Analysis of cancer metabolism with high-throughput technologies

<p>Abstract</p> <p>Background</p> <p>Recent advances in genomics and proteomics have allowed us to study the nuances of the Warburg effect – a long-standing puzzle in cancer energy metabolism – at an unprecedented level of detail. While modern next-generation sequencing technologies are extremely powerful, the lack of appropriate data analysis tools makes this study difficult. To meet this challenge, we developed a novel application for comparative analysis of gene expression and visualization of RNA-Seq data.</p> <p>Results</p> <p>We analyzed two biological samples (normal human brain tissue and human cancer cell lines) with high-energy, metabolic requirements. We calculated digital topology and the copy number of every expressed transcript. We observed subtle but remarkable qualitative and quantitative differences between the citric acid (TCA) cycle and glycolysis pathways. We found that in the first three steps of the TCA cycle, digital expression of aconitase 2 (<it>ACO2</it>) in the brain exceeded both citrate synthase (<it>CS</it>) and isocitrate dehydrogenase 2 (<it>IDH2</it>), while in cancer cells this trend was quite the opposite. In the glycolysis pathway, all genes showed higher expression levels in cancer cell lines; and most notably, digital gene expression of glyceraldehyde-3-phosphate dehydrogenase (<it>GAPDH</it>) and enolase (<it>ENO</it>) were considerably increased when compared to the brain sample.</p> <p>Conclusions</p> <p>The variations we observed should affect the rates and quantities of ATP production. We expect that the developed tool will provide insights into the subtleties related to the causality between the Warburg effect and neoplastic transformation. Even though we focused on well-known and extensively studied metabolic pathways, the data analysis and visualization pipeline that we developed is particularly valuable as it is global and pathway-independent.</p

Accessing a Hidden Conformation of the Maltose Binding Protein Using Accelerated Molecular Dynamics

Periplasmic binding proteins (PBPs) are a large family of molecular transporters that play a key role in nutrient uptake and chemotaxis in Gram-negative bacteria. All PBPs have characteristic two-domain architecture with a central interdomain ligand-binding cleft. Upon binding to their respective ligands, PBPs undergo a large conformational change that effectively closes the binding cleft. This conformational change is traditionally viewed as a ligand induced-fit process; however, the intrinsic dynamics of the protein may also be crucial for ligand recognition. Recent NMR paramagnetic relaxation enhancement (PRE) experiments have shown that the maltose binding protein (MBP) - a prototypical member of the PBP superfamily - exists in a rapidly exchanging (ns to µs regime) mixture comprising an open state (approx 95%), and a minor partially closed state (approx 5%). Here we describe accelerated MD simulations that provide a detailed picture of the transition between the open and partially closed states, and confirm the existence of a dynamical equilibrium between these two states in apo MBP. We find that a flexible part of the protein called the balancing interface motif (residues 175–184) is displaced during the transformation. Continuum electrostatic calculations indicate that the repacking of non-polar residues near the hinge region plays an important role in driving the conformational change. Oscillations between open and partially closed states create variations in the shape and size of the binding site. The study provides a detailed description of the conformational space available to ligand-free MBP, and has implications for understanding ligand recognition and allostery in related proteins

