GapMind for catabolism of small carbon sources


L-lactate catabolism

Analysis of pathway L-lactate in 35 genomes

Genome Best path
Acidovorax sp. GW101-3H11 lctP, lctO, acs
Azospirillum brasilense Sp245 lctP, lutA, lutB, lutC
Bacteroides thetaiotaomicron VPI-5482 lctP, lldE, lldF, lldG
Burkholderia phytofirmans PsJN mctP, lldE, lldF, lldG
Caulobacter crescentus NA1000 lctP, L-LDH
Cupriavidus basilensis 4G11 lctP, lutA, lutB, lutC
Dechlorosoma suillum PS lctP, lctO, acs
Desulfovibrio vulgaris Hildenborough lctP, lutA, lutB, lutC
Desulfovibrio vulgaris Miyazaki F lctP, lutA, lutB, lutC
Dinoroseobacter shibae DFL-12 Shew_2731, Shew_2732, L-LDH
Dyella japonica UNC79MFTsu3.2 lctP, L-LDH
Echinicola vietnamensis KMM 6221, DSM 17526 Shew_2731, Shew_2732, lldE, lldF, lldG
Escherichia coli BW25113 lctP, L-LDH
Herbaspirillum seropedicae SmR1 lctP, L-LDH
Klebsiella michiganensis M5al lctP, L-LDH
Magnetospirillum magneticum AMB-1 lctP, lutA, lutB, lutC
Marinobacter adhaerens HP15 Shew_2731, Shew_2732, lutA, lutB, lutC
Paraburkholderia bryophila 376MFSha3.1 mctP, lldE, lldF, lldG
Pedobacter sp. GW460-11-11-14-LB5 lctP, L-LDH
Phaeobacter inhibens BS107 lctP, L-LDH
Pseudomonas fluorescens FW300-N1B4 lctP, lutA, lutB, lutC
Pseudomonas fluorescens FW300-N2C3 lctP, lutA, lutB, lutC
Pseudomonas fluorescens FW300-N2E2 lctP, lutA, lutB*, lutC
Pseudomonas fluorescens FW300-N2E3 lctP, lutA, lutB, lutC
Pseudomonas fluorescens GW456-L13 lctP, lutA, lutB, lutC
Pseudomonas putida KT2440 lctP, L-LDH
Pseudomonas simiae WCS417 lctP, L-LDH
Pseudomonas stutzeri RCH2 lctP, lutA, lutB, lutC
Shewanella amazonensis SB2B Shew_2731, Shew_2732, lldE, lldF, lldG
Shewanella loihica PV-4 Shew_2731, Shew_2732, lldE, lldF, lldG
Shewanella oneidensis MR-1 Shew_2731, Shew_2732, lldE, lldF, lldG
Shewanella sp. ANA-3 Shew_2731, Shew_2732, lldE, lldF, lldG
Sinorhizobium meliloti 1021 lctP, L-LDH
Sphingomonas koreensis DSMZ 15582 lctP, L-LDH
Synechococcus elongatus PCC 7942 lctP, lctO, acs

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.



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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:

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:

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:

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, or view the source code.

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