The Pivotal Role of Galectin-3 in Viral Infection: A Multifaceted Player in Host-Pathogen Interactions.

Galectin-3 (Gal-3), a beta-galactoside-binding lectin, plays a pivotal role in various cellular processes, including immune responses, inflammation, and cancer progression. This comprehensive review aims to elucidate the multifaceted functions of Gal-3, starting with its crucial involvement in viral entry through facilitating viral attachment and catalyzing internalization. Furthermore, Gal-3 assumes significant roles in modulating immune responses, encompassing the activation and recruitment of immune cells, regulation of immune signaling pathways, and orchestration of cellular processes such as apoptosis and autophagy. The impact of Gal-3 extends to the viral life cycle, encompassing critical phases such as replication, assembly, and release. Notably, Gal-3 also contributes to viral pathogenesis, demonstrating involvement in tissue damage, inflammation, and viral persistence and latency elements. A detailed examination of specific viral diseases, including SARS-CoV-2, HIV, and influenza A, underscores the intricate role of Gal-3 in modulating immune responses and facilitating viral adherence and entry. Moreover, the potential of Gal-3 as a biomarker for disease severity, particularly in COVID-19, is considered. Gaining further insight into the mechanisms and roles of Gal-3 in these infections could pave the way for the development of innovative treatment and prevention options for a wide range of viral diseases.

Disease ecology of bats--the Canadian scene

Bats are hosts to a range of pathogens, which include zoonotic pathogens and pathogens of conservation concern. Brock Fenton's research on bat ecology has always balanced clear communication of potential health risks associated with bats and the need to communicate these risks precisely to avoid unnecessary persecution of bats. Here, we integrate Brock's work in the field of disease ecology with that of his students and collaborators and consider the potential advantages of studying disease ecology of bats within the Canadian context. The broad distribution of a few common species across the vast landscape of present-day Canada provides an opportunity to untangle the impacts of environmental variation on host-pathogen interactions and disease severity, particularly in the context of climate change. The varying migratory strategies and social structure of the bat species found in Canada could also facilitate informative interspecific studies to better understand how bat health is affected by interactions among rapid environmental changes, physiological traits, and the social behaviour of different species. We propose a series of priority research questions and approaches that could further our understanding of bat health and disease ecology in Canada, inspired by the work of Brock, his colleagues, and students.

Transcriptome in Human Mycoses

Mycoses are infectious diseases caused by fungi, which incidence has increased in recent decades due to the increasing number of immunocompromised patients and improved diagnostic tests. As eukaryotes, fungi share many similarities with human cells, making it difficult to design drugs without side effects. Commercially available drugs act on a limited number of targets and have been reported fungal resistance to commonly used antifungal drugs. Therefore, elucidating the pathogenesis of fungal infections, the fungal strategies to overcome the hostile environment of the host, and the action of antifungal drugs is essential for developing new therapeutic approaches and diagnostic tests. Large-scale transcriptional analyses using microarrays and RNA sequencing (RNA-seq), combined with improvements in molecular biology techniques, have improved the study of fungal pathogenicity. Such techniques have provided insights into the infective process by identifying molecular strategies used by the host and pathogen during the course of human mycoses. This chapter will explore the latest discoveries regarding the transcriptome of major human fungal pathogens. Further we will highlight genes essential for host–pathogen interactions, immune response, invasion, infection, antifungal drug response, and resistance. Finally, we will discuss their importance to the discovery of new molecular targets for antifungal drugs. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2014, 2022.

Antiviral immune response reveals host-specific virus infections in natural ant populations.

Hosts can carry many viruses in their bodies, but not all of them cause disease. We studied ants as a social host to determine both their overall viral repertoire and the subset of actively infecting viruses across natural populations of three subfamilies: the Argentine ant (Linepithema humile, Dolichoderinae), the invasive garden ant (Lasius neglectus, Formicinae) and the red ant (Myrmica rubra, Myrmicinae). We used a dual sequencing strategy to reconstruct complete virus genomes by RNA-seq and to simultaneously determine the small interfering RNAs (siRNAs) by small RNA sequencing (sRNA-seq), which constitute the host antiviral RNAi immune response. This approach led to the discovery of 41 novel viruses in ants and revealed a host ant-specific RNAi response (21 vs. 22 nt siRNAs) in the different ant species. The efficiency of the RNAi response (sRNA/RNA read count ratio) depended on the virus and the respective ant species, but not its population. Overall, we found the highest virus abundance and diversity per population in Li. humile, followed by La. neglectus and M. rubra. Argentine ants also shared a high proportion of viruses between populations, whilst overlap was nearly absent in M. rubra. Only one of the 59 viruses was found to infect two of the ant species as hosts, revealing high host-specificity in active infections. In contrast, six viruses actively infected one ant species, but were found as contaminants only in the others. Disentangling spillover of disease-causing infection from non-infecting contamination across species is providing relevant information for disease ecology and ecosystem management.

