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Critical steps to CGAS: scientific protocol, study design, phenotype definition, candidate gene and variant selection, haplotype analysis and linkage disequilibrium (LD), sample size estimation, statistical significance and p-value, exploratory data analysis (EDA) and quality control of genotyping data, testing statistical association and functional analysis (adapted from, David 2021)
To support a causal association, case–control genetic association studies can be validated through replication. Candidate gene association studies in targeted biological pathways are useful for the replication of genome-wide results. They are also more adaptable to identify lower minor allele frequency variants and thus more prone to a functional analysis follow-up. Meta-analysis is a statistical method for analyzing large collections of results from independent association studies including candidate gene and genome-wide association studies. Meta-analysis can be used to compile results from methodologically uniform studies (conventional application) (Skol et al. 2006; Zondervan and Cardon 2007) or from various methodological approaches (Li et al. 2020c; Pairo-Castineira et al. 2021; Parkinson et al. 2020). Meta-analysis of genome-wide overlap (MAGO), or Meta-analysis of overlapping SNVs from genome-wide approaches, provides a relevant means of aggregating evidence for causal association. There is an increased need for replication strategies by which to infer causality for candidate gene selection and validation, pointing to the possible role of other candidate pathway-related targets
Illustration of different definitions of phenotype leading up to different results in case–control genetic association studies
COVID-19 is a new complex multisystem disease caused by the novel coronavirus SARS-CoV-2. In slightly over 2 years, it infected nearly 500 million and killed 6 million human beings worldwide, causing an unprecedented coronavirus pandemic. Currently, the international scientific community is engaged in elucidating the molecular mechanisms of the pathophysiology of SARS-CoV-2 infection as a basis of scientific developments for the future control of COVID-19. Global exome and genome analysis efforts work to define the human genetics of protective immunity to SARS-CoV-2 infection. Here, we review the current knowledge regarding the SARS-CoV-2 infection, the implications of COVID-19 to Public Health and discuss genotype to phenotype association approaches that could be exploited through the selection of candidate genes to identify the genetic determinants of severe COVID-19.
The major histocompatibility complex (MHC) plays a key role in immune defense, and the Mhc genes of cynomolgus macaque display a high degree of polymorphism. Based on their geographic distribution, different populations of cynomolgus macaques are recognized. Here we present the characterization of the Mhc class I and II repertoire of a large pedigreed group of cynomolgus macaques originating from the mainland north of the isthmus of Kra (N = 42). Segregation analyses resulted in the definition of 81 unreported Mafa-A/B/DRB/DQ/DP haplotypes, which include 32 previously unknown DRB regions. In addition, we report 13 newly defined Mafa-A/B/DRB/DQ/DP haplotypes in a group of cynomolgus macaques originating from the mainland south of the isthmus of Kra/Maritime Southeast Asia (N = 16). A relatively high level of sharing of Mafa-A (51%) and Mafa-B (40%) lineage groups is observed between the populations native to the north and the south of isthmus of Kra. At the allelic level, however, the Mafa-A/B haplotypes seem to be characteristic of a population. An overall comparison of all currently known data revealed that each geographic population has its own specific combinations of Mhc class I and II haplotypes. This illustrates the dynamic evolution of the cynomolgus macaque Mhc region, which was most likely generated by recombination and maintained by selection due to the differential pathogenic pressures encountered in different geographic areas.
Sampling locations. Preserved continuous forests (CO1 and CO2) are denoted with white circles, and forest fragments (FR1-FR4) are denoted with red triangles. For each of the six focal species for genetic analyses, two populations were sampled, one from a continuous site and one from a fragmented site. See Table S2 for sample sizes and species associated with each site
MHC IIB haplotype network for four focal species. Circle size is proportional to haplotype frequency, colors correspond to the populations in which each haplotype is found, and the length of the links between haplotype circles correspond to the genetic distance between haplotypes. XL was the only haplotype found in more than one species (B. semilineata and D. branneri). Photos by A. M. Belasen and T. Y. James
MHC IIB summary statistics across all focal species. Sampling region (SP = São Paulo, BA = Bahia) is specified in parentheses after each species’ name. A,B MHC IIB immunogenetic diversity erodes in fragmented populations as measured by both expected heterozygosity (A) and nucleotide diversity (B). Dark green bars represent populations from continuous forests, and light green bars represent populations from fragmented forests. Asterisks represent a significant difference according 95% confidence Intervals shown by error bars. C Genetic differentiation (fixation index, FST) at MHC IIB vs. ddRAD markers. ddRAD FST mean values are shown by red bars with 95% CI error bars, and MHC IIB FST values are shown by pink bars
Bd incidence across populations and MHC IIB genotypes. (A) and (B) include data from six São Paulo amphibian species, while (C) only includes data from the four species that were genotyped for MHC IIB (see text for details). ABd prevalence in São Paulo across habitat types and species ecologies (forest specialists in dark gray, habitat generalists in light gray). B Log-transformed Bd infection loads across species ecologies (forest specialists in dark gray, habitat generalists in light gray). C Proportion of Bd-infected (black) versus uninfected (white) frogs according the MHC IIB allelic genotype
Habitat fragmentation and infectious diseases threaten wildlife globally, but the interactions of these threats are poorly understood. For instance, while habitat fragmentation can impact genetic diversity at neutral loci, the impacts on disease-relevant loci are less well-studied. We examined the effects of habitat fragmentation in Brazil’s Atlantic Forest on amphibian genetic diversity at an immune locus related to antigen presentation and detection (MHC IIB Exon 2). We used a custom high-throughput assay to sequence a fragment of MHC IIB and quantified Batrachochytrium dendrobatidis (Bd) infections in six frog species in two Atlantic Forest regions. Habitat fragmentation was associated with genetic erosion at MHC IIB Exon 2. This erosion was most severe in forest specialists. Significant Bd infections were detected only in one Atlantic Forest region, potentially due to relatively higher elevation. In this region, forest specialists showed an increase in both Bd prevalence and infection loads in fragmented habitats. Reduced population-level MHC IIB diversity was associated with increased Bd infection risk. On the individual level, MHC IIB heterozygotes exhibited a trend toward reduced Bd infection risk, although this was marginally non-significant. Our results suggest that habitat fragmentation increases Bd infection susceptibility in amphibians, mediated at least in part through erosion of immunogenetic diversity. Our findings have implications for management of fragmented populations in the face of emerging infectious diseases.
Structural organisation of KIR proteins
Haplotype gene content
Killer immunoglobulin-like receptors (KIR) regulate the function of natural killer cells through interactions with various ligands on the surface of cells, thereby determining whether natural killer (NK) cells are to be activated or inhibited from killing the cell being interrogated. The genes encoding these proteins display extensive variation through variable gene content, copy number and allele polymorphism. The combination of KIR genes and their ligands is implicated in various clinical settings including haematopoietic stem cell and solid organ transplant and infectious disease progression. The determination of KIR genes has been used as a factor in the selection of optimal stem cell donors with haplotype variations in recipient and donor giving differential clinical outcomes. Methods to determine KIR genes have primarily involved ascertaining the presence or absence of genes in an individual. With the more recent introduction of massively parallel clonal next-generation sequencing and single molecule very long read length third-generation sequencing, high-resolution determination of KIR alleles has become feasible. Determining the extent and functional impact of allele variation has the potential to lead to further optimisation of clinical outcomes as well as a deeper understanding of the functional properties of the receptors and their interactions with ligands. This review summarizes recently published high-resolution KIR genotyping methods and considers the various advantages and disadvantages of the approaches taken. In addition the application of allele level genotyping in the setting of transplantation and infectious disease control is discussed.
Heritable polymorphisms within the human IgG locus, collectively termed allotypes, have often been linked by statistical associations, but rarely mechanistically, to a wide range of disease states. One potential explanation for these associations is that IgG allotype alters host cell receptors’ affinity for IgG, dampening or enhancing an immune response depending on the nature of the change and the receptors. In this work, a panel of allotypic antibody variants were evaluated using multiplexed, label-free biophysical methods and cell-based functional assays to determine what effect, if any, human IgG polymorphisms have on antibody function. While we observed several differences in FcγR affinity among allotypes, there was little evidence of dramatically altered FcγR-based effector function or antigen recognition activity associated with this aspect of genetic variability.
The Notch pathway is a highly conserved signaling pathway involved in the regulation of cell proliferation and differentiation. However, the relationships between Notch pathway-related genes (NPRGs), immunosuppression, and immunotherapy resistance of hepatocellular carcinoma (HCC) remain unclear. Gene expression data and clinical information were extracted from GSE14520, GSE36376, GSE76427, LIRI-JP, TCGA-LIHC, GSE20140, GSE27150, and IMvigor210 datasets. A consensus clustering analysis based on 10 NPRGs was performed to determine the molecular subtypes, and then a notchScore was constructed based on differentially expressed and prognostic genes between molecular subtypes. Two molecular subgroups with significantly distinct survival and immune cell infiltration were identified. Then, a notchScore was constructed to quantify the Notch index of each patient with HCC. Next, we investigated the correlations between the clinical characteristics and the notchScore using logistic regression. Furthermore, multivariate Cox analysis showed that a high notchScore was an independent predictor of poor overall survival (OS) in the TCGA and LIRI-JP datasets and was associated with higher pathological stages. Additionally, a high notchScore was associated with higher immune cells, higher ESTIMATE score, higher immune score, higher stromal score, higher immune checkpoint, and lower tumor purity, which was consistent with the “immunity tidal model theory.” Importantly, a high notchScore was sensitive to immunotherapy. Additionally, GSEA indicated that several GO and KEGG items associated with apoptosis, immune-related pathways, and cell cycle signal pathways were significantly enriched in the high notchScore phenotype pathway. Our findings propose that a high notchScore is a prognostic biomarker and correlates with immune infiltration and sensitivity to immunotherapy in HCC.
Multiple novel immunoglobulin-like transcripts (NILTs) have been identified from salmon, trout, and carp. NILTs typically encode activating or inhibitory transmembrane receptors with extracellular immunoglobulin (Ig) domains. Although predicted to provide immune recognition in ray-finned fish, we currently lack a definitive framework of NILT diversity, thereby limiting our predictions for their evolutionary origin and function. In order to better understand the diversity of NILTs and their possible roles in immune function, we identified five NILT loci in the Atlantic salmon (Salmo salar) genome, defined 86 NILT Ig domains within a 3-Mbp region of zebrafish (Danio rerio) chromosome 1, and described 41 NILT Ig domains as part of an alternative haplotype for this same genomic region. We then identified transcripts encoded by 43 different NILT genes which reflect an unprecedented diversity of Ig domain sequences and combinations for a family of non-recombining receptors within a single species. Zebrafish NILTs include a sole putative activating receptor but extensive inhibitory and secreted forms as well as membrane-bound forms with no known signaling motifs. These results reveal a higher level of genetic complexity, interindividual variation, and sequence diversity for NILTs than previously described, suggesting that this gene family likely plays multiple roles in host immunity.
Crohn’s disease (CD), a subtype of inflammatory bowel disease (IBD), has increasing prevalence in the world. Due to the lack of cure strategy, most patients with CD develop progressive disease companying with a series of serious complications. Therefore, exploring molecular mechanism differences between active and inactive CD will help in the screening of predict markers and therapeutic targets. In this study, we analyzed differentially expressed genes (DEGs) and molecular pathways through between active and inactive CD patients. In addition, the abundance of 22 immune cell types were assessed by using the CIBERSORT. The hub DEGs were screened out by the CytoHubba in Cytoscape, followed by the least absolute shrinkage and selection operator (LASSO) regression. Finally, the clinical predictive model was constructed by binary logistic regression model. The diagnostic efficacy was tested by receiver operating characteristic (ROC) curve and verified in independent datasets. The results showed that there were 137 DEGs between the active and inactive CD. Most of them were involved in regulating the immunity process. In addition, the decreased abundance of CD8 T cells and the increased abundance of M0, M1 macrophages, and neutrophils were closely related to CD activation. CXCL9, C3AR1, IL1B, and TLR4 were the hub gene and can be applied to the prediction of CD activation. Our results provided important targets for the prediction of CD activation and the selection of therapeutic targets.
