A new tier 2 PGDB for Pseudomonas putida KT2440

We are pleased to announce that we have recently upgraded our Pseudomonas putida KT2440 database to a tier 2 status, meaning it has undergone moderate curation. The KT2440 strain is a plasmid-free derivative of the toluene-degrading organism Pseudomonas putida mt-2, which was originally isolated in 1963 under the name Pseudomonas arvilla mt-2. Unlike strain mt-2, strain KT2440 does not carry the pWW0 TOL plasmid, which is required for toluene degradation. Strain KT2440 is the first Gram-negative soil bacterium to be certified as a safety strain by the Recombinant DNA Advisory Committee and is considered the best characterized saprophytic Pseudomonad that has retained its ability to survive and function in the environment. It is used worldwide as a workhorse for genetic and physiological studies and for the development of biotechnological applications.

This upgrade included the following curation activities:

• Propagation of cumulative changes in MetaCyc compounds and reactions since the KT2440 PGDB was created, synchronizing the two databases. 
• Rerun of the pathway prediction algorithm, followed by human inspection and pruning of some of the predicted pathways. The pathway count grew by 25, for a total of 330. The number of enzymatic reactions grew from 2,081 to 2,313.
• Creation of a large number of protein complexes, increasing the total number of complexes from 37 to 180.
• Propagation of strain KT2440 enzymes that were previously curated in MetaCyc into this PGDB.
• Curation of several new pathways that were previously not present in MetaCyc.
• Literature-based curation of 65 proteins.
• Literature-based curation of 247 transcriptional regulatory interactions.

Some of the newly curated pathways that may be of particular interest:

• Carbon metabolism in this organism is unusual. Pseudomonas putida KT2440 lacks a functional Embden-Meyerhof-Parnas (EMP) pathway, and 90% of consumed sugar is converted into D-gluconate, entering central carbon metabolism as D-gluconate 6-phosphate, which is processed in two parallel channels: it is either converted to pyruvate via the Entner-Doudoroff shunt, or processed by enzymes of the pentose phosphate pathway into D-glyceraldehyde 3-phosphate. This complex process is described by the pathway “glucose metabolism (Pseudomonas putida)”.
• The fluorescence of Fluorescent Pseudomonads is due to the production of pyoverdine siderophores. Pyoverdines are species-specific, with more than 100 types reported. A new pathway describes the biosynthesis of pyoverdine G4R, the type produced by this organism.
• Similarly, there is a wide variability in the composition of Lipid A, a major component of bacterial lipopolysaccharides. Three new pathways describe the biosynthesis of P. putida-specific (Kdo)₂-lipid A.

Access to tier 2 PGDBs requires a subscription to the BioCyc website.

Regulation Roundup

BioCyc includes a range of different kinds of regulation data, but because there are many different ways that biological processes are regulated, that data is distributed over different parts of the site and is accessed in different ways.  This post is an attempt to summarize all the different types of regulation we represent, to help you find the information you are seeking.

BioCyc contains the following types of regulatory information:

• Regulation of enzymatic activity, usually by small molecules or metal ions.  This includes required enzyme cofactors, allosteric activation and inhibition, competitive inhibition, and other forms of enzyme modulation.
• Regulation of gene transcription or translation.  This includes regulation of transcription by transcription factors and sigma factors, attenuation, and regulation of translation by small RNAs, riboswitches, and regulatory proteins.
• Regulation by protein modification, such as phosphorylation or binding of a ligand, to determine whether or not a protein is in its active form.

When looking at a gene page, the various regulatory influences on the gene and its product are summarized in the Regulation Summary Diagram at the top of the Summary tab (if this diagram is not present, then there is no regulatory data available for that organism). Mousing over any element of this diagram describes its particular mode of regulatory action.  More detailed information is available in the other tabs, as described below.

