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1.
Toxins (Basel). 2019 Jan 12;11(1). pii: E35. doi: 10.3390/toxins11010035.

Content/Potency Assessment of Botulinum Neurotoxin Type-A by Validated Liquid Chromatography Methods and Bioassays.

Author information

1
Postgraduate Programme in Pharmaceutical Sciences; Federal University of Santa Maria, Santa Maria 97105-900, Brazil. bxavier28@gmail.com.
2
Postgraduate Programme in Pharmaceutical Sciences; Federal University of Santa Maria, Santa Maria 97105-900, Brazil. rafaelaperobelli@gmail.com.
3
Postgraduate Programme in Pharmaceutical Sciences; Federal University of Santa Maria, Santa Maria 97105-900, Brazil. mauricio.walter13@gmail.com.
4
Postgraduate Programme in Pharmaceutical Sciences; Federal University of Santa Maria, Santa Maria 97105-900, Brazil. fransantos.biomed@gmail.com.
5
Industrial Pharmacy Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil. sdalmora@terra.com.br.

Abstract

Botulinum neurotoxin type-A (BoNTA) is one of the seven different serotypes (A to G) produced by Clostridium botulinum. A stability-indicating size-exclusion chromatography (SEC) method was developed and validated, and the specificity was confirmed by forced degradation study, interference of the excipients, and peaks purity. The method was applied to assess the content and high-molecular-weight (HMW) forms of BoNTA in biopharmaceutical products, and the results were compared with those of the LD50 mouse bioassay, the T-47D cell culture assay, and the reversed-phase chromatography (RPC) method, giving mean values of 0.71% higher, 0.36% lower, and 0.87% higher, respectively. Aggregated forms showed significant effects on cytotoxicity, as well as a decrease in the bioactivity (p < 0.05). The employment of the proposed method in conjunction with the optimized analytical technologies for the analysis of the intact and altered forms of the biotechnology-derived medicines, in the correlation studies, enabled the demonstration of the capability of each one of the methods and allowed for great improvements, thereby assuring their safe and effective use.

KEYWORDS:

LD50 mouse bioassay; T−47D cell culture; botulinum neurotoxin type A; reversed-phase chromatography; size-exclusion chromatography

PMID:
30642048
DOI:
10.3390/toxins11010035
Free full text
Icon for Multidisciplinary Digital Publishing Institute (MDPI)
2.
Genes (Basel). 2018 Jul 26;9(8). pii: E372. doi: 10.3390/genes9080372.

TERribly Difficult: Searching for Telomerase RNAs in Saccharomycetes.

Author information

1
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. maria@tbi.univie.ac.at.
2
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. thiel@tbi.univie.ac.at.
3
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. romanoch@tbi.univie.ac.at.
4
BioInformatics Group, Fakultät CB Hochschule Mittweida, Technikumplatz 17, D-09648 Mittweida, Germany. alexander.holzenleiter@web.de.
5
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany. alexander.holzenleiter@web.de.
6
Departamento de Ciência da Computação, Instituto de Ciências Exatas, Universidade de Brasília, Campus Universitário⁻Asa Norte, Brasília, DF CEP: 70910-900, Brazil. joaovicers@gmail.com.
7
Departamento de Ciência da Computação, Instituto de Ciências Exatas, Universidade de Brasília, Campus Universitário⁻Asa Norte, Brasília, DF CEP: 70910-900, Brazil. mariaemilia@unb.br.
8
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. michael.wolfinger@univie.ac.at.
9
Center for Anatomy and Cell Biology, Medical University of Vienna, Währingerstraße 13, 1090 Vienna, Austria. michael.wolfinger@univie.ac.at.
10
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. studla@bioinf.uni-leipzig.de.
11
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Competence Center for Scalable Data Services and Solutions, and Leipzig Research Center for Civilization Diseases, Universität Leipzig, D-04107 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
12
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
13
Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA. studla@bioinf.uni-leipzig.de.

Abstract

The telomerase RNA in yeasts is large, usually >1000 nt, and contains functional elements that have been extensively studied experimentally in several disparate species. Nevertheless, they are very difficult to detect by homology-based methods and so far have escaped annotation in the majority of the genomes of Saccharomycotina. This is a consequence of sequences that evolve rapidly at nucleotide level, are subject to large variations in size, and are highly plastic with respect to their secondary structures. Here, we report on a survey that was aimed at closing this gap in RNA annotation. Despite considerable efforts and the combination of a variety of different methods, it was only partially successful. While 27 new telomerase RNAs were identified, we had to restrict our efforts to the subgroup Saccharomycetacea because even this narrow subgroup was diverse enough to require different search models for different phylogenetic subgroups. More distant branches of the Saccharomycotina remain without annotated telomerase RNA.

