Use of Drosophila mutants to investigate the effect of disuse on the maintenance of muscle

The effect of continued muscular inactivity and prolonged paralysis on the structure and function of muscles was investigated in Drosophila melanogaster. A number of flightless mutants was examined to see whether their flight muscles degenerated as a result of disuse. No sign of progressive deterioration was observed in any of these mutants. Further, by producing mosaic flies in which part of the body expressed the temperature-sensitive paralytic mutation shibire^ST139, reversible local paralysis was obtained, and maintained for prolonged periods. Flies in which parts of the leg or flight musculature had been paralysed for several days were examined; no effect of such inactivity on the structure and function of the muscles was observed in any of the flies. These results indicate that in Drosophila continued muscular inactivity does not result in extensive degeneration of the musculature.     

Release factor regulating termination of complete protein in a eukaryotic organism

Polypeptide release reaction was studied using a protein release factor and a physiological substrate containing a complete polypeptide chain attached to monosomes of the insect Tenebrio molitor. The intermediate substrate used for the release reaction was synthesized using a cell-free protein synthesizing system from Tenebrio capable of polypeptide synthesis but not release of the completed chain. This system synthesized predominantly adult cuticular protein. The released product was characterized by chromatography after tryptic digestion; many of the tryptic peptides corresponded to those of cuticule labeled in vivo. The protein release factor was obtained as microsomal wash and was further purified by ammonium sulfate precipitation and column chromatography. It released about 30% of the monosome-bound peptide in the absence of GTP. The remaining 70% of peptidyl-tRNA was released as peptidyl-puromycin in the absence of release factor, but required transferase II and GTP. The peptidyl-puromycin varied in size from dipeptide to almost complete protein. The puromycin reaction was inhibited by diphtheria toxin and NAD and was dependent on GTP, while the release of completed peptide was independent of GTP and not affected by diphtheria toxin and NAD. The release factor was capable of releasing formylmethionine as formylmethionine-puromycin from ribosomes in response to poly(A₃,U). Hence it is suggested that the release factor is responding to UAA as terminating codon.     

Function from within: Autophagy induction by HPSE/heparanase—new possibilities for intervention

HPSE (heparanase) is the predominant enzyme in mammals capable of cleaving heparan sulfate, an activity highly implicated in cellular invasion and tumor metastasis. HPSE expression is induced in many types of cancer and increased HPSE levels are most often associated with increased tumor metastasis and reduced patient survival post operation. In addition, HPSE induction is associated with progression of the primary tumors but the mechanism(s) underlying tumor expansion by HPSE have not been sufficiently resolved. Our results establish a role for heparanase in modulating autophagy in normal and malignant cells, thereby conferring growth advantages as well as resistance to chemotherapy.     

Abundance-based Classifier for the Prediction of Mass Spectrometric Peptide Detectability Upon Enrichment (PPA)

