Biomolecular Fluorescence Complementation Profiling and Artificial Intelligence Structure Prediction of the Kaposi’s Sarcoma-Associated Herpesvirus ORF18 and ORF30 Interaction

Maeda, Yukihide; Watanabe, Tadashi; Izumi, Tatsuro; Kuriyama, Kazushi; Ohno, Shinji; Fujimuro, Masahiro

doi:10.3390/ijms23179647

Cited by 6 publications

(8 citation statements)

References 27 publications

(54 reference statements)

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“…Prediction structure models by the deep-learning algorithm have been widely used as powerful tools for life-science fields, including virology. We previously applied that to a partial vPIC protein-protein interaction model (28). The structure of KSHV ORF34 is predicted by Alphafold2, an algorithm for predicting the protein structure from the amino-acid sequence (29).…”

Section: Resultsmentioning

confidence: 99%

“…Pull-down assays between the vPIC components and ORF34 were performed as described previously (20, 23, 28). In brief, 293T cells were transfected with the indicated plasmids using PEI-MAX (46) and cultured for two days.…”

Section: Methodsmentioning

confidence: 99%

“…ORF34 serves as a network hub for gathering ORF18, ORF31, and ORF66 to ORF24 (20–26). ORF30 binds to only ORF18, not to other vPIC components (21, 26, 28). This protein complex composed of each component is also indispensable for viral L-gene transcription (20–26).…”

Section: Introductionmentioning

confidence: 99%

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Conserved cysteine residues in Kaposi’s sarcoma herpesvirus ORF34 are necessary for viral production and viral pre-initiation complex formation

Watanabe

Narahari

Bhardwaj

et al. 2023

Preprint

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Kaposi’s sarcoma herpesvirus (KSHV) ORF34 is a component of the viral pre-initiation complex (vPIC), a highly conserved piece of machinery essential for late gene expression among beta- and gamma-herpes viruses. KSHV ORF34 is also estimated to be a hub protein, associated with the majority of vPIC components. However, the precise mechanisms underlying how the ORF34 molecule contributes to the vPIC function, including the binding manner to other vPIC components, remain unclear. Therefore, we constructed ORF34 alanine-scanning mutants, in which amino-acid residues that were conserved among other herpesviruses had been replaced by alanine. The mutants were analyzed for their binding functions to other vPIC factors, and then were evaluated for their recovering ability of viral production using the cells harboring ORF34-deficient KSHV-BAC. The results demonstrated that at least four cysteines conserved in ORF34 were crucial for binding to other vPIC components, ORF24 and ORF66, virus production, and late gene transcription and expression. Based on the amino acid sequence of ORF34, these four cysteines were expected to constitute a pair of C-Xn-C consensus motifs. An artificial intelligence-predicted structure model revealed that the four cysteines were present tetrahedrally in an intramolecular fashion. Another prediction algorithm indicated the possible capture of metal cations by ORF34. Furthermore, it was experimentally observed that the elimination of cations by a selective chelator resulted in the loss of ORF34’s binding ability to other vPIC components. In conclusion, our results suggest the functional importance of KSHV ORF34 conserved cysteines for vPIC components assembly and viral replication.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Conserved cysteine residues in Kaposi’s sarcoma herpesvirus ORF34 are necessary for viral production and viral pre-initiation complex formation

Watanabe

Narahari

Bhardwaj

et al. 2023

Preprint

Self Cite

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“…From 2015, there has been an exponential increase in the number of publications that use deep learning in the pathology field in Japan. Examples include gastrointestinal pathology, 122 precision medicine, 123 urothelial carcinoma, 124 ocular pathology, 125 esophageal cancer, 126 lung cancer, 127 thyroid cytology, 128 intestinal diseases, 122,[129][130][131][132][133][134][135] sarcoma, 136 hematological, [137][138][139][140] among others. In the field of malignant lymphoma, Miyoshi H and Ohshima K et al showed how deep learning was capable of high-level computer-aided diagnosis based on H&E slides, including diffuse large B-cell lymphoma, follicular lymphoma, and reactive lymphoid hyperplasia; 141 and Hashimoto N and Takeuchi I et al analyzed several malignant lymphoma cases using immunohistochemical patterns.…”

Section: Artificial Intelligencementioning

confidence: 99%

The pathobiology of follicular lymphoma

Carreras

2023

J Clin Exp Hematopathol

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Follicular lymphoma is one of the most frequent lymphomas. Histologically, it is characterized by a follicular (nodular) growth pattern of centrocytes and centroblasts; mixed with variable immune microenvironment cells. Clinically, it is characterized by diffuse lymphadenopathy, bone marrow involvement, and splenomegaly. It is biologically and clinically heterogeneous. In most patients it is indolent, but others have a more aggressive evolution with relapses; and transformation to diffuse large B-cell lymphoma. Tumorigenesis includes an asymptomatic preclinical phase in which premalignant B-lymphocytes with the t(14;18) chromosomal translocation acquire additional genetic alterations in the germinal centers, and clonal evolution occurs, although not all the cells progress to the tumor stage. This manuscript reviews the pathobiology and clinicopathological characteristics of follicular lymphoma. It includes a description of the physiology of the germinal center, the genetic alterations of BCL2 and BCL6 , the mutational profile, the immune checkpoint, precision medicine, and highlights in the lymphoma classification. In addition, a comment and review on artificial intelligence and machine (deep) learning are made.

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“…However, the nature of the protein-protein interaction of the vPIC factors has not been completely elucidated. Maeda et al characterized the interaction of vPIC factors ORF18 and ORF30 using a bimolecular fluorescence complementation assay, a pull-down assay, and an AlphaFold2 predicted binding model [14]. As a result, they identified four amino acid residues (Leu29, Glu36, His41, and Trp170) of ORF18 that were responsible for its interaction with ORF30.…”

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confidence: 99%