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As the COVID-19 pandemic winds down, it leaves behind the serious concern that future, even more disruptive pandemics may eventually surface. One of the crucial steps in handling the SARS-CoV-2 pandemic was being able to detect the presence of the virus in an accurate and timely manner, to then develop policies counteracting the spread. Nevertheless, as the pandemic evolved, new variants with potentially dangerous mutations appeared. Faced by these developments, it becomes clear that there is a need for fast and reliable techniques to create highly specific molecular tests, able to uniquely identify VOCs. Using an automated pipeline built around evolutionary algorithms, we designed primer sets for SARS-CoV-2 (main lineage) and for VOC, B.1.1.7 (Alpha) and B.1.1.529 (Omicron). Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for the main lineage and each variant in a matter of hours. Preliminary in-silico validation showed that the sequences in the primer sets featured high accuracy. A pilot test in a laboratory setting confirmed the results: the developed primers were favorably compared against existing commercial versions for the main lineage, and the specific versions for the VOCs B.1.1.7 and B.1.1.529 were clinically tested successfully., (© 2023. Springer Nature Limited.)

Background: Type 2 diabetes (T2D) is associated with coronary microvascular dysfunction, which is thought to contribute to compromised diastolic function, ultimately culminating in heart failure with preserved ejection fraction (HFpEF). The molecular mechanisms remain incompletely understood, and no early diagnostics are available. We sought to gain insight into biomarkers and potential mechanisms of microvascular dysfunction in obese mouse (db/db) and lean rat (Goto-Kakizaki) pre-clinical models of T2D-associated diastolic dysfunction., Methods: The microRNA (miRNA) content of circulating extracellular vesicles (EVs) was assessed in T2D models to identify biomarkers of coronary microvascular dysfunction/rarefaction. The potential source of circulating EV-encapsulated miRNAs was determined, and the mechanisms of induction and the function of candidate miRNAs were assessed in endothelial cells (ECs)., Results: We found an increase in miR-30d-5p and miR-30e-5p in circulating EVs that coincided with indices of coronary microvascular EC dysfunction (i.e., markers of oxidative stress, DNA damage/senescence) and rarefaction, and preceded echocardiographic evidence of diastolic dysfunction. These miRNAs may serve as biomarkers of coronary microvascular dysfunction as they are upregulated in ECs of the left ventricle of the heart, but not other organs, in db/db mice. Furthermore, the miR-30 family is secreted in EVs from senescent ECs in culture, and ECs with senescent-like characteristics are present in the db/db heart. Assessment of miR-30 target pathways revealed a network of genes involved in fatty acid biosynthesis and metabolism. Over-expression of miR-30e in cultured ECs increased fatty acid β-oxidation and the production of reactive oxygen species and lipid peroxidation, while inhibiting the miR-30 family decreased fatty acid β-oxidation. Additionally, miR-30e over-expression synergized with fatty acid exposure to down-regulate the expression of eNOS, a key regulator of microvascular and cardiomyocyte function. Finally, knock-down of the miR-30 family in db/db mice decreased markers of oxidative stress and DNA damage/senescence in the microvascular endothelium., Conclusions: MiR-30d/e represent early biomarkers and potential therapeutic targets that are indicative of the development of diastolic dysfunction and may reflect altered EC fatty acid metabolism and microvascular dysfunction in the diabetic heart., (© 2022. The Author(s).)

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.