KEYWORDS:

homology search; non-coding RNA; secondary structure; synteny; telomerase RNA; yeast

3.
BMC Bioinformatics. 2018 May 16;19(1):172. doi: 10.1186/s12859-018-2162-x.

Live neighbor-joining.

Author information

1
Instituto de Computação, Universidade Estadual de Campinas, Cidade Universitária, Campinas, 13083-852, Brazil.
2
Faculdade de Computação, Universidade Federal de Mato Grosso do Sul, Av. Costa e Silva, s/n, Campo Grande, 79070-900, Brazil.
3
Departamento de Ciência da Computação, Universidade de Brasília, Campus Darcy Ribeiro, Brasília, 70910-900, Brazil.
4
Instituto de Ciências Biológicas, Universidade de Brasília, Campus Darcy Ribeiro, Brasília, 70910-900, Brazil.
5
Faculdade de Computação, Universidade Federal de Mato Grosso do Sul, Av. Costa e Silva, s/n, Campo Grande, 79070-900, Brazil. nalvo@facom.ufms.br.

Abstract

BACKGROUND:

In phylogenetic reconstruction the result is a tree where all taxa are leaves and internal nodes are hypothetical ancestors. In a live phylogeny, both ancestral and living taxa may coexist, leading to a tree where internal nodes may be living taxa. The well-known Neighbor-Joining heuristic is largely used for phylogenetic reconstruction.

RESULTS:

We present Live Neighbor-Joining, a heuristic for building a live phylogeny. We have investigated Live Neighbor-Joining on datasets of viral genomes, a plausible scenario for its application, which allowed the construction of alternative hypothesis for the relationships among virus that embrace both ancestral and descending taxa. We also applied Live Neighbor-Joining on a set of bacterial genomes and to sets of images and texts. Non-biological data may be better explored visually when their relationship in terms of content similarity is represented by means of a phylogeny.

CONCLUSION:

Our experiments have shown interesting alternative phylogenetic hypothesis for RNA virus genomes, bacterial genomes and alternative relationships among images and texts, illustrating a wide range of scenarios where Live Neighbor-Joining may be used.

KEYWORDS:

Live phylogeny; Neighbor-joining; Phylogeny

4.
Noncoding RNA. 2017 Dec 20;3(4). pii: E25. doi: 10.3390/ncrna3040025.

Roles of Non-Coding RNA in Sugarcane-Microbe Interaction.

Author information

1
Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil. flaviabqi@gmail.com.
2
Universidade Federal da INTEGRAÇÃO Latino-Americana, Foz do Iguaçu 85866-000, Brazil. cristian.rojas@unila.edu.br.
3
Laboratório de Química e Função de Proteínas e Peptídeos, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes 28013-602, Brazil. cgrativol@uenf.br.
4
Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil. treecko_blaziken@hotmail.com.
5
Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil. mariana.attom@gmail.com.
6
Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil. helfos85@gmail.com.
7
Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil. babicp@hotmail.com.
8
Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil. berenicenagelasl@gmail.com.
9
Departamento de Ciência da Computação, Universidade de Brasília, Brasília 70910-900, Brazil. maciel.lucas@outlook.com.
10
Departamento de Ciência da Computação, Universidade de Brasília, Brasília 70910-900, Brazil. mariaemilia@unb.br.
11
Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro 22451-900, Brazil. earmas@inf.puc-rio.br.
12
Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro 22451-900, Brazil. jentenza@inf.puc-rio.br.
13
Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro 22451-900, Brazil. sergio@inf.puc-rio.br.
14
Fasteris SA, 1228 Plan-les-Ouates, Switzerland. ht_seq@fasteris.com.
15
Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil. hemerly@bioqmed.ufrj.br.
16
Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil. paulof@bioqmed.ufrj.br.

Abstract

Studies have highlighted the importance of non-coding RNA regulation in plant-microbe interaction. However, the roles of sugarcane microRNAs (miRNAs) in the regulation of disease responses have not been investigated. Firstly, we screened the sRNA transcriptome of sugarcane infected with Acidovorax avenae. Conserved and novel miRNAs were identified. Additionally, small interfering RNAs (siRNAs) were aligned to differentially expressed sequences from the sugarcane transcriptome. Interestingly, many siRNAs aligned to a transcript encoding a copper-transporter gene whose expression was induced in the presence of A. avenae, while the siRNAs were repressed in the presence of A. avenae. Moreover, a long intergenic non-coding RNA was identified as a potential target or decoy of miR408. To extend the bioinformatics analysis, we carried out independent inoculations and the expression patterns of six miRNAs were validated by quantitative reverse transcription-PCR (qRT-PCR). Among these miRNAs, miR408-a copper-microRNA-was downregulated. The cleavage of a putative miR408 target, a laccase, was confirmed by a modified 5'RACE (rapid amplification of cDNA ends) assay. MiR408 was also downregulated in samples infected with other pathogens, but it was upregulated in the presence of a beneficial diazotrophic bacteria. Our results suggest that regulation by miR408 is important in sugarcane sensing whether microorganisms are either pathogenic or beneficial, triggering specific miRNA-mediated regulatory mechanisms accordingly.

