Publications by authors named "K Anton Feenstra"

62 Publications

Impact of Preanalytical Factors on the Measurement of Tumor Tissue Biomarkers Using Immunohistochemistry.

J Histochem Cytochem 2021 Mar 1:22155421995600. Epub 2021 Mar 1.

Pathology and Biorepository Core, Van Andel Institute, Grand Rapids, Michigan.

Analysis of formalin-fixed paraffin-embedded (FFPE) tissue by immunohistochemistry (IHC) is commonplace in clinical and research laboratories. However, reports suggest that IHC results can be compromised by biospecimen preanalytical factors. The National Cancer Institute's Biospecimen Preanalytical Variables Program conducted a systematic study to examine the potential effects of delay to fixation (DTF) and time in fixative (TIF) on IHC using 24 cancer biomarkers. Differences in IHC staining, relative to controls with a DTF of 1 hr, were observed in FFPE kidney tumor specimens after a DTF of ≥2 hr. Reductions in H-score and/or staining intensity were observed for c-MET, p53, PAX2, PAX8, pAKT, and survivin, whereas increases were observed for RCC1, EGFR, and CD10. Prolonged TIF of 72 hr resulted in significantly reduced H-scores of CD44 and c-Met in kidney tumor specimens, compared with controls with 12-hr TIF. An elevated probability of altered staining intensity due to DTF was observed for nine antigens, whereas for prolonged TIF an elevated probability was observed for one antigen. Results reported here and elsewhere across tumor types and antigens support limiting DTF to ≤1 hr when possible and fixing tissues in formalin for 12-24 hr to avoid confounding effects of these preanalytical factors on IHC.
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http://dx.doi.org/10.1369/0022155421995600DOI Listing
March 2021

Stability of Ketoprofen Methylester in Plasma of Different Species.

Curr Drug Metab 2020 Dec 17. Epub 2020 Dec 17.

Zoetis, Inc., Veterinary Medicine Research and Development, Kalamazoo, MI 49007. United States.

Background: Pharmacokinetic and pharmacodynamic assessment of ester-containing drugs can be impacted by hydrolysis of the drugs in plasma samples post blood collection. The impact is different in the plasma of different species.

Objective: This study was to evaluate stability of a prodrug, ketoprofen methylester (KME) in commercially purchased and freshly collected plasma of mouse, rat, dog, cat, pig, sheep, cattle and horse.

Methods: KME hydrolysis was determined following its incubation in commercially purchased and freshly collected plasma of those species. Different esterase inhibitors were evaluated for prevention of the hydrolysis in rat, dog and pig plasma.

Results: KME was rapidly hydrolyzed in both commercially purchased and freshly collected plasma of mouse, rat, and horse. The hydrolysis was initially quick and then limited in cat plasma. KME hydrolysis was minimum in commercially purchased plasma of dog, pig, sheep and cattle but substantial in freshly collected plasma of those species. Different esterase inhibitors showed different effects on stability of KME in rat, dog and pig plasma.

Conclusion: These results indicate that plasma of different species has different hydrolytic activities to ester-containing drugs. The activities in commercially purchased and freshly collected plasma may be different and species dependent. Esterase inhibitors have different effects on prevention of hydrolysis of the ester-containing drugs in the plasma of different species.
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http://dx.doi.org/10.2174/1389200221666201217141025DOI Listing
December 2020

Predicting the relationships between gut microbiota and mental disorders with knowledge graphs.

Health Inf Sci Syst 2021 Dec 24;9(1). Epub 2020 Nov 24.

Knowledge Representation and Reasoning (KR&R) Group, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Gut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as 'matchmaker' between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways. We therefore gather the dispersed results that in the existing studies into a structured knowledge base for identifying and predicting the potential relationships between gut microbiota and mental disorders. In this study, we propose to construct a gut microbiota knowledge graph for mental disorder, which named as MiKG4MD. It is extendable by linking to future ontologies by just adding new relationships between existing information and new entities. This extendibility is emphasized for the integration with existing popular ontologies/terminologies, e.g. UMLS, MeSH, and KEGG. We demonstrate the performance of MiKG4MD with three SPARQL query test cases. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationships between gut microbiota and mental disorders.
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http://dx.doi.org/10.1007/s13755-020-00128-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7686388PMC
December 2021

A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community).

F1000Res 2020 22;9. Epub 2020 Apr 22.

Institute of Biotechnology of the Czech Academy of Sciences, Vestec, CZ-25250, Czech Republic.

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect. Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.
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http://dx.doi.org/10.12688/f1000research.20559.1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284151PMC
July 2020

A framework for exhaustive modelling of genetic interaction patterns using Petri nets.

Bioinformatics 2020 04;36(7):2142-2149

Department of Computer Science, Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands.

Motivation: Genetic interaction (GI) patterns are characterized by the phenotypes of interacting single and double mutated gene pairs. Uncovering the regulatory mechanisms of GIs would provide a better understanding of their role in biological processes, diseases and drug response. Computational analyses can provide insights into the underpinning mechanisms of GIs.

