The study of virus-host interactions is essential to achieve a comprehensive understanding of the viral replication process. The commonly used methods are yeast two-hybrid approach and transient expression of a single tagged viral protein in host cells followed by affinity purification of interacting cellular proteins and mass spectrometry analysis (AP-MS). However, by these approaches, virus-host protein-protein interactions are detected in the absence of a real infection, not always correctly compartmentalized, and for the yeast two-hybrid approach performed in a heterologous system. Thus, some of the detected protein-protein interactions may be artificial. Here we describe a new strategy based on recombinant viruses expressing tagged viral proteins to capture both direct and indirect protein partners during the infection (AP-MS in viral context). This way, virus-host protein-protein interacting co-complexes can be purified directly from infected cells for further characterization.

Protein-fragment complementation assays (PCAs) are powerful tools to investigate protein-protein interactions in a cellular context. These are especially useful to study unstable proteins and weak interactions that may not resist protein isolation or purification. The PCA based on the reconstitution of the Gaussia princeps luciferase (split-luc) is a sensitive approach allowing the mapping of protein-protein interactions and the semiquantitative measurement of binding affinity. Here, we describe the split-luc protocol we used to map the viral interactome of measles virus polymerase complex.

OBJECTIVE: The protein-protein interaction (PPI) network alignment has proven to be an efficient technique in the diagnosis and prevention of certain diseases. However, the difficulty in maximizing, at the same time, the two qualities that measure the goodness of alignments (topological and biological quality) has led aligners to produce very different alignments. Thus making a comparative study among alignments of such different qualities a big challenge. Multi-objective optimization is a computer method, which is very powerful in this kind of contexts because both conflicting qualities are considered together. Analysing the alignments of each PPI network aligner with multi-objective methodologies allows you to visualize a bigger picture of the alignments and their qualities, obtaining very interesting conclusions. This paper proposes a comprehensive PPI network aligner study in the multi-objective domain.
METHODS: Alignments from each aligner and all aligners together were studied and compared to each other via Pareto dominance methodologies. The best alignments produced by each aligner and all aligners together for five different alignment scenarios were displayed in Pareto front graphs. Later, the aligners were ranked according to the topological, biological, and combined quality of their alignments. Finally, the aligners were also ranked based on their average runtimes.
RESULTS: Regarding aligners constructing the best overall alignments, we found that SAlign, BEAMS, SANA, and HubAlign are the best options. Additionally, the alignments of best topological quality are produced by: SANA, SAlign, and HubAlign aligners. On the contrary, the aligners returning the alignments of best biological quality are: BEAMS, TAME, and WAVE. However, if there are time constraints, it is recommended to select SAlign to obtain high topological quality alignments and PISwap or SAlign aligners for high biological quality alignments.
CONCLUSIONS: The use of the SANA aligner is recommended for obtaining the best alignments of topological quality, BEAMS for alignments of the best biological quality, and SAlign for alignments of the best combined topological and biological quality. Simultaneously, SANA and BEAMS have above-average runtimes. Therefore, it is suggested, if necessary due to time restrictions, to choose other, faster aligners like SAlign or PISwap whose alignments are also of high quality.

Protein-protein interactions (PPIs) are known to be involved in most cellular functions, and a detailed knowledge of such interactions is essential for studying their role in normal and pathological conditions. Significant progress is being made in the identification of PPIs through advances in computational methods. In particular, the AlphaFold2 machine learning-based model has been shown to accelerate drug discovery process by predicting the 3D structure of protein complexes. In this chapter, a straightforward protocol for predicting interprotein interactions between PAR-3 and its protein partner adapter molecule crk is provided. Such artificial intelligence-based and publicly available approaches can provide a resource for further investigation of therapeutic drug targets.

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

Affinity purification-Mass spectroscopy (AP-MS) is a biochemical technique to identify the novel protein-protein interaction that occurs in the most relevant physiological conditions, whereas co-immunoprecipitation (Co-IP) is used to study the interaction between two known protein partners that are expressed in the native physiological conditions. Both AP-MS and Co-IP techniques are based on the ability of the interacting partners to pull-down with protein of interest. In this chapter, we have explained the AP-MS and Co-IP methods to study protein-protein interactions in the plant cells.

