D-glucuronate catabolism in Rhizobium etli CFN 42

GapMind for catabolism of small carbon sources

D-glucuronate catabolism in Rhizobium etli CFN 42

Best path

Rules

Overview: Glucuronate utilization in GapMind is based on MetaCyc pathways D-glucuronate degradation II (oxidation of 5-keto-4-deoxyglucarate, link), a related pathway via 5-keto-4-deoxyglucarate aldolase (link), or degradation via fructuronate (link). GapMind also includes a variation on the oxidative pathway with a glucarolactonase, as in Pseudomonas putida. MetaCyc pathway I (via L-gulonate and xylitol, link) is not reported in prokaryotes and is not described here.

all:

glucuronate-transport, udh, gci and 5-dehydro-4-deoxyglucarate-degradation
or glucuronate-transport, udh, uxuL, gudD and 5-dehydro-4-deoxyglucarate-degradation
or glucuronate-transport, uxaC, uxuB, uxuA, kdgK and eda
Comment: In pathway II, dehydrogenase udh forms D-glucaro-1,5-lactone, which spontaneously rearranges to D-glucaro-1,4-lactone, and the cycloisomerase gci forms 5-dehydro-4-deoxy-D-glucarate. In P. putida, genetic data suggests that the lactone is hydrolyzed to D-glucarate by uxuL and the dehydratase gudD forms 5-dehydro-4-deoxy-D-glucarate. In the fructuronate pathway, an isomerase (uxaC) converts D-glucuronate to D-fructuronate, followed by oxidation to D-mannonate, dehydration to 2-dehydro-3-deoxy-D-gluconate, phosphorylation to 2-dehydro-3-deoxy-D-gluconate 6-phosphate, and an aldolase reaction to glyceraldehyde-3-phosphate and pyruvate.

5-dehydro-4-deoxyglucarate-degradation:

kdgD and dopDH
or garL, garR and garK
Comment: As part of pathway II, 5-dehydro-4-deoxyglucarate is dehydrated/decarboxylated to 2,5-dioxopentanoate (by kdgD) and oxidized to 2-oxoglutarate (by dopDH). Alternatively, 5-dehydro-4-deoxy-D-glucarate can be consumed by aldolase garL, which forms pyruvate and tartronate semialdehyde (2-hydroxy-3-oxopropionate); tartronate semialdehyde is reduced to D-glycerate and phosphorylated to enter glycolysis.

glucuronate-transport:

exuT
or dctP, dctQ and dctM
Comment: Transporters were identified using query: transporter:glucuronate:D-glucuronate:D-glucopyranuronate:CPD-14488:CPD-12521:CPD-15530

18 steps (15 with candidates)

Or see definitions of steps

Step	Description	Best candidate	2nd candidate
exuT	D-glucuronate:H+ symporter ExuT
udh	D-glucuronate dehydrogenase	RHE_RS27650
gci	D-glucaro-1,4-lactone cycloisomerase	RHE_RS28415	RHE_RS27620
kdgD	5-dehydro-4-deoxyglucarate dehydratase	RHE_RS10535	RHE_RS27660
dopDH	2,5-dioxopentanonate dehydrogenase	RHE_RS16180	RHE_RS29210
Alternative steps:
dctM	D-glucuronate TRAP transporter, large permease component	RHE_RS23925	RHE_RS26780
dctP	D-glucuronate TRAP transporter, solute receptor component	RHE_RS23930
dctQ	D-glucuronate TRAP transporter, small permease component
eda	2-keto-3-deoxygluconate 6-phosphate aldolase	RHE_RS18590	RHE_RS04295
garK	glycerate 2-kinase	RHE_RS09170
garL	5-dehydro-4-deoxy-D-glucarate aldolase	RHE_RS11755	RHE_RS07055
garR	tartronate semialdehyde reductase	RHE_RS23580	RHE_RS28225
gudD	D-glucarate dehydratase
kdgK	2-keto-3-deoxygluconate kinase	RHE_RS15865	RHE_RS06870
uxaC	D-glucuronate isomerase	RHE_RS00460
uxuA	D-mannonate dehydratase	RHE_RS22025	RHE_RS24555
uxuB	D-mannonate dehydrogenase	RHE_RS00455	RHE_RS18810
uxuL	D-glucaro-1,5-lactonase UxuL or UxuF	RHE_RS27655	RHE_RS28940

Confidence: high confidence medium confidence low confidence
transporter – transporters and PTS systems are shaded because predicting their specificity is particularly challenging.

This GapMind analysis is from Apr 09 2024. The underlying query database was built on Sep 17 2021.

Downloads

Candidates (tab-delimited)
Steps (tab-delimited)
Rules (tab-delimited)
Protein sequences (fasta format)
Organisms (tab-delimited)
SQLite3 databases

Related tools

About GapMind

Each pathway is defined by a set of rules based on individual steps or genes. Candidates for each step are identified by using ublast (a fast alternative to protein BLAST) against a database of manually-curated proteins (most of which are experimentally characterized) or by using HMMer with enzyme models (usually from TIGRFam). Ublast hits may be split across two different proteins.

A candidate for a step is "high confidence" if either:

ublast finds a hit to a characterized protein at above 40% identity and 80% coverage, and bits >= other bits+10.
- (Hits to curated proteins without experimental data as to their function are never considered high confidence.)
HMMer finds a hit with 80% coverage of the model, and either other identity < 40 or other coverage < 0.75.

where "other" refers to the best ublast hit to a sequence that is not annotated as performing this step (and is not "ignored").

Otherwise, a candidate is "medium confidence" if either:

ublast finds a hit at above 40% identity and 70% coverage (ignoring otherBits).
ublast finds a hit at above 30% identity and 80% coverage, and bits >= other bits.
HMMer finds a hit (regardless of coverage or other bits).

Other blast hits with at least 50% coverage are "low confidence."

Steps with no high- or medium-confidence candidates may be considered "gaps." For the typical bacterium that can make all 20 amino acids, there are 1-2 gaps in amino acid biosynthesis pathways. For diverse bacteria and archaea that can utilize a carbon source, there is a complete high-confidence catabolic pathway (including a transporter) just 38% of the time, and there is a complete medium-confidence pathway 63% of the time. Gaps may be due to:

our ignorance of proteins' functions,
omissions in the gene models,
frame-shift errors in the genome sequence, or
the organism lacks the pathway.

GapMind relies on the predicted proteins in the genome and does not search the six-frame translation. In most cases, you can search the six-frame translation by clicking on links to Curated BLAST for each step definition (in the per-step page).

For more information, see:

the paper from 2019 on GapMind for amino acid biosynthesis
the 2024 update on GapMind for amino acid biosynthesis
the paper from 2022 on GapMind for carbon sources
the source code
instructions for running GapMind on your computer

If you notice any errors or omissions in the step descriptions, or any questionable results, please let us know

by Morgan Price, Arkin group, Lawrence Berkeley National Laboratory

GapMind for catabolism of small carbon sources