Results: In this study, we present a framework for exhaustive modelling of GI patterns using Petri nets (PN). Four-node models were defined and generated on three levels with restrictions, to enable an exhaustive approach. Simulations suggest ∼5 million models of GIs. Generalizing these we propose putative mechanisms for the GI patterns, inversion and suppression. We demonstrate that exhaustive PN modelling enables reasoning about mechanisms of GIs when only the phenotypes of gene pairs are known. The framework can be applied to other GI or genetic regulatory datasets.

Availability And Implementation: The framework is available at http://www.ibi.vu.nl/programs/ExhMod.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz917DOI Listing
April 2020

Assessment of Inhibition of Bovine Hepatic Cytochrome P450 by 43 Commercial Bovine Medicines Using a Combination of Assays and Pharmacokinetic Data from the Literature.

Drug Metab Lett 2019 ;13(2):123-131

Veterinary Medicine Research and Development, Zoetis, Inc, 333 Portage Street, Kalamazoo, MI-49007, United States.

Background: There has been a lack of information about the inhibition of bovine medicines on bovine hepatic CYP450 at their commercial doses and dosing routes.

Objective: The aim of this work was to assess the inhibition of 43 bovine medicines on bovine hepatic CYP450 using a combination of in vitro assay and Cmax values from pharmacokinetic studies with their commercial doses and dosing routes in the literature.

Methods: Those drugs were first evaluated through a single point inhibitory assay at 3 μM in bovine liver microsomes for six specific CYP450 metabolisms, phenacetin o-deethylation, coumarin 7- hydroxylation, tolbutamide 4-hydroxylation, bufuralol 1-hydroxylation, chlorzoxazone 6-hydroxylation and midazolam 1'-hydroxylation. When the inhibition was greater than 20% in the assay, IC50 values were then determined. The potential in vivo bovine hepatic CYP450 inhibition by those drugs was assessed using a combination of the IC50 values and in vivo Cmax values from pharmacokinetic studies at their commercial doses and administration routes in the literature.

Results: Fifteen bovine medicines or metabolites showed in vitro inhibition on one or more bovine hepatic CYP450 metabolisms with different IC50 values. Desfuroylceftiour (active metabolite of ceftiofur), nitroxinil and flunixin have the potential to inhibit one of the bovine hepatic CYP450 isoforms in vivo at their commercial doses and administration routes. The rest of the bovine medicines had low risks of in vivo bovine hepatic CYP450 inhibition.

Conclusion: This combination of in vitro assay and in vivo Cmax data provides a good approach to assess the inhibition of bovine medicines on bovine hepatic CYP450.
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http://dx.doi.org/10.2174/1872312813666191120094649DOI Listing
May 2020

The potential use of big data in oncology.

Oral Oncol 2019 11 12;98:8-12. Epub 2019 Sep 12.

Department of Head and Neck Oncology and Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands.

In this era of information technology, big data analysis is entering biomedical sciences. But what is big data, where do they come from and what can we do with it? In this commentary, the main sources of big data are explained, especially in (head and neck) oncology. It also touches upon the need to integrate various sources of clinical, pathological and quality-of-life data. It discusses some initiatives in linking of such datasets on a nation-wide scale in the Netherlands. Finally, it touches upon important issues regarding governance, FAIRness of data and the need to bring into place the necessary infrastructures needed to fully exploit the full potential of big data sets in head and neck cancer.
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http://dx.doi.org/10.1016/j.oraloncology.2019.09.003DOI Listing
November 2019

Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit.

Bioinformatics 2019 12;35(24):5315-5317

Department of Computer Science, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands.

Summary: PRALINE 2 is a toolkit for custom multiple sequence alignment workflows. It can be used to incorporate sequence annotations, such as secondary structure or (DNA) motifs, into the alignment scoring, as well as to customize many other aspects of a progressive multiple alignment workflow.

Availability And Implementation: PRALINE 2 is implemented in Python and available as open source software on GitHub: https://github.com/ibivu/PRALINE/.
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http://dx.doi.org/10.1093/bioinformatics/btz572DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954659PMC
December 2019

SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions.

Bioinformatics 2019 11;35(22):4794-4796

IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands.

Motivation: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here, we present a webserver that implements this method efficiently.

Results: With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than 10-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry.

Availability And Implementation: Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz428DOI Listing
November 2019

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern.

PLoS Comput Biol 2019 05 13;15(5):e1007061. Epub 2019 May 13.

Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.
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http://dx.doi.org/10.1371/journal.pcbi.1007061DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6532943PMC
May 2019

Pharmacokinetics and brain distribution of amitraz and its metabolites in rats.

Environ Toxicol Pharmacol 2019 Jan 23;65:40-45. Epub 2018 Nov 23.

Zoetis, Inc., Veterinary Medicine Research and Development, Kalamazoo, MI, 49007, USA.

