Multi-agent model of hepatitis C virus infection

Objectives: The objective of this study is to design a method for modeling hepatitis C virus (HCV) infection using multi-agent simulation and to verify it in practice. Methods and materials: In this paper, first, the modeling of HCV infection using a multi-agent system is compared with the most commonly used model type, which is based on differential equations. Then, the implementation and results of the model using a multi-agent simulation is presented. To find the values of the parameters used in the model, a method using inverted simulation flow and genetic algorithm is proposed. All of the data regarding HCV infection are taken from the paper describing the model based on the differential equation to which the proposed method is compared. Results: Important advantages of the proposed method are noted and demonstrated; these include flexibility, clarity, re-usability and the possibility to model more complex dependencies. Then, the simulation framework that uses the proposed approach is successfully implemented in C++ and is verified by comparing it to the approach based on differential equations. The verification proves that an objective function that performs the best is the function that minimizes the maximal differences in the data. Finally, an analysis of one of the already known models is performed, and it is proved that it incorrectly models a decay in the hepatocytes number by 40%. Conclusions: The proposed method has many advantages in comparison to the currently used model types and can be used successfully for analyzing HCV infection. With almost no modifications, it can also be used for other types of viral infections

2D-PAGE as an effective method of RNA degradome analysis

The continuously growing interest in small regulatory RNA exploration is one of the important factors that have inspired the recent development of new high throughput techniques such as DNA microarrays or next generation sequencing. Each of these methods offers some significant advantages but at the same time each of them is expensive, laborious and challenging especially in terms of data analysis. Therefore, there is still a need to develop new analytical methods enabling the fast, simple and cost-effective examination of the complex RNA mixtures. Recently, increasing attention has been focused on the RNA degradome as a potential source of riboregulators. Accordingly, we attempted to employ a two-dimensional gel electrophoresis as a quick and uncomplicated method of profiling RNA degradome in plant or human cells. This technique has been successfully used in proteome analysis. However, its application in nucleic acids studies has been very limited. Here we demonstrate that two dimensional electrophoresis is a technique which allows one to quickly and cost-effectively identify and compare the profiles of 10–90 nucleotide long RNA accumulation in various cells and organs

Hepatitis C virus quasispecies in chronically infected children subjected to interferon–ribavirin therapy

Accumulating evidence suggests that certain features of hepatitis C virus (HCV), especially its high genetic variability, might be responsible for the low efficiency of anti-HCV treatment. Here, we present a bioinformatic analysis of HCV-1a populations isolated from 23 children with chronic hepatitis C (CHC) subjected to interferon–ribavirin therapy. The structures of the viral quasispecies were established based on a 132-amino-acid sequence derived from E1/E2 protein, including hypervariable region 1 (HVR1). Two types of HCV populations were identified. The first type, found in non-responders, contained a small number of closely related variants. The second type, characteristic for sustained responders, was composed of a large number of distantly associated equal-rank variants. Comparison of 445 HVR1 sequences showed that a significant number of variants present in non-responding patients are closely related, suggesting that certain, still unidentified properties of the pathogen may be key factors determining the result of CHC treatment

Computational prediction of non-enzymatic RNA degradation patterns

Since the beginning of the 21st century, an increasing interest in the research of ribonucleic acids has been observed in response to a surprising discovery of the role that RNA molecules play in the biological systems. It was demonstrated that they do not only take part in the protein synthesis (mRNA, rRNA, tRNA) but also are involved in the regulation of gene expression. Several classes of small regulatory RNAs have been discovered (e.g. microRNA, small interfering RNA, piwiRNA). Most of them are excised from specific double-stranded RNA precursors by enzymes that belong to the RNaseIII family (Drosha, Dicer or Dicer-like proteins). More recently, it has been shown that small regulatory RNAs are also generated as stable intermediates of RNA degradation (the so called RNA fragments originating from tRNA, snRNA, snoRNA etc.). Unfortunately, the mechanisms underlying biogenesis of the RNA fragments remain unclear. It is thought that several factors may be involved in the formation of the RNA fragments. The most important are the specific RNases, RNA-protein interactions and RNA structure. In this work, we focus on the RNA primary and secondary structures as factors influencing the RNA stability and consequently the pattern of RNA fragmentation. Earlier, we identified the major structural factors affecting non-enzymatic RNA degradation. Now, based on these data, we developed a new branch-and-cut algorithm that is able to predict the products of large RNA molecules' hydrolysis in vitro. We also present the experimental data that verify the results generated using this algorithm

