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Phosphatase of regenerating liver (PRL) is an oncogenic protein that promotes tumor progression by directly binding to cyclin M (CNNM) membrane proteins and inhibiting their Mg2+ efflux activity. In this study, we have developed a high-throughput screening system to detect the interactions between PRL and CNNM proteins based on homogenous time-resolved fluorescence resonance energy transfer (HTR-FRET, HTRF). We optimized the tag sequences attached to the recombinant proteins of the CNNM4 CBS domains and PRL3 lacking the carboxyl terminal CAAX motif, and successfully detected the interaction by observing the FRET signal in the mixture of the tagged proteins and fluorophore-conjugated antibodies. Moreover, we performed compound library screening using this system and discovered several compounds that could efficiently inhibit the PRL-CNNM interaction. Characterization of one candidate compound revealed that it was relatively stable compared with thienopyridone, a known inhibitor of the PRL-CNNM interaction. The candidate compound can also inhibit PRL function in cells: suppression of CNNM-dependent Mg2+ efflux, and has sufficient in vitro drug metabolism and pharmacokinetic properties. Overall, these results demonstrate the effectiveness of this screening system for identifying novel inhibitors of the PRL-CNNM interaction, which could contribute to the development of novel anti-cancer drugs. [ABSTRACT FROM AUTHOR]

The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coinfection may result in complications that make treatment more difficult. However, the molecular mechanisms underpinning the interaction between TB and COVID-19 are unclear. Accordingly, transcriptome analysis was used to detect the shared pathways and molecular biomarkers in TB and COVID-19, allowing us to determine the complex relationship between COVID-19 and TB. Two RNA-seq datasets (GSE114192 and GSE163151) from the Gene Expression Omnibus were used to find concerted differentially expressed genes (DEGs) between TB and COVID-19 to identify the common pathogenic mechanisms. A total of 124 common DEGs were detected and used to find shared pathways and drug targets. Several enterprising bioinformatics tools were applied to perform pathway analysis, enrichment analysis and networks analysis. Protein–protein interaction analysis and machine learning was used to identify hub genes (GAS6, OAS3 and PDCD1LG2) and datasets GSE171110, GSE54992 and GSE79362 were used for verification. The mechanism of protein-drug interactions may have reference value in the treatment of coinfection of COVID-19 and TB. [ABSTRACT FROM AUTHOR]

Ras Proteins, play a pivotal role in the proliferation pathways, and Ras mutant forms are well-established cancer drivers. Ras mutations are found in about 30% of all human cancers widely known as challenging diseases to control and treat. The direct inhibition of Ras is challenging and makes Ras an “undruggable” target for many years. The important reason is, that Ras protein has a unique smooth surface and shows different dynamicity upon binding GDP and GTP. As a result, interfering peptides (IPs) targeting the Ras family protein-protein interactions (PPIs) are considered more likely to bind Ras effectively and inhibit the downstream signaling. In this review, we aimed to cover the recent approaches to design the peptides that target Ras family proteins, focusing on in silico methods. In this regard, the anti-cancer peptide development approaches including design and delivery strategies are discussed. Later, more specific methods regarding Ras-specific peptide design are presented. In conclusion, IPs are a promising group of cancer therapeutics to combat Ras mutant cancers. For future perspectives to have these peptides in clinical use, co-inhibition of other cancer targets as well as improving the pharmacokinetic features of peptides are suggested. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV) is an arbovirus of the Flaviviridae genus that has rapidly disseminated from across the Pacific to the Americas. Robust evidence has indicated a crucial role of ZIKV in congenital virus syndrome, including neonatal microcephaly. Moreover, emerging evidence suggests an association between ZIKV infection and the development of an extensive spectrum of central nervous system inflammatory demyelinating diseases (CNS IDD), such as multiple sclerosis–like clinical phenotypes. However, the underlying mechanisms of host-pathogen neuro-immune interactions remain to be elucidated. This study aimed to identify common transcriptional signatures between multiple sclerosis (MS) and ZIKV infection to generate molecular interaction networks, thereby leading to the identification of deregulated processes and pathways, which could give an insight of these underlying molecular mechanisms. Our investigation included publicly available transcriptomic data from MS patients in either relapse or remission (RR-MS) and datasets of subjects acutely infected by ZIKV for both immune peripheral cells and central nervous system cells. The protein-protein interaction (PPI) analysis showed upregulated AP-1 transcription factors (JUN and FOS) among the top hub and bottleneck genes in RR-MS and ZIKV data. Gene enrichment analysis retrieved a remarkable presence of ontologies and pathways linked to oxidative stress responses, immune cell function, inflammation, interleukin signaling, cell division, and transcriptional regulation commonly enriched in both scenarios. Considering the recent findings concerning AP-1 function in immunological tolerance breakdown, regulation of inflammation, and its function as an oxidative stress sensor, we postulate that the ZIKV trigger may contribute as a boost for the activation of such AP-1-regulated mechanisms that could favor the development of MS-like phenotypes following ZIKV infection in a genetically susceptible individual. [ABSTRACT FROM AUTHOR]