Amitraz is an acaricide and insecticide widely used in agriculture and veterinary medicine. Although central nervous system (CNS) toxicity is one of major toxicities following oral ingestion of amitraz, the understanding of the cause of the toxicity is limited. This study evaluated the systemic and brain exposure of amitraz and its major metabolites, BTS27271, 2',4'-formoxylidide, and 2,4-dimethylaniline following administration of amitraz in male Sprague-Dawley rats. Significant metabolism of amitraz was observed following the intravenous and oral administration. Amitraz related metabolites were majority of the total exposure observed, especially following oral administration. BTS27271 had higher brain exposure than amitraz and its other metabolites, which was due to low plasma protein binding but high brain tissue binding of BTS27271. Since BTS27271 has similar or higher toxicity and α-adrenoreceptor agonist potency than amitraz, its exposure in brain tissues may be the major cause of CNS toxicity of amitraz in animals and humans.
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http://dx.doi.org/10.1016/j.etap.2018.11.005DOI Listing
January 2019

Motif-Aware PRALINE: Improving the alignment of motif regions.

PLoS Comput Biol 2018 11 1;14(11):e1006547. Epub 2018 Nov 1.

Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Protein or DNA motifs are sequence regions which possess biological importance. These regions are often highly conserved among homologous sequences. The generation of multiple sequence alignments (MSAs) with a correct alignment of the conserved sequence motifs is still difficult to achieve, due to the fact that the contribution of these typically short fragments is overshadowed by the rest of the sequence. Here we extended the PRALINE multiple sequence alignment program with a novel motif-aware MSA algorithm in order to address this shortcoming. This method can incorporate explicit information about the presence of externally provided sequence motifs, which is then used in the dynamic programming step by boosting the amino acid substitution matrix towards the motif. The strength of the boost is controlled by a parameter, α. Using a benchmark set of alignments we confirm that a good compromise can be found that improves the matching of motif regions while not significantly reducing the overall alignment quality. By estimating α on an unrelated set of reference alignments we find there is indeed a strong conservation signal for motifs. A number of typical but difficult MSA use cases are explored to exemplify the problems in correctly aligning functional sequence motifs and how the motif-aware alignment method can be employed to alleviate these problems.
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http://dx.doi.org/10.1371/journal.pcbi.1006547DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233922PMC
November 2018

Deep sequencing identifies hepatitis B virus core protein signatures in chronic hepatitis B patients.

Antiviral Res 2018 10 16;158:213-225. Epub 2018 Aug 16.

Department of Experimental Immunology, Academic Medical Center, Amsterdam, The Netherlands. Electronic address:

Background: We aimed to identify HBc amino acid differences between subgroups of chronic hepatitis B (CHB) patients.

Methods: Deep sequencing of HBc was performed in samples of 89 CHB patients (42 HBeAg positive, 47 HBeAg negative). Amino acid types were compared using Sequence Harmony to identify subgroup specific sites between HBeAg-positive and -negative patients, and between patients with combined response and non-response to peginterferon/adefovir combination therapy.

Results: We identified 54 positions in HBc where the frequency of appearing amino acids was significantly different between HBeAg-positive and -negative patients. In HBeAg negative patients, 22 positions in HBc were identified which differed between patients with treatment response and those with non-response. The fraction non-consensus sequence on selected positions was significantly higher in HBeAg-negative patients, and was negatively correlated with HBV DNA and HBsAg levels.

Conclusions: Sequence Harmony identified a number of amino acid changes associated with HBeAg-status and response to peginterferon/adefovir combination therapy.
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http://dx.doi.org/10.1016/j.antiviral.2018.08.009DOI Listing
October 2018

Aurora kinase A (AURKA) interaction with Wnt and Ras-MAPK signalling pathways in colorectal cancer.

Sci Rep 2018 05 14;8(1):7522. Epub 2018 May 14.

Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Hyperactivation of Wnt and Ras-MAPK signalling are common events in development of colorectal adenomas. Further progression from adenoma-to-carcinoma is frequently associated with 20q gain and overexpression of Aurora kinase A (AURKA). Interestingly, AURKA has been shown to further enhance Wnt and Ras-MAPK signalling. However, the molecular details of these interactions in driving colorectal carcinogenesis remain poorly understood. Here we first performed differential expression analysis (DEA) of AURKA knockdown in two colorectal cancer (CRC) cell lines with 20q gain and AURKA overexpression. Next, using an exact algorithm, Heinz, we computed the largest connected protein-protein interaction (PPI) network module of significantly deregulated genes in the two CRC cell lines. The DEA and the Heinz analyses suggest 20 Wnt and Ras-MAPK signalling genes being deregulated by AURKA, whereof β-catenin and KRAS occurred in both cell lines. Finally, shortest path analysis over the PPI network revealed eight 'connecting genes' between AURKA and these Wnt and Ras-MAPK signalling genes, of which UBE2D1, DICER1, CDK6 and RACGAP1 occurred in both cell lines. This study, first, confirms that AURKA influences deregulation of Wnt and Ras-MAPK signalling genes, and second, suggests mechanisms in CRC cell lines describing these interactions.
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http://dx.doi.org/10.1038/s41598-018-24982-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5951826PMC
May 2018

Seeing the trees through the forest: sequence-based homo- and heteromeric protein-protein interaction sites prediction using random forest.

