The Drosophila nucleoporin DNup88 localizes DNup214 and CRM1 on the nuclear envelope and attenuates NES-mediated nuclear export

Many cellular responses rely on the control of nucleocytoplasmic transport of transcriptional regulators. The Drosophila nucleoporin Nup88 is selectively required for nuclear accumulation of Rel proteins and full activation of the innate immune response. Here, we investigate the mechanisms underlying its role in nucleocytoplasmic transport. Nuclear import of an nuclear localization signal-enhanced green fluorescent protein (NLS-EGFP) reporter is not affected in DNup88 (members only; mbo) mutants, whereas the level of CRM1-dependent EGFP-nuclear export signal (EGFP-NES) export is increased. We show that the nuclear accumulation of the Drosophila Rel protein Dorsal requires CRM1. DNup88 binds to DNup214 and DCRM1 in vitro, and both proteins become mislocalized from the nuclear rim into the nucleus of mbo mutants. Overexpression of DNup88 is sufficient to relocalize DNup214 and CRM1 on the nuclear envelope and revert the mutant phenotypes. We propose that a major function of DNup88 is to anchor DNup214 and CRM1 on the nuclear envelope and thereby attenuate NES-mediated nuclear export

Distinct functions of the Drosophila Nup153 and Nup214 FG domains in nuclear protein transport

The phenylanine-glycine (FG)–rich regions of several nucleoporins both bind to nuclear transport receptors and collectively provide a diffusion barrier to the nuclear pores. However, the in vivo roles of FG nucleoporins in transport remain unclear. We have inactivated 30 putative nucleoporins in cultured Drosophila melanogaster S2 cells by RNA interference and analyzed the phenotypes on importin α/β−mediated import and CRM1-dependent protein export. The fly homologues of FG nucleoporins Nup358, Nup153, and Nup54 are selectively required for import. The FG repeats of Nup153 are necessary for its function in transport, whereas the remainder of the protein maintains pore integrity. Inactivation of the CRM1 cofactor RanBP3 decreased the nuclear accumulation of CRM1 and protein export. We report a surprisingly antagonistic relationship between RanBP3 and the Nup214 FG region in determining CRM1 localization and its function in protein export. Our data suggest that peripheral metazoan FG nucleoporins have distinct functions in nuclear protein transport events

Metabolic Disorders in Chronic Lung Diseases

Chronic lung diseases represent complex diseases with gradually increasing incidence, characterized by significant medical and financial burden for both patients and relatives. Their increasing incidence and complexity render a comprehensive, multidisciplinary, and personalized approach critically important. This approach includes the assessment of comorbid conditions including metabolic dysfunctions. Several lines of evidence show that metabolic comorbidities, including diabetes mellitus, dyslipidemia, osteoporosis, vitamin D deficiency, and thyroid dysfunction have a significant impact on symptoms, quality of life, management, economic burden, and disease mortality. Most recently, novel pathogenetic pathways and potential therapeutic targets have been identified through large-scale studies of metabolites, called metabolomics. This review article aims to summarize the current state of knowledge on the prevalence of metabolic comorbidities in chronic lung diseases, highlight their impact on disease clinical course, delineate mechanistic links, and report future perspectives on the role of metabolites as disease modifiers and therapeutic targets

CINS: Cell Interaction Network inference from Single cell expression data.

Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging mouse dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions improving on prior methods suggested for cell interaction predictions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS

Bayesian Networks (BN) learned for lung cell types in healthy and IPF individual.

(A) BN for controls (healthy individuals). (B) BN for IPF patients. Nodes represent specific cell types and are colored accordingly, edges represent directed interactions between the cell types. Edge width corresponds to its bootstrap score.</p

Permutation analysis highlights the agreement between the two aging networks.

(A) Leftmost–learning using the Angelidis (15 samples) dataset. (B) Top–Learning combined networks using both Angelidis and real new data. Bottom–Learning combined networks using both Angelidis and permutation of cell type fractions in the new data. (C) Overlap in bootstrapped edges between the original and combined model when using the real data (red dashed line) and the permutation data (blue distribution).</p

Top differential cell type interactions identified by CINS for the IPF dataset.

The IPF-Control column lists the difference in the number of times the edge between the two cells was identified in 100 bootstrap runs for each of the two datasets. Negative values indicate that it was identified more for the Control whereas positive numbers mean that the interaction is more prevalent in IPF. For all listed edges the interaction was only identified in for one of the two datasets (score of 100 or -100).</p

Fastq generation, identification of valid cell barcodes and generation of the gene-cell-matrix from Fendrr-floxed mice.

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