Background. While Zika virus (ZIKV) is mainly transmitted by mosquitoes, numerous cases of sexual transmission have been reported during recent outbreaks. Little is known about which host cell types or entry factors aid in mediating this sexual transmission. Methods. In this study, we investigated ZIKV cell tropism by infecting 2 types of human prostate cells with 3 contemporary ZIKV isolates from persons infected in the Americas. We used real-time quantitative polymerase chain reaction and immunofluorescence analyses to measure infection and flow cytometry to detect entry factor expression. Results. Here we show that ZIKV infects, replicates, and produces infectious virus in prostate stromal mesenchymal stem cells, epithelial cells, and organoids made with a combination of these cells. We also show that prostate cells express several well-characterized flavivirus attachment factors. In contrast, dengue virus does not infect or does not replicate in these prostate cells, although it is known to use similar receptors. Conclusions. Our results indicate that ZIKV favors infection of stromal cells more so than epithelial cells in organoids, possibly indicating a preference for stem cells in general. Overall, these results suggest that ZIKV replication occurs in the human prostate and can account for ZIKV secretion in semen, thus leading to sexual transmission.

Agent-based modelling is becoming widely used for studies in Social Sciences. However, its application faces limitations coming from its bias to software development, which precludes a more active involvement of social researchers. In order to deal with this problem, this work proposes using domain-specific modelling languages based on the socio-psychological Activity Theory. These languages apply agent research to crystallize that theoretical framework in a formal definition suitable for automated processing but close to Social Sciences. The paper focuses on the language for the specification of group constructions such as organizations, norms and shared knowledge. A case study about contradictory decisions in the space shuttle program illustrates the discussion.

Functional annotation is the process that assigns a biological functionality to a deoxyribonucleic acid (DNA) sequence. It requires searching in huge data sets for candidates, and inferring the most appropriate features based on the information found and expert knowledge. When humans perform most of these tasks, results are of a high quality, but there is a bottleneck in processing; when experts are largely replaced by automated tools, annotation is faster but of poorer quality. Combining the automatic annotation with expert systems (ESs) can enhance the quality of the annotation, while effectively reducing experts' workload. This paper presents INFAES, a rule-based ES developed for mimicking the human reasoning in the inference stage of the functional annotation. It integrates knowledge on Biology and heuristics about the use of Bioinformatics tools. Its development adopts state-of-the-art methodologies to facilitate the acquisition and integration of new knowledge. INFAES showed a high performance when compared to the systems developed for the first large-scale community-based critical assessment of protein function annotation (CAFA) [1]. (C) 2015 Elsevier B.V. All rights reserved.

In the last years, the amount of genomic data produced has increased significantly. These data need to be processed and linked to biological information in order to make a meaningful use of them. Automated tools support these tasks, but their results have to be curated by experts, what creates a bottleneck. Although some collaborative community annotation efforts have attempted to address this issue, they have not been overly successful. This paper carries out a social analysis of them and their tools through the Activity Theory framework, looking for limitations and potential solutions. Its conclusions are used to propose requirements for an actual annotation tool, MASSA (Multi-Agent System to Support functional Annotation), which works in functional annotation (i.e., assigning biological functions). The case study reports a preliminary evaluation of their implementation and the analysis of the tool performance according to a state-of-the-art benchmark.

In the last years, the amount of genomic data produced has increased significantly. These data need to be processed and linked to biological information in order to make a meaningful use of them. Automated tools support these tasks, but their results have to be curated by experts, what creates a bottleneck. Although some collaborative community annotation efforts have attempted to address this issue, they have not been overly successful. This paper carries out a social analysis of them and their tools through the Activity Theory framework, looking for limitations and potential solutions. Its conclusions are used to propose requirements for an actual annotation tool, MASSA (Multi-Agent System to Support functional Annotation), which works in junctional annotation (i.e., assigning biological functions). The case study reports a preliminary evaluation of their implementation and the analysis of the tool performance according to a state-of-the-art benchmark.

Functional annotation aims to predict the biological function of DNA sequences. This complex and time-consuming task has to process huge amounts of data and get high quality results. In order to guarantee the quality of the outcome, the annotation should be carried out by human experts, but the great volume of biological data produced lately demands a high degree of automation. The features of this problem (i.e., knowledge-based, distributed resources, and an evolving environment) make it suitable for an agent approach. This paper presents MASSA, a Multi-Agent System to support functional annotation. MASSA combines the potentialities of the agent approach with a Rule-Based Expert System to reproduce the annotation steps, including the human reasoning, at the inference stage. The expert system integrates knowledge on Biology and tools. A case study on the annotation of sequences of four phylogenetically distinct species illustrates the results and use of MASSA.

ABSTRACT The whole-genome shotgun sequence of Rhodococcus ruber strain Chol-4 is presented here. This organism was shown to be able to grow using many steroids as the sole carbon and energy sources. These sequence data will help us to further explore the metabolic abilities of this versatile degrader.

Finding the genes that exist within a DNA sequence and assigning them biological features and functions is one of the biggest challenges of Genomics. This task, called annotation, has to be as accurate and reliable as possible, because this information will be applied in other researches. Ideally, each sequence should be annotated and validated by a human expert, who has the knowledge to infer the most appropriate annotation. Nevertheless, the huge amount of genomic data produced by the new sequencing technologies prevents this practice. Developing expert systems that are able to annotate sequences automatically and emulate the expert involvement in certain key points of the process would enhance the annotation quality. In this work, the CommonKADS methodology is innovatively applied for this purpose. It is used to structure and model the knowledge required to build an expert system able to deal with the functional part of sequence annotation, i.e. establishing the biological purpose of the sequence. This approach provides the first general framework for the aforementioned problem, which can be easily extended to related issues.

Tools-4-Metatool (T4M) is a suite of web-tools, implemented in PERL, which analyses, parses, and manipulates files related to Metatool. Its main goal is to assist the work with Metatool. T4M has two major sets of tools: Analysis and Compare. Analysis visualizes the results of Metatool (convex basis, elementary flux modes, and enzyme subsets) and facilitates the study of metabolic networks. It is composed of five tools: MDigraph, MetaMatrix, CBGraph, EMGraph, and SortEM. Compare was developed to compare different Metatool results from different networks. This set consists of: Compara and ComparaSub which compare network subsets providing outputs in different formats and ComparaEM that seeks for identical elementary modes in two metabolic networks. The suite T4M also includes one script that generates Metatool input: CBasis2Metatool, based on a Metatool output file that is filtered by a list of convex basis' metabolites. Finally, the utility CheckMIn checks the consistency of the Metatool input file. T4M is available at http://solea.quim.ucm.es/t4m.