Identification of 37 Heterogeneous Drug Candidates for Treatment of COVID-19 via a Rational Transcriptomics-Based Drug Repurposing Approach

A year after the initial outbreak, the COVID-19 pandemic caused by SARS-CoV-2 virus remains a serious threat to global health, while current treatment options are insufficient to bring major improvements. The aim of this study is to identify repurposable drug candidates with a potential to reverse transcriptomic alterations in the host cells infected by SARS-CoV-2. We have developed a rational computational pipeline to filter publicly available transcriptomic datasets of SARS-CoV-2-infected biosamples based on their responsiveness to the virus, to generate a list of relevant differentially expressed genes, and to identify drug candidates for repurposing using LINCS connectivity map. Pathway enrichment analysis was performed to place the results into biological context. We identified 37 structurally heterogeneous drug candidates and revealed several biological processes as druggable pathways. These pathways include metabolic and biosynthetic processes, cellular developmental processes, immune response and signaling pathways, with steroid metabolic process being targeted by half of the drug candidates. The pipeline developed in this study integrates biological knowledge with rational study design and can be adapted for future more comprehensive studies. Our findings support further investigations of some drugs currently in clinical trials, such as itraconazole and imatinib, and suggest 31 previously unexplored drugs as treatment options for COVID- 19

Leveraging Machine Learning for the Analysis and Prediction of Influenza A Virus

Influenza, commonly known as flu, is a respiratory disease that poses a significant challenge to global public health due to its high prevalence and potential for serious health complications. The disease is caused by influenza viruses, among which influenza A viruses are of particular concern. These viruses are known for their rapid transmission, potential to cause severe health issues, and frequent mutations, which underscore the need for ongoing research and surveillance. A key aspect of managing influenza outbreaks includes understanding host origins, antigenic properties, and the ability of influenza A viruses to transmit between species, as this knowledge is critical in forecasting outbreaks and developing effective vaccines. Traditional approaches, such as hemagglutination inhibition assays for antigenicity assessment and phylogenetic analysis to determine genetic relationships, host origins and subtypes, have been fundamental in understanding influenza viruses. These methods, while informative, often face limitations in terms of time, resources, and the ability to keep pace with the rapid evolutionary changes of viruses. To mitigate these limitations, this thesis uses advanced machine learning techniques to analyse critical protein sequence data from influenza A viruses, offering an alternative perspective for unravelling the complexities of influenza, and potentially opening new avenues for analysis without strict reliance on prior biological knowledge. The core of the thesis is the application and refinement of predictive models to determine host origins, subtypes, and antigenic relationships of influenza A viruses. These models are evaluated comprehensively, considering factors such as the impact of incomplete sequences, performance across various host taxonomies and individual hosts, as well as the influence of reference databases on model performance. This evaluation illuminates the potential of machine learning to enhance our understanding of influenza A viruses in real-world scenarios, pointing out the ongoing importance of this research in public health