Bioinformatics 2017 May;33(10):1479-1487

Center for Integrative Bioinformatics VU (IBIVU), Amsterdam, HV, The Netherlands.

Motivation: Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains the prediction of the amino acids in a given protein sequence that are involved in protein-protein interactions. Such predictions are typically based on machine learning methods that take advantage of the properties and sequence positions of amino acids that are known to be involved in interaction. In this paper, we evaluate the importance of various features using Random Forest (RF), and include as a novel feature backbone flexibility predicted from sequences to further optimise protein interface prediction.

Results: We observe that there is no single sequence feature that enables pinpointing interacting sites in our Random Forest models. However, combining different properties does increase the performance of interface prediction. Our homomeric-trained RF interface predictor is able to distinguish interface from non-interface residues with an area under the ROC curve of 0.72 in a homomeric test-set. The heteromeric-trained RF interface predictor performs better than existing predictors on a independent heteromeric test-set. We trained a more general predictor on the combined homomeric and heteromeric dataset, and show that in addition to predicting homomeric interfaces, it is also able to pinpoint interface residues in heterodimers. This suggests that our random forest model and the features included capture common properties of both homodimer and heterodimer interfaces.

Availability And Implementation: The predictors and test datasets used in our analyses are freely available ( http://www.ibi.vu.nl/downloads/RF_PPI/ ).

Contact: k.a.feenstra@vu.nl.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btx005DOI Listing
May 2017

BioASF: a framework for automatically generating executable pathway models specified in BioPAX.

Bioinformatics 2016 06;32(12):i60-i69

Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands.

Motivation: Biological pathways play a key role in most cellular functions. To better understand these functions, diverse computational and cell biology researchers use biological pathway data for various analysis and modeling purposes. For specifying these biological pathways, a community of researchers has defined BioPAX and provided various tools for creating, validating and visualizing BioPAX models. However, a generic software framework for simulating BioPAX models is missing. Here, we attempt to fill this gap by introducing a generic simulation framework for BioPAX. The framework explicitly separates the execution model from the model structure as provided by BioPAX, with the advantage that the modelling process becomes more reproducible and intrinsically more modular; this ensures natural biological constraints are satisfied upon execution. The framework is based on the principles of discrete event systems and multi-agent systems, and is capable of automatically generating a hierarchical multi-agent system for a given BioPAX model.

Results: To demonstrate the applicability of the framework, we simulated two types of biological network models: a gene regulatory network modeling the haematopoietic stem cell regulators and a signal transduction network modeling the Wnt/β-catenin signaling pathway. We observed that the results of the simulations performed using our framework were entirely consistent with the simulation results reported by the researchers who developed the original models in a proprietary language.

Availability And Implementation: The framework, implemented in Java, is open source and its source code, documentation and tutorial are available at http://www.ibi.vu.nl/programs/BioASF CONTACT: j.heringa@vu.nl.
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http://dx.doi.org/10.1093/bioinformatics/btw250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908334PMC
June 2016

Construction and Experimental Validation of a Petri Net Model of Wnt/β-Catenin Signaling.

PLoS One 2016 24;11(5):e0155743. Epub 2016 May 24.

Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands.

The Wnt/β-catenin signaling pathway is important for multiple developmental processes and tissue maintenance in adults. Consequently, deregulated signaling is involved in a range of human diseases including cancer and developmental defects. A better understanding of the intricate regulatory mechanism and effect of physiological (active) and pathophysiological (hyperactive) WNT signaling is important for predicting treatment response and developing novel therapies. The constitutively expressed CTNNB1 (commonly and hereafter referred to as β-catenin) is degraded by a destruction complex, composed of amongst others AXIN1 and GSK3. The destruction complex is inhibited during active WNT signaling, leading to β-catenin stabilization and induction of β-catenin/TCF target genes. In this study we investigated the mechanism and effect of β-catenin stabilization during active and hyperactive WNT signaling in a combined in silico and in vitro approach. We constructed a Petri net model of Wnt/β-catenin signaling including main players from the plasma membrane (WNT ligands and receptors), cytoplasmic effectors and the downstream negative feedback target gene AXIN2. We validated that our model can be used to simulate both active (WNT stimulation) and hyperactive (GSK3 inhibition) signaling by comparing our simulation and experimental data. We used this experimentally validated model to get further insights into the effect of the negative feedback regulator AXIN2 upon WNT stimulation and observed an attenuated β-catenin stabilization. We furthermore simulated the effect of APC inactivating mutations, yielding a stabilization of β-catenin levels comparable to the Wnt-pathway activities observed in colorectal and breast cancer. Our model can be used for further investigation and viable predictions of the role of Wnt/β-catenin signaling in oncogenesis and development.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155743PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878796PMC
July 2017

CLUB-MARTINI: Selecting Favourable Interactions amongst Available Candidates, a Coarse-Grained Simulation Approach to Scoring Docking Decoys.

PLoS One 2016 11;11(5):e0155251. Epub 2016 May 11.

Center for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.

