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1.
Int J Mol Sci ; 23(5)2022 Feb 22.
Article in English | MEDLINE | ID: covidwho-1699203

ABSTRACT

Since December 2019, a pandemic of COVID-19 disease, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread across the globe. At present, the Food and Drug Administration (FDA) has issued emergency approval for the use of some antiviral drugs. However, these drugs still have limitations in the specific treatment of COVID-19, and as such, new treatment strategies urgently need to be developed. RNA-interference-based gene therapy provides a tractable target for antiviral treatment. Ensuring cell-specific targeted delivery is important to the success of gene therapy. The use of nanoparticles (NPs) as carriers for the delivery of small interfering RNA (siRNAs) to specific tissues or organs of the human body could play a crucial role in the specific therapy of severe respiratory infections, such as COVID-19. In this review, we describe a variety of novel nanocarriers, such as lipid NPs, star polymer NPs, and glycogen NPs, and summarize the pre-clinical/clinical progress of these nanoparticle platforms in siRNA delivery. We also discuss the application of various NP-capsulated siRNA as therapeutics for SARS-CoV-2 infection, the challenges with targeting these therapeutics to local delivery in the lung, and various inhalation devices used for therapeutic administration. We also discuss currently available animal models that are used for preclinical assessment of RNA-interference-based gene therapy. Advances in this field have the potential for antiviral treatments of COVID-19 disease and could be adapted to treat a range of respiratory diseases.


Subject(s)
COVID-19/therapy , Drug Delivery Systems/methods , Nanoparticles/administration & dosage , RNA, Small Interfering/administration & dosage , RNAi Therapeutics/methods , Animals , COVID-19/epidemiology , COVID-19/virology , Humans , Models, Genetic , Nanoparticles/chemistry , Pandemics/prevention & control , RNA, Small Interfering/chemistry , RNA, Small Interfering/genetics , SARS-CoV-2/physiology
2.
J Cell Mol Med ; 26(5): 1445-1455, 2022 03.
Article in English | MEDLINE | ID: covidwho-1642687

ABSTRACT

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.


Subject(s)
COVID-19/genetics , COVID-19/mortality , Neural Networks, Computer , COVID-19/epidemiology , Complement Activation/genetics , Complement Factor H/genetics , Complement System Proteins/genetics , Female , Greece/epidemiology , Hospitalization/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Male , Middle Aged , Models, Genetic , Morbidity , Polymorphism, Single Nucleotide , Thrombomodulin/genetics
3.
PLoS Comput Biol ; 18(1): e1009804, 2022 01.
Article in English | MEDLINE | ID: covidwho-1637205

ABSTRACT

Nonstructural protein 1 (nsp1) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a 180-residue protein that blocks translation of host mRNAs in SARS-CoV-2-infected cells. Although it is known that SARS-CoV-2's own RNA evades nsp1's host translation shutoff, the molecular mechanism underlying the evasion was poorly understood. We performed an extended ensemble molecular dynamics simulation to investigate the mechanism of the viral RNA evasion. Simulation results suggested that the stem loop structure of the SARS-CoV-2 RNA 5'-untranslated region (SL1) binds to both nsp1's N-terminal globular region and intrinsically disordered region. The consistency of the results was assessed by modeling nsp1-40S ribosome structure based on reported nsp1 experiments, including the X-ray crystallographic structure analysis, the cryo-EM electron density map, and cross-linking experiments. The SL1 binding region predicted from the simulation was open to the solvent, yet the ribosome could interact with SL1. Cluster analysis of the binding mode and detailed analysis of the binding poses suggest residues Arg124, Lys47, Arg43, and Asn126 may be involved in the SL1 recognition mechanism, consistent with the existing mutational analysis.


Subject(s)
COVID-19/virology , Host-Pathogen Interactions/genetics , SARS-CoV-2 , Untranslated Regions/genetics , Viral Nonstructural Proteins , Computational Biology , Humans , Models, Genetic , Molecular Dynamics Simulation , Protein Binding , Protein Biosynthesis , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Viral Nonstructural Proteins/chemistry , Viral Nonstructural Proteins/genetics , Viral Nonstructural Proteins/metabolism
4.
Front Immunol ; 12: 789317, 2021.
Article in English | MEDLINE | ID: covidwho-1593957

ABSTRACT

Background: The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches. Methods: RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules. Results: Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis. Conclusion: This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.