IAMBEE is a web server designed for the Identification of Adaptive Mutations in Bacterial Evolution Experiments (IAMBEE). Input data consist of genotype information obtained from independently evolved clonal populations or strains that show the same adapted behavior (phenotype). To distinguish adaptive from passenger mutations, IAMBEE searches for neighborhoods in an organism-specific interaction network that are recurrently mutated in the adapted populations. This search for recurrently mutated network neighborhoods, as proxies for pathways is driven by additional information on the functional impact of the observed genetic changes and their dynamics during adaptive evolution. In addition, the search explicitly accounts for the differences in mutation rate between the independently evolved populations. Using this approach, IAMBEE allows exploiting parallel evolution to identify adaptive pathways. The web-server is freely available at http://bioinformatics.intec.ugent.be/iambee/ with no login requirement., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Morphogen gradients provide concentration-dependent positional information along polarity axes. Although the dynamics of the establishment of these gradients is well described, precision and noise in the downstream activation processes remain elusive. A simple paradigm to address these questions is the Bicoid morphogen gradient that elicits a rapid step-like transcriptional response in young fruit fly embryos. Focusing on the expression of the major Bicoid target, hunchback (hb), at the onset of zygotic transcription, we used the MS2-MCP approach which combines fluorescent labeling of nascent mRNA with live imaging at high spatial and temporal resolution. Removing 36 putative Zelda binding sites unexpectedly present in the original MS2 reporter, we show that the 750 bp of the hb promoter are sufficient to recapitulate endogenous expression at the onset of zygotic transcription. After each mitosis, in the anterior, expression is turned on to rapidly reach a plateau with all nuclei expressing the reporter. Consistent with a Bicoid dose-dependent activation process, the time period required to reach the plateau increases with the distance to the anterior pole. Despite the challenge imposed by frequent mitoses and high nuclei-to-nuclei variability in transcription kinetics, it only takes 3 minutes at each interphase for the MS2 reporter loci to distinguish subtle differences in Bicoid concentration and establish a steadily positioned and steep (Hill coefficient ~ 7) expression boundary. Modeling based on the cooperativity between the 6 known Bicoid binding sites in the hb promoter region, assuming rate limiting concentrations of the Bicoid transcription factor at the boundary, is able to capture the observed dynamics of pattern establishment but not the steepness of the boundary. This suggests that a simple model based only on the cooperative binding of Bicoid is not sufficient to describe the spatiotemporal dynamics of early hb expression., Competing Interests: The authors have declared that no competing interests exist.

Fly development amazes us by the precision and reproducibility of gene expression, especially since the initial expression patterns are established during very short nuclear cycles. Recent live imaging of hunchback promoter dynamics shows a stable steep binary expression pattern established within the three minute interphase of nuclear cycle 11. Considering expression models of different complexity, we explore the trade-off between the ability of a regulatory system to produce a steep boundary and minimize expression variability between different nuclei. We show how a limited readout time imposed by short developmental cycles affects the gene's ability to read positional information along the embryo's anterior posterior axis and express reliably. Comparing our theoretical results to real-time monitoring of the hunchback transcription dynamics in live flies, we discuss possible regulatory strategies, suggesting an important role for additional binding sites, gradients or non-equilibrium binding and modified transcription factor search strategies., Competing Interests: The authors have declared that no competing interests exist.

Telomeres are maintained in a heterochromatic state that represses transcription of subtelomeric genes, a phenomenon known as telomere position effect. Nevertheless, telomeric DNA is actively transcribed, leading to the synthesis of telomeric repeat-containing noncoding RNA or TERRA. This nuclear noncoding RNA has been proposed to play important roles at telomeres, regulating their silencing, capping, repair and elongation by telomerase. In the budding yeast Saccharomyces cerevisiae, TERRA accumulation is repressed by telomeric silencing and the Rat1 exonuclease. On the other hand, telomere shortening promotes expression of TERRA. So far, little is known about the biological processes that induce TERRA expression in yeast. Understanding the dynamics of TERRA expression and localization is essential to define its function in telomere biology. Here, we aim to study the dynamics of TERRA expression during yeast cell growth. Using live-cell imaging, RNA-FISH and quantitative RT-PCR, we show that TERRA expression is induced as yeast cells undergo diauxic shift, a lag phase during which yeast cells switch their metabolism from anaerobic fermentation to oxidative respiration. This induction is transient as TERRA levels decrease during post-diauxic shift. The increased expression of TERRA is not due to the shortening of telomeres or increased stability of this transcript. Surprisingly, this induction is coincident with a cytoplasmic accumulation of TERRA molecules. Our results suggest that TERRA transcripts may play extranuclear functions with important implications in telomere biology and add a novel layer of complexity in the interplay between telomere biology, metabolism and stress response.

We present the LiveFly toolbox for quantitative analysis of transcription dynamics in live Drosophila embryos. The toolbox allows users to process two-color 3D confocal movies acquired using nuclei-labeling and the fluorescent RNA-tagging system described in the previous chapter and export the nuclei's position as a function of time, their lineages and the intensity traces of the active loci. The toolbox, which is tailored for the context of Drosophila early development, is semiautomatic, and requires minimal user intervention. It also includes a tool to combine data from multiple movies and visualize several features of the intensity traces and the expression pattern.