Regulation Summary Diagrams for three EcoCyc genes, illustrating a range of regulation types.
a) Transcription of trpD is inhibited by TrpR bound to tryptophan; translation of trpD is attenuated by trp-tRNA; the TrpDE enzyme requires Mg²⁺as a cofactor, and its activity is inhibited by tryptophan.
b) Transcription of  oppA is inhibited by several different transcription factors, and requires sigma factor σ28; translation of oppA is activated by a spermidine riboswitch and inhibited by small RNA GcvB with accessory protein Hfq.
c) Transcription of uvrY is inhibited by LexA; translation of uvrY is activated by regulatory protein DeaD; the protein UvrY is converted to its phosphorylated form by BarA-P.

BioCyc Gene Essentiality Data

BioCyc contains gene essentiality datasets for 30 organisms (see listing at end).  These datasets have been both collected from individual publications, and harvested in bulk from the OGEE database (see http://ogee.medgenius.info/browse/).  In some cases, multiple essentiality datasets are available for a given organism (e.g., for E. coli K-12).

BioCyc essentiality datasets are keyed to the conditions of growth (e.g., MOPS medium with 0.4% glucose), since different sets of genes will be essential under different conditions of growth.  If essentiality data is available for a given gene, it will be shown on the gene page, under the essentiality tab.

The Essentiality tab for gene trpA in EcoCyc

In addition, BioCyc provides access to the full lists of essential and non-essential genes for a given dataset.  To obtain such lists:

1. Select your organism of interest using "change organism database" in the upper right corner.
2. Click Analysis menu → Summary Statistics
3. In the bottom table, the table will contain an entry "Gene Essentiality Datasets" if any essentiality datasets are present. Click on those words.
4. In the resulting page, click on the entry in the "Growth Medium" column for the dataset of interest.
5. The resulting pages will show lists of genes that are essential and/or non-essential for growth on this medium.  To manipulate those gene lists, click "Turn into a SmartTable".  SmartTable results can be downloaded as a file and explored interactively.

A section of the page for growth medium M9 medium with 1% glycerol in EcoCyc.

Current BioCyc organisms with gene essentiality data are as follows. To generate the list:

1. Click "change organism database" in upper right corner of page
2. Click "By Organism Properties" tab
3. In "Select Property" selector, select "# Genes with Essentiality Data"
4. Change "has values" selector to "has any value"
5. Click "Find Organisms"

The organism selector showing all databases with gene essentiality datasets

• Agrobacterium fabrum C58
• Bacillus subtilis subtilis 168
• Brevundimonas subvibrioides ATCC 15264
• Burkholderia cenocepacia J2315
• Caulobacter crescentus NA1000
• Escherichia coli CFT073
• Escherichia coli K-12 substr. MG1655 (EcoCyc)
• Escherichia coli O25b:H4-ST131
• Francisella tularensis novicida U112
• Haemophilus influenzae Rd KW20
• Helicobacter pylori 26695
• Homo sapiens
• Mycobacterium tuberculosis H37Rv
• Mycoplasma genitalium G-37
• Mycoplasma pneumoniae M129
• Mycoplasma pulmonis UAB CTIP
• Porphyromonas gingivalis ATCC 33277
• Pseudomonas aeruginosa PAO1
• Pseudomonas aeruginosa UCBPP-PA14
• Rhizobium leguminosarum bv. viciae 3841
• Saccharomyces cerevisiae S288c
• Salmonella enterica enterica CT18
• Salmonella enterica enterica LT2
• Salmonella enterica enterica SL1344
• Salmonella enterica enterica Ty2
• Shewanella oneidensis MR-1
• Sphingomonas wittichii RW1
• Streptococcus pneumoniae D39
• Streptococcus pneumoniae TIGR4; ATCC BAA-334
• Synechococcus elongatus PCC 7942 

A 7th EC Class for Translocases

Six enzyme classes have been recognized since the first Enzyme classification and nomenclature list was first approved by the International Union of Biochemistry in 1961. These were based on the type of reaction catalyzed:

Oxidoreductases (EC 1)
Transferases (EC 2)
Hydrolases (EC 3)
Lyases (EC 4)
Isomerases (EC 5)
Ligases (EC 6)

However, it has become apparent that none of these could describe the important group of enzymes that catalyse the movement of ions or molecules across membranes or their separation within membranes. Several of these involve the hydrolysis of ATP and had been previously classified as ATPases (EC 3.6.3.-), although the hydrolytic reaction is not their primary function.

