Search

Background The vast majority of phylogenetic trees are inferred from molecular sequence data (nucleotides or amino acids) using time-reversible evolutionary models which assume that, for any pair of nucleotide or amino acid characters, the relative rate of X to Y substitution is the same as the relative rate of Y to X substitution. However, this reversibility assumption is unlikely to accurately reflect the actual underlying biochemical and/or evolutionary processes that lead to the fixation of substitutions. Here, we use empirical viral genome sequence data to reveal that evolutionary non-reversibility is pervasive among most groups of viruses. Specifically, we consider two non-reversible nucleotide substitution models: (1) a 6-rate non-reversible model (NREV6) in which Watson-Crick complementary substitutions occur at identical relative rates and which might therefor be most applicable to analyzing the evolution of genomes where both complementary strands are subject to the same mutational processes (such as might be expected for double-stranded (ds) RNA or dsDNA genomes); and (2) a 12-rate non-reversible model (NREV12) in which all relative substitution types are free to occur at different rates and which might therefore be applicable to analyzing the evolution of genomes where the complementary genome strands are subject to different mutational processes (such as might be expected for viruses with single-stranded (ss) RNA or ssDNA genomes). Results Using likelihood ratio and Akaike Information Criterion-based model tests, we show that, surprisingly, NREV12 provided a significantly better fit to 21/31 dsRNA and 20/30 dsDNA datasets than did the general time reversible (GTR) and NREV6 models with NREV6 providing a better fit than NREV12 and GTR in only 5/30 dsDNA and 2/31 dsRNA datasets. As expected, NREV12 provided a significantly better fit to 24/33 ssDNA and 40/47 ssRNA datasets. Next, we used simulations to show that increasing degrees of strand-specific substitution bias decrease the accuracy of phylogenetic inference irrespective of whether GTR or NREV12 is used to describe mutational processes. However, in cases where strand-specific substitution biases are extreme (such as in SARS-CoV-2 and Torque teno sus virus datasets) NREV12 tends to yield more accurate phylogenetic trees than those obtained using GTR. Conclusion We show that NREV12 should, be seriously considered during the model selection phase of phylogenetic analyses involving viral genomic sequences.

Myelodysplastic neoplasms (MDSs) and chronic myelomonocytic leukemia (CMML) are clonal disorders driven by progressively acquired somatic mutations in hematopoietic stem cells (HSCs). Hypomethylating agents (HMAs) can modify the clinical course of MDS and CMML. Clinical improvement does not require eradication of mutated cells and may be related to improved differentiation capacity of mutated HSCs. However, in patients with established disease it is unclear whether (1) HSCs with multiple mutations progress through differentiation with comparable frequency to their less mutated counterparts or (2) improvements in peripheral blood counts following HMA therapy are driven by residual wild-type HSCs or by clones with particular combinations of mutations. To address these questions, the somatic mutations of individual stem cells, progenitors (common myeloid progenitors, granulocyte monocyte progenitors, and megakaryocyte erythroid progenitors), and matched circulating hematopoietic cells (monocytes, neutrophils, and naïve B cells) in MDS and CMML were characterized via high-throughput single-cell genotyping, followed by bulk analysis in immature and mature cells before and after AZA treatment. The mutational burden was similar throughout differentiation, with even the most mutated stem and progenitor clones maintaining their capacity to differentiate to mature cell types in vivo. Increased contributions from productive mutant progenitors appear to underlie improved hematopoiesis in MDS following HMA therapy.

Progressively acquired somatic mutations in hematopoietic stem cells are central to pathogenesis in myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML). They can lead to proliferative advantages, impaired differentiation and progressive cytopenias. MDS or CMML patients with high-risk disease are treated with hypomethylating agents including 5-azacytidine (AZA). Clinical improvement does not require eradication of mutated cells and may be related to improved differentiation capacity of mutated hematopoietic stem and progenitor cells (HSPCs). However, the contribution of mutated HSPCs to steadystate hematopoiesis in MDS and CMML is unclear. To address this, we characterised the somatic mutations of individual stem, progenitor (common myeloid progenitor, granulocyte monocyte progenitor, megakaryocyte erythroid progenitor), and matched circulating (monocyte, neutrophil, naïve B cell) haematopoietic cells in treatment naïve and AZA-treated MDS and CMML via high-throughput single cell genotyping. The mutational burden was similar across multiple hematopoietic cell types, and even the most mutated stem and progenitor clones maintained their capacity to differentiate to mature myeloid and, in some cases, lymphoid cell types in vivo. Our data show that even highly mutated HSPCs contribute significantly to circulating blood cells in MDS and CMML, prior to and following AZA treatment.Key points*Highly mutated HSPCs contribute significantly to circulating blood cells in MDS and CMML, prior to and following AZA treatment.*The mutational burden in matched bone marrow and peripheral blood cells in MDS and CMML was similar throughout myelopoiesis.

