compleXView: a server for the interpretation of protein abundance and connectivity information to identify protein complexes

BCLAF1 drives esophageal squamous cell carcinoma progression through regulation of YTHDF2-dependent SIX1 mRNA degradation

Esophageal cancer ranks among the most prevalent malignant tumors, and esophageal squamous cell carcinoma (ESCC) constitutes its predominant histological form. Despite its impact, a thorough insight into the molecular intricacies of ESCC's development is still incomplete, which hampers the advancement of targeted molecular diagnostics and treatments. Recently, B-cell lymphoma-2-associated transcription factor 1 (BCLAF1) has come under investigation for its potential involvement in tumor biology, yet its specific role and mechanism in ESCC remain unclear. In this study, we observed a marked increase in BCLAF1 expression in ESCC tissues, correlating with advanced tumor stages and inferior patient outcomes. Our comprehensive in vitro and in vivo studies show that BCLAF1 augments glycolytic activity and the proliferation, invasion, and spread of ESCC cells. By employing mass spectrometry, we identified YTHDF2 as a key protein interacting with BCLAF1 in ESCC, with further validation provided by colocalization, co-immunoprecipitation, and GST pull-down assay. Further investigations involving MeRIP-seq and RIP-seq, alongside transcriptomic analysis, highlighted SIX1 mRNA as a molecule significantly upregulated and modified by N6-methyladenosine (m6A) in BCLAF1 overexpressing cells. BCLAF1 was found to reduce the tumor-suppressive activities of YTHDF2, and its effects on promoting glycolysis and cancer progression were shown to hinge on SIX1 expression. This research establishes that BCLAF1 fosters glycolysis and tumor progression in ESCC through the YTHDF2-SIX1 pathway in an m6A-specific manner, suggesting a potential target for future therapeutic intervention.

Identification of potential biomarkers and immune infiltration characteristics in severe asthma

OBJECTIVES

We hope to identify key molecules that can be used as markers of asthma severity and investigate their correlation with immune cell infiltration in severe asthma.

METHODS

An asthma dataset was downloaded from the Gene Expression Omnibus database and then processed by R software to obtain differentially expressed genes (DEGs). First, multiple enrichment platforms were applied to analyze crucial biological processes and pathways and protein-protein interaction networks related to the DEGs. We next combined least absolute shrinkage and selection operator logistic regression and the support vector machine-recursive feature elimination algorithms to screen diagnostic markers of severe asthma. Then, a local cohort consisting of 40 asthmatic subjects (24 with moderate asthma and 16 with severe asthma) was used for biomarker validation. Finally, infiltration of immune cells in asthma bronchoalveolar lavage fluid and their correlation with the screened markers was evaluated by CIBERSORT.

RESULTS

A total of 97 DEGs were identified in this study. Most of these genes are enriched in T cell activation and immune response in the asthma biological process. CC-chemokine receptor 7 (CCR7) and natural killer cell protein 7(NKG7) were identified as markers of severe asthma. The highest area under the ROC curve (AUC) was from a new indicator combining CCR7 and NKG7 (AUC = 0.851, adj. p < 0.05). Resting and activated memory CD4 T cells, activated NK cells, and CD8 T cells were found to be significantly higher in the severe asthma group (adj. p < 0.01). CCR7 and NKG7 were significantly correlated with these infiltrated cells that showed differences between the two groups. In addition, CCR7 was found to be significantly positively correlated with eosinophils (r = 0.38, adj. p < 0.05) infiltrated in bronchoalveolar lavage fluid.

CONCLUSION

CCR7 and NKG7 might be used as potential markers for asthma severity, and their expression may be associated with differences in immune cell infiltration in the moderate and severe asthma groups.

Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons

Mass spectrometry (MS) has become one of the key technologies of structural biology. In this review, the contributions of chemical cross-linking combined with mass spectrometry (XL-MS) for studying three-dimensional structures of proteins and for investigating protein-protein interactions are outlined. We summarize the most important cross-linking reagents, software tools, and XL-MS workflows and highlight prominent examples for characterizing proteins, their assemblies, and interaction networks in vitro and in vivo. Computational modeling plays a crucial role in deriving 3D-structural information from XL-MS data. Integrating XL-MS with other techniques of structural biology, such as cryo-electron microscopy, has been successful in addressing biological questions that to date could not be answered. XL-MS is therefore expected to play an increasingly important role in structural biology in the future.