Analysis of HIV-host interaction on different scales

The human immunodeficiency virus depends on molecular pathways of the host for efficient replication and spread. The intricate network of host-virus interactions shapes the virus\u27; evolution by driving the pathogen to evade immune recognition and constraining it to maintain its capacity to replicate. Study of the HIV-host interactions provides important insights into viral evolution, pathogenicity and potential treatment strategies. This thesis presents an analysis of HIV-host interactions on several scales, ranging from individual protein interactions to whole genomes. On the scale of individual interaction we analyze structural and physical determinants of the interaction between host TRIM5alpha and virus capsid — an interaction of potential therapeutic interest due to the capacity of TRIM5alpha to block retroviral infections. On the scale of viral population we present two studies of a highly variable region of the virus genome involved in the interaction with host cell coreceptors upon virus cell entry. The studies provide insights into the virus evolution and the physicochemical and structural properties related to its interaction with cellular coreceptors. On the scale of the single cell we develop models of HIV cell entry involving virus, host and environmental factors. The models represent a comprehensive picture of the virus phenotype and allow one to view the variability of virus phenotypes on 2D phenotype maps. On the genomic scale we perform a large-scale analysis of all HIV-host interactions. This study reveals insights into general patterns of the host-pathogen evolution and suggests candidate host proteins involved in interactions potentially important for the infection and interesting for further study on other scales. Interactions and processes crucial for the HIV infection reemerge across the scales pointing to the importance of integrative, multi-scale studies of host-pathogen biology.Das Humane Immundefizienz-Virus hängt von molekularen Mechanismen des Wirts für seine effiziente Replikation und Ausbreitung ab. Das komplizierte Netzwerk von Wirt-Virus Interaktionen formt die Evolution des Virus, indem es den Erreger dazu bringt, sich der Erkennung durch das Immunsystem zu entziehen und seine Replikationskapazität aufrecht zu erhalten. Das Studium der HIV-Wirt Interaktionen erlaubt wichtige Einblicke in die viralen Evolution, die Pathogenität des Virus, sowie mögliche Behandlungsstrategien. Diese Arbeit stellt eine Analyse der HIV-Wirt-Interaktionen in mehreren Größenordnungen vor, von einzelnen Protein-Interaktionen bis hin zur Analyse ganzer Genome. In Hinblick auf einzelne Interaktionen untersuchen wir strukturelle und physikalische Determinanten der Interaktion zwischen dem Wirtfaktor TRIM5alpha; und dem viralen Kapsid - eine Interaktion, die von therapeutischem Interesse ist wegen der Fähigkeit von TRIM5alpha, retrovirale Infektionen zu blockieren. In Hinblick auf virale Populationen präsentieren wir zwei Studien einer hochvariablen Region des viralen Genoms, die in der Interaktion des Virus mit zellulären Rezeptoren des Wirts beim viralen Zelleintritt involviert sind. Diese Studien geben Einblick in die virale Evolution und die physikalisch-chemischen und strukturellen Eigenschaften des Virus bezüglich dessen Interaktion mit zellulären Ko-Rezeptoren. Auf der Skala der einzelnen Zelle entwickeln wir Modelle des HIV Zelleintritts welche das Virus, den Wirt und Umgebungsfaktoren berücksichtigen. Diese Modelle bieten ein umfassendes Bild des viralen Phänotyps und erlauben es, die Variabilität des Virus auf 2D-Phänotyp-Karten zu visualisieren. Im genomweiten Maßstab führen wir eine groß angelegte Analyse aller HIV-Wirt-Interaktionen durch. Diese Studie erlaubt Einblicke in allgemeine Muster der Wirt-Pathogen-Evolution und identifiziert Kandidaten für Wirtsproteine, deren Interaktionen potenziell wichtig für die virale Infektion sind und deren weitere Untersuchung in anderen Größenordnungen von Interesse ist. Interaktionen und Prozesse, die von entscheidender Bedeutung für die HIV-Infektion sind zeigen sich wiederholt in allen untersuchten Maßstäben und unterstreichen die Bedeutung einer integrativen und multi-skalaren Untersuchung der Wirt-Pathogen-Biologie

사람세포거대바이러스로 유도된 IL-10 생산에 의한 큰포식세포에서 Mycobacteroides abscessus subspecies massiliense의 증식 촉진

