Protein interaction network for Alzheimer's disease using computational approach

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

Construction of Unified Human Antimicrobial and Immunomodulatory Peptide Database and Examination of Antimicrobial and Immunomodulatory Peptides in Alzheimer's Disease Using Network Analysis of Proteomics Datasets

The reanalysis of genomics and proteomics datasets by bioinformatics approaches is an appealing way to examine large amounts of reliable data. This can be especially true in cases such as Alzheimer's disease, where the access to biological samples, along with well-defined patient information can be challenging. Considering the inflammatory part of Alzheimer's disease, our aim was to examine the presence of antimicrobial and immunomodulatory peptides in human proteomic datasets deposited in the publicly available proteomics database ProteomeXchange (http://www.proteomexchange.org/). First, a unified, comprehensive human antimicrobial and immunomodulatory peptide database, containing all known human antimicrobial and immunomodulatory peptides was constructed and used along with the datasets containing high-quality proteomics data originating from the examination of Alzheimer's disease and control groups. A throughout network analysis was carried out, and the enriched GO functions were examined. Less than 1% of all identified proteins in the brain were antimicrobial and immunomodulatory peptides, but the alterations characteristic of Alzheimer's disease could be recapitulated with their analysis. Our data emphasize the key role of the innate immune system and blood clotting in the development of Alzheimer's disease. The central role of antimicrobial and immunomodulatory peptides suggests their utilization as potential targets for mechanistic studies and future therapies.

Microarray Analysis of Differential Gene Expression in Alzheimer's Disease Identifies Potential Biomarkers with Diagnostic Value

BACKGROUND Alzheimer disease (AD) is a common and fatal subtype of dementia that remains a challenge to diagnose and treat. This study aimed to identify potential biomarkers that influence the prognosis of AD. MATERIAL AND METHODS A total of 6 gene expression profiles from the Gene Expression Omnibus (GEO) database were assessed for their potential as AD biomarkers. We identified differentially expressed genes (DEGs) using the prediction analysis for microarray (PAM) algorithm and obtained hub genes through the analysis of the protein-protein interaction (PPI) network and module analysis. RESULTS We identified 6 gene expression profiles from the GEO database and assessed their potential as AD biomarkers. Shared gene sets were extracted and integrated into large expression profile matrices. We identified 2514 DEGs including 68 upregulated- and 2446 downregulated genes through analysis of the limma package. We screened 379 significant DEGs including 68 upregulated and 307 downregulated genes for their ability to distinguish AD from control samples using PAM algorithm. Functional enrichment of the 379 target genes was produced from Database for Annotation, Visualization and Integrated Discovery.(DAVID) and included histone function, beta receptor signaling, cell growth, and angiogenesis. The downregulated genes were significantly enriched in MAPK signaling, synaptic signaling, neuronal apoptosis and AD associated pathways. Upon analysis of the PPI network, 32 hub genes including ENO2, CCT2, CALM2, ACACB, ATP5B, MDH1, and PP2CA were screened. Of these hub genes, NFKBIA and ACACB were upregulated and 29 genes were downregulated in AD patients. CONCLUSIONS We screened 379 significant DEGs as potential biomarkers of AD using PAM and obtained 32 hub genes through PPI network and module analysis. These findings reveal new potential AD biomarkers with prognostic and therapeutic value.

Computer aided analysis of disease linked protein networks

Proteins can interact in various ways, ranging from direct physical relationships to indirect interactions in a formation of protein-protein interaction network. Diagnosis of the protein connections is critical to identify various cellular pathways. Today constructing and analyzing the protein interaction network is being developed as a powerful approach to create network pharmacology toward detecting unknown genes and proteins associated with diseases. Discovery drug targets regarding therapeutic decisions are exciting outcomes of studying disease networks. Protein connections may be identified by experimental and recent new computational approaches. Due to difficulties in analyzing in-vivo proteins interactions, many researchers have encouraged improving computational methods to design protein interaction network. In this review, the experimental and computational approaches and also advantages and disadvantages of these methods regarding the identification of new interactions in a molecular mechanism have been reviewed. Systematic analysis of complex biological systems including network pharmacology and disease network has also been discussed in this review.