Transcriptomics and Proteomics of SARS-CoV-2 Infection and COVID-19

The emergence of the novel severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) Coronavirus resulted in a global pandemic due to its nature of rapid transmission and variable severities that facilitated its spread worldwide. Correspondingly, owing to advances in molecular technologies, information on this virus is generated at an unprecedented pace. Since the onset of the pandemic, multiple highthroughput "omics" analyses - including transcriptomics and proteomics of different viral infection models - have been made readily available to the research and wider community. The availability and ability to rapidly generate these data facilitate the deciphering of virus–host interactions during SARS-CoV-2 infection - thus enhancing understanding of the viral transmission, host susceptibility, pathogenesis, viral evolution, and disease complications. Such information is vital for eventual applications towards biomarker and treatment discovery against Coronavirus disease 2019 (COVID-19), and can serve as useful models for future pandemic responses. © 2023 by World Scientific Publishing Co. Pte. Ltd.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions.

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Visualisation of Host-Pathogen Communication.

The core of biomedical science is the use of laboratory techniques to support the diagnosis and treatment of disease in clinical settings. Despite tremendous advancement in our understanding of medicine in recent years, we are still far from having a complete understanding of human physiology in homeostasis, let alone the pathology of disease states. Indeed medical advances over the last two hundred years would not have been possible without the invention of and continuous development of visualisation techniques available to research scientists and clinicians. As we have all learned from the recent COVID pandemic, despite advances in modern medicine we still have much to learn regarding infection biology. Indeed antimicrobial resistant (AMR) bacteria are a global threat to human health, meaning research into bacterial pathogenesis is vital. In this chapter, we will briefly describe the nature of microbes and host immune responses before delving into some of the visualisation techniques utilised in the field of biomedical research with a focus on host-pathogen interactions. We will give a brief overview of commonly used techniques from gold standard staining methods, in situ hybridisation, microscopy, western blotting, microbial characterisation, to cutting-edge image flow cytometry and mass spectrometry. Specifically, we will focus on techniques utilised to visualise interactions between the host, our own bodies, and invading organisms including bacteria. We will touch on in vitro and ex vivo modelling methodology with examples utilised to delineate pathogenicity in disease. A better understanding of bacterial biology, immunology and how these fields interact (host-pathogen communications) in biomedical research is integral to developing novel therapeutic approaches which circumvent the need for antibiotics, an important issue as we enter a post-antibiotic era.

Significance of understanding the genomics of host-pathogen interaction in limiting antibiotic resistance development: lessons from COVID-19 pandemic.

The entire world is facing the stiff challenge of COVID-19 pandemic. To overcome the spread of this highly infectious disease, several short-sighted strategies were adopted such as the use of broad-spectrum antibiotics and antifungals. However, the misuse and/or overuse of antibiotics have accentuated the emergence of the next pandemic: antimicrobial resistance (AMR). It is believed that pathogens while transferring between humans and the environment carry virulence and antibiotic-resistant factors from varied species. It is presumed that all such genetic factors are quantifiable and predictable, a better understanding of which could be a limiting step for the progression of AMR. Herein, we have reviewed how genomics-based understanding of host-pathogen interactions during COVID-19 could reduce the non-judicial use of antibiotics and prevent the eruption of an AMR-based pandemic in future.

Inhibition of glycogen synthase kinase-3-beta (GSK3ß) blocks nucleocapsid phosphorylation and SARS-CoV-2 replication.

