Asta Literature Retrieval: Gene Research for Functional Annotation ⚠️ CRITICAL: Gene/Protein Identification Context BEFORE YOU BEGIN RESEARCH: Y...

This report is retrieval-only and is generated directly from Asta results.

• Papers retrieved: 20
• Snippets retrieved: 20

Relevant Papers

[1] Phase-separating fusion proteins drive cancer by dysregulating transcription through ectopic condensates

• Authors: Nazanin Farahi, Tamas Lazar, P. Tompa, Bálint Mészáros, Rita Pancsa
• Year: 2023
• Venue: bioRxiv
• URL: https://www.semanticscholar.org/paper/57a63e3228a18d5d68d54eb8303eeb7c0ae29da6
• DOI: 10.1101/2023.09.20.558425
• Citations: 1
• Summary: It is found that proteins initiating LLPS are frequently implicated in somatic cancers, even surpassing their involvement in neurodegeneration, and protein regions driving condensate formation show an increased association with DNA- or chromatin-binding domains of transcription regulators within OFPs, indicating a common molecular mechanism underlying several soft tissue sarcomas and hematologic malignancies.
• Evidence snippets:
• Snippet 1 (score: 0.760)
  > We defined the subcellular localization for each protein in the human proteome by integrating data from Gene Ontology annotations in UniProt (GOA), UniProt annotations, the Human Transmembrane Proteome (HTP) 121 , MatrixDB 122 , and MatrisomeDB 123 . We divided the UniProt and the Gene Ontology annotations (GOA) into tier 1 (more reliable) and tier 2 (less reliable) annotations, depending on the attached evidence codes. For UniProt, annotations with the evidence codes ECO:0000269 or ECO:0000305 are considered as tier 1, while annotations with evidence codes ECO:0000250, ECO:0000255, or ECO:0000303 are tier 2. For Gene Ontology, annotations with evidence codes IDA, IMP, IPI, IGI, EXP, IBA, IKR, TAS, NAS, IC, or ND are tier 1, while annotations with evidence codes HDA, ISS, ISA, RCA, ISO, ISM, IGC, or IEA are tier 2. Based on these, each protein was assigned exactly one broad localization. It was considered to be a transmembrane protein (TMP), if it is assigned the 'integral component of membrane (GO:0016021)' GO term in tier 1 GOA annotations, or it is annotated as a TMP in HTP with a confidence score over 85, or is annotated in HTP as a TMP with a confidence score above 50 and is also annotated as a TMP in GOA (either tier).

[2] CRONOS: the cross-reference navigation server

• Authors: Brigitte Waegele, I. Dunger, G. Fobo, Corinna Montrone, H. Mewes et al.
• Year: 2008
• Venue: Bioinformatics
• URL: https://www.semanticscholar.org/paper/8c05b3aa0ba01c41ee97c2dc98ea7b5b14ce0e9c
• DOI: 10.1093/bioinformatics/btn590
• PMID: 19010804
• PMCID: 2638938
• Citations: 20
• Summary: CRONOS, a cross-reference server that contains entries from five mammalian organisms presented by major gene and protein information resources, is developed, which shows that the cross-references are highly accurate.
• Evidence snippets:
• Snippet 1 (score: 0.737)
  > In order to detect gene and protein names which are assigned to products of different genes and thus result in erroneous cross-references, dedicated lists are created for each organism separately. Organism-specific lists are necessary, since terms that are ambiguous in one organism might be explicit in another. For example, ADORA2 is an ambiguous gene name in Homo sapiens but not in mouse, and GALT in mouse but not in H.sapiens.
  > In a first step, ambiguous names within the databases were extracted. If a name occurs in at least two entries describing different genes or proteins (splice variants count as one gene/protein), this particular name is marked as ambiguous and is excluded from the mapping process. In a second step, corresponding gene names occurring in the manually annotated sections of RefSeq as well as in UniProt were analyzed. Entries containing the same gene product name and having a one-to-many or many-to-many relation (e.g. one Swiss-Prot entry maps to many RefSeq entries) were scrutinized for misleading annotation. This process is done manually by inspecting additional information like sequence similarity or functional information about the involved entries. In most of the cases, the exclusion of the ambiguous gene names resulted in correct one-to-one relations.
  > As statistical analysis revealed (Supplementary Material S2) that gene names with less than four letters are exceptionally error-prone, only gene names with at least four letters are kept for mapping purposes. However, gene names with less than four letters can be queried, e.g. a search for the tumor suppressor 'p53' reveals the respective entries with the official gene name 'TP53'. Organism-specific lists of ambiguous gene and protein names are available for download on the CRONOS home page.

[3] Avian Immunome DB: an example of a user-friendly interface for extracting genetic information

• Authors: Ralf C. Mueller, Nicolai Mallig, Jacqueline Smith, Lél Eöry, Richard I. Kuo et al.
• Year: 2020
• Venue: BMC Bioinformatics
• URL: https://www.semanticscholar.org/paper/b894d9ca8ea2d653bf1711a0c67dab71d054487c
• DOI: 10.1186/s12859-020-03764-3
• PMID: 33176685
• PMCID: 7661159
• Citations: 6
• Summary: The Avian Immunome DB (Avimm) for easy gene property extraction as exemplified by avian immune genes is presented and described, which contains 1170 distinct avian immune genes with canonical gene symbols and 612 synonyms across 363 bird species.
• Evidence snippets:
• Snippet 1 (score: 0.730)
  > Ever since the advent of commercial next-generation sequencing platforms in the early 2000s with its associated decrease in sequencing costs [1], the number of DNA sequences increased considerably [2]. Generally, these data become publicly accessible in databases provided by projects focussing on different aspects of biological sequence information [3,4]. Ensembl [5] and NCBI [6] for instance, have a strong focus on genome annotation with the help of RNA transcript information while UniProt has a pronounced emphasis on protein-coding genes and biological function of proteins. UniProt's records are either based on manually annotated, non-redundant protein sequences (SwissProt) or on highquality computationally analysed records, which are enriched with automatic annotation (TrEMBL) [7]. Relying on accurate genome annotations and protein descriptions, Gene Ontology (GO) [8,9] categorises gene products and fits them into a computational model of biological systems. Their assignment deploys a controlled vocabulary, so-called GO terms, to link genes and gene products to biological processes, cellular components, or molecular functions.
  > However, genome annotation is not standardised, and each service provider uses their own custom-built annotation pipelines. As a consequence, this often leads to ambiguity in gene names during genome annotation with different gene symbols being given to the same gene or the same gene symbol being given to different, but similar genes. Additionally, since the pre-existing wealth of sequencing information relies on model organisms like human and mouse, there is a strong bias in gene symbols towards those chosen for these species. Particularly for model species, this issue has been partially addressed, for example by the Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC) [10], the Vertebrate Gene Nomenclature Committee (VGNC) [11], or the Chicken Gene Nomenclature Consortium [12]. However, this neither guarantees that gene names are harmonised among these consortia, nor does it keep researchers from assigning alternative gene symbols in their annotations, especially when working with non-model species.

[4] Structure-Aware Mycobacterium tuberculosis Functional Annotation Uncloaks Resistance, Metabolic, and Virulence Genes

• Authors: Samuel J. Modlin, A. Elghraoui, Deepika Gunasekaran, Alyssa M Zlotnicki, N. Dillon et al.
• Year: 2021
• Venue: mSystems
• URL: https://www.semanticscholar.org/paper/76ff9a62b36b32cc10e46e71ffd4dd90344e4706
• DOI: 10.1128/mSystems.00673-21
• PMID: 34726489
• PMCID: 8562490
• Citations: 15
• Summary: This work systematically updates the functional genome annotation of Mycobacterium tuberculosis virulent type strain H37Rv and identifies hundreds of high-confidence candidates for mechanisms of antibiotic resistance, virulence factors, and basic metabolism and other functions key in clinical and basic tuberculosis research.
• Evidence snippets:
• Snippet 1 (score: 0.708)
  > 3. Fig. S2B -match/mismatch colours mixed up? (I think match should be teal and mismatch -red?) 4. Line 162-163: Rv1430 is in UniProt (EC 3.1.1.-) and has been present in Uniprot since version 45 of the gene record: https://www.uniprot.org/uniprot/L7N697. I presume you had conducted your literature analysis before the UniProt entry was updated to include the EC code, so maybe you can add the dates when the data was retrieved from UniProt and other databases you used in the Materials and Methods section? 5. Supplementary text, p. 9, first paragraph. I believe that an unrelated fragment of text was copy-pasted into the second sentence of the paragraph ("Many mutations that altered bacterial clearance...") 6. Supplementary text, p. 12, final paragraph. It should be Rv1191, not Rv1191c. Could you also add a short explanation why you believe it should be classified as a cathepsin (what protein did you transfer this annotation from)?
  > Reviewer #3 (Comments for the Author):
  > In this manuscript, Modlin et al., attempt to tackle the problem of assigning functions to ~1700 hypothetical and/or underannotated genes in the Mycobacterium tuberculosis H37Rv (Mtb) genome. Rapid and accurate annotation of microbial genomes is indeed a very critical and under appreciated part of microbial ecophysiology. This step is especially crucial for pathogenic organisms such as Mtb where accurate functional annotation of these hypothetical proteins could unravel mechanisms which could act as drug targets. The authors employed a two-pronged strategy to define a set of these unannotated or under-annotated genes and to then provide possible functions for many of these genes. First, they undertook a large-scale manual curation of literature to assign functions (including EC numbers for enzymatic functions) to ~575 genes.

[5] Ten steps to get started in Genome Assembly and Annotation

• Authors: Victoria Dominguez Del Angel, Erik Hjerde, L. Sterck, S. Capella-Gutiérrez, C. Notredame et al.
• Year: 2018
• Venue: F1000Research
• URL: https://www.semanticscholar.org/paper/1b1090dcbd0f6a609f0448957b7e464997879ea8
• DOI: 10.12688/f1000research.13598.1
• PMID: 29568489
• PMCID: 5850084
• Citations: 109
• Influential citations: 1
• Summary: Ten steps to facilitate researchers getting started in genome assembly and genome annotation are presented and the importance of data management is stressed, and advice on where to submit data and how to make results Findable, Accessible, Interoperable, and Reusable (FAIR).
• Evidence snippets:
• Snippet 1 (score: 0.708)
  > The ultimate goal of the functional annotation process (Figure 4) is to assign biologically relevant information to predicted polypeptides, and to the features they derive from (e.g. gene, mRNA). This process is especially relevant nowadays in the context of the NGS era due to the capacity of sequencing, assembling, and annotating full genomes in short periods of time, e.g. less than a month. Functional elements could range from putative name and/or symbols for protein-coding genes, e.g. ADH to its putative biological function, e.g. alcohol dehydrogenase, associated gene ontology terms, e.g. GO:0004022, functional sites, e.g. METAL 47 47 Zinc 1, and domains, e.g. IPR002328, among other features. The function of predicted proteins can be computationally inferred based on the similarity between the sequence of interest and other sequences in different public repositories, e.g. BLASTP against Uniprot. Caution should be taken when assigning results merely based on sequence similarity as two evolutionary independent sequences which share some common domains could be considered homologs 62 . Thus, whenever possible, it is better to use orthologous sequences for annotation purposes rather than simply similar sequences 63 . With the growing number of sequences in those public repositories, it is possible to perform various searches and combine obtained results into a consensus annotation. The accurate assignment of the functional elements is a complex process, and the best annotation will involve manual curation.
  > There are two main outcomes of the functional annotation process. The first is the assignment of functional elements to genes. Downstream analysis of these elements allow further understanding of specific genome properties, e.g. metabolic pathways, and similarities compared with closely related species. The second result of the functional annotation is the additional quality check for the predicted gene set. It is possible to identify problematic and/or suspicious genes by the presence of specific domains, suspicious orthology assignment and/or absence of other functional elements, e.g. functional completeness. These Page 13 of 19

[6] Construction of an Ortholog Database Using the Semantic Web Technology for Integrative Analysis of Genomic Data

• Authors: H. Chiba, Hiroyo Nishide, I. Uchiyama
• Year: 2015
• Venue: PLoS ONE
• URL: https://www.semanticscholar.org/paper/7cc805575c642aa8efdc1204383a7662965fbb60
• DOI: 10.1371/journal.pone.0122802
• PMID: 25875762
• PMCID: 4395280
• Citations: 14
• Summary: The ortholog database using the Semantic Web technology can contribute to biological knowledge discovery through integrative data analysis and examples demonstrate that the ortholog information described in RDF can be used to link various biological data such as taxonomy information and Gene Ontology.
• Evidence snippets:
• Snippet 1 (score: 0.707)
  > A typical use of an ortholog database is transferring functional annotations from known genes in model organisms to genes of unknown function in other organisms, on the basis of the conjecture that orthologs are usually functionally conserved. To demonstrate such an application in our database, we showed a query to retrieve ortholog information of a specified protein.
  > Here, we specified a UniProt ID to obtain ortholog information. For describing functional categories of genes, we used Gene Ontology (GO) [24]. The UniProt GO Annotation (UniProt-GOA) database [25] (http://www.ebi.ac.uk/GOA) provides GO term assignment to proteins with evidence codes (http://www.geneontology.org/GO.evidence.shtml). We created an ontology for GO annotation (GOA-O, Table 1, http://purl.jp/bio/11/goa) and described UniProt-GOA data in RDF using it (Table 2). If some model organisms have experimentally verified GO annotations, we can transfer such a validated annotation to orthologs of other organisms.