The genetics of allorecognition has been studied extensively in inbred lines of Hydractinia symbiolongicarpus, in which genetic control is attributed mainly to the highly polymorphic loci allorecognition 1 (Alr1) and allorecognition 2 (Alr2), located within the Allorecognition Complex (ARC). While allelic variation at Alr1 and Alr2 can predict the phenotypes in inbred lines, these two loci do not entirely predict the allorecognition phenotypes in wild-type colonies and their progeny, suggesting the presence of additional uncharacterized genes that are involved in the regulation of allorecognition in this species. Comparative genomics analyses were used to identify coding sequence differences from assembled chromosomal intervals of the ARC and from genomic scaffold sequences between two incompatible H. symbiolongicarpus siblings from a backcross population. New immunoglobulin superfamily (Igsf) genes are reported for the ARC, where five of these genes are closely related to the Alr1 and Alr2 genes, suggesting the presence of multiple Alr-like genes within this complex. Complementary DNA sequence evidence revealed that the allelic polymorphism of eight Igsf genes is associated with allorecognition phenotypes in a backcross population of H. symbiolongicarpus, yet that association was not found between parental colonies and their offspring. Alternative splicing was found as a mechanism that contributes to the variability of these genes by changing putative activating receptors to inhibitory receptors or generating secreted isoforms of allorecognition proteins. Our findings demonstrate that allorecognition in H. symbiolongicarpus is a multigenic phenomenon controlled by genetic variation in at least eight genes in the ARC complex.
Multiple Sclerosis (MS) is a chronic autoimmune, inflammatory neurological disease that is widely associated withGrey and white matter degradation due to the demyelination of axons. Thus exposing the underlying causes of thiscondition can lead to a novel treatment approach for Multiple Sclerosis. The total RNA microarray processed datafrom GEO for Multiple Sclerotic (MS) patients was comprehensively analyzed to find out underlying differencesbetween Grey Matter Lesions (GML), Normal Appearing Grey Matter (NAGM), and Control Grey matter at thetranscriptomics level. Thus, in the current study, we performed various bioinformatics analyses on transcriptionalprofiles of 184 samples including 105 NAGM, 37 GML, and 42 Controls obtained from the NCBI-Bio project(PRJNA543111). First, exploratory data analysis based on gene expression data using Principal Component Analysis(PCA) depicted distinct patterns between GML and CG samples. Subsequently, the Welch’s T-test differential geneexpression analysis identified 1525 significantly differentially expressed genes (p. adj value <0.05, Fold change (>=+/-1.5) between these conditions. This study reveals the genes like CREB3L2, KIF5B, WIPI1, EP300, NDUFA1, ATG101,and TAF4 as the key features that may substantially contribute to loss of cognitive functions in Multiple Sclerosisand several other neurodegenerative disorders. Further, this study also proposes genes associated with Huntington’sdisease in multiple sclerotic patients. Eventually, the results presented here reveal new insights into MS and how itaffects the development of male primary sexual characteristics. (17) (PDF) In Silico Analysis of Transcriptomic Profiling and Affected Biological Pathways in Multiple Sclerosis. Available from: [accessed Jul 26 2022].
Myd88 was highly expressed in mDCs. a Scanning electron microscopy images of imDCs at 6 days and mDCs at 8 days (× 2500). Scale bar: 100 μm. b Transmission electron microscopy images of imDCs at 6 days and mDCs at 8 days (× 4000). Scale bar: 100 μm. c The expression of DCs maturation markers was detected by flow cytometry. d The mRNA and e protein levels of Myd88 in imDCs and mDCs were examined by qRT-PCR and western blot, respectively. *P < 0.05, vs. imDC group
The identification of Ad-Myd88 shRNAs. On the 6th day, DCs from monkeys were infected with Ad-NC or Ad-Myd88 shRNA. a Chromatogram showing the insert sequencing data of different Ad-Myd88 shRNAs. b Ad-Myd88 shRNAs-transfected DCs at 24 h under a fluorescence microscope (× 200). c Myd88 protein expression was examined by western blot analysis in DCs infected with different Ad-Myd88 shRNAs. *P < 0.05, vs. ctrl group; #P < 0.05, vs. shR-NC + LPS group
Myd88 knockdown suppressed DCs maturation. On the 6th day, DCs from monkeys were infected with Ad-NC or Ad-Myd88 shRNA, followed by LPS stimulation. The mRNA a and protein b levels of Myd88 were determined by qRT-PCR and western blot, respectively. c The expression of DC maturation markers was examined by flow cytometry. d DC apoptosis was examined by Annexin V/PI staining. *P < 0.05, vs. ctrl group; #P < 0.05, vs. shR-NC + LPS group
Myd88 knockdown suppressed allogeneic T cell proliferation and activation. On the 6th day, DCs from monkeys were infected with Ad-NC or Ad-Myd88 shRNA, followed by LPS stimulation. a The absorbance at 490 nm in the mixed lymphocyte reaction (MLR). b The cytokine levels of Th1 cells (IL-2, IFN-γ) and Th2 cells (IL-4 and IL-10) in the cell supernatant were examined by ELISA. c The effect of DCs in each group on Th1 and Th2 cell percentages and the Th1/Th2 ratio were determined by flow cytometry. **P < 0.01, vs. ctrl group; ##P < 0.01, vs. Ad-NC group
Immature dendritic cells (imDCs) are activated and mature to initiate an adaptive immune response, resulting in allograft rejection and transplantation failure. Myeloid differentiation factor 88 (Myd88) is a key factor in the Toll-like receptor (TLR) signaling pathway. Here, we investigated the effect of Myd88 silencing on DC function and immune response. CD34 + cells were isolated from the bone marrow of rhesus monkeys by the immunomagnetic bead method and then infected with an adenovirus expressing Myd88-specific short hairpin RNA (sh-Myd88). sh-NC (nontargeting negative control)- or sh-Myd88-infected DCs were treated with lipopolysaccharide (LPS) for another 48 h to induce DCS maturation. The maturation of DCs was identified by immunofluorescence staining for MHCII, CD80, and CD86. DC apoptosis was examined using Annexin V/PI staining. DC-related cytokine levels (IFN-γ and IL-12) were assessed by ELISA. A mixed lymphocyte reaction (MLR) was performed to test the effect of Myd88-silenced DCs on T lymphocytes in vitro. The results showed that compared with control or sh-NC-infected DCs, Myd88-silenced DCs had lower MHCII, CD80, CD86, and DC-related cytokine (IFN-γ and IL-12) levels. Myd88 did not affect the apoptosis of DCs. MLR demonstrated that Myd88 silencing could effectively block LPS-activated T cell proliferation in vitro. These data were consistent with the characteristics of tolerogenic DCs. In conclusion, our data indicated that Myd88 silencing could inhibit the maturation of imDCs and alleviate immune rejection, which provides a reference for immune tolerance in clinical liver transplantation.
Generation of fusion genes by chromosomal recombination events involving KIR3DL20. Chromosomal recombination events might shuffle segments from different genes to generate a novel gene entity. Two distinct events are recorded for the framework gene KIR3DL20. One event involves the recombination with KIR1D, thereby contracting the centromeric haplotype region (blue lines and arrow), whereas the other event involves segment reshuffling with KIR2DL04, and fuses the centromeric and telomeric regions directly (red lines and arrow)
Schematic overview of the KIR region architecture in rhesus macaques and humans. The organization of the most common KIR configurations in rhesus macaques and humans is schematically illustrated. Regions of diversity are in the branched lines. The centromeric and telomeric regions are divided by the double-lined break. Inhibitory and activating KIR genes are indicated by blue and red boxes, respectively. Lineage V and I KIR genes are indicated by yellow and green boxes, respectively, whereas grey boxes represent pseudogenes. In rhesus macaques, KIR3DL20 represents the only framework gene, whereas the presence of all other KIR genes is variable. The telomeric region in rhesus macaque is expanded, and the average number of inhibitory KIR genes is indicated in respect to the number of activating copies, representing the different functional haplotypes defined. As their telomeric KIR gene content displays great diversity, specific genes are not assigned to this region (empty blue and red boxes). The human haplotypes display four framework genes and several regions of diversity. The upper and lower content lines represent the more activating group B and more inhibitory group A KIR haplotypes, respectively. The human KIR haplotype representation is adapted from Abi-Rached and colleagues (Abi-Rached et al. 2010)
Distribution of the functional KIR region configuration profiles in rhesus macaques. The number of identified region configurations with a more inhibitory and more activating profile is designated by the blue bars, together with their subgroups that are distinguished based on the number of activating genes present on a configuration (none or 1 activating gene for inhibitory profiles and 2–5 activating genes for activating profiles). The relative frequencies of these region configuration categories are shown as a percentage by the red bars (right axis). More inhibitory than activating profiles are recorded, but their frequency is nearly equal in the population studied. Most inhibitory profiles contain a single activating entity, whereas the majority of the activating profiles are defined by two activating genes
Distribution of the different KIR genes on inhibitory and activating functional profiles. The frequency of KIR genes that could be identified on both inhibitory (blue bars) and activating (red bars) configuration profiles (A, upper panel) or on only one of the functional profiles (B, lower panel). The framework gene KIR3DL20 is always present
KIR gene co-occurrence and co-exclusion matrix. The matrix displays the co-occurrence and co-exclusion for pairwise KIR genes. The expected frequency is subtracted from the observed frequency for all KIR gene tandems. White backgrounds indicate similar expected and observed frequencies of pairwise KIR genes, indicating a random association. Positive corrected frequencies demonstrate co-occurrence (green background), whereas negative corrected frequencies indicate co-exclusion (red background). KIR3DL20 is present on all haplotypes as a framework gene and therefore will always display identical observed and expected frequencies in relation to any other KIR gene. Only KIR genes that were documented on at least two KIR haplotype configurations were included in the matrix. Significant (p < 0.05) associations are in bold
The role of natural killer (NK) cells is tightly modulated by interactions of killer cell immunoglobulin-like receptors (KIR) with their ligands of the MHC class I family. Several characteristics of the KIR gene products are conserved in primate evolution, like the receptor structures and the variegated expression pattern. At the genomic level, however, the clusters encoding the KIR family display species-specific diversity, reflected by differential gene expansions and haplotype architecture. The human KIR cluster is extensively studied in large cohorts from various populations, which revealed two KIR haplotype groups, A and B, that represent more inhibitory and more activating functional profiles, respectively. So far, genomic KIR analyses in large outbred populations of non-human primate species are lacking. In this study, we roughly quadrupled the number of rhesus macaques studied for their KIR transcriptome (n = 298). Using segregation analysis, we defined 112 unique KIR region configurations, half of which display a more inhibitory profile, whereas the other half has a more activating potential. The frequencies and functional potential of these profiles might mirror the human KIR haplotype groups. However, whereas the human group A and B KIR haplotypes are confined to largely fixed organizations, the haplotypes in macaques feature highly variable gene content. Moreover, KIR homozygosity was hardly encountered in this panel of macaques. This study exhibits highly diverse haplotype architectures in humans and macaques, which nevertheless might have an equivalent effect on the modulation of NK cell activity.
