Quantitative proteomics reveals cellular responses to individual mAb expression and tunicamycin in CHO cells.

Chinese hamster ovary (CHO) cells are popular in the pharmaceutical industry for their ability to produce high concentrations of antibodies and their resemblance to human cells in terms of protein glycosylation patterns. Current data indicate the relevance of CHO cells in the biopharmaceutical industry, with a high number of product commendations and a significant market share for monoclonal antibodies. To enhance the production capabilities of CHO cells, a deep understanding of their cellular and molecular composition is crucial. Genome sequencing and proteomic analysis have provided valuable insights into the impact of the bioprocessing conditions, productivity, and product quality. In our investigation, we conducted a comparative analysis of proteomic profiles in high and low monoclonal antibody-producing cell lines and studied the impact of tunicamycin (TM)-induced endoplasmic reticulum (ER) stress. We examined the expression levels of different proteins including unfolded protein response (UPR) target genes by using label-free quantification techniques for protein abundance. Our results show the upregulation of proteins associated with protein folding mechanisms in low producer vs. high producer cell line suggesting a form of ER stress related to specific protein production. Further, Hspa9 and Dnaja3 are notable candidates activated by the mitochondria UPR and play important roles in protein folding processes in mitochondria. We identified significant upregulation of Nedd8 and Lgmn proteins in similar levels which may contribute to UPR stress. Interestingly, the downregulation of Hspa5/Bip and Pdia4 in response to tunicamycin treatment suggests a low-level UPR activation. KEY POINTS: • Proteome profiling of recombinant CHO cells under mild TM treatment. • Identified protein clusters are associated with the unfolded protein response (UPR). • The compared cell lines revealed noticeable disparities in protein expression levels.

Spinach genomes reveal migration history and candidate genes for important crop traits.

Spinach (Spinacia oleracea) is an important leafy crop possessing notable economic value and health benefits. Current genomic resources include reference genomes and genome-wide association studies. However, the worldwide genetic relationships and the migration history of the crop remained uncertain, and genome-wide association studies have produced extensive gene lists related to agronomic traits. Here, we re-analysed the sequenced genomes of 305 cultivated and wild spinach accessions to unveil the phylogeny and history of cultivated spinach and to explore genetic variation in relation to phenotypes. In contrast to previous studies, we employed machine learning methods (based on Extreme Gradient Boosting, XGBoost) to detect variants that are collectively associated with agronomic traits. Variant-based cluster analyses revealed three primary spinach groups in the Middle East, Asia and Europe/US. Combining admixture analysis and allele-sharing statistics, migration routes of spinach from the Middle East to Europe and Asia are presented. Using XGBoost machine learning models we predict genomic variants influencing bolting time, flowering time, petiole color, and leaf surface texture and propose candidate genes for each trait. This study enhances our understanding of the history and phylogeny of domesticated spinach and provides valuable information on candidate genes for future genetic improvement of the crop.

Genomic basis of seed colour in quinoa inferred from variant patterns using extreme gradient boosting.

Quinoa is an agriculturally important crop species originally domesticated in the Andes of central South America. One of its most important phenotypic traits is seed colour. Seed colour variation is determined by contrasting abundance of betalains, a class of strong antioxidant and free radicals scavenging colour pigments only found in plants of the order Caryophyllales. However, the genetic basis for these pigments in seeds remains to be identified. Here we demonstrate the application of machine learning (extreme gradient boosting) to identify genetic variants predictive of seed colour. We show that extreme gradient boosting outperforms the classical genome-wide association approach. We provide re-sequencing and phenotypic data for 156 South American quinoa accessions and identify candidate genes potentially controlling betalain content in quinoa seeds. Genes identified include novel cytochrome P450 genes and known members of the betalain synthesis pathway, as well as genes annotated as being involved in seed development. Our work showcases the power of modern machine learning methods to extract biologically meaningful information from large sequencing data sets.