[7] LMPD: LIPID MAPS proteome database

• Authors: Dawn Cotter, A. Maer, C. Guda, Brian Saunders, S. Subramaniam
• Year: 2005
• Venue: Nucleic Acids Research
• URL: https://www.semanticscholar.org/paper/265c37b45326b7927e396484751e84e4aeff92d5
• DOI: 10.1093/nar/gkj122
• PMID: 16381922
• PMCID: 1347484
• Citations: 92
• Influential citations: 2
• Summary: The initial release of the LIPID MAPS Proteome Database contains 2959 records, representing human and mouse proteins involved in lipid metabolism, and this LMPD protein list was enhanced with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources.
• Evidence snippets:
• Snippet 1 (score: 0.705)
  > For each record selected from the results summary, all LMPD data relevant to that protein are displayed, with external database IDs linked to their respective resources.
  > Annotations are organized by category: Record Overview, Gene/GO/KEGG Information, UniProt Annotations, and Related Proteins. The record overview contains LMPD_ID, species, description, gene symbols, lipid categories, EC number, molecular weight, sequence length and protein sequence. Gene information includes Entrez Gene ID, chromosome, map location, primary name, primary symbol and alternate names and symbols; Gene Ontology (GO) IDs and descriptions, and KEGG pathway IDs and descriptions. UniProt annotations include primary accession number, entry name and comments such as catalytic activity, enzyme regulation, function and similarity. For related proteins and splice variants, we display source database, database ID, sequence length, and title.

[8] Protein-coding genes in humans and model mammals (mouse, rat and pig): gene identifiers and disambiguation of gene nomenclature retrieved from the Ensembl genome browser

• Authors: Grzegorz R. Juszczak, C. Pareek, U. Czarnik, M. Pierzchała
• Year: 2025
• Venue: BMC Genomics
• URL: https://www.semanticscholar.org/paper/d9089dfc889d790deb49cbc5b4617bda55bcc4da
• DOI: 10.1186/s12864-025-12329-8
• PMID: 41408139
• PMCID: 12822150
• Citations: 2
• Summary: An R script is developed that performs a gene symbol update to current official versions combined with identification of ambiguous symbols and retrieval of other IDs from the Ensembl database and provides a single list of updated symbols with annotation about their ambiguity.
• Evidence snippets:
• Snippet 1 (score: 0.697)
  > Gene nomenclature contains current official symbols and various numbers of synonyms, which pose a challenge to integrating genomic data and increase the probability that different genes share the same symbol. Therefore, we retrieved identifiers assigned to all protein-coding genes in human, mouse, rat and pig genomes that are available in the Ensembl genome browser (release 113) to assess the number of genes, compare species and identify ambiguous symbols. Results: Our analysis revealed that the total number of symbols, both official symbols and synonyms, used to identify protein-coding genes ranges from 16,600 in pigs to 64,580 in mice. Furthermore, the gene nomenclature is not complete because there are also genes without an assigned symbol, which indicates gaps in understanding protein-coding genes, especially in pigs. We also found a large number of gene symbols that map to more than one gene. These symbols might complicate the identification of about 10% of rat and mouse genes and 18% of human protein-coding genes. A simple solution for this problem is the usage of stable gene IDs assigned by scientific institutions and committees (Ensembl, NCBI, RGD, HGNC and VGNC) provided that the genomic information associated with these IDs is retrieved directly from proprietary databases containing the most accurate data. Finally, although gene symbols may pose a problem with unequivocal identification of genes, there are instances when no other identifiers are available in the literature. Therefore, we have developed an R script performing search of the Ensembl database and integrating data to provide a single list of updated symbols with annotation about their ambiguity. Conclusions: Gene symbols are not always reliable and should be reported together with stable IDs to enable unequivocal identification of genes. Therefore, data containing only gene symbols should be used cautiously to avoid misidentification of genes. A solution for this problem is our R script REgeness that performs a gene symbol update to current official versions combined with identification of ambiguous symbols and retrieval of other IDs from the Ensembl database.

[9] Molecular Mechanisms Underlying Response to Influenza in Grey Seals (Halichoerus grypus), a Potential Wild Reservoir

• Authors: Christina M McCosker, E. Unal, Alayna K Gigliotti, Wendy B Puryear, Jonathan A. Runstadler et al.
• Year: 2025
• Venue: Molecular Ecology
• URL: https://www.semanticscholar.org/paper/bebb135aae1c1182d098fce839c9a3df0cfb2b21
• DOI: 10.1111/mec.70012
• PMID: 40613337
• PMCID: 12288799
• Citations: 3
• Summary: It is hypothesized that the combination of down‐ and up‐regulated immune gene expression may prevent overstimulation of the immune response, acting as an adaptation in grey seals to resist IAV‐associated mortality.
• Evidence snippets:
• Snippet 1 (score: 0.687)
  > Top hits were required to have a percent query coverage (QC) ≥ 80 to be used for annotating transcripts. A subsequent blastx search against the Swiss-Prot database (downloaded from NCBI 07/02/2021) for transcripts without a sufficient hit was conducted using the parameters max_target_seqs 2, max_hsps 1, e-value 0.001 and qcov_hsp_perc 80. Genes without a published gene symbol (named 'LOC' + Gene ID in NCBI's database) were assigned a UniProt gene symbol based on the protein annotation listed in the RefSeq entries, when possible. Ultimately, a list of transcript identifiers and gene symbols were compiled into a transcript-to-gene map for subsequent analyses.
  > To further facilitate analyses of gene functions, additional steps were taken to identify the putative function of genes annotated with a symbol that began with 'LOC'. First 'LOC' genes with a protein-coding gene description in NCBI's database were manually assigned the appropriate gene symbol. Transcript sequences of remaining 'LOC' genes were queried through a blastn search against NCBI's nucleotide database (parameters: max target seq = 2, max hsps = 1, evalue = 0.01, perc identity = 90). Results from the blastn search were filtered to exclude hits with query coverage < 90 and hits that included vague terms (e.g., 'uncharacterized', 'genome assembly' and 'chromosome'). Gene symbols were extracted from the first hit for each transcript and assigned as the identity of that gene for genes with only a single transcript with hits or with consistent hits across transcripts. For genes with multiple transcripts that matched different gene symbols, the annotation was manually determined based on the number of transcripts for each gene symbol hit and query coverage/% identity values. For any 'LOC' genes that were identified as a gene already present in the dataset, gene counts were concatenated for further analysis.

[10] PROTGOAT : Improved automated protein function predictions using Protein Language Models

• Authors: Z. Chua, Adarsh Rajesh, Sanju Sinha, P. Adams
• Year: 2024
• Venue: bioRxiv
• URL: https://www.semanticscholar.org/paper/e86cd2c5e29214bcf879ebdd0e885bad3270dee0
• DOI: 10.1101/2024.04.01.587572
• Citations: 10
• Summary: An ensemble method called PROTGOAT is developed based on embeddings extracted from multiple and diverse pre-trained PLMs and existing text information about the protein in published literature that outperforms most current state-of-the-art methods.
• Evidence snippets:
• Snippet 1 (score: 0.685)
  > We used protein GO annotations (Swiss-Prot) from the then latest version of UniProtKB that was available from https://www.uniprot.org/help/downloads on 3 May 2023. We narrowed down the GO terms associated with each protein in our training dataset by selecting only those that were derived from experimental results (evidence code ECO:0000269, ECO:0000314, ECO:0000353, ECO:0000315, ECO:0000316, ECO:0000270), high throughput assays (evidence code ECO:0006056, ECO:0007005, ECO:0007001, ECO:0007003, ECO:0007007), annotated with traceable author statement (evidence code ECO:0000303), or inferred by curator (evidence code ECO:0000305).
  > Many GO terms within the training set are labeled on proteins without the necessary parental GO terms being annotated for the same protein. In our training set we added annotations for every GO label up to the root for each domain. Relationships between GO terms were obtained from https://purl.obolibrary.org/obo/go/go-basic.obo, with parental GO terms defined as GO terms that encompass

[11] The Surface Proteome of Bovine Unsexed and Sexed Spermatozoa

• Authors: P. Pinto-Pinho, Joana Quelhas, Francis Impens, Sara Dufour, Delphi Van Haver et al.
• Year: 2025
• Venue: Animals : an Open Access Journal from MDPI
• URL: https://www.semanticscholar.org/paper/66496158140e8f55a2c2ca8965bd298300ce9ab0
• DOI: 10.3390/ani15040484
• PMID: 40002966
• PMCID: 11852025
• Citations: 2
• Summary: Differences in surface proteins between X- and Y-chromosome-bearing bovine spermatozoa are explored to identify potential targets for sperm sexing by LC-MS/MS analysis, with 5 transmembrane proteins showing promise as markers for X-sperm.
• Evidence snippets:
• Snippet 1 (score: 0.674)
  > The protein sequences were functionally annotated by combining information retrieved from the UniProt database ( [25], accessed on 9 June 2022) and one-to-one fast orthology assignments using the eggNOG-mapper v.2.1.7 tool ( [26], accessed on 9 June 2022), as described in [27]. Briefly, the gene name, protein name, length, Gene Ontology (GO) IDs, and chromosome associated with each protein entry were obtained from UniProt using the retrieve/ID mapping tool. Additionally, gene names, descriptions, and experimentally validated GO IDs were obtained from eggNOG, with consideration given to a taxonomic scope auto-adjusted per query, a minimum hit bit-score of 60, and thresholds of 80% for identity, minimum query coverage, and minimum subject coverage.
  > Out of the 130 detected proteins, 71 (54.6%) had information manually verified by UniProt curators (Supplementary Spreadsheet S1.5). Utilizing the eggNOG-mapper v2.1.7 tool, a total of 122 entries were scanned (Supplementary Spreadsheet S1.6). By combining data from both tools, a total of 123 proteins were characterized with a gene name, and 127 had GO information. However, 2 proteins still lacked information on a descrip-tion, protein, gene, and preferred names. Figure 1 provides a summary of the functional annotation results.
  > tool, a total of 122 entries were scanned (Supplementary Spreadsheet S1.6). By combining data from both tools, a total of 123 proteins were characterized with a gene name, and 127 had GO information. However, 2 proteins still lacked information on a description, protein, gene, and preferred names. Figure 1 provides a summary of the functional annotation results.

