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1. The Species-Specific Acquisition and Diversification of a K1-like Family of Killer Toxins in Budding Yeasts of the Saccharomycotina

   https://doi.org/10.1371/journal.pgen.1009341  

   Fredericks, Lance R.; Lee, Mark D.; Crabtree, Angela M.; Boyer, Josephine M.; Kizer, Emily A.; Taggart, Nathan T.; Roslund, Cooper R.; Hunter, Samuel S.; Kennedy, Courtney B.; Willmore, Cody G.; et al (February 2021, PLOS Genetics)

   Fay, Justin C. (Ed.)

   Killer toxins are extracellular antifungal proteins that are produced by a wide variety of fungi, including Saccharomyces yeasts. Although many Saccharomyces killer toxins have been previously identified, their evolutionary origins remain uncertain given that many of these genes have been mobilized by double-stranded RNA (dsRNA) viruses. A survey of yeasts from the Saccharomyces genus has identified a novel killer toxin with a unique spectrum of activity produced by Saccharomyces paradoxus . The expression of this killer toxin is associated with the presence of a dsRNA totivirus and a satellite dsRNA. Genetic sequencing of the satellite dsRNA confirmed that it encodes a killer toxin with homology to the canonical ionophoric K1 toxin from Saccharomyces cerevisiae and has been named K1-like (K1L). Genomic homologs of K1L were identified in six non- Saccharomyces yeast species of the Saccharomycotina subphylum, predominantly in subtelomeric regions of the genome. When ectopically expressed in S . cerevisiae from cloned cDNAs, both K1L and its homologs can inhibit the growth of competing yeast species, confirming the discovery of a family of biologically active K1-like killer toxins. The sporadic distribution of these genes supports their acquisition by horizontal gene transfer followed by diversification. The phylogenetic relationship between K1L and its genomic homologs suggests a common ancestry and gene flow via dsRNAs and DNAs across taxonomic divisions. This appears to enable the acquisition of a diverse arsenal of killer toxins by different yeast species for potential use in niche competition. 

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2. The prevalence of killer yeasts and double-stranded RNAs in the budding yeast Saccharomyces cerevisiae

   https://doi.org/10.1093/femsyr/foad046  

   Crabtree, Angela M.; Taggart, Nathan T.; Lee, Mark D.; Boyer, Josie M.; Rowley, Paul A. (November 2023, FEMS Yeast Research)

   Abstract Killer toxins are antifungal proteins produced by many species of “killer” yeasts, including the brewer's and baker's yeast Saccharomyces cerevisiae. Screening 1270 strains of S. cerevisiae for killer toxin production found that 50% are killer yeasts, with a higher prevalence of yeasts isolated from human clinical samples and winemaking processes. Since many killer toxins are encoded by satellite double-stranded RNAs (dsRNAs) associated with mycoviruses, S. cerevisiae strains were also assayed for the presence of dsRNAs. This screen identified that 51% of strains contained dsRNAs from the mycovirus families Totiviridae and Partitiviridae, as well as satellite dsRNAs. Killer toxin production was correlated with the presence of satellite dsRNAs but not mycoviruses. However, in most killer yeasts, whole genome analysis identified the killer toxin gene KHS1 as significantly associated with killer toxin production. Most killer yeasts had unique spectrums of antifungal activities compared to canonical killer toxins, and sequence analysis identified mutations that altered their antifungal activities. The prevalence of mycoviruses and killer toxins in S. cerevisiae is important because of their known impact on yeast fitness, with implications for academic research and industrial application of this yeast species. 

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3. Bioinformatic analysis of Chinese hamster ovary host cell protein lipases

   https://doi.org/10.1002/aic.16378  

   MacDonald, Madolyn_L; Hamaker, Nathaniel_K; Lee, Kelvin_H (September 2018, AIChE Journal)

   Complete, accurate genome assemblies are necessary to design targets for genetic engineering strategies. Successful gene knockdowns and knockouts in Chinese hamster ovary (CHO) cells may prevent the expression of difficult‐to‐remove host cell proteins (HCPs). HCPs, if not removed, can cause problems in stability, safety, and efficacy of the biotherapeutic. A significantly improved Chinese hamster (CH) reference genome was used to identify new knockout targets with similar predicted functions and characteristics as the difficult‐to‐remove host cell lipases, LPL, PLBL2, and LPLA2. The CHO‐K1 gene and protein sequences of several of these lipases were corrected using the updated CH genome. Sequence alignments were then used to identify conserved regions that may serve as possible targets for multiple simultaneous gene knockouts. Finally, the comparison of the CHO‐K1 lipase protein sequences to their human orthologs provided insight into which lipases, if persistent in the drug product, could possibly cause immunogenic responses in patients. Topical heading: Biomolecular Engineering, Bioengineering, Biochemicals, Biofuels, and Food. © 2018 American Institute of Chemical EngineersAIChE J, 64: 4247–4254, 2018 