KEYWORDS:

Acidovorax avenae; diazotrophic bacteria; microRNA; pathogen; siRNA

5.
Noncoding RNA. 2017 Mar 4;3(1). pii: E11. doi: 10.3390/ncrna3010011.

PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants.

Author information

1
Departamento de Ciência da Computação, Universidade de Brasília, Brasília-DF 70910-900, Brasil. maciel.lucas@outlook.com.
2
Laboratório de Química e Função de Proteínas e Peptídeos, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes-RJ 28013-602, Brazil. cgrativol@uenf.br.
3
Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro-RJ 21941-901, Brazil. flaviabqi@gmail.com.
4
Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro-RJ 21941-901, Brazil. thaislouise@hotmail.com.
5
Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro-RJ 21941-901, Brazil. phardoim@gmail.com.
6
Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro-RJ 21941-901, Brazil. hemerly@bioqmed.ufrj.br.
7
Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro-RJ 22451-900, Brazil. sergio@inf.puc-rio.br.
8
Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro-RJ 21941-901, Brazil. paulof@bioqmed.ufrj.br.
9
Departamento de Ciência da Computação, Universidade de Brasília, Brasília-DF 70910-900, Brasil. mariaemilia@unb.br.

Abstract

Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM). We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane (Saccharum spp.) and in maize (Zea mays). From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms.

KEYWORDS:

SVM-based workflow; bioinformatics; long intergenic non-coding RNAs; long non-coding RNAs; maize; plants; sugarcane

6.
BMC Genomics. 2017 Oct 18;18(1):804. doi: 10.1186/s12864-017-4178-4.

A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts.

Author information

1
Department of Computer Science, University of Brasilia, ICC Central, Instituto de Ciências Exatas, Campus Universitario Darcy Ribeiro, Asa Norte, CEP: 70910-900, Brasilia, Brazil. hugowschneider@gmail.com.
2
Gerência Regional de Brasilia (GEREB), Oswaldo Cruz Foundation (Fiocruz), Av. L3 Norte, Campus Universitário Darcy Ribeiro, Gleba A, Asa Norte, CEP: 70910-900, Brasília, Brazil.
3
Laboratory of Molecular Biology, University of Brasilia, Instituto de Ciencias Biologicas, Campus Universitario Darcy Ribeiro, Asa Norte, CEP: 70910-900, Brasilia, Brazil.
4
Department of Computer Science, University of Brasilia, ICC Central, Instituto de Ciências Exatas, Campus Universitario Darcy Ribeiro, Asa Norte, CEP: 70910-900, Brasilia, Brazil.
5
Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Hartelstrasse 16-18, Leipzig, D-04107, Germany.

Abstract

BACKGROUND:

In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed when analyzing this data is distinguishing between long non-coding RNAs (lncRNAs) and protein coding transcripts (PCTs). Thus, we present a Support Vector Machine (SVM) based method to distinguish lncRNAs from PCTs, using features based on frequencies of nucleotide patterns and ORF lengths, in transcripts.

METHODS:

The proposed method is based on SVM and uses the first ORF relative length and frequencies of nucleotide patterns selected by PCA as features. FASTA files were used as input to calculate all possible features. These features were divided in two sets: (i) 336 frequencies of nucleotide patterns; and (ii) 4 features derived from ORFs. PCA were applied to the first set to identify 6 groups of frequencies that could most contribute to the distinction. Twenty-four experiments using the 6 groups from the first set and the features from the second set where built to create the best model to distinguish lncRNAs from PCTs.