[12] Mitotic Spindle Proteomics in Chinese Hamster Ovary Cells

• Authors: Mary Kate Bonner, D. Poole, Tao Xu, Ali Sarkeshik, J. Yates et al.
• Year: 2011
• Venue: PLoS ONE
• URL: https://www.semanticscholar.org/paper/8a46e242e657489c1933c76e06a37618f7d1901f
• DOI: 10.1371/journal.pone.0020489
• PMID: 21647379
• PMCID: 3103581
• Citations: 51
• Influential citations: 3
• Summary: This work reports the first proteomic study of the mitotic spindle from Chinese Hamster Ovary (CHO) cells and identifies proteins that are unique to the CHO spindle.
• Evidence snippets:
• Snippet 1 (score: 0.671)
  > The lists of proteins used for the comparison contain more items than listed previously due to expansion out of gene clusters, for example, to allow the updating and comparison of current HGNC gene symbols. These lists of proteins were compared in Microsoft Excel 2011 using PivotTable.
  > The protein set for the CHO midbody was derived from the accession numbers in Table S1 and Table S2 from Skop et al. [9]. Original accession numbers were updated to more recent UniProt accessions (2/2010), and duplicates from different species or different protein isoforms were removed. The unique UniProt accessions were mapped to gene names using UniProt KB Unimart, UniProt dataset [82], and these gene names were confirmed manually as HGNC symbols using HGNC, with ambiguities checked using BLASTP of the sequence corresponding to the original accession number. Accessions that didn't map successfully in Unimart were manually analyzed using BLASTP against the human RefSeq protein set using sequences from the original accessions, combined with TreeFam.org data for the non-human UniProt accessions. HGNC symbols were updated again on 12/14/2010 before comparison with this paper's protein set.
  > The protein set for the HeLa spindle proteome is derived from the 1121 accession numbers in Sauer et al. supplementary table 1 column 2 [17]. Updating the 1116 UniProt accession numbers and 5 IPI accession numbers from 795 rows required several steps. Most proteins were updated to current UniProt accessions using UniProt retrieve. Duplicates were removed. Sequences for the IPI accession numbers and 16 defunct UniProt accession numbers were recovered from other sources on the web, and BLASTP against the human RefSeq protein set with a cutoff of at least 90% identity was used to update some of these accessions. The unique current UniProt accessions were mapped to HGNC symbols using UniProt ID mapping to HGNC IDs. Biomart, database Ensembl Genes 60, dataset GRCh37.p2 [http://uswest.ensembl.org/biomart/martview/]

[13] Discovering and Summarizing Relationships Between Chemicals, Genes, Proteins, and Diseases in PubChem

• Authors: L. Zaslavsky, Tiejun Cheng, A. Gindulyte, Siqian He, Sunghwan Kim et al.
• Year: 2021
• Venue: Frontiers in Research Metrics and Analytics
• URL: https://www.semanticscholar.org/paper/57b86aef9aae576c2ae4199c0b74971f4c195211
• DOI: 10.3389/frma.2021.689059
• PMID: 34322655
• PMCID: 8311438
• Citations: 23
• Influential citations: 1
• Summary: The literature knowledge panels developed and implemented in PubChem help to uncover and summarize important relationships between chemicals, genes, proteins, and diseases by analyzing co-occurrences of terms in biomedical literature abstracts.
• Evidence snippets:
• Snippet 1 (score: 0.670)
  > We decided to prioritize human genes and proteins. The following strategy has been implemented to resolve gene and protein text entities to the most reasonable gene, protein, or enzyme symbol (corresponding to human, when possible):
  > -Try to find a match among Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC) names (Braschi et al., 2019;HUGO, 2021);
  > -Try to find a match among names in The IUPHAR/BPS Guide to Pharmacology (Armstrong et al., 2020; IUPHAR/BPS, 2021); -Try to find matches among names in UniProt (Bateman et al., 2017); -Otherwise, try to match to an enzyme name and resolve to an EC number (Bairoch, 2000;Expassy, 2021).
  > In general, it is very difficult and often impossible to distinguish the name of a gene from the name of the protein encoded by that gene. Therefore, gene and protein names are not strictly distinguished from each other but considered as one category. Therefore, the annotations considered in this study can be grouped into three categories: chemicals, genes/proteins, and diseases.

[14] Network Pharmacology-Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging

• Authors: Yanfei Liu, Yue Liu, Wantong Zhang, Mingyue Sun, Weiliang Weng et al.
• Year: 2020
• Venue: Evidence-based Complementary and Alternative Medicine : eCAM
• URL: https://www.semanticscholar.org/paper/fadeff691eb41dff82e019cc3d38b846fdc605c4
• DOI: 10.1155/2020/8508491
• PMID: 32802136
• PMCID: 7403930
• Citations: 8
• Summary: The study found that flavonoids (quercetin, luteolin, and kaempferol) and beta-sitosterol and the top eight candidate targets, namely, PTGS2, PPARG, DPP4, GSK3B, CCNA2, AR, MAPK14, and ESR1, were selected as the main therapeutic targets of EGb.
• Evidence snippets:
• Snippet 1 (score: 0.665)
  > e validated target proteins of the active components were obtained from the TCMSP database. e target protein name of the active ingredient was converted to the standard target gene name through the UniProt Knowledge Base (UniProtKB, http://www. uniprot.org/). e UniProtKB is the central hub for the collection of functional information on proteins, with accurate, consistent, and rich annotation. e target protein names were inputted into UniProtKB, with the organism restricted to "Homo sapiens," eventually gaining the official symbol.

[15] PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse

• Authors: P. Hornbeck, J. Kornhauser, S. Tkachev, Bin Zhang, E. Skrzypek et al.
• Year: 2011
• Venue: Nucleic Acids Research
• URL: https://www.semanticscholar.org/paper/93e4acf4ba3bca1b379ae8292e73dddb344abd90
• DOI: 10.1093/nar/gkr1122
• PMID: 22135298
• PMCID: 3245126
• Citations: 1605
• Influential citations: 155
• Summary: PhosphoSitePlus (http://www.phosphosite.org) is an open, comprehensive, manually curated and interactive resource for studying experimentally observed post-translational modifications, primarily of human and mouse proteins. It encompasses 1 30 000 non-redundant modification sites, primarily phosphorylation, ubiquitinylation and acetylation. The interface is designed for clarity and ease of navigation. From the home page, users can launch simple or complex searches and browse high-throughput d...
• Evidence snippets:
• Snippet 1 (score: 0.662)
  > Accession numbers from UniPROT KB, NCBI and Ensembl (24)(25)(26), as well as gene symbols from HGNC (27), are curated for all proteins when possible. Basic protein descriptions include information parsed from UniPROT KB (24), and may include additional information from the literature. Descriptions are updated in bulk occasionally. Gene Ontology (28) annotations are parsed from NCBI (25). Editors assign protein types.

[16] An Evidence-Grounded Research Assistant for Functional Genomics and Drug Target Assessment

• Authors: Ksenia Sokolova, Dmitri Kosenkov, Keerthana Nallamotu, S. Vedula, Daniil Sokolov et al.
• Year: 2025
• Venue: bioRxiv
• URL: https://www.semanticscholar.org/paper/f0e30abe5851db793b59fe0d3ce41c67124f3d96
• DOI: 10.64898/2025.12.30.697073
• PMID: 41502944
• PMCID: 12773016
• Citations: 2
• Summary: Alvessa is introduced, an evidence-grounded agentic research assistant designed around verifiability that substantially improves accuracy relative to general-purpose language models and performs comparably to coding-centric agents while producing fully traceable outputs.
• Evidence snippets:
• Snippet 1 (score: 0.661)
  > Extract gene symbols and drug names from the message, but only if they appear verbatim in the input. Reply with a dictionary of entities, without any additional text. It must be a valid JSON object with two keys: drugs and genes.
  > Example question: Is HER2 or PTEN a drug target of neratinib in breast cancer? Example answer: json{"genes": ["HER2", "PTEN"], "drugs": ["neratinib"]} Example invalid answers: 'Found these { "genes"...}', 'The drugs are neratinib...', 'I do not see any genes here.' ["extract_entities", "gencode_gene_node", "query_gwas_by_gene", "variant_annotations", "sei", "expectosc_predictions_agent", "remap_crm_agent"] 2. Variant pathogenicity:
  > ["extract_entities", "query_gwas_by_gene", "variant_annotations", "alphamissense"] 3. Gene-level functional annotation:
  > ["extract_entities", "gencode_gene_node", "humanbase_functions", "uniprot_base", "reactome", "Summarize_bioGRID_GO", "uniprot_gwas", 'clinvar_gene_node'] 4. Protein structure, visualization and druggability:
  > ["extract_entities", "prot", "chembl", "drug_central", "MSigDB"] 5. Protein-protein interactions and gene function questions summaries: ["extract_entities", "Summarize_bioGRID_GO", "Summarize_GO", "AllianceOfGenomes"] 6. Specific protein-protein interactions and gene function questions:

[17] Protein Localization Analysis of Essential Genes in Prokaryotes

• Authors: Chong Peng, Feng Gao
• Year: 2014
• Venue: Scientific Reports
• URL: https://www.semanticscholar.org/paper/69181762648fd77a085b2f93618a71b43b62cf76
• DOI: 10.1038/srep06001
• PMID: 25105358
• PMCID: 4126397
• Citations: 27
• Summary: A comprehensive protein localization analysis of essential genes in 27 prokaryotes including 24 bacteria, 2 mycoplasmas and 1 archaeon has been performed and shows that proteins encoded by essential genes are enriched in internal location sites, while exist in cell envelope with a lower proportion compared with non-essential ones.
• Evidence snippets:
• Snippet 1 (score: 0.656)
  > Bioinformatics Databases. DEG is a database of essential genes (http://www. essentialgene.org/). The newly released DEG 10 has been developed to accommodate the quantitative and qualitative advancements brought by the progressive identification methods. Currently available records of both essential and nonessential genes among a wide range of organisms can be downloaded from DEG 10, making it possible to compare the two different types of genes in many aspects 21 .
  > 27 prokaryotic organisms including 24 bacteria, 2 mycoplasmas and Methanococcus maripaludis S2, the only record of the Archaea domain were selected to analyze the protein localization and GO distribution of the essential and nonessential genes. There are 31 bacterial records corresponding to 27 organisms in the database in total and 26 sets of data were selected in the current study. Streptococcus pneumonia was not chosen for the lack of non-essential genes. Since the essential genes were not genome-widely identified, it's not reasonable to regard the complementary set of essential genes as non-essential genes in Streptococcus pneumonia 29,30 . In the case of multiple records for one organism, the one with the most convincing experimental methods was chosen. The non-essential genes in Methanococcus maripaludis S2 and 13 bacteria such as Escherichia coli MG1655 are obtained based on the original literatures, while non-essential genes in other 12 organisms such as Bacillus subtilis 168 are the complementary set of essential genes. The information of the organisms used in the current study are displayed in Table 1.
  > The three model genomes' subcellular location information and the Gene Ontology (GO) terms used for the analysis in the current study were downloaded from the Universal Protein Resource (UniProt; http://www.uniprot.org). Maintained by the UniProt Consortium, UniProt is committed to providing biologists with a comprehensive, high-quality and freely accessible resource of protein sequences and functional annotation 27 . Among the wealth of annotation data, detailed GO annotation statements are included.

[18] Telomere-to-telomere genome assembly of Phoxinus lagowskii

• Authors: Yanfeng Zhou, Chunhai Chen, Di’an Fang, Chenhe Wang, Yajuan Peng et al.
• Year: 2025
• Venue: Scientific Data
• URL: https://www.semanticscholar.org/paper/23f573aff23769df3b733d508b46f721cadb94ac
• DOI: 10.1038/s41597-025-05367-0
• PMID: 40533492
• PMCID: 12177035
• Citations: 1
• Summary: A T2T (Telomere-to-telomere) genome for P. lagowskii with chromosome-level is reported, serving as an invaluable resource for studies in evolution, comparative genomics, fish breeding applications, and ecological research.
• Evidence snippets:
• Snippet 1 (score: 0.654)
  > The gene set of 24,610 genes were functionally annotated using diamond v0.8.23
  > with an E-value threshold of 1E-5 based on the five databases including NR (NCBI nonredundant protein), TrEMB (http://www.uniprot.org), KOG 42 , KEEG (Kyoto Encyclopedia of Genes and Genomes, http://www. genome.jp/kegg/), Swiss-Prot (http://www.gpmaw.com/html/swiss-prot.html). The protein motifs and domains were identified using the InterProScan with InterPro 93.0 43 . The GO Ontology (GO) was classified from the results of InterProScan 44 . The annotation of 24,599 predicted genes (99.96%) out of the total 24,610 genes can be found by at least one database (Fig. 3b and Table 6). Of these functional proteins, 19,367 genes (~78.7%) were supported by five databases (Fig. 3a).

