Layers Of Biological Information Research Articles

Integration of Multi-Omics Data Using Probabilistic Graph Models and External Knowledge

Background: High-throughput sequencing technologies have revolutionized the ability to perform systems-level biology and elucidate molecular mechanisms of disease through the comprehensive characterization of different layers of biological information. Integration of these heterogeneous layers can provide insight into the underlying biology but is challenged by modeling complex interactions. Objective: We introduce OBaNK: omics integration using Bayesian networks and external knowledge, an algorithm to model interactions between heterogeneous high-dimensional biological data to elucidate complex functional clusters and emergent relationships associated with an observed phenotype. Method: Using Bayesian network learning, we modeled the statistical dependencies and interactions between lipidomics, proteomics, and metabolomics data. The strength of a learned interaction between molecules was altered based on external knowledge. Results : Networks learned from synthetic datasets based on real pathways achieved an average area under the curve score of ~0.85, an improvement of ~0.23 from baseline methods. When applied to real multi-omics data collected during pregnancy, five distinct functional networks of heterogeneous biological data were identified, and the results were compared to other multi-omics integration approaches. Conclusion: OBaNK successfully improved the accuracy of learning interaction networks from data integrating external knowledge, identified heterogeneous functional networks from real data, and suggested potential novel interactions associated with the phenotype. These findings can guide future hypothesis generation. OBaNK source code is available at: https://github.com/bridgettripp/OBaNK.git, and a graphical user interface is available at: http://otulab.unl.edu/OBaNK.

Rearrangement of incomplete multi-omics datasets combined with ComDim for evaluating replicate cross-platform variability and batch influence

Multi-omics studies can highlight the interrelationships among data across different layers of biological information. However, methods for the unsupervised analysis of multi-block data do not take the individual variability across batches into account and cannot deal with omics datasets when they present different numbers of replicates. We have explored three different data arrangement strategies to tackle these limitations. Several multi-block methods can be used to decipher the common variations across blocks and to determine the contribution of each block to each common component. In this study the ComDim method was used to compare these rearrangement strategies for three multi-omics datasets. We found that arranging the data using the ‘replicate by blocks’ strategy, where each block comprises data from only one replicate independently of its data type, provided the most insightful results. ComDim allowed the evaluation of the variability across the replicate blocks, confirming the existence of batch effects in some of the studies. Moreover, since the contributions of these batch effects were separated from the other contributions, the coordinated biological responses common across the different blocks was characterized for each data type.

Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map.

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

In silicoEvaluation of Substrate Binding Site and Rare Codons in the Structure of CYP152A1

Background:The Cytochromes P450 (CYPs) have an essential role in the oxidation of endogenous and exogenous molecules. The CYPs are identified in all domains of life, but the CYP152A1 from Bacillus subtilis is specially considered for clinical and industrial applications. The molecular cloning of a new type of CYP from Bacillus subtilis was reported, previously. Here, we describe the hidden layer of biological information of the CYP152A1 enzyme, which can help researchers for better understanding of enzyme application. In this study, four rare codons of enzyme, including Arg63, Arg187, Arg276, and Arg338 were identified and evaluated using the bioinformatics web servers.Methods:Through in silico modeling of CYP152A1 via the I-TASSER server, the above-mentioned rare codons were studied in the structure of enzyme that may have an important role in the proper folding of CYP152A1. In the following, the substrate binding site of CYP152A1 was studied by AutoDock Vina, and the heme and palmitic acid were considered as the substrates.Results:The results of docking study elucidated the Arg242 in the active site is closely related to the substrate binding site of CYP152A1, which help us to further clarify the mechanism of the enzyme reaction.Conclusion:Studies of these hidden information’s can enhance our understanding of CYP152A1 folding and protein expression challenges. Moreover, identification of rare codons can help in the rational design of new and effective drugs.