Live imaging has been used in recent years for the understanding of dynamic processes in biology, such as embryo development. This was made possible by a combination of advancements in microscopy, leading to improved signal-to-noise ratios and better spatial and temporal resolutions, and by the development of new fluorescence markers, allowing for the quantification of protein expression and transcriptional dynamics in vivo. Here we describe a general protocol, which can be used in standard confocal microscopes to image early Drosophila melanogaster embryos, in order to learn about the transcriptional dynamics of a fluorescently labeled RNA.

Common variable immunodeficiency (CVID) is a syndrome with predominantly defective B cell function. However, abnormalities in the number and function of other lymphocyte subpopulations in peripheral blood (PB) have been described in most patients. We have analysed the distribution of iNKT cell subpopulations in the PB of CVID patients and the ability of these cells to provide in vitro cognate B cell help. The total of iNKT cells was reduced in the PB of CVID patients, especially CD4+, CD4-/CD8- and CCR5+/CXCR3+. These findings were associated with an enrichment of memory-like and a tendency towards a reduction in TNF-α-expressing effector iNKT cells in the peripheral blood mononuclear cells (PBMC) of CVID patients. Moreover, an accumulation of follicular helper iNKT cells in the PB of CVID patients was demonstrated. CVID αGalCer-pulsed iNKT cells are not able to induce autologous B cell proliferation although they do induce proliferation to healthy donor B cells. Interestingly, autologous and heterologous co-cultures did not differ in the amount of immunoglobulin secreted by B cells in vitro. Finally, reduced intracellular SAP expression in iNKT cells and other lymphocytes in the blood from CVID patients was observed. These results provide further insights into the immunological mechanisms underlying the iNKT cell defects and the potential targets to improve B cell help in CVID., (© 2017 The Foundation for the Scandinavian Journal of Immunology.)

Diabetic macroangiopathy is a leading cause of diabetes-related mortality worldwide. Both genetic and environmental factors, through a multitude of underlying molecular mechanisms, contribute to the pathogenesis of diabetic macroangiopathy. MicroRNAs (miRNAs), a class of non-coding RNAs known for their functional diversity and expression specificity, are increasingly recognized for their roles in the initiation and progression of diabetes and diabetic macroangiopathy. In this review, we will describe the biogenesis of miRNAs, and summarize their functions in diabetic macroangiopathy, including atherosclerosis, peripheral artery disease, coronary artery disease, and cerebrovascular disease, which are anticipated to provide new insights into future perspectives of miRNAs in basic, translational and clinical research, ultimately advancing the diagnosis, prevention, and treatment of diabetic macroangiopathy. [ABSTRACT FROM AUTHOR]

The global spread of the SARS-CoV-2 pandemic, originating in Wuhan, China, has had profound consequences on both health and the economy. Traditional alignment-based phylogenetic tree methods for tracking epidemic dynamics demand substantial computational power due to the growing number of sequenced strains. Consequently, there is a pressing need for an alignment-free approach to characterize these strains and monitor the dynamics of various variants. In this work, we introduce a swift and straightforward tool named GenoSig, implemented in C++. The tool exploits the Di and Tri nucleotide frequency signatures to delineate the taxonomic lineages of SARS-CoV-2 by employing diverse machine learning (ML) and deep learning (DL) models. Our approach achieved a tenfold cross-validation accuracy of 87.88% (± 0.013) for DL and 86.37% (± 0.0009) for Random Forest (RF) model, surpassing the performance of other ML models. Validation using an additional unexposed dataset yielded comparable results. Despite variations in architectures between DL and RF, it was observed that later clades, specifically GRA, GRY, and GK, exhibited superior performance compared to earlier clades G and GH. As for the continental origin of the virus, both DL and RF models exhibited lower performance than in predicting clades. However, both models demonstrated relatively higher accuracy for Europe, North America, and South America compared to other continents, with DL outperforming RF. Both models consistently demonstrated a preference for cytosine and guanine over adenine and thymine in both clade and continental analyses, in both Di and Tri nucleotide frequencies signatures. Our findings suggest that GenoSig provides a straightforward approach to address taxonomic, epidemiological, and biological inquiries, utilizing a reductive method applicable not only to SARS-CoV-2 but also to similar research questions in an alignment-free context. [ABSTRACT FROM AUTHOR]

