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<!DOCTYPE html>
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<title>CSF Autophagy/Lysosome Panel Discovery</title>
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</head>
<body>
<div class="progress" id="progress"></div>
<div class="deck" id="deck">
<!-- SLIDE 1: Title -->
<div class="slide title-slide active">
<div class="title-decoration"></div>
<div class="section-label animate-in" style="opacity:0">Karolinska Institutet · Dep. of Clinical Neuroscience & Dep. of Neurobiology, Care Sciences and Society</div>
<h1 class="animate-in" style="opacity:0">CSF Autophagy & Lysosome<br>Biomarker Panel Discovery</h1>
<p class="title-meta animate-in" style="opacity:0">Pontus Plavén-Sigray</p>
<p class="title-meta animate-in" style="opacity:0; margin-top:0.3rem;">Translational pipeline · 12 datasets · ~3,600 samples · Mouse ↔ Human</p>
</div>
<!-- SLIDE 2: Question -->
<div class="slide">
<div class="section-label animate-in" style="opacity:0">The question</div>
<h2 class="animate-in" style="opacity:0">Which autophagy/lysosomal proteins can we <span class="hl">reliably measure in CSF</span> across both species?</h2>
<p class="lead animate-in" style="opacity:0">We need candidates that are <strong>robustly detected</strong> in human and mouse CSF, <strong>biologically relevant</strong> to autophagy/lysosomal pathways, and <strong>actually measurable</strong> with targeted assays.</p>
</div>
<!-- SLIDE 3: Pipeline overview -->
<div class="slide">
<div class="section-label animate-in" style="opacity:0">Methods</div>
<h2 class="animate-in" style="opacity:0">Pipeline architecture</h2>
<div class="pipeline" style="flex-wrap:nowrap;justify-content:center;">
<div class="pipe-step animate-in" style="opacity:0"><span class="step-num">01</span><span class="step-name">Extract & QC</span></div>
<span class="pipe-arrow animate-in" style="opacity:0">→</span>
<div class="pipe-step animate-in" style="opacity:0"><span class="step-num">02</span><span class="step-name">Orthology mapping</span></div>
<span class="pipe-arrow animate-in" style="opacity:0">→</span>
<div class="pipe-step animate-in" style="opacity:0"><span class="step-num">03</span><span class="step-name">Evidence scoring</span></div>
<span class="pipe-arrow animate-in" style="opacity:0">→</span>
<div class="pipe-step animate-in" style="opacity:0"><span class="step-num">04</span><span class="step-name">Autophagy filter</span></div>
</div>
<div class="pipeline" style="flex-wrap:nowrap;justify-content:center;margin-top:0.6rem;">
<div class="pipe-step animate-in" style="opacity:0;animation-delay:0.55s !important;"><span class="step-num">05</span><span class="step-name">Peptide feasibility</span></div>
<span class="pipe-arrow animate-in" style="opacity:0;animation-delay:0.6s !important;">→</span>
<div class="pipe-step animate-in" style="opacity:0;animation-delay:0.65s !important;"><span class="step-num">06</span><span class="step-name">Module validation</span></div>
<span class="pipe-arrow animate-in" style="opacity:0;animation-delay:0.7s !important;">→</span>
<div class="pipe-step animate-in" style="opacity:0;animation-delay:0.75s !important;"><span class="step-num">07</span><span class="step-name">Sensitivity</span></div>
</div>
<p class="lead animate-in" style="opacity:0; margin-top:1.5rem;">Weighted evidence scoring replaces binary intersection. EV data contributes a <span class="hl">small scoring bonus (5%)</span> but is never required for inclusion. All parameters configurable; sensitivity tested across multiple configurations.</p>
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<!-- SLIDE 4: Data -->
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<h2 class="animate-in" style="opacity:0">Input data</h2>
<div class="two-col">
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<h3>🐭 Mouse (9 datasets)</h3>
<ul>
<li><strong>7 CSF datasets</strong> — D1–D5, D7, D8 (789–5,961 proteins)</li>
<li><strong>1 brain lysate</strong> — D6 (7,645 proteins; plausibility layer)</li>
<li><strong>1 ISF</strong> — D9 (789 proteins; orthogonal support)</li>
</ul>
</div>
<div class="card animate-in" style="opacity:0">
<h3>🧬 Human (3 datasets + references)</h3>
<ul>
<li><strong>D11 Astral</strong> — 3,232 proteins × 2,720 samples (primary)</li>
<li><strong>D12</strong> — replication subset of D11 (validation only)</li>
<li><strong>D10</strong> — 990 proteins, independent cohort</li>
<li><strong>R1</strong> — 604 curated autophagy/lysosome genes</li>
</ul>
</div>
</div>
