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Salomon, M. P.

Publications and source records attributed to Salomon, M. P..

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Tempo, mode, and fitness effects of mutation in Caenorhabditis elegans over 400 generations of minimal selection

The mutational process varies at many levels, from within genomes to among taxa. Many mechanisms have been linked to variation in mutation, but understanding of the evolution of the mutational process is rudimentary. Physiological condition is often implicated as a source of variation in microbial mutation rate and may contribute to mutation rate variation in multicellular organisms.\n\nDeleterious mutations are a ubiquitous source of variation in condition. We test the hypothesis that the mutational process depends on the underlying mutation load in two groups of Caenorhabditis elegans mutation accumulation (MA) lines that differ in their starting mutation loads. \"First-Order MA\" (O1MA) lines maintained under minimal selection for [~]250 generations were divided into high-fitness and low-fitness groups and sets of \"second-order MA\" (O2MA) lines derived from each O1MA line were maintained for [~]150 additional generations. Genomes of 48 O2MA lines and their progenitors were sequenced. There is significant variation among O2MA lines in base-substitution rate ({micro}bs), but no effect of initial fitness, whereas the indel rate is greater in high-fitness O2MA lines. Overall, {micro}bs is positively correlated with recombination and proximity to short tandem repeats and negatively correlated with 10 bp and 1 Kb GC content. However, probability of mutation is well-predicted by the three-nucleotide motif. [~]90% of the variance in standing nucleotide variation is explained by mutability. Total mutation rate increased in the O2MA lines, as predicted by the \"drift barrier\" model of mutation rate evolution. These data, combined with experimental estimates of fitness, suggest that epistasis is synergistic.

evolutionary biology

Epigenetic Profiling for the Molecular Classification of Metastatic Brain Tumors

Optimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet these challenges, we generated genome-scale DNA methylomes of the three most frequent types of brain metastases: melanoma, breast, and lung cancers (n=96). Using supervised machine learning and integration of multiple DNA methylomes from normal, primary, and metastatic tumor specimens (n=1,860), we unraveled epigenetic signatures specific to each type of metastatic brain tumor and constructed a three-step DNA methylation-based classifier (BrainMETH) that categorizes brain metastases according to the tissue of origin and therapeutically-relevant subtypes. BrainMETH predictions were supported by routine histopathologic evaluation. We further characterized and validated the most predictive genomic regions in a large cohort of brain tumors (n=165) using quantitative methylation-specific PCR. Our study highlights the importance of brain tumor-defining epigenetic alterations, which can be utilized to further develop DNA methylation profiling as a critical tool in the histomolecular stratification of patients with brain metastases.

cancer biology