Fast and Simple Nuclear Magnetic Resonance Method To Measure Conjugated Linoleic Acid in Beef

Conjugated linoleic acids (CLAs) are a group of linoleic acid isomers that are naturally found in food products originating from ruminants (meat and dairy). These acids have received special attention in recent years due to their potential human health benefits. Research efforts have been proposed to increase the CLA content in beef to improve public health. However, because there are more than 30 million beef cattle used each year by the American food industry, it will be necessary to ensure their content in a large number of samples. Therefore, it is important to have an inexpensive and rapid analytical method to measure CLA content in food products. Because gas chromatography (GC), a current popular method for measuring CLAs, is slow, this paper describes a nuclear magnetic resonance spectroscopy ((1)H NMR) method that is potentially >10 times faster than the GC method. Analyses show a correlation coefficient of 0.97, indicating the capacity of NMR to quantify the CLA content in beef samples. Furthermore, the method proposed herein is simple and does not require sophisticated sample preparation.

Fast Acquisition of (13)C NMR Spectra using the Steady-state Free Precession Sequence

In this paper, we show that the steady-state free precession sequence can be used to acquire (13)C high-resolution nuclear magnetic resonance spectra and applied to qualitative analysis. The analysis of brucine sample using this sequence with 60 degrees flip angle and time interval between pulses equal to 300 ms (acquisition time, 299.7 ms; recycle delay, 300 ms) resulted in spectrum with twofold enhancement in signal-to-noise ratio, when compared to standard (13)C sequence. This gain was better when a much shorter time interval between pulses (100 ms) was applied. The result obtained was more than fivefold enhancement in signal-to-noise ratio, equivalent to more than 20-fold reduction in total data recording time. However, this short time interval between pulses produces a spectrum with severe phase and truncation anomalies. We demonstrated that these anomalies can be minimized by applying an appropriate apodization function and plotting the spectrum in the magnitude mode.

Exercise and skeletal muscle gene expression

1. Skeletal muscle is a complex and heterogenous tissue capable of remarkable adaptation in response to exercise training. The role of gene transcription, as an initial target to control protein synthesis, is poorly understood.
2. Mature myofibres contain several hundred nuclei, all of which maintain transcriptional competency, although the localized responsiveness of nuclei is not well known. Myofibres are capable of hypertrophy. These processes require the activation and myogenic differentiation of mononuclear satellite cells that fuse with the enlarging or repairing myofibre.
3. A single bout of exercise in human subjects is capable of activating the expression of many diverse groups of genes.
4. The impact of repeated exercise bouts, typical of exercise training, on gene expression has yet to receive systematic investigation.
5. The molecular programme elicited by resistance exercise and endurance exercise differs markedly. Muscular hypertrophy following resistance exercise is dependent on the activation of satellite cells and their subsequent myogenic maturation. Endurance exercise requires the simultaneous activation of mitochondrial and nuclear genes to enable mitochondrial biogenesis.
6. Future analysis of the regulation of genes by exercise may combine high-throughput technologies, such as gene-chips, enabling the rapid detection and analysis of changes in the expression of many thousands of genes.

Preemptive job scheduling policy for distributively-owned workstation clusters

Cluster computing has come to prominence as a cost-effective parallel processing tool for solving many complex computational problems. In this paper, we propose a new timesharing opportunistic scheduling policy to support remote batch job executions over networked clusters to be used in conjunction with the Condor Up-Down scheduling algorithm. We show that timesharing approaches can be used in an opportunistic setting to improve both mean job slowdowns and mean response times with little or no throughput reduction. We also show that the proposed algorithm achieves significant improvement in job response time and slowdown as compared to exiting approaches and some recently proposed new approaches.

Dynamical systems for discovering protein complexes and functional modules from biological networks

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NPhard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a “spurious” heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in “spurious” heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.