RESULTS:

This method was trained and tested with human (Homo sapiens), mouse (Mus musculus) and zebrafish (Danio rerio) data, achieving 98.21%, 98.03% and 96.09%, accuracy, respectively. Our method was compared to other tools available in the literature (CPAT, CPC, iSeeRNA, lncRNApred, lncRScan-SVM and FEELnc), and showed an improvement in accuracy by ≈3.00%. In addition, to validate our model, the mouse data was classified with the human model, and vice-versa, achieving ≈97.80% accuracy in both cases, showing that the model is not overfit. The SVM models were validated with data from rat (Rattus norvegicus), pig (Sus scrofa) and fruit fly (Drosophila melanogaster), and obtained more than 84.00% accuracy in all these organisms. Our results also showed that 81.2% of human pseudogenes and 91.7% of mouse pseudogenes were classified as non-coding. Moreover, our method was capable of re-annotating two uncharacterized sequences of Swiss-Prot database with high probability of being lncRNAs. Finally, in order to use the method to annotate transcripts derived from RNA-seq, previously identified lncRNAs of human, gorilla (Gorilla gorilla) and rhesus macaque (Macaca mulatta) were analyzed, having successfully classified 98.62%, 80.8% and 91.9%, respectively.

CONCLUSIONS:

The SVM method proposed in this work presents high performance to distinguish lncRNAs from PCTs, as shown in the results. To build the model, besides using features known in the literature regarding ORFs, we used PCA to identify features among nucleotide pattern frequencies that contribute the most in distinguishing lncRNAs from PCTs, in reference data sets. Interestingly, models created with two evolutionary distant species could distinguish lncRNAs of even more distant species.

KEYWORDS:

Long non-coding RNA (lncRNA); Machine learning; Principal component analysis (PCA); Support vector machine (SVM); lncRNA prediction with nucleotide pattern frequencies and ORF length

PMID:
29047334
PMCID:
PMC5648457
DOI:
10.1186/s12864-017-4178-4
[Indexed for MEDLINE]
Free PMC Article
Icon for BioMed Central Icon for PubMed Central
7.
Talanta. 2017 Jan 1;162:567-573. doi: 10.1016/j.talanta.2016.10.053. Epub 2016 Oct 13.

Evaluation of recombinant human parathyroid hormone by CZE method and its correlation with in vitro bioassay and LC methods.

Author information

1
Postgraduate Program in Pharmaceutical Sciences, Federal University of Santa Maria, 97105-900 Santa Maria-RS, Brazil.
2
Department of Industrial Pharmacy, Federal University of Santa Maria, 97105-900 Santa Maria-RS, Brazil.
3
Department of Industrial Pharmacy, Federal University of Santa Maria, 97105-900 Santa Maria-RS, Brazil. Electronic address: sdalmora@terra.com.br.

Abstract

A stability-indicating capillary zone electrophoresis (CZE) method was validated to assess the content/potency of the recombinant human parathyroid hormone (rhPTH 1-34), using ranitidine as internal standard (IS). A fused-silica capillary, (i.d. of 50µm; effective length of 40cm) was used at 25°C; the applied voltage was 20kV. The background electrolyte solution consisted of 50mmolL-1 sodium dihydrogen phosphate solution at pH 3.0. Injections were performed using a pressure mode at 50 mbar for 45s, with detection by photodiode array (PDA) detector set at 200nm. Separation was obtained with a migration time of 5.3min, and was linear over the concentration range of 0.25-250µgmL-1 (r2 =0.9992). Specificity and stability-indicating capability were established in degradation studies, which also showed that there was no interference of the excipients. The accuracy was 100.28% with bias lower than 0.85%. Analyses of the same batches showed mean differences of the estimated content/potencies of 0.61%, 1.31% higher and 0.86% lower as compared to the validated reversed-phase and size exclusion liquid chromatography methods, and to the UMR-106 cell culture bioassay, respectively, with non-significant differences (p>0.05). Degraded forms were also subjected to the in vitro cytotoxicity test. The results obtained showed the capabilities of each one of the methods, and constitute an alternative strategy to monitor stability, improve the quality control and ensure the batch-to-batch consistency of bulk and finished biotechnology-derived medicine.

KEYWORDS:

Biotechnology-derived medicine; Capillary zone electrophoresis; Liquid chromatography; Recombinant human parathyroid hormone; UMR-106 cell culture bioassay

PMID:
27837872
DOI:
10.1016/j.talanta.2016.10.053
[Indexed for MEDLINE]
Icon for Elsevier Science
8.
J Microbiol Methods. 2016 Dec;131:113-121. doi: 10.1016/j.mimet.2016.10.010. Epub 2016 Oct 19.

High-throughput mutation, selection, and phenotype screening of mutant methanogenic archaea.