Subject(s)
COVID-19/genetics , Gene Expression Profiling/methods , Signal Transduction/genetics , Transcription Factors/genetics , Transcriptome/genetics , COVID-19/epidemiology , COVID-19/virology , Cluster Analysis , Gene Ontology , Gene Regulatory Networks , Humans , Immunity/genetics , Models, Genetic , Pandemics , Protein Interaction Maps/genetics , SARS-CoV-2/physiology
5.
Cells ; 11(1)2021 12 28.
Article in English | MEDLINE | ID: covidwho-1580991

ABSTRACT

Coronavirus disease (COVID-19) spreads mainly through close contact of infected persons, but the molecular mechanisms underlying its pathogenesis and transmission remain unknown. Here, we propose a statistical physics model to coalesce all molecular entities into a cohesive network in which the roadmap of how each entity mediates the disease can be characterized. We argue that the process of how a transmitter transforms the virus into a recipient constitutes a triad unit that propagates COVID-19 along reticulate paths. Intrinsically, person-to-person transmissibility may be mediated by how genes interact transversely across transmitter, recipient, and viral genomes. We integrate quantitative genetic theory into hypergraph theory to code the main effects of the three genomes as nodes, pairwise cross-genome epistasis as edges, and high-order cross-genome epistasis as hyperedges in a series of mobile hypergraphs. Charting a genome-wide atlas of horizontally epistatic hypergraphs can facilitate the systematic characterization of the community genetic mechanisms underlying COVID-19 spread. This atlas can typically help design effective containment and mitigation strategies and screen and triage those more susceptible persons and those asymptomatic carriers who are incubation virus transmitters.


Subject(s)
COVID-19/transmission , Gene Expression Regulation , Genome, Viral/genetics , Genomics/methods , SARS-CoV-2/genetics , Algorithms , COVID-19/epidemiology , COVID-19/virology , Epistasis, Genetic , Genome-Wide Association Study/methods , Humans , Models, Genetic , Pandemics , SARS-CoV-2/pathogenicity , Virulence/genetics
6.
Infect Genet Evol ; 96: 105106, 2021 12.
Article in English | MEDLINE | ID: covidwho-1506080

ABSTRACT

Coronaviruses (especially SARS-CoV-2) are characterized by rapid mutation and wide spread. As these characteristics easily lead to global pandemics, studying the evolutionary relationship between viruses is essential for clinical diagnosis. DNA sequencing has played an important role in evolutionary analysis. Recent alignment-free methods can overcome the problems of traditional alignment-based methods, which consume both time and space. This paper proposes a novel alignment-free method called the correlation coefficient feature vector (CCFV), which defines a correlation measure of the L-step delay of a nucleotide location from its location in the original DNA sequence. The numerical feature is a 16×L-dimensional numerical vector describing the distribution characteristics of the nucleotide positions in a DNA sequence. The proposed L-step delay correlation measure is interestingly related to some types of L+1 spaced mers. Unlike traditional gene comparison, our method avoids the computational complexity of multiple sequence alignment, and hence improves the speed of sequence comparison. Our method is applied to evolutionary analysis of the common human viruses including SARS-CoV-2, Dengue virus, Hepatitis B virus, and human rhinovirus and achieves the same or even better results than alignment-based methods. Especially for SARS-CoV-2, our method also confirms that bats are potential intermediate hosts of SARS-CoV-2.


Subject(s)
Genome, Viral/genetics , Phylogeny , Sequence Analysis, DNA/methods , Coronavirus/genetics , Dengue Virus/genetics , Hepatitis B/genetics , Humans , Models, Genetic , Rhinovirus/genetics , SARS-CoV-2/genetics , Sequence Alignment
7.
Elife ; 102021 10 12.
Article in English | MEDLINE | ID: covidwho-1478420

ABSTRACT

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. A strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. When only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. We systematically examine the impact of several factors on the utility of screening, including: variance explained by the PRS, number of embryos, disease prevalence, parental PRSs, and parental disease status. We consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions, and also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating 'virtual' couples and offspring based on real genomes from schizophrenia and Crohn's disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.


