The potential of translational bioinformatics approaches for pharmacology research

Translational bioinformatics approach to combat cardiovascular disease and cancers

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

Machine learning and bioinformatics-based insights into the potential targets of saponins in Paris polyphylla smith against non-small cell lung cancer

Background: Lung cancer has the highest mortality rate among cancers worldwide, and non-small cell lung cancer (NSCLC) is the major lethal factor. Saponins in Paris polyphylla smith exhibit antitumor activity against non-small cell lung cancer, but their targets are not fully understood.

Methods: In this study, we used differential gene analysis, lasso regression analysis and support vector machine recursive feature elimination (SVM-RFE) to screen potential key genes for NSCLC by using relevant datasets from the GEO database. The accuracy of the signature genes was verified by using ROC curves and gene expression values. Screening of potential active ingredients for the treatment of NSCLC by molecular docking of the reported active ingredients of saponins in Paris polyphylla Smith with the screened signature genes. The activity of the screened components and their effects on key genes expression were further validated by CCK-8, flow cytometry (apoptosis and cycling) and qPCR.

Results: 204 differential genes and two key genes (RHEBL1, RNPC3) stood out in the bioinformatics analysis. Overall survival (OS), First-progression survival (FP) and post-progression survival (PPS) analysis revealed that low expression of RHEBL1 and high expression of RNPC3 indicated good prognosis. In addition, Polyphyllin VI(PPVI) and Protodioscin (Prot) effectively inhibited the proliferation of non-small cell lung cancer cell line with IC50 of 4.46 μM ± 0.69 μM and 8.09 μM ± 0.67μM, respectively. The number of apoptotic cells increased significantly with increasing concentrations of PPVI and Prot. Prot induces G1/G0 phase cell cycle arrest and PPVI induces G2/M phase cell cycle arrest. After PPVI and Prot acted on this cell line for 48 h, the expression of RHEBL1 and RNPC3 was found to be consistent with the results of bioinformatics analysis.

Conclusion: This study identified two potential key genes (RHEBL1 and RNPC3) in NSCLC. Additionally, PPVI and Prot may act on RHEBL1 and RNPC3 to affect NSCLC. Our findings provide a reference for clinical treatment of NSCLC.

Identification of Cysteine Protease Inhibitor CST2 as a Potential Biomarker for Colorectal Cancer

Additional biomarkers for the development and progression of colorectal cancer (CRC) remain to be identified. Hence, the current study aimed to identify potential diagnostic markers for CRC. Analyses of cysteine protease inhibitor [cystatins (CSTs)] expression in CRC samples and its correlation with cancer stage or survival in patients with CRC demonstrated that CRC tissues had greater CST1 and CST2 mRNA expression compared to noncancerous adjacent tissues, while higher CST2 mRNA expression in CRC tissues was correlated with advanced stages and disease-free survival in patients with CRC, encouraging further exploration on the role of CST2 in CRC. Through an online database search and tissue microarray (TMA), we confirmed that CRC samples had higher CST2 expression compared to noncancerous adjacent tissue or normal colorectal tissues at both the mRNA and protein levels. TMA also revealed that colorectal adenoma, CRC, and metastatic CRC tissues exhibited a significantly increased CST2 protein expression. Accordingly, survival analysis demonstrated that the increase in CST2 protein expression was correlated with shorter overall survival of patients with CRC. Moreover, our results found a significant upregulation of CST2 in multiple cancer tissues. Taken together, these findings suggest the potential role of CST2 as a diagnostic and prognostic biomarker for CRC.

A New Data Repository for Pharmacokinetic Natural Product-Drug Interactions: From Chemical Characterization to Clinical Studies

There are many gaps in scientific knowledge about the clinical significance of pharmacokinetic natural product–drug interactions (NPDIs) in which the natural product (NP) is the precipitant and a conventional drug is the object. The National Center for Complimentary and Integrative Health created the Center of Excellence for NPDI Research (NaPDI Center) (www.napdi.org) to provide leadership and guidance on the study of pharmacokinetic NPDIs. A key contribution of the Center is the first user-friendly online repository that stores and links pharmacokinetic NPDI data across chemical characterization, metabolomics analyses, and pharmacokinetic in vitro and clinical experiments (repo.napdi.org). The design is expected to help researchers more easily arrive at a complete understanding of pharmacokinetic NPDI research on a particular NP. The repository will also facilitate multidisciplinary collaborations, as the repository links all of the experimental data for a given NP across the study types. The current work describes the design of the repository, standard operating procedures used to enter data, and pharmacokinetic NPDI data that have been entered to date. To illustrate the usefulness of the NaPDI Center repository, more details on two high-priority NPs, cannabis and kratom, are provided as case studies.

