Computational analysis on the effects of variations in T and B cells. Primary immunodeficiencies and cancer neoepitopes

Computational approaches are essential to study the effects of inborn and somatic variations. Results from such studies contribute to better diagnosis and therapies. Primary immunodeficiencies (PIDs) are rare inborn defects of key immune response genes. Somatic variations are main drivers of most cancers. Large and diverse data on PID genes and proteins can enable systems biology studies on their dynamic effects on T and B cells. Amino acid substitutions (AASs) are somatic variations that drive cancers. However, AASs also cause cancer-associated antigens that are recognized by lymphocytes as non-self, and are called neoantigens. Detail analysis these neoantigens can be performed due to the availability of cancer data from many consortia.The purpose of this thesis was to investigate the effects of PIDs on T and B cells and to explore features of neoepitopes in cancers. The object of the first study was to detect the central T cell-specific protein network. The purpose of the second and third studies were to reconstruct the T and B cell network model and simulate the dynamic effects of PID perturbations. The aim of the fourth study was to characterize neoepitopes from pan-cancer datasets.The immunome interactome was reconstructed, and the links weighed with gene expression correlation of integrated, time series data (Paper I). The significance of the weighted links were computed with the Global Statistical Significance (GloSS) method, and the weighted interactome network was filtered to obtain the central T cell network. Next, the T cell network model was reconstructed from literature mining and the core T cell protein interaction network (Paper II). The B cell network model was reconstructed by mining the literature for central B cell interactions (Paper III). The normalized HillCube software was used to study the dynamic effects of PID perturbations in T and B cells. Proteome-wide amino AASs on putatively derived 8-, 9-, 10-, and 11-mer neoepitopes in 30 cancer types were analyzed with the NetMHC 4.0 software (Paper IV).The interconnectedness of the major T cell pathways are maintained in the central T cell PPI network. Empirical evidence from Gene Ontology term and essential genes enrichment analyses were in support for the central T cell network. In the T and B cell simulations for several knockout PIDs correspond to previous results. In the T cell model, simulations for TCR, PTPRC, LCK, ZAP70 and ITK indicated profound disruption in network dynamics. BCL10, CARD11, MALT1, NEMO and MAP3K14 simulations showed significant effects. In B cell, the simulations for LYN, BTK, STIM1, ORAI1, CD19, CD21 and CD81 indicated profound changes to many proteins in the network. Severe effects were observed in the BCL10, IKKB, knockout CARD11, MALT1, NEMO, IKKB and WIPF1 simulations. No major effects were observed for constitutively active PID proteins. The most likely epitopes are those which are detected by several macromolecular histocompartibility complexes (MHCs) and of several peptide lengths. 0.17% of all variants yield more than 100 neoepitopes. Amino acid distributions indicate that variants at all positions in neoepitopes of any length are, on average, more hydrophobic compared to the wild-type.The core T cell network approach is general and applicable to any system with adequate data. The T and B cell models enable the understanding of the dynamic effects of PID disease processes and reveals several novel proteins that may be of interest when diagnosing and treating immunological defects. The neoepitope characteristics can be employed for targeted cancer vaccine applications in personalized therapies

Identification of core T cell network based on immunome interactome

Background Data-driven studies on the dynamics of reconstructed protein-protein interaction (PPI) networks facilitate investigation and identification of proteins important for particular processes or diseases and reduces time and costs of experimental verification. Modeling the dynamics of very large PPI networks is computationally costly. Results To circumvent this problem, we created a link-weighted human immunome interactome and performed filtering. We reconstructed the immunome interactome and weighed the links using jackknife gene expression correlation of integrated, time course gene expression data. Statistical significance of the links was computed using the Global Statistical Significance (GloSS) filtering algorithm. P-values from GloSS were computed for the integrated, time course gene expression data. We filtered the immunome interactome to identify core components of the T cell PPI network (TPPIN). The interconnectedness of the major pathways for T cell survival and response, including the T cell receptor, MAPK and JAK-STAT pathways, are maintained in the TPPIN network. The obtained TPPIN network is supported both by Gene Ontology term enrichment analysis along with study of essential genes enrichment. Conclusions By integrating gene expression data to the immunome interactome and using a weighted network filtering method, we identified the T cell PPI immune response network. This network reveals the most central and crucial network in T cells. The approach is general and applicable to any dataset that contains sufficient information.BioMed Central open acces

Specific autoantibody profiles and disease subgroups correlate with circulating micro-RNA in systemic sclerosis.

To evaluate the expression profiles of cell-free plasma miRNAs in SSc and to characterize their correlation with disease subgroups (lcSSc and dcSSc) and with autoantibody profiles

