Protein Structure-Guided Hidden Markov Models (HMMs) as A Powerful Method in the Detection of Ancestral Endogenous Viral Elements

Identification and functional analysis of novel protein-encoding sequences related to stress-resistance

Currently, industrial bioproducts are less competitive than chemically produced goods due to the shortcomings of conventional microbial hosts. Thus, is essential developing robust bacteria for improved cell tolerance to process-specific parameters. In this context, metagenomic approaches from extreme environments can provide useful biological parts to improve bacterial robustness. Here, in order to build genetic constructs that increase bacterial resistance to diverse stress conditions, we recovered novel protein-encoding sequences related to stress-resistance from metagenomic databases using an in silico approach based on Hidden-Markov-Model profiles. For this purpose, we used metagenomic shotgun sequencing data from microbial communities of extreme environments to identify genes encoding chaperones and other proteins that confer resistance to stress conditions. We identified and characterized 10 novel protein-encoding sequences related to the DNA-binding protein HU, the ATP-dependent protease ClpP, and the chaperone protein DnaJ. By expressing these genes in Escherichia coli under several stress conditions (including high temperature, acidity, oxidative and osmotic stress, and UV radiation), we identified five genes conferring resistance to at least two stress conditions when expressed in E. coli. Moreover, one of the identified HU coding-genes which was retrieved from an acidic soil metagenome increased E. coli tolerance to four different stress conditions, implying its suitability for the construction of a synthetic circuit directed to expand broad bacterial resistance.

Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

This paper presents a context-aware adaptive assembly assistance system meant to support factory workers by embedding predictive capabilities. The research is focused on the predictor which suggests the next assembly step. Hidden Markov models are analyzed for this purpose. Several prediction methods have been previously evaluated and the prediction by partial matching, which was the most efficient, is considered in this work as a component of a hybrid model together with an optimally configured hidden Markov model. The experimental results show that the hidden Markov model is a viable choice to predict the next assembly step, whereas the hybrid predictor is even better, outperforming in some cases all the other models. Nevertheless, an assembly assistance system meant to support factory workers needs to embed multiple models to exhibit valuable predictive capabilities.

The diversity of endogenous viral elements in insects

We provide an overview of the currently known diversity of viral sequences integrated into insect genomes. Such endogenous viral elements (EVE) have so far been annotated in at least eight insect orders and can be assigned to at least three families of large double-stranded (ds) DNA viruses, at least 22 families of RNA viruses, and three families of single-stranded DNA viruses. The study of these EVE has already produced important insights into insect-virus interactions, including the discovery of a new form of adaptive antiviral immunity. Insect EVE diversity will continue to increase as new insect genomes and exogenous viruses are sequenced, which will continue to make paleovirology a vibrant research field in this group of animals in the years to come.

Virus-like insertions with sequence signatures similar to those of endogenous nonretroviral RNA viruses in the human genome

Understanding the genetics and taxonomy of ancient viruses will give us great insights into not only the origin and evolution of viruses but also how viral infections played roles in our evolution. Endogenous viruses are remnants of ancient viral infections and are thought to retain the genetic characteristics of viruses from ancient times. In this study, we used machine learning of endogenous RNA virus sequence signatures to identify viruses in the human genome that have not been detected or are already extinct. Here, we show that the k-mer occurrence of ancient RNA viral sequences remains similar to that of extant RNA viral sequences and can be differentiated from that of other human genome sequences. Furthermore, using this characteristic, we screened RNA viral insertions in the human reference genome and found virus-like insertions with phylogenetic and evolutionary features indicative of an exogenous origin but lacking homology to previously identified sequences. Our analysis indicates that animal genomes still contain unknown virus-derived sequences and provides a glimpse into the diversity of the ancient virosphere.

Deep Roots and Splendid Boughs of the Global Plant Virome

Land plants host a vast and diverse virome that is dominated by RNA viruses, with major additional contributions from reverse-transcribing and single-stranded (ss) DNA viruses. Here, we introduce the recently adopted comprehensive taxonomy of viruses based on phylogenomic analyses, as applied to the plant virome. We further trace the evolutionary ancestry of distinct plant virus lineages to primordial genetic mobile elements. We discuss the growing evidence of the pivotal role of horizontal virus transfer from invertebrates to plants during the terrestrialization of these organisms, which was enabled by the evolution of close ecological associations between these diverse organisms. It is our hope that the emerging big picture of the formation and global architecture of the plant virome will be of broad interest to plant biologists and virologists alike and will stimulate ever deeper inquiry into the fascinating field of virus-plant coevolution.