Histological interpretation of the rare pleomorphic xanthoastrocytoma (PXA) has been the holy grail for treatment options. However, no stand-alone clinical interventions have been developed owing to the lack of gene expression profiling data in PXA/APXA patients. We first time report the comprehensive analyses of the coding as well as long non-coding RNA (lncRNA) signatures of PXA/APXA patients. Several genes such as IGFBP2, NF1, FOS, ERBB2, and lncRNAs such as NEAT1, HOTAIRM1, and GAS5 known to play crucial roles in glioma patients were also deregulated in PXA patients suggesting the commonality in the molecular signatures. PPI network, co-expression, and lncRNA-mRNA interaction studies unraveled hub genes (such as ERBB2, FOS, RPA1) and networks that may play a critical role in PXA biology. The most enriched pathways based on gene profiles were related to TLR, chemokine, MAPK, Rb, and PI3K-Akt signaling pathways. The lncRNA targets were enriched in glucuronidation, adipogenesis, TGF-beta signaling, EGF/EGFR signaling, and cell cycle pathways. Interestingly, several mRNAs like PARVG, and ABI2 were found to be targeted by multiple lncRNAs suggesting a tight control of their levels. Some of the most prominent lncRNA-mRNA pairs were LOC728730: MRPL9, XLOC_l2_011987: ASIC2, lnc-C1QTNF5-1: RNF26. Notably, several lncRNAs such as lnc-CETP-1, lnc-XRCC3-1, lnc-RPL31-1, lnc-USP13-1, and MAPKAPK5-AS1, and genes such as RPA1, NTRK3, and CNRP1 showed strong correlation to the progression-free survival of PXA patients suggesting their potential as novel biomarkers. Overall, the findings of this study may facilitate the development of a new realm of RNA biology in PXA that may have clinical significance in the future. [ABSTRACT FROM AUTHOR]

Vitiligo is an idiopathic disorder characterized by depigmented patches on the skin due to progressive loss of melanocytes. Several genetic, immunological, and pathophysiological investigations have established vitiligo as a polygenetic disorder with multifactorial etiology. However, no definite model explaining the interplay between these causative factors has been established hitherto. Therefore, we studied the disorder at the system level to identify the key proteins involved by exploring their molecular connectivity in terms of topological parameters. The existing research data helped us in collating 215 proteins involved in vitiligo onset or progression. Interaction study of these proteins leads to a comprehensive vitiligo map with 4845 protein nodes linked with 107,416 edges. Based on centrality measures, a backbone network with 500 nodes has been derived. This has presented a clear overview of the proteins and processes involved and the crosstalk between them. Clustering backbone proteins revealed densely connected regions inferring major molecular interaction modules essential for vitiligo. Finally, a list of top order proteins that play a key role in the disease pathomechanism has been formulated. This includes SUMO2, ESR1, COPS5, MYC, SMAD3, and Cullin proteins. While this list is in fair agreement with the available literature, it also introduces new candidate proteins that can be further explored. A subnetwork of 64 vitiligo core proteins was built by analyzing the backbone and seed protein networks. Our finding suggests that the topology, along with functional clustering, provides a deep insight into the behavior of proteins. This in turn aids in the illustration of disease condition and discovery of significant proteins involved in vitiligo. [ABSTRACT FROM AUTHOR]