GM604 regulates developmental neurogenesis pathways and the expression of genes associated with amyotrophic lateral sclerosis

Background

Amyotrophic lateral sclerosis (ALS) is currently an incurable disease without highly effective pharmacological treatments. The peptide drug GM604 (GM6 or Alirinetide) was developed as a candidate ALS therapy, which has demonstrated safety and good drug-like properties with a favorable pharmacokinetic profile. GM6 is hypothesized to bolster neuron survival through the multi-target regulation of developmental pathways, but mechanisms of action are not fully understood.

Methods

This study used RNA-seq to evaluate transcriptome responses in SH-SY5Y neuroblastoma cells following GM6 treatment (6, 24 and 48 h).

Results

We identified 2867 protein-coding genes with expression significantly altered by GM6 (FDR < 0.10). Early (6 h) responses included up-regulation of Notch and hedgehog signaling components, with increased expression of developmental genes mediating neurogenesis and axon growth. Prolonged GM6 treatment (24 and 48 h) altered the expression of genes contributing to cell adhesion and the extracellular matrix. GM6 further down-regulated the expression of genes associated with mitochondria, inflammatory responses, mRNA processing and chromatin organization. GM6-increased genes were located near GC-rich motifs interacting with C2H2 zinc finger transcription factors, whereas GM6-decreased genes were located near AT-rich motifs associated with helix-turn-helix homeodomain factors. Such motifs interacted with a diverse network of transcription factors encoded by GM6-regulated genes (STAT3, HOXD11, HES7, GLI1). We identified 77 ALS-associated genes with expression significantly altered by GM6 treatment (FDR < 0.10), which were known to function in neurogenesis, axon guidance and the intrinsic apoptosis pathway.

Conclusions

Our findings support the hypothesis that GM6 acts through developmental-stage pathways to influence neuron survival. Gene expression responses were consistent with neurotrophic effects, ECM modulation, and activation of the Notch and hedgehog neurodevelopmental pathways. This multifaceted mechanism of action is unique among existing ALS drug candidates and may be applicable to multiple neurodegenerative diseases.

Electronic supplementary material

The online version of this article (10.1186/s40035-018-0135-7) contains supplementary material, which is available to authorized users.

Polypharmacy in older adults: Association Rule and Frequent-Set Analysis to evaluate concomitant medication use

The aim of this study was to apply Association Rule and Frequent-Set analysis, and novel means of data visualisation to ascertain patterns of medication use and medication combinations contributing to medication group clusters according to geriatric syndrome status in older adults. Participants were community-dwelling men (aged ≥70 years, n=1686), Sydney, Australia. Medication exposure was categorised at medication class level and data were analysed according to geriatric syndrome status (presence of at least one syndrome including frailty, falls, cognitive impairment and urinary incontinence). Association Rule and Frequent-Set analysis were performed to identify "interesting" patterns of medication combinations that occur together. This analysis involves advanced computer algorithms that investigated all possible combinations of medications in the dataset in order to identify those which are observed more or much less frequently than expected. Frequent-Set Analysis demonstrated one unexpected medication combination, antiulcer and antidiabetic medications (3.5% of participants) in the overall population (n=1687). Frequency of medication combinations was similar in participants with (n=666) and without (n=1020) geriatric syndromes. Among participants with geriatric syndromes, the most frequent combinations included antigout with lipid-lowering agents (5.7%) followed by angiotensin II and diuretics combination (22%). This novel methodology can be used to detect common medication combinations overall by data visualisation, and against specific adverse drug reactions such as geriatric syndromes. This methodology may be a valuable pharmacovigilance approach to monitor large databases for the safety of medications.