Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores ((Formula presented.) and (Formula presented.)). By applying a logistic regression with both IPGS, ((Formula presented.) (or indifferently (Formula presented.)) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, supporting their link with COVID-19 severity outcome

The polymorphism L412F in TLR3 inhibits autophagy and is a marker of severe COVID-19 in males

The polymorphism L412F in TLR3 has been associated with several infectious diseases. However, the mechanism underlying this association is still unexplored. Here, we show that the L412F polymorphism in TLR3 is a marker of severity in COVID-19. This association increases in the sub-cohort of males. Impaired macroautophagy/autophagy and reduced TNF/TNFα production was demonstrated in HEK293 cells transfected with TLR3L412F-encoding plasmid and stimulated with specific agonist poly(I:C). A statistically significant reduced survival at 28 days was shown in L412F COVID-19 patients treated with the autophagy-inhibitor hydroxychloroquine (p = 0.038). An increased frequency of autoimmune disorders such as co-morbidity was found in L412F COVID-19 males with specific class II HLA haplotypes prone to autoantigen presentation. Our analyses indicate that L412F polymorphism makes males at risk of severe COVID-19 and provides a rationale for reinterpreting clinical trials considering autophagy pathways. Abbreviations: AP: autophagosome; AUC: area under the curve; BafA1: bafilomycin A1; COVID-19: coronavirus disease-2019; HCQ: hydroxychloroquine; RAP: rapamycin; ROC: receiver operating characteristic; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; TLR: toll like receptor; TNF/TNF-α: tumor necrosis factor

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity.. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores (IPGSph1 and IPGSph2). By applying a logistic regression with both IPGS, (IPGSph2 (or indifferently IPGSph1) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Il valore aggiunto infermieristico nel sistema dei percorsi diagnostici terapeutici assistenziali PDTA dell'Azienda USL 4 di Prato

La motivazione che mi ha spinto a scegliere l’oggetto della tesi, è legata la mio lavoro in quanto sono un’infermiera con la Posizione Organizzativa Integrazione Percorsi Ospedale e Territorio e lavoro presso l’Azienda Usl 4 di Prato, Area Staff Direzione Sanitaria, Unità Funzionale Ricerca Clinica ed Innovazione, e supporto il sistema Aziendale dei PDTA. Nella tesi dimostro il contributo infermieristico nei PDTA che è valore aggiunto orientato all'outocome del paziente

AUMENTO DEGLI INDICI DI CITOLISI EPATICA NEI SOGGETTI CON INFEZIONE DA HIV CHE AVVIANO LA PRIMA LINEA DI TRATTAMENTO ANTIRETROVIRALE: STUDIO OSSERVAZIONALE DI COORTE

Obiettivi: Valutare l'incidenza e i fattori di rischio per il rialzo degli enzimi di citolisi epatica (liver enzyme elevation, LEE) nei pazienti che iniziano una terapia antiretrovirale di prima linea (ART) nella coorte osservazionale prospettica ICONA. Pazienti e metodi: sono stati selezionati per lo studio 6575 pazienti naive alla ART, che hanno iniziato, tra giugno 2009 e dicembre 2017, un regime terapeutico costituito da 2 inibitori nucleosidici della trascrittasi inversa (NRTI) associati ad un inibitore della proteasi potenziato (PI/b) N=2436 (37.1%), ad un inibitore non nucleosidico della trascrittasi inversa (NNRTI) N=2384 (36.3%), o ad un inibitore dell'integrasi (INSTI) N=755 (26.7%). La presenza di co-infezione da parte del virus dell\u2019HBV (HBsAg positivit\ue0) o dell\u2019HCV (HCV-RNA positivit\ue0) \ue8 stata rilevata nel 3.9% e nel 5.8% della popolazione dello studio. I LEE sono stati definiti come aumento di ALT o AST di 65 2.5 x ULN (limite superiore della normale) per i pazienti con valori di base nei limiti della norma o 65 2.5 x il valore basale, per i pazienti con valori di base >ULN. L'analisi di regressione Cox inverse probability weighted \ue8 stata utilizzata per calcolare gli hazard ratio (HR) ed i relativi intervalli di confidenza al 95% (95%CI) per LEE, sulla base del regime terapeutico di prima linea utilizzato e delle caratteristiche basali dei partecipanti allo studio. Risultati: Durante un follow-up complessivo di 20722 anni di osservazione, si sono verificati 183 casi di LEE. Dopo l'aggiustamento per i principali fattori confondenti, il rischio di LEE \ue8 risultato significativamente ridotto nei pazienti trattati con INSTI rispetto a coloro che ricevevano NNRTI (HR 0.46, 95%CI 0.25-0.86), con una significativa riduzione del rischio nel gruppo di pazienti trattati con raltegravir (HR 0.11, 95%CI 0.02-0.84, utilizzando la classe degli NNRTI come riferimento). L'HR per LEE \ue8 risultato significativamente pi\uf9 elevato nei soggetti con co-infezione da HBV o HCV, nei pazienti con infezione da HIV scarsamente controllata e in quelli che hanno contratto l'HIV attraverso la trasmissione omosessuale. Conclusioni: Nel nostro studio, l'utilizzo di INSTI riduce del 54% il rischio di LEE rispetto ad altri regimi. Questo dato potrebbe essere particolarmente importante per la scelta dell'ART in pazienti con fattori di rischio per tossicit\ue0 epatica come le co-infezioni HCV e HBV