<p class="animate-in" style="opacity:0; margin-top:1.2rem; color: var(--text-dim); font-size:0.9rem;">EV dataset (SH-SY5Y cell line) contributes a small scoring bonus (5% weight) but is never required for panel inclusion.</p>
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<!-- SLIDE 5: QC -->
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<div class="section-label animate-in" style="opacity:0">Methods</div>
<h2 class="animate-in" style="opacity:0">Quality control</h2>
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<h3>Sample filtering</h3>
<p>Excluded samples with >80% missing data. Removed lab artefacts (MaxQuant decoys & contaminants).</p>
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<h3>Blood contamination</h3>
<p><span class="hl-warm">235 proteins flagged</span> as likely plasma-derived (albumin, Ig, complement, keratins). Excluded from core panel.</p>
</div>
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<h3>Detectability tiers</h3>
<p>Dataset-aware confidence grading:<br>
<span class="tier tier-a">TIER A</span> robust
<span class="tier tier-b">TIER B</span> moderate<br>
Thresholds scale with dataset size (n=8 → n=2,720).</p>
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<!-- SLIDE 6: Orthology mapping -->
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<h2 class="animate-in" style="opacity:0">Orthology mapping — mouse → human</h2>
<div class="two-col">
<div>
<p class="lead animate-in" style="opacity:0">All mouse gene symbols (D1–D9) mapped to human equivalents via g:Profiler (Ensembl gene trees), so we can compare across species in a single namespace.</p>
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<div class="number" style="color:var(--accent)">89%</div>
<div class="label">Clean 1:1 mappings (8,589 genes) — one mouse gene → one human gene</div>
</div>
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<div class="number" style="color:var(--accent-gold)">5%</div>
<div class="label">Ambiguous 1:many (477 genes) — flagged, kept, penalised −0.1 in scoring</div>
</div>
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<div class="number" style="color:var(--text-dim)">6%</div>
<div class="label">No human ortholog found (~604 genes) — dropped from analysis</div>
</div>
</div>
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<h3>Paralog families</h3>
<p>~30 core autophagy genes had non-trivial mappings due to paralog families (e.g. LC3/GABARAP, ATG4 variants). These were identified upfront by mapping R1 separately and handled explicitly.</p>
<p style="margin-top:0.8rem; font-size:0.85rem; color:var(--text-dim);">Ambiguous cases use an <strong>expand</strong> policy: all possible human matches kept but flagged — nothing silently lost.</p>
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<h2 class="animate-in" style="opacity:0">Evidence scoring</h2>
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<p class="lead">Each protein scored 0–1 from five weighted components. No single source determines inclusion.</p>
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<div style="display:flex; align-items:center; gap:0.8rem; margin:0.6rem 0;">
<div style="width:110px; text-align:right; font-size:0.85rem; color:var(--text-dim);">Human CSF</div>
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<div style="width:30%; height:100%; background:var(--accent); border-radius:5px;"></div>
</div>
<div style="width:30px; font-family:'IBM Plex Mono'; font-size:0.8rem; color:var(--accent);">30%</div>
</div>
<div style="display:flex; align-items:center; gap:0.8rem; margin:0.6rem 0;">
<div style="width:110px; text-align:right; font-size:0.85rem; color:var(--text-dim);">Mouse CSF</div>
<div style="flex:1; height:10px; border-radius:5px; background:var(--surface); overflow:hidden;">
<div style="width:25%; height:100%; background:var(--accent-blue); border-radius:5px;"></div>
</div>
<div style="width:30px; font-family:'IBM Plex Mono'; font-size:0.8rem; color:var(--accent-blue);">25%</div>
</div>
<div style="display:flex; align-items:center; gap:0.8rem; margin:0.6rem 0;">
<div style="width:110px; text-align:right; font-size:0.85rem; color:var(--text-dim);">Autophagy R1</div>
<div style="flex:1; height:10px; border-radius:5px; background:var(--surface); overflow:hidden;">
<div style="width:25%; height:100%; background:var(--accent-gold); border-radius:5px;"></div>
</div>
<div style="width:30px; font-family:'IBM Plex Mono'; font-size:0.8rem; color:var(--accent-gold);">25%</div>
</div>
<div style="display:flex; align-items:center; gap:0.8rem; margin:0.6rem 0;">
