Identifying driver mutations in cancers

All cancers depend upon mutations in critical genes, which confer a selective advantage to the tumour cell. The key to understanding the contribution of a disease-associated mutation to the development and progression of cancer comes from an understanding of the consequences of that mutation on the function of the affected protein, and the impact on the pathways in which that protein is involved. Using data from over 30 different cancers from whole-exome sequencing cancer genomic projects, I analysed over one million somatic mutations. I identified mutational hotspots within domain families by mapping small mutations to equivalent positions in multiple sequence alignments of protein domains. I found that gain of function mutations from oncogenes and loss of function mutations from tumour suppressors are normally found in different domain families and when observed in the same domain families, hotspot mutations are located at different positions within the multiple sequence alignment of the domain. Next, I investigated the ability of seven prediction algorithms to discriminate between driver missense mutations in oncogenes and tumour suppressors. Using 19 features to describe these mutations, I then developed a random forest classifier, MOKCaRF, to distinguish between gain of function and loss of function missense mutations in cancer. MOKCaRF performs significantly better than existing algorithms. I then evaluated the ability of six existing prediction tools to distinguish between pathogenic and neutral mutations for both inframe insertion and inframe deletion mutations. I developed my own classifiers using 11 features that perform better than the current algorithms. Finally, using the algorithms that I developed, as well as changes in copy number and expression data for each gene, I analysed samples from 50 lung cancer patients to identify the actionable targets and potential new drug targets for each tumour

Identifying the impact of inframe insertions and deletions on protein function in cancer

Inframe insertion and deletion mutations (indels) are commonly observed in cancer samples accounting for over 1% of all reported mutations. Few somatic inframe indels have been clinically documented as pathogenic and at present there are few tools to predict which indels drive cancer development. However, indels are a common feature of hereditary disease and several tools have been developed to predict the impact of inframe indels on protein function. In this study, we test whether six of the popular prediction tools can be adapted to test for cancer driver mutations and then develop a new algorithm (IndelRF) that discriminates between recurrent indels in known cancer genes and indels not associated with disease. IndelRF was developed to try and identify somatic, driver, and inframe indel mutations. Using a random forest classifier with 11 features, IndelRF achieved accuracies of 0.995 and 0.968 for insertion and deletion mutations, respectively. Finally, we use IndelRF to classify the inframe indel cancer mutations in the MOKCa database

Identification and analysis of mutational hotspots in oncogenes and tumour suppressors

Background: The key to interpreting the contribution of a disease-associated mutation in the development and progression of cancer is an understanding of the consequences of that mutation both on the function of the affected protein and on the pathways in which that protein is involved. Protein domains encapsulate function and position-specific domain based analysis of mutations have been shown to help elucidate their phenotypes. Results: In this paper we examine the domain biases in oncogenes and tumour suppressors, and find that their domain compositions substantially differ. Using data from over 30 different cancers from whole-exome sequencing cancer genomic projects we mapped over one million mutations to their respective Pfam domains to identify which domains are enriched in any of three different classes of mutation; missense, indels or truncations. Next, we identified the mutational hotspots within domain families by mapping small mutations to equivalent positions in multiple sequence alignments of protein domains We find that gain of function mutations from oncogenes and loss of function mutations from tumour suppressors are normally found in different domain families and when observed in the same domain families, hotspot mutations are located at different positions within the multiple sequence alignment of the domain. Conclusions: By considering hotspots in tumour suppressors and oncogenes independently, we find that there are different specific positions within domain families that are particularly suited to accommodate either a loss or a gain of function mutation. The position is also dependent on the class of mutation. We find rare mutations co-located with well-known functional mutation hotspots, in members of homologous domain superfamilies, and we detect novel mutation hotspots in domain families previously unconnected with cancer. The results of this analysis can be accessed through the MOKCa database (http://strubiol.icr.ac.uk/ extra/MOKCa)