Adenomyosis is a benign uterine disease and affected patients present with symptoms such as menorrhagia, chronic pelvic pain, abnormal uterine bleeding, and infertility. However, the specific mechanisms by which adenomyosis occurs need to be further studied.
Dataset of adenomyosis from our hospital and a public database were analyzed using bioinformatics. Corresponding differentially expressed genes (DEGs) and gene enrichment were detected to explore potential genetic adenomyosis targets.
Clinical data on adenomyosis were accessed based on the pathological specimens of patients with adenomyosis obtained from the Shengjing Hospital. R software was used to screen for DEGs, and volcano and cluster maps were drawn. Adenomyosis datasets (GSE74373) were downloaded from the GEO database. GEO2R online tool was used to screen for DEGs between adenomyosis and normal controls. Genes with P < 0.01 and |logFC| >1 were selected as DEGs. DAVID software was used for functional and pathway enrichment analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on common DEGs to obtain descriptions of the genes. The online database STRING was used for interaction gene retrieval. Moreover, Cytoscape software was used to construct a protein-protein interaction (PPI) network map for common DEGs to visualize potential gene interactions and screen the hub genes.
A total of 845 DEGs were identified in the dataset obtained from Shengjing Hospital. A total of 175 genes were downregulated, and 670 genes were upregulated. In the GSE74373 database, 1679 genes were differentially expressed, 916 genes were downregulated, and 763 genes were upregulated. A total of 40 downregulated and 148 upregulated common DEGs showed potential gene interactions. The top ten upregulated hub genes were CDH1, EPCAM, CLDN7, ESRP1, RAB25, SPINT1, PKP3, TJP3, GRHL2, and CDKN2A.
Genes involved in tight junction may be key in the development of adenomyosis and may provide a potential treatment strategy for adenomyosis.

UNASSIGNED: Keloid is a benign fibroproliferative skin tumor. The respective functions of fibroblasts and vascular endothelial cells in keloid have not been fully studied. The purpose of this study is to identify the respective roles and key genes of fibroblasts and vascular endothelial cells in keloids, which can be used as new targets for diagnosis or treatment.The microarray datasets of keloid fibroblasts and vascular endothelial cells were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for functional enrichment analysis. The search tool for retrieval of interacting genes and Cytoscape were used to construct protein-protein interaction (PPI) networks and analyze gene modules. The hub genes were screened out, and the relevant interaction networks and biological process analysis were carried out.In fibroblasts, the DEGs were significantly enriched in collagen fibril organization, extracellular matrix organization and ECM-receptor interaction. The PPI network was constructed, and the most significant module was selected, which is mainly enriched in ECM-receptor interaction. In vascular endothelial cells, the DEGs were significantly enriched in cytokine activity, growth factor activity and transforming growth factor-β (TGF-β) signaling pathway. Module analysis was mainly enriched in TGF-β signaling pathway. Hub genes were screened out separately.In summary, the DEGs and hub genes discovered in this study may help us understand the molecular mechanisms of keloid, and provide potential targets for diagnosis and treatment.

UNASSIGNED: Radix Isatidis (Banlangen) is a well-known traditional Chinese medicine for the treatment of different diseases and prevention of many body disorders. Besides, it also plays a pivotal role in novel coronavirus pneumonia, coronavirus disease 2019 (COVID-19). However, few researchers know its active ingredients and mechanism of action for COVID-19. To find whether Banlangen has a pharmacological effect on COVID-19. In this research, we systematically analyze Banlangen and COVID-19 through network pharmacology technology. A total of 33 active ingredients in Banlangen, 92 targets of the active ingredients, and 259 appropriate targets of COVID-19 were obtained, with 11 common targets. The analysis of the biological process of gene ontology and the enrichment of Kyoto Encyclopedia of Genes and Genomes signaling pathway suggests that Banlangen participated in the biological processes of protein phosphatase binding, tetrapyrrole binding, the apoptotic process involving cysteine-type endopeptidase activity, etc. The COVID-19 may be treated by regulating advanced glycation end products/a receptor for advanced glycation end products signaling pathway, interleukin-17 signaling pathway, tumor necrosis factor signaling pathway, sphingolipid signaling pathway, and p53 signaling pathway. Banlangen has a potential pharmacological effect on COVID-19, which has the value of further exploration in the following experiment and clinical application.

We hypothesize that intracellular trafficking pathways are altered in chlamydial infected cells to maximize the ability of Chlamydia to scavenge nutrients while not overtly stressing the host cell. Previous data demonstrated the importance of two eukaryotic SNARE proteins, VAMP4 and syntaxin 10 (Stx10), in chlamydial growth and development. Although, the mechanism for these effects is still unknown. To interrogate whether chlamydial infection altered these proteins\' networks, we created BirA*-VAMP4 and BirA*-Stx10 fusion constructs to use the BioID proximity labeling system. While we identified a novel eukaryotic protein-protein interaction between Stx10 and VAPB, we also identified caveats in using the BioID system to study the impact of infection by an obligate intracellular pathogen on SNARE protein networks. The addition of the BirA* altered the localization of VAMP4 and Stx10 during infection with Chlamydia trachomatis serovars L2 and D and Coxiella burnetii Nine Mile Phase II. We also discovered that BirA* traffics to and biotinylates Coxiella-containing vacuoles and, in general, has a propensity for labeling membrane or membrane-associated proteins. While the BioID system identified a novel association for Stx10, it is not a reliable methodology to examine intracellular trafficking pathway dynamics during infection with intracellular pathogens.