Subject(s)
Crohn Disease/genetics , Fertilization in Vitro , Genetic Testing , Models, Genetic , Multifactorial Inheritance , Preimplantation Diagnosis , Schizophrenia/genetics , Computer Simulation , Female , Genetic Predisposition to Disease , Humans , Male , Predictive Value of Tests , Pregnancy , Risk Assessment , Risk Factors
8.
Cancer Prev Res (Phila) ; 14(11): 1021-1032, 2021 11.
Article in English | MEDLINE | ID: covidwho-1463067

ABSTRACT

Up to 10% of patients with pancreatic ductal adenocarcinoma (PDAC) carry underlying germline pathogenic variants in cancer susceptibility genes. The GENetic Education Risk Assessment and TEsting (GENERATE) study aimed to evaluate novel methods of genetic education and testing in relatives of patients with PDAC. Eligible individuals had a family history of PDAC and a relative with a germline pathogenic variant in APC, ATM, BRCA1, BRCA2, CDKN2A, EPCAM, MLH1, MSH2, MSH6, PALB2, PMS2, STK11, or TP53 genes. Participants were recruited at six academic cancer centers and through social media campaigns and patient advocacy efforts. Enrollment occurred via the study website (https://GENERATEstudy.org) and all participation, including collecting a saliva sample for genetic testing, could be done from home. Participants were randomized to one of two remote methods that delivered genetic education about the risks of inherited PDAC and strategies for surveillance. The primary outcome of the study was uptake of genetic testing. From 5/8/2019 to 5/6/2020, 49 participants were randomized to each of the intervention arms. Overall, 90 of 98 (92%) of randomized participants completed genetic testing. The most frequently detected pathogenic variants included those in BRCA2 (N = 15, 17%), ATM (N = 11, 12%), and CDKN2A (N = 4, 4%). Participation in the study remained steady throughout the onset of the Coronavirus disease (COVID-19) pandemic. Preliminary data from the GENERATE study indicate success of remote alternatives to traditional cascade testing, with genetic testing rates over 90% and a high rate of identification of germline pathogenic variant carriers who would be ideal candidates for PDAC interception approaches. PREVENTION RELEVANCE: Preliminary data from the GENERATE study indicate success of remote alternatives for pancreatic cancer genetic testing and education, with genetic testing uptake rates over 90% and a high rate of identification of germline pathogenic variant carriers who would be ideal candidates for pancreatic cancer interception.


Subject(s)
BRCA1 Protein/genetics , BRCA2 Protein/genetics , Genetic Predisposition to Disease , Genetic Testing/methods , Germ-Line Mutation , Pancreatic Neoplasms/genetics , Risk Assessment/methods , Adolescent , Adult , Aged , Aged, 80 and over , Carcinoma, Pancreatic Ductal/genetics , Carcinoma, Pancreatic Ductal/pathology , Carcinoma, Pancreatic Ductal/therapy , Female , Humans , Male , Middle Aged , Models, Genetic , Pancreatic Neoplasms/pathology , Pancreatic Neoplasms/therapy , Patient Participation , Risk Factors , Surveys and Questionnaires , Telemedicine , Young Adult
9.
Infect Genet Evol ; 95: 104812, 2021 11.
Article in English | MEDLINE | ID: covidwho-1461688

ABSTRACT

While the COVID-19 pandemic continues to spread with currently more than 117 million cumulated cases and 2.6 million deaths worldwide as per March 2021, its origin is still debated. Although several hypotheses have been proposed, there is still no clear explanation about how its causative agent, SARS-CoV-2, emerged in human populations. Today, scientifically-valid facts that deserve to be debated still coexist with unverified statements blurring thus the knowledge on the origin of COVID-19. Our retrospective analysis of scientific data supports the hypothesis that SARS-CoV-2 is indeed a naturally occurring virus. However, the spillover model considered today as the main explanation to zoonotic emergence does not match the virus dynamics and somehow misguided the way researches were conducted. We conclude this review by proposing a change of paradigm and model and introduce the circulation model for explaining the various aspects of the dynamic of SARS-CoV-2 emergence in humans.


