Inter-individual variation in expression: a missing link in biomarker biology?

Variability among individuals is generated at the gene expression level

Selection acts on individuals, specifically on their differences. To understand adaptation and responses to change therefore requires knowledge of how variation is generated and distributed across traits. Variation occurs on different biological scales, from genetic through physiological to morphological, yet it is unclear which of these carries the most variability. For example, if individual variation is mainly generated by differences in gene expression, variability should decrease progressively from coding genes to morphological traits, whereas if post-translational and epigenetic effects increase variation, the opposite should occur. To test these predictions, we compared levels of variation among individuals in various measures of gene expression, physiology (including activity), and morphology in two abundant and geographically widespread Antarctic molluscs, the clam Laternula elliptica and the limpet Nacella concinna. Direct comparisons among traits as diverse as heat shock protein QPCR assays, whole transcription profiles, respiration rates, burying rate, shell length, and ash-free dry mass were made possible through the novel application of an established metric, the Wentworth Scale. In principle, this approach could be extended to analyses of populations, communities, or even entire ecosystems. We found consistently greater variation in gene expression than morphology, with physiological measures falling in between. This suggests that variability is generated at the gene expression level. These findings have important implications for refining current biological models and predictions of how biodiversity may respond to climate change.

Urinary proteome analysis and the management of ureteropelvic junction obstruction

Ureteropelvic junction obstruction (UPJO) detected prenatally may over time deteriorate and require surgery, improve, or remain stable, and it may take upwards of 3 years for its natural history to unfold. Clinical decisions for or against operative corrections are usually based on scintigraphy follow-up studies. A non-invasive method for facilitating clinical decisions has recently been presented: urinary proteome analysis utilizing capillary electrophoresis mass spectrometry (CE-MS) has been shown to predict the outcome of UPJO in newborns. The group that developed this assay has now validated their seminal findings and extended the investigations to older age groups (this issue). The results of the blinded analysis correctly identified patients with UPJO who underwent surgery with a sensitivity of 83% (5 of 6 patients) and a specificity of 92% (12 of 13 patients) in infants up to 1 year of age. The validity of the analysis was poor in children >1 year of age with unilateral UPJO. A large number of patients will be needed to answer the question of to what extent the normal variability of urinary proteomes overlap with the variability of the pattern in UPJO beyond early infancy.

Proteomics data mining

Marc Wilkins completed his undergraduate and doctoral studies at Macquarie University, Sydney, Australia. During his doctoral studies, he defined the concept of the proteome and coined the term. After postdoctoral studies in Geneva, Switzerland, during which he co-edited the first book on proteomics, he returned to Australia, where he cofounded the company Proteome Systems. More recently, Marc took a position as Professor of Systems Biology at the University of New South Wales. He has established and directs the NSW Systems Biology Initiative, and is currently researching the role that protein post-translational modifications play in the regulation of protein-interaction networks.

Effect of consecutive repeated sprint and resistance exercise bouts on acute adaptive responses in human skeletal muscle

We examined acute molecular responses in skeletal muscle to repeated sprint and resistance exercise bouts. Six men [age, 24.7 ± 6.3 yr; body mass, 81.6 ± 7.3 kg; peak oxygen uptake, 47 ± 9.9 ml·kg−1·min−1; one repetition maximum (1-RM) leg extension 92.2 ± 12.5 kg; means ± SD] were randomly assigned to trials consisting of either resistance exercise (8 × 5 leg extension, 80% 1-RM) followed by repeated sprints (10 × 6 s, 0.75 N·m torque·kg−1) or vice-versa. Muscle biopsies from vastus lateralis were obtained at rest, 15 min after each exercise bout, and following 3-h recovery to determine early signaling and mRNA responses. There was divergent exercise order-dependent phosphorylation of p70 S6K (S6K). Specifically, initial resistance exercise increased S6K phosphorylation (∼75% P < 0.05), but there was no effect when resistance exercise was undertaken after sprints. Exercise decreased IGF-I mRNA following 3-h recovery (∼50%, P = 0.06) independent of order, while muscle RING finger mRNA was elevated with a moderate exercise order effect ( P < 0.01). When resistance exercise was followed by repeated sprints PGC-1α mRNA was increased (REX1-SPR2; P = 0.02) with a modest distinction between exercise orders. Repeated sprints may promote acute interference on resistance exercise responses by attenuating translation initiation signaling and exacerbating ubiquitin ligase expression. Indeed, repeated sprints appear to generate the overriding acute exercise-induced response when undertaking concurrent repeated sprint and resistance exercise. Accordingly, we suggest that sprint-activities are isolated from resistance training and that adequate recovery time is considered within periodized training plans that incorporate these divergent exercise modes.

Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

Recent advances in biomarker discovery, biocomputing and nanotechnology have raised new opportunities in the emerging fields of personalized medicine (in which disease detection, diagnosis and therapy are tailored to each individual's molecular profile) and predictive medicine (in which genetic and molecular information is used to predict disease development, progression and clinical outcome). Here, we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, in addition to the prospects for and challenges involved in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases.