Detection of driver mutations and genomic signatures in endometrial cancers using artificial intelligence algorithms.

Analyzed endometrial cancer (EC) genomes have allowed for the identification of molecular signatures, which enable the classification, and sometimes prognostication, of these cancers. Artificial intelligence algorithms have facilitated the partitioning of mutations into driver and passenger based on a variety of parameters, including gene function and frequency of mutation. Here, we undertook an evaluation of EC cancer genomes deposited on the Catalogue of Somatic Mutations in Cancers (COSMIC), with the goal to classify all mutations as either driver or passenger. Our analysis showed that approximately 2.5% of all mutations are driver and cause cellular transformation and immortalization. We also characterized nucleotide level mutation signatures, gross chromosomal re-arrangements, and gene expression profiles. We observed that endometrial cancers show distinct nucleotide substitution and chromosomal re-arrangement signatures compared to other cancers. We also identified high expression levels of the CLDN18 claudin gene, which is involved in growth, survival, metastasis and proliferation. We then used in silico protein structure analysis to examine the effect of certain previously uncharacterized driver mutations on protein structure. We found that certain mutations in CTNNB1 and TP53 increase protein stability, which may contribute to cellular transformation. While our analysis retrieved previously classified mutations and genomic alterations, which is to be expected, this study also identified new signatures. Additionally, we show that artificial intelligence algorithms can be effectively leveraged to accurately predict key drivers of cancer. This analysis will expand our understanding of ECs and improve the molecular toolbox for classification, diagnosis, or potential treatment of these cancers.

Quality Control Method and Device for Producing Agarose Micropads.

In brightfield and fluorescence microscopy, capturing images that show well-focused and immobile microorganisms can be challenging. An agarose-based gel pad reduces the variability of results, especially in conditions like uneven specimen staging, variable fluid dynamics, and Brownian motion that plague conventional wet mount setups. To correct these discrepancies during image acquisition, we analyzed three micropad preparation setups. We tested the quality and consistency of pads and images resulting from each setup. Our examination reveals that improved gel pad flatness is associated with better image quality. Moreover, we observe increased consistency in gel pad construction connected to the use of a 3D-printed setup. These findings highlight the technical benefits arising from incorporating micropad-generating platforms that increase the consistency of results in imaging pipelines. Additionally, our use of a quantitative approach to examine pad flatness suggests its inclusion in quality control pipelines to reduce variation in gel pad construction and image quality over time and between investigators. Finally, our use of a 3D-printed setup coupled with a quantitative downstream routine suggests their application in microscopy experiments that involve model organisms relevant to human health and disease.

Early-life caffeine exposure induces morphological changes and altered physiology in Caenorhabditiselegans.

Caffeine, a widely consumed stimulant, is known for its effects on alertness and fatigue reduction by blockade of adenosine receptors. While it holds therapeutic potential, its diverse impacts pose risks, particularly in early development. This study explores the developmental effects of caffeine exposure using Caenorhabditis elegans (C. elegans) as a model organism. We investigated morphological and behavioral changes induced by caffeine exposure at the L1 stage and assessed their impact at the L4 stage, which roughly corresponds to human infancy and adolescence, respectively. Caffeine-exposed worms displayed increased body length, body bends, and pharyngeal pumping rates compared to control worms. These findings indicate heightened food-seeking behavior and greater food intake, leading to the observed morphological changes. While caffeine did not affect other locomotor behaviors, its stimulatory effect on growth and development highlights its significance. This study provides insights into the potential impact of early-life caffeine exposure on long-term health and development, offering a foundation for future research in vertebrates to uncover its implications on metabolism and other metrics of health.

Photo Phenosizer, a rapid machine learning-based method to measure cell dimensions in fission yeast.

Cell metrics such as area, length, and width provide informative data about cell cycle dynamics. Factors that affect these dimensions include environmental conditions and genotypic differences. Fission yeast ( Schizosaccharomyces pombe ) is a rod-shaped ascomycete fungus in which cell cycle progression is linked to changes in cell length. Microscopy work to obtain these metrics places considerable burdens on time and effort. We now report on Photo Phenosizer (PP), a machine learning-based methodology that measures cell dimensions in fission yeast. It does this in an unbiased, automated manner and streamlines workflow from image acquisition to statistical analysis. Using this new approach, we constructed an efficient and flexible pipeline for experiments involving different growth media (YES and EMM) and treatments (Untreated and MMS) as well as different genotypes ( cut6-621 versus wildtype). This methodology allows for the analysis of larger sample sizes and faster image processing relative to manual segmentation. Our findings suggest that researchers using PP can quickly and efficiently determine cell size differences under various conditions that highlight genetic or environmental disruptions.

Comprehensive Genetic Analysis of DGAT2 Mutations and Gene Expression Patterns in Human Cancers.

DGAT2 is a transmembrane protein encoded by the DGAT2 gene that functions in lipid metabolism, triacylglycerol synthesis, and lipid droplet regulation. Cancer cells exhibit altered lipid metabolism and mutations in DGAT2 may contribute to this state. Using data from the Catalogue of Somatic Mutations in Cancer (COSMIC), we analyzed all cancer genetic DGAT2 alterations, including mutations, copy number variations and gene expression. We find that several DGAT2 mutations fall within the catalytic site of the enzyme. Using the Variant Effect Scoring Tool (VEST), we identify multiple mutations with a high likelihood of contributing to cellular transformation. We also found that D222V is a mutation hotspot neighboring a previously discovered Y223H mutation that causes Axonal Charcot-Marie-Tooth disease. Remarkably, Y223H has not been detected in cancers, suggesting that it is inhibitory to cancer progression. We also identify several single nucleotide polymorphisms (SNP) with high VEST scores, indicating that certain alleles in human populations have a pathogenic predisposition. Most mutations do not correlate with a change in gene expression, nor is gene expression dependent on high allele copy number. However, we did identify eight alleles with high expression levels, suggesting that at least in certain cases, the excess DGAT2 gene product is not inhibitory to cellular proliferation. This work uncovers unknown functions of DGAT2 in cancers and suggests that its role may be more complex than previously appreciated.