Insights into exazaphosphorine resistance and possible approaches to its circumvention

The oxazaphosphorines cyclophosphamide, ifosfamide and trofosfamide remain a clinically useful class of anticancer drugs with substantial antitumour activity against a variety of solid tumors and hematological malignancies. A major limitation to their use is tumour resistance, which is due to multiple mechanisms that include increased DNA repair, increased cellular thiol levels, glutathione S-transferase and aldehyde dehydrogenase activities, and altered cell-death response to DNA damage. These mechanisms have been recently re-examined with the aid of sensitive analytical techniques, high-throughput proteomic and genomic approaches, and powerful pharmacogenetic tools. Oxazaphosphorine resistance, together with dose-limiting toxicity (mainly neutropenia and neurotoxicity), significantly hinders chemotherapy in patients, and hence, there is compelling need to find ways to overcome it. Four major approaches are currently being explored in preclinical models, some also in patients: combination with agents that modulate cellular response and disposition of oxazaphosphorines; antisense oligonucleotides directed against specific target genes; introduction of an activating gene (CYP3A4) into tumor tissue; and modification of dosing regimens. Of these approaches, antisense oligonucleotides and gene therapy are perhaps more speculative, requiring detailed safety and efficacy studies in preclinical models and in patients. A fifth approach is the design of novel oxazaphosphorines that have favourable pharmacokinetic and pharmacodynamic properties and are less vulnerable to resistance. Oxazaphosphorines not requiring hepatic CYP-mediated activation (for example, NSC 613060 and mafosfamide) or having additional targets (for example, glufosfamide that also targets glucose transport) have been synthesized and are being evaluated for safety and efficacy. Characterization of the molecular targets associated with oxazaphosphorine resistance may lead to a deeper understanding of the factors critical to the optimal use of these agents in chemotherapy and may allow the development of strategies to overcome resistance.

Drug bioactivation, covalent binding to target proteins and toxicity relevance

A number of therapeutic drugs with different structures and mechanisms of action have been reported to undergo metabolic activation by Phase I or Phase II drug-metabolizing enzymes. The bioactivation gives rise to reactive metabolites/intermediates, which readily confer covalent binding to various target proteins by nucleophilic substitution and/or Schiff's base mechanism. These drugs include analgesics (e.g., acetaminophen), antibacterial agents (e.g., sulfonamides and macrolide antibiotics), anticancer drugs (e.g., irinotecan), antiepileptic drugs (e.g., carbamazepine), anti-HIV agents (e.g., ritonavir), antipsychotics (e.g., clozapine), cardiovascular drugs (e.g., procainamide and hydralazine), immunosupressants (e.g., cyclosporine A), inhalational anesthetics (e.g., halothane), nonsteroidal anti-inflammatory drugs (NSAIDSs) (e.g., diclofenac), and steroids and their receptor modulators (e.g., estrogens and tamoxifen). Some herbal and dietary constituents are also bioactivated to reactive metabolites capable of binding covalently and inactivating cytochrome P450s (CYPs). A number of important target proteins of drugs have been identified by mass spectrometric techniques and proteomic approaches. The covalent binding and formation of drug-protein adducts are generally considered to be related to drug toxicity, and selective protein covalent binding by drug metabolites may lead to selective organ toxicity. However, the mechanisms involved in the protein adduct-induced toxicity are largely undefined, although it has been suggested that drug-protein adducts may cause toxicity either through impairing physiological functions of the modified proteins or through immune-mediated mechanisms. In addition, mechanism-based inhibition of CYPs may result in toxic drug-drug interactions. The clinical consequences of drug bioactivation and covalent binding to proteins are unpredictable, depending on many factors that are associated with the administered drugs and patients. Further studies using proteomic and genomic approaches with high throughput capacity are needed to identify the protein targetsof reactive drug metabolites, and to elucidate the structure-activity relationships of drug's covalent binding to proteins and their clinical outcomes.

A clustering based hybrid system for mass spectrometry data analysis

Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.

A protocol for the combined sub-fractionation and delipidation of lipid-binding proteins by hydrophobic interaction chromatography

Cellular lipids frequently co-purify with lipid binding proteins isolated from tissue extracts or heterologous host systems and as such hinder in vitro ligand binding approaches for which the apo-protein is a prerequisite. Here we present a technique for the complete removal of unesterified fatty acids, phospholipids, steroids and other lipophilic ligands bound to soluble proteins, without protein denaturation. Peroxisome proliferator activated receptor gamma ligand binding domain and intracellular fatty acid binding proteins were expressed in an Escherichia coli host and completely delipidated by hydrophobic interaction chromatography using phenyl sepharose. The delipidation procedure operates at room temperature with complete removal of bound lipids in a single step, as ascertained by mass spectrometry analysis of organic solvent extracts from purified protein samples. The speed and capacity of this method makes it amenable to scale-up and high-throughput applications. The method can also easily be adapted for other lipid binding proteins that require delipidation under native conditions.