Duplicates of genes for major histocompatibility complex (MHC) molecules can be subjected to selection independently and vary markedly in their evolutionary rates, sequence polymorphism, and functional roles. Therefore, without a thorough understanding of their copy number variation (CNV) in the genome, the MHC-dependent fitness consequences within a species could be misinterpreted. Studying the intra-specific CNV of this highly polymorphic gene, however, has long been hindered by the difficulties in assigning alleles to loci and the lack of high-quality genomic data. Here, using the high-quality genome of the Siamese fighting fish (Betta splendens), a model for mate choice studies, and the whole-genome sequencing (WGS) data of 17 Betta species, we achieved locus-specific amplification of their three classical MHC class II genes — DAB1, DAB2, and DAB3. By performing quantitative PCR and depth-of-coverage analysis using the WGS data, we revealed intra-specific CNV at the DAB3 locus. We identified individuals that had two allelic copies (i.e., heterozygous or homozygous) or one allele (i.e., hemizygous) and individuals without this gene. The CNV was due to the deletion of a 20-kb-long genomic region harboring both the DAA3 and DAB3 genes. We further showed that the three DAB genes were under different modes of selection, which also applies to their corresponding DAA genes that share similar pattern of polymorphism. Our study demonstrates a combined approach to study CNV within a species, which is crucial for the understanding of multigene family evolution and the fitness consequences of CNV.
Maximum likelihood trees of S100A7 and its nuclear interactors (FABP5, AIRE, CFTR, CHD4, EGFR, MYC, POT1, TERF1, TERF2) in mammals inferred using IQ-TREE. The analysis was performed with 5000 bootstrap searches and visualized with Interactive Tree of Life. Bootstrap values are indicated with circles
Mapping of interaction residues for S100A7 (PDB: 1PSR) and its binding partners TERF1 (PDB: 3BQO) and CFTR (PDB: 6O1V). Coevolving amino acid residues are shown. The figure was made using the SWISS-MODEL Repository (Bienert et al. 2017)
S100A7, a member of the S100A family of Ca²⁺-binding proteins, is considered a key effector in immune response. In particular, S100A7 dysregulation has been associated with several diseases, including autoimmune disorders. At the nuclear level, S100A7 interacts with several protein-binding partners which are involved in transcriptional regulation and DNA repair. By using the BioGRID and GAAD databases, S100A7 nuclear interactors with a putative involvement in autoimmune diseases were retrieved. We selected fatty acid–binding protein 5 (FABP5), autoimmune regulator (AIRE), cystic fibrosis transmembrane conductance regulator (CFTR), chromodomain helicase DNA-binding protein 4 (CHD4), epidermal growth factor receptor (EGFR), estrogen receptor 1 (ESR1), histone deacetylase 2 (HDAC2), v-myc avian myelocytomatosis viral oncogene homolog (MYC), protection of telomeres protein 1 (POT1), telomeric repeat–binding factor (NIMA-interacting) 1 (TERF1), telomeric repeat–binding factor 2 (TERF2), and Zic family member 1 (ZIC1). Linear correlation coefficients between interprotein distances were calculated with MirrorTree. Coevolution clusters were also identified with the use of a recent version of the Blocks in Sequences (BIS2) algorithm implemented in the BIS2Analyzer web server. Analysis of pair positions identified interprotein coevolving clusters between S100A7 and the binding partners CFTR and TERF1. Such findings could guide further analysis to better elucidate the function of S100A7 and its binding partners and to design drugs targeting for these molecules in autoimmune diseases.
Workshop cluster 1 (WC1) molecules are part of the scavenger receptor cysteine-rich (SRCR) superfamily and act as hybrid co-receptors for the γδ T cell receptor and as pattern recognition receptors for binding pathogens. These members of the CD163 gene family are expressed on γδ T cells in the blood of ruminants. While the presence of WC1⁺ γδ T cells in the blood of goats has been demonstrated using monoclonal antibodies, there was no information available about the goat WC1 gene family. The caprine WC1 multigenic array was characterized here for number, structure and expression of genes, and similarity to WC1 genes of cattle and among goat breeds. We found sequence for 17 complete WC1 genes and evidence for up to 30 SRCR a1 or d1 domains which represent distinct signature domains for individual genes. This suggests substantially more WC1 genes than in cattle. Moreover, goats had seven different WC1 gene structures of which 4 are unique to goats. Caprine WC1 genes also had multiple transcript splice variants of their intracytoplasmic domains that eliminated tyrosines shown previously to be important for signal transduction. The most distal WC1 SRCR a1 domains were highly conserved among goat breeds, but fewer were conserved between goats and cattle. Since goats have a greater number of WC1 genes and unique WC1 gene structures relative to cattle, goat WC1 molecules may have expanded functions. This finding may impact research on next-generation vaccines designed to stimulate γδ T cells.
Costimulatory molecules were considered to be promising and important targets in immunotherapy for various cancers. The present study was intended for generating a costimulatory molecule signature in kidney renal clear cell carcinoma (KIRC), to investigate prognostic implication, elucidate immune atlas, and predict immunotherapy response. All the KIRC samples from the TCGA were randomly divided into the training dataset and the testing dataset in the ratio of 7:3. The Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify 7 key costimulatory molecules which were associated with prognosis and construct a costimulatory molecule prognostic index (CMsPI), which was validated by internal and external datasets and an independent cohort. Patients in the high-CMsPI group had high mortality. Mutation analysis showed the most common mutational genes and variant types. Immune analysis demonstrated CD8⁺ T cells were infiltrated at a high level in the high-CMsPI group. In combination of analysis of the immune relevant gene signature and the biomarkers of immunotherapy, we may infer there were more dysfunctional CD8⁺ T cells in the high-CMsPI group, and the patients of this group were less sensitive to immunotherapy. A nomogram was constructed, and the concordance index was 0.77 (95% CI: 0.74–0.79). Three key signaling pathways were identified to facilitate tumor progression. The CMsPI can be regarded as a promising biomarker for predicting individual prognosis and assessing immunotherapy response in KIRC patients.
Immunoglobulin G (IgG) is an essential antibody in adaptive immunity; a differential expansion of the gene encoding the Fc region (IGHG) of this antibody has been observed in mammals. Like humans, animal biomedical models, such as mice and macaques, have four functional genes encoding 4 IgG subclasses; however, the data for New World monkeys (NWM) seems contentious. Some publications argue for the existence of a single-copy gene for IgG Fc; however, a recent paper has suggested the presence of IgG subclasses in some NWM species. Here, we evaluated the genetic distances and phylogenetic relationships in NWM to assess the presence of IgG subclasses using the sequences of IGHG genes from 13 NWM species recovered from genomic data and lab PCR and cloning-based procedures available in GenBank. The results show that several sequences do not cluster into the expected taxon, probably due to cross-contamination during laboratory procedures, and consequently, they appear to be wrongly assigned. Additionally, several sequences reported as subclasses were shown to be 100% identical in the CH domains. The data presented here suggests that there is not enough evidence to establish the presence of IgG subclasses in NWM.
Leukocyte immunoglobulin-like receptor B1 (LILRB1) is widely expressed on various immune cells and the engagement of LILRB1 to HLA class I and pathogen-derived proteins can modulate the immune response. In the current study, 108 LILRB1 alleles were identified by screening the LILRB1 locus from the 1000 Genomes Phase 3 database. Forty-six alleles that occurred in three or more individuals encode 28 LILRB1 allotypes, and the inferred LILRB1 allotypes were then grouped into 9 LILRB1 D1-D2 variants for further analysis. We found that variants 1, 2, and 3 represent the three most frequent LILRB1 D1-D2 variants and the nine variants show frequency differences in populations. The binding assay demonstrated that variant 1 bound to HLA class I with the highest avidity, and all tested LILRB1 D1-D2 variants bound to HLA-C with lower avidity than to HLA-A and -B. Locus-specific polymorphisms at positions 183, 189, and 268 in HLA class I and dimorphisms in HLA-A (positions 207 and 253) and in HLA-B (position 194) affect their binding to LILRB1. Notably, the electrostatic interaction plays a critical role in the binding of LILRB1 to HLA class I as revealed by electrostatic analysis and by comparison of different binding avidities caused by polymorphisms at positions 72 and 103 of LILRB1. In this paper, we present a comprehensive study of the population genetics and binding abilities of LILRB1. The data will help us better understand the LILRB1-related diversity of the immune system and lay a foundation for functional studies.
of Kd constants obtained from binding studies for pathogens and cancer epitopes in complexes with mAb, plotted against febrile temperatures for each pathology and against the length of epitopes (a) and paratopes (b). Maximal fever values are detailed in Supplementary Table 1 and are not the temperatures that the binding assays were performed at. The symbol size is proportional to the number of amino acid residues on the Ag-Ab contact interface that are involved in complex formation
We herein analyzed all available protein–protein interfaces of the immune complexes from the Protein Data Bank whose antigens belong to pathogens or cancers that are modulated by fever in mammalian hosts. We also included, for comparison, protein interfaces from immune complexes that are not significantly modulated by the fever response. We highlight the distribution of amino acids at these viral, bacterial, protozoan and cancer epitopes, and at their corresponding paratopes that belong strictly to monoclonal antibodies. We identify the “hotspots”, i.e. residues that are highly connected at such interfaces, and assess the structural, kinetic and thermodynamic parameters responsible for complex formation. We argue for an evolutionary pressure for the types of residues at these protein interfaces that may explain the role of fever as a selective force for optimizing antibody binding to antigens.
Comparative synteny analysis of TLR7 and TLR8. The analysis was performed using the NCBI genome data viewer and, when genes were absent, we performed TBLASTN in the available genomes with default parameters. The same genes are represented in similar color across species. The species represented are, from the top to the bottom, Balaenoptera musculus (blue whale), Bos taurus (cattle), Ceratotherium simum (southern white rhinoceros), Canis lupus familiaris (dog), Rhinolophus ferrumequinum (greater horseshoe bat), Oryctolagus cuniculus (European rabbit), Ochotona princeps (American pika), Ochotona curzoniae (Black-lipped pika), Mus musculus (mouse), Homo sapiens (human), Dasypus novemcinctus (Nine-banded armadillo), Loxodonta africana (African elephant), Phascolarctos cinereus (koala), Ornithorhynchus anatinus (Platypus), Gallus gallus (chicken), Crocodylus porosus (Australian saltwater crocodile), Podarcis muralis (common wall lizard), Thamnophis elegans (Western terrestrial garter snake), Chelonia mydas (green sea turtle), Xenopus tropicalis (tropical clawed frog), Protopterus annectens (West African lungfish), Latimeria chalumnae (coelacanth), Danio rerio (zebrafish), Chiloscyllium plagiosum (whitespotted bambooshark), and Ambrlyraja radiata (thorny skate)
TLR8 protein identification by western blot in 16 rabbit tissues: spermatozoa, epididymis, testes, vas deferens, ampoule, vesicular gland, prostate, colon, stomach (pylorus), jejunum, heart, spleen, kidney, brain, lung, and liver. Lysates prepared from rat testes and human spermatozoa were used as positive controls (PC). Results are representative of biological triplicates. Ponceau S staining with 16 rabbit tissues is provided in Supplementary Fig. S3
Phylogenetic tree showing the TLR7 evolutionary rates under a strick molecular clock and calibrated using normally distributed priors for 14 dates of most recent common ancestors of monophyletic groups, with a standard deviation of 2: Mammalia (177 Mya), Theria (159 Mya), Atlantogenata (101 Mya), Boreotheria (96 Mya), Euarchontoglires (90 Mya), Laurasiatheria (89 Mya), Afrotheria (83 Mya), Euarchonta (82 Mya), Glires (82 Mya), Marsupialia (82 Mya), Scrotifera (79 Mya), Euungulata (78 Mya), Ferae (75 Mya), and Xenathra (66 Mya). Runs of 50,000,000 generations were conducted, using the Yule tree prior and the GTR + G + I nucleotide substitution model
Toll-like receptors (TLRs) are one of the most ancient and widely studied innate immune receptors responsible for host defense against invading pathogens. Among the known TLRs, TLR7 and TLR8 sense and recognize single-stranded (ss) RNAs with a dynamic evolutionary history. While TLR8 was lost in birds and duplicated in turtles and crocodiles, TLR7 is duplicated in some birds, but in other tetrapods, there is only one copy. In mammals, with the exception of lagomorphs, TLR7 and TLR8 are highly conserved. Here, we aim to study the evolution of TLR7 and TLR8 in mammals, with a special focus in the order Lagomorpha. By searching public sequence databases, conducting evolutionary analysis, and evaluating gene expression, we were able to confirm that TLR8 is absent in hares but widely expressed in the European rabbit. In contrast, TLR7 is absent in the European rabbit and quite divergent in hares. Our results suggest that, in lagomorphs, more in particular in leporids, TLR7 and TLR8 genes have evolved faster than in any other mammalian group. The long history of interaction with viruses and their location in highly dynamic telomeric regions might explain the pattern observed.
