Tracking the functional meaning of the human oral-microbiome protein-protein interactions

COVID-19 Salivary Protein Profile: Unravelling Molecular Aspects of SARS-CoV-2 Infection

COVID-19 is the most impacting global pandemic of all time, with over 600 million infected and 6.5 million deaths worldwide, in addition to an unprecedented economic impact. Despite the many advances in scientific knowledge about the disease, much remains to be clarified about the molecular alterations induced by SARS-CoV-2 infection. In this work, we present a hybrid proteomics and in silico interactomics strategy to establish a COVID-19 salivary protein profile. Data are available via ProteomeXchange with identifier PXD036571. The differential proteome was narrowed down by the Partial Least-Squares Discriminant Analysis and enrichment analysis was performed with FunRich. In parallel, OralInt was used to determine interspecies Protein-Protein Interactions between humans and SARS-CoV-2. Five dysregulated biological processes were identified in the COVID-19 proteome profile: Apoptosis, Energy Pathways, Immune Response, Protein Metabolism and Transport. We identified 10 proteins (KLK 11, IMPA2, ANXA7, PLP2, IGLV2-11, IGHV3-43D, IGKV2-24, TMEM165, VSIG10 and PHB2) that had never been associated with SARS-CoV-2 infection, representing new evidence of the impact of COVID-19. Interactomics analysis showed viral influence on the host immune response, mainly through interaction with the degranulation of neutrophils. The virus alters the host’s energy metabolism and interferes with apoptosis mechanisms.

Unveiling the Secretome of the Fungal Plant Pathogen Neofusicoccum parvum Induced by In Vitro Host Mimicry

Neofusicoccum parvum is a fungal plant pathogen of a wide range of hosts but knowledge about the virulence factors of N. parvum and host-pathogen interactions is rather limited. The molecules involved in the interaction between N. parvum and Eucalyptus are mostly unknown, so we used a multi-omics approach to understand pathogen-host interactions. We present the first comprehensive characterization of the in vitro secretome of N. parvum and a prediction of protein-protein interactions using a dry-lab non-targeted interactomics strategy. We used LC-MS to identify N. parvum protein profiles, resulting in the identification of over 400 proteins, from which 117 had a different abundance in the presence of the Eucalyptus stem. Most of the more abundant proteins under host mimicry are involved in plant cell wall degradation (targeting pectin and hemicellulose) consistent with pathogen growth on a plant host. Other proteins identified are involved in adhesion to host tissues, penetration, pathogenesis, or reactive oxygen species generation, involving ribonuclease/ribotoxin domains, putative ricin B lectins, and necrosis elicitors. The overexpression of chitosan synthesis proteins during interaction with the Eucalyptus stem reinforces the hypothesis of an infection strategy involving pathogen masking to avoid host defenses. Neofusicoccum parvum has the molecular apparatus to colonize the host but also actively feed on its living cells and induce necrosis suggesting that this species has a hemibiotrophic lifestyle.

New feature extraction from phylogenetic profiles improved the performance of pathogen-host interactions

Motivation

The understanding of pathogen-host interactions (PHIs) is essential and challenging research because this potentially provides the mechanism of molecular interactions between different organisms. The experimental exploration of PHI is time-consuming and labor-intensive, and computational approaches are playing a crucial role in discovering new unknown PHIs between different organisms. Although it has been proposed that most machine learning (ML)–based methods predict PHI, these methods are all based on the structure-based information extracted from the sequence for prediction. The selection of feature values is critical to improving the performance of predicting PHI using ML.

Results

This work proposed a new method to extract features from phylogenetic profiles as evolutionary information for predicting PHI. The performance of our approach is better than that of structure-based and ML-based PHI prediction methods. The five different extract models proposed by our approach combined with structure-based information significantly improved the performance of PHI, suggesting that combining phylogenetic profile features and structure-based methods could be applied to the exploration of PHI and discover new unknown biological relativity.

Availability and implementation

The KPP method is implemented in the Java language and is available at https://github.com/yangfangs/KPP.

Mechanism of a Herbal Formula Associated with Prognosis and Immune Infiltration in LIHC: Transcriptomics Analysis and Molecular Dynamics Simulations

Background

The aim of this study is to explore the interactions between effective monomers of herbal formulas and their therapeutic targets using systems biology approaches which may be a promising approach to unraveling their underlying mechanisms. Shentao Ruangan decoction (STRGD), which has been experimentally, clinically demonstrated to be effective in treating liver hepatocellular carcinoma (LIHC), was selected.

