A Potential Role of Interleukin 10 in COVID-19 Pathogenesis.

Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being investigated. Here, we explored the role of CCR5 and its ligands across the immunologic spectrum of COVID-19. We used a bioinformatics approach to predict and model the immunologic phases of COVID so that effective treatment strategies can be devised and monitored. We investigated 224 individuals including healthy controls and patients spanning the COVID-19 disease continuum. We assessed the plasma and isolated peripheral blood mononuclear cells (PBMCs) from 29 healthy controls, 26 Mild-Moderate COVID-19 individuals, 48 Severe COVID-19 individuals, and 121 individuals with post-acute sequelae of COVID-19 (PASC) symptoms. Immune subset profiling and a 14-plex cytokine panel were run on all patients from each group. B-cells were significantly elevated compared to healthy control individuals (P<0.001) as was the CD14+, CD16+, CCR5+ monocytic subset (P<0.001). CD4 and CD8 positive T-cells expressing PD-1 as well as T-regulatory cells were significantly lower than healthy controls (P<0.001 and P=0.01 respectively). CCL5/RANTES, IL-2, IL-4, CCL3, IL-6, IL-10, IFN-γ, and VEGF were all significantly elevated compared to healthy controls (all P<0.001). Conversely GM-CSF and CCL4 were in significantly lower levels than healthy controls (P=0.01). Data were further analyzed and the classes were balanced using SMOTE. With a balanced working dataset, we constructed 3 random forest classifiers: a multi-class predictor, a Severe disease group binary classifier and a PASC binary classifier. Models were also analyzed for feature importance to identify relevant cytokines to generate a disease score. Multi-class models generated a score specific for the PASC patients and defined as S1 = (IFN-γ + IL-2)/CCL4-MIP-1β. Second, a score for the Severe COVID-19 patients was defined as S2 = (IL-6+sCD40L/1000 + VEGF/10 + 10*IL-10)/(IL-2 + IL-8). Severe COVID-19 patients are characterized by excessive inflammation and dysregulated T cell activation, recruitment, and counteracting activities. While PASC patients are characterized by a profile able to induce the activation of effector T cells with pro-inflammatory properties and the capacity of generating an effective immune response to eliminate the virus but without the proper recruitment signals to attract activated T cells.

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Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the viral pathogen responsible for the current coronavirus disease 2019 (COVID-19) pandemic. As of 19 May 2021, John Hopkins University's COVID-19 tracking platform reported 3.3 million deaths associated with SARS-CoV-2 infection. Currently, the World Health Organization has granted emergency use listing (EUL) to six COVID-19 vaccine candidates. However, much of the pathogenesis observed during SARS-CoV-2 infection remains elusive. To gain insight into the contribution of individual accessory open reading frame (ORF) proteins in SARS-CoV-2 pathogenesis, we used our recently described reverse-genetics system approach to successfully engineer recombinant SARS-CoV-2 (rSARS-CoV-2) constructs; we removed individual viral ORF3a, -6, -7a, -7b, and -8 proteins from them, and we characterized the resulting recombinant viruses in vitro and in vivo. Our results indicate differences in plaque morphology, with ORF-deficient (ΔORF) viruses producing smaller plaques than those of the wild type (rSARS-CoV-2/WT). However, growth kinetics of ΔORF viruses were like those of rSARS-CoV-2/WT. Interestingly, infection of K18 human angiotensin-converting enzyme 2 (hACE2) transgenic mice with the ΔORF rSARS-CoV-2s identified ORF3a and ORF6 as the major contributors of viral pathogenesis, while ΔORF7a, ΔORF7b, and ΔORF8 rSARS-CoV-2s induced pathology comparable to that of rSARS-CoV-2/WT. This study demonstrates the robustness of our reverse-genetics system to generate rSARS-CoV-2 constructs and the major role for ORF3a and ORF6 in viral pathogenesis, providing important information for the generation of attenuated forms of SARS-CoV-2 for their implementation as live attenuated vaccines for the treatment of SARS-CoV-2 infection and associated COVID-19. IMPORTANCE Despite great efforts put forward worldwide to combat the current coronavirus disease 2019 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to be a human health and socioeconomic threat. Insights into the pathogenesis of SARS-CoV-2 and the contribution of viral proteins to disease outcome remain elusive. Our study aims (i) to determine the contribution of SARS-CoV-2 accessory open reading frame (ORF) proteins to viral pathogenesis and disease outcome and (ii) to develop a synergistic platform combining our robust reverse-genetics system to generate recombinant SARS-CoV-2 constructs with a validated rodent model of infection and disease. We demonstrate that SARS-CoV-2 ORF3a and ORF6 contribute to lung pathology and ultimately disease outcome in K18 hACE2 transgenic mice, while ORF7a, ORF7b, and ORF8 have little impact on disease outcome. Moreover, our combinatory platform serves as a foundation for generating attenuated forms of the virus to develop live attenuated vaccines for the treatment of SARS-CoV-2.

