Viral Proteins Acquired from a Host Converge to Simplified Domain Architectures

The infection cycle of viruses creates many opportunities for the exchange of genetic material with the host. Many viruses integrate their sequences into the genome of their host for replication. These processes may lead to the virus acquisition of host sequences. Such sequences are prone to accumulation of mutations and deletions. However, in rare instances, sequences acquired from a host become beneficial for the virus. We searched for unexpected sequence similarity among the 900,000 viral proteins and all proteins from cellular organisms. Here, we focus on viruses that infect metazoa. The high-conservation analysis yielded 187 instances of highly similar viral-host sequences. Only a small number of them represent viruses that hijacked host sequences. The low-conservation sequence analysis utilizes the Pfam family collection. About 5% of the 12,000 statistical models archived in Pfam are composed of viral-metazoan proteins. In about half of Pfam families, we provide indirect support for the directionality from the host to the virus. The other families are either wrongly annotated or reflect an extensive sequence exchange between the viruses and their hosts. In about 75% of cross-taxa Pfam families, the viral proteins are significantly shorter than their metazoan counterparts. The tendency for shorter viral proteins relative to their related host proteins accounts for the acquisition of only a fragment of the host gene, the elimination of an internal domain and shortening of the linkers between domains. We conclude that, along viral evolution, the host-originated sequences accommodate simplified domain compositions. We postulate that the trimmed proteins act by interfering with the fundamental function of the host including intracellular signaling, post-translational modification, protein-protein interaction networks and cellular trafficking. We compiled a collection of hijacked protein sequences. These sequences are attractive targets for manipulation of viral infection

Comparative metagenomic analyses reveal viral-induced shifts of host metabolism towards nucleotide biosynthesis

BACKGROUND: Viral genomes often contain metabolic genes that were acquired from host genomes (auxiliary genes). It is assumed that these genes are fixed in viral genomes as a result of a selective force, favoring viruses that acquire specific metabolic functions. While many individual auxiliary genes were observed in viral genomes and metagenomes, there is great importance in investigating the abundance of auxiliary genes and metabolic functions in the marine environment towards a better understanding of their role in promoting viral reproduction. RESULTS: In this study, we searched for enriched viral auxiliary genes and mapped them to metabolic pathways. To initially identify enriched auxiliary genes, we analyzed metagenomic microbial reads from the Global Ocean Survey (GOS) dataset that were characterized as viral, as well as marine virome and microbiome datasets from the Line Islands. Viral-enriched genes were mapped to a “global metabolism network” that comprises all KEGG metabolic pathways. Our analysis of the viral-enriched pathways revealed that purine and pyrimidine metabolism pathways are among the most enriched pathways. Moreover, many other viral-enriched metabolic pathways were found to be closely associated with the purine and pyrimidine metabolism pathways. Furthermore, we observed that sequential reactions are promoted in pathways having a high proportion of enriched genes. In addition, these enriched genes were found to be of modular nature, participating in several pathways. CONCLUSIONS: Our naïve metagenomic analyses strongly support the well-established notion that viral auxiliary genes promote viral replication via both degradation of host DNA and RNA as well as a shift of the host metabolism towards nucleotide biosynthesis, clearly indicating that comparative metagenomics can be used to understand different environments and systems without prior knowledge of pathways involved

Computational design and designability of gene regulatory networks

Nuestro conocimiento de las interacciones moleculares nos ha conducido hoy hacia una perspectiva ingenieril, donde diseños e implementaciones de sistemas artificiales de regulación intentan proporcionar instrucciones fundamentales para la reprogramación celular. Nosotros aquí abordamos el diseño de redes de genes como una forma de profundizar en la comprensión de las regulaciones naturales. También abordamos el problema de la diseñabilidad dada una genoteca de elementos compatibles. Con este fin, aplicamos métodos heuríticos de optimización que implementan rutinas para resolver problemas inversos, así como herramientas de análisis matemático para estudiar la dinámica de la expresión genética. Debido a que la ingeniería de redes de transcripción se ha basado principalmente en el ensamblaje de unos pocos elementos regulatorios usando principios de diseño racional, desarrollamos un marco de diseño computacional para explotar este enfoque. Modelos asociados a genotecas fueron examinados para descubrir el espacio genotípico asociado a un cierto fenotipo. Además, desarrollamos un procedimiento completamente automatizado para diseñar moleculas de ARN no codificante con capacidad regulatoria, basándonos en un modelo fisicoquímico y aprovechando la regulación alostérica. Los circuitos de ARN resultantes implementaban un mecanismo de control post-transcripcional para la expresión de proteínas que podía ser combinado con elementos transcripcionales. También aplicamos los métodos heurísticos para analizar la diseñabilidad de rutas metabólicas. Ciertamente, los métodos de diseño computacional pueden al mismo tiempo aprender de los mecanismos naturales con el fin de explotar sus principios fundamentales. Así, los estudios de estos sistemas nos permiten profundizar en la ingeniería genética. De relevancia, el control integral y las regulaciones incoherentes son estrategias generales que los organismos emplean y que aquí analizamos.Rodrigo Tarrega, G. (2011). Computational design and designability of gene regulatory networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1417

Feature selection of microarray data using genetic algorithms and artificial neural networks

Microarrays, which allow for the measurement of thousands of gene expression levels in parallel, have created a wealth of data not previously available to biologists along with new computational challenges. Microarray studies are characterized by a low sample number and a large feature space with many features irrelevant to the problem being studied. This makes feature selection a necessary pre-processing step for many analyses, particularly classification. A Genetic Algorithm -Artificial Neural Network (ANN) wrapper approach is implemented to find the highest scoring set of features for an ANN classifier. Each generation relies on the performance of a set of features trained on an ANN for fitness evaluation. A publically-available leukemia microarray data set (Golub et al., 1999), consisting of 25 AML and 47 ALL Leukemia samples, each with 7129 features, is used to evaluate this approach. Results show an increased performance over Golub\u27s initial findings

Establishment of a three-dimensional cell culture system to study tubular structures - A comparative study of neuronal differentiation in zebrafish and in 2D and 3D zebrafish primary cell culture

A three-dimensional cell culture system was established to generate tubular structures and study neuronal differentiation in zebrafish and in 2D and 3D primary cell cultur