Add environmental-toxin overlay on the PD pathways

pd_toxin_target.parquet (79 toxin-gene rows) and pd_toxin_pathway.parquet (76 pathways with toxin evidence rolled up), plus card sections covering the EC mapping, specificity weighting and the direction-of-effect caveat.

README.md

@@ -6,6 +6,7 @@ tags:
 - aidd
 - knowledge-graph
 - parkinsons-disease
+- toxicology
 pretty_name: Biopharma hackathon data
 configs:
 - config_name: targets
@@ -24,6 +25,10 @@ configs:
   data_files: pd_subgraph_enrichment.parquet
 - config_name: pd_tree
   data_files: pd_tree.parquet
+- config_name: pd_toxin_target
+  data_files: pd_toxin_target.parquet
+- config_name: pd_toxin_pathway
+  data_files: pd_toxin_pathway.parquet
 ---
 
 # Biopharma hackathon data
@@ -33,9 +38,10 @@ and nothing joins them:
 
 1. **[GenomeScreen (relational)](#1-genomescreen-as-a-relational-dataset)** — 5 tables, the
    DrugCLIP genome-wide virtual screen parsed into parquet.
-2. **[Parkinson's disease subgraph](#2-parkinsons-disease-subgraph-primekg)** — 3 tables, a
+2. **[Parkinson's disease subgraph](#2-parkinsons-disease-subgraph-primekg)** — 5 tables, a
    pathway-centric neighbourhood extracted from PrimeKG, as a graph and as a
-   disease→pathway→protein→drug tree.
+   disease→pathway→protein→drug tree, plus an environmental-toxin overlay on the same
+   pathways.
 
 ---
 
@@ -153,6 +159,8 @@ enrichment table for filtering it down.
 | `pd_subgraph.parquet` | 162,384 | edge (both orientations — see below) |
 | `pd_subgraph_enrichment.parquet` | 283 | pathway |
 | `pd_tree.parquet` | 54,557 | root→leaf path, disease → pathway → protein → drug |
+| `pd_toxin_target.parquet` | 79 | (environmental toxin, gene) |
+| `pd_toxin_pathway.parquet` | 76 | pathway, with toxin evidence rolled up |
 
 ## How it was built
 
@@ -244,6 +252,65 @@ carry two relation types (usually enzyme + target), so they appear as two rows.
 The tree drops `protein_protein` and `pathway_pathway`, which have no place in a tree; use
 `pd_subgraph.parquet` for those.
 
+## The environmental-toxin overlay
+
+A second, independent line of evidence on the same pathways. Ten environmental toxins with
+an established PD association (paraquat, rotenone, MPTP, manganese, lead, mercury,
+chlorpyrifos, dieldrin, trichloroethylene, tetrachloroethylene) were mapped to their
+literature-co-mentioned gene targets, and those genes joined onto the tree's pathways.
+
+The premise: a toxin that is *causative* for PD implicates the pathways it perturbs, so a
+pathway reached by both the disease-association route and the toxicological route carries a
+stronger prior than either alone. **9 of the 21 enriched pathways contain a toxin target**,
+and the top of that list is the canonical PD tox story.
+
+`pd_toxin_target.parquet` is one row per (toxin, gene): `toxin`, `cid`, `pubmed_mentions`,
+`gene_symbol`, `protein_index` (PrimeKG), `evidence_count`, `n_source_entries`,
+`source_entries`, `mapping_confidence`, `toxin_mapped_fraction`, `in_pd_tree`, `is_pd_seed`,
+`tf`, `idf`, `specificity_weight`.
+
+`pd_toxin_pathway.parquet` rolls that up per pathway and joins to `pd_tree.parquet` on
+`pathway_index`: `n_toxins`, `n_toxin_targets`, `toxin_support`, `toxins`, `toxin_targets`,
+alongside the pathway's `pathway_qvalue`, `n_drugs` and `n_target_drugs`.
+
+Ranked by toxin support among pathways at q < 0.05:
+
+| pathway | support | q | toxins | targets |
+| --- | ---: | ---: | ---: | --- |
+| Detoxification of Reactive Oxygen Species | 3.28 | 1.5e-02 | 6 | CAT, GPX1–3, SOD1–3 |
+| Transcriptional activation of mitochondrial biogenesis | 2.99 | 2.4e-02 | 7 | GABPA, PPARGC1B, SOD2 |
+| FOXO-mediated transcription of oxidative stress genes | 1.98 | 3.3e-02 | 6 | CAT, SOD2 |
+| Interleukin-4 and Interleukin-13 signaling | 1.24 | 6.4e-03 | 4 | AKT1, BCL2, IL6, MAOA, TNF |
+| Catecholamine biosynthesis | 1.05 | 1.6e-02 | 2 | TH |
+| Biogenic amines deaminated by MAOA and MAOB | 0.19 | 6.4e-03 | 1 | MAOA, MAOB |
+
+### How the targets were mapped
+
+The source lists targets as `name [TAG] (count)`, where TAG is a gene symbol *or* an EC
+number. Three properties of that format shape the table:
+
+- **The EC entries carry the mechanism.** MPTP's causal target is monoamine oxidase, which
+  appears only as `EC:1.4.3.4`; rotenone's is complex I, only as `EC:1.6.99.3`. Symbol-only
+  parsing loses both. EC classes are expanded through a hand-curated map, and
+  `mapping_confidence` records how firm each expansion is: `exact` (one human gene),
+  `family` (a small enumerated family — MAOA/MAOB, SOD1–3), or `complex` (complex I core
+  subunits). Large cross-species classes (`peroxidase`, `unspecific monooxygenase`) and
+  non-human ones (photosystem II, lignin peroxidase, bacterial dehalogenases) are dropped
+  rather than guessed at.
+- **A toxin often lists one protein twice**, once by symbol and once by EC — lead has both
+  `catalase [CAT] (576)` and `catalase [EC:1.11.1.6] (234)`. Rows are aggregated to the
+  (toxin, gene) grain and the counts summed; `source_entries` preserves what contributed.
+  A family EC has its evidence split across members rather than replicated, so a family tag
+  cannot outvote a specific symbol.
+- **The gene lists are cross-species.** Influenza PB2, bacterial MERA, rice LOC4326471 and a
+  *Drosophila* GABA receptor all appear. Only symbols resolving to a PrimeKG `gene/protein`
+  node are kept.
+
+`specificity_weight` is `tf × idf`: a gene's share of its toxin's mapped evidence, damped by
+how many toxins name it. CAT and the SODs appear for 6 of the 10 toxins — they are the
+background of the toxicology literature, not a toxin-specific signal, and the weighting is
+what keeps them from dominating.
+
 ## Caveats worth knowing before you use it
 
