Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification.

Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score.

Respiratory syncytial virus infection changes the piwi-interacting RNA content of airway epithelial cells.

Piwi-interacting RNAs (piRNAs) are small non-coding RNAs (sncRNAs) of about 26-32 nucleotides in length and represent the largest class of sncRNA molecules expressed in animal cells. piRNAs have been shown to play a crucial role to safeguard the genome, maintaining genome complexity and integrity, as they suppress the insertional mutations caused by transposable elements. However, there is growing evidence for the role of piRNAs in controlling gene expression in somatic cells as well. Little is known about changes in piRNA expression and possible function occurring in response to viral infections. In this study, we investigated the piRNA expression profile, using a human piRNA microarray, in human small airway epithelial (SAE) cells infected with respiratory syncytial virus (RSV), a leading cause of acute respiratory tract infections in children. We found a time-dependent increase in piRNAs differentially expressed in RSV-infected SAE cells. We validated the top piRNAs upregulated and downregulated at 24 h post-infection by RT-qPCR and identified potential targets. We then used Gene Ontology (GO) tool to predict the biological processes of the predicted targets of the most represented piRNAs in infected cells over the time course of RSV infection. We found that the most significant groups of targets of regulated piRNAs are related to cytoskeletal or Golgi organization and nucleic acid/nucleotide binding at 15 and 24 h p.i. To identify common patterns of time-dependent responses to infection, we clustered the significantly regulated expression profiles. Each of the clusters of temporal profiles have a distinct set of potential targets of the piRNAs in the cluster Understanding changes in piRNA expression in RSV-infected airway epithelial cells will increase our knowledge of the piRNA role in viral infection and might identify novel therapeutic targets for viral lung-mediated diseases.

Cigarette Smoke Condensate Exposure Changes RNA Content of Extracellular Vesicles Released from Small Airway Epithelial Cells.

Exposure to environmental tobacco smoke (ETS) is a known risk factor for the development of chronic lung diseases, cancer, and the exacerbation of viral infections. Extracellular vesicles (EVs) have been identified as novel mediators of cell-cell communication through the release of biological content. Few studies have investigated the composition/function of EVs derived from human airway epithelial cells (AECs) exposed to cigarette smoke condensate (CSC), as surrogates for ETS. Using novel high-throughput technologies, we identified a diverse range of small noncoding RNAs (sncRNAs), including microRNA (miRNAs), Piwi-interacting RNA (piRNAs), and transfer RNA (tRNAs) in EVs from control and CSC-treated SAE cells. CSC treatment resulted in significant changes in the EV content of miRNAs. A total of 289 miRNAs were identified, with five being significantly upregulated and three downregulated in CSC EVs. A total of 62 piRNAs were also detected in our EV preparations, with five significantly downregulated and two upregulated in CSC EVs. We used TargetScan and Gene Ontology (GO) analysis to predict the biological targets of hsa-miR-3913-5p, the most represented miRNA in CSC EVs. Understanding fingerprint molecules in EVs will increase our knowledge of the relationship between ETS exposure and lung disease, and might identify potential molecular targets for future treatments.

Maximum parsimony interpretation of chromatin capture experiments.

We present a new approach to characterizing the global geometric state of chromatin from HiC data. Chromatin conformation capture techniques (3C, and its variants: 4C, 5C, HiC, etc.) probe the spatial structure of the genome by identifying physical contacts between genomic loci within the nuclear space. In whole-genome conformation capture (HiC) experiments, the signal can be interpreted as spatial proximity between genomic loci and physical distances can be estimated from the data. However, observed spatial proximity signal does not directly translate into persistent contacts within the nuclear space. Attempts to infer a single conformation of the genome within the nuclear space lead to internal geometric inconsistencies, notoriously violating the triangle inequality. These inconsistencies have been attributed to the stochastic nature of chromatin conformation or to experimental artifacts. Here we demonstrate that it can be explained by a mixture of cells, each in one of only several conformational states, contained in the sample. We have developed and implemented a graph-theoretic approach that identifies the properties of such postulated subpopulations. We show that the geometrical conflicts in a standard yeast HiC dataset, can be explained by only a small number of homogeneous populations of cells (4 populations are sufficient to reconcile 95,000 most prominent impossible triangles, 8 populations can explain 375,000 top geometric conflicts). Finally, we analyze the functional annotations of genes differentially interacting between the populations, suggesting that each inferred subpopulation may be involved in a functionally different transcriptional program.

Respiratory Syncytial Virus Infection Changes Cargo Composition of Exosome Released from Airway Epithelial Cells.

