Transcriptional profiling of HOXA9-regulated genes in human glioblastoma cell models

The data here described pertain to the article by Pojo et al. (2015) [10] titled “A transcriptomic signature mediated by HOXA9 promotes human glioblastoma initiation, aggressiveness and resistance to temozolomide” (Pojoet al., 2015 [10]). HOX genes are part of the homeobox gene family, which encodes transcription factors crucial during embryonic development (Grier et al., 2005; Pearson et al., 2005 [6,9]) and also in post developmental regulation(Neville et al., 2002; Yamamoto et al., 2003; Takahashi et al., 2004;Morgan 2006 [8,14,13,7]). Alterations interfering with the regulation of these genes may lead to tumorigenesis in adults. Due to their contributions in the control of important cellular processes, the deregulation of HOX genes is ultimately correlated with cancer treatment failure and patients' poor prognosis (Golub et al., 1999; Abdel-Fattah et al., 2006 [5,1]; Costa et al.,2010 [4]; Pojo et al., 2015 [10]). Recently, our studies showed that HOXA9 overexpression is associated with poor prognosis in patients with glioblastoma (GBM), the most common and most malignant primary brain tumor. Mechanistically, HOXA9 is associated with resistance to chemotherapy and with pro-proliferative, pro invasive and anti-apoptotic features (Costa et al., 2010 [4]; Pojo et al., 2015 [10]) in GBM in vitro models. Since HOXA9 is a transcription factor, its target genes can be the true biological effectors of its aggressiveness. In this context, whole genome Agilent's microarrays were used to obtain the full transcriptome of HOXA9 in a variety of GBM cell models, including human immortalized astrocytes, established GBM cell lines, and GBM patient derived cell cultures. Here, we provide detailed methods, including experimental design and microarray data analyses,which can be accessed in Gene Expression Omnibus (GEO) under the accession number GSE56517. Additional interpretation of the data is included and supplemented in (Pojo et al., 2015 [10]).

Effect of fire on a monodominant floating mat of Cyperus giganteus Vahl in a neotropical wetland

The rhizomatous Cyperus giganteus, abundant in the Pantanal wetland, can dominate extense floodable areas as monodominant communities. The Jacadigo lake has a large area of C. giganteus, where we performed an evaluation on community structure during two months in 2010, before it was hit by a wildfire which top-killed the vegetation, compared to ten months post-fire. We utilized 40 plots of 1m × 1m, along permanent trails, assessing two strata: the upper, near the inflorescence of adult plants, and the lower, close to the water level. Our results show that fire does not affect dominance of C. giganteus, as it maintained the same cover as before fire; species richness is not much altered either - 28 before fire and 34 thereafter. Fire changed the floristic composition, due to the annual variation of species and the ability of some plants to colonize gaps and to regrow after fire from underground organs and seeds. The stratification of the vegetation with characteristic species of upper and lower strata was similar after fire.

A transcriptomic signature mediated by HOXA9 promotes human glioblastoma initiation, aggressiveness and resistance to temozolomide

Glioblastoma is the most malignant brain tumor, exhibiting remarkable resistance to treatment. Here we investigated the oncogenic potential of HOXA9 in gliomagenesis, the molecular and cellular mechanisms by which HOXA9 renders glioblastoma more aggressive, and how HOXA9 affects response to chemotherapy and survival. The prognostic value of HOXA9 in glioblastoma patients was validated in two large datasets from TCGA and Rembrandt, where high HOXA9 levels were associated with shorter survival. Transcriptomic analyses identified novel HOXA9-target genes with key roles in cancer-related processes, including cell proliferation, DNA repair, and stem cell maintenance. Functional studies with HOXA9-overexpressing and HOXA9-silenced glioblastoma cell models revealed that HOXA9 promotes cell viability, stemness and invasion, and inhibits apoptosis. Additionally, HOXA9 promoted the malignant transformation of human immortalized astrocytes in an orthotopic in vivo model, and caused tumor-associated death. HOXA9 also mediated resistance to temozolomide treatment in vitro and in vivo via upregulation of BCL2. Importantly, the pharmacological inhibition of BCL2 with the BH3 mimetic ABT-737 reverted temozolomide resistance in HOXA9-positive cells. These data establish HOXA9 as a driver of glioma initiation, aggressiveness and resistance to therapy. In the future, the combination of BH3 mimetics with temozolomide should be further explored as an alternative treatment for glioblastoma.

