14 research outputs found
Consortium_Members
No full textList of Mint Evolutionary Genomics Consortium members and institutional affiliations
Consortium_Members
No full textList of Mint Evolutionary Genomics Consortium members and institutional affiliations
Phylogenomic Mining of the Mints Reveals Multiple Mechanisms Contributing to the Evolution of Chemical Diversity in Lamiaceae
No full textThe evolution of chemical complexity has been a major driver of plant diversification, with novel compounds serving as key innovations. The species-rich mint family (Lamiaceae) produces an enormous variety of compounds that act as attractants and defense molecules in nature and are used widely by humans as flavor additives, fragrances, and anti-herbivory agents. To elucidate the mechanisms by which such diversity evolved, we combined leaf transcriptome data from 48 Lamiaceae species and four outgroups with a robust phylogeny and chemical analyses of three terpenoid classes (monoterpenes, sesquiterpenes, and iridoids) that share and compete for precursors. Our integrated chemical–genomic–phylogenetic approach revealed that: (1) gene family expansion rather than increased enzyme promiscuity of terpene synthases is correlated with mono- and sesquiterpene diversity; (2) differential expression of core genes within the iridoid biosynthetic pathway is associated with iridoid presence/absence; (3) generally, production of iridoids and canonical monoterpenes appears to be inversely correlated; and (4) iridoid biosynthesis is significantly associated with expression of geraniol synthase, which diverts metabolic flux away from canonical monoterpenes, suggesting that competition for common precursors can be a central control point in specialized metabolism. These results suggest that multiple mechanisms contributed to the evolution of chemodiversity in this economically important family. The mint family (Lamiaceae) includes many culturally and economically important species and collectively exhibits an exceptionally high degree of chemical diversity. Using an integrated chemical-genomic-phylogenetic approach, gene family expansion, altered gene expression of key biosynthetic pathway genes, and flux of precursors were shown to underlie the evolution of chemodiversity observed in this chemically rich clade
Data from: Phylogenomic mining of the mints reveals multiple mechanisms contributing to the evolution of chemical diversity in Lamiaceae
No full textThe evolution of chemical complexity has been a major driver of plant diversification, with novel compounds serving as key innovations. The species-rich mint family (Lamiaceae) produces an enormous variety of compounds that act as attractants and defense molecules in nature and are used widely by humans as flavor additives, fragrances, and anti-herbivory agents. To elucidate the mechanisms by which such diversity evolved, we combined leaf transcriptome data from 48 Lamiaceae species and four outgroups with a robust phylogeny and chemical analyses of three terpenoid classes (monoterpenes, sesquiterpenes, iridoids) that share and compete for precursors. Our integrated chemical-genomic-phylogenetic approach revealed that: 1) gene family expansion rather than increased enzyme promiscuity of terpene synthases is correlated with mono- and sesqui-terpene diversity; 2) differential expression of core genes within the iridoid biosynthetic pathway is associated with iridoid presence/absence; 3) generally, production of iridoids and canonical monoterpenes appeared to be inversely correlated; and 4) iridoid biosynthesis was significantly associated with expression of geraniol synthase, which diverts metabolic flux away from canonical monoterpenes, suggesting that competition for common precursors can be a central control point in specialized metabolism. These results suggest that multiple mechanisms contributed to the evolution of chemodiversity in this economically important family
A database-driven approach identifies additional diterpene synthase activities in the mint family (Lamiaceae)
No full textTranscriptomes of all 52 Lamiales species.zip
No full textTranscriptomes of all 52 species generated in this study (Zipped folder)
Global analysis of SNPs, proteins and protein-protein interactions: approaches for the prioritisation of candidate disease genes.
No full textPhDUnderstanding the etiology of complex disease remains a challenge in biology. In recent
years there has been an explosion in biological data, this study investigates machine
learning and network analysis methods as tools to aid candidate disease gene prioritisation,
specifically relating to hypertension and cardiovascular disease.
This thesis comprises four sets of analyses: Firstly, non synonymous single nucleotide
polymorphisms (nsSNPs) were analysed in terms of sequence and structure based properties
using a classifier to provide a model for predicting deleterious nsSNPs. The degree
of sequence conservation at the nsSNP position was found to be the single best attribute
but other sequence and structural attributes in combination were also useful. Predictions
for nsSNPs within Ensembl have been made publicly available.
Secondly, predicting protein function for proteins with an absence of experimental
data or lack of clear similarity to a sequence of known function was addressed. Protein
domain attributes based on physicochemical and predicted structural characteristics
of the sequence were used as input to classifiers for predicting membership of large and
diverse protein superfamiles from the SCOP database. An enrichment method was investigated
that involved adding domains to the training dataset that are currently absent
from SCOP. This analysis resulted in improved classifier accuracy, optimised classifiers
achieved 66.3% for single domain proteins and 55.6% when including domains from
multi domain proteins. The domains from superfamilies with low sequence similarity,
share global sequence properties enabling applications to be developed which compliment
profile methods for detecting distant sequence relationships.