Author information

1
Redox Biology Center, Department of Biochemistry, University of Nebraska-Lincoln, N200 Beadle Center, Lincoln, NE 68588-0664, United States.
2
Morrison Microscopy Core Research Facility, Center for Biotechnology, University of Nebraska-Lincoln, E117 Beadle Center, Lincoln, NE 68588-0664, United States.
3
Nebraska Center for Materials and Nanoscience, University of Nebraska-Lincoln, 855 N 16th St, Lincoln, NE 68588, United States.
4
School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, United States.
5
Department of Animal Science, University of Nebraska-Lincoln, C220K Animal Science, Lincoln, NE 68583, United States.
6
Redox Biology Center, Department of Biochemistry, University of Nebraska-Lincoln, N200 Beadle Center, Lincoln, NE 68588-0664, United States. Electronic address: nbuan2@unl.edu.

Abstract

Bacterial and archaeal genomes can contain 30% or more hypothetical genes with no predicted function. Phylogenetically deep-branching microbes, such as methane-producing archaea (methanogens), contain up to 50% genes with unknown function. In order to formulate hypotheses about the function of hypothetical gene functions in the strict anaerobe, Methanosarcina acetivorans, we have developed high-throughput anaerobic techniques to UV mutagenize, screen, and select for mutant strains in 96-well plates. Using these approaches we have isolated 10 mutant strains that exhibit a variety of physiological changes including increased or decreased growth rate relative to the parent strain when cells use methanol and/or acetate as carbon and energy sources. This method provides an avenue for the first step in identifying new gene functions: associating a genetic mutation with a reproducible phenotype. Mutations in bona fide methanogenesis genes such as corrinoid methyltransferases and proton-translocating F420H2:methanophenazine oxidoreductase (Fpo) were also generated, opening the door to in vivo functional complementation experiments. Irradiation-based mutagenesis such as from ultraviolet (UV) light, combined with modern genome sequencing, is a useful procedure to discern systems-level gene function in prokaryote taxa that can be axenically cultured but which may be resistant to chemical mutagens.

KEYWORDS:

Archaea; Methane; Methanogen; Methanosarcina; Struvite

PMID:
27771305
DOI:
10.1016/j.mimet.2016.10.010
[Indexed for MEDLINE]
Icon for Elsevier Science
9.
Plasmid. 2016 Mar-May;84-85:27-35. doi: 10.1016/j.plasmid.2016.02.003. Epub 2016 Feb 11.

pNEB193-derived suicide plasmids for gene deletion and protein expression in the methane-producing archaeon, Methanosarcina acetivorans.

Author information

1
Redox Biology Center, Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States.
2
Redox Biology Center, Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States. Electronic address: nbuan@unl.edu.

Abstract

Gene deletion and protein expression are cornerstone procedures for studying metabolism in any organism, including methane-producing archaea (methanogens). Methanogens produce coenzymes and cofactors not found in most bacteria, therefore it is sometimes necessary to express and purify methanogen proteins from the natural host. Protein expression in the native organism is also useful when studying post-translational modifications and their effect on gene expression or enzyme activity. We have created several new suicide plasmids to complement existing genetic tools for use in the methanogen, Methanosarcina acetivorans. The new plasmids are derived from the commercially available Escherichia coli plasmid, pNEB193, and cannot replicate autonomously in methanogens. The designed plasmids facilitate markerless gene deletion, gene transcription, protein expression, and purification of proteins with cleavable affinity tags from the methanogen, M. acetivorans.

KEYWORDS:

Archaea; Methanogen; Methanosarcina; Protein expression

PMID:
26876941
PMCID:
PMC4875793
[Available on 2017-03-01]
DOI:
10.1016/j.plasmid.2016.02.003
[Indexed for MEDLINE]
Free PMC Article
Icon for Elsevier Science Icon for PubMed Central
10.
Int J Genomics. 2015;2015:502795. doi: 10.1155/2015/502795. Epub 2015 Oct 19.

Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency.

Author information

1
Computer Science Department, University of Brasilia (UNB), 70910-900 Brasilia, DF, Brazil.
2
Informatics Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), 22451-900 Rio de Janeiro, RJ, Brazil.

Abstract

Rapid advances in high-throughput sequencing techniques have created interesting computational challenges in bioinformatics. One of them refers to management of massive amounts of data generated by automatic sequencers. We need to deal with the persistency of genomic data, particularly storing and analyzing these large-scale processed data. To find an alternative to the frequently considered relational database model becomes a compelling task. Other data models may be more effective when dealing with a very large amount of nonconventional data, especially for writing and retrieving operations. In this paper, we discuss the Cassandra NoSQL database approach for storing genomic data. We perform an analysis of persistency and I/O operations with real data, using the Cassandra database system. We also compare the results obtained with a classical relational database system and another NoSQL database approach, MongoDB.

11.
Archaea. 2015 Oct 12;2015:912582. doi: 10.1155/2015/912582. eCollection 2015.

Production and Application of a Soluble Hydrogenase from Pyrococcus furiosus.