<div style="width:110px; text-align:right; font-size:0.85rem; color:var(--text-dim);">Brain (D6)</div>
<div style="flex:1; height:10px; border-radius:5px; background:var(--surface); overflow:hidden;">
<div style="width:10%; height:100%; background:var(--text-dim); border-radius:5px;"></div>
</div>
<div style="width:30px; font-family:'IBM Plex Mono'; font-size:0.8rem; color:var(--text-dim);">10%</div>
</div>
<div style="display:flex; align-items:center; gap:0.8rem; margin:0.6rem 0;">
<div style="width:110px; text-align:right; font-size:0.85rem; color:var(--text-dim);">EV support</div>
<div style="flex:1; height:10px; border-radius:5px; background:var(--surface); overflow:hidden;">
<div style="width:5%; height:100%; background:rgba(139,149,173,0.4); border-radius:5px;"></div>
</div>
<div style="width:30px; font-family:'IBM Plex Mono'; font-size:0.8rem; color:var(--text-dim);">5%</div>
</div>
</div>
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<h3>Core panel criteria (hard filters)</h3>
<ul>
<li>Tier A/B in <strong>human CSF</strong> (D11)</li>
<li>Tier A/B in <strong>≥1 mouse CSF</strong> dataset</li>
<li>On the <strong>autophagy/lysosome list</strong> (R1)</li>
<li><strong>Not</strong> flagged as plasma-derived</li>
</ul>
<p style="margin-top:0.8rem; font-size:0.85rem;">EV detection is <span class="hl">never required</span> for inclusion. It contributes a small scoring bonus only.</p>
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<h2 class="animate-in" style="opacity:0">Results</h2>
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<!-- SLIDE 8: Funnel -->
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<div class="section-label animate-in" style="opacity:0">Results</div>
<h2 class="animate-in" style="opacity:0">From proteome to panel</h2>
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<div class="number" style="color:var(--text-dim)">9,561</div>
<div class="label">unique proteins scored</div>
</div>
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<div class="number" style="color:var(--accent-blue)">6,684</div>
<div class="label">detected in ≥1 mouse CSF</div>
</div>
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<div class="number" style="color:var(--accent-gold)">3,026</div>
<div class="label">Tier A/B in human Astral</div>
</div>
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<div class="number" style="color:var(--accent-warm)">434</div>
<div class="label">overlap with R1 autophagy list</div>
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<div class="number" style="color:var(--accent)">122</div>
<div class="label">pass all 4 core criteria</div>
</div>
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<div class="number" style="background:var(--gradient-1);-webkit-background-clip:text;-webkit-text-fill-color:transparent;">80</div>
<div class="label">final shortlist (by score)</div>
</div>
</div>
</div>
<!-- SLIDE 9: Top 10 hits -->
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<div class="section-label animate-in" style="opacity:0">Results</div>
<h2 class="animate-in" style="opacity:0">Top 10 candidates</h2>
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<table class="mini-table">
<thead><tr><th>#</th><th>Protein</th><th>Score</th><th></th><th>Feas.</th><th>Mouse</th></tr></thead>
<tbody>
<tr><td>1</td><td class="protein-name">CTSD</td><td>0.975</td><td><span class="score-bar" style="width:97.5%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>2</td><td class="protein-name">CTSB</td><td>0.927</td><td><span class="score-bar" style="width:92.7%"></span></td><td><span class="tier tier-ii">II</span></td><td>7/7</td></tr>
<tr><td>3</td><td class="protein-name">CST3</td><td>0.927</td><td><span class="score-bar" style="width:92.7%"></span></td><td><span class="tier tier-ii">II</span></td><td>7/7</td></tr>
<tr><td>4</td><td class="protein-name">CPQ</td><td>0.926</td><td><span class="score-bar" style="width:92.6%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>5</td><td class="protein-name">PRDX6</td><td>0.926</td><td><span class="score-bar" style="width:92.6%"></span></td><td><span class="tier tier-ii">II</span></td><td>7/7</td></tr>
<tr><td>6</td><td class="protein-name">PARK7</td><td>0.925</td><td><span class="score-bar" style="width:92.5%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>7</td><td class="protein-name">HEXB</td><td>0.922</td><td><span class="score-bar" style="width:92.2%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>8</td><td class="protein-name">GGH</td><td>0.909</td><td><span class="score-bar" style="width:90.9%"></span></td><td><span class="tier tier-ii">II</span></td><td>7/7</td></tr>