Subject(s)
COVID-19/epidemiology , Genome, Viral , Models, Statistical , Pandemics , SARS-CoV-2/genetics , Zoonoses/epidemiology , Animals , COVID-19/transmission , COVID-19/virology , Chiroptera/virology , Eutheria/virology , Humans , Models, Genetic , Retrospective Studies , SARS-CoV-2/growth & development , SARS-CoV-2/pathogenicity , Stochastic Processes , Zoonoses/transmission , Zoonoses/virology
11.
Sci Rep ; 11(1): 18108, 2021 09 13.
Article in English | MEDLINE | ID: covidwho-1406409

ABSTRACT

The progress of the SARS-CoV-2 pandemic requires the design of large-scale, cost-effective testing programs. Pooling samples provides a solution if the tests are sensitive enough. In this regard, the use of the gold standard, RT-qPCR, raises some concerns. Recently, droplet digital PCR (ddPCR) was shown to be 10-100 times more sensitive than RT-qPCR, making it more suitable for pooling. Furthermore, ddPCR quantifies the RNA content directly, a feature that, as we show, can be used to identify nonviable samples in pools. Cost-effective strategies require the definition of efficient deconvolution and re-testing procedures. In this paper we analyze the practical implementation of an efficient hierarchical pooling strategy for which we have recently derived the optimal, determining the best ways to proceed when there are impediments for the use of the absolute optimum or when multiple pools are tested simultaneously and there are restrictions on the throughput time. We also show how the ddPCR RNA quantification and the nested nature of the strategy can be combined to perform self-consistency tests for a better identification of infected individuals and nonviable samples. The studies are useful to those considering pool testing for the identification of infected individuals.


Subject(s)
COVID-19 Nucleic Acid Testing/methods , COVID-19/diagnosis , Diagnostic Tests, Routine/methods , Real-Time Polymerase Chain Reaction/methods , SARS-CoV-2/genetics , Algorithms , COVID-19/epidemiology , COVID-19/virology , Communicable Diseases/diagnosis , Communicable Diseases/virology , Humans , Models, Genetic , Pandemics , RNA, Viral/genetics , Reproducibility of Results , SARS-CoV-2/physiology , Sensitivity and Specificity , Specimen Handling/methods
12.
Mol Biol Evol ; 38(4): 1537-1543, 2021 04 13.
Article in English | MEDLINE | ID: covidwho-1387956

ABSTRACT

The rooting of the SARS-CoV-2 phylogeny is important for understanding the origin and early spread of the virus. Previously published phylogenies have used different rootings that do not always provide consistent results. We investigate several different strategies for rooting the SARS-CoV-2 tree and provide measures of statistical uncertainty for all methods. We show that methods based on the molecular clock tend to place the root in the B clade, whereas methods based on outgroup rooting tend to place the root in the A clade. The results from the two approaches are statistically incompatible, possibly as a consequence of deviations from a molecular clock or excess back-mutations. We also show that none of the methods provide strong statistical support for the placement of the root in any particular edge of the tree. These results suggest that phylogenetic evidence alone is unlikely to identify the origin of the SARS-CoV-2 virus and we caution against strong inferences regarding the early spread of the virus based solely on such evidence.


Subject(s)
COVID-19/virology , Genome, Viral , Mutation , Phylogeny , SARS-CoV-2/genetics , Algorithms , Animals , Bayes Theorem , Evolution, Molecular , Humans , Likelihood Functions , Markov Chains , Models, Genetic , Models, Statistical , Monte Carlo Method , Mutation, Missense , RNA, Viral/genetics , Uncertainty
13.
PLoS Genet ; 16(12): e1009272, 2020 12.
Article in English | MEDLINE | ID: covidwho-1388879

ABSTRACT

The Betacoronaviruses comprise multiple subgenera whose members have been implicated in human disease. As with SARS, MERS and now SARS-CoV-2, the origin and emergence of new variants are often attributed to events of recombination that alter host tropism or disease severity. In most cases, recombination has been detected by searches for excessively similar genomic regions in divergent strains; however, such analyses are complicated by the high mutation rates of RNA viruses, which can produce sequence similarities in distant strains by convergent mutations. By applying a genome-wide approach that examines the source of individual polymorphisms and that can be tested against null models in which recombination is absent and homoplasies can arise only by convergent mutations, we examine the extent and limits of recombination in Betacoronaviruses. We find that recombination accounts for nearly 40% of the polymorphisms circulating in populations and that gene exchange occurs almost exclusively among strains belonging to the same subgenus. Although experimental studies have shown that recombinational exchanges occur at random along the coronaviral genome, in nature, they are vastly overrepresented in regions controlling viral interaction with host cells.


