Airway epithelial homeostasis and planar cell polarity signaling depend on multiciliated cell differentiation

The pseudostratified epithelium of the mouse trachea and human airways contains a population of basal cells expressing Trp-63 (p63) and cytokeratins 5 (Krt5) and Krt14. Using a KRT5-CreER(T2) transgenic mouse line for lineage tracing, we show that basal cells generate differentiated cells during postnatal growth and in the adult during both steady state and epithelial repair. We have fractionated mouse basal cells by FACS and identified 627 genes preferentially expressed in a basal subpopulation vs. non-BCs. Analysis reveals potential mechanisms regulating basal cells and allows comparison with other epithelial stem cells. To study basal cell behaviors, we describe a simple in vitro clonal sphere-forming assay in which mouse basal cells self-renew and generate luminal cells, including differentiated ciliated cells, in the absence of stroma. The transcriptional profile identified 2 cell-surface markers, ITGA6 and NGFR, which can be used in combination to purify human lung basal cells by FACS. Like those from the mouse trachea, human airway basal cells both self-renew and generate luminal daughters in the sphere-forming assay.

The genetic code-the binding specificity of all transfer-RNAs--defines how protein primary structure is determined by DNA sequence. DNA also dictates when and where proteins are expressed, and this information is encoded in a pattern of specific sequence motifs that are recognized by transcription factors. However, the DNA-binding specificity is only known for a small fraction of the approximately 1400 human transcription factors (TFs). We describe here a high-throughput method for analyzing transcription factor binding specificity that is based on systematic evolution of ligands by exponential enrichment (SELEX) and massively parallel sequencing. The method is optimized for analysis of large numbers of TFs in parallel through the use of affinity-tagged proteins, barcoded selection oligonucleotides, and multiplexed sequencing. Data are analyzed by a new bioinformatic platform that uses the hundreds of thousands of sequencing reads obtained to control the quality of the experiments and to generate binding motifs for the TFs. The described technology allows higher throughput and identification of much longer binding profiles than current microarray-based methods. In addition, as our method is based on proteins expressed in mammalian cells, it can also be used to characterize DNA-binding preferences of full-length proteins or proteins requiring post-translational modifications. We validate the method by determining binding specificities of 14 different classes of TFs and by confirming the specificities for NFATC1 and RFX3 using ChIP-seq. Our results reveal unexpected dimeric modes of binding for several factors that were thought to preferentially bind DNA as monomers.

Gene-transfer vectors based on lentiviruses are distinguished by their ability to transduce non-dividing cells. The HIV-1 proteins Matrix, Vpr and Integrase have been implicated in the nuclear import of the viral genome in non-dividing cells. Here we show that a sequence within pol is also required in cis. It contains structural elements previously associated with the progress of reverse transcription in target cells. We restored these elements in cis within late-generation lentiviral vectors. The new vector transduced to a much higher efficiency several types of human primary cells, when both growing and growth-arrested, including haematopoietic stem cells assayed by long-term repopulation of NOD/SCID mice. On in vivo administration into SCID mice, the vector induced higher plasma levels of human clotting factor IX (F.IX) than non-modified vector. Our results indicate that nuclear translocation of the genome is a rate-limiting step in lentiviral infection of both dividing and non-dividing cells, and that it depends on protein and nucleic acid sequence determinants. Full rescue of this step in lentivirus-based vectors improves performance for gene-therapy applications.