A Shotgun Proteomic Platform for a Global Mapping of Lymphoblastoid Cells to Gain Insight into Nasu-Hakola Disease

Nasu-Hakola Disease (NHD) is a recessively inherited systemic leukodystrophy disorder characterized by a combination of frontotemporal presenile dementia and lytic bone lesions. NHD is known to be genetically related to a structural defect of TREM2 and DAP12, two genes that encode for different subunits of the membrane receptor signaling complex expressed by microglia and osteoclast cells. Because of its rarity, molecular or proteomic studies on this disorder are absent or scarce, only case reports based on neuropsychological and genetic tests being reported. In light of this, the aim of this paper is to provide evidence on the potential of a label-free proteomic platform based on the Multidimensional Protein Identification Technology (MudPIT), combined with in-house software and on-line bioinformatics tools, to characterize the protein expression trends and the most involved pathways in NHD. The application of this approach on the Lymphoblastoid cells from a family composed of individuals affected by NHD, healthy carriers and control subjects allowed for the identification of about 3000 distinct proteins within the three analyzed groups, among which proteins anomalous to each category were identified. Of note, several differentially expressed proteins were associated with neurodegenerative processes. Moreover, the protein networks highlighted some molecular pathways that may be involved in the onset or progression of this rare frontotemporal disorder. Therefore, this fully automated MudPIT platform which allowed, for the first time, the generation of the whole protein profile of Lymphoblastoid cells from Nasu-Hakola subjects, could be a valid approach for the investigation of similar neurodegenerative diseases.

Detecting global influence of transcription factor interactions on gene expression in lymphoblastoid cells using neural network models

Background: Transcription factor(TF) interactions are known to regulate target gene(TG) expression in eukaryotes via TF regulatory modules(TRMs). Such interactions can be formed due to co-localizing TFs binding proximally to each other in the DNA sequence or over long distances between distally binding TFs via chromatin looping. While the former type of interaction has been characterized extensively, long distance TF interactions are still largely understudied. Furthermore, most prior approaches have focused on characterizing physical TF interactions without accounting for their effects on TG expression regulation. Understanding TRM based TG expression regulation could aid in understanding diseases caused by disruptions to these mechanisms. In this paper, we present a novel neural network based TRM detection approach that consists of using multi-omics TF based regulatory mechanism information to generate features for building non-linear multilayer perceptron TG expression prediction models in the GM12878 immortalized lymphoblastoid cells. Results: We estimated main effects of 149 individual TFs and interaction effects of 48 distinct combinations of TFs forming TRMs based on their influence on TG expression. We identified several well-known and discovered multiple previously uncharacterized TF interactions within our detected set of TRMs. We further characterized the pairwise TRMs using long distance chromatin looping and motif co-occurrence data. We found that nearly all the TFs constituting TRMs detected by our approach interacted via chromatin looping, and that these TFs further interacted with promoters to influence TG expression through one of four possible regulatory configurations. Conclusion: Here, we have provided a framework for detecting TRMs using neural network models containing multi-omics TF based regulatory features. We have also described these TRMs based on their regulatory potential along with presenting evidence for the possibility of TF interactions forming the TRMs occurring via chromatin looping.

Detecting global influence of transcription factor interactions on gene expression in lymphoblastoid cells using neural network models

Background Transcription factor(TF) interactions are known to regulate target gene(TG) expression in eukaryotes via TF regulatory modules(TRMs). Such interactions can be formed due to co-localizing TFs binding proximally to each other in the DNA sequence or over long distances between distally binding TFs via chromatin looping. While the former type of interaction has been characterized extensively, long distance TF interactions are still largely understudied. Furthermore, most prior approaches have focused on characterizing physical TF interactions without accounting for their effects on TG expression regulation. Understanding TRM based TG expression regulation could aid in understanding diseases caused by disruptions to these mechanisms. In this paper, we present a novel neural network based TRM detection approach that consists of using multi-omics TF based regulatory mechanism information to generate features for building non-linear multilayer perceptron TG expression prediction models in the GM12878 immortalized lymphoblastoid cells.Results We estimated main effects of 149 individual TFs and interaction effects of 48 distinct combinations of TFs forming TRMs based on their influence on TG expression. We identified several well-known and discovered multiple previously uncharacterized TF interactions within our detected set of TRMs. We further characterized the pairwise TRMs using long distance chromatin looping and motif co-occurrence data. We found that nearly all the TFs constituting TRMs detected by our approach interacted via chromatin looping, and that these TFs further interacted with promoters to influence TG expression through one of four possible regulatory configurations. Conclusion Here, we have provided a framework for detecting TRMs using neural network models containing multi-omics TF based regulatory features. We have also described these TRMs based on their regulatory potential along with presenting evidence for the possibility of TF interactions forming the TRMs occurring via chromatin looping.

Epstein-Barr Virus Episome Physically Interacts with Active Regions of the Host Genome in Lymphoblastoid Cells

ABSTRACT The Epstein-Barr virus (EBV) episome is known to interact with the three-dimensional structure of the human genome in infected cells. However, the exact locations of these interactions and their potential functional consequences remain unclear. Recently, high-resolution chromatin conformation capture (Hi-C) assays in lymphoblastoid cells have become available, enabling us to precisely map the contacts between the EBV episome(s) and the human host genome. Using available Hi-C data at a 10-kb resolution, we have identified 15,000 reproducible contacts between EBV episome(s) and the human genome. These contacts are highly enriched in chromatin regions denoted by typical or super enhancers and active markers, including histone H3K27ac and H3K4me1. Additionally, these contacts are highly enriched at loci bound by host transcription factors that regulate B cell growth (e.g., IKZF1 and RUNX3), factors that enhance cell proliferation (e.g., HDGF), or factors that promote viral replication (e.g., NBS1 and NFIC). EBV contacts show nearly 2-fold enrichment in host regions bound by EBV nuclear antigen 2 (EBNA2) and EBNA3 transcription factors. Circular chromosome conformation capture followed by sequencing (4C-seq) using the EBV origin of plasmid replication (oriP) as a “bait” in lymphoblastoid cells further confirmed contacts with active chromatin regions. Collectively, our analysis supports interactions between EBV episome(s) and active regions of the human genome in lymphoblastoid cells. IMPORTANCE EBV is associated with ∼200,000 cancers each year. In vitro, EBV can transform primary human B lymphocytes into immortalized cell lines. EBV-encoded proteins, along with noncoding RNAs and microRNAs, hijack cellular proteins and pathways to control cell growth. EBV nuclear proteins usurp normal transcriptional programs to activate the expression of key oncogenes, including MYC, to provide a proliferation signal. EBV nuclear antigens also repress CDKN2A to suppress senescence. EBV membrane protein activates NF-κB to provide survival signals. EBV genomes are maintained by EBNA1, which tethers EBV episomes to the host chromosomes during mitosis. However, little is known about where EBV episomes are located in interphase cells. In interphase cells, EBV promoters drive the expression of latency genes, while oriP functions as an enhancer for these promoters. In this study, integrative analyses of published lymphoblastoid cell line (LCL) Hi-C data and our 4C-seq experiments position EBV episomes to host genomes with active epigenetic marks. These contact points were significantly enriched for super enhancers. The close proximity of EBV episomes and the super enhancers that are enriched for transcription cofactors or mediators in lymphoblasts may benefit EBV gene expression, suggesting a novel mechanism of transcriptional activation.