Cancers in Agreement? Exploring the Cross-Talk of Cancer Metabolomic and Transcriptomic Landscapes Using Publicly Available Data

One of the major hallmarks of cancer is the derailment of a cell’s metabolism. The multifaceted nature of cancer and different cancer types is transduced by both its transcriptomic and metabolomic landscapes. In this study, we re-purposed the publicly available transcriptomic and metabolomics data of eight cancer types (breast, lung, gastric, renal, liver, colorectal, prostate, and multiple myeloma) to find and investigate differences and commonalities on a pathway level among different cancer types. Topological analysis of inferred graphical Gaussian association networks showed that cancer was strongly defined in genetic networks, but not in metabolic networks. Using different statistical approaches to find significant differences between cancer and control cases, we highlighted the difficulties of high-level data-merging and in using statistical association networks. Cancer transcriptomics and metabolomics and landscapes were characterized by changed macro-molecule production, however, only major metabolic deregulations with highly impacted pathways were found in liver cancer. Cell cycle was enriched in breast, liver, and colorectal cancer, while breast and lung cancer were distinguished by highly enriched oncogene signaling pathways. A strong inflammatory response was observed in lung cancer and, to some extent, renal cancer. This study highlights the necessity of combining different omics levels to obtain a better description of cancer characteristics.

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Transcriptomics to Metabolomics

Advances in Bioinformatics and Biomedical Engineering - Applying Big Data Analytics in Bioinformatics and Medicine ◽

10.4018/978-1-5225-2607-0.ch008 ◽

2018 ◽

pp. 188-206 ◽

Cited By ~ 5

Author(s):

Ankush Bansal ◽

Pulkit Anupam Srivastava

Keyword(s):

Biological Networks ◽

Metabolic Networks ◽

Topological Analysis ◽

In Silico Analysis ◽

Small World ◽

Recent Decade ◽

Bipartite Networks ◽

Metabolomics Data ◽

Scale Free ◽

Gene Regulatory

A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics, proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.

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DEVELOPMENT OF AUTOMATED VISION TECHNIQUES FOR IMMEDIATE ANALYSIS AND CONTROL OF BETALAIN-PRODUCING CELL CULTURES

HortScience ◽

10.21273/hortsci.27.6.572c ◽

1992 ◽

Vol 27 (6) ◽

pp. 572c-572

Author(s):

M.A.L. Smith ◽

I. Dustin ◽

R. Leathers ◽

J-P. Zrÿd

Keyword(s):

Production Systems ◽

Growth Cycle ◽

Cell Counts ◽

Level Data ◽

Immediate Analysis ◽

High Level ◽

And Control ◽

Color Filters ◽

Betalain Pigment ◽

Fine Tune

Natural plant pigments (produced as secondary metabolites in cell culture) can replace controversial synthetic chemical colorants to enhance the appearance of processed foods. Intensive bioreactor-based production systems designed for betalain pigment-producing cultures of Beta vulgaris are still not economically competitive, in part due to the slow, prohibitively expensive, and incomplete conventional methods (HPLC analysis, biomass estimates, cell counts) which must be used to assess culture status. As an alternative, software was written using Semper 6 (a high level programming language for image analysis) for collection of exacting morphometric (spatial) and photometric (spectral) process information from an intense violet cell line. Uniform, crisp images of 1 ml culture samples in multiwell plates were captured macroscopically, and the pattern of pigment production was traced at 3 day intervals over the course of a 15 day growth cycle with monochromatic color filters and image grey level data. Rod-shaped cells and aggregates were automatically sorted and measured using parameters of particle size, density, and circularity. The machine vision method offers greater opportunity to fine-tune cell selection for enhanced pigment content.

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Transcriptomics to Metabolomics

Biotechnology ◽

10.4018/978-1-5225-8903-7.ch014 ◽

2019 ◽

pp. 361-379

Author(s):

Ankush Bansal ◽

Pulkit Anupam Srivastava

Keyword(s):

Biological Networks ◽

Metabolic Networks ◽

Topological Analysis ◽

In Silico Analysis ◽

Small World ◽

Recent Decade ◽

Bipartite Networks ◽

Metabolomics Data ◽

Scale Free ◽

Gene Regulatory

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The MADE Reference Information Model for Interoperable Pervasive Telemedicine Systems

Methods of Information in Medicine ◽

10.3414/me15-02-0013 ◽

2017 ◽

Vol 56 (02) ◽

pp. 180-187 ◽

Cited By ~ 1

Author(s):

Valerie M. Jones ◽

Hermie J. Hermens ◽

Nick L. S. Fung

Keyword(s):

