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Genotype-Informed Supplement Quantification Workflow

The user-facing operational backbone for the platform's personalized-medicine thesis: a five-step closed-loop n=1 pharmacogenomics pipeline that turns "I took some supplement" into "I took N mg of compound X, chosen because my genotype favors it, verified at the dose level, with biomarker readout."

This page composes three previously disconnected wiki threads into a single named workflow: 1. Genotype-informed compound selection (personal-genome-protocol.md) 2. Home / community-biolab batch quantification (quantification-ladder.md + enzyme-quantification-protocol.md + medicinal-mushroom-extract-sops.md) 3. Biomarker-tracked self-experimentation (self-experiment-protocol.md)

Standard self-experiment protocols treat supplement dose / form / timing as a fixed input variable — "take 500 mg of X, see what happens." That framing has a silent failure mode: a batch producing 20% of expected titer is indistinguishable from a mechanism that doesn't work. Without batch QC, every n=1 result is contaminated by invisible dose noise. The quantification ladder converts dose into a verified variable; the personal genome converts compound selection into a genotype-informed variable; the self-experiment protocol tracks the biomarker. Compose all three and every link in the chain is verified rather than assumed.

The closed loop

genotype → compound selection → home or community-biolab production → Tier 2 batch QC → calibrated dose → biomarker tracking → adjust

Every link verifiable. Every link logged.

The five-step workflow

For each intervention the subject considers:

1. Genotype-inform the selection

Per personal-genome-protocol.md §"Gout-specific pharmacogenomic query list" + the unified variant index at gout-genetic-variants.md. Specific variants change compound priority:

  • ABCG2 Q141K (rs2231142) → butyrate emphasis (PPARγ-driven WT ABCG2 induction + HDAC trafficking rescue for the Q141K variant per abcg2-modulators.md §6); pharmacological-chaperone class as the orthogonal small-molecule track (chassis-pending; comp-032)
  • URAT1 gain-of-function variants (uncommon; SLC22A12 not RHUC1-causing) → cordycepin > eurycomanone per comp-015 v2
  • SLC22A12 W258X (RHUC1 carrier) → urate excretion is enhanced; the platform's gut-lumen sink thesis is less load-bearing for these carriers
  • NLRP3 gain-of-function variants (CAPS spectrum; rare) → upweight CP6 (oridonin, BHB) over CP1–CP4
  • HLA-B*58:01 (East Asian / Han Chinese / Korean / Thai ancestry) → exclude allopurinol; route urate-lowering through the gut-lumen sink, alternative uricosurics, or non-XO-inhibitor strategies
  • G6PD deficiency → exclude systemic recombinant uricase (rasburicase, pegloticase contraindicated); gut-lumen approach is plausibly safer but empirically untested

Source-quality requirement: clinical-grade genotyping (rheumatologist-ordered panel or CLIA-grade direct-to-consumer service). Consumer panels (23andMe, AncestryDNA) are not recommended for trial-grade decisions per gout-action-guide.md "This year (advanced)." Consumer panels are useful for personal exploration but should not be the data source when a clinical decision or supplement-stack stratification rides on the variant.

2. Source or produce the compound

Three production routes, choose by track:

  • Engineered koji / engineered yeast for enzyme cassettes (uricase, lactoferrin, etc.) — see engineered-koji-protocol.md for the home-fermentation procedure.
  • Cultivated medicinal mushrooms / extracts for native-compound payloads (cordycepin / pentostatin via whole-fermentate Cordyceps militaris, GLPP via Ganoderma lucidum, ergothioneine via Pleurotus citrinopileatus) — see medicinal-mushroom-extract-sops.md.
  • Commercial supplement purchase when home production isn't tractable or the compound isn't fermentation-accessible (e.g., resistant starch from a documented RS2 source, sodium butyrate from a vendor with reported potency, FDA-approved off-label small molecules via compounding pharmacy per compounding-pharmacy-track.md).

Log batch / lot / source in the self-experiment-protocol.md §7 daily log. Without per-batch identity, downstream QC has no anchor.