Frequency of Tel KIR genes. General view for KIR gene cluster distribution in patients with or without CMV infection. **Statistically significant difference
Cytomegalovirus (CMV) infection is a common complication after organ transplantation. Despite the immunosuppressed state, natural killer (NK) cells remain the major immune defense cells against viral infections in transplanted patients. The present study aimed at elucidating the correlation between the number of inhibitory and activating genes and the incidence of CMV infection in kidney transplanted recipients. Kidney transplanted recipients including 51 CMV⁺ and 50 CMV⁻ were genotyped for the presence or absence of 4 activating (KIR2DS1, KIR2DS4, KIR2DS5, KIR3DS1) and 2 inhibitory (KIR3DL1, KIR2DL5a) genes using polymerase chain reaction sequence-specific primers (PCR-SSP) assay. Our results showed that CMV infection occurred in 50.49% of kidney allograft recipients. In addition, there was a significant correlation between the presence of the KIR2DS1 activating gene in the CMV⁻ group compared to the CMV⁺ group (p = 0.033). The other three activating KIR receptors did not show a correlation with CMV infection. Our results suggest that the prevalence of the KIR activating KIR2DS1 gene may reduce the rate of CMV infection after kidney transplantation in our population.
Schematic of modeled SARS-CoV-2 ORF1ab CoV-2 protein regions highlighted B cell (cyan) and T cell epitopes (pink) represented as surface structure (gray). Potential functional domains are mapped as green color. Pymol was utilized to visualize the positions of forecast epitopes on the 3D structure. A NSP1 region representing predicted B-cell epitopes (cyan) located at position 315 and 801. B NSP2 region representing predicted T cell epitope (pink) at position 1367. C Helicase region representing predicted B cell epitope (cyan) at position 16,793. D Exonuclease region denotes predicted B-cell epitope (cyan) at position 18,459. E RDRP region denotes T cell epitopes (pink) at position 15,999. The table to the right denotes list of predicted B cell and T cell epitopes and their start position. Residues underlined and colored in red denotes specific residues that are predicted to elicit antibody response by using Bepipred linear epitope prediction 2.0
Schematic of modeled SARS-CoV-2 spike and ORF3 proteins (gray) highlighted B cell (cyan) and T cell epitopes (pink) represented as surface structure (gray). Potential receptor-binding region (RBD) and cleavage site are mapped as green color. Pymol was utilized to visualize the positions of forecast epitopes on the 3D structure. A The 3D structure denotes site of both B cell and T cell in SARS-CoV-2 spike protein trimer at position 23,587 (orange color), site of B cell epitopes predicted in SARS-CoV-2 spike protein trimer at position 25,133 (cyan), and site of T cell epitopes predicted in SARS-CoV-2 spike protein trimer at position 23,555 (pink). The table below the spike protein denotes list of predicted B cell and T cell epitopes and their start position. Residues underlined and colored in red denotes specific residues that are predicted to elicit antibody response by using Bepipred linear epitope prediction 2.0. B The 3D structure denotes site of B cell epitope (cyan) predicted in ORF3a-envelope protein at position 25,749 and site of T cell epitopes (pink) predicted at position 25,562. Bepipred Linear Epitope Prediction 2.0 tool was used to predict antibody epitopes (red color) in the B cell epitopes. The table below the spike protein denotes list of predicted B cell and T cell epitopes and their start position. Residues underlined and colored in red denotes specific residues that are predicted to elicit antibody response by using Bepipred linear epitope prediction 2.0
Heat map plot sowing the binding of 50 conserved SARS-CoV-2 epitopes to HLA different variants. IEDB EpiTool analysis was performed for all 73 identified linear epitopes to predict binding to HLA class I molecules, out of which, 50 epitopes bind to HLA-A, B, and C gene variants. Each row indicates an epitope’s sequence, and each column indicates a different HLA variant. The color gradient for each cell of the heatmap plot represents the affinity, which inversely correlates with the IC50 value
Cross-reactivity between different human coronaviruses (HCoVs) might contribute to COVID-19 outcomes. Here, we aimed to predict conserved peptides among different HCoVs that could elicit cross-reacting B cell and T cell responses. Three hundred fifty-one full-genome sequences of HCoVs, including SARS-CoV-2 (51), SARS-CoV-1 (50), MERS-CoV (50), and common cold species OC43 (50), NL63 (50), 229E (50), and HKU1 (50) were downloaded aligned using Geneious Prime 20.20. Identification of epitopes in the conserved regions of HCoVs was carried out using the Immune Epitope Database (IEDB) to predict B- and T-cell epitopes. Further, we identified sequences that bind multiple common MHC and modeled the three-dimensional structures of the protein regions. The search yielded 73 linear and 35 discontinuous epitopes. A total of 16 B-cell and 19 T-cell epitopes were predicted through a comprehensive bioinformatic screening of conserved regions derived from HCoVs. The 16 potentially cross-reactive B-cell epitopes included 12 human proteins and four viral proteins among the linear epitopes. Likewise, we identified 19 potentially cross-reactive T-cell epitopes covering viral proteins. Interestingly, two conserved regions: LSFVSLAICFVIEQF (NSP2) and VVHSVNSLVSSMEVQSL (spike), contained several matches that were described epitopes for SARS-CoV. Most of the predicted B cells were buried within the SARS-CoV-2 protein regions’ functional domains, whereas T-cell stretched close to the functional domains. Additionally, most SARS-CoV-2 predicted peptides (80%) bound to different HLA types associated with autoimmune diseases. We identified a set of potential B cell and T cell epitopes derived from the HCoVs that could contribute to different diseases manifestation, including autoimmune disorders.
Effector T cells, which are abundant but are short-lived after reinfusion into the body, are generally used for T-cell therapy, and antitumor immunity is typically not maintained over the long term. Genetic modification by early differentiated T cells and reinfusion has been shown to enhance antitumor immunity in vivo. This study overexpressed the characteristic transcription factors of differentiated early T cells by transfecting effector T cells with transcription factor recombinant lentivirus (S6 group: BCL6, EOMES, FOXP1, LEF1, TCF7, KLF7; S1 group: BCL6, EOMES, FOXP1, KLF7; S3 group: BCL6, EOMES, FOXP1, LEF1) to induce a sufficient number of effector T cells to dedifferentiate and optimize the transcription factor system. The results revealed that overexpression of early characteristic transcription factors in effector T cells upregulated the expression of early T cell differentiation markers (CCR7 and CD62L), with the S1 group having the highest expression level, while the rising trend of late differentiation marker (CD45RO) expression was suppressed. Moreover, the expression of early differentiation-related genes (ACTN1, CERS6, BCL2) was significantly increased, while the expression of late differentiation-related genes (KLRG-1) and effector function-related genes (GNLY, GZMB, PRF1) was significantly decreased; this difference in expression was more significant in the S1 group than in the other two experimental groups. The antiapoptotic ability of each experimental group was significantly enhanced, while the secretion ability of TNF-α and IFN-γ was weakened, with the effector cytokine secretion ability of the S1 group being the weakest. Transcriptomic analysis showed that the gene expression profile of each experimental group was significantly different from that of the control group, with differences in the gene expression pattern and number of differentially expressed genes in the S1 group compared with the other two experimental groups. The differentially expressed gene enrichment pathways were basically related to the cell cycle, cell division, and immune function. In conclusion, overexpression of early characteristic transcription factors in effector T cells induces their dedifferentiation, and induction of dedifferentiation by the S1 group may be more effective.
Nucleotide diversity profiles for 95-kb genomic pairwise and multiple sequence alignments. The nucleotide diversity profiles that were drawn per 100-bp each based on sequence alignment using A three 88–12-64 haplotypes (Hp.1, Hp.2, and Hp.3), B two 88-88L-64 haplotypes (Hp.4 and Hp.5), and C all five 88–12/88L-64 haplotypes. The locations of DLA and Can-SINE loci are described with light gray and black boxes, respectively, under the scale bar. The position of these boxes above and below the horizontal line is the sense and antisense direction, respectively. Black and red solid lines represent the number of single nucleotide variants (SNVs) and indels at each profile window, respectively. The blue columns link the positions of the DLA loci in each profile
Intralocus and interlocus nucleotide sequence identity of full genomic DLA class I alleles. Average pairwise nucleotide sequence identity score calculated by intralocus (A) and interlocus (B) at each region of 5′ flanking UTR (500-bp upstream from translation start site (TSS)), exons and introns
GARD analysis using genomic sequences determined for 41 DLA class I alleles. A The schematic location of estimated six break points by GARD analysis. Red circles indicate the estimated recombination break points. Each segment among the break points is assigned as segment numbers 1 to 7. White and grey box indicated UTR and CDS of each exon. B Phylogenetic trees show sequence groupings for seven segments identified between recombination break points. The alleles labelled in blue and red letters represent the alleles derived from DLA-88L and DLA-12, respectively. Alignment lengths of each segment are as follows: 432 bp (segment 1), 367 bp (segment 2), 798 bp (segment 3, including exon 2 and 5′ side of exon 3), 164 bp (segment 4, including exon 3), 361 bp (segment 5, including 3′ side of exon 3), 1792 bp (segment 6), and 462 bp (segment 7). C Nucleotide sequence alignment from 5′ flanking region (500 bp upstream from TSS) to exon 8 using the different sites between the DLA-88 or DLA-12 lineage specific nucleotides. The sites that are identical between the DLA-88 or DLA-12 sequences are highlighted with a blue or red background, respectively. The boundaries of each exon and intron are indicated with labelled vertical lines
Gene conversion relative to generation of the DLA-12*004:01 allele. A Phylogenetic trees using nucleotide sequences of exon 1 to intron 1, exon 2, and intron 2 of 41 DLA class I alleles. The DLA-12 and DLA-88L alleles are labelled with red and blue letters, respectively. A black arrow indicates DLA-12*004:01 in each phylogenetic tree. B Nucleotide sequence alignment with DLA-88*028:03, DLA-12*004:01, and DLA-12 consensus sequence without DLA-12*004:01. This sequence alignment was constructed using the sites which were different between DLA-88*028:03 and the DLA-12 consensus sequence (DLA-12_cons). The same nucleotides within DLA-88*028:03 or DLA-12_cons are highlighted with a blue or red background, respectively. The boundaries for each exon and intron are shown by labelled vertical lines. C Amino acid sequence alignment using exon 2 of DLA-88*028:03, DLA-12*004:01, and DLA-12_cons. The amino acid sequence shared with DLA-88*028:03 between alignments are indicated with “.”. Putative peptide binding regions (PBRs) and T cell recognition sites (TRSs) referred from our previous study (Miyamae et al. 2018) are highlighted with an orange and green background, respectively
Putative gene conversion tracts in exon 2–intron 2–exon 3 sequences among DLA-88, DLA-12, and DLA-88L alleles. Red boxes within exon 2 and exon 3 indicate the location of hyper variable regions (HVRs) that were defined in the DLA nomenclature report (Kennedy et al. 2001). Schematic location of the conversion tract 1 to 14 and detailed information about these tracts are described in Table 2
The dog leukocyte antigen (DLA) class I genomic region is located on chromosome 12, and the class I genomic region is composed of at least two distinct haplotypic gene structures, DLA-88–DLA-12 and DLA-88–DLA-88L. However, detailed information of the genomic differences among DLA-88, DLA-12, and DLA-88L are still lacking at the full-length gene level, and therefore, DLA allelic sequences classified for each of these loci are limited in number so far. In this study, we determined the DNA sequence of a 95-kb DLA class I genomic region including DLA-88, DLA-12/88L, and DLA-64 with three DLA homozygous dogs and of 37 full-length allelic gene sequences for DLA-88 and DLA-12/88L loci in 26 DLA class I homozygous dogs. Nucleotide diversity profiles of the 95-kb regions and sequence identity scores of the allelic sequences suggested that DLA-88L is a hybrid gene generated by interlocus and/or intralocus gene conversion between DLA-88 and DLA-12. The putative minimum conversion tract was estimated to be at least an 850-bp segment in length located from the 5´flanking untranslated region to the end of intron 2. In addition, at least one DLA-12 allele (DLA-12*004:01) was newly generated by interlocus gene conversion. In conclusion, the analysis for the occurrence of gene conversion within the dog DLA class I region revealed intralocus gene conversion tracts in 17 of 27 DLA-88 alleles and two of 10 DLA-12 alleles, suggesting that intralocus gene conversion has played an important role in expanding DLA allelic variations.
