Noncoding RNAs evolutionarily extend animal lifespan

Abstract

The mechanisms underlying lifespan evolution in organisms have long been mysterious. However, recent studies have demonstrated that organisms evolutionarily gain noncoding RNAs (ncRNAs) that carry endogenous profound functions in higher organisms1,2, including lifespan3. This study unveils ncRNAs as crucial drivers driving animal lifespan evolution. Species in the animal kingdom evolutionarily increase their ncRNA length in their genomes, coinciding with trimming mitochondrial genome length. This leads to lower energy consumption and ultimately lifespan extension. Notably, during lifespan extension, species exhibit a gradual acquisition of long-life ncRNA motifs while concurrently losing short-life motifs. These longevity-associated ncRNA motifs, such as GGTGCG, are particularly active in key tissues, including the endometrium, ovary, testis, and cerebral cortex. The activation of ncRNAs in the ovary and endometrium offers insights into why women generally exhibit longer lifespans than men. This groundbreaking discovery reveals the pivotal role of ncRNAs in driving lifespan evolution and provides a fundamental foundation for the study of longevity and aging.

Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS‐CoV‐2

“…A recent study used motifs 4 6 and 4 7 to select biologically significant motifs in the human genome. 33 Assembling all variant Frs constructs an Fr matrix. This Fr matrix can be computed using a range of algorithms such as artificial intelligence (AI) transformer models, LSTM models, conventional machine learning models, and even simple statistical methods.…”

“…However, a future application can certainly expand it to additional features, such as (4 4 + 4 5 + 4 6 + 4 7 ), to enhance the discrimination specificity and sensitivity when genomes become complex. A recent study used motifs 4 6 and 4 7 to select biologically significant motifs in the human genome 33 …”

Conceptual breakthroughs of the long noncoding RNA functional system and its endogenous regulatory role in the cancerous regime

“…Based on the algorithm described above, FINET can be widely applied to any type of big data. It has been applied to compute massive heterogeneous data, and its results have been validated [ 1 , 28 , 31 ]. For example, FINET has inferred endogenous regulatory lncRNA networks from all 265k human RNA-seq samples from the SRA database [ 1 ] and revealed endogenous lncRNAs from unannotated regions of the human genome [ 1 , 31 ].…”

“…In addition, FINET unearthed an endogenous regulatory network for all cancers based on TCGA data [ 31 ]. Moreover, FINET has been applied to identify genome sequence motifs in evolutionary studies of the animal kingdom [ 28 ] and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus [ 46 ].…”

“…Fortunately, recent big data studies have significantly advanced our understanding of lncRNA functions and created a conceptual breakthrough in the lncRNA functional system [ 1 , 27 ]. Analysis of multiple large datasets has demonstrated that lncRNAs are evolutionary drivers of animal lifespan across the animal kingdom by lowering energy consumption [ 28 ]. This explains why the human genome requires 98% of noncoding regions that perform broader functions than previously thought with low energy.…”