Contribution of SARS-CoV-2 Accessory Proteins to Viral Pathogenicity in K18 Human ACE2 Transgenic Mice.

Acid sphingomyelinase (ASM) cleaves sphingomyelin into the highly lipophilic ceramide, which forms large gel-like rafts/platforms in the plasma membrane. We showed that SARS-CoV-2 uses these platforms for cell entry. Lowering the amount of ceramide or ceramide blockade due to inhibitors of ASM, genetic downregulation of ASM, anti-ceramide antibodies or degradation by neutral ceramidase protected against infection with SARS-CoV-2. The addition of ceramide restored infection with SARS-CoV-2. Many clinically approved medications functionally inhibit ASM and are called FIASMAs (functional inhibitors of acid sphingomyelinase). The FIASMA fluvoxamine showed beneficial effects on COVID-19 in a randomized prospective study and a prospective open-label real-world study. Retrospective and observational studies showed favorable effects of FIASMA antidepressants including fluoxetine, and the FIASMA hydroxyzine on the course of COVID-19. The ASM/ceramide system provides a framework for a better understanding of the infection of cells by SARS-CoV-2 and the clinical, antiviral, and anti-inflammatory effects of functional inhibitors of ASM. This framework also supports the development of new drugs or the repurposing of “old” drugs against COVID-19.

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The acid sphingomyelinase/ceramide system in COVID-19.

COVID-19 is an acute respiratory distress syndrome and is often accompanied by gastrointestinal symptoms. The SARS-CoV-2 has been traced not only in nasopharyngeal and mid-nasal swabs but also in stool and rectal swabs of COVID-19 patients. The gut microbiota is important for an effective immune response as it ensures that unfavorable immune reactions in lungs and other vital organs are regulated. The human gut-lung microbiota interplay provides a framework for therapies in the treatment and management of several pulmonary diseases and infections. Here, we have collated data from COVID-19 studies, which suggest that bacterial co-infections as well as the gut-lung cross talk may be important players in COVID-19 disease prognosis. Our analyses suggests a role of gut microbiome in pathogen infections as well as in an array of excessive immune reactions during and post COVID-19 infection recovery period.

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Interplay between severities of COVID-19 and the gut microbiome: implications of bacterial co-infections?

IL-6 and IL-10 as predictors of disease severity in COVID-19 patients: results from meta-analysis and regression

Aims SARS-CoV-2, an infectious agent behind the ongoing COVID-19 pandemic, induces high levels of cytokines such as IL-1, IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ etc in infected individuals which contribute towards the underlying disease patho-physiology. Nonetheless, exact association and contribution of every cytokine towards COVID-19 pathology remains poorly understood. Delineation of the role of the cytokines during COVID-19 holds the key of efficient patient management in clinics. This study performed a comprehensive meta-analysis to establish association between induced cytokines and COVID-19 disease severity to help in prognosis and clinical care. Main methods Scientific literature was searched to identify 13 cytokines (IL-1β, IL-2, IL-2R, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12, IL-17, TNF-α and IFN-γ) from 18 clinical studies. Standardized mean difference (SMD) for selected 6 cytokines IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ between severe and non-severe COVID-19 patient groups were summarized using random effects model. A classifier was built using logistic regression model with cytokines having significant SMD as covariates. Key findings Out of 13 cytokines, IL-6 and IL-10 showed statistically significant SMD across the studies synthesized. Classifier with mean values of both IL-6 and IL-10 as covariates performed well with accuracy of ~ 92% that was significantly higher than accuracy reported in literature with IL-6 and IL-10 as individual covariates. Significance Simple panel proposed by us with only two cytokine markers can be used as predictors for fast diagnosis of patients with higher risk of COVID-19 disease deterioration and thus can be managed well for a favourable prognosis.