These enzymes have now been classified under a new EC class of Translocases (EC 7). The reactions catalyzed are designated as transfers from ‘side 1’ to ‘side 2’ because the designations ‘in’ and ‘out’ (or ‘cis’ and ‘trans’), which had been used previously, lack clarity and can be ambiguous. The comments associated with each entry then describe the specific translocations catalysed.

The subclasses designate the types of ion or molecule translocated:

• EC 7.1 contains enzymes catalysing the translocation of hydrons (hydron being the general name for H⁺ in its natural abundance),
• EC 7.2 contains those catalysing the translocation of inorganic cations and their chelates,
• EC 7.3 contains those catalysing the translocation of inorganic anions,
• EC 7.4 contains those catalysing the translocation of amino acids and peptides,
• EC 7.5 contains those catalysing the translocation of carbohydrates and their derivatives
• EC 7.6 contains those catalysing the translocation of other compounds.

The sub-subclasses concern the reaction that provides the driving force for the translocation, where these are relevant:

• EC 7.x.1 translocations linked to oxidoreductase reactions
• EC 7.x.2 translocations linked to the hydrolysis of a nucleoside triphosphate
• EC 7.x.3 translocations linked to the hydrolysis of a diphosphate
• EC 7.x.4 translocations linked to a decarboxylation reaction

Exchange transporters that are not dependent on enzyme-catalyzed reactions, such as the exchange of ions across membranes, are not included. Pores that change conformation between open and closed states in response to phosphorylation or some other catalyzed reaction are classified under EC 5.6 (Macromolecular conformational isomerases).

(prepared by Keith Tipton, originally published at the ExplorEnz enzyme database)

Pan-Genome PGDBs Unify Genomic and Metabolic Data across Related Strains

Pan-Genome PGDBs are a relatively new feature of BioCyc. These BioCyc databases combine in one place information about multiple sequenced genomes for a given species. For example, the Helicobacter pylori Pan-Genome database covers 158 sequenced strains.

The Pan-Genome PGDBs contain one gene object for each orthologous group of genes in the organism. They also contain the union of all metabolic pathways across all the strains. Thus, a Pan-Genome PGDB allows you to quickly assess the full set of gene functions and metabolic pathways across the the known strains.  For example, the gene page shows all orthologs across all strains in the Pan-Genome.  The page for the ftsX gene illustrates this for a gene with relatively few orthologs.

Other gene pages list hundreds of orthologs and synonyms.

You can visit a page for the orthologous genes by following the links in the 'Relationship Links' area near the bottom of the page.

Pan-Genomes provide a way to visualize genes in the genome browser as well.   It's easier to understand this visualization if you know that Pan-Genomes are constructed starting with the PGDB of a base strain and adding other members of the collection of strains one-by-one.  For example, the H. pylori Pan-Genome was constructed by starting with strain 26695.  The visualization is based on dividing groups of orthologous genes into two sets: ortholog groups that include genes that occur in the base strain, and ortholog groups that only include genes in other strains.  

Ortholog groups that include genes from the base strain are collected on a 'chromosome' that preserves the location and direction of the genes from the base strain.  The remaining ortholog groups are mapped in arbitrary order onto an 'artificial replicon'.  The names in the artificial replicon display are based on the (arbitrary) order that PGDBs were added to the Pan-Genome.