Reconstructing genome-scale metabolic models with merlin

The Metabolic Models Reconstruction Using Genome-Scale Information (merlin) tool is a user-friendly Java application that aids the reconstruction of genome-scale metabolic models for any organism that has its genome sequenced. It performs the major steps of the reconstruction process, including the functional genomic annotation of the whole genome and subsequent construction of the portfolio of reactions. Moreover, merlin includes tools for the identification and annotation of genes encoding transport proteins, generating the transport reactions for those carriers. It also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome and thus the localisation of the metabolites involved in the reactions promoted by such enzymes. The gene-proteins-reactions (GPR) associations are automatically generated and included in the model. Finally, merlin expedites the transition from genomic data to draft metabolic models reconstructions exported in the SBML standard format, allowing the user to have a preliminary view of the biochemical network, which can be manually curated within the environment provided by merlin.

Development and application of efficient pathway enumeration algorithms for metabolic engineering applications

Metabolic Engineering (ME) aims to design microbial cell factories towards the production of valuable compounds. In this endeavor, one important task relates to the search for the most suitable heterologous pathway(s) to add to the selected host. Different algorithms have been developed in the past towards this goal, following distinct approaches spanning constraint-based modeling, graph-based methods and knowledge-based systems based on chemical rules. While some of these methods search for pathways optimizing specific objective functions, here the focus will be on methods that address the enumeration of pathways that are able to convert a set of source compounds into desired targets and their posterior evaluation according to different criteria. Two pathway enumeration algorithms based on (hyper)graph-based representations are selected as the most promising ones and are analyzed in more detail: the Solution Structure Generation and the Find Path algorithms. Their capabilities and limitations are evaluated when designing novel heterologous pathways, by applying these methods on three case studies of synthetic ME related to the production of non-native compounds in E. coli and S. cerevisiae: 1-butanol, curcumin and vanillin. Some targeted improvements are implemented, extending both methods to address limitations identified that impair their scalability, improving their ability to extract potential pathways over large-scale databases. In all case-studies, the algorithms were able to find already described pathways for the production of the target compounds, but also alternative pathways that can represent novel ME solutions after further evaluation.

An integrated network visualization framework towards metabolic engineering applications

Background

Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential.

Results

In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks.

Conclusions

The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0420-0) contains supplementary material, which is available to authorized users.

Transcript level and sequence determinants of protein abundance and noise in Escherichia coli

The range over which a protein is expressed, and its cell-to-cell variability, is often thought to be linked to the demand for its activity. Steady-state protein level is determined by multiple mechanisms controlling transcription and translation, many of which are limited by DNA- and RNA-encoded signals that affect initiation, elongation and termination of polymerases and ribosomes. We performed a comprehensive analysis of >100 sequence features to derive a predictive model composed of a minimal non-redundant set of factors explaining 66% of the total variation of protein abundance observed in >800 genes in Escherichia coli. The model suggests that protein abundance is primarily determined by the transcript level (53%) and by effectors of translation elongation (12%), whereas only a small fraction of the variation is explained by translational initiation (1%). Our analyses uncover a new sequence determinant, not previously described, affecting translation initiation and suggest that elongation rate is affected by both codon biases and specific amino acid composition. We also show that transcription and translation efficiency may have an effect on expression noise, which is more similar than previously assumed.

D-Tailor: automated analysis and design of DNA sequences

Motivation: Current advances in DNA synthesis, cloning and sequencing technologies afford high-throughput implementation of artificial sequences into living cells. However, flexible computational tools for multi-objective sequence design are lacking, limiting the potential of these technologies.

Results: We developed DNA-Tailor (D-Tailor), a fully extendable software framework, for property-based design of synthetic DNA sequences. D-Tailor permits the seamless integration of multiple sequence analysis tools into a generic Monte Carlo simulation that evolves sequences toward any combination of rationally defined properties. As proof of principle, we show that D-Tailor is capable of designing sequence libraries comprising all possible combinations among three different sequence properties influencing translation efficiency in Escherichia coli. The capacity to design artificial sequences that systematically sample any given parameter space should support the implementation of more rigorous experimental designs.

