Studies of Complex Biological Systems with Applications to Molecular Medicine: The Need to Integrate Transcriptomic and Proteomic Approaches

Journal of Biomedicine and Biotechnology ◽

10.1155/2011/810242 ◽

2011 ◽

Vol 2011 ◽

pp. 1-19 ◽

Cited By ~ 9

Author(s):

Elena Silvestri ◽

Assunta Lombardi ◽

Pieter de Lange ◽

Daniela Glinni ◽

Rosalba Senese ◽

...

Keyword(s):

Messenger Rna ◽

Dna Microarrays ◽

A Priori ◽

Biological Systems ◽

Molecular Medicine ◽

Biological Research ◽

Mitochondrial Activity ◽

Thyroid State ◽

Integrative Analyses ◽

Altered Thyroid

Omics approaches to the study of complex biological systems with potential applications to molecular medicine are attracting great interest in clinical as well as in basic biological research. Genomics, transcriptomics and proteomics are characterized by the lack of ana prioridefinition of scope, and this gives sufficient leeway for investigators (a) to discern all at once a globally altered pattern of gene/protein expression and (b) to examine the complex interactions that regulate entire biological processes. Two popular platforms in “omics” are DNA microarrays, which measure messenger RNA transcript levels, and proteomic analyses, which identify and quantify proteins. Because of their intrinsic strengths and weaknesses, no single approach can fully unravel the complexities of fundamental biological events. However, an appropriate combination of different tools could lead to integrative analyses that would furnish new insights not accessible through one-dimensional datasets. In this review, we will outline some of the challenges associated with integrative analyses relating to the changes in metabolic pathways that occur in complex pathophysiological conditions (viz. ageing and altered thyroid state) in relevant metabolically active tissues. In addition, we discuss several new applications of proteomic analysis to the investigation of mitochondrial activity.

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Studies of Complex Biological Systems with Applications to Molecular Medicine: The Need to Integrate Transcriptomic and Proteomic Approaches

Omics in Clinical Practice ◽

10.1201/b17137-4 ◽

2014 ◽

pp. 29-70

Author(s):

Elena Silvestri ◽

Assunta Lombardi ◽

Pieter De Lange ◽

Daniela Glinni ◽

Rosalba Senese ◽

...

Keyword(s):

Biological Systems ◽

Molecular Medicine

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Early Days of Biochemical NMR

Biological NMR Spectroscopy ◽

10.1093/oso/9780195094688.003.0008 ◽

1997 ◽

Author(s):

R.G. Shulman

Keyword(s):

Magnetic Resonance ◽

Biological Systems ◽

Biological Molecules ◽

Biological Research ◽

Solid State Physics ◽

Arts And Sciences ◽

Birthday Celebration ◽

Common Interests ◽

The 1960S ◽

The Individual

It was my pleasure to participate in Oleg’s 65th birthday celebration and to reminisce about the early days of Biochemical NMR. Oleg was always there. I remember in the summer in the early 1960s sitting on lawn chairs at a Gordon Conference and discussing the need for a meeting on biochemical NMR. This was to convene those with common interests, and out of this grew the 1964 meeting in Boston, which was the first International Conference on Magnetic Resonance in Biological Systems. In organizing the 1964 meeting Oleg was stalwart, in charge of the local arrangements at the old mansion, home of the American Academy of Arts and Sciences. The venue was much appreciated by the more than 100 attendees, and the smooth arrangements and elegant, although somewhat dowdy locale, contributed to the sense, generated by the meeting, that the field had a coherent scientific core and a meaningful future. In the early days of the 1960s the field of magnetic resonance in biological systems, brought together biannually by the society, had a coherence that was nurtured by the society. In those days the NMR and ESR methods were much less developed than they soon became, so that any reasonably competent spectroscopist could understand all the methods employed. Additionally, because the earlier studies concentrated upon the better understood biological molecules or processes, the breadth of the applications did not baffle a slightly informed biochemist. The rapid advances in definite understanding were thrilling to practitioners in the field, and individual efforts were motivated by a sense that the field was going to grow. By that time NMR was firmly established as a quantitative method in chemistry, solid state physics, and other material sciences so that with the results in hand it was logical to extrapolate to a future in which magnetic resonance could be central to biological research. These high hopes, however, required considerable confidence in extrapolation, because the individual findings were sometimes slight when compared to the exciting cutting edges of biological research.