Conventional instruments for cancer diagnosis including magnetic resonance imaging, computed tomography scan, are expensive and require long-waiting time, whilst the outcomes have not approached to the successful early-stage diagnosis yet. Due to the special properties of graphene-based nanocomposites, e.g., good electrical and thermal conductivity, luminescence, and mechanic flexibility, these ultra-thin two-dimensional nanostructures have been extensively used as platforms for detecting biomolecules and cells. Herein, we discuss the development of two types of graphene and graphene oxide-based biosensors: electrochemical and optical, aimed for tumor detection and early diagnosis of cancer. Moreover, we highlight the challenges of their use as biosensors for cancer detection. Efficient surface modification and suitable bio-conjugation of graphene and graphene oxide is discussed, including key role in improvement of the biocompatibility, and improved performance in terms of selectivity and sensitivity towards the early diagnosis of cancer. [ABSTRACT FROM AUTHOR]

The protein tyrosine phosphatase interacting protein 51 (PTPIP51) is thought to regulate crucial cellular functions such as mitosis, apoptosis, migration, differentiation and communication between organelles as a scaffold protein. These diverse functions are modulated by the tyrosine/serine phosphorylation status of PTPIP51. This review interconnects the insights obtained about the action of PTPIP51 in mitogen-activated protein kinase signaling, nuclear factor kB signaling, calcium homeostasis and chromosomal segregation and identifies important signaling hubs. The interference of PTPIP51 in such multiprotein complexes and their PTPIP51-modulated cross-talk makes PTPIP51 an ideal target for novel drugs such as the small molecule LDC-3. [ABSTRACT FROM AUTHOR]

Drug-drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our assumption was that a query drug (Dq) and a drug to be examined (De) likely have DDI if the drugs in the interaction network of De are structurally similar to Dq. A network of De describes the associations between the drugs and the proteins relating to PK and PD for De. These include target proteins, proteins interacting with target proteins, enzymes, and transporters for De. We constructed logistic regression models for DDI prediction using only 2D structural similarities between each Dq and the drugs in the network of De. The results indicated that our models could effectively predict DDIs. It was found that integrating structural similarity scores of the drugs relating to both PK and PD of De was crucial for model performance. In particular, the combination of the target- and enzyme-related scores provided the largest increase of the predictive power. [ABSTRACT FROM AUTHOR]

Embryonic stem (ES) cells derived from the inner cell mass of developing embryos have tremendous potential in regenerative medicine due to their unique properties: ES cells can be maintained for a prolonged time without changes in their cellular characteristics in vitro (self-renewal), while sustaining the capacity to give rise to all cell types of adult organisms (pluripotency). In addition to the development of protocols to manipulate ES cells for therapeutic applications, understanding how such unique properties are maintained has been one of the key questions in stem cell research. During the past decade, advances in high-throughput technologies have enabled us to systematically monitor multiple layers of gene regulatory mechanisms in ES cells. In this review, we briefly summarize recent findings on global gene regulatory modes in ES cells, mainly focusing on the regulatory factors responsible for transcriptional and epigenetic regulations as well as their modular regulatory patterns throughout the genome. [ABSTRACT FROM AUTHOR]

