DNA analysis is making a valuable contribution to the understanding of human evolution [1]. Much attention has focused on mitochondrial DNA (mtDNA) [2] and the Y chromosome [3] [4], both of which escape recombination and so provide information on maternal and paternal lineages, respectively. It is often assumed that the polymorphisms observed at loci on mtDNA and the Y chromosome are selectively neutral and, therefore, that existing patterns of molecular variation can be used to deduce the histories of populations in terms of drift, population movements, and cultural practices. The coalescence of the molecular phylogenies of mtDNA and the Y chromosome to recent common ancestors in Africa [5] [6], for example, has been taken to reflect a recent origin of modern human populations in Africa. An alternative explanation, though, could be the recent selective spread of mtDNA and Y chromosome haplotypes from Africa in a population with a more complex history [7]. It is therefore important to establish whether there are selective differences between classes (haplotypes) of mtDNA and Y chromosomes and, if so, whether these differences could have been sufficient to influence the distributions of haplotypes in existing populations. A precedent for this hypothesis has been established for mtDNA in that one mtDNA background increases susceptibility to Leber hereditary optic neuropathy [8]. Although studies of nucleotide diversity in global samples of Y chromosomes have suggested an absence of recent selective sweeps or bottlenecks [9], selection may, in principle, be very important for the Y chromosome because it carries several loci affecting male fertility [10] [11] and as many as 5% of males are infertile [11] [12]. Here, we show that one class of infertile males, PRKX/PRKY translocation XX males, arises predominantly on a particular Y haplotypic background. Selection is, therefore, acting on Y haplotype distributions in the population.
Background Cellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types. Results We employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is named HiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) by HiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture. Conclusion We developed HiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy. HiTIMED deconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment.
The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location within the tissue. Recently, a number of deep learning diagnostic aids have been developed to classify digitized biopsy slides. Clinical workflows often involve processing of more than 500 slides per day. But, clinical use of deep learning diagnostic aids would require a preprocessing workflow that is cost-effective, flexible, scalable, rapid, interpretable, and transparent. Here, we present such a workflow, optimized using Dask and mixed precision training via APEX, capable of handling any patch-level or slide level classification and prediction problem. The workflow uses a flexible and fast preprocessing and deep learning analytics pipeline, incorporates model interpretation and has a highly storage-efficient audit trail. We demonstrate the utility of this package on the analysis of a prototypical anatomic pathology specimen, liver biopsies for evaluation of hepatitis from a prospective cohort. The preliminary data indicate that PathFlowAI may become a cost-effective and time-efficient tool for clinical use of Artificial Intelligence (AI) algorithms.
IntroductionThe human brain comprises heterogeneous cell types whose composition can be altered with physiological and pathological conditions. New approaches to discern the diversity and distribution of brain cells associated with neurological conditions would significantly advance the study of brain-related pathophysiology and neuroscience. Unlike single-nuclei approaches, DNA methylation-based deconvolution does not require special sample handling or processing, is cost-effective, and easily scales to large study designs. Existing DNA methylation-based methods for brain cell deconvolution are limited in the number of cell types deconvolvedMethodsUsing DNA methylation profiles of the top cell-type-specific differentially methylated CpGs, we employed a hierarchical modeling approach to deconvolve GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells.ResultsWe demonstrate the utility of our method by applying it to data on normal tissues from various brain regions and in aging and diseased tissues, including Alzheimer’s disease, autism, Huntington’s disease, epilepsy, and schizophrenia.DiscussionWe expect that the ability to determine the cellular composition in the brain using only DNA from bulk samples will accelerate understanding brain cell type composition and cell-type-specific epigenetic states in normal and diseased brain tissues.
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