In Science this week, an international team led by investigators at the University of Houston report the effects of epistatic interactions among mutations on fitness "for the first five mutations to fix in an experimental population of Escherichia coli." The team says that "epistasis depended on the effects of the combined mutations," such that "the larger the expected benefit, the more negative the epistatic effect." As a result, the team says that in its study, epistasis "tended to produce diminishing returns with genotype fitness," though that was not the case for interactions involving one particular mutation, which conferred the opposite effect.
In another paper appearing in this week's issue, the California Institute of Technology's Lulu Qian and Erik Winfree report their creation of "several digital logic circuits, culminating in a four-bit square-root circuit that comprises 130 DNA strands." The duo says its multilayer biochemical circuits "include thresholding and catalysis within every logical operation to perform digital signal restoration," and that, overall, its DNA strand displacement-based design "naturally incorporates other crucial elements for large-scale circuitry, such as general debugging tools, parallel circuit preparation, and an abstraction hierarchy supported by an automated circuit compiler."
Baylor College of Medicine's Christopher McGraw and his colleagues report in a Science paper published online in advance this week that, in an inducible mouse model of Rett syndrome, deletion of Mecp2 in adult animals "recapitulates the germline knockout phenotype, underscoring the ongoing role of MeCP2 in adult neurological function." McGraw et al. say that their study shows that "the effects of early MeCP2 function are lost soon after its deletion," and, as such, Rett syndrome therapies "must be maintained throughout life."
And in Science Signaling, researchers at the Korea Advanced Institute of Science and Technology and at the University of Leicester in the UK this week show report an algorithm to identify the "kernel" of a given complex network — which "maintains the essential regulatory functions for the output under consideration," and "preserves the network dynamics," when complexity is reduced. By applying that algorithm to an integrated network of all human signaling pathways in the Kyoto Encyclopedia of Genes and Genomes database, the team "identified this network's kernel and compared the properties of the kernel to those of the original network," finding that "the percentage of essential genes to the genes encoding nodes outside of the kernel was about 10 percent, whereas [about] 32 percent of the genes encoding nodes within the kernel were essential," it writes. Further, the team also found that "95 percent of the kernel nodes corresponded to Mendelian disease genes."