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Stendahl, A.

Publications and source records attributed to Stendahl, A..

2 recordsLinked to original sources

A naturally occurring variant of MBD4 causes maternal germline hypermutation in primates

As part of an ongoing genome sequencing project at the Oregon National Primate Research Center, we identified a rhesus macaque with a rare homozygous frameshift mutation in the gene Methyl-CpG binding domain 4 (MBD4). MBD4 is responsible for the repair of C>T deamination mutations at CpG locations and has been linked to somatic hypermutation and cancer predisposition in humans. We show here that MBD4-associated hypermutation also affects the germline: the 6 offspring of the MBD4-null dam have a 4-6 fold increase in de novo mutation burden. This excess burden was predominantly C>T mutations at CpG locations consistent with MBD4 loss-of-function in the dam. There was also a significant excess of C>T at CpA sites, indicating an important, underappreciated role for MBD4 to repair deamination in CpA contexts. The MBD4-null dam developed sustained eosinophilia later in life, but we saw no other signs of neoplastic processes associated with MBD4 loss-of-function in humans, nor any obvious disease in the hypermutated offspring. This work provides what is likely the first evidence for a genetic factor causing hypermutation in the maternal germline of a mammal, and adds to the very small list of naturally occurring variants known to modulate germline mutation rates in mammals.

genetics↗

SATINN: An automated neural network-based classification of testicular sections allows for high-throughput histopathology of mouse mutants

MotivationThe mammalian testis is a complex organ with a hierarchical organization that changes smoothly and stereotypically over time in normal adults. While testis histology is already an invaluable tool for identifying and describing developmental differences in evolution and disease, methods for standardized, digital image analysis of testis are needed to expand the utility of this approach. ResultsWe developed SATINN (Software for Analysis of Testis Images with Neural Networks), a multi-level framework for automated analysis of multiplexed immunofluorescence images from mouse testis. This approach uses a convolutional neural network (CNN) to classify nuclei from seminiferous tubules into 7 distinct cell types with an accuracy of 94.2%. These cell classifications are then used in a second-level tubule CNN, which places seminiferous tubules into one of 7 distinct tubule stages with 90.4% accuracy. We further describe numerous cell- and tubule-level statistics that can be derived from wildtype testis. Finally, we demonstrate how the classifiers and derived statistics can be used to rapidly and precisely describe pathology by applying our methods to image data from two mutant mouse lines. Our results demonstrate the feasibility and potential of using computer-assisted analysis for testis histology, an area poised to evolve rapidly on the back of emerging, spatially-resolved genomic and proteomic technologies. Availability and implementationScripts to apply the methods described here are available from http://github.com/conradlab/SATINN.

bioinformatics↗