[19] Protocol for gene annotation, prediction, and validation of genomic gene expansion

• Authors: Quanwei Zhang, Zhengdong D. Zhang
• Year: 2022
• Venue: STAR Protocols
• URL: https://www.semanticscholar.org/paper/af8e3a73daaa04214d43f4ec1d9b1c0fcd42b8e3
• DOI: 10.1016/j.xpro.2022.101692
• PMID: 36125934
• PMCID: 9494284
• Citations: 1
• Summary: A detailed step-by-step protocol for gene annotation, prediction of genomic gene expansion, and its computational and experimental validation is described and steps to discover functionality of each copy of replicated genes are detailed.
• Evidence snippets:
• Snippet 1 (score: 0.654)
  > 3. Gene annotation and functional annotation. a. Gene structure annotation.
  > In addition to gene prediction models, evidence from orthologous protein sequences and transcriptome assembly could be used to improve annotation quality. Protein sequences of orthologous genes can be obtained from UniProt (The UniProt, 2017). Ones from Swiss-Port have been reviewed and thus are of higher quality. Transcriptome assembly may be available from previous studies or can be assembled de novo from RNA-seq reads by Trinity (Haas et al., 2013). High quality transcriptome assembly can be selected as described in (Zhang et al., 2021). Note: Details about gene structure annotation (Holt and Yandell, 2011) can be found at http:// gmod.org/wiki/MAKER_Tutorial, https://darencard.net/blog/2017-05-16-maker-genomeannotation/, and the protocol (Campbell et al., 2014).
  > b. Quality measurement and functional annotation.
  > For each predicted gene, Maker2 provides the annotation edit distance (AED) score, which measures the goodness of fit between its predicted gene structure and its evidence support. The lower the score, the more accurate the prediction. If more than 90% genes with AED scores lower than 0.5, the genome can be considered well annotated. In addition to the AED score, a high proportion of recognizable domains contained in predicted protein -e.g., higher than 50% -also indicates a good annotation. Recognizable protein domains can by scanned by InterProScan (Jones et al., 2014), assigning potential function to predicted genes.
  > Note: Besides the aforementioned quality measurement, we strongly recommend measuring the completeness of the genome assembly and annotation by checking the existence of a set of Benchmarking Universal Single-Copy Orthologs (BUSCO) (Simao et al., 2015). A high-level completeness of genome assembly and annotation is imperative for a better identification of gene expansion. Based on the result of this analysis, researchers can decide whether they need to further improve the genome assembly before predicting gene expansion. A detailed protocol of BUSCO is available at

[20] Quantitative proteomic dataset of whole protein in three melanoma samples of 92.1, 92.1-A and 92.1-B

• Authors: Xi-feng Fei, Xiangtong Xie, X. Ji, Haiyan Tian, F. Sun et al.
• Year: 2022
• Venue: Data in Brief
• URL: https://www.semanticscholar.org/paper/33312bb6cc2cf985d32f3a31cf4fdee6b4e17385
• DOI: 10.1016/j.dib.2022.108592
• PMID: 36164296
• PMCID: 9508510
• Citations: 2
• Influential citations: 1
• Summary: Covering differential proteomes of three cell lines in a pairwise model, the data could be used to further screen the kinesins that play a vital role in regulating the growth of UM.
• Evidence snippets:
• Snippet 1 (score: 0.652)
  > 2.8.1. Annotation methods 2.8.1.1. Functional annotation. UniProt-GOA database was utilized to retrieve Gene Ontology (GO) annotation proteome. First, the identified protein identity was converted to UniProt identity and then mapped to GO identity based on the protein identity. When the identified protein was not annotated by UniProt-GOA, the functional annotation of that protein was conducted using the InterProScan software according to the amino acid sequence alignment approach. All proteins were then classified into 3 groups: molecular function, cellular component and biological process.

Notes

• This provider combines search_papers_by_relevance with snippet_search.
• No synthesis or second-stage model call is performed.

Citations

1. Nazanin Farahi, Tamas Lazar, P. Tompa, Bálint Mészáros, Rita Pancsa (2023). Phase-separating fusion proteins drive cancer by dysregulating transcription through ectopic condensates. bioRxiv. https://www.semanticscholar.org/paper/57a63e3228a18d5d68d54eb8303eeb7c0ae29da6
2. Brigitte Waegele, I. Dunger, G. Fobo, Corinna Montrone, H. Mewes et al. (2008). CRONOS: the cross-reference navigation server. Bioinformatics. https://www.semanticscholar.org/paper/8c05b3aa0ba01c41ee97c2dc98ea7b5b14ce0e9c
3. Ralf C. Mueller, Nicolai Mallig, Jacqueline Smith, Lél Eöry, Richard I. Kuo et al. (2020). Avian Immunome DB: an example of a user-friendly interface for extracting genetic information. BMC Bioinformatics. https://www.semanticscholar.org/paper/b894d9ca8ea2d653bf1711a0c67dab71d054487c
4. Samuel J. Modlin, A. Elghraoui, Deepika Gunasekaran, Alyssa M Zlotnicki, N. Dillon et al. (2021). Structure-Aware Mycobacterium tuberculosis Functional Annotation Uncloaks Resistance, Metabolic, and Virulence Genes. mSystems. https://www.semanticscholar.org/paper/76ff9a62b36b32cc10e46e71ffd4dd90344e4706
5. Victoria Dominguez Del Angel, Erik Hjerde, L. Sterck, S. Capella-Gutiérrez, C. Notredame et al. (2018). Ten steps to get started in Genome Assembly and Annotation. F1000Research. https://www.semanticscholar.org/paper/1b1090dcbd0f6a609f0448957b7e464997879ea8
6. H. Chiba, Hiroyo Nishide, I. Uchiyama (2015). Construction of an Ortholog Database Using the Semantic Web Technology for Integrative Analysis of Genomic Data. PLoS ONE. https://www.semanticscholar.org/paper/7cc805575c642aa8efdc1204383a7662965fbb60
7. Dawn Cotter, A. Maer, C. Guda, Brian Saunders, S. Subramaniam (2005). LMPD: LIPID MAPS proteome database. Nucleic Acids Research. https://www.semanticscholar.org/paper/265c37b45326b7927e396484751e84e4aeff92d5
8. Grzegorz R. Juszczak, C. Pareek, U. Czarnik, M. Pierzchała (2025). Protein-coding genes in humans and model mammals (mouse, rat and pig): gene identifiers and disambiguation of gene nomenclature retrieved from the Ensembl genome browser. BMC Genomics. https://www.semanticscholar.org/paper/d9089dfc889d790deb49cbc5b4617bda55bcc4da
9. Christina M McCosker, E. Unal, Alayna K Gigliotti, Wendy B Puryear, Jonathan A. Runstadler et al. (2025). Molecular Mechanisms Underlying Response to Influenza in Grey Seals (Halichoerus grypus), a Potential Wild Reservoir. Molecular Ecology. https://www.semanticscholar.org/paper/bebb135aae1c1182d098fce839c9a3df0cfb2b21
10. Z. Chua, Adarsh Rajesh, Sanju Sinha, P. Adams (2024). PROTGOAT : Improved automated protein function predictions using Protein Language Models. bioRxiv. https://www.semanticscholar.org/paper/e86cd2c5e29214bcf879ebdd0e885bad3270dee0
11. P. Pinto-Pinho, Joana Quelhas, Francis Impens, Sara Dufour, Delphi Van Haver et al. (2025). The Surface Proteome of Bovine Unsexed and Sexed Spermatozoa. Animals : an Open Access Journal from MDPI. https://www.semanticscholar.org/paper/66496158140e8f55a2c2ca8965bd298300ce9ab0
12. Mary Kate Bonner, D. Poole, Tao Xu, Ali Sarkeshik, J. Yates et al. (2011). Mitotic Spindle Proteomics in Chinese Hamster Ovary Cells. PLoS ONE. https://www.semanticscholar.org/paper/8a46e242e657489c1933c76e06a37618f7d1901f
13. L. Zaslavsky, Tiejun Cheng, A. Gindulyte, Siqian He, Sunghwan Kim et al. (2021). Discovering and Summarizing Relationships Between Chemicals, Genes, Proteins, and Diseases in PubChem. Frontiers in Research Metrics and Analytics. https://www.semanticscholar.org/paper/57b86aef9aae576c2ae4199c0b74971f4c195211
14. Yanfei Liu, Yue Liu, Wantong Zhang, Mingyue Sun, Weiliang Weng et al. (2020). Network Pharmacology-Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging. Evidence-based Complementary and Alternative Medicine : eCAM. https://www.semanticscholar.org/paper/fadeff691eb41dff82e019cc3d38b846fdc605c4
15. P. Hornbeck, J. Kornhauser, S. Tkachev, Bin Zhang, E. Skrzypek et al. (2011). PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Research. https://www.semanticscholar.org/paper/93e4acf4ba3bca1b379ae8292e73dddb344abd90
16. Ksenia Sokolova, Dmitri Kosenkov, Keerthana Nallamotu, S. Vedula, Daniil Sokolov et al. (2025). An Evidence-Grounded Research Assistant for Functional Genomics and Drug Target Assessment. bioRxiv. https://www.semanticscholar.org/paper/f0e30abe5851db793b59fe0d3ce41c67124f3d96
17. Chong Peng, Feng Gao (2014). Protein Localization Analysis of Essential Genes in Prokaryotes. Scientific Reports. https://www.semanticscholar.org/paper/69181762648fd77a085b2f93618a71b43b62cf76
18. Yanfeng Zhou, Chunhai Chen, Di’an Fang, Chenhe Wang, Yajuan Peng et al. (2025). Telomere-to-telomere genome assembly of Phoxinus lagowskii. Scientific Data. https://www.semanticscholar.org/paper/23f573aff23769df3b733d508b46f721cadb94ac
19. Quanwei Zhang, Zhengdong D. Zhang (2022). Protocol for gene annotation, prediction, and validation of genomic gene expansion. STAR Protocols. https://www.semanticscholar.org/paper/af8e3a73daaa04214d43f4ec1d9b1c0fcd42b8e3
20. Xi-feng Fei, Xiangtong Xie, X. Ji, Haiyan Tian, F. Sun et al. (2022). Quantitative proteomic dataset of whole protein in three melanoma samples of 92.1, 92.1-A and 92.1-B. Data in Brief. https://www.semanticscholar.org/paper/33312bb6cc2cf985d32f3a31cf4fdee6b4e17385

The research report should be a detailed narrative explaining the function, biological processes, and localization of the gene product. Citations should be given for all claims.

You should prioritize authoritative reviews and primary scientific literature when conducting research. You can supplement
this with annotations you find in gene/protein databases, but these can be outdated or inaccurate.

We are specifically interested in the primary function of the gene - for enzymes, what reaction is catalyzed, and what is the substrate specificity? For transporters, what is the substrate? For structural proteins or adapters, what is the broader structural role? For signaling molecules, what is the role in the pathway.

We are interested in where in or outside the cell the gene product carries out its function.

We are also interested in the signaling or biochemical pathways in which the gene functions. We are less interested in broad pleiotropic effects, except where these elucidate the precise role.

Include evidence where possible. We are interested in both experimental evidence as well as inference from structure, evolution, or bioinformatic analysis. Precise studies should be prioritized over high-throughput, where available.

Research report: Functional annotation of pedH (UniProt Q88JH0, locus PP_2679) in Pseudomonas putida KT2440

1) Target verification (gene/protein identity)

The target is PedH, encoded by pedH (ordered locus PP_2679) in Pseudomonas putida (KT2440), and described as a lanthanide-dependent pyrroloquinoline quinone (PQQ)-dependent alcohol/ethanol dehydrogenase. This exact locus tag (PP_2679) and function are consistently used in primary studies on the KT2440 PedE/PedH system, matching the UniProt accession Q88JH0 context provided by the user. (wehrmann2018thepeds2pedr2twocomponent pages 2-3, wehrmann2017functionalroleofa pages 4-7, wehrmann2017functionalroleofa pages 1-2)

2) Key concepts and current understanding

2.1 Quinoprotein PQQ-dependent alcohol dehydrogenases (PQQ-ADHs)

PedH belongs to a family of periplasmic quinoprotein alcohol dehydrogenases that use the redox cofactor PQQ to catalyze oxidation of alcohols (and in some cases aldehydes). In P. putida KT2440, PedH and its paralog PedE constitute a periplasmic oxidation system important for detoxification and catabolism of volatile alcohols/aldehydes. (wehrmann2017functionalroleofa pages 1-2, wehrmann2019rareearthelement pages 1-2)

2.2 Lanthanide-dependent versus calcium-dependent paralogs; the REE/lanthanide switch

KT2440 encodes two functionally redundant but metal-differentiated enzymes:
- PedE (PP_2674): Ca2+-dependent PQQ-ADH
- PedH (PP_2679): Ln3+-dependent PQQ-ADH

Their expression is inversely regulated by rare earth element (REE; lanthanide) availability (“REE switch” / “lanthanide switch”), enabling the organism to deploy the metal-appropriate enzyme depending on which metals are bioavailable. (wehrmann2018thepeds2pedr2twocomponent pages 2-3, wehrmann2019rareearthelement pages 2-3)

3) Primary biochemical function of PedH

3.1 Reaction and substrate scope

PedH is a PQQ-dependent alcohol dehydrogenase (EC 1.1.2.- class context) functioning in the periplasmic oxidation system. It oxidizes a broad range of alcohol substrates, including linear and aromatic primary and secondary alcohols, and can show activity with some aldehydes, with substrate scope broadly similar to the Ca-dependent PedE. (wehrmann2017functionalroleofa pages 4-7, wehrmann2017functionalroleofa pages 2-4)

Experimentally supported substrates and pathway contexts in KT2440 include:
- 2-phenylethanol (growth-linked; periplasmic oxidation system) (wehrmann2019rareearthelement pages 1-2, wehrmann2018thepeds2pedr2twocomponent pages 2-3)
- glycerol (initiates an auxiliary glycerol catabolic route; see below) (wehrmann2020thecellularresponse pages 4-6)
- (2S,3S)-2,3-butanediol → acetoin (PedE/PedH responsible for the dehydrogenation step; acetoin then enters metabolism via the acetoin dehydrogenase system) (liu2021dehydrogenationmechanismof pages 1-2)

In vitro assays in the glycerol study detected PedE/PedH activity with 2-phenylethanol and glycerol but not with citrate or glucose under the tested conditions. (wehrmann2020thecellularresponse pages 4-6)

3.2 Lanthanide requirement and specificity (cofactor dependence)

PedH is catalytically active only in the presence of trivalent lanthanides (Ln3+) and is inactive with several tested heavy lanthanides/other trivalent metals. In purified-enzyme assays, activity was observed with light-to-mid lanthanides such as La3+, Ce3+, Pr3+, Nd3+, Sm3+, Gd3+, Tb3+, while Er3+, Yb3+, Y3+, Sc3+ did not support activity under the reported assay conditions; the highest activities were reported with Pr3+ and Nd3+. (wehrmann2017functionalroleofa pages 4-7, wehrmann2017functionalroleofa pages 2-4)

These metal preferences are visualized in the lanthanide-dependence figure for PedH activity across rare earth metal ions. (wehrmann2017functionalroleof media a288382e)

3.3 Quantitative kinetic and binding parameters (selected)

A key mechanistic distinction between PedH and PedE is PedH’s much higher affinity for lanthanides than PedE’s affinity for Ca2+.

From purified-enzyme measurements (selected examples):
- Lanthanide binding affinity (PedH): KD ~25–75 nM; enzyme active over ~10 nM to 100 µM Ln, with peak around ~1 µM (wehrmann2017functionalroleofa pages 4-7, wehrmann2017functionalroleof pages 6-10)
- Ethanol kinetics (PedH vs PedE): Vmax(PedH) ≈ 10.6 U·mg−1 vs Vmax(PedE) ≈ 6.1 U·mg−1; KM(PedH) ~177 µM vs KM(PedE) ~85 µM (wehrmann2017functionalroleofa pages 4-7)

The kinetic parameter panel/table for PedH is shown in the retrieved figure crop. (wehrmann2017functionalroleof media 5ce5386c)

3.4 Structural/sequence features supporting Ln coordination

PedH contains a conserved active-site Asp (contrasting with a Ser in PedE) that is associated with Ln3+ coordination in lanthanide-dependent quinoprotein dehydrogenases. (wehrmann2017functionalroleofa pages 2-4)

4) Cellular localization and physiological role

4.1 Subcellular localization

PedH is explicitly described as part of a periplasmic oxidation system (together with PedE) that supports growth on alcoholic substrates and influences substrate-dependent physiology (e.g., glycerol growth behavior). (wehrmann2019rareearthelement pages 1-2, wehrmann2020thecellularresponse pages 4-6)

4.2 Pathway integration and ecological/physiological roles

Growth on volatile alcohols (e.g., 2-phenylethanol):
PedH is required for efficient growth in conditions where lanthanides drive the system toward PedH usage. For example, in the presence of lanthanides (La3+), a ΔpedH strain fails to grow on 2-phenylethanol under conditions where the REE switch represses reliance on PedE, demonstrating the ecological role of PedH as the Ln-dependent branch of the periplasmic oxidation system. (wehrmann2018thepeds2pedr2twocomponent pages 2-3, wehrmann2018thepeds2pedr2twocomponent pages 3-5)

Glycerol metabolism (auxiliary route):
A substrate-specific lanthanum response study linked PedE/PedH activity to a novel glycerol route: oxidation of glycerol to glyceraldehyde and then to glycerate, followed by phosphorylation by GarK, providing an advantage in lag-phase behavior. PedE/PedH form the “periplasmic oxidation system” implicated in initiating this alternative route. (wehrmann2020thecellularresponse pages 4-6, wehrmann2020thecellularresponse pages 2-4)

2,3-butanediol catabolism:
PedH and PedE were confirmed as the enzymes responsible for dehydrogenation of (2S,3S)-2,3-butanediol to acetoin, feeding acetoin into central metabolism via the acetoin dehydrogenase complex. (liu2021dehydrogenationmechanismof pages 1-2)

5) Regulation and signaling: mechanistic understanding of the REE switch

5.1 PedS2/PedR2 two-component system

A central mechanistic advance for KT2440 is that the REE switch is orchestrated by the PedS2/PedR2 two-component system (TCS):
- No lanthanides: PedS2 phosphorylates PedR2; phosphorylated PedR2 activates pedE transcription and represses pedH.
- Lanthanides present: PedS2 kinase activity is reduced (possibly by Ln binding to the periplasmic region), decreasing PedR2 phosphorylation; this relieves pedH repression and shifts the cell toward PedH-dependent oxidation (with additional positive feedback proposed for PedH). (wehrmann2018thepeds2pedr2twocomponent pages 2-3, wehrmann2018thepeds2pedr2twocomponent pages 8-9)

5.2 Quantitative genetic evidence for the regulatory model

Adaptive evolution of a ΔpedH strain under La3+ selection produced suppressors with pedS2 mutations (e.g., S178P in a HAMP domain), which restored growth and decoupled pedE promoter activity from La3+ availability. For the PedS2 S178P allele, pedE promoter activities were reported as nearly identical ±La3+ (ratio ~0.89 ± 0.02) and increased >24-fold relative to the parental ΔpedH in the absence of La3+. (wehrmann2018thepeds2pedr2twocomponent pages 3-5)

5.3 Lanthanide uptake and metal competition

Efficient growth on 2-phenylethanol at low (nanomolar) lanthanide concentrations depends on an ABC-type transporter encoded by pedA1A2BC; without it, ~100-fold higher La3+ is needed for PedH-dependent growth (while repression of PedE can still occur). Iron (and other metals) can strongly influence the effective Ln threshold for switching, consistent with mismetallation/competition affecting sensing proteins such as PedS2. (wehrmann2019rareearthelement pages 2-3)

6) Recent developments (prioritizing 2023–2024)

6.1 2024 transcriptome-scale “fine-tuning” view of the lanthanide switch

A major 2024 update is that the PedE/PedH lanthanide switch behaves as element-dependent transcript-pool tuning rather than a simple binary “PedE off / PedH on” response.

In Pseudomonas alloputida KT2440 (same KT2440 strain lineage used widely in the PedH literature), Gorniak et al. (published Oct 2024) showed:
- Growth effects at low La: maximal growth rate 1.64 ± 0.13 h−1 and minimum doubling time 0.42 ± 0.03 h at 10–50 nM La, compared to 0.88 ± 0.06 h−1 without added Ln (gorniak2024changesingrowth pages 2-5)
- Element-specific transcriptional tuning: pedE transcript abundance varied dramatically by Ln; for example pedE RPKM was 410.28 ± 56.55 (no Ln), 210.45 ± 27.53 (Er), and 3.25–4.45 (light Ln La–Nd), and pedH expression increased ~1.9–4.2× (Nd–La) (gorniak2024changesingrowth pages 9-11)
- Light vs heavy Ln: heavy Ln (e.g., Er) behave differently from light Ln in gene expression and growth; because PedH is inactive with heavy Ln such as Er/Yb, heavy Ln can impose fitness costs potentially via mismetallation and interference with Ln sensing/signaling (gorniak2024changesingrowth pages 9-11)

6.2 Quantitative lanthanide binding/association at the single-cell level (2024)

The same 2024 work quantified cell-associated lanthanides after exposure to 1 µM Ln using single-cell ICP-MS, reporting (examples): 0.058 ± 0.055 fg La/cell, 0.125 ± 0.086 fg Nd/cell, 0.152 ± 0.106 fg Er/cell, corresponding to ~0.0025%–0.0068% of wet weight. (gorniak2024changesingrowth pages 2-5, gorniak2024changesingrowth pages 11-13)

7) Current applications and real-world implementation potential

7.1 Enzyme and organism-level applications (biotechnology)

PedH-like lanthanide-dependent PQQ-ADHs have been explicitly proposed as enabling technologies: biocatalysts, biosensors, and microbial platforms for biomining/bioleaching/recycling of rare earth metals. (wehrmann2017functionalroleofa pages 2-4, wehrmann2017functionalroleof pages 1-6)

In KT2440 specifically, PedH (and the REE switch) provides a tunable periplasmic oxidation capability supporting growth on volatile alcohols and modulating glycerol physiology; these attributes are relevant for industrial biotechnology contexts where P. putida is used as a robust chassis and where media metal composition can affect performance. (wehrmann2020thecellularresponse pages 4-6, wehrmann2019rareearthelement pages 2-3)

7.2 Ecosystem-scale prevalence supports sourcing and biomining concepts

A recent global-ocean metagenomic analysis (preprint 2023; journal publication Jul 2025) found 6,886 PQQ dehydrogenases in ocean metagenomes, with 56% containing a lanthanide-binding motif, and lanthanide-dependent genes in ~20% of resolved microbial genomes; the authors note some organisms may be targets for lanthanophore-based biomining and purification. While not specific to PedH in KT2440, these statistics strengthen the case that lanthanide-dependent PQQ-DHs are widespread “real-world” biological implementations and potential resources for biotechnology. (voutsinos2025extensiveanddiverse pages 1-3)

8) Expert synthesis and interpretation (evidence-based)

1. Functional core: PedH is best understood as a periplasmic, lanthanide-activated PQQ alcohol dehydrogenase that broadens KT2440’s ability to oxidize diverse alcohols under environmentally realistic Ln conditions, with higher catalytic capacity and much tighter metal binding than the Ca-dependent paralog PedE. (wehrmann2017functionalroleofa pages 4-7, wehrmann2019rareearthelement pages 1-2)