Climate Change and Trophic Response of the Antarctic Bottom Fauna

BACKGROUND: As Earth warms, temperate and subpolar marine species will increasingly shift their geographic ranges poleward. The endemic shelf fauna of Antarctica is especially vulnerable to climate-mediated biological invasions because cold temperatures currently exclude the durophagous (shell-breaking) predators that structure shallow-benthic communities elsewhere. METHODOLOGY/PRINCIPAL FINDINGS: We used the Eocene fossil record from Seymour Island, Antarctic Peninsula, to project specifically how global warming will reorganize the nearshore benthos of Antarctica. A long-term cooling trend, which began with a sharp temperature drop approximately 41 Ma (million years ago), eliminated durophagous predators-teleosts (modern bony fish), decapod crustaceans (crabs and lobsters) and almost all neoselachian elasmobranchs (modern sharks and rays)-from Antarctic nearshore waters after the Eocene. Even prior to those extinctions, durophagous predators became less active as coastal sea temperatures declined from 41 Ma to the end of the Eocene, approximately 33.5 Ma. In response, dense populations of suspension-feeding ophiuroids and crinoids abruptly appeared. Dense aggregations of brachiopods transcended the cooling event with no apparent change in predation pressure, nor were there changes in the frequency of shell-drilling predation on venerid bivalves. CONCLUSIONS/SIGNIFICANCE: Rapid warming in the Southern Ocean is now removing the physiological barriers to shell-breaking predators, and crabs are returning to the Antarctic Peninsula. Over the coming decades to centuries, we predict a rapid reversal of the Eocene trends. Increasing predation will reduce or eliminate extant dense populations of suspension-feeding echinoderms from nearshore habitats along the Peninsula while brachiopods will continue to form large populations, and the intensity of shell-drilling predation on infaunal bivalves will not change appreciably. In time the ecological effects of global warming could spread to other portions of the Antarctic coast. The differential responses of faunal components will reduce the endemic character of Antarctic subtidal communities, homogenizing them with nearshore communities at lower latitudes

On the analysis of sedimentation velocity in the study of protein complexes

Sedimentation velocity analytical ultracentrifugation has experienced a significant transformation, precipitated by the possibility of efficiently fitting Lamm equation solutions to the experimental data. The precision of this approach depends on the ability to account for the imperfections of the experiment, both regarding the sample and the instrument. In the present work, we explore in more detail the relationship between the sedimentation process, its detection, and the model used in the mathematical data analysis. We focus on configurations that produce steep and fast-moving sedimentation boundaries, such as frequently encountered when studying large multi-protein complexes. First, as a computational tool facilitating the analysis of heterogeneous samples, we introduce the strategy of partial boundary modeling. It can simplify the modeling by restricting the direct boundary analysis to species with sedimentation coefficients in a predefined range. Next, we examine factors related to the experimental detection, including the magnitude of optical aberrations generated by out-of-focus solution columns at high protein concentrations, the relationship between the experimentally recorded signature of the meniscus and the meniscus parameter in the data analysis, and the consequences of the limited radial and temporal resolution of the absorbance optical scanning system. Surprisingly, we find that large errors can be caused by the finite scanning speed of the commercial absorbance optics, exceeding the statistical errors in the measured sedimentation coefficients by more than an order of magnitude. We describe how these effects can be computationally accounted for in SEDFIT and SEDPHAT

Hypofractionated radiotherapy for prostate cancer

In the last few years, hypofractionated external beam radiotherapy has gained increasing popularity for prostate cancer treatment, since sufficient evidence exists that prostate cancer has a low alpha/beta ratio, lower than the one of the surrounding organs at risk and thus there is a potential therapeutic benefit of using larger fractionated single doses. Apart from the therapeutic rationale there are advantages such as saving treatment time and medical resources and thereby improving patient's convenience. While older trials showed unsatisfactory results in both standard and hypofractionated arm due to insufficient radiation doses and non-standard contouring of target volumes, contemporary randomized studies have reported on encouraging results of tumor control mostly without an increase of relevant side effects, especially late toxicity. Aim of this review is to give a detailed analysis of relevant, recently published clinical trials with special focus on rationale for hypofractionation and different therapy settings

Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications

<p>Abstract</p> <p>Background</p> <p>The need for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering.</p> <p>Results</p> <p>In this work we performed whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between <it>Saccharomyces cerevisiae </it>strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the <it>Saccharomyces </it>Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,596 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being nonsynonymous (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (<it>GAL1</it>, <it>GAL10</it>) and ergosterol biosynthetic pathway (<it>ERG8</it>, <it>ERG9</it>). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function.</p> <p>Conclusions</p> <p>With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at <url>http://www.sysbio.se/cenpk</url>.</p

