Molecular Evolution of DNA Topoisomerase III Beta (TOP3B) in Metazoa

Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma

Hepatitis B virus (HBV) causes liver cancer, which is the third most common cause of cancer-related death worldwide. Chronic inflammation via HBV in the host hepatocytes causes hepatocyte remodeling (hepatocyte transformation and immortalization) and hepatocellular carcinoma (HCC). Recognizing cancer stages accurately to optimize early screening and diagnosis is a primary concern in the outlook of HBV-induced hepatocyte remodeling and liver cancer. Genomic signatures play important roles in addressing this issue. Recently, machine learning (ML) models and bioinformatics analysis have become very important in discovering novel genomic signatures for the early diagnosis, treatment, and prognosis of HBV-induced hepatic cell remodeling and HCC. We discuss the recent literature on the ML approach and bioinformatics analysis revealed novel genomic signatures for diagnosing and forecasting HBV-associated hepatocyte remodeling and HCC. Various genomic signatures, including various microRNAs and their associated genes, long noncoding RNAs (lncRNAs), and small nucleolar RNAs (snoRNAs), have been discovered to be involved in the upregulation and downregulation of HBV-HCC. Moreover, these genetic biomarkers also affect different biological processes, such as proliferation, migration, circulation, assault, dissemination, antiapoptosis, mitogenesis, transformation, and angiogenesis in HBV-infected hepatocytes.

Discovering novel prognostic biomarkers of hepatocellular carcinoma using eXplainable Artificial Intelligence

Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tools and novel therapeutic strategies, this study focuses on the discovery and application of novel genetic biomarkers for HCC using explainable artificial intelligence (XAI). Despite advances in HCC research, current biomarkers like Alpha-fetoprotein (AFP) exhibit limitations in sensitivity and specificity, necessitating a shift towards more precise and reliable markers. This paper presents an innovative multi-model XAI and a probabilistic causal inference framework to identify and validate key genetic biomarkers for HCC prognosis. Our methodology involved analyzing clinical and gene expression data to identify potential biomarkers with prognostic significance. The study utilized robust AI models validated against extensive gene expression datasets, demonstrating not only the predictive accuracy but also the clinical relevance of the identified biomarkers through explainable metrics. The findings highlight the importance of biomarkers such as TOP3B, SSBP3, and COX7A2L, which were consistently influential across multiple models, suggesting their role in improving the predictive accuracy for HCC prognosis beyond AFP. Notably, the study also emphasizes the relevance of these biomarkers to the Hispanic population, aligning with the larger goal of demographic-specific research. The application of XAI in biomarker discovery represents a significant advancement in HCC research, offering a more nuanced understanding of the disease and laying the groundwork for improved diagnostic and therapeutic strategies.

Variation of Structure and Cellular Functions of Type IA Topoisomerases across the Tree of Life

Topoisomerases regulate the topological state of cellular genomes to prevent impediments to vital cellular processes, including replication and transcription from suboptimal supercoiling of double-stranded DNA, and to untangle topological barriers generated as replication or recombination intermediates. The subfamily of type IA topoisomerases are the only topoisomerases that can alter the interlinking of both DNA and RNA. In this article, we provide a review of the mechanisms by which four highly conserved N-terminal protein domains fold into a toroidal structure, enabling cleavage and religation of a single strand of DNA or RNA. We also explore how these conserved domains can be combined with numerous non-conserved protein sequences located in the C-terminal domains to form a diverse range of type IA topoisomerases in Archaea, Bacteria, and Eukarya. There is at least one type IA topoisomerase present in nearly every free-living organism. The variation in C-terminal domain sequences and interacting partners such as helicases enable type IA topoisomerases to conduct important cellular functions that require the passage of nucleic acids through the break of a single-strand DNA or RNA that is held by the conserved N-terminal toroidal domains. In addition, this review will exam a range of human genetic disorders that have been linked to the malfunction of type IA topoisomerase.

Discovering Novel Prognostic Biomarkers of Hepatocellular Carcinoma using eXplainable Artificial Intelligence

Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tools and novel therapeutic strategies, this study focuses on the discovery and application of novel genetic biomarkers for HCC using explainable artificial intelligence (XAI). Despite advances in HCC research, current biomarkers like Alpha-fetoprotein (AFP) exhibit limitations in sensitivity and specificity, necessitating a shift towards more precise and reliable markers. This paper presents an innovative XAI framework to identify and validate key genetic biomarkers for HCC prognosis. Our methodology involved analyzing clinical and gene expression data to identify potential biomarkers with prognostic significance. The study utilized robust AI models validated against extensive gene expression datasets, demonstrating not only the predictive accuracy but also the clinical relevance of the identified biomarkers through explainable metrics. The findings highlight the importance of biomarkers such as TOP3B, SSBP3, and COX7A2L, which were consistently influential across multiple models, suggesting their role in improving the predictive accuracy for HCC prognosis beyond AFP. Notably, the study also emphasizes the relevance of these biomarkers to the Hispanic population, aligning with the larger goal of demographic-specific research. The application of XAI in biomarker discovery represents a significant advancement in HCC research, offering a more nuanced understanding of the disease and laying the groundwork for improved diagnostic and therapeutic strategies.