How to explore what is hidden? A review of techniques for vascular tissue expression profile analysis

Abstract The evolution of plants to efficiently transport water and assimilates over long distances is a major evolutionary success that facilitated their growth and colonization of land. Vascular tissues, namely xylem and phloem, are characterized by high specialization, cell heterogeneity, and diverse cell components. During differentiation and maturation, these tissues undergo an irreversible sequence of events, leading to complete protoplast degradation in xylem or partial degradation in phloem, enabling their undisturbed conductive function. Due to the unique nature of vascular tissue, and the poorly understood processes involved in xylem and phloem development, studying the molecular basis of tissue differentiation is challenging. In this review, we focus on methods crucial for gene expression research in conductive tissues, emphasizing the importance of initial anatomical analysis and appropriate material selection. We trace the expansion of molecular techniques in vascular gene expression studies and discuss the application of single-cell RNA sequencing, a high-throughput technique that has revolutionized transcriptomic analysis. We explore how single-cell RNA sequencing will enhance our knowledge of gene expression in conductive tissues

Towards Prediction of HCV Therapy Efficiency

We investigate a correlation between genetic diversity of hepatitis C virus population and the level of viral RNA accumulation in patient blood. Genetic diversity is defined as the mean Hamming distance between all pairs of virus RNA sequences representing the population. We have found that a low Hamming distance (i.e. low genetic diversity) correlates with a high RNA level; symmetrically, high diversity corresponds to a low RNA level. We contend that the obtained correlation strength justifies the use of the viral RNA level as a measure enabling prediction of efficiency of an established therapy. We also propose that patient qualification for therapy, based on viral RNA level, improves its efficiency

RNA-Seq-based analysis of differential gene expression associated with hepatitis C virus infection in a cell culture

Hepatitis C virus (HCV) infection is one of the major causes of chronic liver diseases. Unfortunately, the mechanisms of HCV infection-induced liver injury and host-virus interactions are still not well recognized. To better understand these processes we determined the changes in the host gene expression that occur during HCV infection of Huh-7.5 cells. As a result, we identified genes that may contribute to the immune and metabolic cellular responses to infection. Pathway enrichment analysis indicated that HCV induced an increased expression of genes involved in mitogen-activated protein kinases signaling, adipocytokine signaling, cell cycle and nitrogen metabolism. In addition, the enrichment analyses of processes and molecular functions revealed that the up-regulated genes were mainly implicated in the negative regulation of phosphorylation. Construction of the pathway-gene-process network enabled exploration of a much more complex landscape of molecular interactions. Consequently, several essential processes altered by HCV infection were identified: negative regulation of cell cycle, response to endoplasmic reticulum stress, response to reactive oxygen species, toll-like receptor signaling and pattern recognition receptor signaling. The analyses of genes whose expression was decreased upon HCV infection showed that the latter were engaged in the metabolism of lipids and amino acids. Moreover, we observed disturbance in the cellular antiviral defense. Altogether, our results demonstrated that HCV infection elicits host response that includes a very wide range of cellular mechanisms. Our findings significantly broaden the understanding of complex processes that accompany HCV infection. Consequently, they may be used for developing new host-oriented therapeutic strategies

Small RNA fragments derived from multiple RNA classes – the missing element of multi-omics characteristics of the hepatitis C virus cell culture model

Abstract Background A pool of small RNA fragments (RFs) derived from diverse cellular RNAs has recently emerged as a rich source of functionally relevant molecules. Although their formation and accumulation has been connected to various stress conditions, the knowledge on RFs produced upon viral infections is very limited. Here, we applied the next generation sequencing (NGS) to characterize RFs generated in the hepatitis C virus (HCV) cell culture model (HCV-permissive Huh-7.5 cell line). Results We found that both infected and non-infected cells contained a wide spectrum of RFs derived from virtually all RNA classes. A significant fraction of identified RFs accumulated to similar levels as miRNAs. Our analysis, focused on RFs originating from constitutively expressed non-coding RNAs, revealed three major patterns of parental RNA cleavage. We found that HCV infection induced significant changes in the accumulation of low copy number RFs, while subtly altered the levels of high copy number ones. Finally, the candidate RFs potentially relevant for host-virus interactions were identified. Conclusions Our results indicate that RFs should be considered an important component of the Huh-7.5 transcriptome and suggest that the main factors influencing the RF biogenesis are the RNA structure and RNA protection by interacting proteins. The data presented here significantly complement the existing transcriptomic, miRnomic, proteomic and metabolomic characteristics of the HCV cell culture model