<tr><td>9</td><td class="protein-name">GAA</td><td>0.902</td><td><span class="score-bar" style="width:90.2%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>10</td><td class="protein-name">LAMP1</td><td>0.896</td><td><span class="score-bar" style="width:89.6%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
</tbody>
</table>
<table class="mini-table">
<thead><tr><th>#</th><th>Protein</th><th>Score</th><th></th><th>Feas.</th><th>Mouse</th></tr></thead>
<tbody>
<tr><td>11</td><td class="protein-name">ATP6AP1</td><td>0.887</td><td><span class="score-bar" style="width:88.7%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>12</td><td class="protein-name">NPC2</td><td>0.884</td><td><span class="score-bar" style="width:88.4%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>13</td><td class="protein-name">GM2A</td><td>0.884</td><td><span class="score-bar" style="width:88.4%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>14</td><td class="protein-name">EPDR1</td><td>0.874</td><td><span class="score-bar" style="width:87.4%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>15</td><td class="protein-name">IDS</td><td>0.873</td><td><span class="score-bar" style="width:87.3%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>16</td><td class="protein-name">CTSS</td><td>0.872</td><td><span class="score-bar" style="width:87.2%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>17</td><td class="protein-name">CTSZ</td><td>0.870</td><td><span class="score-bar" style="width:87.0%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>18</td><td class="protein-name">PPT1</td><td>0.869</td><td><span class="score-bar" style="width:86.9%"></span></td><td><span class="tier tier-i">I</span></td><td>6/7</td></tr>
<tr><td>19</td><td class="protein-name">MAN2B1</td><td>0.869</td><td><span class="score-bar" style="width:86.9%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
<tr><td>20</td><td class="protein-name">HEXA</td><td>0.865</td><td><span class="score-bar" style="width:86.5%"></span></td><td><span class="tier tier-i">I</span></td><td>7/7</td></tr>
</tbody>
</table>
</div>
<p class="animate-in" style="opacity:0; margin-top:0.8rem; font-size:0.85rem; color:var(--text-dim);">Feas. = peptide feasibility tier for MS (I = ≥2 conserved proteotypic peptides mouse↔human; II = human-only). Mouse = datasets detected in (of 7). All 20 in all 7 mouse CSF datasets except PPT1 (6/7). Dominated by lysosomal enzymes and autophagy effectors.</p>
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<!-- SLIDE 10: Detection robustness -->
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<div class="section-label animate-in" style="opacity:0">Results</div>
<h2 class="animate-in" style="opacity:0">Detection robustness <span style="font-family:'IBM Plex Sans';font-weight:300;font-size:0.6em;color:var(--text-dim);">(of 80 shortlisted)</span></h2>
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<div class="stat animate-in" style="opacity:0">
<div class="number" style="color:var(--accent)">79</div>
<div class="label">Tier A in human Astral (D11)</div>
</div>
<div class="stat animate-in" style="opacity:0">
<div class="number" style="color:var(--accent-blue)">78</div>
<div class="label">confirmed in replication (D12)</div>
</div>
<div class="stat animate-in" style="opacity:0">
<div class="number" style="color:var(--accent-gold)">78</div>
<div class="label">detected in mouse brain (D6)</div>
</div>
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<div class="number" style="color:var(--text-dim)">28</div>
<div class="label">found in all 7 mouse CSF sets</div>
</div>
</div>
<p class="lead animate-in" style="opacity:0; margin-top:1.5rem;">Nearly all candidates are independently replicated, brain-expressed, and cross-species detected.</p>
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<div class="section-label animate-in" style="opacity:0">Results</div>
<h2 class="animate-in" style="opacity:0">Peptide feasibility — can we measure these?</h2>
<div class="two-col">
<div>
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<div class="number" style="color:var(--accent)">99 <span style="font-size:0.4em; color:var(--text-dim); font-family:'IBM Plex Sans';">of 122</span></div>
<div class="label"><span class="tier tier-i">TIER I</span> ≥2 conserved proteotypic peptides (mouse ↔ human)</div>
</div>
<div class="stat animate-in" style="opacity:0; width:100%;">
