Omics · study · 2026
Systematic Perturbation of Cultures of K. Phaffii Show Gene Signatures Associated with 1 Specific Productivity of Recombinant Proteins [K. phaffii S-953 RNA-seq]
Listed in NCBI GEO
Demand for recombinant proteins is rapidly growing, driven by their use as 16 biotherapeutics, vaccine components, industrial enzymes, and food ingredients.
Description
The growing market requires novel strategies for increasing specific productivity (qp) of cellular hosts. Systems-level frameworks have been used to improve productivity, but have had difficulty relating complex cellular pathways with protein expression.
Here, we demonstrate a method for mapping relationships between gene expression signatures and complex phenotypes related to recombinant protein productivity. Our approach induces systematic perturbations in cultures of K. Phaffii using varied co-feeds of carbon sources. The different carbon sources significantly impacted cell growth, specific productivity, and transcriptional states.
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With these data, we identified scale-independent metagenes that explained significant transcriptomic variance and strongly associated with complex cellular phenotypes, including qp and response to methanol induction for both IgG1 and VHH. We used these results to identify and knockout 31 novel gene targets whose expression inversely correlated with productivity. Ten of these genes improved productivity of IgG1 by up to 3x, and 17 genes increased productivity of VHH by up to 1.7x without impacting cell growth.
This study identified ten groups of carbon co-feeds which induce similar transcriptomic states. We systematically perturbated cultures of K. phaffii using these co-feeds and identified phenotype-associated metagenes. Using the productivity-correlated metagene, we identified 10-17 genes which improved recombinant protein productivity when knocked out.
This framework for relating gene signatures to complex cellular phenotypes could provide useful metrics for assessing 37 production processes and identifying new targets for cellular engineering.
Links
Get the data
- GEO FTP directory ftp.ncbi.nlm.nih.gov/geo/series/GSE302nnn/GSE302181 ↗
download · from NCBI GEO
Where it is published
- GEO accession page ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE302181 ↗
landing page · from NCBI GEO
Documentation and papers
- PRJNA1289154 ncbi.nlm.nih.gov/bioproject/PRJNA1289154 ↗
project · from NCBI GEO
- PubMed 41639851 pubmed.ncbi.nlm.nih.gov/41639851 ↗
publication · from NCBI GEO
Topics
- Stated by source
- Expression profiling by high throughput sequencing
- From keywords
- Life Sciences
- Inferred from text
- RNA sequencing 65%
Provenance · 1 source records, 7 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| NCBI GEO | GSE302181 | 14 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · NCBI GEO | connector:ncbi_geo@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · NCBI GEO | connector:ncbi_geo@1.0.0 | |
| concepts[method].geo_series_type:expression-profiling-by-high-throughput-sequencing | source · NCBI GEO | connector:ncbi_geo@1.0.0 | /gdstype |
| concepts[modality].local:modality:rna-seq | enrichment · NCBI GEO | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · NCBI GEO | connector:ncbi_geo@1.0.0 | /summary |
| publication_date | source · NCBI GEO | connector:ncbi_geo@1.0.0 | |
| title | source · NCBI GEO | connector:ncbi_geo@1.0.0 | /title |