A New Approach for the Diagnosis of Myelodysplastic Syndrome Subtypes Based on Protein Interaction Analysis

Myelodysplastic syndromes (MDS) are a heterogeneous group of hematological malignancies with a high risk of transformation to acute myeloid leukemia (AML). MDS are associated with posttranslational modifications of proteins and variations in the protein expression levels. In this work, we present a novel interactomic diagnostic method based on both protein array and surface plasmon resonance biosensor technology, which enables monitoring of protein-protein interactions in a label-free manner. In contrast to conventional methods based on the detection of individual biomarkers, our presented method relies on measuring interactions between arrays of selected proteins and patient plasma. We apply this method to plasma samples obtained from MDS and AML patients, as well as healthy donors, and demonstrate that even a small protein array comprising six selected proteins allows the method to discriminate among different MDS subtypes and healthy donors.

Protein-Protein Interactions and Aggregation Inhibitors in Alzheimer's Disease

BACKGROUND

Alzheimer's Disease (AD), a multifaceted disorder, involves complex pathophysiology and plethora of protein-protein interactions. Thus such interactions can be exploited to develop anti-AD drugs.

OBJECTIVE

The interaction of dynamin-related protein 1, cellular prion protein, phosphoprotein phosphatase 2A and Mint 2 with amyloid β, etc., studied recently, may have critical role in progression of the disease. Our objective has been to review such studies and their implications in design and development of drugs against the Alzheimer's disease.

METHODS

Such studies have been reviewed and critically assessed.

RESULTS

Review has led to show how such studies are useful to develop anti-AD drugs.

CONCLUSION

There are several PPIs which are current topics of research including Drp1, Aβ interactions with various targets including PrPC, Fyn kinase, NMDAR and mGluR5 and interaction of Mint2 with PDZ domain, etc., and thus have potential role in neurodegeneration and AD. Finally, the multi-targeted approach in AD may be fruitful and opens a new vista for identification and targeting of PPIs in various cellular pathways to find a cure for the disease.

Genome-wide expression profiling of glioblastoma using a large combined cohort

Glioblastomas (GBMs), are the most common intrinsic brain tumors in adults and are almost universally fatal. Despite the progresses made in surgery, chemotherapy, and radiation over the past decades, the prognosis of patients with GBM remained poor and the average survival time of patients suffering from GBM was still short. Discovering robust gene signatures toward better understanding of the complex molecular mechanisms leading to GBM is an important prerequisite to the identification of novel and more effective therapeutic strategies. Herein, a comprehensive study of genome-scale mRNA expression data by combining GBM and normal tissue samples from 48 studies was performed. The 147 robust gene signatures were identified to be significantly differential expression between GBM and normal samples, among which 100 (68%) genes were reported to be closely associated with GBM in previous publications. Moreover, function annotation analysis based on these 147 robust DEGs showed certain deregulated gene expression programs (e.g., cell cycle, immune response and p53 signaling pathway) were associated with GBM development, and PPI network analysis revealed three novel hub genes (RFC4, ZWINT and TYMS) play important role in GBM development. Furthermore, survival analysis based on the TCGA GBM data demonstrated 38 robust DEGs significantly affect the prognosis of GBM in OS (p < 0.05). These findings provided new insights into molecular mechanisms underlying GBM and suggested the 38 robust DEGs could be potential targets for the diagnosis and treatment.

Clinical and Environmental Correlates of Serum BDNF: A Descriptive Study with Plausible Implications for AD Research

BACKGROUND

Brain derived neurotrophic factor (BDNF) may play a central role in the pathogenesis of Alzheimer's disease (AD) through neurotrophic effects on basal cholinergic neurons. Reduced serum levels of BDND are observed among AD patients and may predict AD risk. Nevertheless, knowledge about factors associated with its levels in blood is lacking.

OBJECTIVE

To identify clinical and demographic correlates of serum BDNF levels.

METHODS

BDNF was measured from serum collected between 1992-1996 and 1998-2001 in participants from the Original and Offspring cohorts of the Framingham Study, respectively. A cross-sectional analysis was done to evaluate the relationship between clinical measures and BDNF levels using standard linear regression and stepwise models. Analyses were conducted in the total sample and separately in each cohort, and were adjusted for age and sex.