3. Tier 2 batch QC via the quantification ladder

Use the matched assay from quantification-ladder.md:

  • Cordycepin: diazo-coupling colorimetric assay
  • Ergothioneine: Ellman's reagent
  • Total polysaccharide (GLPP): phenol-sulfuric method
  • Uricase activity: 293 nm UV absorbance
  • Lactoferrin: protein-quantification + iron-saturation readout (see enzyme-quantification-protocol.md)

Output: a per-batch potency number (mg compound per gram dried product, or activity units per gram). Calibrate once at Tier 3 (vendor or community-biolab analytical assay — HPLC, GC-MS, LC-MS) if available; track each subsequent batch at Tier 2 against the Tier 3 anchor. This is the calibrate-once / track-batches-cheap operating model that makes home QC sustainable.

4. Calibrate dose against batch potency

A batch returning 50% of expected potency means the subject takes 2× the gram weight to hit the same calibrated dose — or notes the silent underdosing as a confound in §7 of the self-experiment protocol. Without this step, batch variation produces invisible noise in the biomarker readout.

The discipline isn't "always hit the target dose." The discipline is "always know whether you hit it, and if not, by how much." A subject who knows they delivered 60% of target can attribute partial biomarker movement correctly; a subject who assumes they delivered 100% can't.

5. Track biomarkers per self-experiment-protocol.md §3–§4

With dose closed as a verified variable, any biomarker movement is attributable to dose × biology, not dose × batch-variation × biology. The four-biomarker panel + serum UA quarterly is the canonical readout for gout-context interventions. Adjust the intervention based on the result.

Worked example — ABCG2 Q141K heterozygous carrier, butyrate-emphasis stack

A subject genotyped via a clinical-grade panel returns ABCG2 Q141K heterozygous (rs2231142 C/A). Per abcg2-modulators.md, butyrate is the dual-mechanism lever for this genotype: PPARγ-driven ABCG2 induction acts on the wild-type allele, and HDAC-inhibitor trafficking rescue acts on the Q141K variant. The standard supplement-stack recommendation elevates butyrate via fermentable-fiber-rich diet + targeted butyrate-producing probiotics or direct butyrate-ester supplementation.

Workflow application:

  1. Genotype: Q141K heterozygous, confirmed via clinical lab (not 23andMe).
  2. Selection: Butyrate-emphasis stack — fermentable-fiber dietary baseline (resistant starch, inulin, RS2-type sources) + optional direct sodium butyrate supplementation.
  3. Source: Resistant starch from a known source (e.g., Bob's Red Mill unmodified potato starch, a documented RS2 source); sodium butyrate from a documented supplement vendor with reported potency.
  4. Tier 2 batch QC — exposure-proxy tier, NOT input-verification tier (clarified 2026-05-22 per comp-038 + sweep 2026-05-20 Connection #4 + Priority Action #4): Indirect readout — stool SCFA panel (butyrate + acetate + propionate) at week 4 of intervention vs. baseline. This step operates at the exposure-proxy tier, not the input-verification tier — it answers "did butyrate eventually show up in the colon?" but it does NOT answer "did the supplement bottle contain the labeled dose?" For every other compound class in this workflow (cordycepin via diazo-coupling, EGT via Ellman's reagent, GLPP via phenol-sulfuric, engineered-strain uricase via 293 nm UV), the Tier 2 assay verifies the input directly. For butyrate, no such input-verification assay exists at the Tier 2 level (per comp-038 YELLOW verdict, 2026-05-20). The "calibrate once at Tier 3, track batches at Tier 2" discipline (per quantification-ladder.md) is partially broken at step 4 for butyrate.