Cancer is not rare anywhere in the world now, and the global burden of cancer continues to increase largely every year. Previous research on infections and cancers reported that, about 17.8 % of the cancers worldwide, which are over 1.9 million cases of cancer, are related to viral infections. At least six oncoviruses, cancer-causing viruses, have been known so far, which include hepatitis B virus, hepatitis C virus, Epstein-Barr virus (EBV or HHV-4), human papillomavirus, human T lymphotropic virus type 1, Kaposi's sarcoma-associated herpesvirus (KSHV or HHV-8), but the pathogenic mechanism is far from being completely understood. In this study, assuming that finding human proteins significantly similar to viral oncoproteins leads to a categorization of the cancer-related pathways that are currently not clearly known, we analyzed different types of virus-caused cancers based on their similarity in order to clarify the unknown cancer mechanisms. As a result, we obtained several potential tumor pathways that may be significant and essential in oncogenic cancer process, which will be helpful for further study on cancer mechanisms and the development of new drug targets. [ABSTRACT FROM AUTHOR]

In order to explore the differential effects of TGF-beta3 and BMP2 on chondrogenesis in mesenchymal stem cells (MSCs), the gene expression profiles of MSC treated with TGF-beta3 and BMP2 were subjected to systematic analysis on the gene and functional level. The gene expression profiles of MSCs (downloaded from Gene Expression Omnibus database) in the early and later stages, induced with TGF-beta2 and BMP2, were analyzed using packages within R software and the differentially expressed genes (DEGs) were screened. The DEGs both in the two experimental groups were subjected to Gene Ontology and pathway enrichment analysis. The protein–protein interaction (PPI) networks of the DEGs were constructed using cytoscape software. Among the DEGs, 1,194 genes were up-regulated and 580 genes were down-regulated. The up-regulated genes were mainly enriched in the TGF-beta and cell cycle signaling pathways and down-regulated genes were mainly enriched in the insulin-mediated signal pathway, metabolic pathway of fructose and mannose, and glycolysis/gluconeogenesis pathway. Based on the PPI network analysis, the genes of KIAA0101, NEDD4, and TINF2 were confirmed to be important on chondrogenesis. The analysis of DEGs both in TGF-beta3 and BMP2 treated MSCs indicates that the genes are mainly involved in the cell cycle and intracellular signaling pathways. Also the similar gene expression profile can be achieved by transcription factors or microRNAs (miR-199a-5p and miR-31-5p) based on our prediction, which can provide a new approach for the treatment of cartilage injury. [ABSTRACT FROM AUTHOR]

Functional protein microarray is an important tool for high-throughput and large-scale systems biology studies. Besides the progresses that have been made for protein microarray fabrication, significant advancements have also been achieved for applying protein microarrays on determining a variety of protein biochemical activities. Among these applications, detection of protein binding properties, such as protein-protein interactions (PPIs), protein-DNA interactions (PDIs), protein-RNA interactions, and antigen-antibody interactions, are straightforward and have substantial impacts on many research fields. In this review, we will focus on the recent progresses in protein-protein, protein-DNA, protein-RNA, protein-small molecule, protein-lipid, protein-glycan, and antigen-antibody interactions. We will also discuss the challenges and future directions of protein microarray technologies. We strongly believe that protein microarrays will soon become an indispensible tool for both basic research and clinical applications. [ABSTRACT FROM AUTHOR]

Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules-groups of vertices within which connections are dense but between which they are sparse. Identifying these modules is likely through capturing the biologically meaningful interactions. In recent years, many algorithms have been developed for detecting such structures. These algorithms, however, are computationally demanding, which limits their applications. In this paper, we propose a fast iterative-clique percolation method (ICPM) for identifying overlapping functional modules in protein-protein interaction (PPI) networks. Our method is based on clique percolation method (CPM), and it not only considers the degree of nodes to minimize the search space (the vertices in k-cliques must have the degree of k − 1 at least), but also converts k-cliques to ( k − 1)-cliques. It finds k-cliques by appending one node to ( k − 1)-cliques. By testing our method on PPI networks, our analysis of the yeast PPI network suggests that most of these modules have well-supported biological significance. [ABSTRACT FROM AUTHOR]