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PNAS Papers on Chemotherapy-Induced Cardiac Injury Target, Viral Insertions, SMRT Methylation Profiling

Researchers from Ohio State University and elsewhere present findings from a study looking at strategies for reducing cardiac injury in individuals treated with the chemotherapy drug doxorubicin. By profiling transcriptome patterns in stem cell-derived cardiomyocyte cells derived from doxorubicin-treated patients, the team narrowed in OCT3, which codes for an organic cation transporter that helps the drug build up in cardiac cells. With RNA sequencing and other analyses on cardiomyocyte cells from doxorubicin-treated or -untreated mice, the authors went on to show that lower-than-usual levels of OCT3 or OCT3 inhibition could protect the animals from short- or long-term cardiovascular problems by muting cardiotoxicity-related signaling downstream of OCT3. "These findings not only shed light on the etiology of doxorubicin-induced cardiac toxicity," they report, "but also provide a rationale for the identification of targeted intervention strategies to prevent this debilitating side effect." 

A Kyoto University-led team describes insertions in the human genome that share signatures with ancient, endogenous RNA viruses. The researchers uncovered the sequences with the help of machine learning approach focused on "non-retroviral endogenous RNA virus element," or nrEVE, signatures that did not match known viruses in the human reference genome, pointing to ancient bornavirus-like insertions and insertions stemming from previously unappreciated viruses in the human genome and other animal genomes. "Our study suggests that unexplored virus-derived sequences may be a part of the evolutionary origins of such complex genomic sequences," they write. "Viral machinery coded in endogenous retroviruses and nrEVEs are frequently co-opted or repurposed for novel cellular functions. Therefore, unveiling hidden viral insertions in animal genomes will provide insight into the novelty of animal genomes driven by lateral gene transfer from viruses." GenomeWeb has more on this study, here.

The Chinese University of Hong Kong's Dennis Lo and colleagues outline a single-molecule real-time sequencing (SMRT) strategy for directly profiling cytosine methylation patterns across the genome without bisulfite treatment. The team relied on machine learning to come up with a so-called "holistic kinetic model" for picking up 5-methylcytosine (5mC) patterns based on subtle changes in DNA polymerase enzyme kinetic signals during SMRT — an approach that reportedly detected 5mC with almost 90 percent sensitivity and around 94 percent specificity in the samples considered. "We have developed an approach for holistically making use of kinetic signals and sequence context to realize the genome-wide detection of cytosine methylation by SMRT sequencing," the authors write, adding that HK model-based methylation analysis may "open up many new possibilities for studying the genetics and epigenetics in different organisms and may be useful in many molecular diagnostic applications (e.g., in oncology)." GenomeWeb also covers this paper in more depth, here.