The function of a large percentage of proteins is modulated by post-translational modifications (PTMs). Currently, mass spectrometry (MS) is the only proteome-wide technology that can identify PTMs. Unfortunately, the inability to detect a PTM by MS is not proof that the modification is not present. The detectability of peptides varies significantly making MS potentially blind to a large fraction of peptides. Learning from published algorithms that generally focus on predicting the most detectable peptides we developed a tool that incorporates protein abundance into the peptide prediction algorithm with the aim to determine the detectability of every peptide within a protein. We tested our tool, “Peptide Prediction with Abundance” (PPA), on in-house acquired as well as published data sets from other groups acquired on different instrument platforms. Incorporation of protein abundance into the prediction allows us to assess not only the detectability of all peptides but also whether a peptide of interest is likely to become detectable upon enrichment. We validated the ability of our tool to predict changes in protein detectability with a dilution series of 31 purified proteins at several different concentrations. PPA predicted the concentration dependent peptide detectability in 78% of the cases correctly, demonstrating its utility for predicting the protein enrichment needed to observe a peptide of interest in targeted experiments. This is especially important in the analysis of PTMs. PPA is available as a web-based or executable package that can work with generally applicable defaults or retrained from a pilot MS data set.Post-translational modification (PTM)¹ of proteins is a key regulatory mechanism in the vast majority of biological processes. Historically, to follow PTMs, site-specific antibodies had to be generated in a time-consuming and laborious process associated with high failure rates. Mass spectrometry (MS) holds enormous promise in PTM analysis as it is currently the only technique that has the ability to both discover, localize, and quantify proteome-wide modifications (1). Recent advances in instrumentation and method optimization makes it possible to detect the complete yeast proteome within one hour (2), an ever increasing proportion of the human proteome (3–6), and more than 10,000 phosphorylation sites in a single MS experiment (7, 8). As a result one of the major publicly available databases (www.phosphosite.org (9)) has curated >200,000 phosphorylation sites.Although the number of proteins and PTMs that can be identified is impressive, many modifications have still not been identified in any MS-based experiment. The identification and quantification of biologically relevant modifications is challenging for three reasons: (1) many proteins of interest are of very low abundance rendering them difficult to detect and quantify; (2) many modifications sites are present at substoichiometric quantities, further reducing their detectability; and (3) as large scale proteomics is based on the detection of peptides after a proteolytic digest, and the detectability of a peptide is determined by its physiochemical properties (10), many peptides from highly abundant proteins are never detected. This is particularly important, as there is a shift in the use of MS-based proteomics from large scale, unbiased, discovery-focused experiments toward directed experiments for accurate and precise quantification of biologically relevant PTMs. Protein and peptide enrichment strategies and/or targeted MS experiments like single reaction monitoring (SRM) (11) have increased the number of detectable peptides; however, both of these methods are laborious, and often not successful, that is, the peptide carrying the modification of interest is still not observed as it is fundamentally very difficult to detect.Protein enrichment is the method choice for most experimentalists, but there is no current way to determine whether this is likely to succeed prior to engaging in lengthy biochemical and/or analytical experiments. In an effort to gauge the chances of success for detecting a particular peptide we sought to develop an algorithm that can predict both the chances of detecting a particular peptide and, more importantly, what enrichment it would take to detect a particular peptide that is not easily detected. Here we present such a tool that predicts the detectability and estimates an enrichment factor, i.e. an increase in signal over the background that is necessary to actually detect a particular peptide. Our algorithm development was motivated by two premises: (1) In silico methods have been developed that focus on the prediction of easily detectable “proteotypic” peptides (peptides that are likely to provide the best detection sensitivity) with good accuracy (12–15). (2) Comprehensive proteome studies have shown that the number of detected peptides per protein, and thus the sequence coverage, varies with protein abundance (which is the basis for spectral counting-based protein quantification (16, 17)). We find that incorporation of protein abundance in a peptide classification tool improves the accuracy of the prediction of peptide detectability allowing us to predict the detectability of all peptides within a protein as well as the amount of enrichment needed to detect a peptide of interest.We used a set of 120 purified in vitro expressed proteins as a training set to develop a prediction tool. We deliver this in the form of a web-based interface that provides information about: (1) the probability of detecting the different tryptic peptides of a protein, and (2) the fold enrichment that would be required to bring a peptide of interest into the detectable range. This tool will help guide researchers in their efforts to monitor particular peptides and their modified cognates by MS, specifically, in prioritizing their efforts toward enriching proteins where they would be likely to be able to detect a peptide or modification of interest.     

