Rapid membrane protein topology prediction

State-of-the-art methods for topology of α-helical membrane proteins are based on the use of time-consuming multiple sequence alignments obtained from PSI-BLAST or other sources. Here, we examine if it is possible to use the consensus of topology prediction methods that are based on single sequences to obtain a similar accuracy as the more accurate multiple sequence-based methods. Here, we show that TOPCONS-single performs better than any of the other topology prediction methods tested here, but ~6% worse than the best method that is utilizing multiple sequence alignments. AVAILABILITY AND IMPLEMENTATION: TOPCONS-single is available as a web server from http://single.topcons.net/ and is also included for local installation from the web site. In addition, consensus-based topology predictions for the entire international protein index (IPI) is available from the web server and will be updated at regular intervals.     

A genetic screen identifies BRCA2 and PALB2 as key regulators of G2 checkpoint maintenance

To identify key connections between DNA-damage repair and checkpoint pathways, we performed RNA interference screens for regulators of the ionizing radiation-induced G2 checkpoint, and we identified the breast cancer gene BRCA2. The checkpoint was also abrogated following depletion of PALB2, an interaction partner of BRCA2. BRCA2 and PALB2 depletion led to premature checkpoint abrogation and earlier activation of the AURORA A-PLK1 checkpoint-recovery pathway. These results indicate that the breast cancer tumour suppressors and homologous recombination repair proteins BRCA2 and PALB2 are main regulators of G2 checkpoint maintenance following DNA-damage.     

Thioredoxin 80-activated-monocytes (TAMs) inhibit the replication of intracellular pathogens

Background

Thioredoxin 80 (Trx80) is an 80 amino acid natural cleavage product of Trx, produced primarily by monocytes. Trx80 induces differentiation of human monocytes into a novel cell type, named Trx80-activated-monocytes (TAMs).

Principal Findings

In this investigation we present evidence for a role of TAMs in the control of intracellular bacterial infections. As model pathogens we have chosen Listeria monocytogenes and Brucella abortus which replicate in the cytosol and the endoplasmic reticulum respectively. Our data indicate that TAMs efficiently inhibit intracellular growth of both L. monocytogenes and B. abortus. Further analysis shows that Trx80 activation prevents the escape of GFP-tagged L. monocytogenes into the cytosol, and induces accumulation of the bacteria within the lysosomes. Inhibition of the lysosomal activity by chloroquine treatment resulted in higher replication of bacteria in TAMs compared to that observed in control cells 24 h post-infection, indicating that TAMs kill bacteria by preventing their escape from the endosomal compartments, which progress into a highly degradative phagolysosome.

Significance

Our results show that Trx80 potentiates the bactericidal activities of professional phagocytes, and contributes to the first line of defense against intracellular bacteria.     

Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index

Background

Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI) diet improved weight maintenance.

Objective

To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study.

Design

Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured.

Results

Angiotensin I converting enzyme (ACE) was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.

Conclusions

A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss.

Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00390637","term_id":"NCT00390637"}}NCT00390637     

Report from the second cytomegalovirus and immunosenescence workshop

The Second International Workshop on CMV & Immunosenescence was held in Cambridge, UK, 2-4th December, 2010. The presentations covered four separate sessions: cytomegalovirus and T cell phenotypes; T cell memory frequency, inflation and immunosenescence; cytomegalovirus in aging, mortality and disease states; and the immunobiology of cytomegalovirus-specific T cells and effects of the virus on vaccination. This commentary summarizes the major findings of these presentations and references subsequently published work from the presenter laboratory where appropriate and draws together major themes that were subsequently discussed along with new areas of interest that were highlighted by this discussion.     

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

Background

Network inference methods reconstruct mathematical models of molecular or genetic networks directly from experimental data sets. We have previously reported a mathematical method which is exclusively data-driven, does not involve any heuristic decisions within the reconstruction process, and deliveres all possible alternative minimal networks in terms of simple place/transition Petri nets that are consistent with a given discrete time series data set.

