Elucidating the interaction of CF airway epithelial cells and rhinovirus: Using the host-pathogen relationship to identify future therapeutic strategies

© 2018 Ling, Garratt, Lassmann, Stick. Chronic lung disease remains the primary cause of mortality in cystic fibrosis (CF). Growing evidence suggests respiratory viral infections are often more severe in CF compared to healthy peers and contributes to pulmonary exacerbations (PEx) and deterioration of lung function. Rhinovirus is the most prevalent respiratory virus detected, particularly during exacerbations in children with CF < 5 years old. However, even though rhinoviral infections are likely to be one of the factors initiating the onset of CF lung disease, there is no effective targeted treatment. A better understanding of the innate immune responses by CF airway epithelial cells, the primary site of infection for viruses, is needed to identify why viral infections are more severe in CF. The aim of this review is to present the clinical impact of virus infection in both young children and adults with CF, focusing on rhinovirus infection. Previous in vitro and in vivo investigations looking at the mechanisms behind virus infection will also be summarized. The review will finish on the potential of transcriptomics to elucidate the host-pathogen responses by CF airway cells to viral infection and identify novel therapeutic targets

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Identification of common key genes and pathways between type 1 diabetes and multiple sclerosis using transcriptome and interactome analysis

Purpose: Type 1 diabetes (T1D) and multiple sclerosis (MS) are classified as T cell-mediated autoimmune diseases. Although convergent evidence proposed common genetic architecture for autoimmune diseases, it remains a challenge to identify them. This study aimed to determine common gene signature and pathways in T1D and MS via systems biology approach. Methods: Gene expression profiles of peripheral blood mononuclear cells (PBMCs) and pancreatic-β cells in T1D as well as PBMCs and cerebrospinal fluid (CSF) in MS were analyzed in our previous published data, and differential expressed genes were integrated with protein�protein interactions data to construct Query�Query PPI (QQPPI) networks. In this study, QQPPI networks were further analyzed to investigate more central genes, functional modules and complexes shared in T1D and MS progression. Lastly, the interaction of common genes with drugs was also explored. Results: Several cytokines such as IL-23A, IL-32, IL-34, and IL-37 tend to be differentially expressed in both diseases. In addition, PSMA1, MYC, SRPK1, YBX1, HNRNPM, NF-κB2, IKBKE, RAC1, FN1, ARRB2, ESR1, HSP90AB1, and PPP1CA were common high central genes in QQPPI networks corresponding to each disease. Proteasome, spliceosome, immune responses, apoptosis, cellular communication/signaling transduction mechanism, interaction with environment, and activity of intercellular mediators were shared biological processes in T1D and MS. Finally, azathioprine, melatonin, resveratrol, and geldanamycin identified as prioritized drugs for the treatment of patients with T1D and MS. Conclusions: This study represented novel key genes and pathways shared between T1D and MS, which may facilitate the identification of potential therapeutic targets in these diseases. © 2020, Springer Science+Business Media, LLC, part of Springer Nature

Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

Background The involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein–protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease. Methods Gene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MS vs. control) and PBMCs (relapse vs. remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression. Results The networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapse vs. remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MS vs. control) and CDC37, MAP3K3, MYC genes in PBMCs (relapse vs. remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways. Discussion This study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets

Assessment of Post-Radiation Time Effect on Gene Expression Profiles of Saccharomyces cerevisiae Samples After Appling a UV Laser: Effect of UV Laser on Saccharomyces cerevisiae Gene Expression

Introduction: Widespread application of lasers in different fields of medicine implies more investigations into the molecular mechanism of laser effects on the human body. Network analysis of the dysregulated genes of Saccharomyces cerevisiae samples are irradiated by a UV laser and harvested 30 minutes after radiation compared with a 15-minute group is the aim of this research.Methods: The significantly dysregulated genes interacted via the STRING database, and the central nodes were determined by “Networkanalyzer” application of Cytoscape software. The critical genes and the related biological terms were identified via action map analysis and gene ontology assessment.Results: The gene expression profiles of the samples with 30-minute post-radiation time were different from the samples with 15 minutes of post-radiation time. 9 potent central genes, 50% of which were similar to the nodes of the 15-minute group, were identified. The terms “positive regulation of telomere maintenance” were targeted in the two sample groups.Conclusion: In spite of large alterations in the gene expression profiles of the samples, the results indicated that the main affected biological term for the 15-minute and 30-minute groups was similar. DOI:10.34172/jlms.2021.91