Antiaging — Effect of Stem Cells on Aging and Stem Cell Aging

Aging is defined broadly as the normal progressive process, consequently leading to growing vulnerability to disease and death. A major challenge lies in dissecting the underlying mechanisms of aging with conventional experiments due to the complexity of and multicontributions to the aging process, reflecting a need for investigation into it in various aspects. For this reason, the age process has currently been subjected to OMICS technologies including genomics, transcriptomics, proteomics, and metabolomics, allowing the exploration of age-related changes in a multifactorial manner. In addition, since age-dependent decline in stem cell function is almost identical to the biological age, stem cells have used to understand “aging” and to investigate key reverse factors for “antiaging”. This suggests that a range of new approaches are needed to reveal the unknown biological basis for aging at a variety of different molecular levels using stem cells as a tool of normal aging process and can further apply fundamental aspects in biological aging and longevity

Early Diagnosis of Brain Diseases Using Artificial Intelligence and EV Molecular Data: A Proposed Noninvasive Repeated Diagnosis Approach

Brain-derived extracellular vesicles (BDEVs) are released from the central nervous system. Brain-related research and diagnostic techniques involving BDEVs have rapidly emerged as a means of diagnosing brain disorders because they are minimally invasive and enable repeatable measurements based on body fluids. However, EVs from various cells and organs are mixed in the blood, acting as potential obstacles for brain diagnostic systems using BDEVs. Therefore, it is important to screen appropriate brain EV markers to isolate BDEVs in blood. Here, we established a strategy for screening potential BDEV biomarkers. To collect various molecular data from the BDEVs, we propose that the sensitivity and specificity of the diagnostic system could be enhanced using machine learning and AI analysis. This BDEV-based diagnostic strategy could be used to diagnose various brain diseases and will help prevent disease through early diagnosis and early treatment

Early Diagnosis of Brain Diseases Using Artificial Intelligence and EV Molecular Data: A Proposed Noninvasive Repeated Diagnosis Approach

Brain-derived extracellular vesicles (BDEVs) are released from the central nervous system. Brain-related research and diagnostic techniques involving BDEVs have rapidly emerged as a means of diagnosing brain disorders because they are minimally invasive and enable repeatable measurements based on body fluids. However, EVs from various cells and organs are mixed in the blood, acting as potential obstacles for brain diagnostic systems using BDEVs. Therefore, it is important to screen appropriate brain EV markers to isolate BDEVs in blood. Here, we established a strategy for screening potential BDEV biomarkers. To collect various molecular data from the BDEVs, we propose that the sensitivity and specificity of the diagnostic system could be enhanced using machine learning and AI analysis. This BDEV-based diagnostic strategy could be used to diagnose various brain diseases and will help prevent disease through early diagnosis and early treatment

Functional analysis of the molecular interactions of TATA box-containing genes and essential genes.

Genes can be divided into TATA-containing genes and TATA-less genes according to the presence of TATA box elements at promoter regions. TATA-containing genes tend to be stress-responsive, whereas many TATA-less genes are known to be related to cell growth or "housekeeping" functions. In a previous study, we demonstrated that there are striking differences among four gene sets defined by the presence of TATA box (TATA-containing) and essentiality (TATA-less) with respect to number of associated transcription factors, amino acid usage, and functional annotation. Extending this research in yeast, we identified KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways that are statistically enriched in TATA-containing or TATA-less genes and evaluated the possibility that the enriched pathways are related to stress or growth as reflected by the individual functions of the genes involved. According to their enrichment for either of these two gene sets, we sorted KEGG pathways into TATA-containing-gene-enriched pathways (TEPs) and essential-gene-enriched pathways (EEPs). As expected, genes in TEPs and EEPs exhibited opposite results in terms of functional category, transcriptional regulation, codon adaptation index, and network properties, suggesting the possibility that the bipolar patterns in these pathways also contribute to the regulation of the stress response and to cell survival. Our findings provide the novel insight that significant enrichment of TATA-binding or TATA-less genes defines pathways as stress-responsive or growth-related

Degrees and connectivity in the interaction network.

<p>(a) The mean degrees of molecular interactions are significantly higher (p<0.05) in genes of EEPs than in genes of TEPs. (b) Genes of EEPs were over-represented for higher-scored or highly interconnected clusters, whereas those of TEPs were over-represented for less-scored clusters. The X-axis represents clusters that are displayed in the order of the scores, with the cluster in the left corner having the highest scores.</p

Enrichment of metabolic pathways and enzyme.

<p>(a) There is a marked difference in the enriched metabolic pathways between TATA and essential genes. (b) Oxidoreductases are enriched in the enzyme set (197 enzymes) of the TEPs, whereas transferases are enriched in the enzyme set (124) of the EEPs. The red dashed lines correspond to the negative log transformation of an adjusted p-value of 0.05.</p

Functional comparison of EEPs and TEPs.

<p>(a) Genes of TEPs tend to be associated with a greater number of transcription factors than EEPs. (b) The transcription of TEPs is preferentially associated with the SAGA complex, a regulator of transcription that is known to be related to the stress response. The red dashed lines correspond to the negative log transformation of an adjusted p-value of 0.05. (c) Most TEPs exhibit a higher CAI (codon adaptation index) compared with the EEPs.</p

Comparison of pathways in TEPs and EEPs on metabolic map.

<p>(a) TEPs and (b) EEPs are associated with different modules in the metabolic pathways analysis; these are highlighted in color (red for TEPs and blue for EEPs). Most of the TEPs in the metabolic pathways are associated with carbohydrate metabolism. Circles indicate compounds in the metabolic pathways.</p

Marked differences in genes and pathways between EEPs and TEPs.

<p>(a) The heat map of the adjusted p-values obtained from the enrichment test showed that EEPs are obviously different from TEPs. Rows represent genes and columns pathways (pathway ID); colors represent the negative log transformation of the adjusted p-values, showing the extent to which essential and TATA genes are enriched in the corresponding pathways. (b) Essential-gene-enriched pathways (EEPs) and (c) TATA-containing-gene-enriched pathways (TEPs). The enriched pathways were sorted according to the negative log of the adjusted p-value. The red dashed lines correspond to the log transformation of an adjusted p-value of 0.05.</p