T-CLASS: An Online Tool for the Identification and Classification of Aging and Senescence Using Transcriptome Data.

Area of Science:

• Gerontology and molecular biology
• Bioinformatics and computational biology

Background:

• Transcriptome analysis is crucial in aging research but identifying key molecular changes remains difficult.
• Existing methods for gene selection from transcriptome data often fall short in accurately representing aging and longevity paradigms.

Purpose of the Study:

• To introduce Transcriptomic CLassification via Adaptive learning of Signature States (T-CLASS), an online tool for identifying optimal gene sets from transcriptome data.
• To assess T-CLASS's effectiveness in classifying aging and longevity-related transcriptomic changes across diverse biological models.

Main Methods:

• Development of T-CLASS, an adaptive learning tool for gene signature identification.
• Systematic evaluation of T-CLASS using datasets from Caenorhabditis elegans longevity studies, cellular senescence models (mouse and human), and human sarcopenia.
• Comparison of T-CLASS performance against existing machine and deep learning-based gene selection tools.

Main Results:

• T-CLASS demonstrated robust and high classification performance across various aging-related datasets.
• The tool successfully classified transcriptomic changes induced by ten lifespan-extending small molecules in C. elegans.
• Experimental validation confirmed the effects of rifampicin and atracurium in C. elegans longevity studies.

Conclusions:

• T-CLASS is a powerful and practical tool for uncovering and classifying molecular changes associated with aging and longevity.
• The tool aids in understanding physiological alterations resulting from genetic and pharmacological interventions affecting aging processes.
• T-CLASS facilitates the identification of novel targets for interventions aimed at promoting healthy aging.