Roles of NOD-like receptors (NLRs). NLRs are involved in the activation of NF-kB-dependent gene expression and inflammasome-dependent cell death and immune responses. Besides their role in the defense against pathogens, NLRs have also unknown functions in the reproductive system. Evolution has shaped host–pathogen interactions and control mechanisms of reproduction in mammals
NOD2 is a pseudogene in pangolins. a Gene locus of NOD2 in the Malayan pangolin, dog, cattle, and human. Genes are represented by arrows pointing into the direction of transcription. The mutated NOD2 gene is shown as a broken arrow. b Inactivating mutations in multiple exons of pangolin NOD2. Positions of frame-shift and premature stop mutations are indicated by vertical arrows. Exons of NOD2 of pangolin, dog, cattle and human are shown as boxes. c Nucleotide sequence alignment of a segment of exon 1 that contains a conserved frame-shift mutation (red shading) leading to a premature stop codon (grey shading) in three species of pangolins. Nucleotides identical in all species are shown with blue fonts. d The absence ( −) or presence ( +) of intact NOD2 in 6 species was used to map the NOD2 gene loss (flash symbol) on a simplified phylogenetic tree of mammals. Species: Malayan pangolin (Manis javanica), Chinese pangolin (Manis pentadactyla), tree pangolin (Phataginus tricuspis), dog (Canis lupus familiaris), cattle (Bos taurus), and human (Homo sapiens)
Absence of NLRC4 and NAIP in pangolins. a Gene locus of NLRC4. Genes are represented by arrows pointing into the direction of transcription. Intact NLRC4 genes are shown as blue arrows. Red broken arrows indicate inactivating mutations of NLRC4 in caniformia, represented by dog and ermine. NLRC4 is absent in pangolins. Loss of NAIP was mapped (red flash symbols) onto a simplified phylogenetic tree of mammals. b Gene locus of NAIP. Genes are represented by arrows pointing into the direction of transcription. Intact NAIP genes are shown as blue arrows. NAIP is absent in pangolins, dog, and ermine. Loss of NAIP was mapped (red flash symbols) onto a simplified phylogenetic tree of mammals. Species: Malayan pangolin (Manis javanica), Chinese pangolin (Manis pentadactyla), cat (Felis catus), ermine (Mustela erminea), dog (Canis lupus familiaris), cattle (Bos taurus), and human (Homo sapiens)
Absence of multiple NLRP genes in pangolins. Gene loci of NLRP1a, NLRP2 and NLRP7b, NLRP4, NLRP5, NLRP8, NLRP9, NLRP11, and NLRP13c, NLRP10d, and NLRP14e of Malayan pangolin, dog, cattle, and human are schematically depicted. Intact NLRP genes are shown as blue arrows with white numbers indicating the number of the NLRP gene. Red broken arrows indicate NLRP genes that are inactivated by mutations. Grey arrows indicate evolutionarily conserved genes flanking NLRP genes. White arrows represent genes that are not conserved across species. Species: Pangolin (Manis javanica), dog (Canis lupus familiaris), cattle (Bos taurus), and human (Homo sapiens)
NOD-like receptors (NLRs) are sensors of pathogen-associated molecular patterns with critical roles in the control of immune responses and programmed cell death. Recent studies have revealed inter-species differences in mammalian innate immune genes and a particular degeneration of nucleic acid sensing pathways in pangolins, which are currently investigated as potential hosts for zoonotic pathogens. Here, we used comparative genomics to determine which NLR genes are conserved or lost in pangolins and related mammals. We show that NOD2 , which is implicated in sensing bacterial muramyl dipeptide and viral RNA, is a pseudogene in pangolins, but not in any other mammalian species investigated. NLRC4 and NAIP are absent in pangolins and canine carnivorans, suggesting convergent loss of cytoplasmic sensing of bacterial flagellin in these taxa. Among NLR family pyrin domain containing proteins (NLRPs), skin barrier-related NLRP10 has been lost in pangolins after the evolutionary divergence from Carnivora. Strikingly, pangolins lack all NLRPs associated with reproduction (germ cells and embryonic development) in other mammals, i.e., NLRP2 , 4 , 5 , 7 , 8 , 9 , 11 , 13 , and 14 . Taken together, our study shows a massive degeneration of NLR genes in pangolins and suggests that these endangered mammals may have unique adaptations of innate immunity and reproductive cell biology.
Patients with mucormycosis associated with Covid-19, diabetes, and corticosteroid
Covid-19 and mucormycosis (black fungus) severity in India
Prescriptions for predominant orbital involvement and surgical debridement in mucormycosis cases
The catastrophic phase of Covid-19 turns the table over with the spread of its disastrous transmission network throughout the world. Covid-19 associated with mucormycosis fungal infection accompanied by opportunistic comorbidities have emerged the myriad of complications and manifestations. We searched the electronic databases of Google Scholar, PubMed, Springer, and Elsevier until June 05, 2021, using keywords. We retrieved the details of confirmed and suspected mucormycosis patients associated with Covid-19. We analyzed the case reports, treatment given for Covid-19, steroids used, associated comorbidities, mucormycosis site involved, and patients survived or dead. Overall, 102 patients of mucormycosis associated with Covid-19 have been reported from India. Mucormycosis was predominant in males (69.6%) rather than females (19.6%), and most of the patients were active Covid-19 cases (70.5%). Steroids were mostly used (68.6%) for the treatment of Covid-19 followed by remdesivir (10.7%). Patients were suffering from diabetes mellitus (88.2%) and severe diabetic ketoacidosis (11.7%). Mucormycosis affects the sino-nasal (72.5%), orbit (24.5%), central nervous system (18.6%), and maxillary necrosis (13.7%) of the patients. The Mortality rate was recorded as 23.5%, and recovery rate was 2.9%. Diabetes mellitus cases are highest in India as compared to other countries, and prevalent use of steroids with the background of Covid-19 becomes an opportunistic environment for mucormycosis fungal infection to survive.
Structure of G-quadruplex. a. QGRS sequences contain G tract b. loop structures and c. loop sequences that may form G-quadruplex
Location of QGRS/PQS sequences in SARS-CoV2 genome. Most of the QGRSs/PQSs are located upstream of genes
G-quadruplex structure or Putative Quadruplex Sequences (PQSs) are abundant in human, microbial, DNA, or RNA viral genomes. These sequences in RNA viral genome play critical roles in integration into human genome as LTR (Long Terminal Repeat), genome replication, chromatin rearrangements, gene regulation, antigen variation (Av), and virulence. Here, we investigated whether the genome of SARS-CoV2, an RNA virus, contained such potential G-quadruplex structures. Using bioinformatic tools, we searched for such sequences and found thirty-seven (forward strand (twenty-five) + reverse strand (Twelve)) QGRSs (Quadruplex forming G-Rich Sequences)/PQSs in SARS-CoV2 genome. These sequences are dispersed mainly in the upstream of SARS-CoV2 genes. We discuss whether existing PQS/QGRS ligands could inhibit the SARS-CoV2 replication and gene transcription as has been observed in other RNA viruses. Further experimental validation would determine the role of these G-quadruplex sequences in SARS-CoV2 genome function to survive in the host cells and identify therapeutic agents to destabilize these PQSs/QGRSs.
The flowchart of the proposed model
The ROC curves of different features on Strain
The ROC curves of different features on Sind
The contribution of each feature. a Training dataset. b Independent dataset
Cancer is a terrible disease, recent studies reported that tumor T cell antigens (TTCAs) may play a promising role in cancer treatment. Since experimental methods are still expensive and time-consuming, it is highly desirable to develop automatic computational methods to identify tumor T cell antigens from the huge amount of natural and synthetic peptides. Hence, in this study, a novel computational model called iTTCA-MFF was proposed to identify TTCAs. In order to describe the sequence effectively, the physicochemical (PC) properties of amino acid and residue pairwise energy content matrix (RECM) were firstly employed to encode peptide sequences. Then, two different approaches including covariance and Pearson’s correlation coefficient (PCC) were used to collect discriminative information from PC and RECM matrixes. Next, an effective feature selection approach called the least absolute shrinkage and selection operator (LAASO) was adopted to select the optimal features. These selected optimal features were fed into support vector machine (SVM) for identifying TTCAs. We performed experiments on two different datasets, experimental results indicated that the proposed method is promising and may play a complementary role to the existing methods for identifying TTCAs. The datasets and codes can be available at
Sensors of bacteria activating the IMD pathway. The IMD pathway is activated after recognition of Gram-negative bacteria, through the DAP-type peptidoglycans (PGNs) present in their cell wall and that may be released during bacterial growth and division or upon attack by AntiMicrobial Peptides (AMPs). DAP-type PGNs are sensed, or enzymatically inactivated, by members of the PeptidoGlycan Recognition Protein (PGRP) family. Polymeric DAP-type PGNs can be detected by PGRP-LCx whereas the monomeric DAP-type PGNs tracheal cytotoxin (TCT) is sensed by a PGRP-LCx-PGRP-LCa dimer. PGRP-LE functions as an intracellular sensor of TCT in barrier epithelia such as the gut or the Malpighian tubules and also in octopaminergic neurons. The cryptic RIP Homotypic Interaction Motif (cRHIM) domain found in PGRP-LCx, -LCa and -LE allows their oligomerization that seeds the formation of amyloid fibers, that the IMD adapter elongates through its own cRHIM domain. The amyloid fibers triggers downstream IMD pathway signaling that ultimately activates the Relish transcription factor. This process can be negatively regulated by the induction of the PIRK repressor. PGRP-SCs and PGRP-LB prevent detrimental IMD pathway activation by degrading polymeric or monomeric PGNs into metabolites unable to trigger the IMD pathway, a process that is counteracted by the binding of PGRP-SD to PGN. PGRP-LF may prevent the formation of PGRP-LC multimers and hence negatively regulate the initiation of IMD pathway signaling. PGRP-LA may enhance IMD pathway activation solely in barrier epithelia (not shown)
Sensors of microorganisms activating the Toll pathway. Lys-type peptidoglycans (LYS-PGNs) found at the surface of Gram-positive bacteria are sensed by a complex of GNBP1 and PGRP-SA, the former processing LYS-PGN for detection by PGRP-SA. ß-glucans found at the surface of fungi can be detected by GNBP3. GNBP1 and GNBP3 can activate ModSP through their enzymatically-inactive ß-glucanase domain, that in turn activates Grass. Next, Grass is thought to activate both PSH and Hayan, which function redundantly. The sensing of microbial proteases through Persephone (PSH) bait domain leads to the formation of a cleaved pre-activated inactive PSH that gets further processed by the circulating 26-29-p cathepsin protease to a mature PSH. PSH can also be directly activated by subtilisin (not shown). Active PSH and Hayan can both activate SPE, that processes Spätzle to activate the Toll intracellular signaling pathway. The Necrotic serpin avoids the spontaneous self-activation of PSH. In addition to the activation of Toll intracellular signaling, Hayan activates the Sp7 protease as well as the two prophenoloxidases PPO1 and PPO2. These extracellular enzymes are involved in microbial killing and activation of melanization, which may be separate processes
Drosophila phagocytosis receptors and opsonins. Eater, NimC1 and Draper are three members of the Nimrod family, that are characterized by a subtype of epidermal growth factor (EGF) repeat called Nimrod (NIM) repeat. Eater and Draper can detect bacteria, although NimC1 can recognize yeast zymosan particles. Croquemort and Sr-C1 are two members of the scavenger receptor family that sense bacteria. The integrin made by αPS3 and βυ was shown to be involved in the phagocytosis of S. aureus. Some thioester-containing proteins (TEPs) and some members of the Nimrod B-type subfamily (NimBs) are soluble molecules that bind to various microbes and promote their engulfment by phagocytes (opsonins). NimBs can bind bacteria and TEPs had been shown to recognize both bacteria and yeasts. (Adapted from Melcarne et al. 2019a)
Sensing viral infections in Drosophila. Double-stranded (ds) RNAs are sensed by Dicer-2, that together with R2D2 process them into siRNAs, one strand of which is loaded on AGO2. The involvement of AGO2 allows the formation of the RISC complex that targets the mRNA corresponding to the complementary siRNA sequence. In parallel, siRNAs induce the expression of Vago. dsRNAs are also sensed by the cGAS-like receptor 1 (cGLR1) that synthesizes 3’2’cGAMP cyclic dinucleotides. Another sensor, cGLR2, is activated by an unidentified ligand, and synthesizes 3’2’cGAMP and 2’3’cGAMP. These two cGAMPs then activates Drosophila melanogaster STING. The latter activates the terminal part of the IMD pathway by interacting with IKKß independently of IKKγ. This STING-Relish axis controls the production of specific genes referred to as STING-regulated genes. The JAK-STAT pathway is likely induced indirectly by cytosolic components such as α-actinin that are released from infected cells lysed by viral infections (not shown)
Insects occupy a central position in the biosphere. They are able to resist infections even though they lack an adaptive immune system. Drosophila melanogaster has been used as a potent genetic model to understand innate immunity both in invertebrates and vertebrates. Its immune system includes both humoral and cellular arms. Here, we review how the distinct immune responses are triggered upon sensing infections, with an emphasis on the mechanisms that lead to systemic humoral immune responses. As in plants, the components of the cell wall of microorganisms are detected by dedicated receptors. There is also an induction of the systemic immune response upon sensing the proteolytic activities of microbial virulence factors. The antiviral response mostly relies on sensing double-stranded RNAs generated during the viral infection cycle. This event subsequently triggers either the viral short interfering RNA pathway or a cGAS-like/STING/NF-κB signaling pathway.
