In a paper published online in advance in Cell this week, researchers at the University of California, San Francisco, along with their collaborators, describe the "phenotypic landscape of a bacterial cell." In characterizing the E. coli phenome, the team found "thousands of mutant growth phenotypes … [and] new functional connections between genes." By integrating chemical genomics approaches with quantitative fitness measurements, the team was able to investigate whether each gene was essential, and consequently, "understand higher order organization of the bacterial chromosome," the authors write.
In the most recent issue of Cell Metabolism, investigators at the University of Miami Miller School of Medicine, along with their colleague at the University of Wisconsin-Madison, present a "sensitive, pragmatic approach to mtDNA mutation detection," dubbed Mito-Seq. Using their method, the Miller-led team "observed an increase of at least two orders of magnitude in the number of mtDNA single nucleotide variants in Polg mutator mice compared to controls." They also identified control region multimers that "contained heterogeneous breakpoints and ... excluded the majority of mtDNA genes," which the authors say have implications for mtDNA integrity maintenance.
Over in Cell Stem Cell, a public-private research collaboration in Japan proposes that "TIM-3 is a promising target to selectively kill acute myeloid leukemia stem cells." More specifically, using human AML in immunodeficient mouse models, the team found that TIM-3+ cells reconstituted the disease state. The team also found that "an anti-human TIM-3 mouse IgG2a antibody having complement-dependent and antibody-dependent cellular cytotoxic activities" blocked AML engraftment in the mice, but did not harm normal hematopoietic stem cells.
A team led by investigators at the University of California, Santa Barbara, identified two distinct categories of human pluripotent stem cells that they found through miRNA expression studies. In the most recent issue of Cell Stem Cell, the team says that its transcriptomic analysis of iPSCs and ESCs, among other cells lines, revealed that "several gene sets related to p53 distinguished these" cell types, and that the "overexpression of the p53-targeting miRNAs miR-92 and miR-141 in iPSCs was sufficient to change their classification status."