The nuclear envelope (NE) is a double-membrane system surrounding the nucleus of eukaryotic cells. A large number of proteins are localized in the NE, performing a wide variety of functions, from the bidirectional exchange of molecules between the cytoplasm and the nucleus to chromatin tethering, genome organization, regulation of signaling cascades, and many others. Despite its importance, several aspects of the NE, including its protein–protein interactions, remain understudied. In this work, we present NucEnvDB, a publicly available database of NE proteins and their interactions. Each database entry contains useful annotation including a description of its position in the NE, its interactions with other proteins, and cross-references to major biological repositories. In addition, the database provides users with a number of visualization and analysis tools, including the ability to construct and visualize protein–protein interaction networks and perform functional enrichment analysis for clusters of NE proteins and their interaction partners. The capabilities of NucEnvDB and its analysis tools are showcased by two informative case studies, exploring protein–protein interactions in Hutchinson–Gilford progeria and during SARS-CoV-2 infection at the level of the nuclear envelope.

Background: At present, the complexity that governs the associations between different biological entities is understood better than ever before, owing to high-throughput techniques and systems biology. Networks of interactions are necessary not only for the visualization of these complex relationships but also because their analysis tends to be valuable for the extraction of novel biological knowledge. Methods: For this reason, we constructed a disease–protein–drug network, focusing on a category of rare protein-misfolding diseases, known as amyloidoses, and on other pathological conditions also associated with amyloid deposition. Apart from the amyloidogenic proteins that self-assemble into fibrils, we also included other co-deposited proteins found in amyloid deposits. Results: In this work, protein–protein, protein–drug, and disease–drug associations were collected to create a heterogenous network. Through disease-based and drug-based analyses, we highlighted commonalities between diseases and proposed an approved drug with prospects of repurposing. Conclusions: The identified disease associations and drug candidates are proposed for further study that will potentially help treat diseases associated with amyloid deposition.

Background: Amyloidogenic proteins, a heterogenous group of proteins characterized by their ability to form amyloid fibrils, lead to pathological conditions when they undergo abnormal folding and self-assembly. Missense single-nucleotide polymorphisms (msSNPs) may occur in their sequence, disrupting the normal structure and function of these proteins, pushing them towards amyloidogenesis. Methods: A comprehensive dataset of amyloidogenic proteins was created and their msSNPs were collected and mapped on their amino acid sequence. The chi squared test, logistic regression and the bootstrap method were used to ascertain the statistical significance of the results. Results: The distribution of pathogenic and benign msSNPs highlighted the predicted amyloidogenic segments as hotspots for pathogenic msSNPs. Analysis of the change in residue properties and pathogenicity status revealed that the substitution of negatively charged residues by any other type of residue tends to be pathogenic. Furthermore, certain substitutions were found to be more likely pathogenic than average. Additionally, a case study of APP, a key protein in Alzheimer’s disease, is used as an example. Conclusions: This study will hopefully showcase the importance of amyloidogenic protein msSNPs as well as spark an interest in research of the mechanisms that lead to the formation of amyloid deposits under the scope of pathogenic msSNPs.

Transthyretin amyloidosis (ATTR) is a human systemic disorder, caused by deposition of transthyretin amyloid fibrils in various organs and tissues. Significant difficulties in studying ATTR include heterogeneous symptomatology, its complex pathological phenotype and a limited understanding of underlying mechanisms. This is an attempt to explore ATTR through the collection of potentially related proteins and the construction of a protein–protein interaction network (PPIN). A set of 158 potential ATTR-related proteins was collected with data extracted from biological databases and through mining biomedical literature. A PPIN was constructed to explore the connectivity of these proteins and determine whether they are likely related to transthyretin and ATTR. Central proteins were identified through topological analysis of the connected network and selected for enrichment analysis. This resulted in a network of 121 interconnected potential ATTR-related proteins, 19 of which were identified as central. Terms associated with the central proteins included other diseases and complications, such as Alzheimer’s disease and inflammation, and amyloidogenic proteins, such as APP and APOA1. Lastly, key terms and central proteins are discussed within the framework of the existing ATTR literature. Overall, this network can guide future research on ATTR, and the central proteins represent promising targets for further exploration.

Background: Exosomes are a group of extracellular vesicles that are released by almost all mammalian cell types and engage in intracellular communication. Studies conducted in recent years have shown that exosomes are involved in a variety of diseases, where they may act as “vehicles” for the transmission of biomolecules and biomolecular information. Amyloidoses constitute a critical subgroup of these diseases, caused by extracellular deposition or intracellular inclusions of insoluble protein fibrils in cells and tissues. However, how exosomes are involved in these diseases remains largely unexplored. Methods: To detect possible links between amyloid proteins and exosomes, protein data from amyloidosis-isolated exosomes were collected and visualized using biological networks. Results: This biomedical informatics approach for the analysis of interaction networks, in combination with the existing literature, highlighted the involvement of exosomes in amyloidosis while strengthening existing hypotheses regarding their mechanism of action. Conclusion: This work is focused on exosomes from patients with Alzheimer’s disease and identifies important amyloidogenic proteins found in exosomes. These proteins can be used for future research in the field of exosome-based biomarkers of amyloidosis and potential prognostic or preventive approaches.

Alzheimer’s disease (AD) affects an increasing number of people as the human population ages. The main pathological feature of AD, amyloid plaques, consists of the key protein amyloid-β and other co-deposited proteins. These co-deposited proteins and their protein interactors could hold some additional functional insights into AD pathophysiology. For this work, proteins found on amyloid plaques were collected from the AmyCo database. A protein–protein and protein–drug interaction network was constructed with data from the IntAct and DrugBank databases, respectively. In total, there were 12 proteins co-deposited on amyloid plaques that reportedly interact with 513 other proteins and are targets of 72 drugs. These drugs were shown to be almost entirely distinct from the panel of drugs currently approved by the FDA for AD and their corresponding protein targets. In conclusion, this work demonstrates the potential for drug repurposing of drugs that target proteins found in amyloid plaques.