Advanced microscopy technologies enable rapid response to SARS-CoV-2 pandemic

Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions

Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and CLas bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.

Molecular Imaging of ACE2 Expression in Infectious Disease and Cancer

Angiotensin-converting enzyme 2 (ACE2) is a cell-surface receptor that plays a critical role in the pathogenesis of SARS-CoV-2 infection. Through the use of ligands engineered for the receptor, ACE2 imaging has emerged as a valuable tool for preclinical and clinical research. These can be used to visualize the expression and distribution of ACE2 in tissues and cells. A variety of techniques including optical, magnetic resonance, and nuclear medicine contrast agents have been developed and employed in the preclinical setting. Positron-emitting radiotracers for highly sensitive and quantitative tomography have also been translated in the context of SARS-CoV-2-infected and control patients. Together this information can be used to better understand the mechanisms of SARS-CoV-2 infection, the potential roles of ACE2 in homeostasis and disease, and to identify potential therapeutic modulators in infectious disease and cancer. This review summarizes the tools and techniques to detect and delineate ACE2 in this rapidly expanding field.

Concentration of SARS-CoV-2-Infected Cell Culture Supernatants for Detection of Virus-like Particles by Scanning Electron Microscopy

There is currently a need for new rapid viral diagnostic electron microscopy methods. Although the gold standard remains the transmission electron microscopy (TEM) negative staining method for electron microscopic examination of samples containing a virus, difficulties can arise when the virus particle content of the sample that has to be examined is poor. Such samples include supernatants of virus-infected cells that can be difficult to examine, as sometimes only a few virus particles are released in the culture medium upon infection. In addition to TEM, scanning electron microscopy (SEM) can also be used for visualizing virus particles. One advantage of SEM over TEM is its ability to rapidly screen several large specimens, such as microscopy slides. In this study, we investigated this possibility and tested different coating molecules as well as the effect of centrifugation for analyzing SARS-CoV-2-virus-infected cell culture supernatants deposited on microscopy glass slides by SEM. We found that centrifugation of 25XConcanavalinA-coated microscopy glass slides in shell vials provided an improved method for concentrating SARS-CoV-2-virus-infected cell supernatants for virus-like particle detection by SEM.

Novel photonic methods for diagnosis of SARS-CoV-2 infection

The COVID-19 pandemic that began in March 2020 continues in many countries. The ongoing pandemic makes early diagnosis a crucial part of efforts to prevent the spread of SARS-CoV-2 infections. As such, the development of a rapid, reliable, and low-cost technique with increased sensitivity for detection of SARS-CoV-2 is an important priority of the scientific community. At present, nucleic acid-based techniques are primarily used as the reference approach for the detection of SARS-CoV-2 infection. However, in several cases, false positive results have been observed with these techniques. Due to the drawbacks associated with existing techniques, the development of new techniques for the diagnosis of COVID-19 is an important research activity. We provide an overview of novel diagnostic methods for SARS-CoV-2 diagnosis that integrate photonic technology with artificial intelligence. Recent developments in emerging diagnostic techniques based on the principles of advanced molecular spectroscopy and microscopy are considered.

Studying SARS-CoV-2 with Fluorescence Microscopy

The COVID-19 pandemic caused by SARS-CoV-2 coronavirus deeply affected the world community. It gave a strong impetus to the development of not only approaches to diagnostics and therapy, but also fundamental research of the molecular biology of this virus. Fluorescence microscopy is a powerful technology enabling detailed investigation of virus-cell interactions in fixed and live samples with high specificity. While spatial resolution of conventional fluorescence microscopy is not sufficient to resolve all virus-related structures, super-resolution fluorescence microscopy can solve this problem. In this paper, we review the use of fluorescence microscopy to study SARS-CoV-2 and related viruses. The prospects for the application of the recently developed advanced methods of fluorescence labeling and microscopy-which in our opinion can provide important information about the molecular biology of SARS-CoV-2-are discussed.