Background: Coronary microvascular dysfunction (CMD) is a frequent complication of diabetes mellitus (DM) characterized by challenges in both diagnosis and intervention. Circulating levels of microRNAs are increasingly recognized as potential biomarkers for cardiovascular diseases. Methods: Serum exosomes from patients with DM, DM with coronary microvascular dysfunction (DM-CMD) or DM with coronary artery disease (DM-CAD) were extracted for miRNA sequencing. The expression of miR-16-2-3p was assessed in high glucose-treated human aortic endothelial cells and human cardiac microvascular endothelial cells. Fluorescence in situ hybridization (FISH) was used to detect miR-16-2-3p within the myocardium of db/db mice. Intramyocardial injection of lentivirus overexpressing miR-16-2-3p was used to explore the function of the resulting gene in vivo. Bioinformatic analysis and in vitro assays were carried out to explore the downstream function and mechanism of miR-16-2-3p. Wound healing and tube formation assays were used to explore the effect of miR-16-2-3p on endothelial cell function. Results: miR-16-2-3p was upregulated in circulating exosomes from DM-CMD, high glucose-treated human cardiac microvascular endothelial cells and the hearts of db/db mice. Cardiac miR-16-2-3p overexpression improved cardiac systolic and diastolic function and coronary microvascular reperfusion. In vitro experiments revealed that miR-16-2-3p could regulate fatty acid degradation in endothelial cells, and ACADM was identified as a potential downstream target. MiR-16-2-3p increased cell migration and tube formation in microvascular endothelial cells. Conclusions: Our findings suggest that circulating miR-16-2-3p may serve as a biomarker for individuals with DM-CMD. Additionally, miR-16-2-3p appears to alleviate coronary microvascular dysfunction in diabetes by modulating ACADM-mediated fatty acid degradation in endothelial cells. [ABSTRACT FROM AUTHOR]

Nucleic acid tests (NATs) are considered as gold standard in molecular diagnosis. To meet the demand for onsite, point-of-care, specific and sensitive, trace and genotype detection of pathogens and pathogenic variants, various types of NATs have been developed since the discovery of PCR. As alternatives to traditional NATs (e.g., PCR), isothermal nucleic acid amplification techniques (INAATs) such as LAMP, RPA, SDA, HDR, NASBA, and HCA were invented gradually. PCR and most of these techniques highly depend on efficient and optimal primer and probe design to deliver accurate and specific results. This chapter starts with a discussion of traditional NATs and INAATs in concert with the description of computational tools available to aid the process of primer/probe design for NATs and INAATs. Besides briefly covering nanoparticles-assisted NATs, a more comprehensive presentation is given on the role CRISPR-based technologies have played in molecular diagnosis. Here we provide examples of a few groundbreaking CRISPR assays that have been developed to counter epidemics and pandemics and outline CRISPR biology, highlighting the role of CRISPR guide RNA and its design in any successful CRISPR-based application. In this respect, we tabularize computational tools that are available to aid the design of guide RNAs in CRISPR-based applications. In the second part of our chapter, we discuss machine learning (ML)- and deep learning (DL)-based computational approaches that facilitate the design of efficient primer and probe for NATs/INAATs and guide RNAs for CRISPR-based applications. Given the role of microRNA (miRNAs) as potential future biomarkers of disease diagnosis, we have also discussed ML/DL-based computational approaches for miRNA-target predictions. Our chapter presents the evolution of nucleic acid-based diagnosis techniques from PCR and INAATs to more advanced CRISPR/Cas-based methodologies in concert with the evolution of deep learning (DL)- and machine learning (ml)-based computational tools in the most relevant application domains., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Background and Aims: Coronavirus disease 2019 (COVID‐19), caused by the SARS‐CoV‐2 novel coronavirus, is a highly communicable disease that gave rise to the ongoing pandemic. Despite prompt action across many laboratories in many countries, effective management of this disease is still out of reach. The focus of this review is to describe various vaccination approaches and nanomedicine‐based delivery systems against COVID‐19. Methods: The articles included in this study were searched and added from different electronic databases, including PubMed, Scopus, Cochrane, Embase, and preprint databases. Results: Mass immunization with vaccines is currently at the forefront of COVID‐19 infection control. Such vaccines are live attenuated vaccines, inactivated vaccines, nucleic acid‐based vaccines, protein subunit vaccines, viral‐vector vaccines, and virus‐like particle platforms. However, many promising avenues are currently being explored in laboratory and clinical settings, including treatment options, prevention, diagnosis, and management of the disease. Soft nanoparticles like lipid nanoparticles (solid lipid nanoparticles (SLNPs), liposomes, nanostructured lipid carriers, nanoemulsions, and protein nanoparticles play an essential role in nanomedicine. Because of their unique and excellent properties, nanomedicines have potential applications in treating COVID‐19 disease. Conclusions: This review work provides an overview of the therapeutic aspects of COVID‐19, including vaccination and the role of nanomedicines in the diagnosis, treatment, and prevention of COVID‐19. [ABSTRACT FROM AUTHOR]