Large-scale identification of native binding orientations is crucial for understanding the role of protein-protein interactions in their biological context. Measuring binding free energy is the method of choice to estimate binding strength and reveal the relevance of particular conformations in which proteins interact. In a recent study, we successfully applied coarse-grained molecular dynamics simulations to measure binding free energy for two protein complexes with similar accuracy to full-atomistic simulation, but 500-fold less time consuming. Here, we investigate the efficacy of this approach as a scoring method to identify stable binding conformations from thousands of docking decoys produced by protein docking programs. To test our method, we first applied it to calculate binding free energies of all protein conformations in a CAPRI (Critical Assessment of PRedicted Interactions) benchmark dataset, which included over 19000 protein docking solutions for 15 benchmark targets. Based on the binding free energies, we ranked all docking solutions to select the near-native binding modes under the assumption that the native-solutions have lowest binding free energies. In our top 100 ranked structures, for the 'easy' targets that have many near-native conformations, we obtain a strong enrichment of acceptable or better quality structures; for the 'hard' targets without near-native decoys, our method is still able to retain structures which have native binding contacts. Moreover, in our top 10 selections, CLUB-MARTINI shows a comparable performance when compared with other state-of-the-art docking scoring functions. As a proof of concept, CLUB-MARTINI performs remarkably well for many targets and is able to pinpoint near-native binding modes in the top selections. To the best of our knowledge, this is the first time interaction free energy calculated from MD simulations have been used to rank docking solutions at a large scale.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155251PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4864233PMC
July 2017

Assessment of inhibition of porcine hepatic cytochrome P450 enzymes by 48 commercial drugs.

Vet J 2016 May 16;211:26-31. Epub 2016 Mar 16.

Veterinary Medicine Research and Development, Zoetis, Inc, 333 Portage Street, Kalamazoo, MI 49007, USA.

Drug interactions due to inhibition of hepatic cytochrome P450 (CYP450) enzymes are not well understood in veterinary medicine. Forty-eight commercial porcine medicines were selected to evaluate their potential inhibition on porcine hepatic CYP450 enzymes at their commercial doses and administration routes. Those drugs were first assessed through a single point inhibitory assay at 3 µM in porcine liver microsomes for six specific CYP450 metabolisms (phenacetin o-deethylation, coumarin 7-hydroxylation, tolbutamide 4-hydroxylation, bufuralol 1-hydroxylation, chlorozoxazone 6-hydroxylation and midazolam 1'-hydroxylation). When the inhibition was > 10% in the single point inhibitory assay, IC50 values (inhibitory concentrations that decrease biotransformation of selected substrate by 50%) were determined. Overall, 17 drugs showed in vitro inhibition on one or more porcine hepatic CYP450 metabolisms with different IC50 values. The potential in vivo porcine hepatic CYP450 inhibition by those drugs was assessed by combining the in vitro data and in vivo Cmax (maximum plasma concentrations from pharmacokinetic studies of the porcine medicines at their commercial doses and administration routes). Three drugs showed high potential inhibition to one or two porcine hepatic CYP450 isoforms at their commercial doses and administration routes, while seven drugs had medium risk and seven had low risk of such in vivo inhibition. These data are useful to prevent potential drug interactions in veterinary medical practice.
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http://dx.doi.org/10.1016/j.tvjl.2016.03.011DOI Listing
May 2016

Sequence specificity between interacting and non-interacting homologs identifies interface residues--a homodimer and monomer use case.

BMC Bioinformatics 2015 Oct 8;16:325. Epub 2015 Oct 8.

Center for Integrative Bioinformatics VU (IBIVU), Vrije University Amsterdam, De Boelelaan 1081A, 1081 HV, Amsterdam, The Netherlands.

Background: Protein families participating in protein-protein interactions may contain sub-families that have different binding characteristics, ranging from right binding to showing no interaction at all. Composition differences at the sequence level in these sub-families are often decisive to their differential functional interaction. Methods to predict interface sites from protein sequences typically exploit conservation as a signal. Here, instead, we provide proof of concept that the sequence specificity between interacting versus non-interacting groups can be exploited to recognise interaction sites.

Results: We collected homodimeric and monomeric proteins and formed homologous groups, each having an interacting (homodimer) subgroup and a non-interacting (monomer) subgroup. We then compiled multiple sequence alignments of the proteins in the homologous groups and identified compositional differences between the homodimeric and monomeric subgroups for each of the alignment positions. Our results show that this specificity signal distinguishes interface and other surface residues with 40.9% recall and up to 25.1% precision.

Conclusions: To our best knowledge, this is the first large scale study that exploits sequence specificity between interacting and non-interacting homologs to predict interaction sites from sequence information only. The performance obtained indicates that this signal contains valuable information to identify protein-protein interaction sites.
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http://dx.doi.org/10.1186/s12859-015-0758-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4599308PMC
October 2015

HIV-1 replication fitness of HLA-B*57/58:01 CTL escape variants is restored by the accumulation of compensatory mutations in gag.

PLoS One 2013 5;8(12):e81235. Epub 2013 Dec 5.