Novel approach to abuse the hyperactive K-Ras pathway for adenoviral gene therapy of colorectal cancer

BackgroundFunctional activation of oncogenic K-Ras signaling pathway plays an important role in the early events of colorectal carcinogenesis (CRC). K-Ras proto-oncogene is involved in 35–40% of CRC cases. Mutations in the Ras gene trigger the transduction of proliferative and anti-apoptotic signals, even in the absence of extra cellular stimuli. The objective of the current study was to use a gene-targeting approach to kill human CRC cells selectively harboring mutated K-Ras.ResultsA recombinant adenovirus that carries a lethal gene, PUMA, under the control of a Ras responsive promoter (Ad-Py4-SV40-PUMA) was used selectively to target CRC cells (HCT116, SW480, DLD1 and RIE-Ras) that possess a hyperactive Ras pathway while using HT29 and RIE cells as a control that harbors wild type Ras and exhibit very low Ras activity. Control vector, without the Ras responsive promoter elements was used to assess the specificity of our “gene therapy” approach. Both adenoviral vectors were assed in vitro and in xenograft model in vivo. Ad-Py4-SV40-PUMA showed high potency to induce ~ 50% apoptosis in vitro, to abolish completely tumor formation by infecting cells with the Ad-Py4-SV40-PUMA prior xenografting them in nude mice and high ability to suppress by ~ 35% tumor progression in vivo in already established tumors.ConclusionsSelective targeting of CRC cells with the activated Ras pathway may be a novel and effective therapy in CRC. The high potency of this adenoviral vector may help to overcome an undetectable micro metastasis that is the major hurdle in challenging with CRC.     

The interplay between pH sensitivity and label-free protein detection in immunologically modified nano-scaled field-effect transistor

We present experimental results in order to establish a correlation between pH sensitivity of immunologically modified nano-scaled field-effect transistor (NS-ImmunoFET) with their sensing capacity for label-free detection. The NS-ImmunoFETs are fabricated from silicon-on-insulator (SOI) wafers and are fully-depleted with thickness of ~20 nm. The data shows that higher sensitivity to pH entails enhanced sensitivity to analyte detection. This suggests that the mechanism of analyte detection as pure electrostatic perturbation induced by antibody-analyte interaction is over simplified. The fundamental assumption, in existing models for field-effect sensing mechanism assumes that the analyte molecules do not directly interact with the surface but rather stand 'deep' in the solution and away from the dielectric surface. Recent studies clearly provide contradicting evidence demonstrating that antibodies lie down flat on the surface. These observations led us to propose that the proteins that cover the gate area intimately interact with active sites on the surface thus forming a network of interacting sites. Since sensitivity to pH is directly correlated with the amount of amphoteric sites, we witness a direct correlation between sensitivity to pH and analyte detection. The highest and lowest threshold voltage shift for a label-free and specific detection of 6.5 nM IgG were 40 mV and 2.3 mV for NS-ImmunoFETs with pH sensitivity of 35 mV/decade and 15 mV/decade, respectively. Finally, physical modeling of the NS-ImmunoFET is presented and charge of a single IgG protein at pH 6 is calculated. The obtained value is consistent with charge of IgG protein cited in literature.     

Lying patterns of high producing healthy dairy cows after calving in commercial herds as affected by age,environmental conditions and production

An animal expresses its physiological and well-being status by its behaviour. Changes in behaviour can be associated with health, production or well-being problems and therefore with the profitability of the farm. The objectives of the present study were to analyse lying patterns of healthy cows, collected with a commercial behaviour sensor, in early lactation in relation to environmental conditions, age of the cow and production performance. In future, these results may be used as a ‘baseline’ for detection of alterations in behaviour that indicate health problems. The study involved 210 healthy multiparous Israeli Holstein cows in three commercial dairy farms. Only healthy cows during the first 28 days after calving were included in this study. Data were analysed in relation to calving season, age of cows and correlation between milk production and lying time.The results show that lying time increased significantly with age and is significantly (P < 0.05) higher in winter than in summer (summer lactation 2: 491 ± 17 min/day (mean ± SD), summer lactation 3 and more: 520 ± 25 min/day, winter lactation 2: 531 ± 25 min/day, winter lactation 3 and more: 579 ± 38 min/day).The proportion of positively and negatively milk production and lying time correlated cows is affected by calving season.This study indicates that behaviour variables in early lactation are affected by calving season, lactation number and type of correlation between milk production and lying time.