2. Metal-driven niche adaptation: The PedE/PedH system is not simply redundant; it is regulated by a dedicated sensory TCS (PedS2/PedR2) and appears tuned for fluctuating metal microenvironments (e.g., rhizosphere/soil), enabling rapid switching to the metal that yields higher fitness on available substrates. (wehrmann2018thepeds2pedr2twocomponent pages 2-3, wehrmann2019rareearthelement pages 2-3)

3. 2024 refinement: The latest KT2440 work shows the “lanthanide switch” is element-discriminating and quantitatively tuned at transcript and growth levels; this is consistent with a model where sensing/signaling and enzyme metallation can be perturbed by non-utilizable heavy lanthanides, potentially causing mismetallation and suboptimal expression of PedE. (gorniak2024changesingrowth pages 9-11, gorniak2024changesingrowth pages 2-5)

9) Key quantitative data highlights (recent and foundational)

• Enzyme kinetics/affinity (PedH): KD(Ln) ~25–75 nM; Vmax(ethanol) ≈ 10.6 U·mg−1; KM(ethanol) ~177 µM (wehrmann2017functionalroleofa pages 4-7)
• Metal specificity: Active with La/Ce/Pr/Nd/Sm/Gd/Tb, inactive with Er/Yb/Y/Sc under tested conditions (wehrmann2017functionalroleofa pages 4-7)
• 2024 growth benefit: growth rate 1.64 ± 0.13 h−1 at 10–50 nM La vs 0.88 ± 0.06 h−1 without Ln (gorniak2024changesingrowth pages 2-5)
• 2024 transcript tuning: pedE RPKM 410.28 ± 56.55 (no Ln) vs 3.25–4.45 (light Ln); pedH ~1.9–4.2× higher with light Ln; pedE:pedH ratio flips toward pedH with La/Nd/mix (gorniak2024changesingrowth pages 9-11)
• 2024 single-cell Ln association: 0.058 ± 0.055 fg La/cell (examples) after 1 µM exposure (gorniak2024changesingrowth pages 2-5)

Summary table

The following table compiles the main functional and regulatory claims, quantitative parameters, and 2024 updates with URLs/DOIs.

Table: This table condenses the key functional, biochemical, regulatory, and recent 2024 findings for PedH (PP_2679/Q88JH0) in Pseudomonas putida KT2440. It is designed as a data-rich reference linking each major claim to specific primary sources and URLs/DOIs.

Notes on evidence limits

The provided evidence set supports periplasmic localization and broad biochemical activity, but does not fully resolve the in vivo physiological electron acceptor chain (e.g., specific cytochromes or membrane electron carriers) for PedH in KT2440; the studies commonly use DCPIP-based assays for activity quantification and focus on growth/regulation outcomes rather than the complete periplasmic electron transfer network. (wehrmann2020thecellularresponse pages 4-6)

References

1. (wehrmann2018thepeds2pedr2twocomponent pages 2-3): Matthias Wehrmann, Charlotte Berthelot, Patrick Billard, and Janosch Klebensberger. The peds2/pedr2 two-component system is crucial for the rare earth element switch in pseudomonas putida kt2440. mSphere, Aug 2018. URL: https://doi.org/10.1128/msphere.00376-18, doi:10.1128/msphere.00376-18. This article has 41 citations and is from a peer-reviewed journal.

2. (wehrmann2017functionalroleofa pages 4-7): Matthias Wehrmann, Patrick Billard, Audrey Martin-Meriadec, Asfaw Zegeye, and Janosch Klebensberger. Functional role of lanthanides in enzymatic activity and transcriptional regulation of pyrroloquinoline quinone-dependent alcohol dehydrogenases in pseudomonas putida kt2440. mBio, Jul 2017. URL: https://doi.org/10.1128/mbio.00570-17, doi:10.1128/mbio.00570-17. This article has 218 citations and is from a domain leading peer-reviewed journal.

3. (wehrmann2017functionalroleofa pages 1-2): Matthias Wehrmann, Patrick Billard, Audrey Martin-Meriadec, Asfaw Zegeye, and Janosch Klebensberger. Functional role of lanthanides in enzymatic activity and transcriptional regulation of pyrroloquinoline quinone-dependent alcohol dehydrogenases in pseudomonas putida kt2440. mBio, Jul 2017. URL: https://doi.org/10.1128/mbio.00570-17, doi:10.1128/mbio.00570-17. This article has 218 citations and is from a domain leading peer-reviewed journal.

4. (wehrmann2019rareearthelement pages 1-2): Matthias Wehrmann, Charlotte Berthelot, Patrick Billard, and Janosch Klebensberger. Rare earth element (ree)-dependent growth of pseudomonas putida kt2440 relies on the abc-transporter peda1a2bc and is influenced by iron availability. Frontiers in Microbiology, Oct 2019. URL: https://doi.org/10.3389/fmicb.2019.02494, doi:10.3389/fmicb.2019.02494. This article has 40 citations and is from a peer-reviewed journal.

5. (wehrmann2019rareearthelement pages 2-3): Matthias Wehrmann, Charlotte Berthelot, Patrick Billard, and Janosch Klebensberger. Rare earth element (ree)-dependent growth of pseudomonas putida kt2440 relies on the abc-transporter peda1a2bc and is influenced by iron availability. Frontiers in Microbiology, Oct 2019. URL: https://doi.org/10.3389/fmicb.2019.02494, doi:10.3389/fmicb.2019.02494. This article has 40 citations and is from a peer-reviewed journal.

6. (wehrmann2017functionalroleofa pages 2-4): Matthias Wehrmann, Patrick Billard, Audrey Martin-Meriadec, Asfaw Zegeye, and Janosch Klebensberger. Functional role of lanthanides in enzymatic activity and transcriptional regulation of pyrroloquinoline quinone-dependent alcohol dehydrogenases in pseudomonas putida kt2440. mBio, Jul 2017. URL: https://doi.org/10.1128/mbio.00570-17, doi:10.1128/mbio.00570-17. This article has 218 citations and is from a domain leading peer-reviewed journal.

7. (wehrmann2020thecellularresponse pages 4-6): Matthias Wehrmann, Maxime Toussaint, Jens Pfannstiel, Patrick Billard, and Janosch Klebensberger. The cellular response to lanthanum is substrate specific and reveals a novel route for glycerol metabolism in pseudomonas putida kt2440. mBio, Apr 2020. URL: https://doi.org/10.1128/mbio.00516-20, doi:10.1128/mbio.00516-20. This article has 29 citations and is from a domain leading peer-reviewed journal.

8. (liu2021dehydrogenationmechanismof pages 1-2): Yidong Liu, Xiuqing Wang, Liting Ma, Min Lü, Wen Zhang, Chuanjuan Lü, Chao Gao, Ping Xu, and Cuiqing Ma. Dehydrogenation mechanism of three stereoisomers of butane-2,3-diol in pseudomonas putida kt2440. Frontiers in Bioengineering and Biotechnology, Aug 2021. URL: https://doi.org/10.3389/fbioe.2021.728767, doi:10.3389/fbioe.2021.728767. This article has 6 citations.

9. (wehrmann2017functionalroleof media a288382e): Matthias Wehrmann, Patrick Billard, Audrey Martin-Meriadec, Asfaw Zegeye, and Janosch Klebensberger. Functional role of lanthanides in enzymatic activity and transcriptional regulation of pyrroloquinoline quinone-dependent alcohol dehydrogenases in pseudomonas putida kt2440. mBio, Jul 2017. URL: https://doi.org/10.1128/mbio.00570-17, doi:10.1128/mbio.00570-17. This article has 218 citations and is from a domain leading peer-reviewed journal.

10. (wehrmann2017functionalroleof pages 6-10): Matthias Wehrmann, Patrick Billard, Audrey Martin Meriadec, Asfaw Zegeye, and Janosch Klebensberger. Functional role of lanthanides in enzymatic activity and transcriptional regulation of pqq-dependent alcohol dehydrogenases in pseudomonas putida kt2440. bioRxiv, May 2017. URL: https://doi.org/10.1101/140046, doi:10.1101/140046. This article has 1 citations.

11. (wehrmann2017functionalroleof media 5ce5386c): Matthias Wehrmann, Patrick Billard, Audrey Martin-Meriadec, Asfaw Zegeye, and Janosch Klebensberger. Functional role of lanthanides in enzymatic activity and transcriptional regulation of pyrroloquinoline quinone-dependent alcohol dehydrogenases in pseudomonas putida kt2440. mBio, Jul 2017. URL: https://doi.org/10.1128/mbio.00570-17, doi:10.1128/mbio.00570-17. This article has 218 citations and is from a domain leading peer-reviewed journal.

12. (wehrmann2018thepeds2pedr2twocomponent pages 3-5): Matthias Wehrmann, Charlotte Berthelot, Patrick Billard, and Janosch Klebensberger. The peds2/pedr2 two-component system is crucial for the rare earth element switch in pseudomonas putida kt2440. mSphere, Aug 2018. URL: https://doi.org/10.1128/msphere.00376-18, doi:10.1128/msphere.00376-18. This article has 41 citations and is from a peer-reviewed journal.

13. (wehrmann2020thecellularresponse pages 2-4): Matthias Wehrmann, Maxime Toussaint, Jens Pfannstiel, Patrick Billard, and Janosch Klebensberger. The cellular response to lanthanum is substrate specific and reveals a novel route for glycerol metabolism in pseudomonas putida kt2440. mBio, Apr 2020. URL: https://doi.org/10.1128/mbio.00516-20, doi:10.1128/mbio.00516-20. This article has 29 citations and is from a domain leading peer-reviewed journal.

14. (wehrmann2018thepeds2pedr2twocomponent pages 8-9): Matthias Wehrmann, Charlotte Berthelot, Patrick Billard, and Janosch Klebensberger. The peds2/pedr2 two-component system is crucial for the rare earth element switch in pseudomonas putida kt2440. mSphere, Aug 2018. URL: https://doi.org/10.1128/msphere.00376-18, doi:10.1128/msphere.00376-18. This article has 41 citations and is from a peer-reviewed journal.

15. (gorniak2024changesingrowth pages 2-5): Linda Gorniak, Sarah Luise Bucka, Bayan Nasr, Jialan Cao, Steffen Hellmann, Thorsten Schäfer, Martin Westermann, Julia Bechwar, and Carl-Eric Wegner. Changes in growth, lanthanide binding, and gene expression in pseudomonas alloputida kt2440 in response to light and heavy lanthanides. Oct 2024. URL: https://doi.org/10.1128/msphere.00685-24, doi:10.1128/msphere.00685-24. This article has 5 citations and is from a peer-reviewed journal.

16. (gorniak2024changesingrowth pages 9-11): Linda Gorniak, Sarah Luise Bucka, Bayan Nasr, Jialan Cao, Steffen Hellmann, Thorsten Schäfer, Martin Westermann, Julia Bechwar, and Carl-Eric Wegner. Changes in growth, lanthanide binding, and gene expression in pseudomonas alloputida kt2440 in response to light and heavy lanthanides. Oct 2024. URL: https://doi.org/10.1128/msphere.00685-24, doi:10.1128/msphere.00685-24. This article has 5 citations and is from a peer-reviewed journal.

17. (gorniak2024changesingrowth pages 11-13): Linda Gorniak, Sarah Luise Bucka, Bayan Nasr, Jialan Cao, Steffen Hellmann, Thorsten Schäfer, Martin Westermann, Julia Bechwar, and Carl-Eric Wegner. Changes in growth, lanthanide binding, and gene expression in pseudomonas alloputida kt2440 in response to light and heavy lanthanides. Oct 2024. URL: https://doi.org/10.1128/msphere.00685-24, doi:10.1128/msphere.00685-24. This article has 5 citations and is from a peer-reviewed journal.

