Transcriptome in Human Mycoses

Mycoses are infectious diseases caused by fungi, which incidence has increased in recent decades due to the increasing number of immunocompromised patients and improved diagnostic tests. As eukaryotes, fungi share many similarities with human cells, making it difficult to design drugs without side effects. Commercially available drugs act on a limited number of targets and have been reported fungal resistance to commonly used antifungal drugs. Therefore, elucidating the pathogenesis of fungal infections, the fungal strategies to overcome the hostile environment of the host, and the action of antifungal drugs is essential for developing new therapeutic approaches and diagnostic tests. Large-scale transcriptional analyses using microarrays and RNA sequencing (RNA-seq), combined with improvements in molecular biology techniques, have improved the study of fungal pathogenicity. Such techniques have provided insights into the infective process by identifying molecular strategies used by the host and pathogen during the course of human mycoses. This chapter will explore the latest discoveries regarding the transcriptome of major human fungal pathogens. Further we will highlight genes essential for host–pathogen interactions, immune response, invasion, infection, antifungal drug response, and resistance. Finally, we will discuss their importance to the discovery of new molecular targets for antifungal drugs. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2014, 2022.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints.

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

Haploidentical CD3+ TCR αß/CD19+ depleted HSCT for MHC Class II deficiency and persistent SARS-CoV-2 pneumonitis.

Background & Objectives: SARS-CoV-2 infection leads to coronavirus disease 2019 (COVID-19), which can range from a mild illness to a severe phenotype characterised by acute respiratory distress, needing mechanical ventilation. Children with combined immunodeficiencies might be unable to mount a sufficient cellular and humoral immune response against Covid-19 and have persistent disease. The authors describe a child with combined immunodeficiency, with favorable post-HSCT course following a haploidentical haematopoietic stem cell transplant in the presence of persistent SARS-CoV-2 infection. Methods & results: A 13-month-old girl with MHC class II deficiency developed persistent pre-HSCT SARS-CoV-2 infection. Faced with a significant challenge of balancing the risk of progressive infection due to incompetent immune system with the danger of inflammatory pneumonitis peri-immune reconstitution post-HSCT, she underwent a maternal (with a recent history of Covid-19 infection) haploidentical haematopoietic stem cell transplant. The patient received Regdanvimab® (post stem cell infusion) and Remdesivir (pre and post stem cell infusion). We noted a gradual increase in the Ct (cycle threshold) values, implying reduction in viral RNA with concomitant expansion in the CD3 lymphocyte subset and clinical/radiological improvement. Conclusions: Combination of adoptive transfer of maternal CD45RO+ memory add-back T-lymphocytes after haploidentical HSCT, use of Regdanvimab® (SARS-CoV-2 neutralising monoclonal antibody) and Remdesivir may have led to the successful outcome in our patient with severe immunodeficiency, undergoing HSCT. Our case highlights the role of novel antiviral strategies (monoclonal antibodies and CD45RO+ memory T-lymphocytes) in contributing to viral clearance in a challenging clinical scenario.

Engineering of a Multi-Epitope Subunit Vaccine Against SASRS-CoV-2 Through the Viroinformatic Approach

The COVID-19 outbreak has infected millions of people worldwide, but no vaccine has been discovered to combat it efficiently. This research aims to design a multi-epitope vaccine using highly efficient B- and T-cell epitopes from the SARS-CoV-2 Surabaya isolate through a viroinformatic approach. First, the putative epitopes were linked together to develop tertiary structures and then docked with toll-like receptor 4 (TLR-4) that demonstrated a robust interaction with a low eigenvalue of 4.816138 e¡06. Furthermore, the structure's high immunogenic response was observed and successfully cloned into the expression vector pET28a (þ). This implies that the designed vaccine can prove effective in combating SARS-CoV-2. © 2022 University of Kerbala.

Population-Predicted MHC Class II Epitope Presentation of SARS-CoV-2 Structural Proteins Correlates to the Case Fatality Rates of COVID-19 in Different Countries.

We observed substantial differences in predicted Major Histocompatibility Complex II (MHCII) epitope presentation of SARS-CoV-2 proteins for different populations but only minor differences in predicted MHCI epitope presentation. A comparison of this predicted epitope MHC-coverage revealed for the early phase of infection spread (till day 15 after reaching 128 observed infection cases) highly significant negative correlations with the case fatality rate. Specifically, this was observed in different populations for MHC class II presentation of the viral spike protein (p-value: 0.0733 for linear regression), the envelope protein (p-value: 0.023), and the membrane protein (p-value: 0.00053), indicating that the high case fatality rates of COVID-19 observed in some countries seem to be related with poor MHC class II presentation and hence weak adaptive immune response against these viral envelope proteins. Our results highlight the general importance of the SARS-CoV-2 structural proteins in immunological control in early infection spread looking at a global census in various countries and taking case fatality rate into account. Other factors such as health system and control measures become more important after the early spread. Our study should encourage further studies on MHCII alleles as potential risk factors in COVID-19 including assessment of local populations and specific allele distributions.

Prediction of putative epitope-based vaccine against all corona virus strains for the Chinese population: Approach toward development of vaccine.

Currently, the whole world is facing the coronavirus disease-19 pandemic. As of now, approximately 0.15 million people around the globe are infected with the novel coronavirus. In the last decade, two strains of the coronavirus family, severe acute respiratory syndrome-related coronavirus and Middle East respiratory syndrome coronavirus, also resulted in epidemics in south Asian and the Middle Eastern countries with high mortality rate. This scenario demands the development of a putative vaccine which may provide immunity against all current and new evolving coronavirus strains. In this study, we designed an epitope-based vaccine using an immunoinformatic approach. This vaccine may protect against all coronavirus strains. The vaccine is developed by considering the geographical distribution of coronavirus strains and host genetics (Chinese population). Nine experimentally validated epitopes sequences from coronavirus strains were used to derive the variants considering the conservancy in all strains. Further, the binding affinities of all derived variants were checked with most abundant human leukocyte antigen alleles in the Chinese population. Three major histocompatibility complex (MHC) Class I epitopes from spike glycoprotein and nucleoprotein showed sufficient binding while one MHC Class II epitope from spike glycoprotein was found to be an effective binder. A cocktail of these epitopes gave more than 95% population coverage in the Chinese population. Moreover, molecular dynamics simulation supported the aforementioned predictions. Further, in vivo studies are needed to confirm the immunogenic potential of these vaccines.