Availability: Source code is available for download at https://sourceforge.net/projects/dtailor/

Contact:aparkin@lbl.gov or cambray.guillaume@gmail.com

Supplementary information:Supplementary data are available at Bioinformatics online (D-Tailor Tutorial).

Re-annotation of the genome sequence of Helicobacter pylori 26695

Helicobacter pylori is a pathogenic bacterium that colonizes the human epithelia, causing duodenal and gastric ulcers, and gastric cancer. The genome of H. pylori 26695 has been previously sequenced and annotated. In addition, two genome-scale metabolic models have been developed. In order to maintain accurate and relevant information on coding sequences (CDS) and to retrieve new information, the assignment of new functions to Helicobacter pylori 26695s genes was performed in this work. The use of software tools, on-line databases and an annotation pipeline for inspecting each gene allowed the attribution of validated EC numbers and TC numbers to metabolic genes encoding enzymes and transport proteins, respectively. 1212 genes encoding proteins were identified in this annotation, being 712 metabolic genes and 500 non-metabolic, while 191 new functions were assignment to the CDS of this bacterium. This information provides relevant biological information for the scientific community dealing with this organism and can be used as the basis for a new metabolic model reconstruction.

Prevalence and predictors of nonadherence to hemodialysis

BACKGROUND

The prevalence of nonadherence to dialysis (NAD) presents a wide variation, depending on the parameters used and demographic regions studied. This study aimed to assess the prevalence and predictors of NAD of patients with chronic kidney disease undergoing hemodialysis (HD).

MATERIALS AND METHODS

This was a cross-sectional study with 255 adult patients receiving HD for >3 months. Skipping a session per month, shortening a session for at least 10 min, phosphorus >7.5 mg/dl, potassium >6.0 mmol/l and interdialytic weight gain (IDWG) >5.7% of body weight were indicative of NAD. The association of sociodemographic and clinical variables with NAD was assessed using logistic regression.

RESULTS

Mean age was 50 ± 13.1 years, 62.7% were male, 85.5% were of African descent and 62% were married. The prevalence rates of NAD were: 49% of shortening sessions, 18% of hyperkalemia, 12% of hyperphosphatemia, 9% of IDWG >5.7% of dry weight and 8% of skipping HD. Independent predictors of NAD were: age ≤50 years, not being married, living alone, living in Salvador, attending dialysis without a companion, ethnic African descent, Kt/V <1.3 and residual diuresis <100 ml/day.

CONCLUSION

NAD is frequent and distinct sociodemographic and clinical variables predict different parameters.

TNA4OptFlux--a software tool for the analysis of strain optimization strategies

Background

Rational approaches for Metabolic Engineering (ME) deal with the identification of modifications that improve the microbes’ production capabilities of target compounds. One of the major challenges created by strain optimization algorithms used in these ME problems is the interpretation of the changes that lead to a given overproduction. Often, a single gene knockout induces changes in the fluxes of several reactions, as compared with the wild-type, and it is therefore difficult to evaluate the physiological differences of the in silico mutant. This is aggravated by the fact that genome-scale models per se are difficult to visualize, given the high number of reactions and metabolites involved.

Findings

We introduce a software tool, the Topological Network Analysis for OptFlux (TNA4OptFlux), a plug-in which adds to the open-source ME platform OptFlux the capability of creating and performing topological analysis over metabolic networks. One of the tool’s major advantages is the possibility of using these tools in the analysis and comparison of simulated phenotypes, namely those coming from the results of strain optimization algorithms. We illustrate the capabilities of the tool by using it to aid the interpretation of two E. coli strains designed in OptFlux for the overproduction of succinate and glycine.

Conclusions

Besides adding new functionalities to the OptFlux software tool regarding topological analysis, TNA4OptFlux methods greatly facilitate the interpretation of non-intuitive ME strategies by automating the comparison between perturbed and non-perturbed metabolic networks. The plug-in is available on the web site http://www.optflux.org, together with extensive documentation.