Background: In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2. Results: In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256. Conclusions: The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs. [ABSTRACT FROM AUTHOR]

Background: As the rapidly evolving characteristic of SARS-CoV-2 could result in false negative diagnosis, the use of as much sequence data as possible is key to the identification of conserved viral sequences. However, multiple alignment of massive genome sequences is computationally intensive. Objective: To extract conserved sequences from SARS-CoV-2 genomes for the design of diagnostic PCR primers using a bioinformatics approach that can handle massive genomic sequences efficiently. Methods: A total of 230,163 full-length viral genomes were retrieved from the NCBI SARS-CoV-2 Resources and GISAID EpiCoV database. This number was reduced to 14.11% following removal of 5′-/3′-untranslated regions and sequence dereplication. Fast, reference-based, multiple sequence alignments identified conserved sequences and specific primer sets were designed against these regions using a conventional tool. Primer sets chosen among the candidates were evaluated by in silico PCR and RT-qPCR. Results: Out of 17 conserved sequences (totaling 4.3 kb), two primer sets targeting the nsp2 and ORF3a genes were picked that exhibited > 99.9% in silico amplification coverage against the original dataset (230,163 genomes) when a 5% mismatch between the primers and target was allowed. In addition, the primer sets successfully detected nine SARS-CoV-2 variant RNA samples (Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Eta, Iota, and Kappa) in experimental RT-qPCR validations. Conclusion: In addition to the RdRp, E, N, and S genes that are targeted commonly, our approach can be used to identify novel primer targets in SARS-CoV-2 and should be a priority strategy in the event of novel SARS-CoV-2 variants or other pandemic outbreaks. [ABSTRACT FROM AUTHOR]

The high spread rate of SARS-CoV-2 virus has put the researchers all over the world in a demanding situation. The need of the hour is to develop novel learning algorithms that can effectively learn a general pattern by training with fewer genome sequences of coronavirus. Learning from very few training samples is necessary and important during the beginning of a disease outbreak when sequencing data is limited. This is because a successful detection and isolation of patients can curb the spread of the virus. However, this poses a huge challenge for machine learning and deep learning algorithms as they require huge amounts of training data to learn the pattern and distinguish from other closely related viruses. In this paper, we propose a new paradigm – Neurochaos Learning (NL) for classification of coronavirus genome sequence that addresses this specific problem. NL is inspired from the empirical evidence of chaos and non-linearity at the level of neurons in biological neural networks. The average sensitivity, specificity and accuracy for NL are 0.998, 0.999 and 0.998 respectively for the multiclass classification problem (SARS-CoV-2, Coronaviridae, Metapneumovirus, Rhinovirus and Influenza) using leave one out crossvalidation. With just one training sample per class for 1000 independent random trials of training, we report an average macro F1-score > 0.99 for the classification of SARS-CoV-2 from SARS-CoV-1 genome sequences. We compare the performance of NL with K-nearest neighbours (KNN), logistic regression, random forest, SVM, and naïve Bayes classifiers. We foresee promising future applications in genome classification using NL with novel combinations of chaotic feature engineering and other machine learning algorithms. [ABSTRACT FROM AUTHOR]

Recent pandemic of COVID-19 (Coronavirus) caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has been growing lethally with unusual speed. It has infected millions of people and continues a mortifying influence on the global population's health and well-being. In this situation, genome sequence analysis and advanced artificial intelligence techniques may help researchers and medical experts to understand the genetic variants of COVID-19 or SARS-CoV-2. Genome sequence analysis of COVID-19 is crucial to understand the virus's origin, behavior, and structure, which might help produce/develop vaccines, antiviral drugs, and efficient preventive strategies. This paper introduces an artificial intelligence based system to perform genome sequence analysis of COVID-19 and alike viruses, e.g., SARS, middle east respiratory syndrome, and Ebola. The system helps to get important information from the genome sequences of different viruses. We perform comparative data analysis by extracting basic information of COVID-19 and other genome sequences, including information of nucleotides composition and their frequency, tri-nucleotide compositions, count of amino acids, alignment between genome sequences, and their DNA similarity information. We use different visualization methods to analyze these viruses' genome sequences and, finally, apply machine learning based classifier support vector machine to classify different genome sequences. The data set of different virus genome sequences are obtained from an online publicly accessible data center repository. The system achieves good classification results with an accuracy of 97% for COVID-19, 96%, SARS, and 95% for MERS and Ebola genome sequences, respectively. [ABSTRACT FROM AUTHOR]