You can search for genes either by name (e.g., 'abc') or an identifier combining a strain name and id joined with an underscore (e.g., HPHPP11_0013) by entering the name in the search box at the top right.  Either quick search or gene search will take you to the page containing the gene and its orthologs in the Pan-Genome PGDB. 

The cellular overview diagram provides a way to visualize reactions associated with genes shared by all members of the Pan-Genome. You can also visualize those reactions that are unique to a single organism within the Pan-Genome.  In the screen shot below, the reactions shared by all organisms in the H. pylori Pan-Genome are shown in red and those unique to a single organism are purple.

To create a diagram like this on BioCyc web site, select a Pan-Genome PGDB, and bring up the cellular overview. In the operations menu, choose Highlight Genes -> By Pan-Genome Core Genes.  The core genes are the set of genes shared by all organism databases in the Pan-Geneome.  Chose Highlight Genes -> By Pan-Genome Unique Genes, to highlight reactions associate with genes that are unique to a single organism database.

In the desktop, show the Cellular Overview, then from Overviews -> Highlight -> Highlight the core genome followed by Overviews -> Highlight the unique genes will achieve the same highlighting.


More details on how Pan-Genome PGDBs are created and how to use them are provided here.

You can select a Pan-Genome PGDB by entering the phrase “pan-genome” in the change organism database dialogue. Here's our current list of Pan-Genome PGDBs and the number of strains that each one contains. Over time we will be adding Pan-Genome PGDBs for additional species, and regenerating existing Pan-Genome PGDBs to include additional strains.

Clostridioides difficile 10 strains

Escherichia coli 374 strains

Helicobacter pylori 158 strains

Listeria monocytogenes 35 strains

Mycobacterium tuberculosis 24 strains

Pseudomonas aeruginosa 24 strains

Salmonella enterica 113 strains

Shigella flexneri 9 strains

Vibrio cholerae 81 strains

Generating a SmartTable of Orthologous Genes Across Multiple BioCyc Genomes

This post shows how, given a list of gene names and/or identifiers from one organism, to retrieve the orthologous genes in a second organism.    For example, this procedure could be used to find the orthologs in EcoCyc of a set of genes of uncertain function in another organism, potentially providing insights about their functions. 

The BioCyc project generates a database of orthologous genes between many of the organism Pathway Genome Databases (PGDBs) we maintain.  These orthologs are stored in a central MySQL database that is separate from the individual BioCyc PGDBs.  This post describes a way to retrieve orthologs for a list of genes using a SmartTable.  We don’t recommend generating a ortholog list for a whole genome because of performance problems that, as of June 2018, we are working to resolve.   

Since we will be using a PGDB in addition to EcoCyc, you will need a BioCyc subscription (not just a free account) to follow through this demonstration.  Here’s a screenshot of the final SmartTable, which you can access directly here. We will begin with a file of 88 genes from E. coli strain B str. REL606 and determine their orthologs in EcoCyc.  The columns in the resulting SmartTable are as follows:  the list of gene names from the input file, their ‘ECB’ accession IDs genes, the names of orthologous genes from EcoCyc, and  ‘b-number’ accession IDs from EcoCyc.  We also show how to add a column containing gene product names.

Here is the step-by-step procedure.

1.     Go to the bottom of this post and cut and paste the list of gene identifiers into a text editor such as textedit or atom.  Save the file as 88EcoliGenes.txt.

2.     Go to BioCyc.org and login and use change organism database to change your organism to Escherichia coli B str. REL606. 

3.     Open the ‘Smart Tables’ menu and choose the ‘My Smart Tables’ command.  It actually doesn’t matter which of the commands in the menu you choose.

4.     You’ll find the operations menu in the upper right corner of the SmartTables page.  Under the ‘New’ command, you’ll find ‘Smart Table from Uploaded File’.  Choose that command. 