Author information

1
Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.

Abstract

Hydrogen gas is a potential renewable alternative energy carrier that could be used in the future to help supplement humanity's growing energy needs. Unfortunately, current industrial methods for hydrogen production are expensive or environmentally unfriendly. In recent years research has focused on biological mechanisms for hydrogen production and specifically on hydrogenases, the enzyme responsible for catalyzing the reduction of protons to generate hydrogen. In particular, a better understanding of this enzyme might allow us to generate hydrogen that does not use expensive metals, such as platinum, as catalysts. The soluble hydrogenase I (SHI) from the hyperthermophile Pyrococcus furiosus, a member of the euryarchaeota, has been studied extensively and used in various biotechnological applications. This review summarizes the strategies used in engineering and characterizing three different forms of SHI and the properties of the recombinant enzymes. SHI has also been used in in vitro systems for hydrogen production and NADPH generation and these systems are also discussed.

PMID:
26543406
PMCID:
PMC4620386
DOI:
10.1155/2015/912582
[Indexed for MEDLINE]
Free PMC Article
Icon for Hindawi Limited Icon for PubMed Central
12.
J Bioinform Comput Biol. 2015 Dec;13(6):1550021. doi: 10.1142/S0219720015500213. Epub 2015 Jun 24.

Knowledge-based reasoning to annotate noncoding RNA using multi-agent system.

Author information

1
* Department of Computer Science, University of Brasília, Campus Universitário Darcy Ribeiro Prédio CIC/EST, ASA Norte, Brasília-DF,CEP: 70910-900, Brazil.
2
† Leônidas and Maria Deane Research Center (Fiocruz Amazônia), Rua Teresina, 476 Adrianópolis, Manaus-AM, CEP: 69027-070, Brazil.
3
‡ Department of Cellular Biology, Institute of Biology, University of Brasília, Campus Universitário Darcy Ribeiro, Prédio do Institute de Biologia, ASA Norte, Brasília-DF,CEP: 70910-900, Brazil.
4
§ Department of Computer Science and the Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107, Leipzig, Germany.

Abstract

Noncoding RNAs (ncRNAs) have been focus of intense research over the last few years. Since characteristics and signals of ncRNAs are not entirely known, researchers use different computational tools together with their biological knowledge to predict putative ncRNAs. In this context, this work presents ncRNA-Agents, a multi-agent system to annotate ncRNAs based on the output of different tools, using inference rules to simulate biologists' reasoning. Experiments with data from the fungus Saccharomyces cerevisiae allowed to measure the performance of ncRNA-Agents, with better sensibility, when compared to Infernal, a widely used tool for annotating ncRNA. Besides, data of the Schizosaccharomyces pombe and Paracoccidioides brasiliensis fungi identified novel putative ncRNAs, which demonstrated the usefulness of our approach. NcRNA-Agents can be be found at: http://www.biomol.unb.br/ncrna-agents.

KEYWORDS:

Noncoding RNA annotation; fungi ncRNA annotation; knowledge-based reasoning; multi-agent system

PMID:
26223200
DOI:
10.1142/S0219720015500213
[Indexed for MEDLINE]
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13.
Genet Mol Res. 2015 Jun 18;14(2):6744-61. doi: 10.4238/2015.June.18.18.

Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

Author information

1
Departamento de Ciência da Computação, Instituto de Ciências Exatas, Universidade de Brasília, Brasília, DF, Brasil shanass@unb.br.
2
Departamento de Ciência da Computação, Instituto de Ciências Exatas, Universidade de Brasília, Brasília, DF, Brasil.
3
Departamento de Ciência da Computação, Universidade de São Paulo, São Carlos, SP, Brasil.
4
Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, GO, Brasil.
5
Departamento de Ecologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, GO, Brasil.

Abstract

Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

PMID:
26125883
DOI:
10.4238/2015.June.18.18
[Indexed for MEDLINE]
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14.
Genome Announc. 2014 Oct 9;2(5). pii: e01027-14. doi: 10.1128/genomeA.01027-14.

Draft Genome Sequence of FT9, a Novel Bacillus cereus Strain Isolated from a Brazilian Thermal Spring.

Author information

1
Department of Cellular Biology, University of Brasilia, Brasília, Brazil tainaraiol@unb.br marlts@unb.br.
2
Department of Computer Science, University of Brasilia, Brasília, Brazil.
3
Department of Cellular Biology, University of Brasilia, Brasília, Brazil.
4
Department of Computing and Statistics, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.
5
Institute of Computing, UNICAMP, Campinas, São Paulo, Brazil.
6
Department of Biochemistry, University of São Paulo, São Paulo, Brazil.