Department of Experimental Immunology, Sanquin Research, Landsteiner Laboratory, and Center for Infectious Diseases and Immunity Amsterdam (CINIMA), Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.

Expression of HLA-B*57 and the closely related HLA-B*58:01 are associated with prolonged survival after HIV-1 infection. However, large differences in disease course are observed among HLA-B*57/58:01 patients. Escape mutations in CTL epitopes restricted by these HLA alleles come at a fitness cost and particularly the T242N mutation in the TW10 CTL epitope in Gag has been demonstrated to decrease the viral replication capacity. Additional mutations within or flanking this CTL epitope can partially restore replication fitness of CTL escape variants. Five HLA-B*57/58:01 progressors and 5 HLA-B*57/58:01 long-term nonprogressors (LTNPs) were followed longitudinally and we studied which compensatory mutations were involved in the restoration of the viral fitness of variants that escaped from HLA-B*57/58:01-restricted CTL pressure. The Sequence Harmony algorithm was used to detect homology in amino acid composition by comparing longitudinal Gag sequences obtained from HIV-1 patients positive and negative for HLA-B*57/58:01 and from HLA-B*57/58:01 progressors and LTNPs. Although virus isolates from HLA-B*57/58:01 individuals contained multiple CTL escape mutations, these escape mutations were not associated with disease progression. In sequences from HLA-B*57/58:01 progressors, 5 additional mutations in Gag were observed: S126N, L215T, H219Q, M228I and N252H. The combination of these mutations restored the replication fitness of CTL escape HIV-1 variants. Furthermore, we observed a positive correlation between the number of escape and compensatory mutations in Gag and the replication fitness of biological HIV-1 variants isolated from HLA-B*57/58:01 patients, suggesting that the replication fitness of HLA-B*57/58:01 escape variants is restored by accumulation of compensatory mutations.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0081235PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3855271PMC
March 2015

Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins.

Bioinformatics 2014 Feb 22;30(3):326-34. Epub 2013 Nov 22.

Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Netherlands Bioinformatics Centre (NBIC), Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands and Department of Biological Psychology, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands.

Motivation: To assess whether two proteins will interact under physiological conditions, information on the interaction free energy is needed. Statistical learning techniques and docking methods for predicting protein-protein interactions cannot quantitatively estimate binding free energies. Full atomistic molecular simulation methods do have this potential, but are completely unfeasible for large-scale applications in terms of computational cost required. Here we investigate whether applying coarse-grained (CG) molecular dynamics simulations is a viable alternative for complexes of known structure.

Results: We calculate the free energy barrier with respect to the bound state based on molecular dynamics simulations using both a full atomistic and a CG force field for the TCR-pMHC complex and the MP1-p14 scaffolding complex. We find that the free energy barriers from the CG simulations are of similar accuracy as those from the full atomistic ones, while achieving a speedup of >500-fold. We also observe that extensive sampling is extremely important to obtain accurate free energy barriers, which is only within reach for the CG models. Finally, we show that the CG model preserves biological relevance of the interactions: (i) we observe a strong correlation between evolutionary likelihood of mutations and the impact on the free energy barrier with respect to the bound state; and (ii) we confirm the dominant role of the interface core in these interactions. Therefore, our results suggest that CG molecular simulations can realistically be used for the accurate prediction of protein-protein interaction strength.

Availability And Implementation: The python analysis framework and data files are available for download at http://www.ibi.vu.nl/downloads/bioinformatics-2013-btt675.tgz.
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http://dx.doi.org/10.1093/bioinformatics/btt675DOI Listing
February 2014

Veterinary and human biobanking practices: enhancing molecular sample integrity.

Vet Pathol 2014 Jan 13;51(1):270-80. Epub 2013 Nov 13.

Van Andel Research Institute, 333 Bostwick Avenue NE, Grand Rapids, MI 49503, USA. Email:

Animal models have historically informed veterinary and human pathophysiology. Next-generation genomic sequencing and molecular analyses using analytes derived from tissue require integrative approaches to determine macroanalyte integrity as well as morphology for imaging algorithms that can extend translational applications. The field of biospecimen science and biobanking will play critical roles in tissue sample collection and processing to ensure the integrity of macromolecules, aid experimental design, and provide more accurate and reproducible downstream genomic data. Herein, we employ animal experiments to combine protein expression analysis by microscopy with RNA integrity number and quantitative measures of morphologic changes of autolysis. These analyses can be used to predict the effect of preanalytic variables and provide the basis for standardized methods in tissue sample collection and processing. We also discuss the application of digital imaging with quantitative RNA and tissue-based protein measurements to show that genomic methods augment traditional in vivo imaging to support biospecimen science. To make these observations, we have established a time course experiment of murine kidney tissues that predicts conventional measures of RNA integrity by RIN analysis and provides reliable and accurate measures of biospecimen integrity and fitness, in particular for time points less than 3 hours post-tissue resection.
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http://dx.doi.org/10.1177/0300985813510532DOI Listing
January 2014

HIV-1 envelope glycoprotein signatures that correlate with the development of cross-reactive neutralizing activity.

Retrovirology 2013 Sep 23;10:102. Epub 2013 Sep 23.