Mutational patterns in oncogenes and tumour suppressors

All cancers depend upon mutations in critical genes, which confer a selective advantage to the tumour cell. Knowledge of these mutations is crucial to understanding the biology of cancer initiation and progression, and to the development of targeted therapeutic strategies. The key to understanding the contribution of a disease-associated mutation to the development and progression of cancer, comes from an understanding of the consequences of that mutation on the function of the affected protein, and the impact on the pathways in which that protein is involved. In this paper we examine the mutation patterns observed in oncogenes and tumour suppressors, and discuss different approaches that have been developed to identify driver mutations within cancers that contribute to the disease progress. We also discuss the MOKCa database where we have developed an automatic pipeline that structurally and functionally annotates all proteins from the human proteome that are mutated in cancer

Chondroprotective Effects of Grapefruit (Citrus paradisi Macfad.) Juice in a Complete Freund’s Adjuvant Rat Model of Knee Osteoarthritis

Osteoarthritis (OA) is a common disorder that can affect any joint in the human body. This study aimed to examine the anti-arthritic properties of high and low doses of grapefruit juice (GFJ), as grapefruit appears to contain anti-inflammatory biochemicals. Forty male Sprague–Dawley rats weighing 170–180 g were divided into five groups. These groups comprised the untreated control group and osteoarthritic (Osteo) rats administered intra-articular injections of Freund’s complete adjuvant (CFA; 0.5 mL; 1 mg/mL) as follows: OA rats administered low doses of GFJ (Osteo+GFJ (low); 5 mL/kg body weight (BW)); OA rats administered high doses of GFJ (Osteo+GFJ (high); 27 mL/kg BW); and OA rats administered diclofenac sodium (Osteo+Diclo) as a reference drug. Injections of CFA induced OA, as indicated by a significant increase in the serum levels of the inflammatory biomarkers C-reactive protein (CRP), interleukin-1β (IL-1β), and (prostaglandin (PGE2), as well as matrix metalloproteinases (MMP-1) and cathepsin K. The synovial levels of glycosaminoglycans (GAGs), tumor necrosis factor (TNF-α), and interleukin 6 (IL-6) also increased, with a concomitant reduction in osteocalcin levels. The administration of either high or low doses of GFJ reduced CRP, IL-1β, PGE2, MMP-1, cathepsin K, and osteocalcin while increasing the synovial levels of GAGs, TNF-α, and IL-6, slowing cartilage degradation and boosting joint function. The results showed comparable histopathological and biochemical responses. A comparison of the treatments showed that high-dose GFJ had a greater chondroprotective effect than low-dose GFJ

Use of Metagenomic Whole Genome Shotgun Sequencing Data in Taxonomic Assignment of Dipterygium glaucum Rhizosphere and Surrounding Bulk Soil Microbiomes, and Their Response to Watering

The metagenomic whole genome shotgun sequencing (mWGS) approach was used to detect signatures of the rhizosphere microbiomes of Dipterygium glaucum and surrounding bulk soil microbiomes, and to detect differential microbial responses due to watering. Preliminary results reflect the reliability of the experiment and the rationality of grouping microbiomes. Based on the abundance of non-redundant genes, bacterial genomes showed the highest level, followed by Archaeal and Eukaryotic genomes, then, the least abundant viruses. Overall results indicate that most members of bacteria have a higher abundance/relative abundance (AB/RA) pattern in the rhizosphere towards plant growth promotion, while members of eukaryota have a higher pattern in bulk soil, most likely acting as pathogens. The results also indicate the contribution of mycorrhiza (genus Rhizophagus) in mediating complex mutualistic associations between soil microbes (either beneficial or harmful) and plant roots. Some of these symbiotic relationships involve microbes of different domains responding differentially to plant root exudates. Among these are included the bacterial genus Burkholderia and eukaryotic genus Trichoderma, which have antagonistic activities against the eukaryotic genus Fusarium. Another example involves Ochrobactrum phage POA1180, its bacterial host and plant roots. One of the major challenges in plant nutrition involves other microbes that manipulate nitrogen levels in the soil. Among these are the microbes that perform contraversal actions of nitrogen fixation (the methanogen Euryarchaeota) and ammonia oxidation (Crenarchaeota). The net nitrogen level in the soil is originally based on the AB/RA of these microbes and partially on the environmental condition. Watering seems to influence the AB/RA of a large number of soil microbes, where drought-sensitive microbes (members of phyla Acidobacteria and Gemmatimonadetes) showed an increased AB/RA pattern after watering, while others (Burkholderia and Trichoderma) seem to be among microbes assisting plants to withstand abiotic stresses. This study sheds light on the efficient use of mWGS in the taxonomic assignment of soil microbes and in their response to watering. It also provides new avenues for improving biotic and abiotic resistance in domestic plant germplasm via the manipulation of soil microbes

