Nutrition, growth and production of Cherax Destructor Clark, 1936 (Decapoda, Parastacidae)

In this study the nutrition, growth and production of C. destructor was examined. Selected nutritional requirements of juvenile animals were determined under controlled conditions with the aim of developing a pelleted diet for use in hatcheries, nurseries and growout situations. The best developed diet was assessed for its potential as a supplementary feed for animals cultured in earthen environments. The protein requirements were first determined simultaneously with an evaluation of the effect of replacing animal protein (fishmeal) by soybean meal. Juveniles were reared communally for 59 d on isoenergetic diets containing 15-30% protein and graded levels of soybean meal (0-60%, of protein). When soybean meal was included at a level of 40-60%, growth was reduced relative to that achieved with control diets containing 15% and 20% protein, but this was not the case at a 20% soybean meal substitution level. A two-way interaction occurred between dietary protein and soybean meal content. Higher protein feeds enabled higher soybean meal inclusion levels without significantly affecting growth. Protein increases of 5% produced better growth at the 40% and 60% soybean meal substitution levels. This effect was less pronounced in the control and the 20% soybean meal diets. Carcass %protein increased and %lipid decreased as dietary protein increased. A similar effect occurred by increasing the soybean meal level to 60%. No obvious trend in carcass moisture, energy, and ash occurred. A protein requirement of 30% was apparent when fish meal and soybean meal were included in diets at levels of 20% and 24% (dry matter) respectively. Alternative protein sources to soybean meal were subsequently identified. Juveniles were maintained for 12 weeks on isoenergetic diets containing 30% protein and differing in the primary source of protein used, with meat, snail, soybean, yabby, and zooplankton meals comprising the major protein ingredient. No significant difference occurred in mean weight (MW), percentage weight gain (%WG), SGR or survival among diets. Food conversion ratios (FCR) were low, with a minimum value of 0.95 for the snail-based diet. The apparent net protein utilisation (ANPU) varied from 29.6% (zooplankton-based diet) to 41.2% (snail-based diet). Carcass composition varied with diet, with the greatest difference occurring in carapace colour. Animals fed the zooplankton-based diet developed the strongest, most natural pigmentation. A new combination of previously used protein-based ingredients was subsequently tested with reference to two yabby species, Cherax albidus and Cherax destructor, that were grown simultaneously in identical conditions. Juvenile male animals were reared individually for 20 weeks on isoenergetic diets containing 15% or 30% protein with fish meal, soybean meal, yabby meal and wheat products forming the basis of the diets. C albidus grew the fastest and utilised the food the most effectively. Carcass composition was influenced by diet with the 30% protein diet resulting in an increase in carcass protein and ash and a decrease in carcass lipid and energy relative to the low protein diet. Carcass moisture and calcium were not affected by diet. The intermoult period (IP) was highly dependent on the premoult weight (W) but the mean moult increment (WI, as weight) was independent of the PM. The orbital carapace length (OCL) and the abdominal length (ABL) %moult increments generally declined with an increase in PM whereas the propus length (PL) %moult increment generally increased. The IP, WI, %OCL, %ABL, and %PL moult increments varied according to diet and to species. Elevated dietary protein caused a reduction to the IP (for similar sized animals) by 11 d and 7 d and an increase to the WI by 85% and 81% in C. albidus and C destructor respectively. Dietary induced morphological changes also occurred. Animals of a standard OCL (both species) had significantly larger abdomens when fed the higher protein diet. Growth on the best developed diet was compared to the growth obtained on a natural diet of freshwater zooplankton. Juveniles were reared individually for 12 weeks on the two diets. The MW, %WG and SGR were higher for the zooplankton diet. Carcass composition was influenced by diet and the zooplankton fed animals had a higher carcass %protein, %lipid, %ash and %fibre content and were more richly pigmented than animals fed pellets. The IP and the WI were highly dependent on the PM and varied according to diet; feeding with zooplankton reduced the IP by 1.2 days and increased the WI by 13.7% compared to pellets. Nutrient digestibility was determined for the pelleted diets evaluated in the growth trials. Protein digestibility (PD) and dry matter digestibility (DMD), using chromic oxide (Cr2O3) as an exogenous marker, were high for all diets, at around 93% and 83% respectively. Ash digestibility varied considerably from 17% to 73% for the snail and yabby meal diets respectively. Crude fibre digestibility was around 50% and probably indicates cellulase activity. Alternative markers to Cr2O3 were evaluated. Ash was considered to be the most suitable alternative to Cr2O3, providing a reasonable, albeit lower, estimate of nutrient digestibility. Cr2O3 and ash were preferentially excreted whereas fibre was retained in the digestive system for a longer period, consequently, the collection of a particular fraction of the deposited faeces (late or early) substantially affected the digestibility coefficients. In earthen-based environments, animals fed the best developed diet were compared to animals cultured using a forage crop of clover (Trifolium repens). Three supplementary feeding strategies representing varying levels of management intensity were evaluated in a series of trials conducted in ponds and pond microcosms. Growth on pellets consistently exceeded that obtained with the forage crop, with final MW being 67-159% higher than that using clover and appeared to be the result of direct pellet consumption and from a pellet fertiliser effect (on the sediment). Within-pond DMD and PD were high and similar for each treatment (DMD = 51-58%; PD = 