patients (n=150), age-matched healthy controls (n=100), and patients with other motor neuron diseases (n=50) followed for 24 months. Key measurements include 16S rRNA and shotgun metagenomic sequencing of fecal samples, serum zonulin and lipopolysaccharide levels as gut barrier markers, cytokine profiling, metabolomics analysis of short-chain fatty acids and other microbial metabolites, and comprehensive clinical assessments using ALSFRS-R scores. The study's innovation lies in its longitudinal design capturing microbiome dynamics during disease progression, integration of multiple gut-barrier biomarkers with detailed clinical phenotyping, and inclusion of appropriate control groups to establish ALS-specific signatures. Advanced bioinformatics will identify microbial taxa and functional pathways associated with disease severity and progression rates. This research could revolutionize ALS understanding by establishing gut dysfunction as a therapeutic target and developing microbiome-based biomarkers for prognosis and treatment monitoring.
This experiment directly tests predictions arising from the following hypotheses:
Gut Barrier Permeability-α-Synuclein Axis Modulation
Enteric Nervous System Prion-Like Propagation Blockade
Phase 1 (Months 1-6): Recruit participants through ALS clinics and neurology departments. Obtain informed consent and baseline assessments including ALSFRS-R, demographic data, medication history, and dietary questionnaires. Collect baseline biosamples: fecal samples for microbiome analysis, blood for gut barrier markers (zonulin, LPS, I-FABP), and serum for cytokine profiling (IL-1β, TNF-α, IL-6, IL-10). Phase 2 (Months 3-24): Conduct longitudinal follow-up visits every 3 months. Repeat clinical assessments and biosample collection at each visit. Perform 16S rRNA sequencing using V3-V4 primers on Illumina MiSeq platform. Conduct shotgun metagenomic sequencing on subset of samples (n=60 per group) using Illumina NovaSeq. Measure gut permeability markers using ELISA kits: serum zonulin (Immundiagnostik), LPS (Hycult Biotech), and intestinal fatty acid binding protein. Analyze fecal and serum metabolites using LC-MS/MS targeting short-chain fatty acids, bile acids, and tryptophan metabolites. Phase 3 (Months 18-30): Integrate multi-omics data using machine learning approaches. Perform correlation analyses between microbiome features and clinical progression rates. Validate findings in independent cohort (n=75). Statistical analysis includes linear mixed-effects models for longitudinal data, differential abundance testing using DESeq2, and machine learning classification using random forest algorithms.
Expected Outcomes
1. ALS patients will demonstrate significantly reduced microbiome alpha diversity compared to controls (Shannon index 20-30% lower, p<0.01)
2. Identification of ALS-specific microbial signature characterized by depletion of beneficial bacteria (Bifidobacterium, Faecalibacterium) and enrichment of pro-inflammatory taxa (Enterobacteriaceae)
3. ALS patients will exhibit 2-3 fold higher serum zonulin and LPS levels indicating compromised gut barrier integrity (p<0.001)
4. Strong correlation between gut barrier dysfunction markers and disease progression rate (ALSFRS-R decline correlation coefficient r>0.6)
5. Reduced fecal short-chain fatty acid concentrations (butyrate, acetate) by 40-50% in ALS patients correlating with inflammatory cytokine elevation
6. Machine learning model incorporating microbiome and gut barrier features will predict disease progression with >80% accuracy (AUC>0.80)
Success Criteria
• Identification of reproducible ALS-specific microbiome signature with effect size >1.0 and FDR-corrected p<0.05
• Demonstration of significant gut barrier compromise in >60% of ALS patients vs <10% of controls
• Establishment of correlation between microbiome dysbiosis severity and ALSFRS-R progression rate (r>0.5, p<0.01)
• Development of microbiome-based classifier distinguishing ALS from controls and disease mimics with sensitivity >85% and specificity >80%
• Validation of gut-barrier biomarker panel showing 2-fold or greater difference between ALS patients and controls
• Publication of findings enabling translation to therapeutic interventions targeting gut-brain axis in ALS
TARGET GENE
AHR/BDNF/CASP1
MODEL SYSTEM
human
ESTIMATED COST
$6,550,000
TIMELINE
49 months
PATHWAY
N/A
SOURCE
wiki
PRIMARY OUTCOME
Validate Microbiome-Gut Barrier Signatures in ALS — Experiment Design
Phase 1 (Months 1-6): Recruit participants through ALS clinics and neurology departments. Obtain informed consent and baseline assessments including ALSFRS-R, demographic data, medication history, and dietary questionnaires. Collect baseline biosamples: fecal samples for microbiome analysis, blood for gut barrier markers (zonulin, LPS, I-FABP), and serum for cytokine profiling (IL-1β, TNF-α, IL-6, IL-10). Phase 2 (Months 3-24): Conduct longitudinal follow-up visits every 3 months. Repeat clinical assessments and biosample collection at each visit. Perform 16S rRNA sequencing using V3-V4 primers on Illumina MiSeq platform. Conduct shotgun metagenomic sequencing on subset of samples (n=60 per group) using Illumina NovaSeq.