Practical user guidance — pick one of three approaches:

  • (a) Vendor with published third-party potency verification. Some sodium-butyrate vendors publish independent COA (certificate of analysis) showing butyrate-ester content vs. label claim. Where available, this substitutes for an in-house Tier 2 input check. Verify the COA is recent and lot-matched.
  • (b) One-time Tier 3 GC-MS anchor on a single batch. Send one batch to a GC-MS lab for absolute butyrate quantification (~$80–150). Treat this as the calibration anchor for that vendor + lot combination. As long as the vendor's manufacturing process is stable across lots, the anchor remains representative — but the calibration is vendor-specific and lot-class-specific, not universal.
  • © Accept the exposure-proxy limitation explicitly. Use the stool SCFA panel as documented, but interpret negative results with appropriate caution: a low stool butyrate could mean the supplement was under-dosed, OR that the microbiome didn't convert it, OR that the mechanism didn't work in this patient. Document the ambiguity in the experiment log so future analysis can re-examine if input-verification becomes available.

Direct supplement quantification by HPLC-UV or electrochemical SCFA profiling is plausible (per comp-038's next-step path) but research-grade and not workflow-ready today. The workflow design IS sound; the butyrate-specific limitation is a class-level methodology gap (see "Tier 2 assay gap for microbiome-derived metabolites" below + the class-level Open Question added to open-questions.md 2026-05-22). 5. Track biomarkers: Serum UA quarterly + the standard four-biomarker panel per §4. Predicted effect from comp-019: WT/WT non-Q141K cohort sees larger ΔSUA than Q141K heterozygotes under the substrate-limited gut-lumen uricase regime; for Q141K-positive subjects, the rescue mechanism (HDAC inhibition) is the dominant lever and per-patient response can be larger if it activates. Track UA trajectory at 3-month intervals.

Compound uncertainty: butyrate × exertion trigger (added 2026-05-22 per sweep 2026-05-20 Contradiction #1). A Q141K-positive subject following this worked example who flares after an exertion event faces a four-way attribution problem at n=1. Without a Tier 2 butyrate input-verification assay AND without an exertion-challenge protocol that monitors urate kinetics, a post-exertion flare could be (a) the butyrate intervention failing (HDAC trafficking rescue insufficient to handle the exertion-driven urate load); (b) the butyrate dose being wrong (supplement underdosed, but unverifiable at step 4); © the exertion trigger acting via a mechanism the butyrate intervention doesn't address (mechanical shedding rather than metabolic overload, per mechanical-flare-triggers.md §"Candidate mechanism #5 — metabolic overload via exertion/fatigue"); (d) regression to the mean. The four explanations are not distinguishable at n=1 without (i) a Tier 2 butyrate-potency assay (closes option b), (ii) an exertion-challenge test with serial spot urinary urate/creatinine ratios (discriminates the metabolic-overload mechanism from mechanical shedding for option c — serum UA + pain logs alone under-resolve renal-clearance kinetics per mechanical-flare-triggers.md), or (iii) a genotype-stratified n-of-many cohort (beyond n=1 resolution). Subjects executing this worked example should be aware of the compound uncertainty and document exertion exposure + post-exertion biomarker draws when feasible, so retrospective analysis can at least disambiguate (a) + (b) from © + (d).

What this example does NOT claim: - Does NOT claim butyrate alone produces clinically meaningful ΔSUA — gated by H08 — Gut-Lumen Sink Platform Thesis and the absence of a typical-gout Phase 2b RCT. - Does NOT claim the SCFA stool panel is mechanistically equivalent to a direct butyrate-supplement potency assay — it's an indirect exposure-proxy at step 4, explicitly documented as such above. - DOES illustrate the workflow shape: every link in the chain is verified rather than assumed — except the butyrate input-verification at step 4, which is documented as the class-level methodology gap it is.

Worked example — OCTN1/SLC22A4 variant carrier, substrate-engineered ergothioneine cultivation (added 2026-05-22)

A second worked example demonstrating the workflow with home cultivation as the production route (rather than supplement purchase) AND with no class-level Tier 2 methodology gap (Ellman's reagent for ergothioneine is well-established; HILIC-HPLC Tier 3 anchor exists; the workflow closes the loop without the exposure-proxy caveat that gates the Q141K butyrate example). This example serves three purposes simultaneously: (a) makes the medicinal-mushroom track visible as a first-class production route in the workflow (per sweep 2026-05-20 Connection #3); (b) demonstrates substrate-level intervention as a distributed-contributor lever per medicinal-mushroom-extract-sops.md SOP-7 (per sweep 2026-05-20 Priority Action #1); © operationalizes the EGT dry-run proposed in sweep 2026-05-20 Riskiest Assumption #1 — the cheapest path to a fully-documented end-to-end execution of the workflow on a compound class where every Tier 2 piece already works.