Data_Sheet_1_Abundant antibiotic resistance genes in rhizobiome of the human edible Moringa oleifera medicinal plant.ZIP

Moringa oleifera (or the miracle tree) is a wild plant species widely grown for its seed pods and leaves, and is used in traditional herbal medicine. The metagenomic whole genome shotgun sequencing (mWGS) approach was used to characterize antibiotic resistance genes (ARGs) of the rhizobiomes of this wild plant and surrounding bulk soil microbiomes and to figure out the chance and consequences for highly abundant ARGs, e.g., mtrA, golS, soxR, oleC, novA, kdpE, vanRO, parY, and rbpA, to horizontally transfer to human gut pathogens via mobile genetic elements (MGEs). The results indicated that abundance of these ARGs, except for golS, was higher in rhizosphere of M. oleifera than that in bulk soil microbiome with no signs of emerging new soil ARGs in either soil type. The most highly abundant metabolic processes of the most abundant ARGs were previously detected in members of phyla Actinobacteria, Proteobacteria, Acidobacteria, Chloroflexi, and Firmicutes. These processes refer to three resistance mechanisms namely antibiotic efflux pump, antibiotic target alteration and antibiotic target protection. Antibiotic efflux mechanism included resistance-nodulation-cell division (RND), ATP-binding cassette (ABC), and major facilitator superfamily (MFS) antibiotics pumps as well as the two-component regulatory kdpDE system. Antibiotic target alteration included glycopeptide resistance gene cluster (vanRO), aminocoumarin resistance parY, and aminocoumarin self-resistance parY. While, antibiotic target protection mechanism included RbpA bacterial RNA polymerase (rpoB)-binding protein. The study supports the claim of the possible horizontal transfer of these ARGs to human gut and emergence of new multidrug resistant clinical isolates. Thus, careful agricultural practices are required especially for plants used in circles of human nutrition industry or in traditional medicine.</p

Data_Sheet_2_Abundant antibiotic resistance genes in rhizobiome of the human edible Moringa oleifera medicinal plant.docx

Moringa oleifera (or the miracle tree) is a wild plant species widely grown for its seed pods and leaves, and is used in traditional herbal medicine. The metagenomic whole genome shotgun sequencing (mWGS) approach was used to characterize antibiotic resistance genes (ARGs) of the rhizobiomes of this wild plant and surrounding bulk soil microbiomes and to figure out the chance and consequences for highly abundant ARGs, e.g., mtrA, golS, soxR, oleC, novA, kdpE, vanRO, parY, and rbpA, to horizontally transfer to human gut pathogens via mobile genetic elements (MGEs). The results indicated that abundance of these ARGs, except for golS, was higher in rhizosphere of M. oleifera than that in bulk soil microbiome with no signs of emerging new soil ARGs in either soil type. The most highly abundant metabolic processes of the most abundant ARGs were previously detected in members of phyla Actinobacteria, Proteobacteria, Acidobacteria, Chloroflexi, and Firmicutes. These processes refer to three resistance mechanisms namely antibiotic efflux pump, antibiotic target alteration and antibiotic target protection. Antibiotic efflux mechanism included resistance-nodulation-cell division (RND), ATP-binding cassette (ABC), and major facilitator superfamily (MFS) antibiotics pumps as well as the two-component regulatory kdpDE system. Antibiotic target alteration included glycopeptide resistance gene cluster (vanRO), aminocoumarin resistance parY, and aminocoumarin self-resistance parY. While, antibiotic target protection mechanism included RbpA bacterial RNA polymerase (rpoB)-binding protein. The study supports the claim of the possible horizontal transfer of these ARGs to human gut and emergence of new multidrug resistant clinical isolates. Thus, careful agricultural practices are required especially for plants used in circles of human nutrition industry or in traditional medicine.</p