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Topologically significant directed random walk with applied walker network in cancer environment

Seah CS, Kasim S, Saedudin RR, Md Fudzee MF, Mohamad MS, Hassan R, et al.

Pak J Pharm Sci, 2019 May;32(3 Special):1395-1408.
PMID: 31551221

Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients.

Matched MeSH terms: Protein Interaction Maps/genetics
Fulltext The FBXW7-NOTCH interactome: A ubiquitin proteasomal system-induced crosstalk modulating oncogenic transformation in human tissues

Kar R, Jha SK, Ojha S, Sharma A, Dholpuria S, Raju VSR, et al.

Cancer Rep (Hoboken), 2021 08;4(4):e1369.
PMID: 33822486 DOI: 10.1002/cnr2.1369

BACKGROUND: Ubiquitin ligases or E3 ligases are well programmed to regulate molecular interactions that operate at a post-translational level. Skp, Cullin, F-box containing complex (or SCF complex) is a multidomain E3 ligase known to mediate the degradation of a wide range of proteins through the proteasomal pathway. The three-dimensional domain architecture of SCF family proteins suggests that it operates through a novel and adaptable "super-enzymatic" process that might respond to targeted therapeutic modalities in cancer.
RECENT FINDINGS: Several F-box containing proteins have been characterized either as tumor suppressors (FBXW8, FBXL3, FBXW8, FBXL3, FBXO1, FBXO4, and FBXO18) or as oncogenes (FBXO5, FBXO9, and SKP2). Besides, F-box members like βTrcP1 and βTrcP2, the ones with context-dependent functionality, have also been studied and reported. FBXW7 is a well-studied F-box protein and is a tumor suppressor. FBXW7 regulates the activity of a range of substrates, such as c-Myc, cyclin E, mTOR, c-Jun, NOTCH, myeloid cell leukemia sequence-1 (MCL1), AURKA, NOTCH through the well-known ubiquitin-proteasome system (UPS)-mediated degradation pathway. NOTCH signaling is a primitive pathway that plays a crucial role in maintaining normal tissue homeostasis. FBXW7 regulates NOTCH protein activity by controlling its half-life, thereby maintaining optimum protein levels in tissue. However, aberrations in the FBXW7 or NOTCH expression levels can lead to poor prognosis and detrimental outcomes in patients. Therefore, the FBXW7-NOTCH axis has been a subject of intense study and research over the years, especially around the interactome's role in driving cancer development and progression. Several studies have reported the effect of FBXW7 and NOTCH mutations on normal tissue behavior. The current review attempts to critically analyze these mutations prognostic value in a wide range of tumors. Furthermore, the review summarizes the recent findings pertaining to the FBXW7 and NOTCH interactome and its involvement in phosphorylation-related events, cell cycle, proliferation, apoptosis, and metastasis.
CONCLUSION: The review concludes by positioning FBXW7 as an effective diagnostic marker in tumors and by listing out recent advancements made in cancer therapeutics in identifying protocols targeting the FBXW7-NOTCH aberrations in tumors.

Matched MeSH terms: Protein Interaction Maps/genetics*
Systematic Analysis of Protein Interaction Network Associated with Azoospermia

Sabetian S, Shamsir MS

Int J Mol Sci, 2016 Nov 10;17(11).
PMID: 27834916

Non-obstructive azoospermia is a severe infertility factor. Currently, the etiology of this condition remains elusive with several possible molecular pathway disruptions identified in the post-meiotic spermatozoa. In the presented study, in order to identify all possible candidate genes associated with azoospermia and to map their relationship, we present the first protein-protein interaction network related to azoospermia and analyze the complex effects of the related genes systematically. Using Online Mendelian Inheritance in Man, the Human Protein Reference Database and Cytoscape, we created a novel network consisting of 209 protein nodes and 737 interactions. Mathematical analysis identified three proteins, ar, dazap2, and esr1, as hub nodes and a bottleneck protein within the network. We also identified new candidate genes, CREBBP and BCAR1, which may play a role in azoospermia. The gene ontology analysis suggests a genetic link between azoospermia and liver disease. The KEGG analysis also showed 45 statistically important pathways with 31 proteins associated with colorectal, pancreatic, chronic myeloid leukemia and prostate cancer. Two new genes and associated diseases are promising for further experimental validation.

Matched MeSH terms: Protein Interaction Maps/genetics*
Fulltext Delineating the Plausible Molecular Vaccine Candidates and Drug Targets of Multidrug-Resistant Acinetobacter baumannii

Mujawar S, Mishra R, Pawar S, Gatherer D, Lahiri C

Front Cell Infect Microbiol, 2019;9:203.
PMID: 31281799 DOI: 10.3389/fcimb.2019.00203

Nosocomial infections have become alarming with the increase of multidrug-resistant bacterial strains of Acinetobacter baumannii. Being the causative agent in ~80% of the cases, these pathogenic gram-negative species could be deadly for hospitalized patients, especially in intensive care units utilizing ventilators, urinary catheters, and nasogastric tubes. Primarily infecting an immuno-compromised system, they are resistant to most antibiotics and are the root cause of various types of opportunistic infections including but not limited to septicemia, endocarditis, meningitis, pneumonia, skin, and wound sepsis and even urinary tract infections. Conventional experimental methods including typing, computational methods encompassing comparative genomics, and combined methods of reverse vaccinology and proteomics had been proposed to differentiate and develop vaccines and/or drugs for several outbreak strains. However, identifying proteins suitable enough to be posed as drug targets and/or molecular vaccines against the multidrug-resistant pathogenic bacterial strains has probably remained an open issue to address. In these cases of novel protein identification, the targets either are uncharacterized or have been unable to confer the most coveted protection either in the form of molecular vaccine candidates or as drug targets. Here, we report a strategic approach with the 3,766 proteins from the whole genome of A. baumannii ATCC19606 (AB) to rationally identify plausible candidates and propose them as future molecular vaccine candidates and/or drug targets. Essentially, we started with mapping the vaccine candidates (VaC) and virulence factors (ViF) of A. baumannii strain AYE onto strain ATCC19606 to identify them in the latter. We move on to build small networks of VaC and ViF to conceptualize their position in the network space of the whole genomic protein interactome (GPIN) and rationalize their candidature for drugs and/or molecular vaccines. To this end, we propose new sets of known proteins unearthed from interactome built using key factors, KeF, potent enough to compete with VaC and ViF. Our method is the first of its kind to propose, albeit theoretically, a rational approach to identify crucial proteins and pose them for candidates of vaccines and/or drugs effective enough to combat the deadly pathogenic threats of A. baumannii.

Matched MeSH terms: Protein Interaction Maps/genetics