P13.07 Multi-omics as a tool for elucidating temozolomide resistance in glioblastoma multiforme

Abstract BACKGROUND Glioblastoma multiforme is the most common and aggressive brain tumor in adults, with an average overall survival of 14 months. Current standard of care consists of tumor resection followed by radiotherapy with concomitant temozolomide and adjuvant temozolomide. However, glioblastoma recurs in all patients. The causes reside in the enhanced invasiveness and resistance to treatment, giving a clear indication that recurrent and resistant glioblastoma biology must be understood better in order to achieve future treatment strategies to benefit the patients. The complex nature of recurrent glioblastoma makes its understanding still a challenging achievement in the field. Nowadays multi-omics approaching is developing further and further and it may be used to unravel, by combining different layers of biological information, a comprehensive view of the changes occurring during the treatment. MATERIAL AND METHODS A discovery set of 13 primary patient-derived glioblastoma stem-like cultures were analysed, comprising selected resistant, induced resistant and with pre-existing resistance conditions. A characterization of transcriptome, proteome and phosphoproteome was performed using RNAseq and liquid chromatography mass spectrometry. Additional 10 paired primary and recurrent tumor tissues were utilized as a validation set. The data obtained was visualised, explored and integrated through TIBCO Spotfire, Ingenuity Pathway Analysis, STRING and COREMINE medical software. RESULTS Genetic regulatory processes such as DNA repair mechanism, mRNA splicing and chromatin assembly were shown to be common over-represented trends in resistant and recurrent glioblastomas as a result of increased genomic instability and stress deriving from acute an repeated temozolomide exposure. Due to the immense heterogeneity of glioblastomas, other proteins and genes here identified as differentially expressed need a further investigation as they also may play an important role in relevant biological processes in a patient-specific way. CONCLUSION This study provides further understanding of glioblastoma biology revealing an association with processes of recurrence and temozolomide resistance, moreover offering potential therapeutic targets for better treatment options for glioblastoma patients.

Integrating transcriptome and epigenome: putting together the pieces of the type 2 diabetes pathogenesis puzzle.

The complex pathophysiological process of type 2 diabetes (T2D) involves perturbation of gene expression, leading to derangement of multiple physiological processes in tissues that are important in glucose homeostasis (1). Interactions of environmental factors with genetic and epigenetic variants are likely to play an important role; however, the relative contribution of each component to gene expression linked to T2D pathogenesis could differ among individuals. Understanding the molecular mechanisms underlying this interaction is crucial for developing novel therapeutic and preventative strategies for T2D. Incomplete concordance of T2D in monozygotic (MZ) twins (considered genetically identical) is indicative that nongenetic/environmental factors can play a prominent role in T2D susceptibility and pathogenesis (2). During pre- and postnatal life, environmental factors (including dietary factors) in each tissue microenvironment interact with cellular genetic regulatory architecture and may modulate gene expression. Environmental factors also may modulate gene expression by causing epigenetic modifications of the genome, including DNA methylation (Fig. 1). Thus, tracing molecular events through layers of biological information, including DNA methylation, gene expression, genotype, and physiological data, is required for solving the puzzle of the etiology of T2D. Figure 1 A causal model of T2D pathogenesis. Environmental factors (including dietary factors) may modulate the expression of genes by interacting with cellular genetic regulatory architecture or by causing epigenetic modifications of the genome, including DNA methylation. Perturbation of gene expression in tissues important for glucose homeostasis causes derangement of multiple physiological processes and leads to the pathogenesis of T2D. An integrative functional ‘omics paradigm that traces molecular events through layers of biological information is required for solving the puzzle of the etiology of T2D. epiSNP, methylation and other epigenetic regulatory single …

Accessing Cancer Metabolic Pathways by the Use of Microarray Technology

Cancer metabolism is currently re-evaluated by the research community with the aim to investigate possible opportunities for the development of targeted therapies. Firstly discovered by Warburg et al. in the beginning of the last century, it is now a widely accepted hypothesis that cancer cells possess a severely deregulated form of glycolysis also under aerobic conditions. Accompanied by a deregulated glycolysis is an increasing dependence on glucose and glutamine, this characteristic offers a striking opportunity for new kinds of anti-cancer drugs. A feasible approach in this endeavour is the combined use of metabolic and transcriptomic information. Microarrays provide nowadays a reliable way for accessing the transcriptomic layer, even higher layers of biological information are in the scope. In this review we present the possibilities and also the limitations of this technique starting from the early phase of the microarray to the modern concepts of bioinformatics and systems biology. By highlighting also clinicopathological possibilities it is demonstrated that microarray technology is able to integrate various layers of biological information. Case studies incorporating aspects of cancer metabolism into therapy relevant applications and some potential new targets of cancer metabolism for novel cancer therapies are pointed out. These new cancer therapies can lead to the establishment of personalized medicine by use of custom based microarray platforms introducing treatment options in clinical decision making.