The sharp increase of COVID-19 cases in late 2020 has made Brazil the new epicenter of the ongoing SARS-CoV-2 pandemic. The novel viral lineages P.1 (Variant of Concern Gamma) and P.2, respectively identified in the Brazilian states of Amazonas and Rio de Janeiro, have been associated with potentially higher transmission rates and antibody neutralization escape. In this study, we performed the whole-genome sequencing of 185 samples isolated from three out of the five Brazilian regions, including Amazonas (North region), Rio Grande do Norte, Paraíba and Bahia (Northeast region), and Rio de Janeiro (Southeast region) in order to monitor the spread of SARS-CoV-2 lineages in Brazil in the first months of 2021. Here, we showed a widespread dispersal of P.1 and P.2 across Brazilian regions and, except for Amazonas, P.2 was the predominant lineage identified in the sampled states. We estimated the origin of P.2 lineage to have happened in February, 2020 and identified that it has differentiated into new clades. Interstate transmission of P.2 was detected since March, but reached its peak in December, 2020 and January, 2021. Transmission of P.1 was also high in December and its origin was inferred to have happened in August 2020. We also confirmed the presence of lineage P.7, recently described in the southernmost region of Brazil, to have spread across the Northeastern states. P.1, P.2 and P.7 are descended from the ancient B.1.1.28 strain, which co-dominated the first phase of the pandemic in Brazil with the B.1.1.33 strain. We also identified the occurrence of a new lineage descending from B.1.1.33 that convergently carries the E484K mutation, N.9. Indeed, the recurrent report of many novel SARS-CoV-2 genetic variants in Brazil could be due to the absence of effective control measures resulting in high SARS-CoV2 transmission rates. Altogether, our findings provided a landscape of the critical state of SARS-CoV-2 across Brazil and confirm the need to sustain continuous sequencing of the SARS-CoV-2 isolates worldwide in order to identify novel variants of interest and monitor for vaccine effectiveness. Author summary: Since its first detection in December 2019, the SARS-CoV-2 has evolved into more than a thousand recognized lineages. Several of these lineages are known to have higher transmissibility or better escape from the immune system. One of them is the P.1 lineage, also known as the variant of concern Gamma. It was first discovered in Brazil in December 2020 and has quickly replaced the previous lineage dominating the country, P.2. We used genomic data from SARS-CoV-2 samples that were collected in the first months of 2021 to analyze how P.1, P.2 and other lineages had spread across Brazil. Our research has identified that P.1 lineage was already present in several states of Brazil almost two months before its first detection in the state of Amazonas. Our work sheds light on the importance of continuous monitoring of SARS-CoV-2 lineages in historically understudied regions to early detect and control the spread of new variants of concern. [ABSTRACT FROM AUTHOR]

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has made a serious public health and economic crisis worldwide which united global efforts to develop rapid, precise, and cost-efficient diagnostics, vaccines, and therapeutics. Numerous multi-disciplinary studies and techniques have been designed to investigate and develop various approaches to help frontline health workers, policymakers, and populations to overcome the disease. While these techniques have been reviewed within individual disciplines, it is now timely to provide a cross-disciplinary overview of novel diagnostic and therapeutic approaches summarizing complementary efforts across multiple fields of research and technology. Accordingly, we reviewed and summarized various advanced novel approaches used for diagnosis and treatment of COVID-19 to help researchers across diverse disciplines on their prioritization of resources for research and development and to give them better a picture of the latest techniques. These include artificial intelligence, nano-based, CRISPR-based, and mass spectrometry technologies as well as neutralizing factors and traditional medicines. We also reviewed new approaches for vaccine development and developed a dashboard to provide frequent updates on the current and future approved vaccines. [ABSTRACT FROM AUTHOR]

