Integrating high-throughput genetics and neuroimaging technologies promises greater information on neurobiological anomalies in neurodegenerative diseases

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Gene Expression ◽  
Neurodegenerative Diseases ◽  
High Throughput ◽  
Morphological Changes ◽  
Brain Area ◽  
Temporal Distribution ◽  
Brain Regions ◽  
Brain Morphology ◽  
Expression Data ◽  
Imaging Research

The integration of high-throughput genomics and neuroimaging technology has the promise of providing more information about neurobiological irregularities in neurodegenerative illnesses. Transcriptomics-derived connections provide insight into the molecular trajectory of neurodegeneration, prioritizing particular systems and networks while also considering other aspects, including neuropathology and cognition. Causal links between gene expression and brain morphology are unknown, however. If omics systems have a wide impact upstream, they can influence morphological changes identified by MRI. Gene expression is a signal indicating a process already underway in a diseased brain area if it is downstream of structural brain changes. More study on people in the early stages of disease may give insights into the temporal connection between anatomical and expression problems.One such thought is molecular stereotactic propagation. Changes in gene expression may travel across the brain, according to this notion, through tractography trails. They follow the successive pattern of afflicted regions and the temporal distribution of sensitive locations. In addition, cell motility genes are often overexpressed in vulnerable locations. On the other hand, the data on gene expression and its relevance to structural change propagation is still conflicting. The role of immunological processes and motility-related genes in neurodegeneration appears to be validated by expression data.There are no therapies available for neurodegenerative diseases. Symptom development and diagnosis is often delayed due to advanced MRI and clinical stages. Early diagnosis is crucial since therapy interventions should ideally aim at commencing pathogenic processes as soon as possible to avoid the onset of disease and restrict the course of disease. This requires dependable neurodegenerative biomarkers with diagnostic validity. Unfortunately, transcriptomics still has several important limitations. There is a paucity of high-quality expression data encompassing a large number of brain regions, expression data generally collected from healthy persons, comparisons with neuroimaging data from degenerative cohorts, and a lack of consistent approach for transcriptional imaging research. By overcoming this barrier, researchers can uncover prodromal stages of neurodegeneration and therapeutic molecular targets.

Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa

2015 ◽  
Vol 11 (11) ◽  
pp. 3137-3148
Author(s):  
Nazanin Hosseinkhan ◽  
Peyman Zarrineh ◽  
Hassan Rokni-Zadeh ◽  
Mohammad Reza Ashouri ◽  
Ali Masoudi-Nejad
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Pseudomonas Aeruginosa ◽  
Systems Biology ◽  
Gene Expression Data ◽  
High Throughput ◽  
Expression Data ◽  
P Gene ◽  
Stress Related Genes ◽  
High Throughput Gene Expression

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data.

scholarly journals Quantitative RT-PCR analyses of five evolutionary conserved genes in Alligator brains during development

2011 ◽  
Vol 2 (4) ◽  
Author(s):  
Sarah Wilson ◽  
Tianli Zhu ◽  
Rajesh Khanna ◽  
Michael Pritz
Keyword(s):  
Gene Expression ◽  
Brain Area ◽  
Brain Regions ◽  
Adult Brain ◽  
Time Interval ◽  
Conserved Genes ◽  
Mrna Gene ◽  
Alligator Mississipiensis ◽  
Mrna Gene Expression ◽  
Present Gene

AbstractGene expression was investigated in the major brain subdivisions (telencephalon, diencephalon, midbrain and hindbrain) in a representative reptile, Alligator mississipiensis, during the later stages of embryonic development. The following genes were examined: voltage-gated sodium channel isoforms: NaV1.1 and NaV1.2; synaptic vesicle 2a (SV2a); synaptophysin; and calbindin 2. With the exception of synaptophysin, which was only expressed in the telencephalon, all genes were expressed in all brain regions sampled at the time periods examined. For NaV1.1, gene expression varied according to brain area sampled. When compared with NaV1.1, the pattern of NaV1.2 gene expression differed appreciably. The gene expression of SV2a was the most robust of any of the genes examined. Of the other genes examined, although differences were noted, no statistically significant changes were found either between brain part or time interval. Although limited, the present analysis is the first quantitative mRNA gene expression study in any reptile during development. Together with future experiments of a similar nature, the present gene expression results should determine which genes are expressed in major brain areas at which times during development in Alligator. When compared with other amniotes, these results will prove useful for determining how gene expression during development influences adult brain structure.

GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists

2020 ◽  
pp. 580-592
Author(s):  
Libi Hertzberg ◽  
Assif Yitzhaky ◽  
Metsada Pasmanik-Chor
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
User Interface ◽  
High Throughput ◽  
Graphical User Interface ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Expression Data ◽  
Batch Correction ◽  
User Friendly

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.

scholarly journals Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression

2019 ◽  
Vol 36 (3) ◽  
pp. 782-788 ◽  
Author(s):  
Jiebiao Wang ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Empirical Bayes ◽  
Brain Regions ◽  
Tissue Level ◽  
Supplementary Information ◽  
Specific Gene ◽  
Expression Data ◽  
Cell Type ◽  
Multiple Measurements ◽  
Cell Type Specific

Abstract Motivation Patterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects. Results Complementing these approaches, we develop a method to estimate subject- and cell-type-specific (CTS) gene expression from tissue using an empirical Bayes method that borrows information across multiple measurements of the same tissue per subject (e.g. multiple regions of the brain). Analyzing expression data from multiple brain regions from the Genotype-Tissue Expression project (GTEx) reveals CTS expression, which then permits downstream analyses, such as identification of CTS expression Quantitative Trait Loci (eQTL). Availability and implementation We implement this method as an R package MIND, hosted on https://github.com/randel/MIND. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data

2008 ◽  
Vol 5 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Nicola Segata ◽  
Enrico Blanzieri ◽  
Corrado Priami
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
High Throughput ◽  
System Level ◽  
Expression Data ◽  
Differential Analysis ◽  
Computational Systems Biology ◽  
Microarray Gene Expression ◽  
High Throughput Data ◽  
Computational Systems

Summary The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.

scholarly journals BBCancer: an expression atlas of blood-based biomarkers in the early diagnosis of cancers

2019 ◽  
Author(s):  
Zhixiang Zuo ◽  
Huanjing Hu ◽  
Qingxian Xu ◽  
Xiaotong Luo ◽  
Di Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Early Detection ◽  
Cancer Patients ◽  
High Throughput ◽  
Early Detection Of Cancer ◽  
Circular Rnas ◽  
Expression Data ◽  
Blood Samples ◽  
Cancer Types ◽  
High Throughput Gene Expression

Abstract The early detection of cancer holds the key to combat and control the increasing global burden of cancer morbidity and mortality. Blood-based screenings using circulating DNAs (ctDNAs), circulating RNA (ctRNAs), circulating tumor cells (CTCs) and extracellular vesicles (EVs) have shown promising prospects in the early detection of cancer. Recent high-throughput gene expression profiling of blood samples from cancer patients has provided a valuable resource for developing new biomarkers for the early detection of cancer. However, a well-organized online repository for these blood-based high-throughput gene expression data is still not available. Here, we present BBCancer (http://bbcancer.renlab.org/), a web-accessible and comprehensive open resource for providing the expression landscape of six types of RNAs, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), microRNAs (miRNAs), circular RNAs (circRNAs), tRNA-derived fragments (tRFRNAs) and Piwi-interacting RNAs (piRNAs) in blood samples, including plasma, CTCs and EVs, from cancer patients with various cancer types. Currently, BBCancer contains expression data of the six RNA types from 5040 normal and tumor blood samples across 15 cancer types. We believe this database will serve as a powerful platform for developing blood biomarkers.

scholarly journals Clustering of high throughput gene expression data

2012 ◽  
Vol 39 (12) ◽  
pp. 3046-3061 ◽  
Author(s):  
Harun Pirim ◽  
Burak Ekşioğlu ◽  
Andy D. Perkins ◽  
Çetin Yüceer
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
High Throughput ◽  
Expression Data ◽  
High Throughput Gene Expression
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