A subject genotyped via a clinical-grade panel returns OCTN1 / SLC22A4 variant carrier — common variants (e.g., rs1050152, ~40–50% allele frequency in European populations) reduce OCTN1's transport capacity for ergothioneine into target tissues. The variant doesn't break the transporter outright; it shifts the dose-response curve, suggesting that elevated dietary EGT intake may be needed to reach equivalent tissue concentrations in variant carriers vs wild-type. Ergothioneine has anti-oxidative, anti-inflammatory, and Nrf2-pathway activity relevant to gout adjacent pathways (per medicinal-mushroom-complement-track.md).

Workflow application:

  1. Genotype: OCTN1 / SLC22A4 variant carrier, confirmed via clinical-grade panel (one-shot test; ~$30–60 single-SNP PCR via Quest/LabCorp, or consumer-array raw data if 23andMe / Ancestry kit on file — rs1050152 is on standard arrays).
  2. Selection: Elevated EGT priority. Dietary EGT comes overwhelmingly from mushrooms (the highest fungal EGT producer is Pleurotus citrinopileatus — golden oyster — at ~7.0 mg/g dry weight; P. ostreatus and P. eryngii also produce substantial EGT). For a variant carrier, the dose target is the upper end of the dietary-mushroom-derived plasma EGT range (~10–25 µM rather than ~5 µM baseline).
  3. Source / produce: Two paths, either alone or combined.
  4. Path A — Commercial dried P. citrinopileatus fruiting body (vendor with published EGT content per gram dry weight; this is the lower-friction option for the first n=1 cycle).
  5. Path B — Home cultivation on methionine-supplemented substrate per SOP-7. Per medicinal-mushroom-extract-sops.md SOP-7, L-methionine 2 mM in mycelial culture produces a 1.7–3.1× EGT yield boost (Lee 2009 PMC3749454). Substrate kit + pharmacy-grade methionine (amino acid supplement; ~$15/kg) → grow P. citrinopileatus per the kit's standard protocol → harvest dried fruiting body. This is the distributed-contributor substrate-engineering execution — the variant carrier produces their own EGT-elevated mushroom batch with food-grade reagents at kitchen scale.
  6. Tier 2 batch QC — INPUT-VERIFICATION TIER (contrast with the Q141K example): Per SOP-6 + SOP-3 (ergothioneine row), the Tier 2 assay is Ellman's reagent (DTNB) thiol detection with smartphone colorimetry at 412 nm. DTNB → 412 nm yellow on free thiol; EGT's free thiol is the substrate. Reagent is pharmacy-accessible (~$25 for enough DTNB for ~50 assays). Calibrate against the Tier 3 EGT-quantified reference batch (HILIC-HPLC with stable-isotope ²H₉-EGT internal standard per SOP-3; outsourced to a community-biolab or contract lab at ~$80–150 per batch). The calibrate-once-at-Tier-3 / track-batches-at-Tier-2 discipline works cleanly here — every link in the chain has a verified input measurement. No exposure-proxy substitution. This is what the workflow looks like when the methodology infrastructure is complete.
  7. Track biomarkers: Standard four-biomarker panel per §4. Add EGT-specific biomarkers: serum ergothioneine (LC-MS/MS, send-out; ~$80–120) at baseline + 8–12 weeks; urinary 8-oxo-deoxyguanosine (oxidative-DNA-damage marker; standard send-out, ~$60) at baseline + endpoint as a downstream functional readout (EGT's anti-oxidative activity should reduce 8-oxodG if the dose is reaching mitochondria). The OCTN1-variant prediction is that a higher dietary EGT dose is required to reach the same serum EGT concentration as in wild-type carriers; the n=1 read is the dose-response shape, not absolute level.

