Transcriptional biomarkers in diagnostics of genetic diseases: possibilities, problems and prospects of application

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Abstract

Quantitative analysis of gene transcription is widely used in various fields of biology, and in medicine it is used for diagnostics and profiling of genetic diseases. Recently, transcriptome studies using large-scale next-generation sequencing have become very popular. Transcriptome studies allow us to establish which cellular processes are active at a given time, to identify transcriptome dynamics in various tissues or conditions, for example, during ontogenesis or physiological adaptation, and to identify differentially expressed genes in case of disease. A significant change in the transcription level of one or more genes in a pathological condition may be sufficient for diagnosis, that is, it can serve as a transcriptional biomarker of the disease. However, in some cases, a change in the transcription level can be an indicator of mutations, including those leading to splicing disorders, transcriptional activity of mobile elements and pseudogenes. The review considers cases in which the transcription level may be useful for identifying the genetic causes of a disease, and discusses the issues that should be taken into account when using transcription as a diagnostic marker. In the future, it can be expected that specialized target panels based on transcription analysis will be used not only as a diagnostic and prognostic tool, but also as a predictor of structural abnormalities in the genome, which will contribute to the development of new effective strategies for the treatment of genetic diseases.

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About the authors

L. N. Nefedova

Lomonosov Moscow State University

Author for correspondence.
Email: nefedova@mail.bio.msu.ru

Faculty of Biology

 

Russian Federation, 119991 Moscow

T. N. Krasnova

Lomonosov Moscow State University

Email: nefedova@mail.bio.msu.ru

Faculty of Fundamental Medicine

Russian Federation, 119991 Moscow

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2. Fig. 1. Environmental and molecular genetic mechanisms that can influence gene transcription levels. Legend: LOF – loss of function; GOF – gain of function; NMD – nonsense-mediated mRNA degradation; TF – transcription factor; ME – mobile element

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3. Fig. 2. Algorithm for developing a targeted panel based on gene transcription for disease diagnosis and prediction of specific structural changes in the genome

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