 - **Every edge in `pd_subgraph.parquet` appears twice.** PrimeKG stores undirected edges in
@@ -265,6 +332,39 @@ The tree drops `protein_protein` and `pathway_pathway`, which have no place in a
   protein interactions, not compounds with a PD rationale. Weight by target specificity, or
   restrict to a pathway you care about, before reading anything into the ordering.
 
+- **PrimeKG's drug nodes are all of DrugBank, and it carries no approval status.** Every
+  `drug_id` is a `DB…` accession, but the set includes experimental entries, cofactors and
+  crystallographic ligands — NADH, copper and glutathione adducts all appear as `target`
+  drugs. There is no column to filter on; join against a DrugBank approval list if you need
+  approved compounds only.
+
+Specific to the toxin overlay:
+
+- **A shared target does not mean a shared direction of effect, and PrimeKG cannot tell you
+  which.** `drug_relation` distinguishes target from enzyme, never agonist from antagonist.
+  If a toxin causes PD by inhibiting a protein, a drug that also inhibits it is a risk, not a
+  therapy. `SLC6A3` is the clearest case: DAT-mediated uptake is how MPP+ enters dopaminergic
+  neurons, so a DAT blocker is protective while amphetamine — on the same target list — is
+  not. Read the overlay as nominating *pathways*, and get direction from DrugBank's action
+  field before selecting compounds within one.
+- **The counts are literature co-mentions, not affinities or assays.** A high count means the
+  toxin and the gene are frequently discussed together, which tracks how well-studied a gene
+  is at least as much as how relevant it is.
+- **The source lists only the top 8 of 25 targets per toxin.** Document frequencies — and so
+  every `idf` — are computed over that truncated view, which biases `idf` upward for genes
+  that fall below the cut for some toxins.
+- **Two toxins are mostly unmappable.** `toxin_mapped_fraction` is 0.38 for tetrachloroethylene
+  and 0.40 for trichloroethylene; the rest of their lists is bacterial or fungal. Because `tf`
+  is a share of *mapped* evidence, the survivors absorb the whole share and their weights are
+  inflated. Treat anything below ~0.5 as soft.
+- **`toxin_support` is a raw sum, so it rewards large pathways.** Unfiltered, `Neutrophil
+  degranulation` (478 proteins, q = 0.21) tops the ranking. The enrichment q-value already
+  controls for size — filter on it first, then rank by support.
+- **10 toxin targets are absent from the tree entirely**, including all seven complex I core
+  subunits — the most PD-relevant mechanism in the set. They resolve to valid PrimeKG
+  `protein_index` values but sit in no PD-bridged pathway, so they contribute nothing to
+  `pd_toxin_pathway.parquet`. Filter `in_pd_tree = false` to see them.
+
 ## Usage
 
 ```python
@@ -294,6 +394,17 @@ conn.execute(f"""
     WHERE drug_relation = 'target' AND pathway_qvalue < 0.05
     GROUP BY 1 ORDER BY pathways DESC, proteins DESC LIMIT 25
 """).fetchall()
+
+# convergent evidence: drugs targeting proteins in pathways that are both
+# statistically enriched for PD proteins and hit by a PD-associated toxin
+conn.execute(f"""
+    SELECT t.drug_name, t.protein_name, x.pathway_name,
+           x.toxins, x.toxin_support, x.pathway_qvalue
+    FROM '{base}/pd_tree.parquet' t
+    JOIN '{base}/pd_toxin_pathway.parquet' x USING (pathway_index)
+    WHERE t.drug_relation = 'target' AND x.pathway_qvalue < 0.05
+    ORDER BY x.toxin_support DESC, t.drug_name
+""").fetchall()
 ```
 
 ## Source and license
@@ -305,6 +416,12 @@ the data itself, which is distributed via Harvard Dataverse
 own ~20 constituent primary sources. Check those before redistributing. The `license` field in
 this repo's metadata (CC-BY-4.0) describes the GenomeScreen tables above.
 
+The toxin overlay adds a second source: the toxin list and its gene targets come from
+**PubChem** (compound records and their literature-co-mentioned genes) and PubMed mention
+counts, retrieved via the PubChem PUG-REST API. PubChem aggregates from many depositors whose
+individual terms vary; see https://www.ncbi.nlm.nih.gov/home/about/policies/. The EC-to-gene
+map and the specificity weighting are original to this dataset.
+
 > Chandak, P., Huang, K., Zitnik, M. *Building a knowledge graph to enable precision medicine.*
 > Nature Scientific Data 10, 67 (2023).
 

pd_toxin_pathway.parquet

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pd_toxin_target.parquet

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