Abstract

A Bacillus cereus strain, FT9, isolated from a hot spring in the midwest region of Brazil, had its entire genome sequenced.

15.
BMC Bioinformatics. 2013;14 Suppl 11:S6. doi: 10.1186/1471-2105-14-S11-S6. Epub 2013 Nov 4.

Provenance in bioinformatics workflows.

Abstract

In this work, we used the PROV-DM model to manage data provenance in workflows of genome projects. This provenance model allows the storage of details of one workflow execution, e.g., raw and produced data and computational tools, their versions and parameters. Using this model, biologists can access details of one particular execution of a workflow, compare results produced by different executions, and plan new experiments more efficiently. In addition to this, a provenance simulator was created, which facilitates the inclusion of provenance data of one genome project workflow execution. Finally, we discuss one case study, which aims to identify genes involved in specific metabolic pathways of Bacillus cereus, as well as to compare this isolate with other phylogenetic related bacteria from the Bacillus group. B. cereus is an extremophilic bacteria, collected in warm water in the Midwestern Region of Brazil, its DNA samples having been sequenced with an NGS machine.

PMID:
24564294
PMCID:
PMC3816297
DOI:
10.1186/1471-2105-14-S11-S6
[Indexed for MEDLINE]
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16.
J Comput Biol. 2013 Jan;20(1):30-7. doi: 10.1089/cmb.2012.0219.

Live phylogeny.

Author information

1
Institute of Computing, University of Campinas, Campinas, Brazil. gpt@ic.unicamp.br

Abstract

The live phylogeny problem generalizes the phylogeny problem while admitting the existence of living ancestors among the taxonomic objects. This problem suits the case of fast-evolving species, like virus, and the construction of phylogenies for nonbiological objects like documents, images, and database records. In this article, we formalize the live phylogeny problem for distances and character states and introduce polynomial-time algorithms for particular versions of the problems. We believe that more general versions of the problems are NP-hard and that many heuristic and approximation approaches may be developed as solution strategies.

PMID:
23294270
DOI:
10.1089/cmb.2012.0219
[Indexed for MEDLINE]
17.
Talanta. 2012 May 30;94:1-7. doi: 10.1016/j.talanta.2012.03.015. Epub 2012 Mar 11.

Stability-indicating capillary zone electrophoresis method for the assessment of recombinant human granulocyte-macrophage colony-stimulating factor and its correlation with reversed-phase liquid chromatography method and bioassay.

Author information

1
Department of Industrial Pharmacy, Federal University of Santa Maria, 97105-900 Santa Maria, RS, Brazil. sdalmora@terra.com.br

Abstract

A stability-indicating capillary zone electrophoresis (CZE) method was validated for the analysis of granulocyte-macrophage colony-stimulating factor (rhGM-CSF) using leuprorelin acetate (LA), as internal standard (IS). A fused-silica capillary (75 μm i.d.; effective length, 72 cm) was used at 25 °C; the applied voltage was 12 kV. The background electrolyte solution consisted of 50mM di-sodium hydrogen phosphate solution at pH 8.8. Injections were performed using a pressure mode at 50 mbar for 9s, with detection by photodiode array detector set at 200 nm. Specificity and stability-indicating capability were established in degradation studies, which also showed that there was no interference of the excipients. The method was linear over the concentration range of 2.5-200 μg mL(-1) (r(2)=0.9995) and the limit of detection (LOD) and limit of quantitation (LOQ) were 0.79 μg mL(-1) and 2.5 μg mL(-1), respectively. The accuracy was 99.14% with bias lower than 1.40%. The method was applied to the quantitative analysis of biopharmaceutical formulations, and the results were correlated to those of a validated reversed-phase LC method (RP-LC), and an in vitro bioassay, showing non-significant differences (p>0.05).

PMID:
22608407
DOI:
10.1016/j.talanta.2012.03.015
[Indexed for MEDLINE]
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18.
Genes (Basel). 2012 Jul 5;3(3):378-90. doi: 10.3390/genes3030378.

Clustering rfam 10.1: clans, families, and classes.

Author information

1
Department of Computer Science, Institute of Exact Sciences, University of Brasília, Brasília 70910-900, Brazil. felipe.lessa@gmail.com.
2
Department of Cellular Biology, Institute of Biology, University of Brasília, Brasília 70910-900, Brazil. tainaraiol@gmail.com.
3
Department of Cellular Biology, Institute of Biology, University of Brasília, Brasília 70910-900, Brazil. brigido@unb.br.
4
Department of Mathematics, University of Brasília, Brasília 70910-900, Brazil. daniele@mat.unb.br.
5
Department of Computer Science, Institute of Exact Sciences, University of Brasília, Brasília 70910-900, Brazil. mariaemilia@unb.br.
6
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany. studla@bioinf.uni-leipzig.de.