Birds are important hosts for many RNA viruses, including influenza A virus, Newcastle disease virus, West Nile virus and coronaviruses. Innate defense against RNA viruses in birds involves detection of viral RNA by pattern recognition receptors. Several receptors of different classes are involved, such as endosomal toll-like receptors and cytoplasmic retinoic acid–inducible gene I-like receptors, and their downstream adaptor proteins. The function of these receptors and their antagonism by viruses is well established in mammals; however, this has received less attention in birds. These receptors have been characterized in a few bird species, and the completion of avian genomes will permit study of their evolution. For each receptor, functional work has established ligand specificity and activation by viral infection. Engagement of adaptors, regulation by modulators and the supramolecular organization of proteins required for activation are incompletely understood in both mammals and birds. These receptors bind conserved nucleic acid agonists such as single- or double-stranded RNA and generally show purifying selection, particularly the ligand binding regions. However, in birds, these receptors and adaptors differ between species, and between individuals, suggesting that they are under selection for diversification over time. Avian receptors and signalling pathways, like their mammalian counterparts, are targets for antagonism by a variety of viruses, intent on escape from innate immune responses.
Leukocyte immunoglobulin-like receptor loci within the leukocyte receptor complex on human chromosome 19
Murine paired immunoglobulin-like receptor cluster within the leukocyte receptor complex on mouse chromosome 7
LILR and PIR structure
Host immunity is classically divided into “innate” and “adaptive.” While the former has always been regarded as the first, rapid, and antigen-nonspecific reaction to invading pathogens, the latter represents the more sophisticated and antigen-specific response that has the potential to persist and generate memory. Recent work however has challenged this dogma, where murine studies have successfully demonstrated the ability of innate immune cells (monocytes and macrophages) to acquire antigen-specific memory to allogeneic major histocompatibility complex (MHC) molecules. The immunoreceptors so far identified that mediate innate immune memory are the paired immunoglobulin-like receptors (PIRs) in mice, which are orthologous to human leukocyte immunoglobulin-like receptors (LILRs). These receptor families are mainly expressed by the myelomonocytic cell lineage, suggesting an important role in the innate immune response. In this review, we will discuss the role of immunoglobulin-like receptors in the development of innate immune memory across species.
Simplified phyletic distribution of metazoan IgSF-containing molecules. Schematic cladogram representation of major phyla following bilaterian divergence, emphasizing major clades where a “pass-through” tubular gut evolved. Phyla, such as Arthropoda, Mollusca, and Chordata, with IgSF-containing immune effectors discussed herein, are highlighted. Representatives of IgSF-containing molecules with immune-related functions have yet to be described in phyla represented by smaller characters. Protein structures of Dscam in arthropods, FREPs in gastropod molluscs and VCBPs in protochordates are reported. VIgSF, immunoglobulin superfamily variable domain; FNIII, fibronectin type III domain; FReD, fibrinogen-related domain; CDB, chitin-binding domain
Digestive tract compartmentalization and main features of vertebrate and invertebrate gut mucosal epithelium are represented (reprinted from (Dishaw et al. 2012; Liberti et al. 2021). The simplified cladogram (left side) reveals major clades and representatives discussed: the Protostomes, which include Arthropods, such as the fruit fly Drosophila melanogaster and the Molluscs, which include gastropods like Biomphalaria glabrata, and Deuterostomes, which include Chordates such as Branchiostoma floridae, Ciona robusta and, vertebrates like Homo sapiens. The digestive tract is highlighted in each, revealing distinct compartmentalization, indicated by different colored arrows (in each case, the anterior portion of the gut, not shown, includes an often complex and derived pharynx structure). In D. melanogaster, yellow arrow points to the foregut, whereas the midgut and the hindgut are blue and magenta arrows, respectively. In B. glabrata, B. floridae, C. robusta, and H. sapiens, the stomach and the intestine are indicated with orange and green arrows, respectively. For simplification, the human small and large intestines are labeled as “intestine.” (right side) Illustration of gut mucosal immunity, emphasizing barrier defense strategies of invertebrates and vertebrates. In invertebrates, the gut epithelium can consist of distinct epithelial lineages that represent a primary barrier of defense, governed by innate immune phenomena characterized by the secretion of mucus (that often consists also of chitin fibers), antimicrobial peptides (AMPs), and soluble immune molecules such as immunoglobulin superfamily (IgSF) molecules. The mucus layers are often colonized by diverse microorganisms, including bacteria, viruses, and fungi. In the basolateral side, a distinct population of hemocytes, i.e., granular amoebocytes, resides in the laminar connective tissue and express diverse pattern recognition receptors (PRRs), also present on overlying epithelial cells, that can be used for sampling microbes. In vertebrates, barrier defenses of gut epithelium are also characterized by distinct epithelial lineages and include secretion of mucus (that organizes as a compact and firmly attached inner layer and a looser outer layer), AMPs, and other soluble immune molecules like antibodies. The secreted outer mucus layers are often colonized by diverse microorganisms, including bacteria, viruses, and fungi. On the basolateral surface of the epithelium, the innate immune response is coupled with the specialized adaptive immune system. Indeed, innate immune cells, like dendritic cells (DCs) that populate this area, sample luminal antigens via PRRs and present them to the adaptive immune system that includes gut-specific lymphocytes of both T and B cell lineages, thus triggering the maturation of immunity and the recruitment of additional cell types
Ciona VCBP-C gut localization and interactions with microorganisms. In Ciona gut epithelium, VCBP-C molecules are produced and secreted into the gut lumen by the epithelial cells of the digestive tract and can interact with diverse components of microbiota, such as bacteria and fungi. a Sections of stomach epithelium showing localization of VCBP-C (magenta) in both granules of cells localized in the crypts (arrows) and in the mucus lining the epithelium (arrowhead). b Immunogold staining, using specific anti-VCBP-C antibody, reveals VCBP-C bound to experimentally introduced bacteria, such as Bacillus cereus, localized both in the lumen and adjacent to stomach epithelium wall (arrows) (reprinted from (Dishaw et al. 2011). c Biofilm assay of Shewanella sp. grown for 4–5 days in the presence/absence of recombinant VCBP-C protein and visualized by crystal violet (CV) staining. Plates are shown with dried (left image) or solubilized/dissolved stain (in acetic acid; right image) that can be used for crude quantification of biofilm abundance, with a microplate reader at OD560 (reprinted from (Liberti et al. 2021). d Immunofluorescence with specific anti-VCBP-C antibody on fungal spores isolated from liquid culture of Penicillium sp. and incubated with recombinant VCBP-C protein shows binding of VCBP-C (magenta) to chitin molecules localized in specific regions of spore surface (e.g., bud scars, arrows). The specific binding of VCBP-C to chitin molecule is confirmed by co-localization with wheat germ agglutinin (WGA) staining (green), a lectin known to recognize chitin on fungal surfaces (reprinted from (Liberti et al. 2019). e Immunofluorescence with recombinant IgG1-Fc-chitin binding domain (CBD) of VCBP-C (IgG1-Fc-CBD-C) probe on whole Penicillium sp. fungi grown in liquid medium reveals binding of the CBD-C (green) probe to chitin molecules localized in specific regions of the fungal hyphae (arrow) (reprinted from (Liberti et al. 2019). Asterisk, gut lumen. White dotted lines highlight the surface morphology of the epithelium. Scale bar: a 100 μm; b 2 μm; d 10 μm; e 25 μm
The origins of a “pass-through” gut in early bilaterians facilitated the exploration of new habitats, motivated the innovation of feeding styles and behaviors, and helped drive the evolution of more complex organisms. The gastrointestinal tract has evolved to consist of a series of interwoven exchanges between nutrients, host immunity, and an often microbe-rich environmental interface. Not surprisingly, animals have expanded their immune repertoires to include soluble effectors that can be secreted into luminal spaces, e.g., in the gut, facilitating interactions with microbes in ways that influence their settlement dynamics, virulence, and their interaction with other microbes. The immunoglobulin (Ig) domain, which is also found in some non-immune molecules, is recognized as one of the most versatile recognition domains lying at the interface of innate and adaptive immunity; among vertebrates, secreted Igs are known to play crucial roles in the management of gut microbial communities. In this mini-review, we will focus on secreted immune effectors possessing Ig-like domains in invertebrates, such as the fibrinogen-related effector proteins first described in the gastropod Biomphalaria glabrata, the Down syndrome cellular adhesion molecule first described in the arthropod, Drosophila melanogaster, and the variable region-containing chitin-binding proteins of the protochordates. We will highlight our current understanding of their function and their potential role, if not yet recognized, in the establishment and maintenance of host-microbial interfaces and argue that these Igs are likely also essential to microbiome management.