Exosomes are microvesicles known to carry biologically active molecules, including RNA, DNA and proteins. Viral infections can induce profound changes in exosome composition, and exosomes have been implicated in viral transmission and pathogenesis. No information is current available regarding exosome composition and function during infection with Respiratory Syncytial Virus (RSV), the most important cause of lower respiratory tract infections in children. In this study, we characterized exosomes released from RSV-infected lung carcinoma-derived A549 cells. RNA deep sequencing revealed that RSV exosomes contain a diverse range of RNA species like messenger and ribosomal RNA fragments, as well as small noncoding RNAs, in a proportion different from exosomes isolated from mock-infected cells. We observed that both RNA and protein signatures of RSV were present in exosomes, however, they were not able to establish productive infection in uninfected cells. Exosomes isolated from RSV-infected cells were able to activate innate immune response by inducing cytokine and chemokine release from human monocytes and airway epithelial cells. These data suggest that exosomes may play an important role in pathogenesis or protection against disease, therefore understating their role in RSV infection may open new avenues for target identification and development of novel therapeutics.

G-Quadruplexes Involving Both Strands of Genomic DNA Are Highly Abundant and Colocalize with Functional Sites in the Human Genome.

The G-quadruplex is a non-canonical DNA structure biologically significant in DNA replication, transcription and telomere stability. To date, only G4s with all guanines originating from the same strand of DNA have been considered in the context of the human nuclear genome. Here, I discuss interstrand topological configurations of G-quadruplex DNA, consisting of guanines from both strands of genomic DNA; an algorithm is presented for predicting such structures. I have identified over 550,000 non-overlapping interstrand G-quadruplex forming sequences in the human genome--significantly more than intrastrand configurations. Functional analysis of interstrand G-quadruplex sites shows strong association with transcription initiation, the results are consistent with the XPB and XPD transcriptional helicases binding only to G-quadruplex DNA with interstrand topology. Interstrand quadruplexes are also enriched in origin of replication sites. Several topology classes of interstrand quadruplex-forming sequences are possible, and different topologies are enriched in different types of structural elements. The list of interstrand quadruplex forming sequences, and the computer program used for their prediction are available at the web address http://moment.utmb.edu/allquads.

Inferring genome-wide functional modulatory network: a case study on NF-κB/RelA transcription factor.

How different pathways lead to the activation of a specific transcription factor (TF) with specific effects is not fully understood. We model context-specific transcriptional regulation as a modulatory network: triplets composed of a TF, target gene, and modulator. Modulators usually affect the activity of a specific TF at the posttranscriptional level in a target gene-specific action mode. This action may be classified as enhancement, attenuation, or inversion of either activation or inhibition. As a case study, we inferred, from a large collection of expression profiles, all potential modulations of NF-κB/RelA. The predicted modulators include many proteins previously not reported as physically binding to RelA but with relevant functions, such as RNA processing, cell cycle, mitochondrion, ubiquitin-dependent proteolysis, and chromatin modification. Modulators from different processes exert specific prevalent action modes on distinct pathways. Modulators from noncoding RNA, RNA-binding proteins, TFs, and kinases modulate the NF-κB/RelA activity with specific action modes consistent with their molecular functions and modulation level. The modulatory networks of NF-κB/RelA in the context epithelial-mesenchymal transition (EMT) and burn injury have different modulators, including those involved in extracellular matrix (FBN1), cytoskeletal regulation (ACTN1), and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a long intergenic nonprotein coding RNA, and tumor suppression (FOXP1) for EMT, and TXNIP, GAPDH, PKM2, IFIT5, LDHA, NID1, and TPP1 for burn injury.

Correcting positional correlations in Affymetrix® genome chips.

We report and model a previously undescribed systematic error causing spurious excess correlations that depend on the distance between probes on Affymetrix® microarrays. The phenomenon affects pairs of features with large chip separations, up to over 100 probes apart. The effect may have a significant impact on analysis of correlations in large collections of expression data, where the systematic experimental errors are repeated in many data sets. Examples of such studies include analysis of functions and interactions in groups of genes, as well as global properties of genomes. We find that the average correlations between probes on Affymetrix microarrays are larger for smaller chip distances, which points out to a previously undescribed positional artifact. The magnitude of the artifact depends on the design of the chip, and we find it to be especially high for the yeast S98 microarray, where spurious excess correlations reach 0.1 at a distance of 50 probes. We have designed an algorithm to correct this bias and provide new data sets with the corrected expression values. This algorithm was successfully implemented to remove the positional artifact from the S98 chip data while preserving the integrity of the data.

The 3D organization of the yeast genome correlates with co-expression and reflects functional relations between genes.

The spatial organization of eukaryotic genomes is thought to play an important role in regulating gene expression. The recent advances in experimental methods including chromatin capture techniques, as well as the large amounts of accumulated gene expression data allow studying the relationship between spatial organization of the genome and co-expression of protein-coding genes. To analyse this genome-wide relationship at a single gene resolution, we combined the interchromosomal DNA contacts in the yeast genome measured by Duan et al. with a comprehensive collection of 1,496 gene expression datasets. We find significant enhancement of co-expression among genes with contact links. The co-expression is most prominent when two gene loci fall within 1,000 base pairs from the observed contact. We also demonstrate an enrichment of inter-chromosomal links between functionally related genes, which suggests that the non random nature of the genome organization serves to facilitate coordinated transcription in groups of genes.