Relation between lipoprotein subfractions and TSH levels in the cardiovascular risk among women with subclinical hypothyroidism

OBJECTIVE

Subclinical hypothyroidism (SCH) is a common condition associated with increased cardiovascular risk. A standard treatment is yet to be established, as there is no consensus on the TSH cut-off values which should be used as indicators. Thus, the aim of this study was to assess cardiovascular risk in patients with SCH and to differentiate it according to TSH levels.

DESIGN

This was an observational study conducted in an academic medical centre.

PATIENTS

The study population consisted of 95 middle-aged women recently diagnosed with SCH and 65 euthyroid controls.

MEASUREMENTS

We measured anthropometric parameters, lipid cardiovascular risk markers and lipoprotein subclasses of HDL and LDL.

RESULTS

Patients with SCH exhibited a significant increase in triglycerides and atherogenic index of plasma and a significant reduction in HDL-cholesterol with respect to the control group after adjusted by age and BMI. A similar lipid profile was observed in both SCH groups. However, patients with TSH levels higher than 10 mIU/l showed a significant reduction in LDL particle size, which was associated with a higher prevalence of atherogenic pattern B.

CONCLUSIONS

Our findings indicate that cardiovascular risk is affected in patients with TSH levels over 10 mIU/l, who have a lipid profile characteristic of atherogenic dyslipidemia.

Parallel evolutionary computation in bioinformatics applications

A large number of optimization problems within the field of Bioinformatics require methods able to handle its inherent complexity (e.g. NP-hard problems) and also demand increased computational efforts. In this context, the use of parallel architectures is a necessity. In this work, we propose ParJECoLi, a Java based library that offers a large set of metaheuristic methods (such as Evolutionary Algorithms) and also addresses the issue of its efficient execution on a wide range of parallel architectures. The proposed approach focuses on the easiness of use, making the adaptation to distinct parallel environments (multicore, cluster, grid) transparent to the user. Indeed, this work shows how the development of the optimization library can proceed independently of its adaptation for several architectures, making use of Aspect-Oriented Programming. The pluggable nature of parallelism related modules allows the user to easily configure its environment, adding parallelism modules to the base source code when needed. The performance of the platform is validated with two case studies within biological model optimization.

An integrated computational environment for elementary modes analysis of biochemical networks

Elementary flux modes (EFMs) have been claimed as one of the most promising approaches for pathway analysis. These are a set of vectors that emerge from the stoichiometric matrix of a biochemical network through the use of convex analysis. The computation of all EFMs of a given network is an NP-hard problem and existing algorithms do not scale well. Moreover, the analysis of results is difficult given the thousands or millions of possible modes generated. In this work, we propose a new plug-in, running on top of the OptFlux Metabolic Engineering workbench (Rocha et al., 2010), whose aims are to ease the analysis of these results and explore synergies among EFM analysis, phenotype simulation and strain optimisation. Two case studies are shown to illustrate the capabilities of the proposed tool.

Effective immune rejection of advanced metastasized cancer

A cellular cancer therapy is described with unique efficiency even in late-stage disease. in situ activated tumor-immune T cells, induced in allogeneic, tumor-resistant, MHC identical but superantigen different donor mice (B10.D2) could transfer strong graft-versus-leukemia (GvL) effects accompanied by only mild graft-versus-host (GvH) reactivity. Systemic immune cell transfer into 5 Gy irradiated DBA/2 mice bearing up to 4 week established syngeneic tumors and macrometastases led to massive infiltration of tumor tissues by CD4 and CD8 donor T lymphocytes. Upon interaction of immune CD4 donor T cells with host antigen presenting cells in synergy with immune CD8 donor T cells attacking the tumor cells directly, primary tumors (1.5 cm diameter) were encapsulated and rejected from the skin and liver metastases eradicated. For the first time, such adoptive cellular immunotherapy (ADI) was followed in individual live animals by P-31-NMR spectroscopy of primary tumors. An approximately 25,000 fold excess of metastatic tumor cells could be rejected as revealed quantitatively by FACScan analysis of lacZ gene transfected tumor cells.