Animals and plants have NLRs (nucleotide-binding leucine-rich repeat receptors) that recognize the presence of pathogens and initiate innate immune responses. In plants, there are three types of NLRs distinguished by their N-terminal domain: the CC (coiled-coil) domain NLRs, the TIR (Toll/interleukin-1 receptor) domain NLRs and the RPW8 (resistance to powdery mildew 8)-like coiled-coil domain NLRs. CC-NLRs (CNLs) and TIR-NLRs (TNLs) generally act as sensors of effectors secreted by pathogens, while RPW8-NLRs (RNLs) signal downstream of many sensor NLRs and are called helper NLRs. Recent studies have revealed three dimensional structures of a CNL (ZAR1) including its inactive, intermediate and active oligomeric state, as well as TNLs (RPP1 and ROQ1) in their active oligomeric states. Furthermore, accumulating evidence suggests that members of the family of lipase-like EDS1 (enhanced disease susceptibility 1) proteins, which are uniquely found in seed plants, play a key role in providing a link between sensor NLRs and helper NLRs during innate immune responses. Here, we summarize the implications of the plant NLR structures that provide insights into distinct mechanisms of action by the different sensor NLRs and discuss plant NLR-mediated innate immune signalling pathways involving the EDS1 family proteins and RNLs.
For over half a century, deciphering the origins of the genomic loci that form the jawed vertebrate adaptive immune response has been a major topic in comparative immunogenetics. Vertebrate adaptive immunity relies on an extensive and highly diverse repertoire of tandem arrays of variable (V), diversity (D), and joining (J) gene segments that recombine to produce different immunoglobulin (Ig) and T cell receptor (TCR) genes. The current consensus is that a recombination-activating gene (RAG)-like transposon invaded an exon of an ancient innate immune VJ-bearing receptor, giving rise to the extant diversity of Ig and TCR loci across jawed vertebrates. However, a model for the evolutionary relationships between extant non-recombining innate immune receptors and the V(D)J receptors of the jawed vertebrate adaptive immune system has only recently begun to come into focus. In this review, we provide an overview of non-recombining VJ genes, including CD8β, CD79b, natural cytotoxicity receptor 3 (NCR3/NKp30), putative remnants of an antigen receptor precursor (PRARPs), and the multigene family of signal-regulatory proteins (SIRPs), that play a wide range of roles in immune function. We then focus in detail on the VJ-containing novel immune-type receptors (NITRs) from ray-finned fishes, as recent work has indicated that these genes are at least 50 million years older than originally thought. We conclude by providing a conceptual model of the evolutionary origins and phylogenetic distribution of known VJ-containing innate immune receptors, highlighting opportunities for future comparative research that are empowered by this emerging evolutionary perspective.
Principal component analysis of the 24 scRNA-seq samples shows clustering of BS-90 and M-line hemocytes into two distinct groups based on the transcriptomic profiles. granulocytes (G) of both strains (red), hyalinocytes (H) of both strains (blue) group cluster based on snail strain with PC1 describing 7.9% and PC2 describing 6.3% of variability between the two groups. M-line clustering is highlighted in the blue oval and BS-90 clustering highlighted in the red oval
Comparison of the immune-related transcripts observed in high abundance within the hemocyte subtype A and snail strain B scRNA-seq datasets. Experimental groups are identified outside each relevant venn diagram node with the total number of immune-related transcript identified in parentheses. Specific immune factors within each region of the Venn diagram can be found in Supplemental Table 4
The immune cells of the snail Biomphalaria glabrata are classified into hyalinocyte and granulocyte subtypes. Both subtypes are essential for the proper functioning of the snail immune response, which we understand best within the context of how it responds to challenge with the human parasite Schistosoma mansoni. Granulocytes are adherent phagocytic cells that possess conspicuous granules within the cell cytoplasm. Hyalinocytes, on the other hand, are predominantly non-adherent and are known to produce a handful of anti-S. mansoni immune effectors. While our understanding of these cells has progressed, an in-depth comparison of the functional capabilities of each type of immune cell has yet to be undertaken. Here, we present the results of a single-cell RNA-seq study in which single granulocytes and hyalinocytes from S. mansoni-susceptible M-line B. glabrata and S. mansoni-resistant BS-90 B. glabrata are compared without immune stimulation. This transcriptomic analysis supports a role for the hyalinocytes as producers of immune effectors such as biomphalysin and thioester-containing proteins. It suggests that granulocytes are primarily responsible for producing fibrinogen-related proteins and are armed with various pattern-recognition receptors such as toll-like receptors with a confirmed role in the anti-S. mansoni immune response. This analysis also confirms that the granulocytes and hyalinocytes of BS-90 snails are generally more immunologically prepared than their M-line counterparts. As the first single-cell analysis of the transcriptional profiles of B. glabrata immune cells, this study provides crucial context for understanding the B. glabrata immune response. It sets the stage for future investigations into how each immune cell subtype differs in its response to various immunological threats.
Typical domain structures of TRIMs and NLRs in vertebrates. A All TRIMs share the structure of RING-BBox-CC, but class I and class IV TRIMs are the only ones with a C-terminal B30.2. In class I TRIMs, a fibronectin (fn3) domain is also present. B All NLRs share the central structure of NACHT-LRRs, but only NLR-C genes have the FISNA extension for NACHT and can have a C-terminal B30.2. Asterisks mark NLR-C domain structures not present in zebrafish, including the novel CARD-NLR-C-(B30.2) genes and the NLRs with an N-terminal C3HC4/RING. Domains that can optionally be either present or absent in different members of the family are surrounded with brackets
Central points for the geographic ranges of each species of fish in the study. Each black dot represents one species. Note that the dots do not show sampling sites of sequenced individuals. Instead, they indicate that the area from which other fish of that species could be sampled from is centered on that point. In our dataset, almost all dots mapped to the vertical black line in the center where longitude = 0 correspond to species with a circum-global/-polar/-Antarctic range, with one exception: the common dragonet who inhabits the Atlantic basin at longitudes ranging from –32 to + 32
Taxonomic relationships of the species used for the study. The top half of the tree is presented on the left side and contains only members of the clade Euteleostei, including stickleback, perch, cichlids, Antarctic icefish, croakers, anglerfish, pufferfish, flatfish, guppy, medaka, killifish, and ballan wrasse. The bottom half of the tree is on the right side and contains some more euteleosts (e.g., gobies, salmonids, pike, cod, capelin, seahorse, and its close relatives), but also catfish, cyprinids (zebrafish, carp, goldfish), herring, eels, bonytongues, bichirs, sturgeon, garfish, latimeria, tetrapods, sharks and rays, lampreys, hagfish, and, as an outgroup, the Japanese amphioxus. Based on the NCBI taxonomy database
Statistics for B30.2 containing immune receptors. A Violin plots showing the distribution of B30.2-containing gene copy numbers across species. B Violin plots showing the distribution of the minimal number of scaffolds that comprise 25% of all B30.2-containing genes of a specific type, again across species. C Scatterplot showing the relationship between numbers of the two different immune receptor types. D Scatterplot showing the relationship between the numbers of scaffolds that comprise 25% of genes of two different immune receptor types. E, F Violin plots showing the distribution of gene type copy numbers between species living in different types of water types (marine vs freshwater)
Modeling results. The three plots present the values of regression slopes, along with 95% confidence intervals. On the right side, the amount of phylogenetic signal in the data (lambda) is shown and ranges from 0 to 1. In our models, the slope parameter is an indicator of the impact that the predictor variables tend to have on copy numbers and can be interpreted as the amount of increase that would be caused by a one-unit-increase in the predictor variable while statistically holding other variables in the model constant. Negative values mean a negative impact, but impact nonetheless. In the “Habitat” field, freshwater has been defined as − 1 and marine as + 1 so a positive value can be interpreted as marine fish having more copies in general, and negative as freshwater fish having more copies. In all cases, the value of 0 means that the copy numbers are not affected. The slope values can be assigned statistical significance if the 95% confidence interval does not cross zero. For the sake of clarity, asterisks were added to the plots based on p-values reported in model summaries: *p < 0.05. **p < 0.01. ***p < 0.001
B30.2 domains, also known as PRY/SPRY, are key components of specific subsets of two large families of proteins involved in innate immunity: the tripartite motif proteins (TRIMs) and the Nod-like receptors (NLRs). TRIM proteins are important, often inducible factors of antiviral innate immunity, targeting multiple steps of viral cycles through a variety of mechanisms. NLRs prime and regulate systemic innate defenses, especially against bacteria, and control inflammation. Large TRIM and NLR subsets characterized by the presence of a B30.2 domain have been reported from a few fish species including zebrafish and seem to be strongly prone to gene duplication/expansion. Here, we performed a large-scale survey of these receptors across about 150 fish genomes, focusing on ray-finned fishes. We assessed the number and genomic distribution of domains and domain combinations associated with TRIMs, NLRs, and other genes containing B30.2 domains and looked for gene expansion patterns across fish groups. We then used a model to test the impact of taxonomy, genome size, and environmental variables on the copy numbers of these genes. Our findings reveal novel domain structures, clade-specific gains and losses. They also assist with the timing of the gene expansions, reveal patterns associated with the MHC, and lay the groundwork for further studies delving deeper into the forces that drive the copy number variation of immune genes on a species level.
a Two Hydractinia colonies growing next to each other on a glass microscope slide. b Two colonies that have fused permanently to form a chimeric colony. The yellow dashed line indicates the approximate border between the tissues of each original colony. c End stage of aggressive rejection. The colony on the left has overgrown and killed the colony on the right
a Linkage map of the ARC, showing the regions that have been sequenced using BAC contigs. Alr1 is surrounded by several Alr-like sequences (purple arrows). Alr2 is downstream of two Alr2 pseudogenes (gray arrows). b Domain architecture of Alr1 and Alr2 proteins. The proteins are drawn to scale. c Hypothesized in vivo interactions that occur when two Hydractinia colonies meet. The schematic shows the expression of Alr1 and Alr2 on the surface of a Hydractinia cell. Co-dominant expression leads to gene products of both alleles on the cell surface. These are color coded to indicate different alleles. Fusion occurs when colonies have at least one allelic isoform capable of homophilic binding for both Alr1 and Alr2. Transitory fusion occurs when either Alr1 or Alr2 cannot find a binding partner. Rejection occurs when there is no homophilic binding
Hydractinia symbiolongicarpus is a colonial hydroid and a long-standing model system for the study of invertebrate allorecognition. The Hydractinia allorecognition system allows colonies to discriminate between their own tissues and those of unrelated conspecifics that co-occur with them on the same substrate. This recognition mediates spatial competition and mitigates the risk of stem cell parasitism. Here, I review how we have come to our current understanding of the molecular basis of allorecognition in Hydractinia. To date, two allodeterminants have been identified, called Allorecognition 1 (Alr1) and Allorecognition 2 (Alr2), which occupy a genomic region called the allorecognition complex (ARC). Both genes encode highly polymorphic cell surface proteins that are capable of homophilic binding, which is thought to be the mechanism of self/non-self discrimination. Here, I review how we have come to our current understanding of Alr1 and Alr2. Although both are members of the immunoglobulin superfamily, their evolutionary origins remain unknown. Moreover, existing data suggest that the ARC may be home to a family of Alr-like genes, and I speculate on their potential functions.