Action Plan ◽

Information Model ◽

Data Types ◽

Reference Information ◽

Level Data ◽

Concurrent Processes ◽

Control Instruction ◽

High Level ◽

And Control ◽

Data Flow Model

SummaryObjectives: The main objective is to develop and validate a reference information model (RIM) to support semantic interoperability of pervasive telemedicine systems. The RIM is one component within a larger, computer-interpretable "MADE language" developed by the authors in the context of the MobiGuide project. To validate our RIM, we applied it to a clinical guideline for patients with gestational diabetes mellitus (GDM).Methods: The RIM is derived from a generic data flow model of disease management which comprises a network of four types of concurrent processes: Monitoring (M), Analysis (A), Decision (D) and Effectuation (E). This resulting MADE RIM, which was specified using the formal Vienna Development Method (VDM), includes six main, high-level data types representing measurements, observations, abstractions, action plans, action instructions and control instructions.Results: The authors applied the MADE RIM to the complete GDM guideline and derived from it a domain information model (DIM) comprising 61 archetypes, specifically 1 measurement, 8 observation, 10 abstraction, 18 action plan, 3 action instruction and 21 control instruction archetypes. It was observed that there are six generic patterns for transforming different guideline elements into MADE archetypes, although a direct mapping does not exist in some cases. Most notable examples are notifications to the patient and/or clinician as well as decision conditions which pertain to specific stages in the therapy.Conclusions: The results provide evidence that the MADE RIM is suitable for modelling clinical data in the design of pervasive tele-medicine systems. Together with the other components of the MADE language, the MADE RIM supports development of pervasive telemedicine systems that are interoperable and independent of particular clinical applications.

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Compiler from El programing language: improvement and development

Digital technology security ◽

10.17212/2782-2230-2021-2-9-19 ◽

2021 ◽

pp. 9-19

Author(s):

Nikita Zelenchuk ◽

◽

Ekaterina Pristavka ◽

Aleksandr Maliavko ◽

◽

...

Keyword(s):

Data Structures ◽

Current Version ◽

Control Structures ◽

Front End ◽

Initial Stage ◽

Level Data ◽

Arbitrary Precision ◽

High Level ◽

And Control ◽

Computational Processes

The implementation of the new multi-paradigm (functionally- imperative) programming language El, developed at the Department of Computer Science of the Novosibirsk State Technical University, in the form of a compiler is associated with the need to find ways to solve a number of complex problems. The current version of the compiler does implement only partially functionality of the language and generates far from optimal executable codes. In this paper, we consider the problem of an efficient compilation of an El-program, taking into account the need to implement new high-level data structures (two-sided lists, vectors with special forms of access, and a number of others) and control structures of the language, which make it possible to uniformly define cyclic and branching computational processes, as well as those laid down in the language a mechanism for explicitly controlling the mutability of variables. The tasks of improving and developing a compiler organized according to the classical multi-platform scheme are briefly considered, in which the front-end (lexical, syntactic, and semantic analyzers) converts the program to be translated into pseudocode of a single format, and used efficient infrastructure for building LLVM compilers as a back-end that turns pseudocode into executable code for different platforms. Execution of all possible operations on elements of high-level data structures (lists, tuples, vectors), as well as on arbitrary-precision numbers, has been moved to the runtime support library and, accordingly, can be deeply optimized. For this structure, the outlined ways of solving the problem of developing and improving the compiler by deep reforming and optimization of the chain of transformations of the translated program implemented by the front-end are formulated. At the initial stage, it is planned to implement a new compiler for two platforms: Linux and Windows.

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Metabolic networks of plasma and joint fluid base on differential correlation

PLoS ONE ◽

10.1371/journal.pone.0247191 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0247191

Author(s):

Bingyong Xu ◽

Hong Su ◽

Ruya Wang ◽

Yixiao Wang ◽

Weidong Zhang

Keyword(s):

Metabolic Networks ◽

Topological Analysis ◽

Joint Fluid ◽

Female Group ◽

Metabolomics Data ◽

Joint Replacement Surgery ◽

Metabolic Characteristics ◽

Differential Correlation ◽

Knee Oa ◽

Significant Difference

Whether osteoarthritis (OA) is a systemic metabolic disorder remains controversial. The aim of this study was to investigate the metabolic characteristics between plasma and knee joint fluid (JF) of patients with advanced OA using a differential correlation metabolic (DCM) networks approach. Plasma and JF were collected during the joint replacement surgery of patients with knee OA. The biological samples were pretreated with standard procedures for metabolite analysis. The metabolic profiling was conducted by means of liquid mass spectrometry coupled with a AbsoluteIDQ kit. A DCM network approach was adopted for analyzing the metabolomics data between the plasma and JF. The variation in the correlation of the pairwise metabolites was quantified across the plasma and JF samples, and networks analysis was used to characterize the difference in the correlations of the metabolites from the two sample types. Core metabolites that played an important role in the DCM networks were identified via topological analysis. One hundred advanced OA patients (50 men and 50 women) were included in this study, with an average age of 65.0 ± 7.6 years (65.6 ± 7.1 years for females and 64.4 ± 8.1 years for males) and a mean BMI of 32.6 ± 5.8 kg/m2 (33.4 ± 6.3 kg/m2 for females and 31.7 ± 5.3 kg/m2 for males). Age and BMI matched between the male and female groups. One hundred and forty-five nodes, 567 edges, and 131 nodes, 407 edges were found in the DCM networks (p < 0.05) of the female and male groups, respectively. Six metabolites in the female group and 5 metabolites in the male group were identified as key nodes in the network. There was a significant difference in the differential correlation metabolism networks of plasma and JF that may be related to local joint metabolism. Focusing on these key metabolites may help uncover the pathogenesis of knee OA. In addition, the differential metabolic correlation between plasma and JF mostly overlapped, indicating that these common correlations of pairwise metabolites may be a reflection of systemic characteristics of JF and that most significant correlation variations were just a result of "housekeeping” biological reactions.

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