Biology subjects

Robertson, D. L.

Publications and source records attributed to Robertson, D. L..

4 recordsLinked to original sources

bioRxiv2018

Using set theory to reduce redundancy in pathway sets

1.1.01 BackgroundThe consolidation of pathway databases, such as KEGG[1], Reactome[2]and ConsensusPathDB[3], has generated widespread biological interest, however the issue of pathway redundancy impedes the use of these consolidated datasets. Attempts to reduce this redundancy have focused on visualizing pathway overlap or merging pathways, but the resulting pathways may be of heterogeneous sizes and cover multiple biological functions. Efforts have also been made to deal with redundancy in pathway data by consolidating enriched pathways into a number of clusters or concepts. We present an alternative approach, which generates pathway subsets capable of covering all of genes presented within either pathway databases or enrichment results, generating substantial reductions in redundancy.\n\n1.02 ResultsWe propose a method that uses set cover to reduce pathway redundancy, without merging pathways. The proposed approach considers three objectives: removal of pathway redundancy, controlling pathway size and coverage of the gene set. By applying set cover to the ConsensusPathDB dataset we were able to produce a reduced set of pathways, representing 100% of the genes in the original data set with 74% less redundancy, or 95% of the genes with 88% less redundancy. We also developed an algorithm to simplify enrichment data and applied it to a set of enriched osteoarthritis pathways, revealing that within the top ten pathways, five were redundant subsets of more enriched pathways. Applying set cover to the enrichment results removed these redundant pathways allowing more informative pathways to take their place.\n\n1.03 ConclusionOur method provides an alternative approach for handling pathway redundancy, while ensuring that the pathways are of homogeneous size and gene coverage is maximised. Pathways are not altered from their original form, allowing biological knowledge regarding the data set to be directly applicable. We demonstrate the ability of the algorithms to prioritise redundancy reduction, pathway size control or gene set coverage. The application of set cover to pathway enrichment results produces an optimised summary of the pathways that best represent the differentially regulated gene set.

bioinformatics↗

bioRxiv2018

Network controllability: viruses are driver agents in dynamic molecular systems

In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However in an intra-cellular network it is unclear what control means. To address this limitation we use viral infection, specifically HIV-1 and HCV, as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two dynamic states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamic host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The driver nodes used by the virus are distributed throughout the pathways in specific locations enabling effective control of the cell via the high control centrality of the viral and targeted host nodes. Furthermore, this viral infection of the human system permits discrimination between available network-control models, and demonstrates the minimum-dominating set (MDS) method better accounts for how biological information and signals are transferred than the maximum matching (MM) method as it identified most of the HIV-1 proteins as critical driver nodes and goes beyond identifying receptors as the only critical driver nodes. This is because MDS, unlike MM, accounts for the inherent non-linearity of signalling pathways. Our results demonstrate control-theory gives a more complete and dynamic understanding of the viral hijack mechanism when compared with previous analyses limited to static single-state networks.

systems biology↗

bioRxiv2017

Protein Structural Disorder Of The Envelope V3 Loop Contributes To The Switch In Human Immunodeficiency Virus Type 1 Cell Tropism

Human immunodeficiency virus type 1 (HIV-1) envelope gp120 is partly an intrinsically disordered (unstructured/disordered) protein as it contains regions that do not fold into well-defined protein structures. These disordered regions play important roles in HIVs life cycle, particularly, V3 loop-dependent cell entry, which determines how the virus uses two coreceptors on immune cells, the chemokine receptors CCR5 (R5), CXCR4 (X4) or both (R5X4 virus). Most infecting HIV-1 variants utilise CCR5, while a switch to CXCR4-use occurs in the majority of infections. Why does this rewiring event occur in HIV-1 infected patients? As changes in the charge of the V3 loop are associated with this receptor switch and it has been suggested that charged residues promote structure disorder, we hypothesise that the intrinsic disorder of the V3 loop plays a role in determining cell tropism. To test this we use three independent data sets of gp120 to analyse V3 loop disorder. We find that the V3 loop of X4 virus has significantly higher intrinsic disorder tendency than R5 and R5X4 virus, while R5X4 virus has the lowest. These results indicate that structural disorder plays an important role in determining HIV-1 cell tropism and CXCR4 binding. We speculate that changes in N-linked glycosylation associated with tropism change (from R5 to X4) are required to stabilise the V3 loop with increased disorder tendency during HIV-1 evolution. We discuss the potential evolutionary mechanisms leading to the fixation of disorder promoting mutations and the adaptive potential of protein structural disorder in viral host adaptation.\n\nIMPORTANCEHIV-1 cell entry relies on the V3 loop of its heavily glycosylated envelope protein gp120 to bind to a host coreceptor CCR5 or CXCR4. Unraveling the mechanism whereby HIV-1 switches host coreceptor is critical to understanding HIV-1 pathogenesis and development of novel intervention strategies. However, a mechanistic understanding of the switch is limited as no gp120-CCR5/CXCR4 complex is available, due to the intrinsically disordered nature of the V3 loop responsible for coreceotor swtich. We hypothesise that shifts of V3 disorder may contribute to HIV-1 coreceptor switch and cell tropism. In this study we compared the disorder tendency of the V3 loop before and after the coreceptor switch. We find that the coreceptor switch is associated with a significant increase of V3 loop disorder from CCR5 to CXCR4 using. This result provides a mechanistic explanation of coreceptor switch that increasingly disordered V3 loop results in use of a different host coreceptor.

bioinformatics↗

bioRxiv2016

Metagenomic Binning through Multi-resolution Genomic Binary Patterns

Bioinformatics methods typically use textual representations of genetic information, represented computationally as strings or sub-strings of the characters A, T, G and C. Image processing methods offer a rich source of alternative descriptors as they are designed to work in the presence of noisy data without the need for exact matching. We introduce a method, multi-resolution local binary patterns (MLBP) from image processing to extract local texture changes from nucleotide sequence data. We apply this feature space to the alignment-free binning of metagenomic data. The effectiveness of MLBP is demonstrated using both simulated and real human gut microbial communities. The intuition behind our method is the MLBP feature vectors permit sequence comparisons without the need for explicit pairwise matching. Sequence reads or contigs can then be represented as vectors and their texture compared efficiently using state-of-the-art machine learning algorithms to perform dimensionality reduction to capture eigengenome information and perform clustering (here using RSVD and BH-tSNE). We demonstrate this approach outperforms existing methods based on k-mer frequency. The image processing method, MLBP, thus offers a viable alternative feature space to textual representations of sequence data. The source code for our Multi-resolution Genomic Binary Patterns method can be found at https://github.com/skouchaki/MrGBP.

bioinformatics↗