Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions

The development of effective vaccines is crucial for combating current and emerging pathogens. Despite significant advances in the field of vaccine development there remain numerous challenges including the lack of standardized data reporting and curation practices, making it difficult to determine correlates of protection from experimental and clinical studies. Significant gaps in data and knowledge integration can hinder vaccine development which relies on a comprehensive understanding of the interplay between pathogens and the host immune system. In this review, we explore the current landscape of vaccine development, highlighting the computational challenges, limitations, and opportunities associated with integrating diverse data types for leveraging artificial intelligence (AI) and machine learning (ML) techniques in vaccine design. We discuss the role of natural language processing, semantic integration, and causal inference in extracting valuable insights from published literature and unstructured data sources, as well as the computational modeling of immune responses. Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. Through data harmonization and integration, the development of safe and effective vaccines can be accelerated to improve public health outcomes. Looking to the future, we highlight the need for collaborative efforts among researchers, data scientists, and public health experts to realize the full potential of AI-assisted vaccine design and streamline the vaccine development process

Coupled transcriptome and proteome analysis of human lymphotropic tumor viruses: insights on the detection and discovery of viral genes

<p>Abstract</p> <p>Background</p> <p>Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV) are related human tumor viruses that cause primary effusion lymphomas (PEL) and Burkitt's lymphomas (BL), respectively. Viral genes expressed in naturally-infected cancer cells contribute to disease pathogenesis; knowing which viral genes are expressed is critical in understanding how these viruses cause cancer. To evaluate the expression of viral genes, we used high-resolution separation and mass spectrometry coupled with custom tiling arrays to align the viral proteomes and transcriptomes of three PEL and two BL cell lines under latent and lytic culture conditions.</p> <p>Results</p> <p>The majority of viral genes were efficiently detected at the transcript and/or protein level on manipulating the viral life cycle. Overall the correlation of expressed viral proteins and transcripts was highly complementary in both validating and providing orthogonal data with latent/lytic viral gene expression. Our approach also identified novel viral genes in both KSHV and EBV, and extends viral genome annotation. Several previously uncharacterized genes were validated at both transcript and protein levels.</p> <p>Conclusions</p> <p>This systems biology approach coupling proteome and transcriptome measurements provides a comprehensive view of viral gene expression that could not have been attained using each methodology independently. Detection of viral proteins in combination with viral transcripts is a potentially powerful method for establishing virus-disease relationships.</p

Sialic Acid on Herpes Simplex Virus Type 1 Envelope Glycoproteins Is Required for Efficient Infection of Cells

Herpes simplex virus type 1 (HSV-1) envelope proteins are posttranslationally modified by the addition of sialic acids to the termini of the glycan side chains. Although gC, gD, and gH are sialylated, it is not known whether sialic acids on these envelope proteins are functionally important. Digestion of sucrose gradient purified virions for 4 h with neuraminidases that remove both α2,3 and α2,6 linked sialic acids reduced titers by 1,000-fold. Digestion with a α2,3-specific neuraminidase had no effect, suggesting that α2,6-linked sialic acids are required for infection. Lectins specific for either α2,3 or α2,6 linkages blocked attachment and infection to the same extent. In addition, the mobility of gH, gB, and gD in sodium dodecyl sulfate-polyacrylamide gel electrophoresis gels was altered by digestion with either α2,3 specific neuraminidase or nonspecific neuraminidases, indicating the presence of both linkages on these proteins. The infectivity of a gC-1-null virus, ΔgC2-3, was reduced to the same extent as wild-type virus after neuraminidase digestion, and attachment was not altered. Neuraminidase digestion of virions resulted in reduced VP16 translocation to the nucleus, suggesting that the block occurred between attachment and entry. These results show for the first time that sialic acids on HSV-1 virions play an important role in infection and suggest that targeting virion sialic acids may be a valid antiviral drug development strategy

Addition of a C-Terminal Cysteine Improves the Anti-Herpes Simplex Virus Activity of a Peptide Containing the Human Immunodeficiency Virus Type 1 TAT Protein Transduction Domain