18. (wehrmann2017functionalroleof pages 1-6): Matthias Wehrmann, Patrick Billard, Audrey Martin Meriadec, Asfaw Zegeye, and Janosch Klebensberger. Functional role of lanthanides in enzymatic activity and transcriptional regulation of pqq-dependent alcohol dehydrogenases in pseudomonas putida kt2440. bioRxiv, May 2017. URL: https://doi.org/10.1101/140046, doi:10.1101/140046. This article has 1 citations.

19. (voutsinos2025extensiveanddiverse pages 1-3): Marcos Y. Voutsinos, Jillian F. Banfield, and Harry-Luke O. McClelland. Extensive and diverse lanthanide-dependent metabolism in the ocean. The ISME Journal, Jul 2025. URL: https://doi.org/10.1101/2023.07.25.550467, doi:10.1101/2023.07.25.550467. This article has 10 citations.

20. (skovran2019lanthanidesinmethylotrophy pages 1-2): Elizabeth Skovran, Charumathi Raghuraman, and Norma Cecilia Martinez-Gomez. Lanthanides in methylotrophy. Current issues in molecular biology, 33:101-116, Jan 2019. URL: https://doi.org/10.21775/cimb.033.101, doi:10.21775/cimb.033.101. This article has 49 citations.

Artifacts

• Edison artifact artifact-00
  

Citations

1. wehrmann2020thecellularresponse pages 4-6
2. liu2021dehydrogenationmechanismof pages 1-2
3. wehrmann2017functionalroleofa pages 4-7
4. wehrmann2017functionalroleofa pages 2-4
5. wehrmann2019rareearthelement pages 2-3
6. gorniak2024changesingrowth pages 2-5
7. gorniak2024changesingrowth pages 9-11
8. voutsinos2025extensiveanddiverse pages 1-3
9. wehrmann2017functionalroleofa pages 1-2
10. wehrmann2019rareearthelement pages 1-2
11. wehrmann2017functionalroleof pages 6-10
12. wehrmann2020thecellularresponse pages 2-4
13. gorniak2024changesingrowth pages 11-13
14. wehrmann2017functionalroleof pages 1-6
15. skovran2019lanthanidesinmethylotrophy pages 1-2
16. https://doi.org/10.1128/mbio.00570-17;
17. https://doi.org/10.1128/msphere.00376-18
18. https://doi.org/10.3389/fmicb.2019.02494;
19. https://doi.org/10.1128/mbio.00570-17
20. https://doi.org/10.1128/mbio.00516-20;
21. https://doi.org/10.3389/fbioe.2021.728767
22. https://doi.org/10.1128/mbio.00516-20
23. https://doi.org/10.1128/msphere.00376-18;
24. https://doi.org/10.1128/msphere.00685-24
25. https://doi.org/10.1101/2023.07.25.550467;
26. https://doi.org/10.21775/cimb.033.101
27. https://doi.org/10.1128/msphere.00376-18,
28. https://doi.org/10.1128/mbio.00570-17,
29. https://doi.org/10.3389/fmicb.2019.02494,
30. https://doi.org/10.1128/mbio.00516-20,
31. https://doi.org/10.3389/fbioe.2021.728767,
32. https://doi.org/10.1101/140046,
33. https://doi.org/10.1128/msphere.00685-24,
34. https://doi.org/10.1101/2023.07.25.550467,
35. https://doi.org/10.21775/cimb.033.101,

Deep Research Report: pedH (PSEPK)

Generated using OpenAI Deep Research API

pedH Gene (Pseudomonas putida KT2440) – Comprehensive Annotation Report

Function and Molecular Mechanism

The pedH gene of P. putida KT2440 encodes a pyrroloquinoline quinone-dependent alcohol dehydrogenase (PQQ-ADH) that is notable for its lanthanide-dependent enzyme activity (pmc.ncbi.nlm.nih.gov). PedH (locus tag PP_2679) catalyzes the periplasmic oxidation of a broad range of alcohols – including linear aliphatic alcohols, aromatic alcohols such as 2-phenylethanol, secondary alcohols, and even some aldehydes – into their corresponding aldehydes or acids (pmc.ncbi.nlm.nih.gov) (pubmed.ncbi.nlm.nih.gov). This enzyme uses PQQ as a redox cofactor and requires trivalent lanthanide ions (e.g. La³⁺, Ce³⁺, Nd³⁺) in its active site for catalysis, in contrast to its homolog PedE which is Ca²⁺-dependent (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). A critical active-site residue substitution (Asp in PedH vs Ser in PedE) enables PedH to coordinate lanthanides and confers its lanthanide reliance (pmc.ncbi.nlm.nih.gov). Mechanistically, PedH oxidizes alcohol substrates by transferring electrons via PQQ to an electron acceptor (a cytochrome c component), linking alcohol catabolism to the respiratory chain (pubmed.ncbi.nlm.nih.gov). Purified PedH is enzymatically inactive without lanthanides but, upon addition of La³⁺ or other light rare earth metals, it exhibits robust dehydrogenase activity, often with kinetics (V_max and specific activity) exceeding those of the Ca²⁺-dependent PedE (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). These characteristics establish PedH as the first described lanthanide-dependent quinoprotein alcohol dehydrogenase in a non-methylotrophic bacterium (pmc.ncbi.nlm.nih.gov). In summary, PedH functions as a PQQ-containing oxidoreductase (EC 1.1.2.8, quinoprotein alcohol dehydrogenase) that plays a key role in oxidizing a variety of volatile and aromatic alcohols into aldehydes, using lanthanide cofactors for its catalytic activity (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). Molecular Function GO terms: quinoprotein alcohol dehydrogenase activity (PQQ-dependent ethanol/alcohol dehydrogenase), PQQ binding, metal ion binding (lanthanide ion binding).

Cellular Localization and Components

PedH is a periplasmic enzyme associated with the inner membrane periplasmic space of P. putida. It is synthesized with an N-terminal signal peptide that directs its export to the periplasm, where it folds and incorporates the PQQ cofactor (and metal ion) (pubmed.ncbi.nlm.nih.gov). In the periplasm, PedH functions together with a periplasmic c-type cytochrome electron carrier to relay electrons into the respiratory chain (pubmed.ncbi.nlm.nih.gov). The ped gene cluster includes a small cytochrome c component required for electron transfer from PedH to cytochrome oxidases (pubmed.ncbi.nlm.nih.gov). PedH itself is a soluble periplasmic quinoprotein dehydrogenase, not anchored in the membrane, though it operates at the periplasmic interface of the cytoplasmic membrane. Experimental and bioinformatic analyses indicate PedH is exported likely via the Sec pathway (or TAT pathway if cofactor-loaded), ensuring its localization in the periplasmic compartment where its alcohol substrates (which can cross the outer membrane) are available (pubmed.ncbi.nlm.nih.gov). There is no evidence that PedH is present in the cytosol or external medium; its activity in oxidizing alcohols occurs in the periplasmic space, consistent with the need to pass electrons to periplasmic cytochromes and ultimately to the electron transport chain (pubmed.ncbi.nlm.nih.gov). Cellular Component GO terms: periplasmic space, intracellular membrane-bounded periplasmic space (periplasmic side of inner membrane), quinoprotein alcohol dehydrogenase complex (periplasmic enzyme complex with PQQ and cytochrome c).

Biological Processes Involvement

PedH is integral to aromatic alcohol catabolism and detoxification in Pseudomonas putida. It is part of the 2-phenylethanol degradation (Ped) pathway, which converts 2-phenylethanol into phenylacetaldehyde and onward to phenylacetic acid (pubmed.ncbi.nlm.nih.gov). The ped cluster (genes pedS1R1ABCS2R2DEFGHI) encodes the complete upper pathway for 2-phenylethanol utilization, including transport and two PQQ-ADHs (PedE and PedH) that perform the initial oxidation step (pubmed.ncbi.nlm.nih.gov). PedH specifically oxidizes 2-phenylethanol to phenylacetaldehyde (in the presence of lanthanides), which is then further oxidized by the aldehyde dehydrogenase PedI to phenylacetic acid (pubmed.ncbi.nlm.nih.gov). This pathway feeds into the phenylacetyl-CoA catabolon (Paa pathway) for complete mineralization of aromatic compounds (pubmed.ncbi.nlm.nih.gov). Beyond aromatic alcohols, PedH (along with PedE) also contributes to the metabolism of other growth substrates: for example, both enzymes were shown to oxidize ethylene glycol to glycolaldehyde, initiating ethylene glycol catabolism in P. putida (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). More generally, the redundant PedE/PedH system is crucial for growth on various volatile organic compounds (VOCs) as sole carbon sources (pmc.ncbi.nlm.nih.gov). Mutant studies demonstrate that P. putida requires at least one of these PQQ-ADH enzymes to efficiently grow on primary alcohols; in their absence, or if the wrong metal cofactor is available, growth is impaired (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). Thus, PedH enables P. putida to utilize a broad spectrum of alcohols (including environmental pollutants and plant-derived aromatics) by oxidizing them to aldehydes for further metabolism (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). Biological Process GO terms: 2-phenylethanol catabolic process, ethanol metabolic process (ethanol oxidation), aromatic compound catabolic process, ether/alcohol metabolic process, response to alcohol (induction by presence of alcohol substrate), volatile organic compound catabolic process.

Disease Associations and Phenotypes

There are no known direct disease associations for the pedH gene, as Pseudomonas putida KT2440 is a non-pathogenic soil bacterium commonly used in biotechnology and bioremediation. P. putida generally does not cause disease in healthy organisms, and pedH’s function is linked to environmental nutrient utilization rather than virulence. However, P. putida can be an opportunistic pathogen in rare cases; even in those scenarios, pedH is not implicated in pathogenicity or host interaction. Instead, the importance of pedH is seen in metabolic phenotypes. Notably, a ΔpedH knockout strain exhibits a specific growth defect on certain substrates under lanthanide-rich conditions: in the presence of lanthanum (which represses the alternate enzyme PedE), a pedH-null mutant fails to grow on 2-phenylethanol as sole carbon source (pmc.ncbi.nlm.nih.gov). This phenotype reflects PedH’s essential role in alcohol utilization when lanthanides are available – without PedH, P. putida cannot effectively oxidize 2-phenylethanol (or similar alcohols) if PedE is downregulated (pmc.ncbi.nlm.nih.gov). In contrast, under calcium-only conditions (no lanthanides), a pedH mutant can still grow on alcohols because PedE compensates. Aside from carbon source utilization phenotypes, there are no reported morphological or viability defects associated with pedH disruption. In summary, pedH is not linked to human disease, but its deletion yields a metabolic phenotype: inability to catabolize certain alcohols when reliance on lanthanide-dependent dehydrogenase is required (pmc.ncbi.nlm.nih.gov). (No specific GO disease terms; phenotypic effect relates to metabolic process failure under certain conditions.)

Protein Domains and Structural Features

PedH is a quinoprotein dehydrogenase belonging to the family of Type II PQQ-dependent alcohol dehydrogenases (ADHs). Its primary structure contains an N-terminal signal peptide (for periplasmic targeting) followed by a large eight-bladed β-propeller domain that harbors the PQQ prosthetic group (www.ncbi.nlm.nih.gov). This β-propeller fold forms the active site pocket where PQQ is non-covalently bound and where substrate oxidation occurs (www.ncbi.nlm.nih.gov). The active site also binds a metal ion cofactor: PedH’s cofactor preference is for lanthanides (Ln³⁺) due to a conserved Asp residue in the coordination sphere, whereas Ca²⁺ cannot adequately activate the enzyme (pmc.ncbi.nlm.nih.gov). A key active-site Aspartate (Asp) (in place of the Serine found in Ca²⁺-dependent enzymes) is responsible for coordinating the lanthanide ion and is critical for PedH’s catalytic activity with Ln³⁺ (pmc.ncbi.nlm.nih.gov). PedH is a monomeric or homodimeric enzyme in solution; like other quinoprotein ADHs (e.g. ExaA of P. aeruginosa), PedH likely forms a homodimer in the periplasm, with each subunit binding one PQQ and one metal ion (go.drugbank.com) (pmc.ncbi.nlm.nih.gov). Unlike some quinohemoproteins, PedH does not contain a covalently bound heme c within its polypeptide; instead, electron transfer is mediated by an external c-type cytochrome (encoded separately in the cluster) (pubmed.ncbi.nlm.nih.gov). In the PedH sequence, motifs characteristic of PQQ-binding enzymes are present (such as the conserved glutamate and tyrosine residues that interact with PQQ) and a conserved His-x-[Ser/Asp]-x-x-Gly motif that helps ligate the metal ion. No transmembrane regions are present, consistent with PedH being periplasmic and soluble. Additionally, PedH’s amino acid composition gives it a relatively basic isoelectric point (pI ~8.7) (journals.asm.org), which may facilitate interaction with acidic cytochromes in the periplasm. Overall, PedH’s structural features include its signal peptide, PQQ-binding β-propeller domain, a metal-binding active site tuned for lanthanides, and the capability to interact with partner electron carriers. Key domains: PQQ-dependent ADH domain (8-blade propeller), lanthanide-binding site (Asp-containing loop), and regions for dimer interface and cytochrome interaction. Protein Domain GO terms: pyrroloquinoline quinone binding, metal ion binding, beta-propeller domain (structure), electron transfer activity (via cytochrome interaction).

