Identity
Canonical name: Bacteroides thetaiotaomicron. 1
Known alias: Btheta. 1
Stable external identifier: No stable external identifier was provided in the source report. 1
Bacteroides thetaiotaomicron was the sole Bacteroidetes organism among the 14 organisms analyzed in the annotation-gap discovery study. 1
Key study findings
The organism had the lowest annotation-gap resolution rate in the study: 3 of 15 gapfilled reaction-organism pairs were resolved, corresponding to exactly 20.0%. 1 Its 20.0% resolution rate was the low end of a 3.5-fold range across organisms, compared with the highest reported rate of 71.4% for klebsiella-michiganensis. 1
The study associated the lower resolution rate with greater phylogenetic and metabolic divergence from the proteobacterial majority, but presented this as an interpretation rather than a directly tested causal explanation. 1 The study’s dataset was phylogenetically biased: 12 of 14 organisms were Proteobacteria, leaving Bacteroides thetaiotaomicron as the sole Bacteroidetes organism. 1
A later co-fitness/pangenome analysis refines this interpretation by showing that Btheta had 287 genomes, 4,649 pangenome clusters, and 328,455 co-fitness pairs available for analysis. 2 Its pairwise co-fitness signal was weak but positive: delta phi was +0.001 across 242,676 evaluated pairs, with mean phi 0.067 for cofit pairs and 0.067 for matched random pairs (p<1e-6). 2 This supports the broader conclusion that laboratory co-fitness only weakly predicts pangenome co-occurrence, while showing that Btheta had sufficient genomic variation for the comparison. 2
The same analysis found a stronger coordinated-module signal in Btheta: 22 of 36 independent component analysis (ICA) modules were significant, with mean within-module phi=0.192 versus a null mean of 0.086. 2 This supports the hypothesis that multi-gene coordinated regulation can constrain co-inheritance more strongly than pairwise functional relationships alone, but does not directly explain the organism’s gapfilling resolution rate. 2
The costly-plus-dispensable gene analysis adds that Bacteroides thetaiotaomicron contributed 14.0% of its genes to this quadrant, the second-highest organism-level proportion after Pseudomonas stutzeri RCH2 at 21.5%. 3 This refines the existing pangenome context by placing Btheta toward the high end of costly, non-conserved gene burden, while the report does not establish whether this reflects mobile-element expansion, strain-specific biology, or another cause. 3
The organism was included in a workflow combining metabolic-model gapfilling, Fitness Browser phenotypes, pangenome annotations, GapMind pathway evidence, and BLAST homology to assign candidate genes to gapfilled reactions. 1 Fitness Browser measurements used random-barcode transposon sequencing (RB-TnSeq), a method that measures genome-wide mutant fitness with barcoded transposon libraries. 1
Interpretation and limitations
Bacteroides thetaiotaomicron’s result suggests that annotation-gap resolution may be less effective for phylogenetically distant clades with divergent metabolism, but the source study did not establish this as a general rule from this single Bacteroidetes organism. 1 The co-fitness analysis refines the available context: Btheta showed a strong module-level co-inheritance pattern despite its weak pairwise signal, indicating that aggregate pangenome organization and gapfilling evidence measure related but distinct properties. 2
The 14.0% costly-plus-dispensable proportion supports treating Btheta’s accessory genome as biologically informative rather than simply inert genomic background, but this is a cross-organism classification result and does not explain its 20.0% gapfilling resolution rate. 3 The costly-plus-dispensable analysis also interprets this gene class overall as enriched for mobile genetic elements and recent acquisitions, but provides no Btheta-specific enrichment measurement; that organism-level mechanism therefore remains a hypothesis rather than an established finding for Btheta. 3
The study noted that better-annotated reference genomes and stronger Fitness Browser coverage were associated with higher organism-level resolution, so differences in annotation quality and experimental coverage may also contribute to the organism’s lower rate. 1 The co-fitness study also cautioned that prevalence ceilings make co-occurrence comparisons most informative for organisms with substantial auxiliary gene content; it did not establish that Btheta’s module signal caused or improved annotation-gap resolution. 2
The report did not provide organism-specific evidence sufficient to distinguish the effects of phylogenetic distance, metabolic divergence, annotation quality, Fitness Browser coverage, and accessory-genome composition. 1 The costly-plus-dispensable classification additionally cautions that burden was defined as max_fit > 1 in any experiment and that binary core/accessory status and sequence-linking thresholds limit interpretation. 3
Related pages
- Annotation-Gap Discovery via Phenotype-Fitness-Pangenome-Gapfilling Integration — source summary containing the organism-level resolution results and study limitations.
- Co-fitness and Co-inheritance in Bacterial Pangenomes — source summary of Btheta’s pairwise co-fitness and ICA-module co-inheritance results.
- The 5,526 Costly + Dispensable Genes — cross-organism analysis placing Btheta’s costly-plus-dispensable gene proportion in context.
- metabolic-model-gapfilling — the organism’s 3-of-15 gapfilled-pair resolution result contributes to cross-organism assessment of gapfilling-based annotation inference.
- cofitness-network-architecture — covers the weak pairwise and stronger module-level co-inheritance signals.
- condition-specific-fitness — links carbon-source fitness measurements to annotation-gap resolution.
- pangenome-integration — covers pangenome conservation evidence used in candidate-gene assignment and co-inheritance analysis.
- multi-omics-integration — covers integration of fitness, annotation, sequence-homology, pathway, and metabolic-model evidence.