Department of Medical Microbiology, Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands.

Background: Current HIV-1 envelope glycoprotein (Env) vaccines are unable to induce cross-reactive neutralizing antibodies. However, such antibodies are elicited in 10-30% of HIV-1 infected individuals, but it is unknown why these antibodies are induced in some individuals and not in others. We hypothesized that the Envs of early HIV-1 variants in individuals who develop cross-reactive neutralizing activity (CrNA) might have unique characteristics that support the induction of CrNA.

Results: We retrospectively generated and analyzed env sequences of early HIV-1 clonal variants from 31 individuals with diverse levels of CrNA 2-4 years post-seroconversion. These sequences revealed a number of Env signatures that coincided with CrNA development. These included a statistically shorter variable region 1 and a lower probability of glycosylation as implied by a high ratio of NXS versus NXT glycosylation motifs. Furthermore, lower probability of glycosylation at position 332, which is involved in the epitopes of many broadly reactive neutralizing antibodies, was associated with the induction of CrNA. Finally, Sequence Harmony identified a number of amino acid changes associated with the development of CrNA. These residues mapped to various Env subdomains, but in particular to the first and fourth variable region as well as the underlying α2 helix of the third constant region.

Conclusions: These findings imply that the development of CrNA might depend on specific characteristics of early Env. Env signatures that correlate with the induction of CrNA might be relevant for the design of effective HIV-1 vaccines.
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http://dx.doi.org/10.1186/1742-4690-10-102DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3849187PMC
September 2013

Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model.

Bioinformatics 2013 Jul;29(13):i80-8

IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, AIMMS Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, De Boelelaan 1081, NKI-AVL The Netherlands.

Motivation: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes.

Results: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as 'stepping stones' for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or 'trigger' is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btt243DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694641PMC
July 2013

Interaction of 14-3-3 proteins with the estrogen receptor alpha F domain provides a drug target interface.

Proc Natl Acad Sci U S A 2013 May 15;110(22):8894-9. Epub 2013 May 15.

Department of Structural Biology, Faculty Earth and Life Sciences, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands.

Estrogen receptor alpha (ERα) is involved in numerous physiological and pathological processes, including breast cancer. Breast cancer therapy is therefore currently directed at inhibiting the transcriptional potency of ERα, either by blocking estrogen production through aromatase inhibitors or antiestrogens that compete for hormone binding. Due to resistance, new treatment modalities are needed and as ERα dimerization is essential for its activity, interference with receptor dimerization offers a new opportunity to exploit in drug design. Here we describe a unique mechanism of how ERα dimerization is negatively controlled by interaction with 14-3-3 proteins at the extreme C terminus of the receptor. Moreover, the small-molecule fusicoccin (FC) stabilizes this ERα/14-3-3 interaction. Cocrystallization of the trimeric ERα/14-3-3/FC complex provides the structural basis for this stabilization and shows the importance of phosphorylation of the penultimate Threonine (ERα-T(594)) for high-affinity interaction. We confirm that T(594) is a distinct ERα phosphorylation site in the breast cancer cell line MCF-7 using a phospho-T(594)-specific antibody and by mass spectrometry. In line with its ERα/14-3-3 interaction stabilizing effect, fusicoccin reduces the estradiol-stimulated ERα dimerization, inhibits ERα/chromatin interactions and downstream gene expression, resulting in decreased cell proliferation. Herewith, a unique functional phosphosite and an alternative regulation mechanism of ERα are provided, together with a small molecule that selectively targets this ERα/14-3-3 interface.
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http://dx.doi.org/10.1073/pnas.1220809110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3670367PMC
May 2013

Bioinformatics and systems biology: bridging the gap between heterogeneous student backgrounds.

Brief Bioinform 2013 Sep 19;14(5):589-98. Epub 2013 Apr 19.

Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands. Tel.: +31 20 59 87816; Fax: +31 20 59 87653;

Teaching students with very diverse backgrounds can be extremely challenging. This article uses the Bioinformatics and Systems Biology MSc in Amsterdam as a case study to describe how the knowledge gap for students with heterogeneous backgrounds can be bridged. We show that a mix in backgrounds can be turned into an advantage by creating a stimulating learning environment for the students. In the MSc Programme, conversion classes help to bridge differences between students, by mending initial knowledge and skill gaps. Mixing students from different backgrounds in a group to solve a complex task creates an opportunity for the students to reflect on their own abilities. We explain how a truly interdisciplinary approach to teaching helps students of all backgrounds to achieve the MSc end terms. Moreover, transferable skills obtained by the students in such a mixed study environment are invaluable for their later careers.
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http://dx.doi.org/10.1093/bib/bbt023DOI Listing
September 2013

Pericyte coverage of differentiated vessels inside tumor vasculature is an independent unfavorable prognostic factor for patients with clear cell renal cell carcinoma.

Cancer 2013 Jan 18;119(2):313-24. Epub 2012 Jul 18.

State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangdong, China.

Background: The objective of this study was to evaluate the effect of pericyte coverage (PC) of differentiated tumor microvessels on the prognosis of patients with clear cell renal cell carcinoma (CCRCC).

