Project description:Organisms possessing genetic codes with unassigned codons raise the question of how cellular machinery resolves such codons and how this could impact horizontal gene transfer. Here, we use a genomically recoded Escherichia coli to examine how organisms address translation at unassigned UAG codons, which obstruct propagation of UAG-containing viruses and plasmids. Using mass spectrometry, we show that recoded organisms resolve translation at unassigned UAG codons via near-cognate suppression, dramatic frameshifting from at least -3 to +19 nucleotides, and rescue by ssrA-encoded tmRNA, ArfA, and ArfB. We then demonstrate that deleting tmRNA restores expression of UAG-ending proteins and propagation of UAG-containing viruses and plasmids in the recoded strain, indicating that tmRNA rescue and nascent peptide degradation is the cause of impaired virus and plasmid propagation. The ubiquity of tmRNA homologs suggests that genomic recoding is a promising path to impair horizontal gene transfer and confer genetic isolation in diverse organisms.
Project description:Classifying leukemias of ambiguous lineage as either acute myeloid leukemia or acute lymphoid leukemia using microRNA expression profiling
Project description:Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We utilized deep learning to characterize these enhancer codes and devised three novel metrics to compare cell types in the telencephalon between mammals and birds. To this end, we generated single-cell multiome and spatially-resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous non-neuronal and GABAergic cell types show a high degree of similarity across vertebrates, while excitatory neurons of the mammalian neocortex and avian pallium exhibit varying degrees of similarity. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep layer neurons. With this study, we present generally applicable deep learning approaches to characterize and compare cell types solely based on genomic sequences.
Project description:Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We utilized deep learning to characterize these enhancer codes and devised three novel metrics to compare cell types in the telencephalon between mammals and birds. To this end, we generated single-cell multiome and spatially-resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous non-neuronal and GABAergic cell types show a high degree of similarity across vertebrates, while excitatory neurons of the mammalian neocortex and avian pallium exhibit varying degrees of similarity. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep layer neurons. With this study, we present generally applicable deep learning approaches to characterize and compare cell types solely based on genomic sequences.