RESULTS

BDNF was measured in 3,689 participants (mean age 65 years, 56% women; 82% Offspring). Cigarette smoking and high total cholesterol were associated with elevated BDNF levels, and history of atrial fibrillation was associated with decreased levels. Elevated BDNF levels were related to greater physical activity and lower Tumor Necrosis Factor-α levels in Offspring. Stepwise models also revealed associations with statin use, alcohol consumption and Apolipoprotein Eε4 genotype.

CONCLUSION

Serum BDNF correlates with various metabolic, inflammatory and life-style measures which in turn have been linked with risk of AD. Future studies of serum BDNF should adjust for these correlates and are needed to further explore the underlying interplay between BDNF and other factors in the pathophysiology of cognitive impairment and AD.

Protein complex finding and ranking: An application to Alzheimer's disease

Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein-protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast and a few other model organisms. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called ComFiR to detect such protein complexes and further rank diseased complexes based on a query disease. We have shown that it has better performance in identifying protein complexes from human PPI data. This method is evaluated in terms of positive predictive value, sensitivity and accuracy. We have introduced a ranking approach and showed its application on Alzheimer's disease.

Proteomics and lipidomics in the human brain

Proteomics and lipidomics are powerful tools to the large-scale study of proteins and lipids, respectively. Several methods can be employed with particular benefits and limitations in the study of human brain. This is a review of the rationale use of current techniques with particular attention to limitations and pitfalls inherent to each one of the techniques, and more importantly, to their use in the study of post-mortem brain tissue. These aspects are cardinal to avoid false interpretations, errors and unreal expectancies. Other points are also stressed as exemplified in the analysis of human neurodegenerative diseases which are manifested by disease-, region-, and stage-specific modifications commonly in the context of aging. Information about certain altered protein clusters and proteins oxidatively damaged is summarized for Alzheimer and Parkinson diseases.

Proteomic analysis of the cullin 4B interactome using proximity-dependent biotinylation in living cells

Cullin 4B (CUL4B) mutations have been implicated in mental retardation and dopamine-related behaviors due to disruptions in their interaction with cullin-RING E3 ligases (CRLs). Thus, further identification of CUL4B substrates can increase the knowledge of protein homeostasis and illuminate the role of CUL4B in neuropsychiatric disease. However, the transient nature of the coupling between CUL4B and its substrates is difficult to detect in vivo using current approaches, thus hampers efforts to investigate functions of CRLs within unperturbed living systems. In this study, we sought to discover CUL4B interactants with or without dopamine stimulation. BirA (118G) proximity-dependent biotin labeling combined with LC-MS was employed to biotinylate and identify transient and weak interactants of CUL4B. After purification with streptavidin beads and identified by LC-MS, a total of 150 biotinylated proteins were identified at baseline condition, 53 of which are well-known CUL4B interactants. After dopamine stimulation, 29 proteins disappeared and were replaced by 21 different protein interactants. The altered CUL4B interactants suggest that CUL4B regulates protein turnover and homeostasis in response to dopamine stimulation. Our results demonstrate the potential of this approach to identify novel CUL4B-related molecules in respond to cellular stimuli, which may be applied to other types of signaling pathways.

Improving analytical methods for protein-protein interaction through implementation of chemically inducible dimerization

When investigating interactions between two proteins with complementary reporter tags in yeast two-hybrid or split GFP assays, it remains troublesome to discriminate true- from false-negative results and challenging to compare the level of interaction across experiments. This leads to decreased sensitivity and renders analysis of weak or transient interactions difficult to perform. In this work, we describe the development of reporters that can be chemically induced to dimerize independently of the investigated interactions and thus alleviate these issues. We incorporated our reporters into the widely used split ubiquitin-, bimolecular fluorescence complementation (BiFC)- and Förster resonance energy transfer (FRET)- based methods and investigated different protein-protein interactions in yeast and plants. We demonstrate the functionality of this concept by the analysis of weakly interacting proteins from specialized metabolism in the model plant Arabidopsis thaliana. Our results illustrate that chemically induced dimerization can function as a built-in control for split-based systems that is easily implemented and allows for direct evaluation of functionality.