Why this example is load-bearing for the platform's "the workflow works end-to-end" claim. The Q141K worked example has a documented step-4 input-verification gap (butyrate-specific Tier 2 methodology class-gap). The OCTN1 / EGT worked example has no such gap — every step has a validated assay infrastructure, and the home-cultivation route via SOP-7's methionine substrate engineering means the production side is also contributor-accessible. Total cost: ~$500 for the first cycle (genotype + cultivation kit + methionine + DTNB reagent + one Tier 3 HPLC anchor + serum EGT baseline+endpoint). Total time: 8–12 weeks (cultivation cycle + assay turnaround + biomarker tracking). This is the cheapest path to a fully-documented end-to-end execution of the workflow — the dry-run that converts the workflow from "specified" to "field-validated at n=1 with no methodology caveats" (see "Multi-user pilot validation" follow-up below; this example is the pre-pilot single-subject anchor).

What this example does NOT claim: - Does NOT claim EGT supplementation reduces gout flares at n=1 — EGT's gout-relevance is mechanistic (anti-oxidative / anti-inflammatory / Nrf2) rather than a documented anti-gout intervention. - Does NOT claim the OCTN1 variant × EGT dose interaction predicts an effect size at the gout-flare level — the dose-response shape is the n=1 readout, not flare-frequency outcome. - DOES illustrate the workflow shape with no class-level Tier 2 methodology gap — contrast with the Q141K butyrate worked example above. - DOES operationalize the cheapest end-to-end workflow dry-run (per sweep 2026-05-20 Riskiest Assumption #1).

Pattern library — variant → pathway vulnerability → bypass intervention (added 2026-05-22)

The two worked examples above (Q141K × butyrate and OCTN1 × EGT) plus the dietary-CP0 stratification documented in complement-c5a-gout.md §9.7 (CFH Y402H × rosmarinic acid / luteolin / Houttuynia / Helicteres) instantiate a single reusable pattern. The pattern is the platform's strategic asset; the workflow above is its operational expression.

Pattern statement:

Identify a genetic variant that produces a pathway-level vulnerability → identify an intervention class that bypasses or rescues that specific vulnerability mechanism (rather than acting through the broken protein) → recommend the bypass intervention specifically to carriers of the variant, who should benefit more from it than wild-type carriers under the bypass logic.

Confirmed instances (documented in the OE corpus):

Variant Pathway vulnerability Bypass intervention Status
ABCG2 Q141K (rs2231142) Misfolded transporter; reduced intestinal urate efflux Butyrate — PPARγ-driven WT ABCG2 induction + HDAC-inhibitor trafficking rescue for the variant Documented abcg2-modulators.md §6 + worked example above
OCTN1 / SLC22A4 variant (rs1050152 et al.) Reduced EGT transport capacity → lower tissue EGT for equivalent dietary intake Elevated dietary EGT to shift the dose-response upward Documented worked example above (added 2026-05-22)
CFH Y402H (rs1061170) Weakened Factor H alternative-pathway complement regulation → elevated C5a generation on MSU crystals Dietary CP0 candidates (rosmarinic acid, luteolin, Houttuynia, Helicteres) operating upstream of Factor H rather than through Factor H Documented complement-c5a-gout.md §9.7 + cfh-mechanism-dissociation-cp0-candidates-computational.md. AMD-paradox counter-evidence flagged. UKB ↔ AoU biobank cross-tab is the empirical falsification gate.