89-92%). In the control pond, DMD and PD increased with each successive flood. The faecal egestion rate (PER) decreased with each successive flood in all ponds, and is negatively related to animal weight and to foregut fullness (FF) according to power curves. FF was consistently lowest in the control pond. Mean FF was 48.5%, 62.3%, and 26.7% for the pellet, crop and control ponds respectively. FF increased to the third flood in each pond. The foregut protein content was high in all samples and the mean values were 33.9%, 32.7% and 35.6% for the pellet, crop and control ponds respectively. Foregut ash was highly variable within each pond and is inversely related to the foregut protein content. In the control and pellet ponds the highest foregut ash content occurred during flood 1. The culture system (aquaria or pond) strongly influenced the composition of the foregut content. The foregut of animals fed the manufactured diet (B2) in ponds contained approximately 176% more ash and 5% more protein than the foregut of animals fed in bare-bottom tanks. The FF of the tank fed animals was approximately 45% higher than the FF of pond fed animals after a similar feeding period. Base-line yields for extensive production systems appeared to be around 400kg ha-1. The supplementary addition of T. repens produced yields of approximately 635kg ha-1 (in ponds) to around 1086kg ha-1 (in tanks). The sequential addition of cut-clover to tanks stimulated growth to levels approaching those achieved on pellets. Yabbies stocked into ponds at 15-20 m-2 with a mean weight of 2.67g and fed a 30% protein pelleted diet for 100 d, resulted in a yield of approximately 1117kg ha-1, but only 2% of the population were above a marketable size of 50g. The feed utilisation indices were better for animals reared on pellets in bare-bottom tanks than in earthen environments, indicating some degree of pellet wastage when natural feeds are simultaneously present. High apparent food conversion ratios and low protein efficiency ratios occurred when the forage crop was provided. A considerable quantity of the dry matter and protein content of the forage crop was either inefficiently utilised or directed into other production pathways. Sowing a forage crop into pond microcosms to which a pelleted diet was also provided, did not enhance growth performance. Pelleted feed inputs at a rate of approximately 129g m-2 to 198g m-2 (dry matter) and 38g -2 to 64g m-2 (protein) over 70-100 d resulted in acceptable growth and feed utilisation indices for animals reared in ponds and pond microcosms. Forage crop inputs of approximately 533g m-2 to 680g m-2 (as dry matter) or 84g m-2 to 177g m-2 (as protein) over a 70-100 d period produced reasonable growth rates but poor feed utilisation indices. Low inputs of dry matter (from 113-296g m-2) and protein (from 24-54g m-2) from clover were sufficient to maintain high growth rates in pond microcosms for around 28 d. In ponds, a very low level of 21g m-2 (dry matter) and 4.3g m-2 (protein) was sufficient for around 3 weeks. Forage depletion appeared to occur beyond week 3-4 and was probably a major growth limiting factor. The mean hepatosomatic index (HSI) was 9.44, 7.68, and 6.79 for the pellet, crop, and control ponds respectively. The relationship between hepatopancreas weight and overall animal weight was significantly different between treatments. The hepatopancreas of pellet-fed animals had the highest %lipid and lowest %ash, %protein, %carbohydrate and %moisture content. In terms of absolute quantities, the only major difference in hepatopancreas composition between treatments occurred for lipid and dry matter content. The hepatopancreas of the pellet-fed animals was a cream/cream-yellow colour and was very fragile, whereas in the other ponds it was a more ‘natural’ bright yellow colour and was structurally more robust. C. destructor has a capacious foregut, being approximately 5 times the volume of similar sized Penaeids. The foregut volume (V, ml) of the yabby is related to animal weight (W, g) according to V = 0.048 W0.9543. Animals that were starved for 96 h and then fed diet B2 were almost completely foil after 30 min. The ‘apparent enzymatic response’ of animals fed various natural and artificial diets in tanks was evaluated. Nutrient processing time and the enzymatic response following ingestion appeared to be regulated by the chemical and physical properties of the diet. For the natural feeds, foregut protein was 1.2% higher (for zooplankton) and up to 300% higher (for detritus) than dietary protein, whereas ash was 7.5% higher (zooplankton) and 46-63% lower (detritus) than dietary ash. For animals fed diet B2 after 48 h without food, FF was approximately half that of 96 h starved animals after a similar feeding period but foregut protein and ash contents were similar. Finally, the physiological and morphological attributes elucidated in this study are discussed with reference to the ecology of the yabby. High growth rates, excellent feed utilisation indices and high digestibility coefficients for a wide range of diet-types illustrate nutritional flexibility. A capacious foregut, a large hepatopancreas with a high energy storage capacity, the ability to partition and preferentially excrete the low nutrient value inorganic component of the diet, the capacity to alter body form, nutrient processing time and enzymatic secretions in relation to diet-type, and modified behaviour according to feed availability also demonstrate plasticity/adaptability/flexibility. The combined effect of these important characteristics ensures survival in environments that may be adverse and highly variable in terms of nutrient availability. Collectively the morphological and digestive traits elucidated in this study reflect the generalist-type nature of C destructor and indicate that a polytrophic classification still seems appropriate. Several priority areas for further nutrition research are identified and recommendations are made regarding the best-practices to use in the commercial culture of the yabby. Of paramount importance is the further clarification of the nutritional requirements and feeding preferences of animals in various phases of development.

