Doxorubicin (Adriamycin) is an anthracycline chemotherapy agent effective in treating a wide range of malignancies with a well–established dose–response cardiotoxic side effect that can lead to heart failure. At present, it is not possible to predict which patients will be affected by doxorubicin-induced cardiotoxicity (DIC). Here we demonstrate that patient–specific human induced pluripotent stem cell–derived cardiomyocytes (hiPSC–CMs) can recapitulate individual patients’ predilection to DIC at the single cell level. hiPSC–CMs derived from breast cancer patients who suffered clinical DIC are consistently more sensitive to doxorubicin toxicity, demonstrating decreased cell viability, mitochondrial and metabolic function, calcium handling, and antioxidant pathway activity, along with increased reactive oxygen species (ROS) production compared to hiPSC–CMs from patients who did not experience DIC. Together, our data indicate that hiPSC–CMs are a suitable platform for identifying and verifying the genetic basis and molecular mechanisms of DIC.

Rapid and inexpensive sequencing technologies are making it possible to collect whole genome sequence data on multiple individuals from a population. This type of data can be used to quickly identify genes that control important ecological and evolutionary phenotypes by finding the targets of adaptive natural selection, and we therefore refer to such approaches as "reverse ecology." To quantify the power gained in detecting positive selection using population genomic data, we compare three statistical methods for identifying targets of selection: the McDonald-Kreitman test, the mkprf method, and a likelihood implementation for detecting d N /d S > 1. Because the first two methods use polymorphism data we expect them to have more power to detect selection. However, when applied to population genomic datasets from human, fly, and yeast, the tests using polymorphism data were actually weaker in two of the three datasets. We explore reasons why the simpler comparative method has identified more genes under selection, and suggest that the different methods may really be detecting different signals from the same sequence data. Finally, we find several statistical anomalies associated with the mkprf method, including an almost linear dependence between the number of positively selected genes identified and the prior distributions used. We conclude that interpreting the results produced by this method should be done with some caution.

We develop a novel synthetic biology platform for rapid, scalable expression of fungal biosynthetic genes and encoded metabolites.

The low costs of array‐synthesized oligonucleotide libraries are empowering rapid advances in quantitative and synthetic biology. However, high synthesis error rates, uneven representation, and lack of access to individual oligonucleotides limit the true potential of these libraries. We have developed a cost‐effective method called Recombinase Directed Indexing (REDI), which involves integration of a complex library into yeast, site‐specific recombination to index library DNA, and next‐generation sequencing to identify desired clones. We used REDI to generate a library of ~3,300 DNA probes that exhibited > 96% purity and remarkable uniformity (> 95% of probes within twofold of the median abundance). Additionally, we created a collection of ~9,000 individually accessible CRISPR interference yeast strains for > 99% of genes required for either fermentative or respiratory growth, demonstrating the utility of REDI for rapid and cost‐effective creation of strain collections from oligonucleotide pools. Our approach is adaptable to any complex DNA library, and fundamentally changes how these libraries can be parsed, maintained, propagated, and characterized.

The protein inference problem represents a major challenge in shotgun proteomics. Here we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorious probabilistic model for protein inference, and provide practical algoritmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results.