Juliane C. Dohm

Table beet ( Beta vulgaris subsp. vulgaris var. vulgaris ), also known as red beet, garden beet, or beetroot, is an important crop with both nutritional and agricultural value, yet high-quality genomic resources for this taxon are limited. We present the chromosome-scale genome assembly RefTab-1 based on PacBio and Hi-C data of the inbred table beet genotype W357B. The assembly has a size of 668.6 Mbp and an N50 of 72 Mbp, with 96% of the assembly anchored to nine pseudo-chromosomes. We annotated 64% repetitive DNA, 24,863 protein-coding genes, and 10,405 non-coding RNA loci including a 5S rDNA cluster on chromosome 4. Genome-wide methylation profiling using EM-seq revealed high levels of cytosine methylation in repeats and context-specific methylation in gene bodies and promoters. We propose three terpene synthase candidate genes based on functional annotation and based on their location near a genetic marker on chromosome 8 previously associated with geosmin concentration, responsible for the earthy aroma of table beet. Comparative analysis with sugar beet indicated overall structural conservation and a 9 Mbp inversion on chromosome 9. These high-quality resources for table beet will enhance genetic, epigenetic, and comparative studies in Beta vulgaris , advancing beet biology and breeding programs.

Key Message: This study provides the first genomic evidence for a candidate self-incompatibility locus in beets ( Beta vulgaris ), identifying T2/S-RNase candidate genes and a complex S-locus on beet chromosome 2. A comparison of self-incompatible and self-compatible beet taxa reveals structural degeneration linked to mating system shifts. This work establishes a genomics-first framework for investigating self-incompatibility in understudied taxa and offers important implications for crop breeding and the evolution of plant reproductive systems. The majority of flowering plants maintain various mechanisms to prevent self-fertilization. One such mechanism, the genetic self-incompatibility (SI) system, exists in about half of angiosperms. SI systems have been described for only few taxa, as their diversity and lack of homology renders their study complicated. Yet, an RNase-based SI system (RSI) has emerged as widespread and possibly ancestral to eudicots. RSI has been recognized in at least six plant families and lately also in Caryophyllales. Here, we utilize the accumulated knowledge of RSI systems and high-quality genomic resources to investigate the presence of RSI in beet, Beta vulgaris. The genus Beta encompasses both self-compatible and self-incompatible taxa. We examined the genes of five beet genome assemblies to identify a candidate for the female component of RSI, a T2/S-RNase, and its corresponding candidate S-locus. A locus on beet chromosome 2 met our criteria, supported by phylogenetics data, sequence characteristics, and expression patterns that are unique to S-RNases. The candidate S-locus in Beta was highly repetitive with gene copy number variation observed in different cultivars. By comparing the candidate S-locus of B. vulgaris with that of the self-compatible wild beet B. patula , we hypothesize a potential link between reproductive modes and S-locus architecture. Our analysis suggests that S-RNase genes derived from an ancestral and highly conserved gene family are present and active in Beta , fulfilling a reproductive function. Employing a genomics-first approach, this work contributes insights into T2/S-RNase evolution and lays the groundwork toward elucidating SI in sugar beet and its relatives.

Table beet (Beta vulgaris subsp. vulgaris var. vulgaris), also known as red beet, garden beet, or beetroot, is an important crop with both nutritional and agricultural value, yet high-quality genomic resources for this taxon are limited. We present the chromosome-scale genome assembly RefTab-1 based on PacBio and Hi-C data of the inbred table beet genotype W357B. The assembly has a size of 668.6 Mbp and an N50 of 72 Mbp, with 96% of the assembly anchored to nine pseudo-chromosomes. We annotated 64% repetitive DNA, 24,863 protein-coding genes, and 10,405 non-coding RNA loci including a 5S rRNA cluster on chromosome 4. Genome-wide methylation profiling using EM-seq revealed high levels of cytosine methylation in repeats and context-specific methylation in gene bodies and promoters. We propose three terpene synthase candidate genes based on functional annotation and based on their location near a genetic marker on chromosome 8 previously associated with geosmin concentration, responsible for the earthy aroma of table beet. Comparative analysis with sugar beet indicated overall structural conservation and a 9 Mbp inversion on chromosome 9. These high-quality resources for table beet will enhance genetic, epigenetic, and comparative studies in Beta vulgaris, advancing beet biology and breeding programs.

