Functions and consequences of AID/APOBEC-mediated DNA and RNA deamination.

The AID/APOBEC polynucleotide cytidine deaminases have historically been classified as either DNA mutators or RNA editors based on their first identified nucleic acid substrate preference. DNA mutators can generate functional diversity at antibody genes but also cause genomic instability in cancer. RNA editors can generate informational diversity in the transcriptome of innate immune cells, and of cancer cells. Members of both classes can act as antiviral restriction factors. Recent structural work has illuminated differences and similarities between AID/APOBEC enzymes that can catalyse DNA mutation, RNA editing or both, suggesting that the strict functional classification of members of this family should be reconsidered. As many of these enzymes have been employed for targeted genome (or transcriptome) editing, a more holistic understanding will help improve the design of therapeutically relevant programmable base editors.

Characterization of Heart Diseases per Single Lead Using ECG Images and CNN-2D.

Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly diminishes mortality risk and enhances the efficacy of treatment. However, fast and efficient recognition necessitates continuous monitoring, encompassing not only specific clinical conditions but also diverse lifestyles. Consequently, an increasing number of studies are striving to automate and progress in the identification of different cardiopathies. Notably, the assessment of electrocardiograms (ECGs) is crucial, given that it serves as the initial diagnostic test for patients, proving to be both the simplest and the most cost-effective tool. This research employs a customized architecture of Convolutional Neural Network (CNN) to forecast heart diseases by analyzing the images of both three bands of electrodes and of each single electrode signal of the ECG derived from four distinct patient categories, representing three heart-related conditions as well as a spectrum of healthy controls. The analyses are conducted on a real dataset, providing noteworthy performance (recall greater than 80% for the majority of the considered diseases and sometimes even equal to 100%) as well as a certain degree of interpretability thanks to the understanding of the importance a band of electrodes or even a single ECG electrode can have in detecting a specific heart-related pathology.

Deep neural networks ensemble to detect COVID-19 from CT scans.

Research on Coronavirus Disease 2019 (COVID-19) detection methods has increased in the last months as more accurate automated toolkits are required. Recent studies show that CT scan images contain useful information to detect the COVID-19 disease. However, the scarcity of large and well balanced datasets limits the possibility of using detection approaches in real diagnostic contexts as they are unable to generalize. Indeed, the performance of these models quickly becomes inadequate when applied to samples captured in different contexts (e.g., different equipment or populations) from those used in the training phase. In this paper, a novel ensemble-based approach for more accurate COVID-19 disease detection using CT scan images is proposed. This work exploits transfer learning using pre-trained deep networks (e.g., VGG, Xception, and ResNet) evolved with a genetic algorithm, combined into an ensemble architecture for the classification of clustered images of lung lobes. The study is validated on a new dataset obtained as an integration of existing ones. The results of the experimental evaluation show that the ensemble classifier ensures effective performance, also exhibiting better generalization capabilities.

An efficient method to enrich for knock-out and knock-in cellular clones using the CRISPR/Cas9 system.

Clustered Regularly Interspaced Short Palindromic Repeats-associated protein 9 nuclease (CRISPR/Cas9) and Transcription Activator-Like Effector Nucleases (TALENs) are versatile tools for genome editing. Here we report a method to increase the frequency of Cas9-targeted cellular clones. Our method is based on a chimeric construct with a Blasticidin S Resistance gene (bsr) placed out-of-frame by a surrogate target sequence. End joining of the CRISPR/Cas9-induced double-strand break on the surrogate target can place the bsr in frame, thus providing temporary resistance to Blasticidin S: this is used to enrich for cells where Cas9 is active. By this approach, in a real experimental setting, we disrupted the Aicda gene in ~70% of clones from CH12F3 lymphoma cells (>40% biallelically). With the same approach we knocked in a single nucleotide to reconstruct the frame of Aicda in these null cells, restoring the function in ~37% of the clones (less than 10% by the standard approach). Targeting of single nucleotide changes in other genes yielded analogous results. These results support our enrichment method as an efficient tool in genome editing.

A systematic review on artificial intelligence techniques for detecting thyroid diseases.

The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.

ADAR1-mediated RNA editing promotes B cell lymphomagenesis.

Diffuse large B cell lymphoma (DLBCL) is one of the most common types of aggressive lymphoid malignancies. Here, we explore the contribution of RNA editing to DLBCL pathogenesis. We observed that DNA mutations and RNA editing events are often mutually exclusive, suggesting that tumors can modulate pathway outcomes by altering sequences at either the genomic or the transcriptomic level. RNA editing targets transcripts within known disease-driving pathways such as apoptosis, p53 and NF-κB signaling, as well as the RIG-I-like pathway. In this context, we show that ADAR1-mediated editing within MAVS transcript positively correlates with MAVS protein expression levels, associating with increased interferon/NF-κB signaling and T cell exhaustion. Finally, using targeted RNA base editing tools to restore editing within MAVS 3'UTR in ADAR1-deficient cells, we demonstrate that editing is likely to be causal to an increase in downstream signaling in the absence of activation by canonical nucleic acid receptor sensing.

ADAR RNA editing on antisense RNAs results in apparent U-to-C base changes on overlapping sense transcripts.

Despite hundreds of RNA modifications described to date, only RNA editing results in a change in the nucleotide sequence of RNA molecules compared to the genome. In mammals, two kinds of RNA editing have been described so far, adenosine to inosine (A-to-I) and cytidine to uridine (C-to-U) editing. Recent improvements in RNA sequencing technologies have led to the discovery of a continuously growing number of editing sites. These methods are powerful but not error-free, making routine validation of newly-described editing sites necessary. During one of these validations on DDX58 mRNA, along with A-to-I RNA editing sites, we encountered putative U-to-C editing. These U-to-C edits were present in several cell lines and appeared regulated in response to specific environmental stimuli. The same findings were also observed for the human long intergenic non-coding RNA p21 (hLincRNA-p21). A more in-depth analysis revealed that putative U-to-C edits result from A-to-I editing on overlapping antisense RNAs that are transcribed from the same loci. Such editing events, occurring on overlapping genes transcribed in opposite directions, have recently been demonstrated to be immunogenic and have been linked with autoimmune and immune-related diseases. Our findings, also confirmed by deep transcriptome data, demonstrate that such loci can be recognized simply through the presence of A-to-I and U-to-C mismatches within the same locus, reflective A-to-I editing both in the sense-oriented transcript and in the cis-natural antisense transcript (cis-NAT), implying that such clusters could be a mark of functionally relevant ADAR1 editing events.

Rapid, adaptable and sensitive Cas13-based COVID-19 diagnostics using ADESSO.

During the ongoing COVID-19 pandemic, PCR testing and antigen tests have proven critical for helping to stem the spread of its causative agent, SARS-CoV-2. However, these methods suffer from either general applicability and/or sensitivity. Moreover, the emergence of variant strains creates the need for flexibility to correctly and efficiently diagnose the presence of substrains. To address these needs we developed the diagnostic test ADESSO (Accurate Detection of Evolving SARS-CoV-2 through SHERLOCK (Specific High Sensitivity Enzymatic Reporter UnLOCKing) Optimization) which employs Cas13 to diagnose patients in 1 h without sophisticated equipment. Using an extensive panel of clinical samples, we demonstrate that ADESSO correctly identifies infected individuals at a sensitivity and specificity comparable to RT-qPCR on extracted RNA and higher than antigen tests for unextracted samples. Altogether, ADESSO is a fast, sensitive and cheap method that can be applied in a point of care setting to diagnose COVID-19 and can be quickly adjusted to detect new variants.

C-to-U RNA Editing: From Computational Detection to Experimental Validation.

The AID/APOBEC family of enzymes are cytidine deaminases that act upon DNA and RNA. Among APOBECs, the best characterized family member to act on RNA is the enzyme APOBEC1. APOBEC1-mediated RNA editing plays a key role in lipid metabolism and in maintenance of brain homeostasis. Editing can be easily detected in RNA-seq data as a cytosine to thymine (C-to-T) change with regard to the reference. However, there are many other sources of base conversions relative to reference, such as PCR errors, SNPs, and even DNA editing by mutator APOBECs. Furthermore, APOBEC1 exhibits disparate activity in different cell types, with respect to which transcripts are edited and the level to which they are edited. When considering these potential sources of error and variability, an RNA-seq comparison between wild-type APOBEC1 sample and a matched control with an APOBEC1 knockout is a reliable method for the discrimination of true sites edited by APOBEC1. Here we present a detailed description of a method for studying APOBEC1 RNA editing, specifically in the murine macrophage cell line RAW 264.7. Our method covers the production of an APOBEC1 knockout cell line using the CRISPR/Cas9 system, through to experimental validation and quantification of editing sites (where we discuss a recently published algorithm (termed MultiEditR) which allows for the detection and quantification of RNA editing from Sanger sequencing). Importantly, this same protocol can be adapted to any RNA modification detectable by RNA-seq analysis for which the responsible protein is known.

MultiEditR: The first tool for the detection and quantification of RNA editing from Sanger sequencing demonstrates comparable fidelity to RNA-seq.

We present MultiEditR (Multiple Edit Deconvolution by Inference of Traces in R), the first algorithm specifically designed to detect and quantify RNA editing from Sanger sequencing (z.umn.edu/multieditr). Although RNA editing is routinely evaluated by measuring the heights of peaks from Sanger sequencing traces, the accuracy and precision of this approach has yet to be evaluated against gold standard next-generation sequencing methods. Through a comprehensive comparison to RNA sequencing (RNA-seq) and amplicon-based deep sequencing, we show that MultiEditR is accurate, precise, and reliable for detecting endogenous and programmable RNA editing.

RNA Editors, Cofactors, and mRNA Targets: An Overview of the C-to-U RNA Editing Machinery and Its Implication in Human Disease.

One of the most prevalent epitranscriptomic modifications is RNA editing. In higher eukaryotes, RNA editing is catalyzed by one of two classes of deaminases: ADAR family enzymes that catalyze A-to-I (read as G) editing, and AID/APOBEC family enzymes that catalyze C-to-U. ADAR-catalyzed deamination has been studied extensively. Here we focus on AID/APOBEC-catalyzed editing, and review the emergent knowledge regarding C-to-U editing consequences in the context of human disease.

Discovery of ß-Adrenergic Receptors Blocker-Carbonic Anhydrase Inhibitor Hybrids for Multitargeted Antiglaucoma Therapy.

The combination of a ß-adrenergic receptors (AR) blocker and a carbonic anhydrase (CA, EC 4.2.1.1) inhibitor in eye drops formulations is one of the most clinically used treatment for glaucoma. A novel approach consisting of single-molecule, multitargeted compounds for the treatment of glaucoma is proposed here by designing compounds which concomitantly interact with the ß-adrenergic and CA targets. Most derivatives of the two series of benzenesulfonamides incorporating 2-hydroxypropylamine moieties reported here exhibited striking efficacy against the target hCA II and XII, whereas a subset of compounds also showed significant modulation of ß1- and ß2-ARs. X-ray crystallography studies provided rationale for the observed hCA inhibition. The best dual-agents decreased IOP more effectively than clinically used dorzolamide, timolol, and the combination of them in an animal model of glaucoma. The reported evidence supports the proof-of-concept of ß-ARs blocker-CAI hybrids for antiglaucoma therapy with an innovative mechanism of action.