Biology subjects

Senyuz, S.

Publications and source records attributed to Senyuz, S..

4 recordsLinked to original sources

bioRxiv2024

An Inflammation Centered Perspective to the Mechanisms and Interactions Related to Vascular Cognitive Impairment

A major health burden for the elderly, vascular cognitive impairment (VCI) is a disease that combines cognitive (CD) and cardiovascular (CVD) components. The molecular mechanisms underlying this disease are poorly understood, and our work attempts to bridge this knowledge gap by building protein-protein interaction (PPI) networks of CD and CVD. Our earlier research not only showed how well these two primary components work together, but also hinted at the potential role of inflammation in the development of VCI. For this reason, we decided to examine the relationship between inflammation and VCI in further detail.We identified the top three most connected clusters, which could represent significant modules, enriched these clusters with alternative conformations, and used PRISM to predict the interactions between the conformations. We proposed putative VCI-related interactions, such as NFKBIA-RELA and the proteasome complex, as well as their effects. The five interactions that we discovered have a higher predicted binding affinity when one of the conformations is mutated: LTF-SNCA, FGA-LTF, UBE2D1-VCP, ERBB4-INS, and NFE2L2-VCP. Additionally, since VCP has a conformational mutation linked to dementia, we proposed that the cancer-related protein BRCA1 may have implications for VCI. BRCA1s interaction with both wild-type and mutant XRCC4 and LIG4 suggests the significance of the DNA damage response pathway which can be shared between VCI and cancer.Altogether, our results suggest various pathways and interactions that can act as targets for therapeutic interventions or early diagnosis of VCI.

systems biology↗

bioRxiv2023

Understanding Molecular Links of Vascular Cognitive Impairment: Selective Interaction between Mutant APP, TP53, and MAPKs

Vascular cognitive impairment (VCI) is an understudied cerebrovascular disease. As it can result in a significant amount of functional and cognitive disabilities, it is vital to reveal proteins related to it. Our study focuses on revealing proteins related to this complex disease by deciphering the crosstalk between cardiovascular and cognitive diseases. We build protein-protein interaction networks related to cardiovascular and cognitive diseases. After merging these networks, we analyze the network to extract the hub proteins and their interactors. We found the clusters on this network and built the structural protein-protein interaction network of the most connected cluster on the network. We analyzed the interactions of this network with molecular modeling via PRISM. PRISM predicted several interactions that can be novel in the context of VCI-related interactions. Two mutant forms of APP (V715M and L723P), previously not connected to VCI, were discovered to interact with other proteins. Our findings demonstrate that two mutant forms of APP interact differently with TP53 and MAPKs. Furthermore, TP53, AKT1, PARP1, and FGFR1 interact with MAPKs through their mutant conformations. We hypothesize that these interactions might be crucial for VCI. We suggest that these interactions and proteins can act as early VCI markers or as possible therapeutic targets.

systems biology↗

bioRxiv2023

DiPPI: A curated dataset for drug-like molecules in protein-protein interfaces

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large dataset for drug-like molecules in protein interfaces. We further present DiPPI (Drugs in Protein-Protein Interfaces), a two-module website to facilitate the search for such molecules and their properties by exploiting our dataset in drug repurposing studies. In the interface module of the website, we extracted several properties of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we curated a list of drug-like small molecules and FDA-approved drugs from various databases and extracted those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinskis rules and various molecular descriptors, are also calculated and made available on the website to guide the selection of drug molecules. Our dataset contains 534,203 interfaces for 98,632 proteins, of which 55,135 are detected to bind to a drug-like molecule. 2,214 drug-like molecules are deposited on our website, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data; and is freely available at http://interactome.ku.edu.tr:8501.

bioinformatics↗

bioRxiv2023

Revealing Shared Proteins and Pathways in Cardiovascular and Cognitive Diseases Using Protein Interaction Network Analysis

One of the primary goals of systems medicine is detecting putative proteins and pathways involved in disease progression and pathological phenotypes. Vascular Cognitive Impairment (VCI) is a heterogeneous condition manifesting as cognitive impairment resulting from vascular factors. The precise mechanisms underlying this relationship remain unclear, which poses challenges for experimental research. Here, we applied computational approaches like systems biology to unveil and select relevant proteins and pathways related to VCI by studying the crosstalk between cardiovascular and cognitive diseases. In addition, we specifically included signals related to oxidative stress, a common etiologic factor tightly linked to aging, a major determinant of VCI. Our results show that pathways associated with oxidative stress are quite relevant, as most of the prioritized vascular-cognitive genes/proteins were enriched in these pathways. Our analysis provided a short list of proteins that could be contributing to VCI: DOLK, TSC1, ATP1A1, MAPK14, YWHAZ, CREB3, HSPB1, PRDX6, and LMNA. Moreover, our experimental results suggest a high implication of glycative stress, generating oxidative processes and post-translational protein modifications through advanced glycation end-products (AGEs). We propose that these products interact with their specific receptors (RAGE) and Notch signaling to contribute to the etiology of VCI.

systems biology↗