Reactogenicity, safety and antibody response, after one and two doses of mRNA-1273 in seronegative and seropositive healthcare workers

SCOPUS: le.jinfo:eu-repo/semantics/publishe

PHENOTYPIC CHARACTERIZATION OF NOVEL ANTIVIRALS FOR THE TREATMENT OF MULTIDRUG RESISTANT HIV-1 AND EMERGING VIRUSES

Abstract Phenotypic characterization of novel antivirals for the treatment of multidrug resistant HIV-1 and emerging viruses Doctoral Research School of Medical Biotechnologies – Cycle XXXV Supervisor: Maurizio Zazzi; Candidate: Federica Giammarino Background The need for new antiviral drugs has increased overtime due to the worldwide circulation of different viruses together with the increased frequency and diversity of new outbreaks. The ideal option for a prompt response against both emerging and re-emerging viruses is represented by the use and the development of direct acting antiviral agents. During my PhD I was involved in several projects focused on the evaluation of the antiviral activity of licensed and investigational antiviral drugs against Human Immunodeficiency (HIV-1), West Nile (WNV), Dengue (DENV) and SARS-CoV-2 viruses. Results and discussion Doravirine The antiviral activity of the NNRTI doravirine was evaluated against viruses harbouring different patterns of NNRTI resistance mutations in two studies. Globally, our data confirmed that the antiviral activity of doravirine may be compromised by the presence of multiple NNRTI resistance mutations, even in the absence of specific doravirine mutations. A third study was focused on the role of the natural polymorphism of the reverse transcriptase V106I. Our results indicate that it minimally affects the susceptibility to doravirine in clinical isolates and that it does not impact the genetic barrier to resistance as compared to reference wild-type virus, while viruses including the NNRTI resistant mutation V106A or V106M rapidly showed viral breakthrough under doravirine pressure due to the reduced susceptibility. Islatravir Our study confirmed the decrease of susceptibility of the investigational NRTTI islatravir due to the presence of M184V mutation. The clinical impact of NRTI mutations in the activity of islatravir has still to be defined and the threshold of fold-change values associated to reduced activity in vivo remains to be established. Ibalizumab The combinatorial activity of ibalizumab together with other antivirals, both approved and investigational, was evaluated through a newly developed cell-based assay consisting in the infection of the MOLT4-R5 cell line with the wild-type strains NL4-3 and AD8, and by the analysis of the results using the innovative software SynergyFinderPlus. Our data suggest that ibalizumab positively interacts with other antivirals with possible synergistic effects in select cases. Further studies are needed to determine the impact of Env variability and viral tropism in combination with other entry inhibitors. Development of a Cell-Based Immunodetection Assay for Simultaneous Screening of Antiviral Compounds Inhibiting Zika and Dengue Virus Replication An easy-to-perform and fast flavivirus immunodetection assay (IA) was developed to determine antiviral activity of promising compounds against ZIKV and DENV. The system, validated with references compounds against both viruses, was able to distinguish between the inhibitory effect of molecules targeting the early and the post-budding phase of viral replication cycle. Evaluation of sofosbuvir activity and resistance profile against West Nile virus in vitro Since the activity of sofosbuvir has been documented against different flaviviruses, we investigated whether it may exert an activity also against WNV. In both cell-based and enzymatic assays sofosbuvir was able to inhibit WNV replication in the low micromolar range. Moreover, in vitro selection and molecular docking experiments indicated that HCV and WNV share a similar sofosbuvir resistance pattern. ORIGINALE CHEMIAE in Antiviral Strategy - Origin and Modernization of Multi-Component Chemistry as a Source of Innovative Broad Spectrum Antiviral Strategy The “ORIGINALE CHEMIAE in Antiviral Strategy” project aims to identify promising broad-spectrum antivirals by taking advantage of the Multi-Component Chemistry strategy. Following the synthetization of molecules, their antiviral activity was determined in in vitro standardized virus-cell systems against DENV, WNV, HIV-1 and SARS-CoV-2. We identified eight molecules able to inhibit at least one of the viruses tested. However, their low selectivity indexes indicate the need to further improve the design of these molecules to increase the antiviral activity and/or reduce the cell toxicity in order to identify candidates for preclinical testing in animal models. Monoclonal antibodies and antivirals vs. SARS-CoV-2 After the development of a quantitative live-virus microneutralization assay, we evaluated the efficacy of licensed monoclonal Antibodies (mAbs) and the antiviral drugs remdesivir, nirmaltrevir and molnupiravir against different circulating SARS-CoV-2 variants. Our results showed that these drugs, contrary to the mAbs, retained activity against all tested variants. Conclusions A continuous challenge for public health is represented by the control of viral infections. Both vaccines and antiviral drugs may synergistically help to reduce the spread and the fatality of acute viral diseases and chronic infections. All the studies described in this thesis emphasize the role of the laboratory of virology within all the steps of the in vitro investigation of antiviral drugs, from the identification of molecules endowed with antiviral activity to the definition of the mechanism of action