Methods

Bioactive ingredients and drug targets of STRGD were retrieved from the traditional Chinese medicine systems pharmacology database and analysis platform and BATMAN-TCM databases. LIHC-related differentially expressed genes (DEGs) and key modules were identified by a weighted gene coexpression network analysis using The Cancer Genome Atlas data. The Kaplan–Meier analysis was used to investigate the relationship between STRGD tumor targets and patients survival. The CIBERSORT deconvolution algorithm was used to analyze the correlation between STRGD tumor targets and infiltrating immune cells. Enrichment analysis was used to analyze biological functions. Interactions between STRGD compounds and LIHC-immune-related genes were investigated using molecular docking and MDS.

Results

We identified 24 STRGD tumor targets, which were found to be correlated with survival and the level of immune cell infiltration in LIHC patients. Immune infiltration, gene set enrichment, and Kyoto Encyclopedia of Genes and Genomes analyses highlighted the roles of T and B cell subsets, which were both related to activator protein 1 (AP1), in STRGD action. Docking studies and HPLC indicated that tanshinone IIA is the main compound of STRGD in LIHC treatment, and MDS showed that the potential LIHC-immune-related targets 1FOS and 1JUN firmly bind to tanshinone IIA.

Conclusions

The mechanisms of STRGD in improving the immune and survival status of LIHC patients include interactions between STRGD compounds and LIHC-immune-related targets. The findings of this study can guide research studies on the potential usefulness of tanshinone IIA in the development of drugs targeting 1JUN and 1FOS for the treatment of LIHC.

Saliva as a Source of Biomarkers for Periodontitis and Periimplantitis

Saliva has the potential to be used as a diagnostic and monitoring tool for various diseases if biomarkers of an adequate sensitivity and specificity could be identified. Several reviews and even meta-analyses have been performed in recent years, which have found some candidate biomarkers for periodontitis, like macrophage inflammatory protein-1 alpha, interleukin-1ß, interleukin-6, matrix metalloproteinase-8, or hemoglobin. However, none of those are currently in use to replace conventional periodontal diagnostics with a periodontal probe. For periimplantitis, to date, heterogeneity of different study protocols and implant types did not permit to discover clear biomarkers, which were able to distinguish between healthy and diseased implants. Few proinflammatory cytokines, similar to periodontitis, have been characterized as adjunct tools to clinical diagnosis. The additional determination of antimicrobial peptides, bone turnover markers, and bacteria could help to enhance sensitivity and specificity in a combined model for periodontitis and periimplantitis. Furthermore, proteomic approaches might be preferred over single biomarker determinations. A global consensus is also needed to harmonize salivary sampling methods as well as procedures of biomarker analysis to ensure future comparability.

The oralome and its dysbiosis: New insights into oral microbiome-host interactions

The oralome is the summary of the dynamic interactions orchestrated between the ecological community of oral microorganisms (comprised of up to approximately 1000 species of bacteria, fungi, viruses, archaea and protozoa - the oral microbiome) that live in the oral cavity and the host. These microorganisms form a complex ecosystem that thrive in the dynamic oral environment in a symbiotic relationship with the human host. However, the microbial composition is significantly affected by interspecies and host-microbial interactions, which in turn, can impact the health and disease status of the host. In this review, we discuss the composition of the oralome and inter-species and host-microbial interactions that take place in the oral cavity and examine how these interactions change from healthy (eubiotic) to disease (dysbiotic) states. We further discuss the dysbiotic signatures associated with periodontitis and caries and their sequalae, (e.g., tooth/bone loss and pulpitis), and the systemic diseases associated with these oral diseases, such as infective endocarditis, atherosclerosis, diabetes, Alzheimer's disease and head and neck/oral cancer. We then discuss current computational techniques to assess dysbiotic oral microbiome changes. Lastly, we discuss current and novel techniques for modulation of the dysbiotic oral microbiome that may help in disease prevention and treatment, including standard hygiene methods, prebiotics, probiotics, use of nano-sized drug delivery systems (nano-DDS), extracellular polymeric matrix (EPM) disruption, and host response modulators.