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Cotton is one of the most important commercial crops as the source of natural fiber, oil and fodder. To protect it from harmful pest populations number of newer transgenic lines have been developed. For quick expression checks in successful agriculture qPCR (quantitative polymerase chain reaction) have become extremely popular. The selection of appropriate reference genes plays a critical role in the outcome of such experiments as the method quantifies expression of the target gene in comparison with the reference. Traditionally most commonly used reference genes are the “house-keeping genes”, involved in basic cellular processes. However, expression levels of such genes often vary in response to experimental conditions, forcing the researchers to validate the reference genes for every experimental platform. This study presents a data science driven unbiased genome-wide search for the selection of reference genes by assessing variation of > 50,000 genes in a publicly available RNA-seq dataset of cotton species Gossypium hirsutum. Five genes (TMN5, TBL6, UTR5B, AT1g65240 and CYP76B6) identified by data-science driven analysis, along with two commonly used reference genes found in literature (PP2A1 and UBQ14) were taken through qPCR in a set of 33 experimental samples consisting of different tissues (leaves, square, stem and root), different stages of leaf (young and mature) and square development (small, medium and large) in both transgenic and non-transgenic plants. Expression stability of the genes was evaluated using four algorithms - geNorm, BestKeeper, NormFinder and RefFinder. Based on the results we recommend the usage of TMN5 and TBL6 as the optimal candidate reference genes in qPCR experiments with normal and transgenic cotton plant tissues. AT1g65240 and PP2A1 can also be used if expression study includes squares. This study, for the first time successfully displays a data science driven genome-wide search method followed by experimental validation as a method of choice for selection of stable reference genes over the selection based on function alone.

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Genome wide search to identify reference genes candidates for gene expression analysis in Gossypium hirsutum

Dysregulation of BCL2 is a pathophysiology observed in haematological malignancies. For implementation of available treatment-options it is preferred to know the relative quantification of BCL2 mRNA with appropriate reference genes. For the choice of reference genes-(i) Reference Genes were selected by assessing variation of >60,000 genes from 4 RNA-seq datasets of haematological malignancies followed by filtering based on their GO biological process annotations and proximity of their chromosomal locations to known disease translocations. Selected genes were experimentally validated across various haematological malignancy samples followed by stability comparison using geNorm, NormFinder, BestKeeper and RefFinder. (ii) 43 commonly used Reference Genes were obtained from literature through extensive systematic review. Levels of BCL2 mRNA was assessed by qPCR normalized either by novel reference genes from this study or GAPDH, the most cited reference gene in literature and compared. The analysis showed PTCD2, PPP1R3B and FBXW9 to be the most unregulated genes across lymph-nodes, bone marrow and PBMC samples unlike the Reference Genes used in literature. BCL2 mRNA level shows a consistent higher expression in haematological malignancy patients when normalized by these novel Reference Genes as opposed to GAPDH, the most cited Reference Gene. These reference genes should also be applicable in qPCR platforms using Taqman probes and other model systems including cell lines and rodent models. Absence of sample from healthy-normal individual in diagnostic cases call for careful selection of Reference Genes for relative quantification of a biomarker by qPCR.BCL2 can be used as molecular diagnostics only if normalized with a set of reference genes with stable yet low levels of expression across different types of haematological malignancies.

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Vishnupriyan K

Papers

IL-6 and IL-10 as predictors of disease severity in COVID-19 patients: results from meta-analysis and regression

IL-6 and IL-10 as predictors of disease severity in COVID-19 patients: results from meta-analysis and regression

Genome wide search to identify reference genes candidates for gene expression analysis in Gossypium hirsutum

Relative quantification of BCL2 mRNA for diagnostic usage needs stable uncontrolled genes as reference.