Gene expression trends and protein features effectively complement each other in gene function prediction

Transcriptome Analysis to Shed Light on the Molecular Mechanisms of Early Responses to Cadmium in Roots and Leaves of King Grass (Pennisetum americanum × P. purpureum)

King grass, a hybrid grass between pearl millet and elephant grass, has many excellent characteristics such as high biomass yield, great stress tolerance, and enormous economic and ecological value, which makes it ideal for development of phytoremediation. At present, the physiological and molecular response of king grass to cadmium (Cd) stress is poorly understood. Transcriptome analysis of early response (3 h and 24 h) of king grass leaves and roots to high level Cd (100 µM) has been investigated and has shed light on the molecular mechanism underlying Cd stress response in this hybrid grass. Our comparative transcriptome analysis demonstrated that in combat with Cd stress, king grass roots have activated the glutathione metabolism pathway by up-regulating glutathione S-transferases (GSTs) which are a multifunctional family of phase II enzymes that detoxify a variety of environmental chemicals, reactive intermediates, and secondary products of oxidative damages. In roots, early inductions of phenylpropanoid biosynthesis and phenylalanine metabolism pathways were observed to be enriched in differentially expressed genes (DEGs). Meanwhile, oxidoreductase activities were significantly enriched in the first 3 h to bestow the plant cells with resistance to oxidative stress. We also found that transporter activities and jasmonic acid (JA)-signaling might be activated by Cd in king grass. Our study provided the first-hand information on genome-wide transcriptome profiling of king grass and novel insights on phytoremediation.

Gene networks underlying convergent and pleiotropic phenotypes in a large and systematically-phenotyped cohort with heterogeneous developmental disorders

Readily-accessible and standardised capture of genotypic variation has revolutionised our understanding of the genetic contribution to disease. Unfortunately, the corresponding systematic capture of patient phenotypic variation needed to fully interpret the impact of genetic variation has lagged far behind. Exploiting deep and systematic phenotyping of a cohort of 197 patients presenting with heterogeneous developmental disorders and whose genomes harbour de novo CNVs, we systematically applied a range of commonly-used functional genomics approaches to identify the underlying molecular perturbations and their phenotypic impact. Grouping patients into 408 non-exclusive patient-phenotype groups, we identified a functional association amongst the genes disrupted in 209 (51%) groups. We find evidence for a significant number of molecular interactions amongst the association-contributing genes, including a single highly-interconnected network disrupted in 20% of patients with intellectual disability, and show using microcephaly how these molecular networks can be used as baits to identify additional members whose genes are variant in other patients with the same phenotype. Exploiting the systematic phenotyping of this cohort, we observe phenotypic concordance amongst patients whose variant genes contribute to the same functional association but note that (i) this relationship shows significant variation across the different approaches used to infer a commonly perturbed molecular pathway, and (ii) that the phenotypic similarities detected amongst patients who share the same inferred pathway perturbation result from these patients sharing many distinct phenotypes, rather than sharing a more specific phenotype, inferring that these pathways are best characterized by their pleiotropic effects.

Developmental disorders occur in ∼3% of live births, and exhibit a broad range of abnormalities including: intellectual disability, autism, heart defects, and other neurological and morphological problems. Often, patients are grouped into genetic syndromes which are defined by a specific set of mutations and a common set of abnormalities. However, many mutations are unique to a single patient and many patients present a range of abnormalities which do not fit one of the recognized genetic syndromes, making diagnosis difficult. Using a dataset of 197 patients with systematically described abnormalities, we identified molecular pathways whose disruption was associated with specific abnormalities among many patients. Importantly, patients with mutations in the same pathway often exhibited similar co-morbid symptoms and thus the commonly disrupted pathway appeared responsible for the broad range of shared abnormalities amongst these patients. These findings support the general concept that patients with mutations in distinct genes could be etiologically grouped together through the common pathway that these mutated genes participate in, with a view to improving diagnoses, prognoses and therapeutic outcomes.

On position-specific scoring matrix for protein function prediction

While genome sequencing projects have generated tremendous amounts of protein sequence data for a vast number of genomes, substantial portions of most genomes are still unannotated. Despite the success of experimental methods for identifying protein functions, they are often lab intensive and time consuming. Thus, it is only practical to use in silico methods for the genome-wide functional annotations. In this paper, we propose new features extracted from protein sequence only and machine learning-based methods for computational function prediction. These features are derived from a position-specific scoring matrix, which has shown great potential in other bininformatics problems. We evaluate these features using four different classifiers and yeast protein data. Our experimental results show that features derived from the position-specific scoring matrix are appropriate for automatic function annotation.

Knowledge-based data analysis comes of age

The emergence of high-throughput technologies for measuring biological systems has introduced problems for data interpretation that must be addressed for proper inference. First, analysis techniques need to be matched to the biological system, reflecting in their mathematical structure the underlying behavior being studied. When this is not done, mathematical techniques will generate answers, but the values and reliability estimates may not accurately reflect the biology. Second, analysis approaches must address the vast excess in variables measured (e.g. transcript levels of genes) over the number of samples (e.g. tumors, time points), known as the 'large-p, small-n' problem. In large-p, small-n paradigms, standard statistical techniques generally fail, and computational learning algorithms are prone to overfit the data. Here we review the emergence of techniques that match mathematical structure to the biology, the use of integrated data and prior knowledge to guide statistical analysis, and the recent emergence of analysis approaches utilizing simple biological models. We show that novel biological insights have been gained using these techniques.