GSK3ß has been proposed to have an essential role in Coronaviridae infections. Screening of a targeted library of GSK3ß inhibitors against both SARS-CoV-2 and HCoV-229E to identify broad-spectrum anti-Coronaviridae inhibitors resulted in the identification of a high proportion of active compounds with low toxicity to host cells. A selected lead compound, T-1686568, showed low micromolar, dose-dependent activity against SARS-CoV-2 and HCoV-229E. T-1686568 showed efficacy in viral-infected cultured cells and primary 2D organoids. T-1686568 also inhibited SARS-CoV-2 variants of concern Delta and Omicron. Importantly, while inhibition by T-1686568 resulted in the overall reduction of viral load and protein translation, GSK3ß inhibition resulted in cellular accumulation of the nucleocapsid protein relative to the spike protein. Following identification of potential phosphorylation sites of Coronaviridae nucleocapsid, protein kinase substrate profiling assays combined with Western blotting analysis of nine host kinases showed that the SARS-CoV-2 nucleocapsid could be phosphorylated by GSK3ß and PKCa. GSK3ß phosphorylated SARS-CoV-2 nucleocapsid on the S180/S184, S190/S194 and T198 phospho-sites, following previous priming in the adjacent S188, T198 and S206, respectively. Such inhibition presents a compelling target for broad-spectrum anti-Coronaviridae compound development, and underlies the mechanism of action of GSK3ß host-directed therapy against this class of obligate intracellular pathogens.

Comparative Upper Respiratory Tract Transcriptomic Profiling Reveals a Potential Role of Early Activation of Interferon Pathway in Severe COVID-19.

Infection with SARS-CoV-2 results in Coronavirus disease 2019 (COVID-19) is known to cause mild to acute respiratory infection and sometimes progress towards respiratory failure and death. The mechanisms driving the progression of the disease and accumulation of high viral load in the lungs without initial symptoms remain elusive. In this study, we evaluated the upper respiratory tract host transcriptional response in COVID-19 patients with mild to severe symptoms and compared it with the control COVID-19 negative group using RNA-sequencing (RNA-Seq). Our results reveal an upregulated early type I interferon response in severe COVID-19 patients as compared to mild or negative COVID-19 patients. Moreover, severely symptomatic patients have pronounced induction of interferon stimulated genes (ISGs), particularly the oligoadenylate synthetase (OAS) family of genes. Our results are in concurrence with other studies depicting the early induction of IFN-I response in severe COVID-19 patients, providing novel insights about the ISGs involved.

SPARSEMODr: Rapidly simulate spatially explicit and stochastic models of COVID-19 and other infectious diseases.

Building realistically complex models of infectious disease transmission that are relevant for informing public health is conceptually challenging and requires knowledge of coding architecture that can implement key modeling conventions. For example, many of the models built to understand COVID-19 dynamics have included stochasticity, transmission dynamics that change throughout the epidemic due to changes in host behavior or public health interventions, and spatial structures that account for important spatio-temporal heterogeneities. Here we introduce an R package, SPARSEMODr, that allows users to simulate disease models that are stochastic and spatially explicit, including a model for COVID-19 that was useful in the early phases of the epidemic. SPARSEMOD stands for SPAtial Resolution-SEnsitive Models of Outbreak Dynamics, and our goal is to demonstrate particular conventions for rapidly simulating the dynamics of more complex, spatial models of infectious disease. In this report, we outline the features and workflows of our software package that allow for user-customized simulations. We believe the example models provided in our package will be useful in educational settings, as the coding conventions are adaptable, and will help new modelers to better understand important assumptions that were built into sophisticated COVID-19 models.

Mechanosensing view of SARS-CoV-2 infection by a DNA nano-assembly.

The mechanical force between a virus and its host cell plays a critical role in viral infection. However, characterization of the virus-cell mechanical force at the whole-virus level remains a challenge. Herein, we develop a platform in which the virus is anchored with multivalence-controlled aptamers to achieve transfer of the virus-cell mechanical force to a DNA tension gauge tether (Virus-TGT). When the TGT is ruptured, the complex of binding module-virus-cell is detached from the substrate, accompanied by decreased host cell-substrate adhesion, thus revealing the mechanical force between whole-virus and cell. Using Virus-TGT, direct evidence about the biomechanical force between SARS-CoV-2 and the host cell is obtained. The relative mechanical force gap (<10 pN) at the cellular level between the wild-type virus to cell and a variant virus to cell is measured, suggesting a possible positive correlation between virus-cell mechanical force and infectivity. Overall, this strategy provides a new perspective to probe the SARS-CoV-2 mechanical force.

