Pan-cancer and single-cell modelling of genomic alterations through gene expression

Cancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression, and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study, we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single-cell sequencing

Momordica charantia, a nutraceutical approach for inflammatory related diseases

Momordica charantia, commonly called bitter melon, is a plant belonging to Cucurbitaceae family known for centuries for its pharmacological activities, and nutritional properties. Due to the presence of many bioactive compounds, some of which possess potent biological actions, this plant is used in folk medicine all over the world for the treatment of different pathologies, mainly diabetes, but also cancer, and other inflammation-associated diseases. It is widely demonstrated that M. charantia extracts contribute in lowering glycaemia in patients affected by type 2 diabetes. However, the majority of existing studies on M. charantia bioactive compounds were performed only on cell lines and in animal models. Therefore, because the real impact of bitter melon on human health has not been thoroughly demonstrated, systematic clinical studies are needed to establish its efficacy and safety in patients. Besides, both in vitro and in vivo studies have demonstrated that bitter melon may also elicit toxic or adverse effects under different conditions. The aim of this review is to provide an overview of anti-inflammatory and anti-neoplastic properties of bitter melon, discussing its pharmacological activity as well as the potential adverse effects. Even if a lot of literature is available about bitter melon as antidiabetic drug, few papers discuss the anti-inflammatory and anti-cancer properties of this plant

Spondylocarpotarsal synostosis syndrome: \u201cBat wings\u201d spinal fusions and \u201cladybug\u201d carpal coalitions

No abstract availabl

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA)

The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a recently established multimodal single cell analysis technique combining the immunophenotyping capabilities of antibody labeling and cell sorting with the resolution of single-cell RNA sequencing (scRNA-seq). By simply adding a 12-bp nucleotide barcode to antibodies (cell hashing), CITE-seq can be used to sequence antibody-bound tags alongside the cellular mRNA, thus reducing costs of scRNA-seq by performing it at the same time on multiple barcoded samples in a single run. Here, we illustrate an ideal CITE-seq data analysis workflow by characterizing the transcriptome of SH-SY5Y neuroblastoma cell line, a widely used model to study neuronal function and differentiation. We obtained transcriptomes from a total of 2879 single cells, measuring an average of 1600 genes/cell. Along with standard scRNA-seq data handling procedures, such as quality checks and cell filtering procedures, we performed exploratory analyses to identify most stable genes to be possibly used as reference housekeeping genes in qPCR experiments. We also illustrate how to use some popular R packages to investigate cell heterogeneity in scRNA-seq data, namely Seurat, Monocle, and slalom. Both the CITE-seq dataset and the code used to analyze it are freely shared and fully reusable for future research

Mirror Surface Check on Solar Troughs by Optical Profilometry

Abstract Linear parabolic collectors usually need profilometric control since the reflector surface can be imperfectly manufactured. Optical profile assessment is generally addressed to detect small localised defects. The paper proposes two optical devices that were developed simulating profile measurements on linear parabolic mirrors. Solar troughs are employed in thermal plants and concentrating photovoltaic systems. The profilometer examines the reflector surface operating on a plane transversal to the linear axis of the trough collector. Then the detection is repeated displacing the optical device along the linear collector axis. The first profilometer includes a shifted laser source and a target placed at the collector focal distance. The second profilometer has a fixed target and a linear laser source, which is approximately located in the focal position of the solar mirror. Ray-tracing simulations and practical tests are illustrated for both optical devices

Quantitative imaging techniques for the assessment of osteoporosis and sarcopenia

Bone and muscle are two deeply interconnected organs and a strong relationship between them exists in their development and maintenance. The peak of both bone and muscle mass is achieved in early adulthood, followed by a progressive decline after the age of 40. The increase in life expectancy in developed countries resulted in an increase of degenerative diseases affecting the musculoskeletal system. Osteoporosis and sarcopenia represent a major cause of morbidity and mortality in the elderly population and are associated with a significant increase in healthcare costs. Several imaging techniques are currently available for the non-invasive investigation of bone and muscle mass and quality. Conventional radiology, dual energy X-ray absorptiometry (DXA), computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound often play a complementary role in the study of osteoporosis and sarcopenia, depicting different aspects of the same pathology. This paper presents the different imaging modalities currently used for the investigation of bone and muscle mass and quality in osteoporosis and sarcopenia with special emphasis on the clinical applications and limitations of each technique and with the intent to provide interesting insights into recent advances in the field of conventional imaging, novel high-resolution techniques and fracture risk

Early Response to the Plant Toxin Stenodactylin in Acute Myeloid Leukemia Cells Involves Inflammatory and Apoptotic Signaling

Stenodactylin, a highly toxic type 2 ribosome-inactivating protein purified from the caudex of Adenia stenodactyla Harms, is a potential anticancer drug candidate. Previous studies demonstrated that stenodactylin induces apoptosis and necroptosis in treated cells, involving the production of reactive oxygen species. We analyzed the effect of stenodactylin on Raji and Ramos (Human Burkitt’s lymphoma cells) and MOLM-13 (acute myeloid leukemia cells). Moreover, we focused on the early events in MOLM-13 cells that characterize the cellular response to the toxin by whole-genome microarray analysis of gene expression. Treatment with stenodactylin induced the depurination of 28S rRNA within 4 h and increased the phosphorylation of p38 and JNK. A time-dependent activation of caspase 1, 2, 8, 9, 3/7 was also observed. Genome-wide gene expression microarray analysis revealed early changes in the expression of genes involved in the regulation of cell death, inflammation and stress response. After 4 h, a significant increase of transcript level was detectable for ATF3, BTG2, DUSP1, EGR1, and JUN. Increased upstream JUN signaling was also confirmed at protein level. The early response to stenodactylin treatment involves inflammatory and apoptotic signaling compatible with the activation of multiple cell death pathways. Because of the above described properties toward acute myeloid leukemia cells, stenodactylin may be a promising candidate for the design of new immunoconjugates for experimental cancer treatment

Single-cell gene network analysis and transcriptional landscape of MYCN-amplified neuroblastoma cell lines

Neuroblastoma (NBL) is a pediatric cancer responsible for more than 15% of cancer deaths in children, with 800 new cases each year in the United States alone. Genomic amplification of the MYC oncogene family member MYCN characterizes a subset of high-risk pediatric neuroblastomas. Several cellular models have been implemented to study this disease over the years. Two of these, SK-N-BE-2-C (BE2C) and Kelly, are amongst the most used worldwide as models of MYCN-Amplified human NBL. Here, we provide a transcriptome-wide quantitative measurement of gene expression and transcriptional network activity in BE2C and Kelly cell lines at an unprecedented single-cell resolution. We obtained 1105 Kelly and 962 BE2C unsynchronized cells, with an average number of mapped reads/cell of roughly 38,000. The single-cell data recapitulate gene expression signatures previously generated from bulk RNA-Seq. We highlight low variance for commonly used housekeeping genes between different cells (ACTB, B2M and GAPDH), while showing higher than expected variance for metallothionein transcripts in Kelly cells. The high number of samples, despite the relatively low read coverage of single cells, allowed for robust pathway enrichment analysis and master regulator analysis (MRA), both of which highlight the more mesenchymal nature of BE2C cells as compared to Kelly cells, and the upregulation of TWIST1 and DNAJC1 transcriptional networks. We further defined master regulators at the single cell level and showed that MYCN is not constantly active or expressed within Kelly and BE2C cells, independently of cell cycle phase. The dataset, alongside a detailed and commented programming protocol to analyze it, is fully shared and reusable