Unaudited candidate instances (queued as Phase 2 audit):

  • URAT1 W258X loss-of-function variants → reduced renal urate reabsorption → carriers may benefit more from uricosurics (lesinurad, probenecid) OR from siRNA-URAT1 (sirna-urat1-modality.md) than wild-type carriers, because the variant has already done part of the URAT1-blocking work. Loss-of-function carriers are protective for gout at baseline — the question is whether the therapeutic response curve differs for carriers vs non-carriers when uricosurics are prescribed.
  • NLRP3 CAPS gain-of-function variants (NLRP3 R260W, D305N, etc. — cryopyrin-associated periodic syndromes) → constitutive NLRP3 activation. Carriers with gout-overlap phenotype may benefit more from direct NLRP3 inhibitors (CP2-CP4) than from upstream priming interventions (CP0-CP1), because the priming step is bypassed by the gain-of-function. Different "upstream vs downstream of the broken protein" logic from the Q141K and CFH examples — here the variant makes the downstream node hot, so downstream blockade is the bypass.
  • HLA-B*58:01 carriers → severe hypersensitivity reaction to allopurinol (the standard first-line ULT) → carriers must route ULT through non-XO-inhibitor pathways (uricosurics, gut-lumen sink via engineered koji, or future modalities). Pharmacogenetic contraindication rather than mechanism-bypass, but the operational pattern is structurally identical: variant → vulnerability (here drug-class toxicity) → bypass class.

How to use the pattern. When a new genetic variant is added to gout-genetic-variants.md, ask: (1) what specific pathway step does the variant break or amplify? (2) is there a known intervention class that operates around that step (upstream, downstream, or via a different protein in the same pathway)? (3) if yes, write a worked example like the Q141K / OCTN1 / CFH examples above. If no, queue the variant as "named vulnerability, no bypass intervention identified" — the absence is itself useful information about where the platform's discovery engine should look.

What the pattern does NOT claim. Carriers benefiting more than wild-type is the mechanistic prediction, not an empirical fact for any of the unaudited candidates. The CFH × AMD counter-evidence (Vavvas 2018, Merle 2015) is the empirical case study that the pattern doesn't always hold — AMD interventions that work through Factor H paradoxically harm carriers, while the OE prediction is that AMD interventions are not analogous to the dietary-CP0 candidates the OE corpus identifies (which work upstream of Factor H). Whether the upstream-bypass logic holds in gout for CFH carriers is empirically open and gated on the UKB ↔ AoU cross-tab. The same falsification structure applies to every new pattern instance: predict carrier-benefit direction, design a falsification test, accept null or inverted results as productive information.

Cross-references: gout-genetic-variants.md (variant catalog), abcg2-modulators.md (Q141K instance), cfh-mechanism-dissociation-cp0-candidates-computational.md (CFH instance), medicinal-mushroom-complement-track.md (OCTN1 / EGT instance + medicinal mushroom track), modality-chokepoint-matrix.md (intervention class taxonomy useful for identifying bypass routes).

Why this exists

Two failure modes the workflow blocks:

1. Silent underdosing. Without batch QC, a subject who "did the protocol" but happened to source a 20%-potency batch will conclude the mechanism doesn't work. With batch QC, they see the dose was 0.2× target and either re-dose against verified potency or flag the source for replacement.

2. Genotype-blind selection. Without genotype-informed selection, a Q141K homozygote will get the same recommendation as a Q141K-negative subject, even though their response curves are different. Stratified selection puts the right compound class in front of the right genotype.

The workflow is the operational instantiation of the platform's "open-source, democratized, rigorous" thesis. Open-source: every step uses methods documented in the wiki. Democratized: every step is achievable at home or via a community biolab. Rigorous: every step is verified, not assumed.

How this fits with H09 (Community Fermentation Reliability)

H09 is the platform-level test of whether home / community-biolab fermentation can reliably deliver therapeutic doses. The workflow above assumes H09 holds — that home-produced fermentate has enough cordycepin / lactoferrin / uricase activity to matter. If H09 fails, the workflow's step 2 (Source or produce) reshapes: genotype-informed selection (step 1) still works, dose calibration (step 3-4) still works, biomarker tracking (step 5) still works, but home-production routes through commercial supplement vendors with verified potency rather than home fermentation. The workflow shape survives even if the home-production assumption fails.

Open follow-ups

Tier 3 anchor library

A growing list of compound-specific Tier 3 anchors (GC-MS / HPLC / spectrophotometric vendor or community-biolab assays) that the Tier 2 home assays calibrate against. Currently scattered across medicinal-mushroom-extract-sops.md, enzyme-quantification-protocol.md, and quantification-ladder.md. Consolidating into a single anchor table is queued for when enough Tier 3 entries land — premature today; ~6+ entries justifies the index.

