Functional gene analysis of individual response to challenge of SIVmac239 in M. mulatta PBMC culture

AbstractIt has previously been shown in macaques that individual animals exhibit varying responses to challenge with the same strain of SIV. We attempted to elucidate these differences using functional genomics and correlate them to biological response. Unfractionated PBMC from three rhesus macaques were isolated, activated, and infected with SIVmac239. Interestingly, one of the three animals used for these experiments exhibited a completely unique response to infection relative to the other two. After repeated attempts to infect the PBMC from this animal, little or no infectivity was seen across the time points considered, and corresponding to this apparent lack of infection, few genes were seen to be differentially expressed when compared to mock-infected cells. For the remaining two animals, gene expression analysis showed that while they exhibited responses for the same groups of pathways, these responses included differences specific to the individual animal at the gene level. In instances where the patterns of differential gene expression differed between these animals, the genes being differentially expressed were associated with the same categories of biological process, mainly immune response and cell signaling. At the pathway level, these animals again exhibited similar responses that could be predicted based on the experimental conditions. Even in these expected results, the degree of response and the specific genes being regulated differed greatly from animal to animal. The differences in gene expression on an individual level have the potential to be used as markers in identification of animals suitable for lentiviral infection experiments. Our results highlight the importance of individual variation in response to viral challenge

Systematic Approaches towards the Development of Host-Directed Antiviral Therapeutics

Since the onset of antiviral therapy, viral resistance has compromised the clinical value of small-molecule drugs targeting pathogen components. As intracellular parasites, viruses complete their life cycle by hijacking a multitude of host-factors. Aiming at the latter rather than the pathogen directly, host-directed antiviral therapy has emerged as a concept to counteract evolution of viral resistance and develop broad-spectrum drug classes. This approach is propelled by bioinformatics analysis of genome-wide screens that greatly enhance insights into the complex network of host-pathogen interactions and generate a shortlist of potential gene targets from a multitude of candidates, thus setting the stage for a new era of rational identification of drug targets for host-directed antiviral therapies. With particular emphasis on human immunodeficiency virus and influenza virus, two major human pathogens, we review screens employed to elucidate host-pathogen interactions and discuss the state of database ontology approaches applicable to defining a therapeutic endpoint. The value of this strategy for drug discovery is evaluated, and perspectives for bioinformatics-driven hit identification are outlined

Anti-diabetic and phytochemical analysis of sutherlandia frutescens extracts

In Africa, the importance of medicinal plants in folklore medicine and their contribution to primary healthcare is well recognized. Across the continent, local herbal mixtures still provide the only therapeutic option for about 80% of the population. The vast floral diversity and the intrinsic ethnobotanical knowledge has been the backbone of localized traditional herbal medical practices. In Africa, an estimated 5400 of the 60000 described plant taxa possess over 16300 therapeutic uses. Similarly, with a therapeutic flora comprising of approximately 650 species, herbal medical practitioners in South Africa, make use of a plethora of plants to treat different human diseases and infections. Over the years, studies have identified numerous plant species with potential against chronic metabolic diseases including type 2 diabetes mellitus (T2DM). Globally, the incidence and prevalence of T2DM have reached epidemic proportions affecting people of all ages, nationalities and ethnicity. Considered the fourth leading cause of deaths by disease, T2DM is a global health crisis with an estimated diagnosis and mortality frequency of 1 every 5 seconds and 1 every 7 seconds respectively. Though the exact pathophysiology of T2DM is not entirely understood, initial peripheral insulin resistance in adipose tissue, liver, and skeletal muscle with subsequent pancreatic β-cell dysfunction resulting from an attempt to compensate for insulin resistance is a common feature of the disease. The current approach to treating T2DM is the use of oral antidiabetic agents (OAAs), insulin, and incretin-based drugs in an attempt to achieve glycaemic control and maintain glucose homeostasis. However, conventional anti-T2DM drugs have been shown to have limited efficacies and serious adverse effects. Hence, the need for newer, more efficacious and safer anti-T2DM agents. Sutherlandia frutescens subsp. microphylla is a flowering shrub of the pea family (Fabaceae/Leguminaceae) found mainly in the Western Cape and Karoo regions of Southern Africa. Concoctions of various parts of the plant are used in the management of different ailments including T2DM. However, despite extensive biological and pharmacological studies, few analyses exist of the chemical constituents of S. frutescens and no Triple Time of Flight Liquid Chromatography with Mass Spectrometry (Triple TOF LC/MS/MS) analysis has been performed. The initial aim of this study was to investigate the phytochemical profile of hot aqueous, cold aqueous, 80% ethanolic, 100% ethanolic, 80% methanolic and 100% methanolic extracts of a single source S. frutescens plant material using colorimetric and spectrophotometric analysis. The hot aqueous extractant was found to be the best extractant for S. frutescens, yielding 1.99 g of crude extract from 16 g fresh powdered plant material. This data suggests that application of heat and water as the extractant (hot aqueous) could play a vital role in extraction of bioactive compounds from S. frutescens and also justifies the traditional use of a tea infusion of S. frutescens. Colorimetric analysis revealed the presence of flavonoids, flavonols, tannins, and phenols in all extracts with varying intensity. The organic extracts 100% methanol, 80% and 100% ethanol exhibited high color intensity (+++) for flavonoids and flavonols respectively, while all the extracts exhibited a moderate color intensity (++) for tannins and phenols. Spectrophotometric analysis of S. frutescens extracts revealed that all the organic extracts contained a significantly higher concentration (in mg/g of extract) of flavonols and tannins when compared to the aqueous extracts. All extracts contained approximately equal levels of phenols. These data confirm the presence of all four groups of bioactive phytocompounds in the S. frutescens extracts used in this study, and also confirm that different solvent extractants possess the capability to differentially extract specific groups of phytocompounds. in individual extracts. Further comparison of these compounds with online databases of anti-diabetic phytocompounds led to the preliminary identification of 10 possible anti-diabetic compounds; α-Pinene, Limonene, Sabinene, Carvone, Myricetin, Rutin, Stigmasterol, Emodin, Sarpagine and Hypoglycin B in crude and solid phase extraction (SPE) fractions of S. frutesecens. Furthermore, using two hepatic cell lines (Chang and HepG2) as an in-vtro model system, the anti-T2DM properties of crude aqueous and organic extracts of S. frutescents was investigated and compared. Both aqueous and organic extracts of S. frutescens were found to decrease gluconeogenesis, increase glucose uptake and decrease lipid accumulation (Triacylglycerol, Diacylglycerol, and Monoacylglycerol) in Chang and HepG2 hepatic cell cultures made insulin resistant (IR) following exposure to high concentration of insulin and fructose. Using real-time quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), the aqueous and organic extracts of S. frutescens were confirmed to regulate the expression of Vesicle-associated membrane protein 3 (VAMP3), Mitogen-activated protein kinase 8 (MAPK8), and Insulin receptor substrate 1 (IRS1) in insulin resistant hepatic cells. IR-mediated downregulation of VAMP3, MAPK8, and IRS1 mRNA in IR HepG2 hepatic cell cultures was reversed in the presence of aqueous and organic extracts of S. frutescens. The hot aqueous extract displayed the highest activity in all the assays, while all the organic extracts displayed similar potency. In conclusion, this study reports that aqueous and organic extracts of S. frutescens possess numerous anti-diabetic compounds that can be further investigated for the development of new, more efficacious and less toxic anti-diabetic agents. The presence of multiple compounds in a single extract does suggest a synergistic or combinatorial therapeutic effect. These findings support the burgeoning body of in-vivo and in-vitro literature evidence on the anti-diabetic properties of S. frutescens and its use in folklore medicine