Normal hypertrophy accompanied by phosphoryation and activation of AMP-activated protein kinase α1 following overload in LKB1 knockout mice

The activation of the AMP-activated protein kinase (AMPK) and inhibition of the mammalian target of rapamycin complex 1 (mTORC1) is hypothesized to underlie the fact that muscle growth following resistance exercise is decreased by concurrent endurance exercise. To directly test this hypothesis, the capacity for muscle growth was determined in mice lacking the primary upstream kinase for AMPK in skeletal muscle, LKB1. Following either 1 or 4 weeks of overload, there was no difference in muscle growth between the wild type (wt) and LKB1−/− mice (1 week: wt, 38.8 ± 7.75%; LKB1−/−, 27.8 ± 12.98%; 4 week: wt, 75.8 ± 15.2%; LKB1−/−, 85.0 ± 22.6%). In spite of the fact that the LKB1 had been knocked out in skeletal muscle, the phosphorylation and activity of the α1 isoform of AMPK were markedly increased in both the wt and the LKB1−/− mice. To identify the upstream kinase(s) responsible, we studied potential upstream kinases other than LKB1. The activity of both Ca2+–calmodulin-dependent protein kinase kinase α(CaMKKα) (5.05 ± 0.86-fold) and CaMKKβ (10.1 ± 2.59-fold) increased in the overloaded muscles, and this correlated with their increased expression. Phosphorylation of TAK-1 also increased 10-fold following overload in both the wt and LKB1 mice. Even though the α1 isoform of AMPK was activated by overload, there were no increases in expression of mitochondrial proteins or GLUT4, indicating that the α1 isoform is not involved in these metabolic adaptations. The phosphorylation of TSC2, an upstream regulator of the TORC1 pathway, at the AMPK site (Ser1345) was increased in response to overload, and this was not affected by LKB1 deficiency. Taken together, these data suggest that the α1 isoform of AMPK is preferentially activated in skeletal muscle following overload in the absence of metabolic adaptations, suggesting that this isoform might be important in the regulation of growth but not metabolism.

Isolation and characterisation of Arabidopsis RAD30 and ERCC1

This thesis describes the isolation and characterisation of two plant genes, AtERCC1 and AtRAD30. Evidence from protein homology comparisons, and functional complementation, in vitro mutagenesis, or interaction assays suggests the involvement of these genes in the repair or tolerance, respectively, of UV-induced DNA change.

MSP119 miniproteins can serve as targets for invasion inhibitory antibodies in plasmodium falciparum provided they contain the correct domains for cell surface trafficking.