학위논문(박사) -- 서울대학교대학원 : 의과대학 의학과, 2021.8. 김지연.사람세포거대바이러스 (HCMV)는 β-herpesvirus에 속하여 전세계 인구의 약 60~80%가 감염되어 있다. 초기 감염의 경우에는 증상이 미미하지만 평생 잠복감염의 형태로 체내에 존재하기도 한다. 사람세포거대바이러스는 세포주기를 조절하고 사이토카인의 조절을 통하여 숙주의 면역 체계에 영향을 주게 되어 효율적인 감염의 형태를 조성하거나, 숙주 내 바이러스 확산이 가능한 환경을 구축한다. US2, 3, 11, UL111A 등과 같은 면역회피 단백을 암호화하고 있어 숙주의 신호전달과 활성을 조절하기도 한다. 이러한 숙주 면역 억제 환경에서는 숙주의 면역 체계가 병원균을 방어할 수 없는 상황이 될 수 있다. 사람세포거대바이러스가 감염된 숙주의 면역체계를 지배하게 되면 다른 병원체가 동일한 숙주에 감염될 수 있다. 이러한 예로, 사람세포거대바이러스와 면역결핍바이러스의 동시감염이나, 결핵의 동시감염 사례가 보고된 바 있다. 이러한 이유로, 사람세포거대바이러스가 잠복 감염된 상황에서 Mycobacteroides abscessus subspecies massiliense (M. abscessus subsp. massiliense)가 공동감염이 될 가능성이 더 높은지의 여부를 확인하고자 하였다. 비결핵항산균 (non-tuberculous mycobacteria, NTM) 중 강력한 약물내성을 갖는 종인 Mycobateroides abscessus (MAB) complex는 빠르게 증식하는 마이코박테리아로, 큰포식세포 내부에서 주로 증식한다. 사람세포거대바이러스의 감염이 M. abscessus subsp. massiliense 질병의 위험을 증가시키는지, 또는 사람세포거대바이러스 혈증의 소인이 M. abscessus subsp. massiliense 질병 발생 인자와 유사할 수 있는지의 여부는 알려진 바가 없다. 전사체 분석을 통해 큰포식세포가 두 병원체에 동시에 감염되었을 때 숙주의 IFN-r, TNF-a, IL-1의 면역 반응이 약해지고 M. abscessus subsp. massiliense에 대한 항박테리아성 면역 반응이 줄어드는 것을 확인했다. 큰포식세포에 사람세포거대바이러스가 감염되었을 때 cmvIL-10의 분비가 증가하였고 이는 숙주세포 IL-10의 신호 전달과 유사한 방식으로 숙주의 IL-10 수용체에 결합한다. 큰포식세포에 HCMV가 감염되었을 때 숙주의 IL-10 분비를 촉진하였고, IL-10 의존적 방식으로 M. abscessus subsp. massiliense의 증식을 촉진하였다. 바이러스 감염 후에 생산되는 cmvIL-10을 제거하면 숙주의 IL-10 생산이 감소하였고, 숙주의 IL-10을 중화시킨 경우에는 박테리아의 증식이 감소하는 결과를 확인하였다. 이러한 연구를 통해 HCMV 감염이 IL-10 분비를 증가시키고 M. abscessus subsp. massiliense 침입 시 숙주의 저하된 면역상태로 인해 동시 감염이 호발하는 현상을 설명할 수 있었다.Human cytomegalovirus (HCMV) is a β-herpesvirus and has a seroprevalence of approximately 60-80% of the world’s population. In initial infections, the symptoms are minimal but it can establish a lifelong latent infection in the body. HCMV activity or a latent infection regulates the cell cycle and cytokines, which affects the host innate immune system and enables severe infection spreading within the human body. HCMV encodes immune evasion proteins such as US2, 3, 11 and UL111A which exacerbates the host immune response. In such a host immune-suppressive environment, there is a risk that the host immune system may not be able to adequately defend against pathogens. When HCMV controls the immune system of an infected host, other pathogens can infect the same host. As examples, HCMV and HIV or HCMV and Mycobacterium tuberculosis co-infection cases have been reported. Therefore, this study investigated whether or not Mycobacteroides abscessus subspecies massiliense (M. abscessus subsp. massiliense) is more likely to induce co-infection under HCMV latent infection. M. abscessus subsp. massiliense, a rapidly growing Mycobacterium (RGM), is the most drug-resistant pathogen among the nontuberculous mycobacteria (NTM). It is uncertain whether HCMV infection itself increases the risk of M. abscessus subsp. massiliense infection or a predisposition to HCMV viremia is the bacterial disease factor. As demonstrated by transcriptomic analysis, HCMV infection suppressed the regulatory pathways of interferon-gamma (IFN-γ), tumor necrosis factor-alpha (TNF-α), and interleukin-1 (IL-1), inhibiting the immune responses to M. abscessus subsp. massiliense co-infection in macrophages. In addition, the HCMV infection cycle regulates the IL-10 signaling cascade of the host cell. It was confirmed that cmvIL-10 signals and binds to receptors in a manner similar to host IL-10. Macrophage increased IL-10 secretion when infected with HCMV, in IL-10 dependent manner and promoted M. abscessus subsp. massiliense proliferation. When the cmvIL-10 produced after virus infection was removed, the production of host IL-10 reduced, and if the host IL-10 was neutralized, I found that the bacterial proliferation reduced. HCMV infection increases IL-10 secretion and provides a basis for the host immune response in the context of pathogenic M. abscessus subsp. massiliense co-infection.List of tables and figures. 1 Abbreviations. 3 Introduction 7 Materials and Methods. 15 Results 22 Discussion. 49 Reference. 53 Abstract in Korean 69박