Abstract

The Rfam database contains information about non-coding RNAs emphasizing their secondary structures and organizing them into families of homologous RNA genes or functional RNA elements. Recently, a higher order organization of Rfam in terms of the so-called clans was proposed along with its "decimal release". In this proposition, some of the families have been assigned to clans based on experimental and computational data in order to find related families. In the present work we investigate an alternative classification for the RNA families based on tree edit distance. The resulting clustering recovers some of the Rfam clans. The majority of clans, however, are not recovered by the structural clustering. Instead, they get dispersed into larger clusters, which correspond roughly to well-described RNA classes such as snoRNAs, miRNAs, and CRISPRs. In conclusion, a structure-based clustering can contribute to the elucidation of the relationships among the Rfam families beyond the realm of clans and classes.

19.
PLoS Genet. 2011 Oct;7(10):e1002345. doi: 10.1371/journal.pgen.1002345. Epub 2011 Oct 27.

Comparative genomic analysis of human fungal pathogens causing paracoccidioidomycosis.

Author information

1
Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

Abstract

Paracoccidioides is a fungal pathogen and the cause of paracoccidioidomycosis, a health-threatening human systemic mycosis endemic to Latin America. Infection by Paracoccidioides, a dimorphic fungus in the order Onygenales, is coupled with a thermally regulated transition from a soil-dwelling filamentous form to a yeast-like pathogenic form. To better understand the genetic basis of growth and pathogenicity in Paracoccidioides, we sequenced the genomes of two strains of Paracoccidioides brasiliensis (Pb03 and Pb18) and one strain of Paracoccidioides lutzii (Pb01). These genomes range in size from 29.1 Mb to 32.9 Mb and encode 7,610 to 8,130 genes. To enable genetic studies, we mapped 94% of the P. brasiliensis Pb18 assembly onto five chromosomes. We characterized gene family content across Onygenales and related fungi, and within Paracoccidioides we found expansions of the fungal-specific kinase family FunK1. Additionally, the Onygenales have lost many genes involved in carbohydrate metabolism and fewer genes involved in protein metabolism, resulting in a higher ratio of proteases to carbohydrate active enzymes in the Onygenales than their relatives. To determine if gene content correlated with growth on different substrates, we screened the non-pathogenic onygenale Uncinocarpus reesii, which has orthologs for 91% of Paracoccidioides metabolic genes, for growth on 190 carbon sources. U. reesii showed growth on a limited range of carbohydrates, primarily basic plant sugars and cell wall components; this suggests that Onygenales, including dimorphic fungi, can degrade cellulosic plant material in the soil. In addition, U. reesii grew on gelatin and a wide range of dipeptides and amino acids, indicating a preference for proteinaceous growth substrates over carbohydrates, which may enable these fungi to also degrade animal biomass. These capabilities for degrading plant and animal substrates suggest a duality in lifestyle that could enable pathogenic species of Onygenales to transfer from soil to animal hosts.

PMID:
22046142
PMCID:
PMC3203195
DOI:
10.1371/journal.pgen.1002345
[Indexed for MEDLINE]
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20.
Genet Mol Res. 2011 Feb 22;10(1):321-5. doi: 10.4238/vol10-1gmr1026.

Development of microsatellite markers for the endangered Neotropical tree species Tibouchina papyrus (Melastomataceae).

Author information

1
Laboratório de Genética e Biodiversidade, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, GO, Brasil. tellesmpc@gmail.com

Abstract

We isolated and characterized 12 microsatellite loci for Tibouchina papyrus (Melastomataceae), an endangered species with narrow and disjunct range, endemics to a few localities in "cerrado rupestre" from Central Brazil. These microsatellites were obtained by sequencing of a genomic shotgun library for primer design. Leaves from 96 individuals collected in the three known local populations were genotyped using the 12 primers designed to analyze the polymorphisms at each locus. The number of alleles per locus ranged from one to six; two loci were monomorphic. Among the polymorphic loci, expected heterozygosities ranged from 0.161 to 0.714. Combined paternity exclusion probability was 0.957 and combined genetic identity (0.051) was high for studies on parentage. Tibouchina papyrus is a rare and endemic tree species of outcrop quartzite and sandstone soils, with highly isolated populations, which may have lead to the low degree of polymorphism that we detected. Also, motifs of most loci are larger than dinucleotide, which typically display lower levels of polymorphism.

PMID:
21365547
DOI:
10.4238/vol10-1gmr1026
[Indexed for MEDLINE]
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