Natural products have a significant role in the development of new drugs, being relevant the pentacyclic triterpenes extracted from Olea europaea L. Anticancer effect of uvaol, a natural triterpene, has been scarcely studied. The aim of this study was to understand the anticancer mechanism of uvaol in the HepG2 cell line. Cytotoxicity results showed a selectivity effect of uvaol with higher influence in HepG2 than WRL68 cells used as control. Our results show that uvaol has a clear and selective anticancer activity in HepG2 cells supported by a significant anti-migratory capacity and a significant increase in the expression of HSP-60. Furthermore, the administration of this triterpene induces cell arrest in the G0/G1 phase, as well as an increase in the rate of cell apoptosis. These results are supported by a decrease in the expression of the anti-apoptotic protein Bcl2, an increase in the expression of the pro-apoptotic protein Bax, together with a down-regulation of the AKT/PI3K signaling pathway. A reduction in reactive oxygen species (ROS) levels in HepG2 cells was also observed. Altogether, results showed anti-proliferative and pro-apoptotic effect of uvaol on hepatocellular carcinoma, constituting an interesting challenge in the development of new treatments against this type of cancer.

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Hydroxytyrosol (HT), the main representative of polyphenols of olive oil, has been described as one of the most powerful natural antioxidants, also showing anti-inflammatory, antimicrobial, cardioprotective and anticancer activity in different type of cancers, but has been little studied in hematological neoplasms. The objective of this work was to evaluate the anticancer potential of HT in acute human leukemia T cells (Jurkat and HL60) and the anti-inflammatory potential in murine macrophages (Raw264.7). For this, cytotoxicity tests were performed for HT, showing IC50 values, at 24 h, for Jurkat, HL60 and Raw264.7 cells, of 27.3 µg·mL−1, 109.8 µg·mL−1 and 45.7 µg·mL−1, respectively. At the same time, HT caused cell arrest in G0/G1 phase in both Jurkat and HL60 cells by increasing G0/G1 phase and significantly decreasing S phase. Apoptosis and cell cycle assays revealed an antiproliferative effect of HT, decreasing the percentage of dividing cells and increasing apoptosis. Furthermore, HT inhibited the PI3K signaling pathway and, consequently, the MAPK pathway was activated. Inflammation tests revealed that HT acts as an anti-inflammatory agent, reducing NO levels in Raw264.7 cells previously stimulated by lipopolysaccharide (LPS). These processes were confirmed by the changes in the expression of the main markers of inflammation and cancer. In conclusion, HT has an anticancer and anti-inflammatory effect in the cell lines studied, which were Raw264.7, Jurkat, and HL60, and could be used as a natural drug in the treatment of liquid cancers, leukemias, myelomas and lymphomas.

Meniere disease (MD) is a rare disorder of the inner ear defined by sensorineural hearing loss (SNHL) associated with episodes of vertigo and tinnitus. The phenotype is variable, and it may be associated with other comorbidities such as migraine, respiratory allergies, and several autoimmune disorders. The condition has a significant heritability according to epidemiological and familial segregation studies. Familial MD is found in 10% of cases, the most frequently found genes being OTOG, MYO7A, and TECTA, previously associated with autosomal dominant and recessive non-syndromic SNHL. These findings suggest a new hypothesis where proteins involved in the extracellular structures in the apical surface of sensory epithelia (otolithic and tectorial membranes) and proteins in the stereocilia links would be key elements in the pathophysiology of MD. The ionic homeostasis of the otolithic and tectorial membranes could be critical to suppress the innate motility of individual hair cell bundles. Initially, focal detachment of these extracellular membranes may cause random depolarization of hair cells and will explain changes in tinnitus loudness or trigger vertigo attacks in early stages of MD. With the progression of the disease, a larger detachment will lead to an otolithic membrane herniation into the horizontal semicircular canal with dissociation in caloric and head impulse responses. Familial MD shows different types of inheritance, including autosomal dominant and compound recessive patterns and implementation of genetic testing will improve our understanding of the genetic structure of MD.

Background: The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to improve the accuracy and speed of diagnosis by analyzing large amounts of genomic data and identifying complex multiallelic patterns that may be associated with specific diseases. In this systematic review, we aimed to identify the methodological trends and the ML application areas in rare genetic diseases. Methods: We performed a systematic review of the literature following the PRISMA guidelines to search studies that used ML approaches to enhance the diagnosis of rare genetic diseases. Studies that used DNA-based sequencing data and a variety of ML algorithms were included, summarized, and analyzed using bibliometric methods, visualization tools, and a feature co-occurrence analysis. Findings: Our search identified 22 studies that met the inclusion criteria. We found that exome sequencing was the most frequently used sequencing technology (59%), and rare neoplastic diseases were the most prevalent disease scenario (59%). In rare neoplasms, the most frequent applications of ML models were the differential diagnosis or stratification of patients (38.5%) and the identification of somatic mutations (30.8%). In other rare diseases, the most frequent goals were the prioritization of rare variants or genes (55.5%) and the identification of biallelic or digenic inheritance (33.3%). The most employed method was the random forest algorithm (54.5%). In addition, the features of the datasets needed for training these algorithms were distinctive depending on the goal pursued, including the mutational load in each gene for the differential diagnosis of patients, or the combination of genotype features and sequence-derived features (such as GC-content) for the identification of somatic mutations. Conclusions: ML algorithms based on sequencing data are mainly used for the diagnosis of rare neoplastic diseases, with random forest being the most common approach. We identified key features in the datasets used for training these ML models according to the objective pursued. These features can support the development of future ML models in the diagnosis of rare genetic diseases. Keywords: artificial intelligence, rare diseases, precision medicine, rare variants, DNA-sequencing, genomics

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Summary The web platform 3DBionotes-WS integrates multiple Web Services and an interactive Web Viewer to provide a unified environment in which biological annotations can be analyzed in their structural context. Since the COVID-19 outbreak, new structural data from many viral proteins have been provided at a very fast pace. This effort includes many cryogenic Electron Microscopy (cryo-EM) studies, together with more traditional ones (X-rays, NMR), using several modeling approaches and complemented with structural predictions. At the same time, a plethora of new genomics and interactomics information (including fragment screening and structure-based virtual screening efforts) have been made available from different servers. In this context we have developed 3DBionotes-COVID-19 as an answer to: (1) The need to explore multi-omics data in a unified context with a special focus on structural information and (2) the drive to incorporate quality measurements, especially in the form of advanced validation metrics for cryogenic Electron Microscopy. Availability https://3dbionotes.cnb.csic.es/ws/covid19 Supplementary information Supplementary data are available at Bioinformatics online.

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