Light Microscopy Combined with Computational Image Analysis Uncovers Virus-Specific Infection Phenotypes and Host Cell State Variability

Abstract: The study of virus infection phenotypes and variability plays a critical role in understanding viral pathogenesis and host response. Virus-host interactions can be investigated by light and various label-free microscopy methods, which provide a powerful tool for the spatiotemporal analysis of patterns at the cellular and subcellular levels in live or fixed cells. Analysis of microscopy images is increasingly complemented by sophisticated statistical methods and leverages artificial intelligence (AI) to address the tasks of image denoising, segmentation, classification, and tracking. Work in this thesis demonstrates that combining microscopy with AI techniques enables models that accurately detect and quantify viral infection due to the virus-induced cytopathic effect (CPE). Furthermore, it shows that statistical analysis of microscopy image data can disentangle stochastic and deterministic factors that contribute to viral infection variability, such as the cellular state. In summary, the integration of microscopy and computational image analysis offers a powerful and flexible approach for studying virus infection phenotypes and variability, ultimately contributing to a more advanced understanding of infection processes and creating possibilities for the development of more effective antiviral strategies

Dynamic, but not necessarily disordered, human-virus interactions mediated through slims in viral proteins

Most viruses have small genomes that encode proteins needed to perform essential enzy-matic functions. Across virus families, primary enzyme functions are under functional constraint; however, secondary functions mediated by exposed protein surfaces that promote interactions with the host proteins may be less constrained. Viruses often form transient interactions with host proteins through conformationally flexible interfaces. Exposed flexible amino acid residues are known to evolve rapidly suggesting that secondary functions may generate diverse interaction potentials between viruses within the same viral family. One mechanism of interaction is viral mimicry through short linear motifs (SLiMs) that act as functional signatures in host proteins. Viral SLiMs display specific patterns of adjacent amino acids that resemble their host SLiMs and may occur by chance numerous times in viral proteins due to mutational and selective processes. Through mimicry of SLiMs in the host cell proteome, viruses can interfere with the protein interaction network of the host and utilize the host-cell machinery to their benefit. The overlap between rapidly evolving protein regions and the location of functionally critical SLiMs suggest that these motifs and their functional potential may be rapidly rewired causing variation in pathogenicity, infectivity, and virulence of related viruses. The following review provides an overview of known viral SLiMs with select examples of their role in the life cycle of a virus, and a discussion of the structural properties of experimentally validated SLiMs highlighting that a large portion of known viral SLiMs are devoid of predicted intrinsic disorder based on the viral SLiMs from the ELM database

Systematic Experimental Determination of Functional Constraints on Proteins and Adaptive Potential of Mutations: A Dissertation