Antibodies from malaria-exposed individuals can agglutinate merozoites released from Plasmodium schizonts, thereby preventing them from invading new erythrocytes. Merozoite coat proteins attached to the plasma membrane are major targets for host antibodies and are therefore considered important malaria vaccine candidates. Prominent among these is the abundant glycosylphosphatidylinositol (GPI)-anchored merozoite surface protein 1 (MSP1) and particularly its C-terminal fragment (MSP1(19)) comprised of two epidermal growth factor (EGF)-like modules. In this paper, we revisit the role of agglutination and immunity using transgenic fluorescent marker proteins. We describe expression of heterologous MSP1(19)'miniproteins' on the surface of Plasmodium falciparum merozoites. To correctly express these proteins, we determined that GPI-anchoring and the presence of a signal sequence do not allow default export of proteins from the endoplasmic reticulum to merozoite surface and that extra sequence elements are required. The EGFs are insufficient for correct trafficking unless they are fused to additional residues that normally reside upstream of this fragment. Antibodies specifically targeting the surface-expressed miniprotein can inhibit erythrocyte invasion in vitro despite the presence of endogenous MSP1. Using a line expressing a green fluorescent protein-MSP1 fusion protein, we demonstrate that one mode of inhibition by antibodies targeting the MSP1(19) domain is the rapid agglutinating of merozoites prior to erythrocyte attachment.

Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

Evolution of distinct EGF domains with specific functions

EGF domains are extracellular protein modules cross-linked by three intradomain disulfides. Past studies suggest the existence of two types of EGF domain with three-disulfides, human EGF-like (hEGF) domains and complement C1r-like (cEGF) domains, but to date no functional information has been related to the two different types, and they are not differentiated in sequence or structure databases. We have developed new sequence patterns based on the different C-termini to search specifically for the two types of EGF domains in sequence databases. The exhibited sensitivity and specificity of the new pattern-based method represents a significant advancement over the currently available sequence detection techniques. We re-annotated EGF sequences in the latest release of Swiss-Prot looking for functional relationships that might correlate with EGF type. We show that important post-translational modifications of three-disulfide EGFs, including unusual forms of glycosylation and post-translational proteolytic processing, are dependent on EGF subtype. For example, EGF domains that are shed from the cell surface and mediate intercellular signaling are all hEGFs, as are all human EGF receptor family ligands. Additional experimental data suggest that functional specialization has accompanied subtype divergence. Based on our structural analysis of EGF domains with three-disulfide bonds and comparison to laminin and integrin-like EGF domains with an additional interdomain disulfide, we propose that these hEGF and cEGF domains may have arisen from a four-disulfide ancestor by selective loss of different cysteine residues.

Functions for S. cerevisiae Swd2p in 3' end formation of specific mRNAs and snoRNAs and global histone 3 lysine 4 methylation

The Saccharomyces cerevisiae WD-40 repeat protein Swd2p associates with two functionally distinct multiprotein complexes: the cleavage and polyadenylation factor (CPF) that is involved in pre-mRNA and snoRNA 3′ end formation and the SET1 complex (SET1C) that methylates histone 3 lysine 4. Based on bioinformatic analysis we predict a seven-bladed β-propeller structure for Swd2p proteins. Northern, transcriptional run-on and in vitro 3′ end cleavage analyses suggest that temperature sensitive swd2 strains were defective in 3′ end formation of specific mRNAs and snoRNAs. Protein–protein interaction studies support a role for Swd2p in the assembly of 3′ end formation complexes. Furthermore, histone 3 lysine 4 di-and tri-methylation were adversely affected and telomeres were shortened in swd2 mutants. Underaccumulation of the Set1p methyltransferase accounts for the observed loss of SET1C activity and suggests a requirement for Swd2p for the stability or assembly of this complex. We also provide evidence that the roles of Swd2p as component of CPF and SET1C are functionally independent. Taken together, our results establish a dual requirement for Swd2p in 3′ end formation and histone tail modification.

Identifying foldable regions in protein sequence from the hydrophobic signal

Structural genomics initiatives aim to elucidate representative 3D structures for the majority of protein families over the next decade, but many obstacles must be overcome. The correct design of constructs is extremely important since many proteins will be too large or contain unstructured regions and will not be amenable to crystallization. It is therefore essential to identify regions in protein sequences that are likely to be suitable for structural study. Scooby-Domain is a fast and simple method to identify globular domains in protein sequences. Domains are compact units of protein structure and their correct delineation will aid structural elucidation through a divide-and-conquer approach. Scooby-Domain predictions are based on the observed lengths and hydrophobicities of domains from proteins with known tertiary structure. The prediction method employs an A*-search to identify sequence regions that form a globular structure and those that are unstructured. On a test set of 173 proteins with consensus CATH and SCOP domain definitions, Scooby-Domain has a sensitivity of 50% and an accuracy of 29%, which is better than current state-of-the-art methods. The method does not rely on homology searches and, therefore, can identify previously unknown domains.