Results

We fundamentally extended the previously published algorithm to consider catalysis and inhibition of the reactions that occur in the underlying network. The results of the reconstruction algorithm are encoded in the form of an extended Petri net involving control arcs. This allows the consideration of processes involving mass flow and/or regulatory interactions. As a non-trivial test case, the phosphate regulatory network of enterobacteria was reconstructed using in silico-generated time-series data sets on wild-type and in silico mutants.

Conclusions

The new exact algorithm reconstructs extended Petri nets from time series data sets by finding all alternative minimal networks that are consistent with the data. It suggested alternative molecular mechanisms for certain reactions in the network. The algorithm is useful to combine data from wild-type and mutant cells and may potentially integrate physiological, biochemical, pharmacological, and genetic data in the form of a single model.     

Predictive and prognostic impact of TP53 mutations and MDM2 promoter genotype in primary breast cancer patients treated with epirubicin or paclitaxel

Background

TP53 mutations have been associated with resistance to anthracyclines but not to taxanes in breast cancer patients. The MDM2 promoter single nucleotide polymorphism (SNP) T309G increases MDM2 activity and may reduce wild-type p53 protein activity. Here, we explored the predictive and prognostic value of TP53 and CHEK2 mutation status together with MDM2 SNP309 genotype in stage III breast cancer patients receiving paclitaxel or epirubicin monotherapy.

Experimental Design

Each patient was randomly assigned to treatment with epirubicin 90 mg/m² (n = 109) or paclitaxel 200 mg/m² (n = 114) every 3rd week as monotherapy for 4–6 cycles. Patients obtaining a suboptimal response on first-line treatment requiring further chemotherapy received the opposite regimen. Time from last patient inclusion to follow-up censoring was 69 months. Each patient had snap-frozen tumor tissue specimens collected prior to commencing chemotherapy.

Principal Findings

While TP53 and CHEK2 mutations predicted resistance to epirubicin, MDM2 status did not. Neither TP53/CHEK2 mutations nor MDM2 status was associated with paclitaxel response. Remarkably, TP53 mutations (p = 0.007) but also MDM2 309TG/GG genotype status (p = 0.012) were associated with a poor disease-specific survival among patients having paclitaxel but not patients having epirubicin first-line. The effect of MDM2 status was observed among individuals harbouring wild-type TP53 (p = 0.039) but not among individuals with TP53 mutated tumors (p>0.5).

Conclusion

TP53 and CHEK2 mutations were associated with lack of response to epirubicin monotherapy. In contrast, TP53 mutations and MDM2 309G allele status conferred poor disease-specific survival among patients treated with primary paclitaxel but not epirubicin monotherapy.     

Dynamics of resistance development to imatinib under increasing selection pressure: a combination of mathematical models and in vitro data

In the last decade, cancer research has been a highly active and rapidly evolving scientific area. The ultimate goal of all efforts is a better understanding of the mechanisms that discriminate malignant from normal cell biology in order to allow the design of molecular targeted treatment strategies. In individual cases of malignant model diseases addicted to a specific, ideally single oncogene, e.g. Chronic myeloid leukemia (CML), specific tyrosine kinase inhibitors (TKI) have indeed been able to convert the disease from a ultimately life-threatening into a chronic disease with individual patients staying in remission even without treatment suggestive of operational cure. These developments have been raising hopes to transfer this concept to other cancer types. Unfortunately, cancer cells tend to develop both primary and secondary resistance to targeted drugs in a substantially higher frequency often leading to a failure of treatment clinically. Therefore, a detailed understanding of how cells can bypass targeted inhibition of signaling cascades crucial for malignant growths is necessary. Here, we have performed an in vitro experiment that investigates kinetics and mechanisms underlying resistance development in former drug sensitive cancer cells over time in vitro. We show that the dynamics observed in these experiments can be described by a simple mathematical model. By comparing these experimental data with the mathematical model, important parameters such as mutation rates, cellular fitness and the impact of individual drugs on these processes can be assessed. Excitingly, the experiment and the model suggest two fundamentally different ways of resistance evolution, i.e. acquisition of mutations and phenotype switching, each subject to different parameters. Most importantly, this complementary approach allows to assess the risk of resistance development in the different phases of treatment and thus helps to identify the critical periods where resistance development is most likely to occur.