Type: dataset. In this corpus the MetaCyc pathway class hierarchy is not used directly from MetaCyc but from a provenance file shipped with ModelSEEDDatabase, MetaCyc_Pathways.tbl 1.
MetaCyc is a curated database of metabolic pathways with a curator-validated class hierarchy. In this corpus its pathway classes were taken from the modelseed database file /global_share/KBaseUtilities/ModelSEEDDatabase/Biochemistry/Aliases/Provenance/MetaCyc_Pathways.tbl, and its pathway identifiers (e.g. PWY-5920) are the units reported by humann3 pathway profiling 1,2.
Use in the IBD pathway analysis
The first Pillar 3 notebook (NB07a) of Metagenome-Prioritized Phage Cocktails for Crohn’s Disease and IBD ran a within-IBD-substudy meta-analysis of Crohn’s disease (CD) versus non-IBD samples on fact_pathway_abundance (HUMAnN3 MetaCyc, CMD_IBD only). Three substudies were robust (HallAB_2017, IjazUZ_2017, NielsenHB_2014) and one was boundary (LiJ_2014, nonIBD = 10). VilaAV_2018 was excluded (CD = 216, nonIBD = 0). After a 10%-prevalence filter, 575 unstratified MetaCyc pathways were reduced to 409 2.
This differential-abundance (DA) analysis found 52 CD-up MetaCyc pathways (FDR<0.10, |effect|>0.5), where FDR is the false discovery rate. The permutation null mean was 0.077. It also found 137 pathway–pathobiont attribution pairs at |ρ_meta|>0.4. The report does not agree with itself about which pair ranks first. Its overview names heme biosynthesis (PWY-5920) ↔ escherichia-coli at ρ=0.640 as the top hit and reads it as recapitulating AIEC biology. NB07a’s ranked attribution table, however, puts heme biosynthesis from glycine at rank 19 (ρ_meta 0.640) and puts GLYOXYLATE-BYPASS (glyoxylate cycle) at rank 1 (ρ_meta 0.797). The heme pair is therefore the overview’s headline example, not the strongest correlation 2.
Name regex versus MetaCyc classes
The v1.7 H3a (b) verdict, “FAIL — degenerate”, came from a regex on pathway names. Only 44/409 background pathways matched the 7 a-priori category patterns. PWY-5920 (superpathway of heme biosynthesis from glycine) was silently put in “0_other” because “heme” / “iron” were not in the regex set. The v1.8 retest replaced the regex with structured MetaCyc class assignments and expanded to 12 IBD-relevant themes, including iron/heme acquisition, fat metabolism / glyoxylate, anaerobic respiration, purine/pyrimidine recycling, and aromatic AA / chorismate / indole 2.
The central digest explains the miss. A regex on the PWY-5920 name does not match “iron”, but MetaCyc’s curator-validated class hierarchy places PWY-5920 under HEME-SYN, Heme-b-Biosynthesis, Cofactor-Biosynthesis and Tetrapyrrole-Biosynthesis. The v1.7 “0_other” bucket was hiding 15+ heme/iron pathways 1.
In the digest’s account of v1.8, 357/409 (87 %) of pathways had MetaCyc class data and 262/409 (64 %) were assigned to ≥ 1 IBD theme. The analysis used a per-theme fishers-exact-test (CD-up × in-theme) with benjamini-hochberg-fdr correction across 12 themes. The verdict was SUPPORTED: iron/heme acquisition was the dominant CD-up theme (OR = 8.1, FDR 7e-6; 15 of 52 CD-up pathways) 1.
The project report states that the MetaCyc class hierarchy gave H3a (b) SUPPORTED. It gives the hierarchy’s coverage as 90 % of 575 HUMAnN3 pathways, and elsewhere as 514/575 pathways categorized across 12 themes. Iron/heme acquisition was the dominant CD-up theme (OR=8.1, FDR 7e-6; 15 of 52 CD-up pathways), an 8.1× enriched dominant theme. Two other themes were also enriched: H. hathewayi (hungatella hathewayi) purine/pyrimidine recycling (OR=4.9) and TMA/choline (OR=9.3). The verdict reversed on the same data and the same DA pipeline, driven by ontology choice. The report calls the iron/heme result causally consistent with the broader IBD-iron literature. Within this corpus it rests on one project’s cohort analysis. The result feeds iron acquisition and pathobiont expansion 2.
Recommended default for category enrichment
The report calls v1.7 → v1.8 a major scientific reversal driven entirely by category-schema choice. It encodes the lesson as plan norm N17: prefer an ontology / class hierarchy over name-pattern regex for pathway / gene / metabolite categorization wherever feasible, and keep regex as a sensitivity check. It states that ModelSEEDDatabase ships a usable MetaCyc class hierarchy with 90 % coverage of HUMAnN3 outputs, which should be the default for any pathway category-enrichment test in BERIL projects 2. This is the main case behind ontology-and-category-schema-sensitivity.
The central digest also records that ModelSEEDDatabase ships a usable MetaCyc class hierarchy at the MetaCyc_Pathways.tbl path, with 90 %+ coverage of HUMAnN3 outputs 1.
Coverage figures as reported
The sources state the coverage of the MetaCyc class hierarchy differently, depending on the denominator, and the figures are not reconciled here. The digest gives 357/409 (87 %) of prevalence-filtered pathways with class data and 90 %+ coverage of HUMAnN3 outputs 1. The project report gives 90 % coverage of 575 HUMAnN3 pathways and 514/575 pathways categorized 2.