Subject(s)
Betacoronavirus/classification , Betacoronavirus/genetics , Recombination, Genetic/genetics , Spike Glycoprotein, Coronavirus/genetics , Crossing Over, Genetic/genetics , Genes, Viral/genetics , Genome, Viral/genetics , Host Specificity/genetics , Models, Genetic , Polymorphism, Genetic , SARS-CoV-2/classification , SARS-CoV-2/genetics , Viral Tropism/genetics
14.
Genome Biol Evol ; 13(10)2021 10 01.
Article in English | MEDLINE | ID: covidwho-1370777

ABSTRACT

Owing to a lag between a deleterious mutation's appearance and its selective removal, gold-standard methods for mutation rate estimation assume no meaningful loss of mutations between parents and offspring. Indeed, from analysis of closely related lineages, in SARS-CoV-2, the Ka/Ks ratio was previously estimated as 1.008, suggesting no within-host selection. By contrast, we find a higher number of observed SNPs at 4-fold degenerate sites than elsewhere and, allowing for the virus's complex mutational and compositional biases, estimate that the mutation rate is at least 49-67% higher than would be estimated based on the rate of appearance of variants in sampled genomes. Given the high Ka/Ks one might assume that the majority of such intrahost selection is the purging of nonsense mutations. However, we estimate that selection against nonsense mutations accounts for only ∼10% of all the "missing" mutations. Instead, classical protein-level selective filters (against chemically disparate amino acids and those predicted to disrupt protein functionality) account for many missing mutations. It is less obvious why for an intracellular parasite, amino acid cost parameters, notably amino acid decay rate, is also significant. Perhaps most surprisingly, we also find evidence for real-time selection against synonymous mutations that move codon usage away from that of humans. We conclude that there is common intrahost selection on SARS-CoV-2 that acts on nonsense, missense, and possibly synonymous mutations. This has implications for methods of mutation rate estimation, for determining times to common ancestry and the potential for intrahost evolution including vaccine escape.


Subject(s)
COVID-19/virology , Mutation , SARS-CoV-2/genetics , Codon Usage , Codon, Nonsense , Evolution, Molecular , Humans , Models, Genetic , Mutation Rate , Mutation, Missense , Polymorphism, Single Nucleotide , Selection, Genetic , Silent Mutation
15.
PLoS Comput Biol ; 17(7): e1009128, 2021 07.
Article in English | MEDLINE | ID: covidwho-1360635

ABSTRACT

If they undergo new mutations at each replication cycle, why are RNA viral genomes so fragile, with most mutations being either strongly deleterious or lethal? Here we provide theoretical and numerical evidence for the hypothesis that genetic fragility is partly an evolutionary response to the multiple population bottlenecks experienced by viral populations at various stages of their life cycles. Modelling within-host viral populations as multi-type branching processes, we show that mutational fragility lowers the rate at which Muller's ratchet clicks and increases the survival probability through multiple bottlenecks. In the context of a susceptible-exposed-infectious-recovered epidemiological model, we find that the attack rate of fragile viral strains can exceed that of more robust strains, particularly at low infectivities and high mutation rates. Our findings highlight the importance of demographic events such as transmission bottlenecks in shaping the genetic architecture of viral pathogens.


