Biology subjects

Parkinson, N.

Publications and source records attributed to Parkinson, N..

6 recordsLinked to original sources

bioRxiv2026

A genome-wide CRISPR knockout screen identifies host dependency factors and potential viral-induced cell death regulators for porcine deltacoronavirus infection

Porcine deltacoronavirus (PDCoV) is a globally circulating coronavirus of swine that causes severe enteric disease and mortality in neonatal piglets. Its broad host range and zoonotic potential raises concerns about continued spillover into human populations. However, the host factors that govern PDCoV infection remain largely unexplored. Identifying host factors involved in virus infection may uncover novel therapeutic targets for antiviral drug development or targets for gene editing to create resistant pigs. Here, we conducted a genome-wide CRISPR knockout (GeCKO) screen to identify host factors contributing to PDCoV infection in swine testicular (ST) cells. Small guide RNAs (sgRNAs) targeting the ANPEP gene, expressing the main receptor porcine aminopeptidase N for PDCoV, were enriched in PDCoV-resistant cells, demonstrating the validity of the screen. Moreover, we identified transmembrane protein 30A (TMEM30A), an accessory {beta}-subunit of the P4-ATPase flippase complex, as a host dependency factor for PDCoV infection along with several potential PDCoV-induced cell death regulators, including DET1, IFNAR1, RFWD2, STAT1 and TP53. We further demonstrated that PDCoV attachment and internalization into cells were significantly impaired in TMEM30A knockout ST cells, suggesting that TMEM30A is important in the early stages of the PDCoV life cycle. Because TMEM30A also contributes to SARS-CoV-2 replication, future studies are warranted to investigate its mechanistic role in the context of coronavirus infection.

molecular biology↗

bioRxiv2025

Identification of key host genes for influenza A virus in avian cells using a genome-wide CRISPR-Cas9 screen

Influenza A virus (IAV) causes major economic losses to the poultry industry and poses a zoonotic threat to human health. Potential pandemic outbreaks are underpinned by the ability of the virus to jump from one species to another. Host-virus interactions can dictate the success of such events and while systematic studies have successfully mapped host virus interactions in human cells, few studies have been performed in relevant animal host cell lines. Here, we conducted two independent genome-wide CRISPR/Cas9 knockout screens in chicken lung epithelial cells infected with either the human-adapted PR8 vaccine strain or the avian UDL 3:5 reassortant virus encoding PR8 HA, NA and M segments. Rather than selecting solely for cell survival, we used anti-M2 antibody staining and fluorescence-activated cell sorting to capture host factors influencing multiple stages of the IAV life cycle. Across both screens, we identified 104 genes required for efficient replication in chicken cells, including 16 with strong effects (log2 fold change > 2). Comparative analysis with published human screens revealed 17 conserved host factors, 19 human-specific factors, and 42 chicken-specific factors, highlighting potential species-specific interactions. Top hits included genes involved in sialic acid biosynthesis and N-linked glycosylation--SLC35A1, SLC35A2, and the avian-specific influenza polymerase cofactor ANP32A. Functional validation demonstrated that MOGS, MGAT1, DENR, DMXL1, ENO1, IPO9, KLF6, PTAR1, and TSG101 contribute to multiple stages of the IAV life cycle. In particular, MOGS and MGAT1 were essential for N-glycan processing and modulated cell-surface sialic acid abundance, with strain- and species-specific effects. These findings define a genetic landscape of IAV dependency factors in chicken cells and suggest shared and species-specific host requirements that could impact cross-species transmission.

cell biology↗

bioRxiv2022

Hypoxia shapes the immune landscape in lung injury promoting inflammation persistence

Acute Respiratory Distress Syndrome (ARDS), an often-fatal complication of pulmonary or systemic inflammation, has no cure. Hypoxemia is a defining feature, yet its impact on inflammation is often neglected. Patients with ARDS are monocytopenic early in the onset of the disease. Endotoxin or Streptococcus pneumoniae acute lung injury (ALI) in the context of hypoxia replicates this finding, through hypoxia-driven suppression of type I interferon signalling. This results in failed lung monocyte-derived interstitial macrophages (IM) niche expansion and unchecked neutrophilic inflammation. Administration of colony stimulating factor 1 (CSF1) rescues the monocytopenia, alters the circulating classical monocyte phenotype in hypoxic endotoxin-driven ALI and enables lung IM population expansion, thus limiting lung injury in endotoxin- and virally-induced hypoxic ALI. Hypoxia directly alters immune dynamics to the detriment of the host and manipulation of this aberrant response offers new therapeutic strategies for ARDS.

cell biology↗

bioRxiv2022

Systematic comparison of ranking aggregation methods for gene lists in experimental results

A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The results of a group of studies answering the same, or similar, questions can be combined by meta-analysis to find a consensus or a more reliable answer. Ranking aggregation methods can be used to combine gene lists from various sources in meta-analyses. Evaluating a ranking aggregation method on a specific type of dataset before using it is required to support the reliability of the result since the property of a dataset can influence the performance of an algorithm. Evaluation of aggregation methods is usually based on a simulated database especially for the algorithms designed for gene lists because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. In this study, a group of existing methods and their variations which are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data was used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomics data, with various heterogeneity of quality, noise level, and a mix of unranked and ranked data using 20000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (NSCLC), and bacteria (macrophage apoptosis) was performed. We summarise our evaluation results in terms of a simple flowchart to select a ranking aggregation method for genomics data.

bioinformatics↗

bioRxiv2019

Functional Annotation of Human Long Non-Coding RNAs via Molecular Phenotyping

Long non-coding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes and yet, their functions remain largely unknown. We systematically knockdown 285 lncRNAs expression in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNA exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest to-date lncRNA knockdown dataset with molecular phenotyping (over 1,000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

genomics↗

bioRxiv2019

Comprehensive characterisation of molecular host-pathogen interactions in influenza A virus-infected human macrophages

Macrophages in the lung detect and respond to influenza A virus (IAV), determining the nature of the immune response. Using terminal depth 5-RNA sequencing (CAGE) we quantify transcriptional activity of both host and pathogen over a 24-hour timecourse of IAV infection in primary human monocyte-derived macrophages (MDM). We use a systems approach to describe the transcriptional landscape of the host response to IAV contrasted with bacterial lipopolysaccharide treated MDMs, observing a failure of IAV-treated MDMs to induce feedback inhibitors of inflammation. Systematic comparison of host RNA sequences incorporated into viral mRNA (\"snatched\") against a complete survey of background RNA in the host cell enables an unbiased quantification of over-represented features of snatched host RNAs. We detect preferential snatching of RNAs associated with snRNA transcription and demonstrate that cap-snatching avoids transcripts encoding host ribosomal proteins, which are required by IAV for replication.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC=\"FIGDIR/small/670919v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (34K):\norg.highwire.dtl.DTLVardef@b05478org.highwire.dtl.DTLVardef@79a519org.highwire.dtl.DTLVardef@462739org.highwire.dtl.DTLVardef@1b91eb6_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical Abstract(A) Overview of bioinformatics pipeline. (B) Host gene expression reveals that human macrophages exposed to IAV exhibit sustained production of key inflammatory mediators and failure to induce expression of feedback inhibitors of inflammation. (C) Unbiased comparison with total background RNA expression demonstrates that IAV cap-snatching has a strong preference for, and aversion to, different groups of host transcripts.

immunology↗