Differing coronavirus genres alter shared host signaling pathways upon viral infection.

Coronaviruses are important viral pathogens across a range of animal species including humans. They have a high potential for cross-species transmission as evidenced by the emergence of COVID-19 and may be the origin of future pandemics. There is therefore an urgent need to study coronaviruses in depth and to identify new therapeutic targets. This study shows that distant coronaviruses such as Alpha-, Beta-, and Deltacoronaviruses can share common host immune associated pathways and genes. Differentially expressed genes (DEGs) in the transcription profile of epithelial cell lines infected with swine acute diarrhea syndrome, severe acute respiratory syndrome coronavirus 2, or porcine deltacoronavirus, showed that DEGs within 10 common immune associated pathways were upregulated upon infection. Twenty Three pathways and 21 DEGs across 10 immune response associated pathways were shared by these viruses. These 21 DEGs can serve as focused targets for therapeutics against newly emerging coronaviruses. We were able to show that even though there is a positive correlation between PDCoV and SARS-CoV-2 infections, these viruses could be using different strategies for efficient replication in their cells from their natural hosts. To the best of our knowledge, this is the first report of comparative host transcriptome analysis across distant coronavirus genres.

Comparative Transcriptome Profiling of Human and Pig Intestinal Epithelial Cells after Porcine Deltacoronavirus Infection.

Porcine deltacoronavirus (PDCoV) is an emerging infectious disease of swine with zoonotic potential. Phylogenetic analysis suggests that PDCoV originated recently from a host-switching event between birds and mammals. Little is known about how PDCoV interacts with its differing hosts. Human-derived cell lines are susceptible to PDCoV infection. Herein, we compare the gene expression profiles of an established host swine cells to potential emerging host human cells after infection with PDCoV. Cell lines derived from intestinal lineages were used to reproduce the primary sites of viral infection in the host. Porcine intestinal epithelial cells (IPEC-J2) and human intestinal epithelial cells (HIEC) were infected with PDCoV. RNA-sequencing was performed on total RNA extracted from infected cells. Human cells exhibited a more pronounced response to PDCoV infection in comparison to porcine cells with more differentially expressed genes (DEGs) in human, 7486, in comparison to pig cells, 1134. On the transcriptional level, the adoptive host human cells exhibited more DEGs in response to PDCoV infection in comparison to the primary pig host cells, where different types of cytokines can control PDCoV replication and virus production. Key immune-associated DEGs and signaling pathways are shared between human and pig cells during PDCoV infection. These included genes related to the NF-kappa-B transcription factor family, the interferon (IFN) family, the protein-kinase family, and signaling pathways such as the apoptosis signaling pathway, JAK-STAT signaling pathway, inflammation/cytokine-cytokine receptor signaling pathway. MAP4K4 was unique in up-regulated DEGs in humans in the apoptosis signaling pathway. While similarities exist between human and pig cells in many pathways, our research suggests that the adaptation of PDCoV to the porcine host required the ability to down-regulate many response pathways including the interferon pathway. Our findings provide an important foundation that contributes to an understanding of the mechanisms of PDCoV infection across different hosts. To our knowledge, this is the first report of transcriptome analysis of human cells infected by PDCoV.

Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties.

Understanding complexity in physical, biological, social and information systems is predicated on describing interactions amongst different components. Advances in genomics are facilitating the high-throughput identification of molecular interactions, and graphs are emerging as indispensable tools in explaining how the connections in the network drive organismal phenotypic plasticity. Here, we describe the architectural organization and associated emergent topological properties of gene regulatory networks (GRNs) that describe protein-DNA interactions (PDIs) in several model eukaryotes. By analyzing GRN connectivity, our results show that the anticipated scale-free network architectures are characterized by organism-specific power law scaling exponents. These exponents are independent of the fraction of the GRN experimentally sampled, enabling prediction of properties of the complete GRN for an organism. We further demonstrate that the exponents describe inequalities in transcription factor (TF)-target gene recognition across GRNs. These observations have the important biological implication that they predict the existence of an intrinsic organism-specific trans and/or cis regulatory landscape that constrains GRN topologies. Consequently, architectural GRN organization drives not only phenotypic plasticity within a species, but is also likely implicated in species-specific phenotype.

Design of Knowledge Bases for Plant Gene Regulatory Networks.

Developing a knowledge base that contains all the information necessary for the researcher studying gene regulation in a particular organism can be accomplished in four stages. This begins with defining the data scope. We describe here the necessary information and resources, and outline the methods for obtaining data. The second stage consists of designing the schema, which involves defining the entire arrangement of the database in a systematic plan. The third stage is the implementation, defined by actualization of the database by using software according to a predefined schema. The final stage is development, where the database is made available to users in a web-accessible system. The result is a knowledgebase that integrates all the information pertaining to gene regulation, and which is easily expandable and transferable.

A Maize Gene Regulatory Network for Phenolic Metabolism.

The translation of the genotype into phenotype, represented for example by the expression of genes encoding enzymes required for the biosynthesis of phytochemicals that are important for interaction of plants with the environment, is largely carried out by transcription factors (TFs) that recognize specific cis-regulatory elements in the genes that they control. TFs and their target genes are organized in gene regulatory networks (GRNs), and thus uncovering GRN architecture presents an important biological challenge necessary to explain gene regulation. Linking TFs to the genes they control, central to understanding GRNs, can be carried out using gene- or TF-centered approaches. In this study, we employed a gene-centered approach utilizing the yeast one-hybrid assay to generate a network of protein-DNA interactions that participate in the transcriptional control of genes involved in the biosynthesis of maize phenolic compounds including general phenylpropanoids, lignins, and flavonoids. We identified 1100 protein-DNA interactions involving 54 phenolic gene promoters and 568 TFs. A set of 11 TFs recognized 10 or more promoters, suggesting a role in coordinating pathway gene expression. The integration of the gene-centered network with information derived from TF-centered approaches provides a foundation for a phenolics GRN characterized by interlaced feed-forward loops that link developmental regulators with biosynthetic genes.

Important biological information uncovered in previously unaligned reads from chromatin immunoprecipitation experiments (ChIP-Seq).

Establishing the architecture of gene regulatory networks (GRNs) relies on chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) methods that provide genome-wide transcription factor binding sites (TFBSs). ChIP-Seq furnishes millions of short reads that, after alignment, describe the genome-wide binding sites of a particular TF. However, in all organisms investigated an average of 40% of reads fail to align to the corresponding genome, with some datasets having as much as 80% of reads failing to align. We describe here the provenance of previously unaligned reads in ChIP-Seq experiments from animals and plants. We show that a substantial portion corresponds to sequences of bacterial and metazoan origin, irrespective of the ChIP-Seq chromatin source. Unforeseen was the finding that 30%-40% of unaligned reads were actually alignable. To validate these observations, we investigated the characteristics of the previously unaligned reads corresponding to TAL1, a human TF involved in lineage specification of hemopoietic cells. We show that, while unmapped ChIP-Seq read datasets contain foreign DNA sequences, additional TFBSs can be identified from the previously unaligned ChIP-Seq reads. Our results indicate that the re-evaluation of previously unaligned reads from ChIP-Seq experiments will significantly contribute to TF target identification and determination of emerging properties of GRNs.