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RGBM: Regularized Gradient Boosting Machines for the Identification of Transcriptional Regulators of Discrete Glioma Subtypes

10.1101/132670 ◽

2017 ◽

Cited By ~ 1

Author(s):

Raghvendra Mall ◽

Luigi Cerulo ◽

Khalid Kunji ◽

Halima Bensmail ◽

Thais S. Sabedot ◽

...

Keyword(s):

Large Scale ◽

Dna Microarrays ◽

Target Genes ◽

Gene Knockout ◽

Biological Activities ◽

Expression Profiles ◽

A Priori ◽

Gradient Boosting ◽

Gene Expressions ◽

Cellular Phenotypes

AbstractThe transcription factors (TF) which regulate gene expressions are key determinants of cellular phenotypes. Reconstructing large-scale genome-wide networks which capture the influence of TFs on target genes are essential for understanding and accurate modelling of living cells. We propose RGBM: a gene regulatory network (GRN) inference algorithm, which can handle data from heterogeneous information sources including dynamic time-series, gene knockout, gene knockdown, DNA microarrays and RNA-Seq expression profiles. RGBM allows to use an a priori mechanistic of active biding network consisting of TFs and corresponding target genes. RGBM is evaluated on the DREAM challenge datasets where it surpasses the winners of the competitions and other established methods for two evaluation metrics by about 10-15%.We use RGBM to identify the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators driving transformation of the G-CIMP-high into the G-CIMP-low subtype of glioma and PA-like into LGm6-GBM, thus, providing a clue to the yet undetermined nature of the transcriptional events driving the evolution among these novel glioma subtypes.RGBM is available for download on CRAN at https://cran.rproject.org/web/packages/RGBM/index.html

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Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data for Chinese Glioma Patients

10.1101/2020.01.20.911982 ◽

2020 ◽

Cited By ~ 3

Author(s):

Zheng Zhao ◽

Ke’nan Zhang ◽

Qiangwei Wang ◽

Guanzhang Li ◽

Fan Zeng ◽

...

Keyword(s):

Dna Methylation ◽

Survival Data ◽

Messenger Rna ◽

Genomic Data ◽

Biological Research ◽

Analysis Tool ◽

Functional Genomic ◽

Functional Genomic Data ◽

Who Grade ◽

Genome Atlas

AbstractGliomas are the most common and malignant intracranial tumours in adults. Recent studies have shown that functional genomics greatly aids in the understanding of the pathophysiology and therapy of glioma. However, comprehensive genomic data and analysis platforms are relatively limited. In this study, we developed the Chinese Glioma Genome Atlas (CGGA, http://www.cgga.org.cn), a user-friendly data portal for storage and interactive exploration of multi-dimensional functional genomic data that includes nearly 2,000 primary and recurrent glioma samples from Chinese cohorts. CGGA currently provides access to whole-exome sequencing (286 samples), messenger RNA sequencing (1,018 samples) and microarray (301 samples), DNA methylation microarray (159 samples), and microRNA microarray (198 samples) data, as well as detailed clinical data (e.g., WHO grade, histological type, critical molecular genetic information, age, sex, chemoradiotherapy status and survival data). In addition, we developed an analysis tool to allow users to browse mutational, mRNA/microRNA expression, and DNA methylation profiles and perform survival and correlation analyses of specific glioma subtypes. CGGA greatly reduces the barriers between complex functional genomic data and glioma researchers who seek rapid, intuitive, and high-quality access to data resources and enables researchers to use these immeasurable data sources for biological research and clinical application. Importantly, the free provision of data will allow researchers to quickly generate and provide data to the research community.

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Inferring cell cycle phases from a partially temporal network of protein interactions

10.1101/2021.03.26.437187 ◽

2021 ◽

Author(s):

Maxime Lucas ◽

Arthur Morris ◽

Alex Townsend-Teague ◽

Laurent Tichit ◽

Bianca H Habermann ◽

...