Systemic juvenile idiopathic arthritis (sJIA) begins with fever, rash, and high-grade systemic inflammation but commonly progresses to a persistent afebrile arthritis. The basis for this transition is unknown. To evaluate a role for lymphocyte polarization, we characterized T cells from patients with acute and chronic sJIA using flow cytometry, mass cytometry, and RNA sequencing. Acute and chronic sJIA each featured an expanded population of activated Tregs uncommon in healthy controls or in children with nonsystemic JIA. In acute sJIA, Tregs expressed IL-17A and a gene expression signature reflecting Th17 polarization. In chronic sJIA, the Th17 transcriptional signature was identified in T effector cells (Teffs), although expression of IL-17A at the protein level remained rare. Th17 polarization was abrogated in patients responding to IL-1 blockade. These findings identify evolving Th17 polarization in sJIA that begins in Tregs and progresses to Teffs, likely reflecting the impact of the cytokine milieu and consistent with a biphasic model of disease pathogenesis. The results support T cells as a potential treatment target in sJIA.

Drug repositioning is the process of finding new therapeutic uses for existing, approved drugs-a process thathas value when considering the exorbitant costs of novel drug development. Several computational strategies exist as a way to predict these alternative applications. In this study, we used datasets on: (1) human biological drug targets and (2) disease-associated genes and, based on a direct functional interaction between them, searched for potential opportunities for drug repositioning. From the set of 1125 unique drug targets and their 88 490 interactions with disease-associated genes, 30 drug targets were analyzed and (3) discussed in detail for the purpose of this article. The current indications of the drugs thattarget them were validated through the interactions, and new opportunities for repositioning were predicted. Among the set of drugs for potential repositioning werebenzodiazepines for the treatment of autism spectrum disorders; nortriptyline for the treatment of melanoma, glioma and other cancers; and vitamin B6 in prevention of spontaneous abortions and cleft palate birth defects. Special emphasis was also placed on those new potential indications that pertained to orphan diseases-these are diseases whose rarity means that development of novel treatment is not financially viable. This computational drug repositioning approach uses existing information on drugs and drug targets, and insights into the genetic basis of disease, as a means to systematically generate the most probable new uses for the drugs on offer, and in this way harness their true therapeutic power.

Populations worldwide currently face several public health challenges, including growing prevalence of infections and the emergence of new pathogenic organisms. The cost and risk associated with drug development make the development of new drugs for several diseases, especially orphan or rare diseases, unappealing to the pharmaceutical industry. Proof of drug safety and efficacy is required before market approval, and rigorous testing makes the drug development process slow, expensive and frequently result in failure. This failure is often because of the use of irrelevant targets identified in the early steps of the drug discovery process, suggesting that target identification and validation are cornerstones for the success of drug discovery and development. Here, we present a large-scale data-driven integrative computational framework to extract essential targets and processes from an existing disease-associated data set and enhance target selection by leveraging drug-target-disease association at the systems level. We applied this framework to tuberculosis and Ebola virus diseases combining heterogeneous data from multiple sources, including protein-protein functional interaction, functional annotation and pharmaceutical data sets. Results obtained demonstrate the effectiveness of the pipeline, leading to the extraction of essential drug targets and to the rational use of existing approved drugs. This provides an opportunity to move toward optimal target-based strategies for screening available drugs and for drug discovery. There is potential for this model to bridge the gap in the production of orphan disease therapies, offering a systematic approach to predict new uses for existing drugs, thereby harnessing their full therapeutic potential.

RNA-seq analysis involves multiple steps, from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates, objects and wrapper functions to post-process bcbio RNA sequencing output data. bcbioRNASeq helps automate the generation of high-level RNA-seq reports, facilitating the quality control analyses, identification of differentially expressed genes and functional enrichment analyses.