Simplified phylogenetic tree showing the evolutionary position of C. elegans (in Nematoda, in bold) relative to selected Metazoan lineages. The black square marks the base of the Ecdysozoa, the red the Protostomia/Deuterostomia junction. Deuterostomia contains 2 clades, the chordates (e.g. vertebrates, including mammals), and ambulacrarians (e.g. echinoderms, including starfish). The dashed line leads to the Lophotrochozoa (not shown). The names of the branches that include species containing at least one predicted Rel homology domain (RHD) protein are in green. The pattern of conservation supports several independent losses of RHD genes, including one on the Nematoda lineage. The recent discovery of NF-kB orthologues in the marine nematode Laxus oneistus (Paredes et al. 2021) confirms that the widespread loss of RHD genes among Nematoda did occur subsequent to their evolutionary divergence from Tardigrada
A Comparing Akirin function in Drosophila and C. elegans. In Drosophila (left), binding of peptidoglycan to the appropriate peptidoglycan recognition protein (PGRP) activates the IMD pathway, leading to a Rel family transcription factor. Akirin bridges chromatin remodellers of the SWItch/Sucrose Non-Fermentable family (SWI/SNF) to the Rel protein and acts as a positive regulator of defence gene expression. In C. elegans, activation of a G-protein coupled receptor (GPCR) by an endogenous ligand (HPLA) triggers a downstream p38 MAPK cascade. Akirin here interacts with the NuRD chromatin remodelling complex, including the protein LIN-40, and a POU-class transcription factor. Akirin needs to dissociate from its target loci if defence genes are to be expressed. Figure modified from one kindly provided by O. Zugasti. B Clustering of 280 genes that act as positive regulators of the antifungal innate immune response (left) and of 144 genes that are up-regulated upon fungal infection (right; adapted from Thakur et al. 2021) on the basis of their patterns of conservation (red indicates the presence of an orthologue) across 113 species (coloured bar at bottom), ranging from Archaea and bacteria (grey; left) to chordates, including human (brown; right). Among the invertebrates (yellow), the 5 Caenorhabditis species are indicated by the pink box
Three examples of innate immune recognition in C. elegans. Left: An as yet uncharacterised compound from pathogenic oomycetes acts via a pair of proteins containing C-type lectin domains that function non-redundantly in specific chemosensory neurons (M. Barkoulas, personal communication) to up-regulate the expression of genes including a family of chitinase-like (chil) that alter the properties of the cuticle. Middle: a breach of the cuticle by physical injury, mutation of specific cuticle components, or by infection with Drechmeria coniospora, increases the level of HPLA. This activates the GPCR DCAR-1 and the downstream p38 MAPK pathway, leading to increased expression of AMPs such as NLP-29, and the insulin-like protein INS-11. These contribute either directly to defence or signal to other tissues as part of a coordinated response to infection. Right: A specific small RNA (sRNA) called P11 and produced by Pseudomonas aeruginosa is taken up by intestinal cells and processed. This results in the generation and transmission of successive signal(s) to the germline and then from the germline to the neurons involved in pathogen aversive behaviour. The formation of virus-like particles by the Cer1 retrotransposon, hypothesised to carry a specific RNA molecule, allows a worm exposed to P11 to affect the behaviour of naïve worms and progeny through both vertical and horizontal transmission
The natural environment of the free-living nematode Caenorhabditis elegans is rich in pathogenic microbes. There is now ample evidence to indicate that these pathogens exert a strong selection pressure on C. elegans, and have shaped its genome, physiology, and behaviour. In this short review, we concentrate on how C. elegans stands out from other animals in terms of its immune repertoire and innate immune signalling pathways. We discuss how C. elegans often detects pathogens because of their effects on essential cellular processes, or organelle integrity, in addition to direct microbial recognition. We illustrate the extensive molecular plasticity that is characteristic of immune defences in C. elegans and highlight some remarkable instances of lineage-specific innovation in innate immune mechanisms.
A schematic diagram to indicate the genes in the chicken MHC and adjacent chromosomal regions that are likely to be involved in innate or adaptate immune responses (downward-pointing arrows underneath the line depicting the genomic sequence), in comparison to the classical class I gene BF2 and the classical class II B gene BLB2 involved in the adaptive response (upward-pointing arrows above the line). To be clear, some multigene families have copy number variation, so the exact number of genes is not implied in this diagram. There are genes that are involved in supplying peptides to the class I and class II molecules that are not depicted
Organisation of regions on chicken chromosome 16, as currently understood. A Depiction of chromosome 16, based on analysis by FISH, radiation hybrids, genetics, southern blotting and sequencing. B, B locus; GC, G + C-rich region of PO1 repeats; Y, Rfp-Y region; NOR, nucleolar organiser region; BLA, class II A gene; fB, factor B gene; ORs, olfactory receptor genes; SRCRs, scavenger receptor with cysteine repeat genes. Double-headed arrows indicate recombination frequencies between B and BLA, fB and Rfp-Y, and B and Rfp-Y. B Region of the B locus currently sequenced, including the BF-BL region, the TRIM region and the BG region. Genes represented by boxes. Rising and falling stripes indicate genes of the classical class I and class II presentation system, respectively; stippled indicate class II region genes; black indicates lectin-like genes and pseudogenes; horizontal stripes indicate TRIM family genes; vertical stripes indicate BG genes. Names of genes above indicate transcription from left to right, below indicate transcription from right to left. (Figure modified from Kaufman 2013)
Compared to the major histocompatibility complex (MHC) of typical mammals, the chicken BF/BL region is small and simple, with most of the genes playing central roles in the adaptive immune response. However, some genes of the chicken MHC are almost certainly involved in innate immunity, such as the complement component C4 and the lectin-like receptor/ligand gene pair BNK and Blec. The poorly expressed classical class I molecule BF1 is known to be recognised by natural killer (NK) cells and, analogous to mammalian immune responses, the classical class I molecules BF1 and BF2, the CD1 homologs and the butyrophilin homologs called BG may be recognised by adaptive immune lymphocytes with semi-invariant receptors in a so-called adaptate manner. Moreover, the TRIM and BG regions next to the chicken MHC, along with the genetically unlinked Y and olfactory/scavenger receptor regions on the same chromosome, have multigene families almost certainly involved in innate and adaptate responses. On this chicken microchromosome, the simplicity of the adaptive immune gene systems contrasts with the complexity of the gene systems potentially involved in innate immunity.
Biases in immune gene expression in brain tissue in Kentish plover. A Sex-specific immune gene expression where positive values indicate male-biased expression and negative a female bias. B Immune expression bias in relation to habitat. Colours indicate chromosomal location of the differentially expressed genes. The horizontal dashed line indicates a false discovery rate (FDR) threshold of 0.05
Heatmap of 11 significantly differentially expressed immune genes in 24 male and female Kentish plovers sampled in China. Colours represent normalised average expression counts (log2(n + 1)). Bottom column names refer to females (F) and males (M) from Bohai Bay (BB) and Qinghai Lake (QL)
Males and females often exhibit differences in behaviour, life histories and ecology, many of which typically are reflecting in their brains. Neuronal protection and maintenance include complex processes led by the microglia, which also interacts with metabolites such as hormones or immune components. Despite increasing interest in sex-specific brain function in laboratory animals, the significance of sex-specific immune activation in the brain of wild animals along with the variables that could affect it, are widely lacking. Here, we use the Kentish plover (Charadrius alexandrinus) to study sex differences in expression of immune genes in the brain of adult males and females, in two wild populations breeding in contrasting habitats: a coastal sea-level population and a high-altitude inland population in China. Our analysis yielded 379 genes associated with immune function. We show a significant male-biased immune gene upregulation. Immune gene expression in the brain did not differ in upregulation between the coastal and inland populations. We discuss the role of dosage compensation in our findings and their evolutionary significance mediated by sex-specific survival and neuronal deterioration. Similar expression profiles in the coastal and inland populations suggest comparable genetic control by the microglia and possible similarities in pathogen pressures between habitats. We call for further studies on gene expressions of males and females in wild population to understand the implications of immune function for life-histories and demography in natural systems.
Geographic information for 10 sampling locations (Pop) of R. amarus in the Czech Republic, along with the number of col- lected fish (N), geographic location (latitude, longitude), the number of MHC-DAB1 variants (A) and DAB1 allelic richness (Ar, the aver- age number of variants present in 1000 random samples of 19 fishes drawn from the same population, with one Standard Deviation). The datum for geographic coordinates is WGS84
Polymorphism of the major histocompatibility complex (MHC), DAB1 gene was characterized for the first time in the European bitterling ( Rhodeus amarus ), a freshwater fish employed in studies of host-parasite coevolution and mate choice, taking advantage of newly designed primers coupled with high-throughput amplicon sequencing. Across 221 genotyped individuals, we detected 1–4 variants per fish, with 28% individuals possessing 3–4 variants. We identified 36 DAB1 variants, and they showed high sequence diversity mostly located within predicted antigen-binding sites, and both global and codon-specific excess of non-synonymous mutations. Despite deep divergence between two major allelic lineages, functional diversity was surprisingly low (3 supertypes). Overall, these findings suggest the role of positive and balancing selection in promotion and long-time maintenance of DAB1 polymorphism. Further investigations will clarify the role of pathogen-mediated selection to drive the evolution of DAB1 variation.
Since 2019, the world was involved with SARS-CoV-2 and consequently, with the announcement by the World Health Organization that COVID-19 was a pandemic, scientific were an effort to obtain the best approach to combat this global dilemma. The best way to prevent the pandemic from spreading further is to use a vaccine against COVID-19. Here, we report the design of a recombinant multi-epitope vaccine against the four proteins spike or crown (S), membrane (M), nucleocapsid (N), and envelope (E) of SARS-CoV-2 using immunoinformatics tools. We evaluated the most antigenic epitopes that bind to HLA class 1 subtypes, along with HLA class 2, as well as B cell epitopes. Beta-defensin 3 and PADRE sequence were used as adjuvants in the structure of the vaccine. KK, GPGPG, and AAY linkers were used to fuse the selected epitopes. The nucleotide sequence was cloned into pET26b(+) vector using restriction enzymes XhoI and NdeI, and HisTag sequence was considered in the C-terminal of the construct. The results showed that the proposed candidate vaccine is a 70.87 kDa protein with high antigenicity and immunogenicity as well as non-allergenic and non-toxic. A total of 95% of the selected epitopes have conservancy with similar sequences. Molecular docking showed a strong binding between the vaccine structure and tool-like receptor (TLR) 7/8. The docking, molecular dynamics, and MM/PBSA analysis showed that the vaccine established a stable interaction with both structures of TLR7 and TLR8. Simulation of immune stimulation by this vaccine showed that it evokes immune responses related to humoral and cellular immunity.
Distribution of centromeric (A) and telomeric (B) KIR haplotypes, independent of ligands, based on the chromosomal locations (C) among control group and COVID-19 patient groups (grey boxes represent the framework genes)
ROC curves show the efficacy of prediction of severe disease for COVID-19 by our model constructed with (the bold line) or without KIR genotypesVisual abstract of this manuscript
Associations between inherited Killer Immunoglobulin-like Receptor (KIR) genotypes and the severity of multiple RNA virus infections have been reported. This prospective study was initiated to investigate if such an association exists for COVID-19. In this cohort study performed at Ankara University, 132 COVID-19 patients (56 asymptomatic, 51 mild-intermediate, and 25 patients with severe disease) were genotyped for KIR and ligands. Ankara University Donor Registry (n:449) KIR data was used for comparison. Clinical parameters (age, gender, comorbidities, blood group antigens, inflammation biomarkers) and KIR genotypes across cohorts of asymptomatic, mild-intermediate, or severe disease were compared to construct a risk prediction model based on multivariate binary logistic regression analysis with backward elimination method. Age, blood group, number of comorbidities, CRP, D-dimer, and telomeric and centromeric KIR genotypes (tAA, tAB1, and cAB1) along with their cognate ligands were found to differ between cohorts. Two prediction models were constructed; both included age, number of comorbidities, and blood group. Inclusion of the KIR genotypes in the second prediction model exp (-3.52 + 1.56 age group - 2.74 blood group (type A vs others) + 1.26 number of comorbidities - 2.46 tAB1 with ligand + 3.17 tAA with ligand) increased the predictive performance with a 92.9% correct classification for asymptomatic and 76% for severe cases (AUC: 0.93; P < 0.0001, 95% CI 0.88, 0.99). This novel risk model, consisting of KIR genotypes with their cognate ligands, and clinical parameters but excluding earlier published inflammation-related biomarkers allow for the prediction of the severity of COVID-19 infection prior to the onset of infection. This study is listed in the National COVID-19 clinical research studies database. Graphical abstract
Top-cited authors
Morten Nielsen
  • Technical University of Denmark
Ole Lund
  • Technical University of Denmark
Søren Buus
  • University of Copenhagen
Alessandro Sette
  • La Jolla Institute for Allergy & Immunology
Bjoern Peters
  • La Jolla Institute for Allergy & Immunology