Huntington disease somatic expansion: CAG repeat somatic instability in striatal neurons driven by MMR pathway — MSH3 inhibition reduces expansion rate and delays phenotype in mouse models

Genetic or pharmacological inhibition of MSH3 (a mismatch repair pathway component) in striatal neurons of Huntington's disease (HD) mouse models carrying expanded CAG repeats (≥100 CAG) will reduce the rate of somatic CAG repeat expansion by ≥30% compared to wild-type MSH3 controls, and this reduction will correlate with a statistically significant delay (≥15%) in onset of motor and behavioral phenotypes as measured by rotarod performance, open-field activity, and striatal neuronal loss at matched timepoints.

Minimum Viable Test (MVT) — 3-arm study using zQ175 knock-in mice:

• Arm 1: zQ175 heterozygous; Msh3+/+ (HD control, n=20/sex)
• Arm 2: zQ175 heterozygous; Msh3+/− (partial inhibition, n=20/sex)
• Arm 3: zQ175 heterozygous; Msh3−/− (full inhibition, n=20/sex)
• Arm 4: Wild-type C57BL/6; Msh3+/+ (non-HD control, n=10/sex)

Primary endpoint: Somatic CAG repeat length distribution in striatal DNA at 6 and 12 months via small-pool PCR (SP-PCR) and Oxford Nanopore long-read sequencing. Secondary endpoints: Rotarod (4-40 rpm accelerating, 5 trials/week from month 3), open-field (30 min, monthly), grip strength (monthly), body weight (weekly). Tertiary endpoints: Striatal volume by MRI at 6 and 12 months; DARPP-32 IHC; HTT aggregate burden (EM48 antibody); MSH3 protein levels by Western blot and IHC.

Full Validation adds:

• AAV-shRNA-MSH3 striatal injection arm (stereotaxic, bilateral, month 1) to test therapeutic delivery
• Patient-derived iPSC striatal neuron validation (3 HD patient lines, 2 control lines)
• Transcriptomic profiling (snRNA-seq) of striatum at 6 months to assess off-target MMR pathway effects

# Experimental Validation Pipeline — MSH3/HD Somatic Expansion
# Pseudocode for data collection, analysis, and decision logic

class HDMsh3ValidationStudy:
    
    def __init__(self):
        self.arms = {
            'HD_Msh3_WT':   {'genotype': 'zQ175_het/Msh3+/+', 'n': 40},
            'HD_Msh3_HET':  {'genotype': 'zQ175_het/Msh3+/-', 'n': 40},
            'HD_Msh3_KO':   {'genotype': 'zQ175_het/Msh3-/-', 'n': 40},
            'WT_control':   {'genotype': 'WT/Msh3+/+',        'n': 20}
        }
        self.timepoints_months = [1, 3, 6, 9, 12]
        self.sacrifice_months  = [6, 12]
        self.abort_flags = []
    
    # ── PHASE 0: Colony Validation ──────────────────────────────────────
    def validate_colony(self, mouse_id):
        cag_length = fragment_analysis(mouse_id, locus='HTT')
        msh3_genotype = pcr_genotype(mouse_id, gene='Msh3')
        msh3_protein  = western_blot(tissue='striatum', antibody='anti-MSH3')
        
        if not (175 <= cag_length <= 210):
            flag_abort("CAG length out of range: {}".format(cag_length))
        
        knockdown_pct = (1 - msh3_protein['KO'] / msh3_protein['WT']) * 100
        if knockdown_pct < 70:
            flag_abort("Insufficient MSH3 knockdown: {:.1f}%".format(knockdown_pct))
        
        return {'cag': cag_length, 'msh3_kd': knockdown_pct}
    
    # ── PHASE 1: Behavioral Monitoring ──────────────────────────────────
    def behavioral_battery(self, mouse_id, month):
        results = {}
        results['rotarod']    = rotarod_test(rpm_start=4, rpm_end=40, duration=300)
        results['open_field'] = open_field_test(duration=1800, arena_cm=40)
        results['grip']       = grip_strength_test(trials=3)
        results['weight']     = weigh_mouse()
        