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4. Genomic language model predicts protein co-regulation and function

   https://doi.org/10.1038/s41467-024-46947-9  

   Hwang, Yunha; Cornman, Andre L.; Kellogg, Elizabeth H.; Ovchinnikov, Sergey; Girguis, Peter R. (April 2024, Nature Communications)

   <bold>Abstract</bold> Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function paradigm from massive protein sequence datasets. However, to date, limited attempts have been made in extending this continuum to include higher order genomic context information. Evolutionary processes dictate the specificity of genomic contexts in which a gene is found across phylogenetic distances, and these emergent genomic patterns can be leveraged to uncover functional relationships between gene products. Here, we train a genomic language model (gLM) on millions of metagenomic scaffolds to learn the latent functional and regulatory relationships between genes. gLM learns contextualized protein embeddings that capture the genomic context as well as the protein sequence itself, and encode biologically meaningful and functionally relevant information (e.g. enzymatic function, taxonomy). Our analysis of the attention patterns demonstrates that gLM is learning co-regulated functional modules (i.e. operons). Our findings illustrate that gLM’s unsupervised deep learning of the metagenomic corpus is an effective and promising approach to encode functional semantics and regulatory syntax of genes in their genomic contexts and uncover complex relationships between genes in a genomic region. 

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5. Comparative Phylogenomic Analysis Reveals Evolutionary Genomic Changes and Novel Toxin Families in Endophytic Liberibacter Pathogens

   https://doi.org/10.1128/Spectrum.00509-21  

   Tan, Yongjun; Wang, Cindy; Schneider, Theresa; Li, Huan; de Souza, Robson Francisco; Tang, Xueming; Swisher Grimm, Kylie D.; Hsieh, Tzung-Fu; Wang, Xu; Li, Xu; et al (October 2021, Microbiology Spectrum)

   Burbank, Lindsey Price (Ed.)

   ABSTRACT Liberibacter pathogens are the causative agents of several severe crop diseases worldwide, including citrus Huanglongbing and potato zebra chip. These bacteria are endophytic and nonculturable, which makes experimental approaches challenging and highlights the need for bioinformatic analysis in advancing our understanding about Liberibacter pathogenesis. Here, we performed an in-depth comparative phylogenomic analysis of the Liberibacter pathogens and their free-living, nonpathogenic, ancestral species, aiming to identify major genomic changes and determinants associated with their evolutionary transitions in living habitats and pathogenicity. Using gene neighborhood analysis and phylogenetic classification, we systematically uncovered, annotated, and classified all prophage loci into four types, including one previously unrecognized group. We showed that these prophages originated through independent gene transfers at different evolutionary stages of Liberibacter and only the SC-type prophage was associated with the emergence of the pathogens. Using ortholog clustering, we vigorously identified two additional sets of genomic genes, which were either lost or gained in the ancestor of the pathogens. Consistent with the habitat change, the lost genes were enriched for biosynthesis of cellular building blocks. Importantly, among the gained genes, we uncovered several previously unrecognized toxins, including new toxins homologous to the EspG/VirA effectors, a YdjM phospholipase toxin, and a secreted endonuclease/exonuclease/phosphatase (EEP) protein. Our results substantially extend the knowledge of the evolutionary events and potential determinants leading to the emergence of endophytic, pathogenic Liberibacter species, which will facilitate the design of functional experiments and the development of new methods for detection and blockage of these pathogens. IMPORTANCE Liberibacter pathogens are associated with several severe crop diseases, including citrus Huanglongbing, the most destructive disease to the citrus industry. Currently, no effective cure or treatments are available, and no resistant citrus variety has been found. The fact that these obligate endophytic pathogens are not culturable has made it extremely challenging to experimentally uncover the genes/proteins important to Liberibacter pathogenesis. Further, earlier bioinformatics studies failed to identify key genomic determinants, such as toxins and effector proteins, that underlie the pathogenicity of the bacteria. In this study, an in-depth comparative genomic analysis of Liberibacter pathogens along with their ancestral nonpathogenic species identified the prophage loci and several novel toxins that are evolutionarily associated with the emergence of the pathogens. These results shed new light on the disease mechanism of Liberibacter pathogens and will facilitate the development of new detection and blockage methods targeting the toxins. 

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