Branching morphogenesis is a general mechanism that increases the surface area of an organ. In chicken feathers, the flat epithelial sheath at the base of the follicle is transformed into periodic branches. How exactly the keratinocytes are organized into this pattern remains unclear. Here we show that in the feather follicle, the pre-branch basal keratinocytes have extensive filopodia, which contract and smooth out after branching. Manipulating the filopodia via small GTPases RhoA/Cdc42 also regulates branch formation. These basal filopodia help interpret the proximal-distal FGF gradient in the follicle. Furthermore, the topological arrangement of cell adhesion via E-Cadherin re-distribution controls the branching process. Periodic activation of Notch signaling drives the differential cell adhesion and contraction of basal filopodia, which occurs only below an FGF signaling threshold. Our results suggest a coordinated adjustment of cell shape and adhesion orchestrates feather branching, which is regulated by Notch and FGF signaling.
Epstein-Barr virus (EBV) is a ubiquitous γ-herpesvirus with latent and lytic cycles. EBV replicates in the stratified epithelium but the nasopharynx is also composed of pseudostratified epithelium with distinct cell types. Latent infection is associated with nasopharyngeal carcinoma (NPC). Here, we show with nasopharyngeal conditionally reprogrammed cells cultured at the air-liquid interface that pseudostratified epithelial cells are susceptible to EBV infection. Donors varied in susceptibility to de novo EBV infection, but susceptible cultures also displayed differences with respect to pathogenesis. The cultures from one donor yielded lytic infection but cells from two other donors were positive for EBV-encoded EBERs and negative for other lytic infection markers. All cultures stained positive for the pseudostratified markers CK7, MUC5AC, α-tubulin in cilia, and the EBV epithelial cell receptor Ephrin receptor A2. To define EBV transcriptional programs by cell type and to elucidate latent/lytic infection-differential changes, we performed single cell RNA-sequencing on one EBV-infected culture that resulted in alignment with many EBV transcripts. EBV transcripts represented a small portion of the total transcriptome (~0.17%). All cell types in the pseudostratified epithelium had detectable EBV transcripts with suprabasal cells showing the highest number of reads aligning to many EBV genes. Several restriction factors (IRF1, MX1, STAT1, C18orf25) known to limit lytic infection were expressed at lower levels in the lytic subcluster. A third of the differentially-expressed genes in NPC tumors compared to an uninfected pseudostratified ALI culture overlapped with the differentially-expressed genes in the latent subcluster. A third of these commonly perturbed genes were specific to EBV infection and changed in the same direction. Collectively, these findings suggest that the pseudostratified epithelium could harbor EBV infection and that the pseudostratified infection model mirrors many of the transcriptional changes imposed by EBV infection in NPC.
Comprehensive characterization of differentially spliced RNA transcripts with nanopore sequencing is limited by bioinformatics tools that are reliant on existing annotations. We have developed FLAME, a bioinformatics pipeline for alternative splicing analysis of gene-specific or transcriptome-wide longread sequencing data. FLAME is a Python-based tool aimed at providing comprehensible quantification of full-length splice variants, reliable de novo recognition of splice sites and exons, and representation of consecutive exon connectivity in the form of a weighted adjacency matrix. Notably, this workflow circumvents issues related to inadequate reference annotations and allows for incorporation of shortread sequencing data to improve the confidence of nanopore sequencing reads. In this study, the Epstein-Barr virus long non-coding RNA RPMS1 was used to demonstrate the utility of the pipeline. RPMS1 is ubiquitously expressed in Epstein-Barr virus associated cancer and known to undergo ample differential splicing. To fully resolve the RPMS1 spliceome, we combined gene-specific nanopore sequencing reads from a primary gastric adenocarcinoma and a nasopharyngeal carcinoma cell line with matched publicly available short-read sequencing datasets. All previously reported splice variants, including putative ORFs, were detected using FLAME. In addition, 32 novel exons, including two intron retentions and a cassette exon, were discovered within the RPMS1 gene.
Infection in the central nervous system is a severe condition associated with high morbidity and mortality. Despite ample testing, the majority of encephalitis and meningitis cases remain undiagnosed. Metagenomic sequencing of cerebrospinal fluid has emerged as an unbiased approach to identify rare microbes and novel pathogens. However, several major hurdles remain, including establishment of individual limits of detection, removal of false positives and implementation of universal controls. Twenty-one cerebrospinal fluid samples, in which a known pathogen had been positively identified by available clinical techniques, were subjected to metagenomic DNA sequencing. Fourteen samples contained minute levels of Epstein-Barr virus. The detection threshold for each sample was calculated by using the total leukocyte content in the sample and environmental contaminants found in the bioinformatic classifiers. Virus sequences were detected in all ten samples, in which more than one read was expected according to the calculations. Conversely, no viral reads were detected in seven out of eight samples, in which less than one read was expected according to the calculations. False positive pathogens of computational or environmental origin were readily identified, by using a commonly available cell control. For bacteria, additional filters including a comparison between classifiers removed the remaining false positives and alleviated pathogen identification. Here we show a generalizable method for identification of pathogen species using DNA metagenomic sequencing. The choice of bioinformatic method mainly affected the efficiency of pathogen identification, but not the sensitivity of detection. Identification of pathogens requires multiple filtering steps including read distribution, sequence diversity and complementary verification of pathogen reads.
The zinc (Zn2+) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins.
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