Comprehensive Structural and Substrate Specificity Classification of the Saccharomyces cerevisiae Methyltransferome

Methylation is one of the most common chemical modifications of biologically active molecules and it occurs in all life forms. Its functional role is very diverse and involves many essential cellular processes, such as signal transduction, transcriptional control, biosynthesis, and metabolism. Here, we provide further insight into the enzymatic methylation in S. cerevisiae by conducting a comprehensive structural and functional survey of all the methyltransferases encoded in its genome. Using distant homology detection and fold recognition, we found that the S. cerevisiae methyltransferome comprises 86 MTases (53 well-known and 33 putative with unknown substrate specificity). Structural classification of their catalytic domains shows that these enzymes may adopt nine different folds, the most common being the Rossmann-like. We also analyzed the domain architecture of these proteins and identified several new domain contexts. Interestingly, we found that the majority of MTase genes are periodically expressed during yeast metabolic cycle. This finding, together with calculated isoelectric point, fold assignment and cellular localization, was used to develop a novel approach for predicting substrate specificity. Using this approach, we predicted the general substrates for 24 of 33 putative MTases and confirmed these predictions experimentally in both cases tested. Finally, we show that, in S. cerevisiae, methylation is carried out by 34 RNA MTases, 32 protein MTases, eight small molecule MTases, three lipid MTases, and nine MTases with still unknown substrate specificity

Revisiting the Myths of Protein Interior: Studying Proteins with Mass-Fractal Hydrophobicity-Fractal and Polarizability-Fractal Dimensions

A robust marker to describe mass, hydrophobicity and polarizability distribution holds the key to deciphering structural and folding constraints within proteins. Since each of these distributions is inhomogeneous in nature, the construct should be sensitive in describing the patterns therein. We show, for the first time, that the hydrophobicity and polarizability distributions in protein interior follow fractal scaling. It is found that (barring ‘all-α’) all the major structural classes of proteins have an amount of unused hydrophobicity left in them. This amount of untapped hydrophobicity is observed to be greater in thermophilic proteins, than that in their (structurally aligned) mesophilic counterparts. ‘All-β’(thermophilic, mesophilic alike) proteins are found to have maximum amount of unused hydrophobicity, while ‘all-α’ proteins have been found to have minimum polarizability. A non-trivial dependency is observed between dielectric constant and hydrophobicity distributions within (α+β) and ‘all-α’ proteins, whereas absolutely no dependency is found between them in the ‘all-β’ class. This study proves that proteins are not as optimally packed as they are supposed to be. It is also proved that origin of α-helices are possibly not hydrophobic but electrostatic; whereas β-sheets are predominantly hydrophobic in nature. Significance of this study lies in protein engineering studies; because it quantifies the extent of packing that ensures protein functionality. It shows that myths regarding protein interior organization might obfuscate our knowledge of actual reality. However, if the later is studied with a robust marker of strong mathematical basis, unknown correlations can still be unearthed; which help us to understand the nature of hydrophobicity, causality behind protein folding, and the importance of anisotropic electrostatics in stabilizing a highly complex structure named ‘proteins’

Genomics in neurodevelopmental disorders: an avenue to personalized medicine

Despite the remarkable number of scientific breakthroughs of the last 100 years, the treatment of neurodevelopmental disorders (e.g., autism spectrum disorder, intellectual disability) remains a great challenge. Recent advancements in genomics, such as whole-exome or whole-genome sequencing, have enabled scientists to identify numerous mutations underlying neurodevelopmental disorders. Given the few hundred risk genes that have been discovered, the etiological variability and the heterogeneous clinical presentation, the need for genotype — along with phenotype- based diagnosis of individual patients has become a requisite. In this review we look at recent advancements in genomic analysis and their translation into clinical practice

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward