Ana-Bárbara García-García

Type 2 Diabetes (T2D) is a complex disease that arises from interaction between genetic and environmental factors. The activation of transposable elements (TEs) and the production of circular extrachromosomal DNA (eccDNA) may represent genetic mechanisms involved in cellular aging, metabolic alterations, and T2D development through distinct pathways. Although the origin and characteristics of TEs and eccDNA differ substantially, eccDNA can in some cases be derived from TEs. This review summarizes the current understanding of these mechanisms and examines the reported associations between T2D and either TEs or eccDNAs. These findings highlight the significant involvement of these molecules in disease pathogenesis, particularly in relation to aging, and underscore their great potential as biomarkers and targets for T2D prevention.

Type 2 diabetes (T2D) is becoming one of the leading health problems in Western societies, diminishing quality of life and consuming a significant share of healthcare resources. This study presents machine learning models for T2D diagnosis and prognosis, developed using heterogeneous data from a Spanish population dataset (Di@bet.es study). The models were trained exclusively on individuals classified as controls and undiagnosed diabetics, ensuring that the results are not influenced by treatment effects or behavioral changes due to disease awareness. Two data domains are considered: environmental (patient lifestyle questionnaires and measurements) and clinical (biochemical and anthropometric measurements). The preprocessing pipeline consists of four key steps: geospatial data extraction, feature engineering, missing data imputation, and quasi-constancy filtering. Two working scenarios (Environmental and Healthcare) are defined based on the features used, and applied to two targets (diagnosis and prognosis), resulting in four distinct models. The feature subsets that best predict the target have been identified based on permutation importance and sequential backward selection, reducing the number of features and, consequently, the cost of predictions. In the Environmental scenario, models achieved an AUROC of 0.86 for diagnosis and 0.82 for prognosis. The Healthcare scenario performed better, with an AUROC of 0.96 for diagnosis and 0.88 for prognosis. A partial dependence analysis of the most relevant features is also presented. An online demo page showcasing the Environmental and Healthcare T2D prognosis models is available upon request. Graphic

The determination of the mutational status in the immunoglobulin variable region is an established prognostic biomarker for chronic lymphocytic leukemia (CLL). The length and inner variability of the variable, diversity, and joining (VDJ) rearranged sequences compromise B-cell clone characterization using next-generation sequencing (NGS), and a standardization is needed to adapt the procedure to the current clinical guidelines. Here, we develop a complete strategy for sequencing the variable domain of the immunoglobulin heavy chain (IGH) locus with a simple, low-cost, and efficient method that enables sequencing using shorter reads (MiSeq 150 × 2), allowing for faster results. Clonality and mutational status determination are performed within the same analysis pipeline. We tested and validated the method using 319 CLL patients previously diagnosed with IGH locus characterized using Sanger sequencing, along with 47 healthy donor samples. The analysis method follows a clone-centered consensus sequence strategy to identify B-cell clones and establish a clonal threshold specific for each patient’s clonality profile, thereby overcoming the limitations of Sanger sequencing which is the gold standard used for determining immunoglobulin heavy variable (IGHV) mutational status.

This research aims to evaluate the Type 2 Diabetes (T2D) diagnosis and prognosis power from heterogeneous environmental, lifestyle and biochemistry data. Model estimation has previously addressed three main actions as: 1) Missingvalue imputation using specific univariant and multivariant imputers accommodated to each particular feature; 2) Quasi-constancy detection in variables; 3) Constructing geographical pollution and rent data from municipality information. Next, different T2D diagnosis and prognosis models are fitted and evaluated, showing increasing performance as more specific features become available while the prediction cost rises as a consequence of requiring more specific data. Finally, four models are obtained: two of them for T2D diagnosis and the other two for T2D prognosis respectively, with performances ranging from 73.3 to 95.41 AUC-ROC. One pair of diagnosis and prognosis models were thought for a global testing that can be done in general locations by only asking general lifestyle-related questions. On the other hand, the other pair, which achieves higher performances, is thought to be applied in a clinical environment where it is easy to obtain more specific biochemistry measures.

The SARS-CoV-2 coronavirus is responsible for the COVID-19 pandemic resulting in a global health emergency. Given its rapid spread and high number of infected individuals, a diagnostic tool for a rapid, simple, and cost-effective detection was essential. In this work, we developed a COVID-19 diagnostic test, that incorporates a human internal control, based on the Reverse Transcription Loop-Mediated Isothermal Amplification (RT-LAMP). When working with synthetic SARS-CoV-2 RNA, the optimized RT-LAMP assay has a sensitivity of 10 viral copies and can be detected by fluorescence in less than 15 min or by the naked eye in 25 min using colorimetric RT-LAMP. To avoid the RNA extraction step, a pre-treatment of the sample was optimized. Subsequently, a validation was performed on 268 trypsin treated samples (including nasopharyngeal, buccal, and nasal exudates) and amplified with colorimetric RT-LAMP to evaluate its sensitivity and specificity in comparison with RT-qPCR of extracted samples. The validation results showed a sensitivity and specificity of 100% for samples with Ct ≤ 30. The rapid, simple, and inexpensive RT-LAMP SARS-CoV-2 extraction-free procedure developed may be an alternative test that could be applied for the detection of SARS-CoV-2 or adapted to detect other viruses present in saliva or nasopharyngeal samples with higher sensitivity and specificity of the antibody test.

Numerous genes involved in different metabolic diseases have been identified, and this number is increasing

Copy number variations (CNVs) are a type of genetic variation involving from 50 base pairs (bps) to millions of bps and, in a general point of view, can include alterations of complete chromosomes. As CNVs mean the gain or loss of DNA sequences, their detection requires specific techniques and analysis. We have developed Easy One-Step Amplification and Labeling for CNV Detection (EOSAL–CNV) by fragment analysis in a DNA sequencer. The procedure is based on a single PCR reaction for amplification and labeling of all fragments included. The protocol includes specific primers for the amplification of the regions of interest with a tail in each of the primers (one for forward and another for the reverse primers) together with primers for tail amplification. One of the primers for tail amplification is labeled with a fluorophore, allowing the amplification and labeling in the same reaction. Combination of several tail pairs and labels allows the detection of DNA fragment by different fluorophores and increases the number of fragments that can be analyzed in one reaction. PCR products can be analyzed without any purification on a DNA sequencer for fragment detection and quantification. Finally, simple and easy calculations allow the detection of fragments with deletions or extra copies. The use of EOSAL–CNV allows simplifying and reducing costs in sample analysis for CNV detection. Key words CNV detection Methodology Genetics PCR multiplex

Patients with high cholesterol and glucose levels are at high risk for cardiovascular disease. The Sterol Regulatory Element Binding Protein (SREBP) system regulates genes involved in lipid, cholesterol and glucose pathways. Autosomal Dominant Hypercholesterolemias (ADHs) are a group of diseases with increased cholesterol levels. They affect 1 out of every 500 individuals. About 20–30% of patients do not present any mutation in the known genes (LDLR, APOB and PCSK9). ADHs constitute a good model to identify the genes involved in the alteration of lipid levels or possible therapeutic targets. In this paper, we studied whether a mutation in the SREBP system could be responsible for ADH and other metabolic alterations present in these patients. Forty-one ADH patients without mutations in the main responsible genes were screened by direct sequencing of SREBP system genes. A luciferase reporter assay of the found mutation and an oral glucose tolerance test in carriers and non-carriers were performed. We found a novel mutation in the SREBF2 gene that increases transcription levels and cosegregates with hypercholesterolemia, and we found increased glucose levels in one family. SREBP2 is known to be involved in cholesterol synthesis, plasma levels and glucose metabolism in humans. The found mutation may involve the SREBF2 gene in hypercholesterolemia combined with hyperglycemia.