The emergence of genome-wide association studies (GWAS) has identified genetic traits and polymorphisms that are associated with the progression of nonalcoholic fatty liver disease (NAFLD). Phospholipase domain-containing 3 and transmembrane 6 superfamily member 2 are genes commonly associated with NAFLD phenotypes. However, there are fewer studies and replicability in lesser-known genes such as LYPLAL1 and glucokinase regulator (GCKR). With the advent of artificial intelligence (AI) in clinical genetics, studies have utilized AI algorithms to identify phenotypes through electronic health records and utilize convolution neural networks to improve the accuracy of variant identification, predict the deleterious effects of variants, and conduct phenotype-to-genotype mapping. Natural language processing (NLP) and machine-learning (ML) algorithms are popular tools in GWAS studies and connect electronic health record phenotypes to genetic diagnoses using a combination of international classification disease (ICD)-based approaches. However, there are still limitations to machine-learning - and NLP-based models, such as the lack of replicability in larger cohorts and underpowered sample sizes, which prevent the accurate prediction of genetic variants that may increase the risk of NAFLD and its progression to advanced-stage liver fibrosis. This may be largely due to the lack of understanding of the clinical consequence in the majority of pathogenic variants. Though the concept of evolution-based AI models and evolutionary algorithms is relatively new, combining current international classification disease -based NLP models with phylogenetic and evolutionary data can improve prediction accuracy and create valuable connections between variants and their pathogenicity in NAFLD. Such developments can improve risk stratification within clinical genetics and research while overcoming limitations in GWAS studies that prevent community-wide interpretations.
Astaxanthin (AXN) is known to have health benefits by epidemiological studies. Therefore, it is of interest to assess the effect of AXN (derived from indigenous unicellular green alga Haematococcus lacustris) to modulate cell cycle arrest, lysosomal acidification and eventually apoptosis using in vitro in A549 lung cancer cells. Natural extracts of astaxanthin were obtained by standardized methods as reported earlier and characterized by standard HPLC and MS. Treatment of A549 cells with AXN (purified fraction) showed significant reduction in cell viability (about 50%) as compared to crude extract at 50µM concentration. Thus, we show the anticancer effects and lysosomal acidification in A549 cells by Astaxanthin from Haematococcus lacustris for further consideration. Together, our results demonstrated the anticancer potential of AXN from Haematococcus lacustris, which is found to be mediated via its ability to induce cell cycle arrest, lysosomal acidification and apoptotic induction.