Sugar beet (Beta vulgaris ssp. vulgaris) is an important crop plant serving as a major source of sugar, particularly in Europe. Sugar beet research using the genotype KWS2320 has a long-standing history, and many datasets and studies exist that use this genotype as a reference. Here, we present a high-quality genome sequence of sugar beet genotype KWS2320 based on long-read sequencing data as well as an evidence-based gene set employing billions of mRNA (messenger RNA)-seq reads as transcript evidence. The assembly, referred to as RefBeet-3.0, was built using Pacific Biosciences data and was integrated with Bionano optical maps, Oxford Nanopore data, and various additional genomic resources. RefBeet-3.0 comprises 648 Mb in nine pseudochromosomes and further sequences with a total N50 size of 61.5 Mb. The gene set BeetSet-3 consists of 28 271 genes of which 25 824 could be functionally annotated based on sequence homology to orthologous groups. The assembly is highly complete and has a high sequence accuracy in absolute terms and in comparison to existing sugar beet assemblies. RefBeet-3.0 and BeetSet-3 will serve as comprehensive resources for future studies on sugar beet and other plants, as well as for breeding activities.

Background: Chinese hamster ovary (CHO) cells are extensively used in the pharmaceutical industry for producing complex proteins, primarily because of their ability to perform human-like post-translational modifications. However, the efficiency of high-quality protein production can vary significantly for monoclonal antibody-producing cell lines, within the CHO host cell lines or by extrinsic factors. Methods: To investigate the complex cellular mechanisms underlying this variability, a phosphoproteomics analysis was performed using label-free quantitative liquid chromatography after a phosphopeptide enrichment of recombinant CHO cells producing two different antibodies and a tunicamycin treatment experiment. Using MaxQuant and Perseus for data analysis, we identified 2109 proteins and quantified 4059 phosphosites. Results: Significant phosphorylation dynamics were observed in nuclear proteins of cells producing the difficult-to-produce 2G12 mAb. It suggests that the expression of 2G12 regulates nuclear pathways based on increases and decreases in phosphorylation abundance. Furthermore, a substantial number of changes in the phosphorylation pattern related to tunicamycin treatment have been detected. TM treatment affects, among other phosphoproteins, the eukaryotic elongation factor 2 kinase (Eef2k). Conclusions: The alterations in the phosphorylation landscape of key proteins involved in cellular processes highlight the mechanisms behind stress-induced cellular responses.

Chinese hamster ovary (CHO) cells are extensively used in the pharmaceutical industry for producing complex proteins, primarily because of their ability to perform human-like post-translational modifications. However, the efficiency of high-quality protein production can vary significantly for mAb producing cell lines, within the CHO host cell lines or by extrinsic factors. To investigate the complex cellular mechanisms underlying this variability, a comprehensive phosphoproteomics analysis was performed using label-free quantitative liquid chromatography after a phospho-peptide enrichment of recombinant CHO-cells producing two different antibodies and a tunicamycin treatment experiment. Using MaxQuant and Perseus for data analysis, we identified 2109 proteins and quantified 4059 phosphosites. Significant phosphorylation dynamics were observed in nuclear proteins of cells producing the difficult-to-produce 2G12 mAb. It suggests that the expression of 2G12 regulates nuclear pathways based on up- and down-regulation of phosphorylation sites. Furthermore, a substantial number of changes in the phosphorylation pattern related to tunicamycin treatment has been detected.TM treatment affects, among other phosphoproteins, the eukaryotic elongation factor 2 kinase (Eef2k). It alters the phosphorylation landscape of key proteins involved in cellular processes including DNA methylation, highlighting the mechanisms behind stress-induced cellular responses.

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.

Summary: 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.