Systems-level reconstruction of kinase phosphosignaling networks regulating endothelial barrier integrity using temporal data.

Phosphosignaling networks control cellular processes. We built kinase-mediated regulatory networks elicited by thrombin stimulation of brain endothelial cells using two computational strategies: Temporal Pathway Synthesizer (TPS), which uses phosphoproetiomics data as input, and Temporally REsolved KInase Network Generation (TREKING), which uses kinase inhibitor screens. TPS and TREKING predicted overlapping barrier-regulatory kinases connected with unique network topology. Each strategy effectively describes regulatory signaling networks and is broadly applicable across biological systems.

Predicting host-based, synthetic lethal antiviral targets from omics data.

Traditional antiviral therapies often have limited effectiveness due to toxicity and the emergence of drug resistance. Host-based antivirals are an alternative, but can cause nonspecific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR knockout (KO) screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting antiviral SL drug targets. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Our comparison of SARS-CoV-2 and influenza infection data revealed potential broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.

Automated, image-based quantification of peroxisome characteristics with perox-per-cell.

SUMMARY: perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. AVAILABILITY AND IMPLEMENTATION: The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nanobody repertoire generated against the spike protein of ancestral SARS-CoV-2 remains efficacious against the rapidly evolving virus.

To date, all major modes of monoclonal antibody therapy targeting SARS-CoV-2 have lost significant efficacy against the latest circulating variants. As SARS-CoV-2 omicron sublineages account for over 90% of COVID-19 infections, evasion of immune responses generated by vaccination or exposure to previous variants poses a significant challenge. A compelling new therapeutic strategy against SARS-CoV-2 is that of single-domain antibodies, termed nanobodies, which address certain limitations of monoclonal antibodies. Here, we demonstrate that our high-affinity nanobody repertoire, generated against wild-type SARS-CoV-2 spike protein (Mast et al., 2021), remains effective against variants of concern, including omicron BA.4/BA.5; a subset is predicted to counter resistance in emerging XBB and BQ.1.1 sublineages. Furthermore, we reveal the synergistic potential of nanobody cocktails in neutralizing emerging variants. Our study highlights the power of nanobody technology as a versatile therapeutic and diagnostic tool to combat rapidly evolving infectious diseases such as SARS-CoV-2.

Multiple receptor tyrosine kinases regulate dengue infection of hepatocytes.

Introduction: Dengue is an arboviral disease causing severe illness in over 500,000 people each year. Currently, there is no way to constrain dengue in the clinic. Host kinase regulators of dengue virus (DENV) infection have the potential to be disrupted by existing therapeutics to prevent infection and/or disease progression. Methods: To evaluate kinase regulation of DENV infection, we performed kinase regression (KiR), a machine learning approach that predicts kinase regulators of infection using existing drug-target information and a small drug screen. We infected hepatocytes with DENV in vitro in the presence of a panel of 38 kinase inhibitors then quantified the effect of each inhibitor on infection rate. We employed elastic net regularization on these data to obtain predictions of which of 291 kinases are regulating DENV infection. Results: Thirty-six kinases were predicted to have a functional role. Intriguingly, seven of the predicted kinases - EPH receptor A4 (EPHA4), EPH receptor B3 (EPHB3), EPH receptor B4 (EPHB4), erb-b2 receptor tyrosine kinase 2 (ERBB2), fibroblast growth factor receptor 2 (FGFR2), Insulin like growth factor 1 receptor (IGF1R), and ret proto-oncogene (RET) - belong to the receptor tyrosine kinase (RTK) family, which are already therapeutic targets in the clinic. We demonstrate that predicted RTKs are expressed at higher levels in DENV infected cells. Knockdown of EPHB4, ERBB2, FGFR2, or IGF1R reduces DENV infection in hepatocytes. Finally, we observe differential temporal induction of ERBB2 and IGF1R following DENV infection, highlighting their unique roles in regulating DENV. Discussion: Collectively, our findings underscore the significance of multiple RTKs in DENV infection and advocate further exploration of RTK-oriented interventions against dengue.

CD4-mediated immunity shapes neutrophil-driven tuberculous pathology.

Pulmonary Mycobacterium tuberculosis (Mtb) infection results in highly heterogeneous lesions ranging from granulomas with central necrosis to those primarily comprised of alveolitis. While alveolitis has been associated with prior immunity in human post-mortem studies, the drivers of these distinct pathologic outcomes are poorly understood. Here, we show that these divergent lesion structures can be modeled in C3HeB/FeJ mice and are regulated by prior immunity. Using quantitative imaging, scRNAseq, and flow cytometry, we demonstrate that Mtb infection in the absence of prior immunity elicits dysregulated neutrophil recruitment and necrotic granulomas. In contrast, prior immunity induces rapid recruitment and activation of T cells, local macrophage activation, and diminished late neutrophil responses. Depletion studies at distinct infection stages demonstrated that neutrophils are required for early necrosis initiation and necrosis propagation at chronic stages, whereas early CD4 T cell responses prevent neutrophil feedforward circuits and necrosis. Together, these studies reveal fundamental determinants of tuberculosis lesion structure and pathogenesis, which have important implications for new strategies to prevent or treat tuberculosis.

Automated, image-based quantification of peroxisome characteristics with perox-per-cell.

perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually-quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. The software is available at https://github.com/AitchisonLab/perox-per-cell.

SARS-CoV-2 Orf6 is positioned in the nuclear pore complex by Rae1 to inhibit nucleocytoplasmic transport.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) accessory protein Orf6 works as an interferon antagonist, in part, by inhibiting the nuclear import activated p-STAT1, an activator of interferon-stimulated genes, and the export of the poly(A) RNA. Insight into the transport regulatory function of Orf6 has come from the observation that Orf6 binds to the nuclear pore complex (NPC) components: Rae1 and Nup98. To gain further insight into the mechanism of Orf6-mediated transport inhibition, we examined the role of Rae1 and Nup98. We show that Rae1 alone is not necessary to support p-STAT1 import or nuclear export of poly(A) RNA. Moreover, the loss of Rae1 suppresses the transport inhibitory activity of Orf6. We propose that the Rae1/Nup98 complex strategically positions Orf6 within the NPC where it alters FG-Nup interactions and their ability to support nuclear transport. In addition, we show that Rae1 is required for normal viral protein production during SARS-CoV-2 infection presumably through its role in supporting Orf6 function.

Deleteome-Tools: Utilizing a compendium of yeast deletion strain transcriptomes to identify co-functional genes.

We developed an R codebase that uses a publicly-available compendium of transcriptomes from yeast single-gene deletion strains - the Deleteome - to predict gene function. Primarily, the codebase provides functions for identifying similarities between the transcriptomic signatures of deletion strains, thereby associating genes of interest with others that may be functionally related. We describe how our tool predicted a novel relationship between the yeast nucleoporin Nup170 and the Ctf18-RFC complex, which was confirmed experimentally, revealing a previously unknown link between nuclear pore complexes and the DNA replication machinery. We also discuss how our strategy for quantifying similarity between deletion strains differs from other approaches and why it has the potential to identify functional relationships that similar approaches may not. Deleteome-Tools is implemented in R and is freely available at https://github.com/AitchisonLab/Deleteome-Tools .