Subject(s)
Evolution, Molecular , Genome, Viral/genetics , Models, Genetic , Computational Biology , Genomic Instability/genetics , Mutation/genetics , RNA, Viral/genetics
16.
Syst Biol ; 71(2): 426-438, 2022 02 10.
Article in English | MEDLINE | ID: covidwho-1358488

ABSTRACT

Phylogenetic trees from real-world data often include short edges with very few substitutions per site, which can lead to partially resolved trees and poor accuracy. Theory indicates that the number of sites needed to accurately reconstruct a fully resolved tree grows at a rate proportional to the inverse square of the length of the shortest edge. However, when inferred trees are partially resolved due to short edges, "accuracy" should be defined as the rate of discovering false splits (clades on a rooted tree) relative to the actual number found. Thus, accuracy can be high even if short edges are common. Specifically, in a "near-perfect" parameter space in which trees are large, the tree length $\xi$ (the sum of all edge lengths) is small, and rate variation is minimal, the expected false positive rate is less than $\xi/3$; the exact value depends on tree shape and sequence length. This expected false positive rate is far below the false negative rate for small $\xi$ and often well below 5% even when some assumptions are relaxed. We show this result analytically for maximum parsimony and explore its extension to maximum likelihood using theory and simulations. For hypothesis testing, we show that measures of split "support" that rely on bootstrap resampling consistently imply weaker support than that implied by the false positive rates in near-perfect trees. The near-perfect parameter space closely fits several empirical studies of human virus diversification during outbreaks and epidemics, including Ebolavirus, Zika virus, and SARS-CoV-2, reflecting low substitution rates relative to high transmission/sampling rates in these viruses.[Ebolavirus; epidemic; HIV; homoplasy; mumps virus; perfect phylogeny; SARS-CoV-2; virus; West Nile virus; Yule-Harding model; Zika virus.].


Subject(s)
COVID-19 , Zika Virus Infection , Zika Virus , Humans , Models, Genetic , Phylogeny , SARS-CoV-2
17.
Epidemiol Infect ; 149: e162, 2021 07 02.
Article in English | MEDLINE | ID: covidwho-1294409

ABSTRACT

Clinical and genetic risk factors for severe coronavirus disease 2019 (COVID-19) are often considered independently and without knowledge of the magnitudes of their effects on risk. Using severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) positive participants from the UK Biobank, we developed and validated a clinical and genetic model to predict risk of severe COVID-19. We used multivariable logistic regression on a 70% training dataset and used the remaining 30% for validation. We also validated a previously published prototype model. In the validation dataset, our new model was associated with severe COVID-19 (odds ratio per quintile of risk = 1.77, 95% confidence interval (CI) 1.64-1.90) and had acceptable discrimination (area under the receiver operating characteristic curve = 0.732, 95% CI 0.708-0.756). We assessed calibration using logistic regression of the log odds of the risk score, and the new model showed no evidence of over- or under-estimation of risk (α = -0.08; 95% CI -0.21-0.05) and no evidence or over-or under-dispersion of risk (ß = 0.90, 95% CI 0.80-1.00). Accurate prediction of individual risk is possible and will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.


Subject(s)
COVID-19 , Models, Genetic , Risk Assessment/methods , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/genetics , COVID-19/physiopathology , Comorbidity , Female , Humans , Male , Models, Statistical , Polymorphism, Single Nucleotide/genetics , ROC Curve , Reproducibility of Results , SARS-CoV-2 , Severity of Illness Index
18.
Hum Genomics ; 15(1): 26, 2021 05 07.
Article in English | MEDLINE | ID: covidwho-1220117

ABSTRACT

BACKGROUND: Mathematical approaches have been for decades used to probe the structure of DNA sequences. This has led to the development of Bioinformatics. In this exploratory work, a novel mathematical method is applied to probe the DNA structure of two related viral families: those of coronaviruses and those of influenza viruses. The coronaviruses are SARS-CoV-2, SARS-CoV-1, and MERS. The influenza viruses include H1N1-1918, H1N1-2009, H2N2-1957, and H3N2-1968. METHODS: The mathematical method used is the slow feature analysis (SFA), a rather new but promising method to delineate complex structure in DNA sequences. RESULTS: The analysis indicates that the DNA sequences exhibit an elaborate and convoluted structure akin to complex networks. We define a measure of complexity and show that each DNA sequence exhibits a certain degree of complexity within itself, while at the same time there exists complex inter-relationships between the sequences within a family and between the two families. From these relationships, we find evidence, especially for the coronavirus family, that increasing complexity in a sequence is associated with higher transmission rate but with lower mortality. CONCLUSIONS: The complexity measure defined here may hold a promise and could become a useful tool in the prediction of transmission and mortality rates in future new viral strains.