        # Early abort check at month 6
        if month == 6:
            group_means = aggregate_by_arm(results['rotarod'])
            if not any_trend_toward_significance(group_means, p_threshold=0.15):
                self.abort_flags.append("No behavioral signal at month 6 — consider study extension or model re-evaluation")
        
        return results
    
    # ── PHASE 2: Somatic Expansion Quantification ────────────────────────
    def measure_somatic_expansion(self, mouse_id, tissues, month):
        expansion_data = {}
        
        for tissue in tissues:  # ['striatum', 'cortex', 'cerebellum', 'liver']
            dna = extract_hmw_dna(tissue, kit='Qiagen_MagAttract')
            
            # Small-pool PCR
            sp_pcr_alleles = []
            for rxn in range(96):
                allele_size = sp_pcr_reaction(dna_ng=0.5, primers='HTT_CAG_flanking')
                sp_pcr_alleles.append(allele_size)
            
            germline_cag  = modal_allele(sp_pcr_alleles)
            expansion_idx = mean(sp_pcr_alleles) - germline_cag
            
            # Nanopore long-read validation
            nanopore_reads = nanopore_sequence(dna, enrichment='CRISPR_Cas9_HTT',
                                               min_reads=500)
            cag_distribution = parse_repeat_length(nanopore_reads)
            
            expansion_data[tissue] = {
                'expansion_index': expansion_idx,
                'sp_pcr_alleles':  sp_pcr_alleles,
                'nanopore_dist':   cag_distribution,
                'germline_cag':    germline_cag
            }
        
        return expansion_data
    
    # ── PHASE 3: Histology Pipeline ──────────────────────────────────────
    def histological_analysis(self, mouse_id):
        brain = perfuse_fix(fixative='4%_PFA')
        sections = cryosection(thickness_um=40, region='striatum')
        
        markers = {
            'DARPP32': count_positive_cells(antibody='anti-DARPP32', method='stereology'),
            'NeuN':    count_positive_cells(antibody='anti-NeuN',    method='stereology'),
            'EM48':    quantify_aggregates(antibody='EM48', metric='percent_area'),
            'MSH3':    quantify_intensity(antibody='anti-MSH3')
        }
        
        striatal_volume = mri_volumetry(sequence='T2_3D', field_strength='7T')
        
        return {'markers': markers, 'volume_mm3': striatal_volume}
    
    # ── PHASE 4: Statistical Analysis ────────────────────────────────────
    def analyze_primary_endpoint(self, expansion_data_all_mice):
        # Primary: EI comparison Msh3-/- vs Msh3+/+ in striatum at 12 months
        EI_KO  = [d['striatum']['expansion_index'] for d in expansion_data_all_mice['HD_Msh3_KO']]
        EI_WT  = [d['striatum']['expansion_index'] for d in expansion_data_all_mice['HD_Msh3_WT']]
        
        stat, p_val    = mann_whitney_u(EI_KO, EI_WT)
        effect_size    = cohens_d(EI_KO, EI_WT)
        pct_reduction  = (mean(EI_WT) - mean(EI_KO)) / mean(EI_WT) * 100
        
        # Tissue specificity check
        for tissue in ['cortex', 'cerebellum', 'liver']:
            EI_tissue_KO = [d[tissue]['expansion_index'] for d in expansion_data_all_mice['HD_Msh3_KO']]
            EI_tissue_WT = [d[tissue]['expansion_index'] for d in expansion_data_all_mice['HD_Msh3_WT']]
            tissue_reduction = (mean(EI_tissue_WT) - mean(EI_tissue_KO)) / mean(EI_tissue_WT) * 100
            
            if tissue_reduction > pct_reduction * 0.7:
                flag_warning("Tissue specificity weak for {}".format(tissue))
        
        # Decision gate
        if pct_reduction >= 30 and p_val < 0.05 and effect_size >= 0.8:
            return "PRIMARY_SUCCESS"
        elif pct_reduction >= 15 and p_val < 0.10:
            return "PARTIAL_SUCCESS — extend study or increase n"
        else:
            return "PRIMARY_FAILURE"
    
    # ── PHASE 5: iPSC Parallel Validation ────────────────────────────────
    def ipsc_validation(self, hd_lines, control_lines):
        results = {}
        for line in hd_lines + control_lines:
            neurons = differentiate_to_striatal_neurons(line, protocol='CHDI_45day')
            
            # MSH3 knockdown
            transfect(neurons, reagent='MSH3_siRNA_SMARTpool', concentration_nM=50)
            kd_efficiency = measure_knockdown(method=['RT-qPCR', 'Western'], timepoint_days=3)
            
            if kd_efficiency < 0.80:
                flag_warning("Insufficient knockdown in line {}".format(line))
            
            # Culture and measure expansion
            culture(neurons, days=30)
            dna = extract_dna(neurons)
            ei  = measure_somatic_expansion(dna, method='SP-PCR+Nanopore')
            results[line] = {'knockdown_pct': kd_efficiency, 'expansion_index': ei}
        
        # Compare HD lines: siRNA vs scrambled
        reduction = compare_groups(results, metric='expansion_index')
        return reduction
    
    # ── ABORT LOGIC ──────────────────────────────────────────────────────
    def check_abort_conditions(self, phase, data):
        if phase == 'colony_validation':
            if data['msh3_kd'] < 50:
                return "ABORT: MSH3 knockdown insufficient — re-derive colony"
            if data['baseline_EI'] < 5:
                return "ABORT: Insufficient baseline somatic expansion — wrong model or age"
        
        if phase == 'month_6_interim':
            if data['EI_reduction_pct'] < 10:
                return "ABORT: No expansion reduction signal — MSH3 not rate-limiting in this model"
            if data['mortality_pct'] > 20:
                return "ABORT: Excess mortality — toxicity confound"
        
        if phase == 'month_9_behavioral':
            if data['rotarod_p_value'] > 0.30:
                return "CONTINUE_WITH_CAUTION: No behavioral signal yet — extend to 15 months"
        
        return "CONTINUE"

# ── COMPUTATIONAL ANALYSIS PIPELINE ──────────────────────────────────────
def nanopore_analysis_pipeline(fast5_dir, reference_genome):
    # Basecalling
    basecalled = guppy_basecall(fast5_dir, model='dna_r10.4.1_e8.2_400bps_hac')
    
    # Alignment
    aligned_bam = minimap2_align(basecalled, reference=reference_genome,
                                  params='-ax map-ont --secondary=no')
    
    # CAG repeat extraction
    repeat_lengths = []
    for read in pysam_fetch(aligned_bam, region='chr4:3,074,877-3,074,940'):
        cag_count = count_repeat_units(read.sequence, motif='CAG')
        repeat_lengths.append(cag_count)
    
    # Distribution statistics
    expansion_index = mean(repeat_lengths) - modal_allele(repeat_lengths)
    variance        = std(repeat_lengths)
    max_expansion   = max(repeat_lengths) - modal_allele(repeat_lengths)
    
    return {
        'expansion_index': expansion_index,
        'variance':        variance,
        'max_expansion':   max_expansion,
        'distribution':    repeat_lengths
    }

def sp_pcr_analysis(capillary_electrophoresis_files):
    allele_sizes = []
    for f in capillary_electrophoresis_files:
        peaks = call_peaks(f, min_height=100, min_separation=3)
        allele_sizes.extend([p.size_bp for p in peaks])
    
    cag_lengths   = convert_bp_to_cag(allele_sizes, ladder='GS