Sequence-function relationship is a fundamental question for many branches of modern biomedical research. It connects the primary sequence of proteins to the function of proteins and fitness of organisms, holding answers for critical questions such as functional consequences of mutations identified in whole genome sequencing and adaptive potential of fast evolving pathogenic viruses and microbes. Many different approaches have been developed to delineate the genotype-phenotype map for different proteins, but are generally limited by their throughput or precision. To systematically quantify the fitness of large numbers of mutations, I modified a novel high throughput mutational scanning approach (EMPIRIC) to investigate the fitness landscape of mutations in important regions of essential proteins from the yeast or RNA viruses. Using EMPIRIC, I analyzed the interplay of the expression level and sequence of Hsp90 on the yeast growth and revealed latent effect of mutations at reduced expression levels of Hsp90. I also examined the functional constraint on the receptor binding site of the Env of Human Immunodeficiency Virus (HIV) and uncovered enhanced receptor binding capacity as a common pathway for adaptation of HIV to laboratory conditions. Moreover, I explored the adaptive potential of neuraminidase (NA) of influenza A virus to a NA inhibitor, oseltamivir, and identified novel oseltamivir resistance mutations with distinct molecular mechanisms. In summary, I applied a high throughput functional genomics approach to map the sequence-function relationship in various systems and examined the evolutionary constraints and adaptive potential of essential proteins ranging from molecular chaperones to drug-targetable viral proteins

Decoding protein networks during porcine epidemic diarrhea virus (PEDV) infection through proteomics

Le virus de la diarrhée épidémique porcine (VDEP) est responsable de graves pertes économiques. Les épidémies de VDEP ont détruit plus de 10% de la population porcine américaine au cours des 3 dernières années. Malheureusement, la compréhension insuffisante des interactions hôte-virus empêche la mise au point d'un vaccin efficace contre le VDEP. Les interactions hôte-virus sont très dynamiques et peuvent impliquer des complexes multiprotéiques. De plus en plus de preuves indiquent que les microvésicules extracellulaires (MVE) et la composition des particules virales jouent un rôle important dans la pathogenèse virale et la modulation de la réponse immunitaire de l'hôte à l'infection. De plus, on pourrait s’attendre à ce que la composition des virions de la diarrhée épidémique porcine (DEP) soit dépendante du type cellulaire, en raison de l'incorporation ou de l'association de protéines de cellules hôtes dans ou avec des virions. Par conséquent, la caractérisation des profils protéomiques des MVEs produits par les cellules infectées par le VDEP, et l'identification des protéines hôtes spécifiquement encapsidées dans les virions sont importantes pour notre compréhension plus approfondie des interactions virus-hôte. Pour atteindre cet objectif, nous avons produit et purifié des virions et des MVE de VDEP et analysé leur composition en protéines en utilisant une approche protéomique. Afin d'étudier la régulation spatiotemporelle de l'infection virale, une certaine optimisation de l'infection par le VDEP était nécessaire. Pour cela, nous avons synchronisé et augmenté l'entrée de virus dans les cellules et étudié les schémas protéomiques des cellules infectées par le VDEP selon un mode de résolution temporelle. Nous avons constaté que l'infection par le VDEP affectait l'abondance de diverses protéines de l'hôte associées aux microvésicules produites par les cellules infectées. Plus précisément, nos données protéomiques ont révélé que les protéines impliquées dans la liaison aux acides nucléiques, les processus métaboliques et les voies de la réponse immunitaire étaient parmi les plus touchées par l'infection. Fait intéressant, les protéines de l'hôte impliquées dans la régulation du cycle cellulaire et du système cytosquelettique ont également été touchées en abondance, ce qui n'est pas étonnant, car plusieurs chercheurs ont rapporté que les protéines cytosquelettiques participent activement au déplacement des composants viraux vers le site d'assemblage et que de nombreux virus manipulent la réparation de l'ADN, ainsi que le cycle cellulaire. La présente étude a démontré IV l’incorporation de nombreuses protéines cellulaires dans les virions de la DEP. De plus, nous avons démontré que les polycations (molécules à charge positive) eu augmente 9-fois l'efficacité de l'entrée et de l'infection du VDEP. Ainsi, les polycations peuvent être utilisés pour optimiser l’infection par le VDEP, et améliorer la production de vaccins. À notre connaissance, il s'agit de la première étude de la composition des virions et des microvésicules de DEP produits par une infection par le VDEP.Porcine epidemic diarrhea virus (PEDV) is responsible for severe economic losses. The PEDV epidemics have destroyed more than 10% of the US swine population in the past 3 years. Unfortunately, the insufficient understanding of virus-host interactions impedes the development of an effective vaccine against PEDV. Virus-host interactions are highly dynamic and may involve multiprotein complexes. Growing evidence indicates that extracellular microvesicles (EMV) and composition of the viral particles play an important role in viral pathogenesis and modulation of host immune responses to infection. Additionally, it could be expected that the composition of porcine epidemic diarrhea (PED) virions is cell type dependent, due to the differential incorporation or association of host cell proteins into or with virions. Consequently, the characterization of the proteomic profiles of the EMV, produced by the PEDV-infected cells, and identification of the host proteins that are specifically encapsidated into the virions are important for our further understanding of virus-host interactions. To accomplish this objective, we produced and purified PEDV virions and EMV and analyzed their protein composition using a proteomic approach. In order to investigate the spatial-temporal regulation of viral infection and due to the low overall infectivity of the virus, a certain optimization of the PEDV infection was needed. To this end, we synchronized and increased virus entry into the cells. This allowed us to study the proteomic patterns of the PEDV-infected cells in a time-resolved mode. We found that PEDV infection affected the abundance of various host proteins associated with microvesicles produced by the infected cells. More precisely, our proteomic data revealed that proteins involved in nucleic acids binding, metabolic processes and immune response pathways were among the most affected by the PEDV infection. Interestingly, host proteins involved in cell cycle regulation and cytoskeletal system also were affected in abundance, which is not surprising since several investigators have reported that cytoskeletal proteins are actively participating in moving the viral components to the assembly site, and that many viruses manipulate DNA repair and cell cycle. The present study has demonstrated the incorporation of numerous cellular proteins into the PED virions. Additionally, we demonstrated that treatment of PEDV virions with polycations (positively charged molecules) induced a nine-fold increase in the efficiency of viral entry and infection. Thus, polycations can be used for the optimization of PEDV infection and improved vaccine production. To the best of our knowledge, this is the first study of the composition of PED virions and microvesicles produced by PEDV infection