Thirdly, a topological analysis of the human protein interactome was performed. The
results were combined with functional annotation and sequence based properties to build
models for predicting hypertension associated proteins. The study found that predicted
hypertension related proteins are not generally associated with network hubs and do
not exhibit high clustering coefficients. Despite this, they tend to be closer and better
connected to other hypertension proteins on the interaction network than would be expected
by chance. Classifiers that combined PPI network, amino acid sequence and functional
properties produced a range of precision and recall scores according to the applied
3
weights.
Finally, interactome properties of proteins implicated in cardiovascular disease and
cancer were studied. The analysis quantified the influential (central) nature of each protein
and defined characteristics of functional modules and pathways in which the disease
proteins reside. Such proteins were found to be enriched 2 fold within proteins that are influential
(p<0.05) in the interactome. Additionally, they cluster in large, complex, highly
connected communities, acting as interfaces between multiple processes more often than
expected. An approach to prioritising disease candidates based on this analysis was proposed.
Each analyses can provide some new insights into the effort to identify novel disease
related proteins for cardiovascular disease
BioSilicoSystems - A Multipronged Approach Towards Analysis and Representation of Biological Data (PhD Thesis)
No full textThe rising field of integrative bioinformatics provides the vital methods to integrate, manage and also to analyze the diverse data and allows gaining new and deeper insights and a clear understanding of the intricate biological systems. The difficulty is not only to facilitate the study of heterogeneous data within the biological context, but it also more fundamental, how to represent and make the available knowledge accessible. Moreover, adding valuable information and functions that persuade the user to discover the interesting relations hidden within the data is, in itself, a great challenge. Also, the cumulative information can provide greater biological insight than is possible with individual information sources. Furthermore, the rapidly growing number of databases and data types poses the challenge of integrating the heterogeneous data types, especially in biology. This rapid increase in the volume and number of data resources drive for providing polymorphic views of the same data and often overlap in multiple resources. 

In this thesis a multi-pronged approach is proposed that deals with various methods for the analysis and representation of the diverse biological data which are present in different data sources. This is an effort to explain and emphasize on different concepts which are developed for the analysis of molecular data and also to explain its biological significance. The hypotheses proposed are in context with various other results and findings published in the past. The approach demonstrated also explains different ways to integrate the molecular data from various sources along with the need for a comprehensive understanding and clear projection of the concept or the algorithm and its results, but with simple means and methods. The multifarious approach proposed in this work comprises of different tools or methods spanning significant areas of bioinformatics research such as data integration, data visualization, biological network construction / reconstruction and alignment of biological pathways. Each tool deals with a unique approach to utilize the molecular data for different areas of biological research and is built based on the kernel of the thesis. Furthermore these methods are combined with graphical representation that make things simple and comprehensible and also helps to understand with ease the underlying biological complexity. Moreover the human eye is often used to and it is more comfortable with the visual representation of the facts
Computational identification and analysis of protein short linear motifs
No full textShort linear motifs (SLiMs) in proteins can act as targets for proteolytic cleavage, sites of post-translational modification, determinants of sub-cellular localization, and mediators of protein-protein interactions. Computational discovery of SLiMs involves assembling a group of proteins postulated to share a potential motif, masking out residues less likely to contain such a motif, down-weighting shared motifs arising through common evolutionary descent, and calculation of statistical probabilities allowing for the multiple testing of all possible motifs. Much of the challenge for motif discovery lies in the assembly and masking of datasets of proteins likely to share motifs, since the motifs are typically short (between 3 and 10 amino acids in length), so that potential signals can be easily swamped by the noise of stochastically recurring motifs. Focusing on disordered regions of proteins, where SLiMs are predominantly found, and masking out non-conserved residues can reduce the level of noise but more work is required to improve the quality of high-throughput experimental datasets (e.g. of physical protein interactions) as input for computational discovery
Improving the resolution of interaction maps: A middleground between high-resolution complexes and genome-wide interactomes
No full textProtein-protein interactions are ubiquitous in Biology and therefore central to understand living organisms. In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps or interactomes for a number of relevant organisms including human. Although the analysis of interaction networks is proving useful, current interactomes provide a blurry and granular picture of the molecular machinery, i.e. unless the structure of the protein complex is known the molecular details of the interaction are missing and sometime is even not possible to know if the interaction between the proteins is direct, i.e. physical interaction or part of functional, not necessary, direct association. Unfortunately, the determination of the structure of protein complexes cannot keep pace with the discovery of new protein-protein interactions resulting in a large, and increasing, gap between the number of complexes that are thought to exist and the number for which 3D structures are available. The aim of the thesis was to tackle this problem by implementing computational approaches to derive structural models of protein complexes and thus reduce this existing gap. Over the course of the thesis, a novel modelling algorithm to predict the structure of protein complexes, V-D2OCK, was implemented. This new algorithm combines structure-based prediction of protein binding sites by means of a novel algorithm developed over the course of the thesis: VORFFIP and M-VORFFIP, data-driven docking and energy minimization. This algorithm was used to improve the coverage and structural content of the human interactome compiled from different sources of interactomic data to ensure the most comprehensive interactome. Finally, the human interactome and structural models were compiled in a database, V-D2OCK DB, that offers an easy and user-friendly access to the human interactome including a bespoken graphical molecular viewer to facilitate the analysis of the structural models of protein complexes. Furthermore, new organisms, in addition to human, were included providing a useful resource for the study of all known interactomes