Genome-Wide Analysis of Binding Sites and Direct Target Genes of the Orphan Nuclear Receptor NR2F1/COUP-TFI

Identification of bona fide direct nuclear receptor gene targets has been challenging but essential for understanding regulation of organismal physiological processes.We describe a methodology to identify transcription factor binding sites and target genes in vivo by intersecting microarray data, computational binding site queries, and evolutionary conservation. We provide detailed experimental validation of each step and, as a proof of principle, utilize the methodology to identify novel direct targets of the orphan nuclear receptor NR2F1 (COUP-TFI). The first step involved validation of microarray gene expression profiles obtained from wild-type and COUP-TFI(-/-) inner ear tissues. Secondly, we developed a bioinformatic tool to search for COUP-TFI DNA binding sites in genomes, using a classification-type Hidden Markov Model trained with 49 published COUP-TF response elements. We next obtained a ranked list of candidate in vivo direct COUP-TFI targets by integrating the microarray and bioinformatics analyses according to the degree of binding site evolutionary conservation and microarray statistical significance. Lastly, as proof-of-concept, 5 specific genes were validated for direct regulation. For example, the fatty acid binding protein 7 (Fabp7) gene is a direct COUP-TFI target in vivo because: i) we identified 2 conserved COUP-TFI binding sites in the Fabp7 promoter; ii) Fapb7 transcript and protein levels are significantly reduced in COUP-TFI(-/-) tissues and in MEFs; iii) chromatin immunoprecipitation demonstrates that COUP-TFI is recruited to the Fabp7 promoter in vitro and in vivo and iv) it is associated with active chromatin having increased H3K9 acetylation and enrichment for CBP and SRC-1 binding in the newborn brain.We have developed and validated a methodology to identify in vivo direct nuclear receptor target genes. This bioinformatics tool can be modified to scan for response elements of transcription factors, cis-regulatory modules, or any flexible DNA pattern

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments

Identification of potential biomarkers in lung cancer as possible diagnostic agents using bioinformatics and molecular approaches

>Magister Scientiae - MScLung cancer remains the leading cause of cancer deaths worldwide, with the majority of cases attributed to non-small cell lung carcinomas. At the time of diagnosis, a large percentage of patients present with advanced stage of disease, ultimately resulting in a poor prognosis. The identification circulatory markers, overexpressed by the tumour tissue, could facilitate the discovery of an early, specific, non-invasive diagnostic tool as well as improving prognosis and treatment protocols. The aim was to analyse gene expression data from both microarray and RNA sequencing platforms, using bioinformatics and statistical analysis tools. Enrichment analysis sought to identify genes, which were differentially expressed (p 2) and had the potential to be secreted into the extracellular circulation, by using Gene Ontology terms of the Cellular Component. Results identified 1 657 statically significant genes between normal and early lung cancer tissue, with only 1 gene differentially expressed (DE) between the early and late stage disease. Following statistical analysis, 171 DE genes selected as potential early stage biomarkers. The overall sensitivity of RNAseq, in comparison to arrays enabled the identification of 57 potential serum markers. These genes of interest were all downregulated in the tumour tissue, and while they did not facilitate the discovery of an ideal diagnostic marker based on the set criteria in this study, their roles in disease initiation and progression require further analysis