Transcriptional regulation of bidirectional promoters: role of NF-Y

ABSTRACT BACKGROUND: About 11% of human genes occur in divergent pairs such that both genes are located on opposite strands of DNA, and their immediate promoters are overlapping. The overlapping proximal promoter of both genes form an intergenic region called a bidirectional promoter which is less than 1000 base pairs in length. There is evidence that some cis-regulatory elements in bidirectional promoters control the transcription of both flanking genes. CCAAT boxes are one of the most abundant cis-regulatory elements in the human genome. NF-Y, a heterotrimeric transcription factor, activates CCAAT boxes and requires both the CCAAT box and specific flanking nucleotides for DNA binding. RESULTS: Using sequence analysis approach, data on the incidence of the NF-Y type CCAAT boxes in bidirectional promoters of both human and mouse genomes was used to deduce the functional and biological significance of NF-Y factor in the transcription mechanism of bidirectional gene pairs. In this study, four major findings were made. Firstly, a considerable number of bidirectional promoters consisted of at least an NF-Y type CCAAT box. This shows a critical role of NF-Y in the underlying bidirectional promoter regulation mechanism. Secondly, forward and reverse orientation of NF-Y type CCAAT boxes occurred in similar proportions in both bidirectional and unidirectional promoters, demonstrating NF-Y's ability to bind its recognition sequence in either orientation. Thirdly, a considerable number of NF-Y type CCAAT boxes were found in their functional position in bidirectional promoters, associating NF-Y to the recruitment of the transcription machinery. Lastly, NF-Y type CCAAT boxes were also significantly distributed further upstream to their functional position, suggesting that NF-Y is potentially connected to transactivating bidirectional promoters. CONCLUSION: These results are in support of NF-Y's essential role in the general and activated transcriptional regulation of bidirectional gene pairs. This work provides an important contribution in understanding the regulatory mechanism of bidirectional promoters and, in turn, their subsequent biomedical applications. Asiasanat:divergent genes, bidirectional promoters, NF-Y, CCAAT box, bidirectional gene pair, head-to-head genes, transcription, regulatio

Transcriptional regulation of bidirectional promoters: role of NF-Y

ABSTRACT BACKGROUND: About 11% of human genes occur in divergent pairs such that both genes are located on opposite strands of DNA, and their immediate promoters are overlapping. The overlapping proximal promoter of both genes form an intergenic region called a bidirectional promoter which is less than 1000 base pairs in length. There is evidence that some cis-regulatory elements in bidirectional promoters control the transcription of both flanking genes. CCAAT boxes are one of the most abundant cis-regulatory elements in the human genome. NF-Y, a heterotrimeric transcription factor, activates CCAAT boxes and requires both the CCAAT box and specific flanking nucleotides for DNA binding. RESULTS: Using sequence analysis approach, data on the incidence of the NF-Y type CCAAT boxes in bidirectional promoters of both human and mouse genomes was used to deduce the functional and biological significance of NF-Y factor in the transcription mechanism of bidirectional gene pairs. In this study, four major findings were made. Firstly, a considerable number of bidirectional promoters consisted of at least an NF-Y type CCAAT box. This shows a critical role of NF-Y in the underlying bidirectional promoter regulation mechanism. Secondly, forward and reverse orientation of NF-Y type CCAAT boxes occurred in similar proportions in both bidirectional and unidirectional promoters, demonstrating NF-Y's ability to bind its recognition sequence in either orientation. Thirdly, a considerable number of NF-Y type CCAAT boxes were found in their functional position in bidirectional promoters, associating NF-Y to the recruitment of the transcription machinery. Lastly, NF-Y type CCAAT boxes were also significantly distributed further upstream to their functional position, suggesting that NF-Y is potentially connected to transactivating bidirectional promoters. CONCLUSION: These results are in support of NF-Y's essential role in the general and activated transcriptional regulation of bidirectional gene pairs. This work provides an important contribution in understanding the regulatory mechanism of bidirectional promoters and, in turn, their subsequent biomedical applications. Asiasanat:divergent genes, bidirectional promoters, NF-Y, CCAAT box, bidirectional gene pair, head-to-head genes, transcription, regulatio

Simulation of the dynamics of primary immunodeficiencies in B cells

Primary immunodeficiencies (PIDs) are a group of over 300 hereditary, heterogeneous, and mainly rare disorders that affect the immune system. Various aspects of immune system and PID proteins and genes have been investigated and facilitate systems biological studies of effects of PIDs on B cell physiology and response. We reconstructed a B cell network model based on data for the core B cell receptor activation and response processes and performed semi-quantitative dynamic simulations for normal and B cell PID failure modes. The results for several knockout simulations correspond to previously reported molecular studies and reveal novel mechanisms for PIDs. The simulations for CD21, CD40, LYN, MS4A1, ORAI1, PLCG2, PTPRC, and STIM1 indicated profound changes to major transcription factor signaling and to the network. Significant effects were observed also in the BCL10, BLNK, BTK, loss-of-function CARD11, IKKB, MALT1, and NEMO, simulations whereas only minor effects were detected for PIDs that are caused by constitutively active proteins (PI3K, gain-of-function CARD11, KRAS, and NFKBIA). This study revealed the underlying dynamics of PID diseases, confirms previous observations, and identifies novel candidates for PID diagnostics and therapy

Tuned parameters of nodes in the Odefy-simulated T cell network model.

<p>Tuned parameters of nodes in the Odefy-simulated T cell network model.</p

Boolean model transformed into its underlying interaction graph.

<p>The network consists of nodes and links derived from the Boolean network model without the AND operator. The interaction graph consists of 85 nodes and 146 links, and represents the underlying interaction network of the model. The nodes are as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176500#pone.0176500.g001" target="_blank">Fig 1</a>. The network shows the paths through which signals from the receptors are channeled through the network to the TFs, which turn on the response genes.</p