An iterative approach of protein function prediction

Background: Current approaches of predicting protein functions from a protein-protein interaction (PPI) dataset are based on an assumption that the available functions of the proteins (a.k.a. annotated proteins) will determine the functions of the proteins whose functions are unknown yet at the moment (a.k.a. un-annotated proteins). Therefore, the protein function prediction is a mono-directed and one-off procedure, i.e. from annotated proteins to un-annotated proteins. However, the interactions between proteins are mutual rather than static and mono-directed, although functions of some proteins are unknown for some reasons at present. That means when we use the similarity-based approach to predict functions of un-annotated proteins, the un-annotated proteins, once their functions are predicted, will affect the similarities between proteins, which in turn will affect the prediction results. In other words, the function prediction is a dynamic and mutual procedure. This dynamic feature of protein interactions, however, was not considered in the existing prediction algorithms.

Results: In this paper, we propose a new prediction approach that predicts protein functions iteratively. This iterative approach incorporates the dynamic and mutual features of PPI interactions, as well as the local and global semantic influence of protein functions, into the prediction. To guarantee predicting functions iteratively, we propose a new protein similarity from protein functions. We adapt new evaluation metrics to evaluate the prediction quality of our algorithm and other similar algorithms. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed approach in predicting unknown protein functions.

Conclusions: The iterative approach is more likely to reflect the real biological nature between proteins when predicting functions. A proper definition of protein similarity from protein functions is the key to predicting functions iteratively. The evaluation results demonstrated that in most cases, the iterative approach outperformed non-iterative ones with higher prediction quality in terms of prediction precision, recall and F-value.

Predicting protein functions from PPI networks using functional aggregation

Predicting protein functions computationally from massive protein–protein interaction (PPI) data generated by high-throughput technology is one of the challenges and fundamental problems in the post-genomic era. Although there have been many approaches developed for computationally predicting protein functions, the mutual correlations among proteins in terms of protein functions have not been thoroughly investigated and incorporated into existing prediction methods, especially in voting based prediction methods. In this paper, we propose an innovative method to predict protein functions from PPI data by aggregating the functional correlations among relevant proteins using the Choquet-Integral in fuzzy theory. This functional aggregation measures the real impact of each relevant protein function on the final prediction results, and reduces the impact of repeated functional information on the prediction. Accordingly, a new protein similarity and a new iterative prediction algorithm are proposed in this paper. The experimental evaluations on real PPI datasets demonstrate the effectiveness of our method.

Iteratively predict protein functions from protein-protein interactions

Current similarity-based approaches of predicting protein functions from protein-protein interaction (PPI) data usually make use of available information in the PPI network to predict functions of un-annotated proteins, and the prediction is a one-off procedure. However the interactions between proteins are more likely to be mutual rather than static and mono-directed. In other words, the un-annotated proteins, once their functions are predicted, will in turn affect the similarities between proteins. In this paper, we propose an innovative iteration algorithm that incorporates this dynamic feature of protein interaction into the protein function prediction, aiming to achieve higher prediction accuracies and get more reasonable results. With our algorithm, instead of one-off function predictions, functions are assigned to an unannotated protein iteratively until the functional similarities between proteins achieve a stable state. The experimental results show that our iterative method can provide better prediction results than one-off prediction methods with higher prediction accuracies, and is stable for large protein datasets.

Dynamically predicting protein functions from semantic associations of proteins

Predicting functions of un-annotated proteins is a significant challenge in the post-genomics era. Among existing computational approaches, exploiting interactions between proteins to predict functions of un-annotated proteins is widely used. However, it remains difficult to extract semantic associations between proteins (i.e. protein associations in terms of protein functionality) from protein interactions and incorporate extracted semantic associations to more effectively predict protein functions. Furthermore, existing approaches and algorithms regard the function prediction as a one-off procedure, ignoring dynamic and mutual associations between proteins. Therefore, deriving and exploiting semantic associations between proteins to dynamically predict functions are a promising and challenging approach for achieving better prediction results. In this paper, we propose an innovative algorithm to incorporate semantic associations between proteins into a dynamic procedure of protein function prediction. The semantic association between two proteins is measured by the semantic similarity of two proteins which is defined by the similarities of functions two proteins possess. To achieve better prediction results, function similarities are also incorporated into the prediction procedure. The algorithm dynamically predicts functions by iteratively selecting functions for the un-annotated protein and updating the similarities between the un-annotated protein and its neighbour annotated proteins until such suitable functions are selected that the similarities no longer change. The experimental results on real protein interaction datasets demonstrated that our method outperformed the similar and non-dynamic function prediction methods. Incorporating semantic associations between proteins into a dynamic procedure of function prediction reflects intrinsic relationships among proteins as well as dynamic features of protein interactions, and therefore, can significantly improve prediction results.