Multi-user pilot validation

The workflow has been instantiated at n=1. The natural next-step gate is an N=5–10 multi-user pilot that validates the workflow under realistic user-variability conditions before the larger H09 community-fermentation trial. Tracked as walkthrough Item 20 (open-question-3 in the 2026-05-15 sweep batch).

Pre-pilot single-subject anchor (added 2026-05-22 per sweep 2026-05-20 Riskiest Assumption #1): the OCTN1 / EGT worked example above is the cheapest path to a fully-documented end-to-end execution of the workflow on a single subject with no class-level methodology gaps. Cost ~$500, time 8–12 weeks. Sequenced before the N=5–10 multi-user pilot — the single-subject execution surfaces operational friction (cultivation timing, reagent sourcing, Tier 2 colorimetry reliability, biomarker turnaround) without the recruitment + coordination overhead of multi-user work. Note distinction from Pass 3's framing of the prior Riskiest Assumption: the workflow has been instantiated at n=1 in pieces (Q141K example specified, OCTN1 example specified, individual Tier 2 assays validated separately), but no subject has executed the FULL five-step pipeline end-to-end with documented Tier 2 batch QC + Tier 3 anchor + biomarker readout published in one place. The EGT worked example provides that anchor. The N=5–10 multi-user pilot then exercises the inter-operator variability dimension (per the Tier 2 inter-operator reproducibility open question under Compound-Specific Questions).

Tier 2 assay gap for microbiome-derived metabolites

The Q141K worked example above uses stool SCFA panel as the Tier 2 batch QC step for butyrate delivery (step 4). The SCFA panel verifies downstream exposure (was butyrate present in stool?) but is NOT a direct potency measurement of the supplement input (how much butyrate was actually delivered, at what tissue site, in what concentration?). The quantification ladder's "calibrate once at Tier 3, track batches at Tier 2" discipline (per quantification-ladder.md) breaks for microbiome-derived metabolites because there's no Tier 2 home assay for butyrate (or any SCFA) that's well-calibrated against a Tier 3 GC-MS anchor at the relevant biological concentration.

This is a known methodology gap, not a workflow failure. It applies to any future intervention relying on microbiome-derived metabolites (SCFAs, bile acids, indoles, lactate, etc.). Three candidate Tier 2 paths worth investigating:

  1. Colorimetric — does a butyrate-specific colorimetric reagent exist at hobbyist-lab affordability? (Most SCFA assays require derivatization + GC-MS.)
  2. Enzymatic — could an enzyme-coupled assay (e.g., acetyl-CoA synthetase-coupled NADH readout) be miniaturized for Tier 2?
  3. Breath hydrogen proxy — a hydrogen breath test correlates loosely with colonic fermentation activity; could it be calibrated as a change-in-butyrate-production proxy rather than an absolute butyrate concentration?

comp-038 (2026-05-20) ran the first desk audit and returned YELLOW: no ready-to-adopt simple/home colorimetric or breath-based butyrate assay surfaced. HPLC-UV is plausible for engineered-strain / culture-supernatant work, and electrochemical fecal SCFA profiling is a promising stool-specific future direction, but both require full-text/protocol review and paired GC-MS validation before adoption. A validated Tier 2 butyrate proxy would still strengthen the workflow not just for Q141K but for any future microbiome-metabolite intervention; comp-038 narrows the next step to a focused Tier 2-vs-GC-MS validation, not a broad assay hunt. See tier-2-butyrate-assay-audit-computational.md.

Tracked from 2026-05-19 sweep-walkthrough Cluster B1; first computational prior completed as comp-038 on 2026-05-20.

Cross-references

Promoted from self-experiment-protocol.md §12 on 2026-05-16 per walkthrough Items 8 + 21 (sweep 8653de9 Connection 2 + Priority Action 2). Both items closed via this promotion.