Subject(s)
Betacoronavirus/classification , Betacoronavirus/genetics , Influenza A virus/classification , Influenza A virus/genetics , Models, Genetic , Betacoronavirus/physiology , Coronavirus Infections/mortality , Coronavirus Infections/transmission , Coronavirus Infections/virology , Evolution, Molecular , Humans , Influenza A virus/physiology , Influenza, Human/mortality , Influenza, Human/transmission , Influenza, Human/virology , Sequence Analysis, DNA , Species Specificity , Time Factors
19.
J Biol Chem ; 296: 100687, 2021.
Article in English | MEDLINE | ID: covidwho-1198855

ABSTRACT

Glucocorticoids are potent anti-inflammatory drugs that are used to treat an extraordinary range of human disease, including COVID-19, underscoring the ongoing importance of understanding their molecular mechanisms. Early studies of GR signaling led to broad acceptance of models in which glucocorticoid receptor (GR) monomers tether repressively to inflammatory transcription factors, thus abrogating inflammatory gene expression. However, newer data challenge this core concept and present an exciting opportunity to reframe our understanding of GR signaling. Here, we present an alternate, two-part model for transcriptional repression by glucocorticoids. First, widespread GR-mediated induction of transcription results in rapid, primary repression of inflammatory gene transcription and associated enhancers through competition-based mechanisms. Second, a subset of GR-induced genes, including targets that are regulated in coordination with inflammatory transcription factors such as NF-κB, exerts secondary repressive effects on inflammatory gene expression. Within this framework, emerging data indicate that the gene set regulated through the cooperative convergence of GR and NF-κB signaling is central to the broad clinical effectiveness of glucocorticoids in terminating inflammation and promoting tissue repair.


Subject(s)
Anti-Inflammatory Agents/therapeutic use , COVID-19/drug therapy , Dexamethasone/therapeutic use , Glucocorticoids/therapeutic use , NF-kappa B/genetics , Receptors, Glucocorticoid/genetics , Animals , COVID-19/immunology , COVID-19/pathology , COVID-19/virology , Gene Expression Regulation , Genomics/methods , Humans , Inflammation/prevention & control , Models, Genetic , NF-kappa B/antagonists & inhibitors , NF-kappa B/immunology , Receptors, Glucocorticoid/agonists , Receptors, Glucocorticoid/immunology , SARS-CoV-2/growth & development , SARS-CoV-2/immunology , SARS-CoV-2/pathogenicity , Signal Transduction , Transcription, Genetic/drug effects , Transcription, Genetic/immunology
20.
PLoS Comput Biol ; 17(2): e1008322, 2021 02.
Article in English | MEDLINE | ID: covidwho-1138558

ABSTRACT

Relaxed clock models enable estimation of molecular substitution rates across lineages and are widely used in phylogenetics for dating evolutionary divergence times. Under the (uncorrelated) relaxed clock model, tree branches are associated with molecular substitution rates which are independently and identically distributed. In this article we delved into the internal complexities of the relaxed clock model in order to develop efficient MCMC operators for Bayesian phylogenetic inference. We compared three substitution rate parameterisations, introduced an adaptive operator which learns the weights of other operators during MCMC, and we explored how relaxed clock model estimation can benefit from two cutting-edge proposal kernels: the AVMVN and Bactrian kernels. This work has produced an operator scheme that is up to 65 times more efficient at exploring continuous relaxed clock parameters compared with previous setups, depending on the dataset. Finally, we explored variants of the standard narrow exchange operator which are specifically designed for the relaxed clock model. In the most extreme case, this new operator traversed tree space 40% more efficiently than narrow exchange. The methodologies introduced are adaptive and highly effective on short as well as long alignments. The results are available via the open source optimised relaxed clock (ORC) package for BEAST 2 under a GNU licence (https://github.com/jordandouglas/ORC).


Subject(s)
Evolution, Molecular , Models, Genetic , Phylogeny , Algorithms , Animals , Bayes Theorem , Computational Biology , Computer Simulation , Databases, Genetic/statistics & numerical data , Likelihood Functions , Markov Chains , Monte Carlo Method , Mutation Rate , Software , Time Factors
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