Previous studies have shown that peptides containing the protein transduction domain (PTD) of the human immunodeficiency virus tat protein (GRKKRRQRRR) were effective inhibitors of herpes simplex virus type 1 (HSV-1) entry (H. Bultmann and C. R. Brandt, J. Biol. Chem. 277:36018-36023, 2002). We now show that the addition of a single cysteine residue to the C terminus of the TAT PTD (TAT-C peptide) improves the antiviral activity against HSV-1 and HSV-2. The principle effect of adding the cysteine was to enable the peptide to inactivate virions and to induce a state of resistance to infection in cells pretreated with peptide. The TAT-C peptide acted extracellularly, immediately blocked entry of adsorbed virus, prevented VP16 translocation to the nucleus, and blocked syncytium formation and cell-cell spread. Thus, TAT-C peptides are fusion inhibitors. The induction of the resistance of cells to infection was rapid, recovered with a half-life of 5 to 6 h, and could be reinduced by peptide treatment. TAT-C bound to heparan sulfate but was a poor competitor for viral attachment. The antiviral activity depended on the net positive charge of the peptide but not on chirality, and a free sulfhydryl group was not essential for antiviral activity because TAT-C dimers were at least as effective as monomers. The unique combination of antiviral activities and low toxicity combine to make TAT-C a strong candidate for further development as a drug to block HSV infection

Computational Tools and Data Integration to Accelerate Vaccine Development: Challenges, Opportunities, and Future Directions

The development of effective vaccines is crucial for combating current and emerging pathogens. Despite significant advances in the field of vaccine development there remain numerous challenges including the lack of standardized data reporting and curation practices, making it difficult to determine correlates of protection from experimental and clinical studies. Significant gaps in data and knowledge integration can hinder vaccine development which relies on a comprehensive understanding of the interplay between pathogens and the host immune system. In this review, we explore the current landscape of vaccine development, highlighting the computational challenges, limitations, and opportunities associated with integrating diverse data types for leveraging artificial intelligence (AI) and machine learning (ML) techniques in vaccine design. We discuss the role of natural language processing, semantic integration, and causal inference in extracting valuable insights from published literature and unstructured data sources, as well as the computational modeling of immune responses. Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. Through data harmonization and integration, the development of safe and effective vaccines can be accelerated to improve public health outcomes. Looking to the future, we highlight the need for collaborative efforts among researchers, data scientists, and public health experts to realize the full potential of AI-assisted vaccine design and streamline the vaccine development process

A Histoplasma capsulatum Lipid Metabolic Map Identifies Antifungal Targets

It is estimated that 150 people die per hour due to the insufficient therapeutic treatments to combat fungal infections. A major hurdle to developing antifungal therapies is the scarce knowledge on the fungal metabolic pathways and mechanisms of virulence.</jats:p

Nipah Virus-Like Particle Egress Is Modulated by Cytoskeletal and Vesicular Trafficking Pathways: a Validated Particle Proteomics Analysis

Nipah virus is a zoonotic biosafety level 4 agent with high mortality rates in humans. The genus to which Nipah virus belongs, Henipavirus , includes five officially recognized pathogens; however, over 20 species have been identified in multiple continents within the last several years. As there are still no vaccines or treatments for NiV infection, elucidating its process of viral particle production is imperative both for targeted drug design as well as for particle-based vaccine development. Developments in high-throughput technologies make proteomic analysis of isolated viral particles a highly insightful approach to understanding the life cycle of pathogens such as Nipah virus. </jats:p

A<i>Histoplasma capsulatum</i>lipid metabolic map identifies antifungal targets

ABSTRACTLipids play a fundamental role in fungal cell biology, being essential cell membrane components and major targets of antifungal drugs. A deeper knowledge of lipid metabolism is key for developing new drugs and a better understanding of fungal pathogenesis. Here we built a comprehensive map of theHistoplasma capsulatumlipid metabolic pathway by incorporating proteomic and lipidomic analyses. We performed genetic complementation and overexpression ofH. capsulatumgenes inSaccharomyces cerevisiaeto validate reactions identified in the map and to determine enzymes responsible for catalyzing orphan reactions. The map led to the identification of both the fatty acid desaturation and the sphingolipid biosynthesis pathways as targets for drug development. We found that the sphingolipid biosynthesis inhibitor myriocin, the fatty acid desaturase inhibitor thiocarlide and the fatty acid analog 10-thiastearic acid inhibitH. capsulatumgrowth in nanomolar to low micromolar concentrations. These compounds also reduced the intracellular infection in an alveolar macrophage cell line. Overall, this lipid metabolic map revealed pathways that can be targeted for drug development.</jats:p