Host-pathogen protein-nucleic acid interactions: A comprehensive review

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.

Proteomics analysis of differentially abundant proteins in the rohu kidney infected with Edwardsiella tarda

Edwardsiella tarda (Et) is a zoonotic gram-negative pathogen with a diverse host range, including fish. However, the in-depth molecular mechanisms underlying the response of Labeo rohita (rohu) kidney to Et are poorly understood. A proteomic and histopathological analysis was performed for the rohu kidney after Et infection. The histopathology of the infected rohu kidney showed vacuolation and necrosis. After LC-MS/MS analysis, ~1240 proteins were identified with ≥2 unique peptides. A total of 96 differentially abundant proteins (DAPs) were observed between the control and Et infected group (ET). Metascape and STRING analysis were used for the gene ontology (GO), and protein-protein interaction network (PPI) for the significant pathways of DAPs. In PPI, low-abundant proteins were mapped to metabolic pathways and oxidative phosphorylation (cox5ab, uqcrfs1). High-abundance proteins were mapped to ribosomes (rplp2), protein process in the ER (hspa8), and immune system (ptgdsb.1, muc2). Our label-free proteomic approach in the rohu kidney revealed abundant enriched proteins involved in vesicle coat (ehd4), complement activation (c3a.1, c9, c7a), phagosome (thbs4, mapk1), metabolic reprogramming (hao1, glud1a), wound healing (vim, alox5), and the immune system (psap) after Et infection. A targeted proteomics approach of multiple reaction monitoring (MRM) validated the DAPs (nprl3, ambp, vmo1a, hspg2, muc2, hao1 and glud1a) between control and ET. Overall, the current analysis of histology and proteome in the rohu kidney provides comprehensive data on pathogenicity and the potential immune proteins against Et.

IFITM1 and IFITM2 inhibit the replication of senecavirus A by positive feedback with RIG-I signaling pathway

The role of host factors in the replication of emerging senecavirus A (SVA) which induced porcine idiopathic vesicular disease (PIVD) distributed worldwide remains obscure. Here, interferon-induced transmembrane (IFITM) protein 1 and 2 inhibit SVA replication by positive feedback with RIG-I signaling pathway was reported. The expression levels of IFITM1 and IFITM2 increased significantly in SVA infected 3D4/21 cells. Infection experiments of cells with over and interference expression of IFITM1 and IFITM2 showed that these two proteins inhibit SVA replication by regulating the expression of interferon beta (IFN-β), IFN-stimulated gene 15 (ISG-15), interleukin 6 (IL-6), IL-8, tumor necrosis factor alpha (TNF-α), IFN regulatory factor-3 (IRF3), and IRF7. Further results showed that antiviral responses of IFITM1 and IFITM2 were achieved by activating retinoic acid-inducible gene I (RIG-I) signaling pathway which in turn enhanced the expression of IFITM1 and IFITM2. It is noteworthy that conserved domains of these two proteins also paly the similar role. These findings provide new data on the role of host factors in infection and replication of SVA and help to develop new agents against the virus.

New insights into nucleic acid sensor AIM2: The potential benefit in targeted therapy for cancer

The AIM2 inflammasome represents a multifaceted oligomeric protein complex within the innate immune system, with the capacity to perceive double-stranded DNA (dsDNA) and engage in diverse physiological reactions and disease contexts, including cancer. While originally conceived as a discerning DNA sensor, AIM2 has demonstrated its capability to discern various nucleic acid variations, encompassing RNA and DNA-RNA hybrids. Through its interaction with nucleic acids, AIM2 orchestrates the assembly of a complex involving multiple proteins, aptly named the AIM2 inflammasome, which facilitates the enzymatic cleavage of proinflammatory cytokines, namely pro-IL-1β and pro-IL-18. This process, in turn, underpins its pivotal biological role. In this review, we provide a systematic summary and discussion of the latest advancements in AIM2 sensing various types of nucleic acids. Additionally, we discuss the modulation of AIM2 activation, which can cause cell death, including pyroptosis, apoptosis, and autophagic cell death. Finally, we fully illustrate the evidence for the dual role of AIM2 in different cancer types, including both anti-tumorigenic and pro-tumorigenic functions. Considering the above information, we uncover the therapeutic promise of modulating the AIM2 inflammasome in cancer treatment.