Network analyses in microbiome based on high-throughput multi-omics data

Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.

Cross-ID: Analysis and Visualization of Complex XL-MS-Driven Protein Interaction Networks

Protein interactions enable much more complex behavior than the sum of the individual protein parts would suggest and represents a level of biological complexity requiring full understanding when unravelling cellular processes. Cross-linking mass spectrometry has emerged as an attractive approach to study these interactions, and recent advances in mass spectrometry and data analysis software have enabled the identification of thousands of cross-links from a single experiment. The resulting data complexity is, however, difficult to understand and requires interactive software tools. Even though solutions are available, these represent an agglomerate of possibilities, and each features its own input format, often forcing manual conversion. Here we present Cross-ID, a visualization platform that links directly into the output of XlinkX for Proteome Discoverer but also plays well with other platforms by supporting a user-controllable text-file importer. The platform includes features like grouping, spectral viewer, gene ontology (GO) enrichment, post-translational modification (PTM) visualization, domains and secondary structure mapping, data set comparison, previsualization overlap check, and more. Validation of detected cross-links is available for proteins and complexes with known structure or for protein complexes through the DisVis online platform ( http://milou.science.uu.nl/cgi/services/DISVIS/disvis/ ). Graphs are exportable in PDF format, and data sets can be exported in tab-separated text files for evaluation through other software.

Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation

The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling.

When human cells or tissues are injured, the body triggers a response known as inflammation to repair the damage and protect itself from further harm. However, if the same issue keeps recurring, the tissues become inflamed for longer periods of time, which may ultimately lead to health problems. This is what could be happening in cardiovascular diseases, where long-term inflammation could damage the heart and blood vessels.

Many different proteins interact with each other to control inflammation; gaining an insight into the nature of these interactions could help to pinpoint the role of each molecular actor. Researchers have used a combination of unbiased, large-scale experimental and computational approaches to develop the interactome, a map of the known interactions between all proteins in humans. However, interactions between proteins can change between cell types, or during disease. Here, Halu et al. aimed to refine the human interactome and identify new proteins involved in inflammation, especially in the context of cardiovascular disease.

Cells called macrophages produce signals that trigger inflammation whey they detect damage in other cells or tissues. The experiments used a technique called proteomics to measure the amounts of all the proteins in human macrophages. Combining these data with the human interactome made it possible to predict new links between proteins known to have a role in inflammation and other proteins in the interactome. Further analysis using other sets of data from macrophages helped identify two new candidate proteins – GBP1 and WARS – that may promote inflammation. Halu et al. then used a genetic approach to deactivate the genes and decrease the levels of these two proteins in macrophages, which caused the signals that encourage inflammation to drop.

These findings suggest that GBP1 and WARS regulate the activity of macrophages to promote inflammation. The two proteins could therefore be used as drug targets to treat cardiovascular diseases and other disorders linked to inflammation, but further studies will be needed to precisely dissect how GBP1 and WARS work in humans.

Histone Interaction Landscapes Visualized by Crosslinking Mass Spectrometry in Intact Cell Nuclei

Cells organize their actions partly through tightly controlled protein-protein interactions-collectively termed the interactome. Here we use crosslinking mass spectrometry (XL-MS) to chart the protein-protein interactions in intact human nuclei. Overall, we identified ∼8,700 crosslinks, of which 2/3 represent links connecting distinct proteins. From these data, we gain insights on interactions involving histone proteins. We observed that core histones on the nucleosomes expose well-defined interaction hot spots. For several nucleosome-interacting proteins, such as USF3 and Ran GTPase, the data allowed us to build low-resolution models of their binding mode to the nucleosome. For HMGN2, the data guided the construction of a refined model of the interaction with the nucleosome, based on complementary NMR, XL-MS, and modeling. Excitingly, the analysis of crosslinks carrying posttranslational modifications allowed us to extract how specific modifications influence nucleosome interactions. Overall, our data depository will support future structural and functional analysis of cell nuclei, including the nucleoprotein assemblies they harbor.