<div class="number" style="color:var(--accent-gold)">23</div>
<div class="label"><span class="tier tier-ii">TIER II</span> Human peptides available; separate mouse assay needed</div>
</div>
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<div class="number" style="color:var(--text-dim)">0</div>
<div class="label">Tier III (not measurable)</div>
</div>
</div>
</div>
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<h3>What this means</h3>
<p>Tier I proteins can be quantified with a <strong>single PRM/MRM assay</strong> using identical peptides in both species — directly bridging preclinical and clinical studies.</p>
<p style="margin-top:0.8rem;">For antibody-based platforms (Olink, ELISA, suspension bead arrays), different feasibility criteria apply — antibody availability and epitope specificity rather than peptide conservation.</p>
</div>
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<!-- SLIDE 12: Module validation -->
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<div class="section-label animate-in" style="opacity:0">Results</div>
<h2 class="animate-in" style="opacity:0">Co-abundance modules — does the panel reflect real biology?</h2>
<div class="two-col">
<div>
<p class="lead animate-in" style="opacity:0">Clustered <strong>all 2,323 proteins</strong> in the Astral dataset by CSF co-variation across 2,720 individuals — independent of any curated list.</p>
<div class="stat-row" style="margin-top:1.2rem;">
<div class="stat animate-in" style="opacity:0">
<div class="number" style="color:var(--accent)">2</div>
<div class="label">modules enriched for<br>autophagy genes (FDR < 0.05)</div>
</div>
<div class="stat animate-in" style="opacity:0">
<div class="number" style="color:var(--accent-blue)">21</div>
<div class="label">of 80 shortlisted proteins<br>in enriched modules</div>
</div>
</div>
</div>
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<h3>Key finding</h3>
<p>~¼ of the panel doesn't just carry an autophagy label — these proteins <strong>co-vary together in CSF</strong>, suggesting coherent pathway-level biology.</p>
<p style="margin-top:0.6rem;">Additionally, <span class="hl-gold">9 hub proteins</span> in enriched modules were <strong>not</strong> on R1 — data-driven candidates that emerged from co-expression, not curation.</p>
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</div>
</div>
<!-- SLIDE 14: AD model cross-check -->
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<div class="section-label animate-in" style="opacity:0">Results</div>
<h2 class="animate-in" style="opacity:0">AD model cross-check — App knock-in mice</h2>
<div class="two-col">
<div>
<p class="lead animate-in" style="opacity:0">Cross-referenced the panel against CSF proteomics from <strong>App<sup>NL-G-F</sup></strong> and <strong>App<sup>NL-F</sup></strong> knock-in mice vs wild-type (3 pairwise comparisons, ~260 proteins each).</p>
<div class="stat-row" style="margin-top:1rem;">
<div class="stat animate-in" style="opacity:0">
<div class="number" style="color:var(--accent)">17</div>
<div class="label">of 80 shortlisted proteins<br>present in AD model data</div>
</div>
<div class="stat animate-in" style="opacity:0">
<div class="number" style="color:var(--accent-warm)">9</div>
<div class="label">significantly altered<br>in ≥1 comparison (p < 0.05)</div>
</div>
</div>
<p class="animate-in" style="opacity:0; margin-top:1rem; font-size:0.88rem; color:var(--text-dim);">Dominant pattern: <strong>lysosomal enzymes are decreased</strong> in CSF of amyloid models — consistent with impaired lysosomal secretion in AD.</p>
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<div class="animate-in" style="opacity:0; overflow-x:auto;">
<table class="mini-table">
<thead><tr><th>Gene</th><th>Rank</th><th>Direction</th><th>p-value</th><th>Comparison</th></tr></thead>
<tbody>
<tr><td class="protein-name">CTSB</td><td>2</td><td style="color:var(--accent-warm)">↓ ↓</td><td>0.017 / 0.002</td><td>NL-F, NL-F vs NL-GF</td></tr>
<tr><td class="protein-name">CPQ</td><td>5</td><td style="color:var(--accent-warm)">↓</td><td>0.003</td><td>NL-F vs WT</td></tr>
<tr><td class="protein-name">HEXB</td><td>6</td><td style="color:var(--accent-warm)">↓</td><td>0.028</td><td>NL-F vs WT</td></tr>
<tr><td class="protein-name">CTSZ</td><td>17</td><td style="color:var(--accent-warm)">↓ ↓</td><td>0.025 / 0.012</td><td>NL-F, NL-F vs NL-GF</td></tr>
<tr><td class="protein-name">MAN2B1</td><td>15</td><td style="color:var(--accent-warm)">↓</td><td>0.046</td><td>NL-F vs WT</td></tr>