Analysis and Prediction of Pathogen Nucleic Acid Specificity for Toll-like Receptors in Vertebrates

Identification of key sequence, expression and function related features of nucleic acid-sensing host proteins is of fundamental importance to understand the dynamics of pathogen-specific host responses. To meet this objective, we considered toll-like receptors (TLRs), a representative class of membrane-bound sensor proteins, from 17 vertebrate species covering mammals, birds, reptiles, amphibians, and fishes in this comparative study. We identified the molecular signatures of host TLRs that are responsible for sensing pathogen nucleic acids or other pathogen-associated molecular patterns (PAMPs), and potentially play important roles in host defence mechanism. Interestingly, our findings reveal that such host-specific features are directly related to the strand (single or double) specificity of nucleic acid from pathogens. However, during host-pathogen interactions, such features were unable to explain the pathogenic PAMP (i.e., DNA, RNA or other) selectivity, suggesting a more complex mechanism. Using these features, we developed a number of machine learning models, of which Random Forest achieved a high performance (94.57% accuracy) to predict strand specificity of TLRs from protein-derived features. We applied the trained model to propose strand specificity of some previously uncharacterized distinct fish-specific novel TLRs (TLR18, TLR23, TLR24, TLR25, TLR27).

Microbial and Host Metabolites at the Backstage of Fever: Current Knowledge about the Co-Ordinate Action of Receptors and Molecules Underlying Pathophysiology and Clinical Implications

Fever represents an elevation of body temperature, that exerts a protective effect against pathogens. Innate immune cells and neurons are implicated in the regulation of body temperature. Pathogen-associated molecular patterns, i.e., lipopolysaccharides from Gram-negative bacteria and peptidoglycan and lipoteichoic acid from Gram-positive bacteria are exogenous pyrogens, that bind to Toll-like receptors on immune and non-immune cells. The subsequent release of pro-inflammatory cytokines [interleukin-1 (IL-1), IL-6 and Tumor necrosis factor-alpha] and their passage through the brain trigger the febrile response. In fact, neurons of the pre-optic area produce prostaglandin E2 (PGE2), that, in turn, bind to the PGE2 receptors; thus, generating fever. Apart from classical non-steroidal anti-inflammatory drugs, i.e., aspirin and acetaminophen, various botanicals are currently used as antipyretic agents and, therefore, their mechanisms of action will be elucidated.

Modified mRNA as a Treatment for Myocardial Infarction

Myocardial infarction (MI) is a severe disease with high mortality worldwide. However, regenerative approaches remain limited and with poor efficacy. The major difficulty during MI is the substantial loss of cardiomyocytes (CMs) with limited capacity to regenerate. As a result, for decades, researchers have been engaged in developing useful therapies for myocardial regeneration. Gene therapy is an emerging approach for promoting myocardial regeneration. Modified mRNA (modRNA) is a highly potential delivery vector for gene transfer with its properties of efficiency, non-immunogenicity, transiency, and relative safety. Here, we discuss the optimization of modRNA-based therapy, including gene modification and delivery vectors of modRNA. Moreover, the effective of modRNA in animal MI treatment is also discussed. We conclude that modRNA-based therapy with appropriate therapeutical genes can potentially treat MI by directly promoting proliferation and differentiation, inhibiting apoptosis of CMs, as well as enhancing paracrine effects in terms of promoting angiogenesis and inhibiting fibrosis in heart milieu. Finally, we summarize the current challenges of modRNA-based cardiac treatment and look forward to the future direction of such treatment for MI. Further advanced clinical trials incorporating more MI patients should be conducted in order for modRNA therapy to become practical and feasible in real-world treatment.

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.

Toward Establishing an Ideal Adjuvant for Non-Inflammatory Immune Enhancement

The vertebrate immune system functions to eliminate invading foreign nucleic acids and foreign proteins from infectious diseases and malignant tumors. Because pathogens and cancer cells have unique amino acid sequences and motifs (e.g., microbe-associated molecular patterns, MAMPs) that are recognized as "non-self" to the host, immune enhancement is one strategy to eliminate invading cells. MAMPs contain nucleic acids specific or characteristic of the microbe and are potential candidates for immunostimulants or adjuvants. Adjuvants are included in many vaccines and are a way to boost immunity by deliberately administering them along with antigens. Although adjuvants are an important component of vaccines, it is difficult to evaluate their efficacy ex vivo and in vivo on their own (without antigens). In addition, inflammation induced by currently candidate adjuvants may cause adverse events, which is a hurdle to their approval as drugs. In addition, the lack of guidelines for evaluating the safety and efficacy of adjuvants in drug discovery research also makes regulatory approval difficult. Viral double-stranded (ds) RNA mimics have been reported as potent adjuvants, but the safety barrier remains unresolved. Here we present ARNAX, a noninflammatory nucleic acid adjuvant that selectively targets Toll-like receptor 3 (TLR3) in antigen-presenting dendritic cells (APCs) to safely induce antigen cross-presentation and subsequently induce an acquired immune response independent of inflammation. This review discusses the challenges faced in the clinical development of novel adjuvants.