5.     In the resulting pop-up window, click the ‘Choose File’ button and select the file you saved out in step 1.  Once you have located and selected the file, review the options below the ‘Choose File’ button in the  upload window.  Since this is a file of gene identifiers, you should keep the ‘Try to make objects of type’ box checked, along with the radio button next to ‘Gene’.  Also leave the other two check boxes checked.  Click the ‘Upload’ button and a new SmartTable with a single column will appear.  If you see a warning message, ignore it and continue.

6.     Add accession numbers for the genes, by locating the ‘ADD PROPERTY COLUMN’ dropdown menu, then choose ‘Accession-1’ from the list.  You’ll see a list of ECB identifiers numbers’ for each of the genes on the first page. 

7.     Now that the table has some basic identifiers for the E. coli B str. REL606 genes, you can proceed to adding orthologs from EcoCyc.  Find the ‘ADD TRANSFORM COLUMN’, and choose the second transform ‘Compare – map to other species PGDB’.  That will bring up a pop-up list of species, which will be rather long.  Scroll down through the list until you find ‘Escherichia coli K-12 substr. MG1655’.  Once you have selected the right organism, click the ‘Go’ button.

8.     Now a column containing ortholog gene names appears.  Each entry is linked to the gene page in its organism’s PGDB.  Try it by clicking on thrL, and notice the organism in the upper left of the gene page.  Clicking on the browser back button will take you back to the table and restore the current PGDB to EcoCyc.

9.     To add a column containing accession numbers of each orthologous gene, select the ortholog column by clicking anywhere in the column header except where the title is displayed.   When the column is selected the header turns darker (and the ‘Gene Name’ column lightens as it is no longer the selected column).  Then, as in step 6,  choose ‘Accession-1’ from the dropdown below ‘Add Property Column.’  A different list of accession numbers appears.  Notice that both lists of accession numbers are simply strings that don’t link to any genes.  A column of product names for the orthologs can be added in a similar fashion.

10.  Finally, suppose you were interested only in those B strain genes having orthologs in the K-12 strain. To remove genes lacking orthologs, select the third column (‘Map to Escherichia…’).  Now look over to the operations menu on the right.  Select the ‘Filter’ command. A  pop-up window titled ‘Column text-value filter’ will appear. 

11.  This dialog has a series of options chained together in a ‘sentence.’  In the second drop-down, change ‘a copy of …’ to ‘this SmartTable’.  In the third option, choose ‘contain an object’.  Click the ‘Go’ button.  You should be left with a SmartTable containing 84 rows, the B / K-12 strain ortholog pairs, as shown in the screen capture at the top of this post.

12.  Finally, you can save the results to a file by selecting the Export command from the Operations menu.  Choose ‘to Spreadsheet file’ and since all of the genes have names in column one, you can save as common names rather than frame IDs, which are more readable in a spreadsheet.

As always, I welcome your questions and comments below.

Here is the example data.  Cut the area between the horizontal lines and paste into a text editor, such as textedit or atom, and save the file as 88Ecoligenes.txt.

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thrL

thrA

thrB

thrC

yaaX

yaaA

yaaJ

talB

mog

satP

yaaI

dnaK

dnaJ

insL-1

mokC

hokC

nhaA

nhaR

ECB_00020

ECB_00021

ECB_00022

insA-1

insB-1

ECB_00025

ECB_00026

rpsT

yaaY

ribF

ileS

lspA

fkpB

ispH

rihC

dapB

carA

carB

caiF

caiE

caiD

caiC

caiB

caiA

caiT

fixA

fixB

fixC

fixX

yaaU

kefF

kefC

folA

apaH

apaG

rsmA

pdxA

surA

lptD

djlA

rluA

hepA

polB

araD

araA

araB

araC

yabI

thiQ

thiP

tbpA

sgrR

setA

leuD

leuC

leuB

leuA

leuL

leuO

ilvI

ilvH

cra

mraZ

rsmH

ftsL

ftsI

murE

murF

mraY

murD

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