Methods: Samples from 2 cohorts of patients with CCRCC (101 Asian patients and 524 US patients) were prepared using 2 different histologic approaches: routine sectioning versus tissue microarray. Then, the samples were immunohistochemically doubled-stained for a pericyte marker (alpha smooth muscle actin [α-SMA]) and a differentiated vessel marker (cluster of differentiation 34 [CD34]), followed by multispectral image capturing and computerized image analyses to quantify the microvessel density (MVD) and the PC of differentiated vessels. The correlations of PC and the MVD:PC ratio with clinicopathologic characteristics were analyzed.

Results: There was an inverse correlation between differentiated MVD and PC. Higher PC correlated with more aggressive clinicopathologic characteristics of CCRCC in both cohorts, including more advanced T-classification, higher pathologic grades, and the occurrence of tumor necrosis. The MVD:PC ratio was an independent favorable prognostic factor for overall and recurrence-free survival in the Asian cohort and for recurrence-free survival in the US cohort. PC also was an independent prognostic factor, with higher PC predicting a poorer outcome. The combination of PC and MVD was better at distinguishing the outcome of patients with CCRCC. PC combined with differentiated MVD or with the MVD:PC ratio provided additional, independent prognostic information to the Leibovich risk model, and that information was used to generate improved risk models.

Conclusions: The authors consistently observed that higher PC was correlated with more aggressive clinicopathologic characteristics. PC was an independent unfavorable prognostic factor. The authors concluded that pericytes should be considered for therapeutic targeting.
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http://dx.doi.org/10.1002/cncr.27746DOI Listing
January 2013

Enabling grand-canonical Monte Carlo: extending the flexibility of GROMACS through the GromPy python interface module.

J Comput Chem 2012 May 28;33(12):1207-14. Epub 2012 Feb 28.

Centre for Integrative Bioinformatics Vrije Universiteit (IBIVU), VU University Amsterdam, De Boelelaan 1081a, 1081HV Amsterdam, The Netherlands; Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525GA Nijmegen, The Netherlands.

We report on a python interface to the GROMACS molecular simulation package, GromPy (available at https://github.com/GromPy). This application programming interface (API) uses the ctypes python module that allows function calls to shared libraries, for example, written in C. To the best of our knowledge, this is the first reported interface to the GROMACS library that uses direct library calls. GromPy can be used for extending the current GROMACS simulation and analysis modes. In this work, we demonstrate that the interface enables hybrid Monte-Carlo/molecular dynamics (MD) simulations in the grand-canonical ensemble, a simulation mode that is currently not implemented in GROMACS. For this application, the interplay between GromPy and GROMACS requires only minor modifications of the GROMACS source code, not affecting the operation, efficiency, and performance of the GROMACS applications. We validate the grand-canonical application against MD in the canonical ensemble by comparison of equations of state. The results of the grand-canonical simulations are in complete agreement with MD in the canonical ensemble. The python overhead of the grand-canonical scheme is only minimal.
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http://dx.doi.org/10.1002/jcc.22947DOI Listing
May 2012

Predictive value of intratumoral microvascular density in patients with advanced non-small cell lung cancer receiving chemotherapy plus bevacizumab.

J Thorac Oncol 2012 Jan;7(1):71-5

State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China.

Introduction: The use of bevacizumab combined with chemotherapy represents a recent advance in clinical oncology for significantly improving the survival of patients who have non-small cell lung cancer (NSCLC). There is an unmet need for biomarkers that can predict response to such treatment and identify patients sensitive to it. Our study was designed to investigate the predictive value of intratumoral microvascular density (MVD) in patients with NSCLC treated with bevacizumab.

Methods: Sixteen patients with NSCLC who underwent chemotherapy combined with bevacizumab were included into this study. Paraffin-embedded tumor samples were sectioned and stained immunohistochemically for the blood vessel markers CD34 and CD31 to characterize the intratumoral vasculature. A computerized image analysis program was used to quantitatively calculate the intratumoral MVD. Treatment response was evaluated by computed tomography scanning.

Results: Two types of blood vessels, undifferentiated (CD31*/CD34*) and differentiated (CD34*), were identified. A positive correlation was found between the largest percentage of tumor shrinkage and the MVD of undifferentiated (CD31*/CD34*) vessels, with Spearman correlation coefficient being 0.576 (p = 0.019). No correlation between tumor shrinkage and differentiated vessel MVD (CD34*) was found. Moreover, seven of the eight patients with more undifferentiated vessels showed a partial response, versus only one of the seven patients with fewer undifferentiated vessels (p = 0.009).

Conclusions: There are two major types of microvessel in lung cancer vasculature. The MVD of undifferentiated vessels is a favorable predictor for patients with NSCLC treated with a chemotherapy regimen plus bevacizumab, with a higher MVD value correlating with better treatment response. Further studies are needed to verify the predictive role of MVD in treatment of NSCLC with bevacizumab.
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http://dx.doi.org/10.1097/JTO.0b013e31823085f4DOI Listing
January 2012