Keyword(s):

Cell Cycle ◽

Protein Interactions ◽

Time Series Data ◽

A Priori ◽

Biological Systems ◽

Series Data ◽

Biological Knowledge ◽

Temporal Network ◽

Protein Protein Interactions ◽

Temporal Organisation

The temporal organisation of biological systems into phases and subphases is often crucial to their functioning. Identifying this multiscale organisation can yield insight into the underlying biological mechanisms at play. To date, however, this identification requires a priori biological knowledge of the system under study. Here, we recover the temporal organisation of the cell cycle of budding yeast into phases and subphases, in an automated way. To do so, we model the cell cycle as a partially temporal network of protein-protein interactions (PPIs) by combining a traditional static PPI network with protein concentration or RNA expression time series data. Then, we cluster the snapshots of this temporal network to infer phases, which show good agreement with our biological knowledge of the cell cycle. We systematically test the robustness of the approach and investigate the effect of having only partial temporal information. The generality of the method makes it suitable for application to other, less well-known biological systems for which the temporal organisation of processes plays an important role.

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Breast and endocrine surgery

10.1093/med/9780199204755.003.0003 ◽

2012 ◽

Author(s):

Matthew D. Gardiner ◽

Neil R. Borley

Keyword(s):

Cancer Diagnosis ◽

Multiple Endocrine Neoplasia ◽

Breast Development ◽

Endocrine Surgery ◽

Basic Principles ◽

Thyroid State ◽

Endocrine Neoplasia ◽

Diagnosis And Classification ◽

Altered Thyroid ◽

Case Based

This chapter begins by discussing the basic principles of oncology, cancer diagnosis and classification, and cancer treatment, before focusing on the key areas of knowledge, namely disorders of breast development and involution, breast cancer assessment and management, goitre, altered thyroid state, thyroid cancer, parathyroid conditions, adrenal conditions, and multiple endocrine neoplasia. The chapter concludes with relevant case-based discussions.

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Thyroid receptor plasticity in striated muscle types: effects of altered thyroid state

AJP Endocrinology and Metabolism ◽

10.1152/ajpendo.1998.274.6.e1018 ◽

1998 ◽

Vol 274 (6) ◽

pp. E1018-E1026 ◽

Cited By ~ 13

Author(s):

Fadia Haddad ◽

Anqi X. Qin ◽

Samuel A. McCue ◽

Kenneth M. Baldwin

Keyword(s):

Skeletal Muscle ◽

Mrna Expression ◽

Type I ◽

Thyroid State ◽

Muscle Types ◽

Slow Twitch ◽

Thyroid Receptor ◽

Altered Thyroid ◽

Fast Twitch ◽

Skeletal Muscle Types

This study examined nuclear thyroid receptor (TR) maximum binding capacity (Bmax), dissociation constant ( K d), and TR isoform (α1, α2, β1) mRNA expression in rodent cardiac, “fast-twitch white,” “fast-twitch red,” and “slow-twitch red” muscle types as a function of thyroid state. These analyses were performed in the context of slow-twitch type I myosin heavy-chain (MHC) expression, a 3,5,3′-triiodothyronine (T3)-regulated gene that displays varying responsiveness to T3 in the above tissues. Nuclear T3 binding analyses show that the skeletal muscle types express more TRs per unit DNA than cardiac muscle, whereas the latter has a lower K d than the former. Altered thyroid state had little effect on either cardiac Bmax or K d, whereas hypothyroidism increased Bmax in the skeletal muscle types without affecting its K d. Cardiac muscle demonstrated the greatest mRNA signal of TR-β1 compared with the other muscle types, whereas the TR-α1mRNA signals were more abundant in the skeletal muscle types, especially fast-twitch red. Hyperthyroidism increased the ratio of β1 to α1 and decreased the ratio of α2- to α1+β1-mRNA signal across the muscle types, whereas hypothyroidism caused the opposite effects. The nuclear T3affinity correlated significantly with the TR-β1 mRNA expression but not with TR-α1 mRNA expression. Collectively, these findings suggest that, despite a divergent pattern of TR mRNA expression in the different muscle types, these patterns follow similar qualitative changes under altered thyroid state. Furthermore, TR expression pattern cannot account for the quantitative and qualitative changes in type I MHC expression that occur in the different muscle types.

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