Dynamically searching for a domain for protein function prediction

The availability of large amounts of protein-protein interaction (PPI) data makes it feasible to use computational approaches to predict protein functions. The base of existing computational approaches is to exploit the known function information of annotated proteins in the PPI data to predict functions of un-annotated proteins. However, these approaches consider the prediction domain (i.e. the set of proteins from which the functions are predicted) as unchangeable during the prediction procedure. This may lead to valuable information being overwhelmed by the unavoidable noise information in the PPI data when predicting protein functions, and in turn, the prediction results will be distorted. In this paper, we propose a novel method to dynamically predict protein functions from the PPI data. Our method regards the function prediction as a dynamic process of finding a suitable prediction domain, from which representative functions of the domain are selected to predict functions of un-annotated proteins. Our method exploits the topological structural information of a PPI network and the semantic relationship between protein functions to measure the relationship between proteins, dynamically select a suitable prediction domain and predict functions. The evaluation on real PPI datasets demonstrated the effectiveness of our proposed method, and generated better prediction results.

Hub-based reliable gene expression algorithm to classify ER+ and ER- breast cancer subtypes

Identifying gene signatures that are associatedwith the estrogen receptor based breast cancer samples is achallenging problem that has significant implications in breastcancer diagnosis and treatment. Various existing approaches foridentifying gene signatures have been developed but are not ableto achieve the satisfactory results because of their severallimitations. Subnetwork-based approaches have shown to be arobust classification method that uses interaction datasets suchas protein-protein interaction datasets. It has been reported thatthese interaction datasets contain many irrelevant interactionsthat have no biological meaning associated with them, and thusit is essential to filter out those interactions which can improvethe classification results. In this paper, we therefore, proposed ahub-based reliable gene expression algorithm (HRGE) thateffectively extracts the significant biologically-relevantinteractions and uses hub-gene topology to generate thesubnetwork based gene signatures for ER+ and ER- breastcancer subtypes. The proposed approach shows the superiorclassification accuracy amongst the other existing classifiers, inthe validation dataset.

Intramuscular heat shock protein 72 and heme oxygenase-1 mRNA are reduced in patients with type 2 diabetes: evidence that insulin resistance is associated with a disturbed antioxidant defense mechanism

To examine whether genes associated with cellular defense against oxidative stress are associated with insulin sensitivity, patients with type 2 diabetes (n = 7) and age-matched (n = 5) and young (n = 9) control subjects underwent a euglycemic-hyperinsulinemic clamp for 120 min. Muscle samples were obtained before and after the clamp and analyzed for heat shock protein (HSP)72 and heme oxygenase (HO)-1 mRNA, intramuscular triglyceride content, and the maximal activities of β-hyroxyacyl-CoA dehydrogenase (β-HAD) and citrate synthase (CS). Basal expression of both HSP72 and HO-1 mRNA were lower (P < 0.05) by 33 and 55%, respectively, when comparing diabetic patients with age-matched and young control subjects, with no differences between the latter groups. Both basal HSP72 (r = 0.75, P < 0.001) and HO-1 (r = 0.50, P < 0.05) mRNA expression correlated with the glucose infusion rate during the clamp. Significant correlations were also observed between HSP72 mRNA and both β-HAD (r = 0.61, P < 0.01) and CS (r = 0.65, P < 0.01). HSP72 mRNA was induced (P < 0.05) by the clamp in all groups. Although HO-1 mRNA was unaffected by the clamp in both the young and age-matched control subjects, it was increased (P < 0.05) ∼70-fold in the diabetic patients after the clamp. These data demonstrate that genes involved in providing cellular protection against oxidative stress are defective in patients with type 2 diabetes and correlate with insulin-stimulated glucose disposal and markers of muscle oxidative capacity. The data provide new evidence that the pathogenesis of type 2 diabetes involves perturbations to the antioxidant defense mechanism within skeletal muscle.