Focusing in on structural genomics : the University of Queensland structural biology pipeline

The flood of new genomic sequence information together with technological innovations in protein structure determination have led to worldwide structural genomics (SG) initiatives. The goals of SG initiatives are to accelerate the process of protein structure determination, to fill in protein fold space and to provide information about the function of uncharacterized proteins. In the long-term, these outcomes are likely to impact on medical biotechnology and drug discovery, leading to a better understanding of disease as well as the development of new therapeutics. Here we describe the high throughput pipeline established at the University of Queensland in Australia. In this focused pipeline, the targets for structure determination are proteins that are expressed in mouse macrophage cells and that are inferred to have a role in innate immunity. The aim is to characterize the molecular structure and the biochemical and cellular function of these targets by using a parallel processing pipeline. The pipeline is designed to work with tens to hundreds of target gene products and comprises target selection, cloning, expression, purification, crystallization and structure determination. The structures from this pipeline will provide insights into the function of previously uncharacterized macrophage proteins and could lead to the validation of new drug targets for chronic obstructive pulmonary disease and arthritis.

Data pre-processing for more effective gene clustering

The high-throughput experimental data from the new gene microarray technology has spurred numerous efforts to find effective ways of processing microarray data for revealing real biological relationships among genes. This work proposes an innovative data pre-processing approach to identify noise data in the data sets and eliminate or reduce the impact of the noise data on gene clustering, With the proposed algorithm, the pre-processed data sets make the clustering results stable across clustering algorithms with different similarity metrics, the important information of genes and features is kept, and the clustering quality is improved. The primary evaluation on real microarray data sets has shown the effectiveness of the proposed algorithm.

Semantic and layered protein function prediction from PPI networks

Background The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein-protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness.

Results We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein's semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions.

Conclusion The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins.

Surveillance of wild birds for avian influenza virus

Recent demand for increased understanding of avian influenza virus in its natural hosts, together with the development of high-throughput diagnostics, has heralded a new era in wildlife disease surveillance. However, survey design, sampling, and interpretation in the context of host populations still present major challenges. We critically reviewed current surveillance to distill a series of considerations pertinent to avian influenza virus surveillance in wild birds, including consideration of what, when, where, and how many to sample in the context of survey objectives. Recognizing that wildlife disease surveillance is logistically and financially constrained, we discuss pragmatic alternatives for achieving probability-based sampling schemes that capture this host-pathogen system. We recommend hypothesis-driven surveillance through standardized, local surveys that are, in turn, strategically compiled over broad geographic areas. Rethinking the use of existing surveillance infrastructure can thereby greatly enhance our global understanding of avian influenza and other zoonotic diseases.

A micromechanical biosensor with interdigitated capacitor readout

The growing demands of high-throughput, accurate and fast response biological or chemical sensors are driving the development of new detection technologies. This paper presents a micromechanical biosensor with capacitive read-out method. The proposed biosensor design consists of a fixed-fixed beam attached to an interdigitated capacitor. Implementation of the interdigitated capacitor design improves the sensitivity of the biosensor. The effects of the electrode thickness, length and the number of electrode fingers on the change of capacitance are investigated. The results show that the percentage change of capacitance is proportional to the number of the electrode fingers. Similarly, the increase in the length of the electrodes results in an increase in the percentage change of the capacitance. However, as the thickness of the electrode increases, the percentage change of the capacitance decreases.

Global and threshold-free transcriptional regulatory networks reconstruction through integrating ChIP-Chip and expression data

Inferring transcriptional regulatory networks from high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed TReNGO (Transcriptional Regulatory Networks reconstruction based on Global Optimization), a global and threshold-free algorithm with simulated annealing for inferring regulatory networks by the integration of ChIP-chip and expression data. Superior to existing methods, TReNGO was expected to find the optimal structure of transcriptional regulatory networks without any arbitrary thresholds or predetermined number of transcriptional modules (TMs). TReNGO was applied to both synthetic data and real yeast data in the rapamycin response. In these applications, we demonstrated an improved functional coherence of TMs and TF (transcription factor)- target predictions by TReNGO when compared to GRAM, COGRIM or to analyzing ChIP-chip data alone. We also demonstrated the ability of TReNGO to discover unexpected biological processes that TFs may be involved in and to also identify interesting novel combinations of TFs.