<tr><td class="protein-name">CTSA</td><td>20</td><td style="color:var(--accent-warm)">↓</td><td>0.014</td><td>NL-F vs NL-GF</td></tr>
<tr><td class="protein-name">CTSS</td><td>24</td><td style="color:var(--accent-warm)">↓</td><td>0.009</td><td>NL-F vs NL-GF</td></tr>
<tr><td class="protein-name">CTSD</td><td>1</td><td style="color:var(--accent-warm)">↓</td><td>0.017</td><td>NL-F vs NL-GF</td></tr>
</tbody>
</table>
<p style="margin-top:0.5rem; font-size:0.78rem; color:var(--text-dim);">↓ = significantly decreased in disease model. CTSB shows inconsistent direction across comparisons (up in NL-GF vs WT).</p>
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<!-- SLIDE 15: EV sensitivity analysis -->
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<div class="section-label animate-in" style="opacity:0">Sensitivity analysis</div>
<h2 class="animate-in" style="opacity:0">How much does EV evidence matter?</h2>
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<h3>Test 1: Up-weight EV to 25%</h3>
<p><span class="hl">72 / 80</span> proteins unchanged. ρ = 0.88. Top 10 stable.</p>
<p style="margin-top:0.5rem;">8 lysosomal enzymes with low EV secretion swap out for 8 vesicle trafficking proteins (VPS35, DNM1L, RAB11B, ATP6V1E1) — biologically expected in EVs.</p>
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<h3>Test 2: Hard EV gate</h3>
<p>Require EV detection → 122 drops to <span class="hl-warm">102</span> candidates. Top 80 loses <strong>6 proteins</strong>.</p>
<p style="margin-top:0.5rem;">Three of those 6 — <span style="font-family:'IBM Plex Mono';font-size:0.85rem;">CPQ, CTSS, CTSZ</span> — are significantly altered in the AD mouse model. The gate trades disease-validated hits for marginal EV-positive candidates.</p>
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<p style="font-size:1rem;">Lysosomal enzymes reach CSF via <strong>direct secretion</strong>, not EV packaging. An EV gate creates a blind spot for exactly the biology we care about. Low EV weight (5–10%) is the right call.</p>
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<!-- SLIDE 14: Parameter robustness -->
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<div class="section-label animate-in" style="opacity:0">Sensitivity analysis</div>
<h2 class="animate-in" style="opacity:0">Stability across all parameter choices</h2>
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<div>
<p class="lead animate-in" style="opacity:0">Pipeline tested under <strong>9 configurations</strong> varying tier stringency and scoring weights (plus the EV-heavy reweighting above).</p>
<div class="stat-row" style="margin-top:1.2rem; flex-direction:column; gap:1rem;">
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<div class="number" style="color:var(--accent)">0.89</div>
<div class="label">Mean Spearman rank correlation across configs (range 0.79–0.98)</div>
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<div class="stat animate-in" style="opacity:0; width:100%;">
<div class="number" style="color:var(--accent-gold)">65 / 80</div>
<div class="label">Mean candidates retained across all configurations</div>
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<h3>Always in the top</h3>
<p>These proteins survived <strong>every configuration</strong>:</p>
<p style="margin-top:0.5rem; font-family:'IBM Plex Mono'; font-size:0.95rem; color:var(--accent); line-height:2;">
CTSD · CTSB · CST3 · CPQ<br>PARK7 · HEXB · GAA · LAMP1
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<!-- SLIDE 15: Summary -->
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<div class="section-label animate-in" style="opacity:0">Summary</div>
<h2 class="animate-in" style="opacity:0">What we have</h2>
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<p>An <span class="hl">80-protein CSF autophagy/lysosomal panel</span> that is robustly detected across both mouse and human, biologically coherent by independent co-expression analysis, stable across analytical choices, and directly amenable to <span class="hl">translational targeted assay development</span> — with the majority of candidates measurable using shared peptide sequences across species.</p>
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<div class="stat-row" style="margin-top:2rem;">
<div class="stat animate-in" style="opacity:0">
<div class="number" style="color:var(--accent)">80</div>
<div class="label">panel proteins</div>
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<div class="stat animate-in" style="opacity:0">
<div class="number" style="color:var(--accent-blue)">65</div>