...
Phase 1 (Months 1-6): Recruit participants through ALS clinics and neurology departments. Obtain informed consent and baseline assessments including ALSFRS-R, demographic data, medication history, and dietary questionnaires. Collect baseline biosamples: fecal samples for microbiome analysis, blood for gut barrier markers (zonulin, LPS, I-FABP), and serum for cytokine profiling (IL-1β, TNF-α, IL-6, IL-10). Phase 2 (Months 3-24): Conduct longitudinal follow-up visits every 3 months. Repeat clinical assessments and biosample collection at each visit. Perform 16S rRNA sequencing using V3-V4 primers on Illumina MiSeq platform. Conduct shotgun metagenomic sequencing on subset of samples (n=60 per group) using Illumina NovaSeq. Measure gut permeability markers using ELISA kits: serum zonulin (Immundiagnostik), LPS (Hycult Biotech), and intestinal fatty acid binding protein. Analyze fecal and serum metabolites using LC-MS/MS targeting short-chain fatty acids, bile acids, and tryptophan metabolites. Phase 3 (Months 18-30): Integrate multi-omics data using machine learning approaches. Perform correlation analyses between microbiome features and clinical progression rates. Validate findings in independent cohort (n=75). Statistical analysis includes linear mixed-effects models for longitudinal data, differential abundance testing using DESeq2, and machine learning classification using random forest algorithms.
Expected Outcomes
1. ALS patients will demonstrate significantly reduced microbiome alpha diversity compared to controls (Shannon index 20-30% lower, p<0.01)
2. Identification of ALS-specific microbial signature characterized by depletion of beneficial bacteria (Bifidobacterium, Faecalibacterium) and enrichment of pro-inflammatory taxa (Enterobacteriaceae)
3. ALS patients will exhibit 2-3 fold higher serum zonulin and LPS levels indicating compromised gut barrier integrity (p<0.001)
4.
...
1. ALS patients will demonstrate significantly reduced microbiome alpha diversity compared to controls (Shannon index 20-30% lower, p<0.01)
2. Identification of ALS-specific microbial signature characterized by depletion of beneficial bacteria (Bifidobacterium, Faecalibacterium) and enrichment of pro-inflammatory taxa (Enterobacteriaceae)
3. ALS patients will exhibit 2-3 fold higher serum zonulin and LPS levels indicating compromised gut barrier integrity (p<0.001)
4. Strong correlation between gut barrier dysfunction markers and disease progression rate (ALSFRS-R decline correlation coefficient r>0.6)
5. Reduced fecal short-chain fatty acid concentrations (butyrate, acetate) by 40-50% in ALS patients correlating with inflammatory cytokine elevation
6. Machine learning model incorporating microbiome and gut barrier features will predict disease progression with >80% accuracy (AUC>0.80)
Success Criteria
• Identification of reproducible ALS-specific microbiome signature with effect size >1.0 and FDR-corrected p<0.05
• Demonstration of significant gut barrier compromise in >60% of ALS patients vs <10% of controls
• Establishment of correlation between microbiome dysbiosis severity and ALSFRS-R progression rate (r>0.5, p<0.01)
• Development of microbiome-based classifier distinguishing ALS from controls and disease mimics with sensitivity >85% and specificity >80%
• Validation of gut-barrier biomarker panel showing 2-fold or greater difference between ALS patients and controls
• Publi
...
• Identification of reproducible ALS-specific microbiome signature with effect size >1.0 and FDR-corrected p<0.05
• Demonstration of significant gut barrier compromise in >60% of ALS patients vs <10% of controls
• Establishment of correlation between microbiome dysbiosis severity and ALSFRS-R progression rate (r>0.5, p<0.01)
• Development of microbiome-based classifier distinguishing ALS from controls and disease mimics with sensitivity >85% and specificity >80%
• Validation of gut-barrier biomarker panel showing 2-fold or greater difference between ALS patients and controls
• Publication of findings enabling translation to therapeutic interventions targeting gut-brain axis in ALS