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:PurposeWe aimed to analyze regional variations in the assignment of International Classification of Diseases, 10th Revision (ICD-10) codes to acute respiratory infections, seeking to identify notable anomalies that suggest diverse diagnoses of the same condition.MethodsWe analyzed national weekly diagnosis data for acute respiratory infections (ICD-10 codes J00-J22) in Poland from 2010 to 2019, covering all 380 county-equivalent administrative regions and encompassing 292 million consultations. Data were aggregated into age brackets. We calculated the Kendall tau correlations between shares of particular diagnoses.ResultsWe found staggering differences across regions in applied diagnoses that persisted even after disaggregating the data into age groups. The differences did not seem to stem from different levels of health care use, as there was no consistent pattern suggesting variability in milder diagnoses. Instead, there were numerous pairs of strongly negatively correlated codes implying classification ambiguity, with the most problematic diagnosis being J06 (acute upper respiratory infections of multiple and unspecified sites), which was used almost interchangeably with a diverse range of others, especially J00 (common cold) and J20 (bronchitis).ConclusionsTo the best of our knowledge, this is the first study using observable anomalies to analyze regional coding variability for the same respiratory infection. Although some of these discrepancies may raise concerns about misdiagnosis, the majority of cases involving interchangeably used codes did not seem to substantially impact treatment or prognosis. This suggests that ICD codes may have clinical ambiguities and could face challenges not only in fulfilling their intended purpose of generating internationally comparable health data but also in their use for comprehensive government health planning.
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.
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.