Categorizing Sequences of Concern by Function To Better Assess Mechanisms of Microbial Pathogenesis.

To identify sequences with a role in microbial pathogenesis, we assessed the adequacy of their annotation by existing controlled vocabularies and sequence databases. Our goal was to regularize descriptions of microbial pathogenesis for improved integration with bioinformatic applications. Here, we review the challenges of annotating sequences for pathogenic activity. We relate the categorization of more than 2,750 sequences of pathogenic microbes through a controlled vocabulary called Functions of Sequences of Concern (FunSoCs). These allow for an ease of description by both humans and machines. We provide a subset of 220 fully annotated sequences in the supplemental material as examples. The use of this compact (∼30 terms), controlled vocabulary has potential benefits for research in microbial genomics, public health, biosecurity, biosurveillance, and the characterization of new and emerging pathogens.

Characteristics of the MicroRNA Expression Profile of Exosomes Released by Vero Cells Infected with Porcine Epidemic Diarrhea Virus.

Exosomes are nanoscale vesicles actively secreted by a variety of cells. They contain regulated microRNA (miRNA), allowing them to function in intercellular communication. In the present study, the role of exosomal miRNAs in porcine epidemic diarrhea virus (PEDV) infection was investigated using exosomes isolated from Vero cells infected with PEDV. The results of transmission electron microscopy observation showed that the exosomes are spherical in shape, uniform in size, and negatively stained in the membrane. Nanoparticle tracking analysis showed that the average exosome particle size is 130.5 nm. The results of miRNA sequencing showed that, compared with the control group, a total of 115 miRNAs are abnormally expressed in the exosomes of infected cells. Of these, 80 miRNAs are significantly upregulated and 35 miRNAs are significantly downregulated. Functional annotation analysis showed that the differentially expressed miRNAs are associated with PEDV infection through interaction with the cAMP, Hippo, TGF-beta, HIF-1, FoxO, MAPK, and Ras signaling pathways. Thus, our findings provide important information about the effects of PEDV infection on exosomal miRNA expression and will aid the search for potential anti-PEDV drug candidates.

Comparative miRNA transcriptomics of macaques and mice reveals MYOC is an inhibitor for Cryptococcus neoformans invasion into the brain.

Cryptococcal meningoencephalitis (CM) is emerging as an infection in HIV/AIDS patients shifted from primarily ART-naive to ART-experienced individuals, as well as patients with COVID-19 and immunocompetent hosts. This fungal infection is mainly caused by the opportunistic human pathogen Cryptococcus neoformans. Brain or central nervous system (CNS) dissemination is the deadliest process for this disease; however, mechanisms underlying this process have yet to be elucidated. Moreover, illustrations of clinically relevant responses in cryptococcosis are currently limited due to the low availability of clinical samples. In this study, to explore the clinically relevant responses during C. neoformans infection, macaque and mouse infection models were employed and miRNA-mRNA transcriptomes were performed and combined, which revealed cytoskeleton, a major feature of HIV/AIDS patients, was a centric pathway regulated in both infection models. Notably, assays of clinical immune cells confirmed an enhanced macrophage "Trojan Horse" in patients with HIV/AIDS, which could be shut down by cytoskeleton inhibitors. Furthermore, myocilin, encoded by MYOC, was found to be a novel enhancer for the macrophage "Trojan Horse," and an enhanced fungal burden was achieved in the brains of MYOC-transgenic mice. Taken together, the findings from this study reveal fundamental roles of the cytoskeleton and MYOC in fungal CNS dissemination, which not only helps to understand the high prevalence of CM in HIV/AIDS but also facilitates the development of novel therapeutics for meningoencephalitis caused by C. neoformans and other pathogenic microorganisms.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens.

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic "Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens," this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.

Increased Abundance of Achromobacter xylosoxidans and Bacillus cereus in Upper Airway Transcriptionally Active Microbiome of COVID-19 Mortality Patients Indicates Role of Co-Infections in Disease Severity and Outcome.

