D-glucuronate catabolism in Pseudomonas simiae WCS417

GapMind for catabolism of small carbon sources

D-glucuronate catabolism in Pseudomonas simiae WCS417

Best path

dctP, dctQ, dctM, udh, uxuL, gudD, kdgD, dopDH

Also see fitness data for the top candidates

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 (17 with candidates)

Or see definitions of steps

Step	Description	Best candidate	2nd candidate
dctP	D-glucuronate TRAP transporter, solute receptor component	PS417_17370
dctQ	D-glucuronate TRAP transporter, small permease component	PS417_17375
dctM	D-glucuronate TRAP transporter, large permease component	PS417_17380	PS417_14900
udh	D-glucuronate dehydrogenase	PS417_17360	PS417_22005
uxuL	D-glucaro-1,5-lactonase UxuL or UxuF	PS417_17365	PS417_07255
gudD	D-glucarate dehydratase	PS417_18895	PS417_23670
kdgD	5-dehydro-4-deoxyglucarate dehydratase	PS417_04195	PS417_07165
dopDH	2,5-dioxopentanonate dehydrogenase	PS417_11015	PS417_04200
Alternative steps:
eda	2-keto-3-deoxygluconate 6-phosphate aldolase	PS417_22100	PS417_00160
exuT	D-glucuronate:H+ symporter ExuT	PS417_14775
garK	glycerate 2-kinase	PS417_13970	PS417_08795
garL	5-dehydro-4-deoxy-D-glucarate aldolase	PS417_17865	PS417_14640
garR	tartronate semialdehyde reductase	PS417_08800	PS417_23720
gci	D-glucaro-1,4-lactone cycloisomerase	PS417_00165
kdgK	2-keto-3-deoxygluconate kinase	PS417_12130	PS417_12565
uxaC	D-glucuronate isomerase
uxuA	D-mannonate dehydratase	PS417_00165
uxuB	D-mannonate dehydrogenase	PS417_12695

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 Sep 17 2021. 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 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