Expression Patterns and Regulation

Expression of pedH is tightly regulated and context-dependent, involving two distinct regulatory systems. First, substrate-induced regulation is mediated by the PedS1/PedR1 two-component system, which responds to the presence of 2-phenylethanol or related aromatic substrates (pubmed.ncbi.nlm.nih.gov). When 2-phenylethanol is available, the sensor kinase PedS1 and response regulator PedR1 activate transcription of the ped cluster (including pedE, pedH, pedI, etc.), ensuring the enzymes needed for 2-phenylethanol uptake and oxidation are produced (pubmed.ncbi.nlm.nih.gov). Consequently, pedH mRNA and PedH protein are strongly induced during growth on 2-phenylethanol as a carbon source (and also induced by structurally similar alcohols or volatile compounds) (pubmed.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). In addition to substrate-dependent control, pedH is subject to the “lanthanide switch” regulatory system: the PedS2/PedR2 two-component system senses rare-earth elements and inversely regulates pedH and pedE (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). In the absence of lanthanides, the Ca²⁺-dependent pedE gene is expressed at high levels while pedH is kept low; when lanthanide ions (e.g. La³⁺ at nanomolar levels) are present, PedS2/R2 represses pedE and strongly induces pedH (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). This dual control optimizes the bacterium’s alcohol oxidation system for the available cofactor: PedH is preferentially produced under lanthanide-rich conditions, whereas PedE dominates when only calcium is available (pmc.ncbi.nlm.nih.gov). Interestingly, PedH itself appears to have a feedback role in regulation – it has been suggested that PedH (perhaps via its cofactor-loaded state or activity) aids in sensing lanthanides and fine-tuning expression of the pedE/pedH genes (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). Reporter gene assays show that pedH promoter activity is extremely sensitive to trace lanthanum, aligning with PedH’s role in a high-affinity lanthanide response network (pmc.ncbi.nlm.nih.gov). Moreover, global transcriptomic and proteomic studies confirm that pedH is highly upregulated when cells are grown on alcohols like ethylene glycol or 2-phenylethanol, and that this induction is modulated by lanthanide availability (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). In summary, pedH expression is induced by substrate (alcohol) presence and regulated by lanthanide levels via two dedicated two-component systems, ensuring the right dehydrogenase is expressed for efficient metabolism. Regulation GO terms: response to alcohol (transcriptional induction by 2-phenylethanol/ethanol), response to metal ion (lanthanide sensing), positive regulation of gene expression by metal ions (lanthanide activates pedH expression), two-component signal transduction system involvement.

Evolutionary Conservation

The ped gene cluster and PedH protein are conserved among certain groups of pseudomonads and related bacteria. P. putida KT2440’s PedH is almost identical (99% amino acid identity) to PedH in P. putida strain U, which first defined the phenylethanol degradation (Ped) pathway (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). This indicates that the ped cluster has been maintained with little divergence in these strains, likely due to its adaptive value in utilizing aromatic compounds. PedH also shows significant homology to PQQ-dependent ethanol dehydrogenases in other species. For instance, P. putida PedH shares ~52% identity with ExaA (the quinoprotein ethanol dehydrogenase of P. aeruginosa) (pmc.ncbi.nlm.nih.gov). Notably, PedE (the Ca²⁺-dependent counterpart) is more closely related to ExaA (84% identity), whereas PedH is more divergent (pmc.ncbi.nlm.nih.gov). This suggests that the lineage leading to PedH acquired unique adaptations (like lanthanide use) not present in the single ADH of P. aeruginosa. Homologs of PedH (lanthanide-dependent PQQ-ADHs) are now known in other non-methylotrophic bacteria as well, hinting at convergent evolution of lanthanide utilization. Within the Pseudomonas putida species complex and closely related Pseudomonas species, the entire ped gene cluster (including pedH) is conserved in synteny (pmc.ncbi.nlm.nih.gov). Comparative genomics shows that P. putida KT2440’s PP_2664–PP_2680 region is structurally similar to the ped cluster in P. putida U and corresponds to the ethanol oxidation gene region in P. aeruginosa (controlled by ErbR) (pmc.ncbi.nlm.nih.gov). This indicates a common evolutionary origin for these alcohol oxidation systems, with diversification (duplication of ADH genes and specialization for lanthanides) in certain lineages (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). The strong conservation between KT2440 and strain U (which differ in isolation source) implies that PedH’s function is ecologically important and under purifying selection. Outside the pseudomonads, analogous lanthanide-dependent PQQ dehydrogenases (sometimes called XoxF or ExaF in methylotrophs) share the critical Asp residue and thus are considered distant functional analogs (pmc.ncbi.nlm.nih.gov). In summary, PedH is highly conserved among P. putida strains and exists as part of a conserved phenylethanol utilization operon, and it is evolutionarily related to other quinoprotein ADHs like those in P. aeruginosa (ExaA) and methylotrophic bacteria (XoxF-type MDHs), reflecting a broader conserved mechanism of PQQ-dependent alcohol oxidation across proteobacteria (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov).

Relevant Gene Ontology (GO) Annotations

• Molecular Function: Quinoprotein alcohol dehydrogenase activity (PQQ-dependent ethanol dehydrogenase activity) – PedH catalyzes the PQQ-mediated oxidation of alcohols (pmc.ncbi.nlm.nih.gov).
• Molecular Function: Pyrroloquinoline quinone binding – PedH binds the PQQ cofactor as a prosthetic group (pubmed.ncbi.nlm.nih.gov).
• Molecular Function: Metal ion binding (lanthanide ion binding) – PedH coordinates a lanthanide (e.g., La³⁺) in its active site for catalytic activity (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov).
• Biological Process: 2-phenylethanol catabolic process – PedH participates in the breakdown of 2-phenylethanol to phenylacetaldehyde as part of phenylacetate metabolism (pubmed.ncbi.nlm.nih.gov).
• Biological Process: Ethanol metabolic process – PedH contributes to oxidation of ethanol and other alcohols for energy derivation (pmc.ncbi.nlm.nih.gov).
• Biological Process: Aromatic compound catabolic process – Through the ped pathway, PedH helps catabolize aromatic alcohols into intermediates for the phenylacetyl-CoA pathway (pubmed.ncbi.nlm.nih.gov).
• Biological Process: Response to lanthanide ion – Expression of pedH is upregulated in response to lanthanide availability, enabling the lanthanide-dependent alcohol oxidation system (pmc.ncbi.nlm.nih.gov).
• Cellular Component: Periplasmic space – PedH is localized to the periplasm of P. putida cells (pubmed.ncbi.nlm.nih.gov).
• Cellular Component: Periplasmic alcohol dehydrogenase complex – PedH functions in the periplasm with PQQ and a partner cytochrome c as an alcohol-oxidizing complex (pubmed.ncbi.nlm.nih.gov).

Key Experimental Evidence and Literature

• Arias et al. (2008) – Identified and characterized the ped gene cluster in P. putida U, demonstrating two pathways (Ped for 2-phenylethanol, Pea for phenylethylamine) converging on phenylacetic acid. They showed PedH is one of two quinoprotein ADHs required for phenylethanol oxidation, working with PQQ and cytochrome c (pubmed.ncbi.nlm.nih.gov) (pubmed.ncbi.nlm.nih.gov). This foundational study defined the gene nomenclature (PedH, etc.) and the necessity of Pqq and Ccm accessory systems for cofactor biosynthesis and cytochrome c maturation (pubmed.ncbi.nlm.nih.gov).
• Mückschel et al. (2012) – Investigated ethylene glycol metabolism in P. putida. They found that PedE and PedH are the periplasmic dehydrogenases initiating ethylene glycol oxidation in KT2440, despite KT2440’s slow growth on EG (pmc.ncbi.nlm.nih.gov). Comparative proteomics and mutagenesis confirmed PedH’s role and noted its high sequence identity with P. putida U PedH (pmc.ncbi.nlm.nih.gov). This study highlighted the broad substrate range of PedE/PedH beyond aromatics.
• Wehrmann et al. (2017) – Described the functional role of lanthanides for PedH in P. putida (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). This mBio article provided the first biochemical characterization of PedH as a lanthanide-dependent enzyme. Key findings were that PedH is inactive without lanthanides but, with nanomolar La³⁺, shows high activity on various alcohols comparable or superior to PedE (pmc.ncbi.nlm.nih.gov). They also discovered that PedH influences its own and pedE gene regulation, acting as part of a sensory system responding to lanthanides (pmc.ncbi.nlm.nih.gov).
• Wehrmann et al. (2018) – Demonstrated the “rare earth switch” controlled by the PedS2/PedR2 system (pmc.ncbi.nlm.nih.gov). Published in mSphere, this study showed that PedS2 is a lanthanide-sensing histidine kinase. In P. putida KT2440, lanthanum triggers PedR2 to upregulate pedH and concurrently repress pedE, an inverse regulatory relationship (pmc.ncbi.nlm.nih.gov). A ΔpedH mutant was unable to grow on 2-phenylethanol with La³⁺ present (pmc.ncbi.nlm.nih.gov), confirming PedH’s essential role under those conditions. This work detailed the molecular mechanism of how bacteria switch between Ca-ADH and Ln-ADH.
• Wehrmann et al. (2019) – Investigated the ABC transporter PedA1A2BC and lanthanide uptake (pmc.ncbi.nlm.nih.gov). They found that an ABC transport system (PedA1A2BC, partly encoded in the ped cluster) is required for efficient internalization of lanthanides in P. putida (pmc.ncbi.nlm.nih.gov). This facilitates PedH’s access to Ln³⁺ in the periplasm and links iron availability to lanthanide utilization, providing insight into how PedH obtains its metal cofactor in vivo.
• Recent Studies (2020–2023) – Ongoing research (e.g., Bassan et al. 2023) examined P. putida KT2440’s response to different lanthanides (light vs heavy) and the fine-tuning of pedH/pedE expression (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). These studies confirmed that light lanthanides optimally activate PedH, whereas heavy lanthanides cause mismetallation and regulatory perturbations (pmc.ncbi.nlm.nih.gov). They further reinforced the model of PedH as part of an elaborate lanthanide-responsive metabolic network in pseudomonads.

Overall, the gene pedH encodes a lanthanide-dependent PQQ enzyme that is central to P. putida’s ability to oxidize and grow on a variety of alcohols, especially aromatic alcohols like 2-phenylethanol. Its function, regulation by rare earth elements, and integration in aromatic catabolic pathways make PedH a compelling example of adaptive enzymatic innovation in environmental bacteria (pmc.ncbi.nlm.nih.gov) (pmc.ncbi.nlm.nih.gov). The detailed experimental evidence from genetic, biochemical, and regulatory studies underpins PedH’s annotation with Gene Ontology terms related to quinoprotein alcohol dehydrogenase activity, periplasmic localization, aromatic alcohol catabolism, and lanthanide-response, all of which are crucial for accurate Gene Ontology curation of this gene.