Machine-learning-based identification of factors that influence molecular virus-host interactions

Viruses are the cause of many infectious diseases such as the pandemic viruses: acquired immune deficiency syndrome (AIDS) and coronavirus disease 2019 (COVID-19). During the infection cycle, viruses invade host cells and trigger a series of virus-host interactions with different directionality. Some of these interactions disrupt host immune responses or promote the expression of viral proteins and exploitation of the host system thus are considered ‘pro-viral’. Some interactions display ‘pro-host’ traits, principally the immune response, to control or inhibit viral replication. Concomitant pro-viral and pro-host molecular interactions on the same host molecule suggests more complex virus-host conflicts and genetic signatures that are crucial to host immunity. In this work, machinelearning-based prediction of virus-host interaction directionality was examined by using data from Human immunodeficiency virus type 1 (HIV-1) infection. Host immune responses to viral infections are mediated by interferons(IFNs) in the initial stage of the immune response to infection. IFNs induce the expression of many IFN-stimulated genes (ISGs), which make the host cell refractory to further infection. We propose that there are many features associated with the up-regulation of human genes in the context of IFN-α stimulation. They make ISGs predictable using machine-learning models. In order to overcome the interference of host immune responses for successful replication, viruses adopt multiple strategies to avoid being detected by cellular sensors in order to hijack the machinery of host transcription or translation. Here, the strategy of mimicry of host-like short linear motifs (SLiMs) by the virus was investigated by using the example of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The integration of in silico experiments and analyses in this thesis demonstrates an interactive and intimate relationship between viruses and their hosts. Findings here contribute to the identification of host dependency and antiviral factors. They are of great importance not only to the ongoing COVID-19 pandemic but also to the understanding of future disease outbreaks