Influence of substitution model selection on protein phylogenetic tree reconstruction

Probabilistic phylogenetic tree reconstruction is traditionally performed under a best-fitting substitution model of molecular evolution previously selected according to diverse statistical criteria. Interestingly, some recent studies proposed that this procedure is unnecessary for phylogenetic tree reconstruction leading to a debate in the field. In contrast to DNA sequences, phylogenetic tree reconstruction from protein sequences is traditionally based on empirical exchangeability matrices that can differ among taxonomic groups and protein families. Considering this aspect, here we investigated the influence of selecting a substitution model of protein evolution on phylogenetic tree reconstruction by the analyses of real and simulated data. We found that phylogenetic tree reconstructions based on a selected best-fitting substitution model of protein evolution are the most accurate, in terms of topology and branch lengths, compared with those derived from substitution models with amino acid replacement matrices far from the selected best-fitting model, especially when the data has large genetic diversity. Indeed, we found that substitution models with similar amino acid replacement matrices produce similar reconstructed phylogenetic trees, suggesting the use of substitution models as similar as possible to a selected best-fitting model when the latter cannot be used. Therefore, we recommend the use of the traditional protocol of selection among substitution models of evolution for protein phylogenetic tree reconstruction.

Evolution of TOP1 and TOP1MT Topoisomerases in Chordata

Type IB topoisomerases relax the torsional stress associated with DNA metabolism in the nucleus and mitochondria and constitute important molecular targets of anticancer drugs. Vertebrates stand out among eukaryotes by having two Type IB topoisomerases acting specifically in the nucleus (TOP1) and mitochondria (TOP1MT). Despite their major importance, the origin and evolution of these paralogues remain unknown. Here, we examine the molecular evolutionary processes acting on both TOP1 and TOP1MT in Chordata, taking advantage of the increasing number of available genome sequences. We found that both TOP1 and TOP1MT evolved under strong purifying selection, as expected considering their essential biological functions. Critical active sites, including those associated with resistance to anticancer agents, were found particularly conserved. However, TOP1MT presented a higher rate of molecular evolution than TOP1, possibly related with its specialized activity on the mitochondrial genome and a less critical role in cells. We could place the duplication event that originated the TOP1 and TOP1MT paralogues early in the radiation of vertebrates, most likely associated with the first round of vertebrate tetraploidization (1R). Moreover, our data suggest that cyclostomes present a specialized mitochondrial Type IB topoisomerase. Interestingly, we identified two missense mutations replacing amino acids in the Linker region of TOP1MT in Neanderthals, which appears as a rare event when comparing the genome of both species. In conclusion, TOP1 and TOP1MT differ in their rates of evolution, and their evolutionary histories allowed us to better understand the evolution of chordates.

Evolutionary History of TOPIIA Topoisomerases in Animals

TOPIIA topoisomerases are required for the regulation of DNA topology by DNA cleavage and re-ligation and are important targets of antibiotic and anticancer agents. Humans possess two TOPIIA paralogue genes (TOP2A and TOP2B) with high sequence and structural similarity but distinct cellular functions. Despite their functional and clinical relevance, the evolutionary history of TOPIIA is still poorly understood. Here we show that TOPIIA is highly conserved in Metazoa. We also found that TOPIIA paralogues from jawed and jawless vertebrates had different origins related with tetraploidization events. After duplication, TOP2B evolved under a stronger purifying selection than TOP2A, perhaps promoted by the more specialized role of TOP2B in postmitotic cells. We also detected genetic signatures of positive selection in the highly variable C-terminal domain (CTD), possibly associated with adaptation to cellular interactions. By comparing TOPIIA from modern and archaic humans, we found two amino acid substitutions in the TOP2A CTD, suggesting that TOP2A may have contributed to the evolution of present-day humans, as proposed for other cell cycle-related genes. Finally, we identified six residues conferring resistance to chemotherapy differing between TOP2A and TOP2B. These six residues could be targets for the development of TOP2A-specific inhibitors that would avoid the side effects caused by inhibiting TOP2B. Altogether, our findings clarify the origin, diversification and selection pressures governing the evolution of animal TOPIIA.