The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental readouts lack sufficient spatiotemporal resolution to separate the contributions from each of these steps. Here, we describe a novel application of Bayesian inference techniques to simultaneously infer the effective parameters of the transcription cycle in real time and at the single-cell level using a two-color MS2/PP7 reporter gene and the developing fruit fly embryo as a case study. Our method enables detailed investigations into cell-to-cell variability in transcription-cycle parameters as well as single-cell correlations between these parameters. These measurements, combined with theoretical modeling, suggest a substantial variability in the elongation rate of individual RNA polymerase molecules. We further illustrate the power of this technique by uncovering a novel mechanistic connection between RNA polymerase density and nascent RNA cleavage efficiency. Thus, our approach makes it possible to shed light on the regulatory mechanisms in play during each step of the transcription cycle in individual, living cells at high spatiotemporal resolution. Author summary: Live cell imaging using fluorescence microscopy provides an exciting way to visualize the transcription cycle in living organisms with great amounts of precision. However, the output of these technologies is often complex and can be hard to interpret. We have developed a computational framework for analyzing the transcription cycle that quantifies rates of RNA initiation, elongation, and cleavage, given input datasets from live cell imaging. Using the developing fruit fly embryo as a case study, we demonstrate that our methodology can quantitatively describe the whole transcription cycle at single-cell resolution. These results allow us to investigate a plethora of avenues, from couplings between different aspects of the transcription cycle at the single-cell level to comparisons with theoretical predictions of distributions of elongation rates across cells. We envision our methodology to provide a unified computational framework for the analysis of transcriptional data obtained from live cell imaging. [ABSTRACT FROM AUTHOR]

Advances in flow cytometry have led to greatly improved primary immunodeficiency (PID) diagnostics. This is due to the fact that patient blood cells in suspension do not require further processing for analysis by flow cytometry, and many PIDs lead to alterations in leukocyte numbers, phenotype, and function. A large portion of current PID assays can be classified as "phenotyping" assays, where absolute numbers, frequencies, and markers are investigated using specific antibodies. Inherent drawbacks of antibody technology are the main limitation to this type of testing. On the other hand, "functional" assays measure cellular responses to certain stimuli. While these latter assays are powerful tools that can be used to detect defects in entire pathways and distinguish variants of significance, it requires samples with robust viability and also skilled processing. In this review, we concentrate on hemophagocytic lymphohistiocytosis (HLH), describing the principles and accuracies of flow cytometric assays that have been proven to assist in the screening diagnosis of primary HLH. [ABSTRACT FROM AUTHOR]

Fc gamma receptors (FcγRs) play a major role in the regulation of humoral immune responses. Single-nucleotide polymorphisms (SNPs) of FCGR2A and FCGR3A can impact the expression level, IgG affinity and function of the CD32 and CD16 FcγRs in response to their engagement by the Fc fragment of IgG. The CD16 isoform encoded by FCGR3A [158V/V] controls the intensity of antibody-dependent cytotoxic alloimmune responses of natural killer cells (NK) and has been identified as a susceptibility marker predisposing patients to cardiac allograft vasculopathy after heart transplant. This study aimed to investigate whether FCGR2A and FCGR3A polymorphisms can also be associated with the clinical outcome of lung transplant recipients (LTRs). The SNPs of FCGR2A ([131R/H], rs1801274) and FCGR3A ([158V/F], rs396991) were identified in 158 LTRs and 184 Controls (CTL). The corresponding distribution of genotypic and allelic combinations was analyzed for potential links with the development of circulating donor-specific anti-HLA alloantibodies (DSA) detected at months 1 and 3 after lung transplant (LTx), the occurrence of acute rejection (AR) and chronic lung allograft dysfunction (CLAD), and the overall survival of LTRs. The FCGR3A [158V/V] genotype was identified as an independent susceptibility factor associated with higher rates of AR during the first trimester after LTx (HR 4.8, p < 0.0001, 95% CI 2.37–9.61), but it could not be associated with the level of CD16- mediated NK cell activation in response to the LTR's DSA, whatever the MFI intensity and C1q binding profiles of the DSA evaluated. The FCGR2A [131R/R] genotype was associated with lower CLAD-free survival of LTRs, independently of the presence of DSA at 3 months (HR 1.8, p = 0.024, 95% CI 1.08–3.03). Our data indicate that FCGR SNPs differentially affect the clinical outcome of LTRs and may be of use to stratify patients at higher risk of experiencing graft rejection. Furthermore, these data suggest that in the LTx setting, specific mechanisms of humoral alloreactivity, which cannot be solely explained by the complement and CD16-mediated pathogenic effects of DSA, may be involved in the development of acute and chronic lung allograft rejection. [ABSTRACT FROM AUTHOR]