<div class="label">Tier I assay-ready<br>(shared peptides)</div>
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<div class="number" style="color:var(--accent-gold)">21</div>
<div class="label">module-validated<br>(co-expression support)</div>
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<div class="number" style="color:var(--accent-warm)">9</div>
<div class="label">novel module-derived<br>candidates</div>
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<!-- SLIDE 16: Module-derived candidates (NEW) -->
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<div class="section-label animate-in" style="opacity:0">Novel candidates</div>
<h2 class="animate-in" style="opacity:0">Module-derived candidates — data over curation</h2>
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<p class="lead animate-in" style="opacity:0">Co-abundance analysis clustered <strong>all</strong> Astral proteins — not just those on the autophagy list. Two modules were enriched for R1 genes. Within those modules, <span class="hl-gold">9 hub proteins</span> were <strong>not</strong> on R1 but co-vary tightly with known autophagy/lysosomal markers.</p>
<p class="animate-in" style="opacity:0; margin-top:1rem; font-size:0.9rem; color:var(--text-dim);">These are <strong>data-driven hypotheses</strong>: proteins the co-expression network says behave like autophagy markers in CSF, despite not being on any curated list. They did not go through the autophagy filter — they emerged independently.</p>
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<table class="module-table">
<thead><tr><th>Gene</th><th>Module</th><th>Connectivity</th></tr></thead>
<tbody>
<tr>
<td class="gene">SELENBP1</td>
<td><span class="module-badge mod-824">824</span></td>
<td><span class="connectivity-bar" style="width:54%; background:var(--accent-blue);"></span> 0.54</td>
</tr>
<tr>
<td class="gene">LGALS3BP</td>
<td><span class="module-badge mod-824">824</span></td>
<td><span class="connectivity-bar" style="width:53%; background:var(--accent-blue);"></span> 0.53</td>
</tr>
<tr>
<td class="gene">ADGRL1</td>
<td><span class="module-badge mod-824">824</span></td>
<td><span class="connectivity-bar" style="width:53%; background:var(--accent-blue);"></span> 0.53</td>
</tr>
<tr>
<td class="gene">GOT1</td>
<td><span class="module-badge mod-824">824</span></td>
<td><span class="connectivity-bar" style="width:52%; background:var(--accent-blue);"></span> 0.52</td>
</tr>
<tr>
<td class="gene">AEBP1</td>
<td><span class="module-badge mod-949">949</span></td>
<td><span class="connectivity-bar" style="width:51%; background:var(--accent);"></span> 0.51</td>
</tr>
<tr>
<td class="gene">DCN</td>
<td><span class="module-badge mod-949">949</span></td>
<td><span class="connectivity-bar" style="width:51%; background:var(--accent);"></span> 0.51</td>
</tr>
<tr>
<td class="gene">F5</td>
<td><span class="module-badge mod-949">949</span></td>
<td><span class="connectivity-bar" style="width:50%; background:var(--accent);"></span> 0.50</td>
</tr>
<tr>
<td class="gene">COL14A1</td>
<td><span class="module-badge mod-949">949</span></td>
<td><span class="connectivity-bar" style="width:47%; background:var(--accent);"></span> 0.47</td>
</tr>
<tr>
<td class="gene">CPVL</td>
<td><span class="module-badge mod-949">949</span></td>
<td><span class="connectivity-bar" style="width:47%; background:var(--accent);"></span> 0.47</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<!-- SLIDE 17: Next steps -->
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<div class="section-label animate-in" style="opacity:0">Next steps</div>
<h2 class="animate-in" style="opacity:0">Where do we go from here?</h2>
<div class="three-col">
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<h3>🔬 Assay development</h3>
<p>PRM/MRM assays for top Tier I candidates using conserved peptides. Alternatively, assess antibody availability for Olink, ELISA, or suspension bead array assays. Validate in independent CSF cohorts.</p>
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<h3>💊 ERAP integration</h3>
<p>Measure panel in rapamycin-treated CSF samples. Test whether mTOR inhibition produces detectable shifts in autophagy/lysosomal protein panel.</p>
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<div class="card animate-in" style="opacity:0">
<h3>🧪 Novel candidates</h3>
<p>Follow up the 9 module-derived proteins. Characterise their relationship to autophagy/lysosomal biology and assess assay feasibility.</p>
</div>
</div>
</div>
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