The modulators of severe COVID-19 have emerged as the most intriguing features of SARS-CoV-2 pathogenesis. This is especially true as we are encountering variants of concern (VOC) with increased transmissibility and vaccination breakthroughs. Microbial co-infections are being investigated as one of the crucial factors for exacerbation of disease severity and complications of COVID-19. A key question remains whether early transcriptionally active microbial signature/s in COVID-19 patients can provide a window for future disease severity susceptibility and outcome? Using complementary metagenomics sequencing approaches, respiratory virus oligo panel (RVOP) and Holo-seq, our study highlights the possible functional role of nasopharyngeal early resident transcriptionally active microbes in modulating disease severity, within recovered patients with sub-phenotypes (mild, moderate, severe) and mortality. The integrative analysis combines patients' clinical parameters, SARS-CoV-2 phylogenetic analysis, microbial differential composition, and their functional role. The clinical sub-phenotypes analysis led to the identification of transcriptionally active bacterial species associated with disease severity. We found significant transcript abundance of Achromobacter xylosoxidans and Bacillus cereus in the mortality, Leptotrichia buccalis in the severe, Veillonella parvula in the moderate, and Actinomyces meyeri and Halomonas sp. in the mild COVID-19 patients. Additionally, the metabolic pathways, distinguishing the microbial functional signatures between the clinical sub-phenotypes, were also identified. We report a plausible mechanism wherein the increased transcriptionally active bacterial isolates might contribute to enhanced inflammatory response and co-infections that could modulate the disease severity in these groups. Current study provides an opportunity for potentially using these bacterial species for screening and identifying COVID-19 patient sub-groups with severe disease outcome and priority medical care. IMPORTANCE COVID-19 is invariably a disease of diverse clinical manifestation, with multiple facets involved in modulating the progression and outcome. In this regard, we investigated the role of transcriptionally active microbial co-infections as possible modulators of disease pathology in hospital admitted SARS-CoV-2 infected patients. Specifically, can there be early nasopharyngeal microbial signatures indicative of prospective disease severity? Based on disease severity symptoms, the patients were segregated into clinical sub-phenotypes: mild, moderate, severe (recovered), and mortality. We identified significant presence of transcriptionally active isolates, Achromobacter xylosoxidans and Bacillus cereus in the mortality patients. Importantly, the bacterial species might contribute toward enhancing the inflammatory responses as well as reported to be resistant to common antibiotic therapy, which together hold potential to alter the disease severity and outcome.

deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host-pathogen protein-protein interactions.

Host-pathogen protein interactions (HPPIs) play vital roles in many biological processes and are directly involved in infectious diseases. With the outbreak of more frequent pandemics in the last couple of decades, such as the recent outburst of Covid-19 causing millions of deaths, it has become more critical to develop advanced methods to accurately predict pathogen interactions with their respective hosts. During the last decade, experimental methods to identify HPIs have been used to decipher host-pathogen systems with the caveat that those techniques are labor-intensive, expensive and time-consuming. Alternatively, accurate prediction of HPIs can be performed by the use of data-driven machine learning. To provide a more robust and accurate solution for the HPI prediction problem, we have developed a deepHPI tool based on deep learning. The web server delivers four host-pathogen model types: plant-pathogen, human-bacteria, human-virus and animal-pathogen, leveraging its operability to a wide range of analyses and cases of use. The deepHPI web tool is the first to use convolutional neural network models for HPI prediction. These models have been selected based on a comprehensive evaluation of protein features and neural network architectures. The best prediction models have been tested on independent validation datasets, which achieved an overall Matthews correlation coefficient value of 0.87 for animal-pathogen using the combined pseudo-amino acid composition and conjoint triad (PAAC_CT) features, 0.75 for human-bacteria using the combined pseudo-amino acid composition, conjoint triad and normalized Moreau-Broto feature (PAAC_CT_NMBroto), 0.96 for human-virus using PAAC_CT_NMBroto and 0.94 values for plant-pathogen interactions using the combined pseudo-amino acid composition, composition and transition feature (PAAC_CTDC_CTDT). Our server running deepHPI is deployed on a high-performance computing cluster that enables large and multiple user requests, and it provides more information about interactions discovered. It presents an enriched visualization of the resulting host-pathogen networks that is augmented with external links to various protein annotation resources. We believe that the deepHPI web server will be very useful to researchers, particularly those working on infectious diseases. Additionally, many novel and known host-pathogen systems can be further investigated to significantly advance our understanding of complex disease-causing agents. The developed models are established on a web server, which is freely accessible at http://bioinfo.usu.edu/deepHPI/.