In buddying yeast, like all eukaryotes examined so far, DNA replication is under temporal control, such that some origins fire early and some late during S phase. This replication timing program is established in G1 phase, where chromatin states are thought to prevent binding of key-limiting initiation factors at late-firing origins. Although many factors are involved in replication initiation, a new player, Rif1, has recently entered the scene, with a spate of papers revealing a global role for the protein in the control of replication initiation timing from yeasts to humans. Since budding yeast Rif1 was known to bind only to telomeric and silent mating loci regions, it remained controversial whether Rif1 acts directly at replication origins or instead influences origin activity indirectly. In this perspective, we discuss our recent finding that Rif1 binds directly to the replication origins that it controls. In this study, we also found that Rif1's regulatory activity at origins is best revealed by an assay (sort-seq) that measures replication in unperturbed, freely cycling cultures, as opposed to commonly used protocols in which cells are first blocked in the G1 phase of the cell cycle by mating pheromone, then released into a synchronous S phase. Finally, we discuss how the sequestration of Rif1 at telomeres, through an interaction with the arrays of Rap1 molecules bound there, plays an important role in limiting Rif1's action primarily to telomere-proximal replication origins. [ABSTRACT FROM AUTHOR]

Heterochromatin domains are stably repressed chromatin structures composed of a core assembly of silencing proteins that condense adjacent nucleosomes. The minimal heterochromatin structure can serve as a platform for recruitment of complementary regulatory factors. We find that a reconstituted budding yeast heterochromatin domain can act as a platform to recruit multiple factors that play a role in regulating heterochromatin function. We uncover the direct interaction between the SIR heterochromatin complex and a chromosomal boundary protein that restricts the spread of heterochromatin. We find that the SIR complex relieves a mechanism of auto-inhibition within the boundary protein Yta7, allowing the Yta7 bromodomain to engage chromatin. Our results suggest that budding yeast shares with other eukaryotes the ability to establish complex heterochromatin domains that coordinate multiple mechanisms of silencing regulation through physical interactions. [ABSTRACT FROM AUTHOR]

Introduction: Common variable immunodeficiency (CVID) is the most prevalent symptomatic humoral deficiency; however, its heterogeneous presentation makes the diagnosis difficult. The present study is aimed to verify the CVID diagnostic criteria as established by the European Society for Immunodeficiencies in 42 CVID patients from our outpatient clinic., Methods: Information was collected from their medical records and when needed, lymphocyte subpopulations in peripheral blood (PB) were performed by flow cytometry., Results: All the patients fulfilled the clinical working definition for CVID and showed decreased serum IgG and IgA at diagnosis. Over two-thirds of the patients had decreased memory B cell percentages. However, the remaining patients exhibited other quantitative B cell defects in PB. Evaluation of vaccination responses was only found in 13 records and 69% were not responsive. None of the patients were subjected to vaccination studies to both, T-cell dependent and independent antigens. The two required tests to evaluate T cell responses were performed in 84.2% of the patients and reported normal. Without the support of third-party payers, only 34.2% of our patients would have completed the required evaluations., Conclusions: Further efforts are needed to speed up CVID diagnosis in low-resourced settings, increasing the availability of the required resources and optimizing the healthcare supply chain., Competing Interests: The authors declare no potential conflicts of interest with respect to research, authorship, and/or publication of this article.

The SARS-CoV-2 variant B.1.1.7 reportedly exhibits substantially higher transmission than the ancestral strain and may generate a major surge of cases before vaccines become widely available, while the P.1 and B.1.351 variants may be equally transmissible and also resist vaccines. All three variants can be sensitively detected by RT-PCR due to an otherwise rare del11288-11296 mutation in orf1ab; B.1.1.7 can also be detected using the common TaqPath kit. Testing, contact tracing, and isolation programs overwhelmed by SARS-CoV-2 could slow the spread of the new variants, which are still outnumbered by tracers in most countries. However, past failures and high rates of mistrust may lead health agencies to conclude that tracing is futile, dissuading them from redirecting existing tracers to focus on the new variants. Here we apply a branching-process model to estimate the effectiveness of implementing a variant-focused testing, contact tracing, and isolation strategy with realistic levels of performance. Our model indicates that bidirectional contact tracing can substantially slow the spread of SARS-CoV-2 variants even in regions where a large fraction of the population refuses to cooperate with contact tracers or to abide by quarantine and isolation requests.