Distinct immunological signatures discriminate severe COVID-19 from non-SARS-CoV-2-driven critical pneumonia.

Immune profiling of COVID-19 patients has identified numerous alterations in both innate and adaptive immunity. However, whether those changes are specific to SARS-CoV-2 or driven by a general inflammatory response shared across severely ill pneumonia patients remains unknown. Here, we compared the immune profile of severe COVID-19 with non-SARS-CoV-2 pneumonia ICU patients using longitudinal, high-dimensional single-cell spectral cytometry and algorithm-guided analysis. COVID-19 and non-SARS-CoV-2 pneumonia both showed increased emergency myelopoiesis and displayed features of adaptive immune paralysis. However, pathological immune signatures suggestive of T cell exhaustion were exclusive to COVID-19. The integration of single-cell profiling with a predicted binding capacity of SARS-CoV-2 peptides to the patients' HLA profile further linked the COVID-19 immunopathology to impaired virus recognition. Toward clinical translation, circulating NKT cell frequency was identified as a predictive biomarker for patient outcome. Our comparative immune map serves to delineate treatment strategies to interfere with the immunopathologic cascade exclusive to severe COVID-19.

A peptide-binding domain shared with an Antarctic bacterium facilitates Vibrio cholerae human cell binding and intestinal colonization.

Vibrio cholerae, the causative agent of the disease cholera, is responsible for multiple pandemics. V. cholerae binds to and colonizes the gastrointestinal tract within the human host, as well as various surfaces in the marine environment (e.g., zooplankton) during interepidemic periods. A large adhesin, the Flagellar Regulated Hemagglutinin A (FrhA), enhances binding to erythrocytes and epithelial cells and enhances intestinal colonization. We identified a peptide-binding domain (PBD) within FrhA that mediates hemagglutination, binding to epithelial cells, intestinal colonization, and facilitates biofilm formation. Intriguingly, this domain is also found in the ice-binding protein of the Antarctic bacterium Marinomonas primoryensis, where it mediates binding to diatoms. Peptide inhibitors of the M. primoryensis PBD inhibit V. cholerae binding to human cells as well as to diatoms and inhibit biofilm formation. Moreover, the M. primoryensis PBD inserted into FrhA allows V. cholerae to bind human cells and colonize the intestine and also enhances biofilm formation, demonstrating the interchangeability of the PBD from these bacteria. Importantly, peptide inhibitors of PBD reduce V. cholerae intestinal colonization in infant mice. These studies demonstrate how V. cholerae uses a PBD shared with a diatom-binding Antarctic bacterium to facilitate intestinal colonization in humans and biofilm formation in the environment.

Revealing novel and conservative T-cell epitopes with MHC B2 restriction on H9N2 avian influenza virus (AIV).

B2 haplotype major histocompatibility complex (MHC) has been extensively reported to confer resistance to various avian diseases. But its peptide-binding motif is unknown, and the presenting peptide is rarely identified. Here, we identified its peptide-binding motif (X-A/V/I/L/P/S/G-X-X-X-X-X-X-V/I/L) in vitro using Random Peptide Library-based MHC I LC-MS/MS analysis. To further clarify the structure basis of motif, we determined the crystal structure of the BF2∗02:01-PB2552-560 complex at 1.9 Å resolution. We found that BF2∗02:01 had a relatively wide antigen-binding groove, and the structural characterization of pockets was consistent with the characterization of peptide-binding motif. The wider features of the peptide-binding motif and increased number of peptides bound by BF2∗02:01 than BF2∗04:01 might resolve the puzzles for the presence of potential H9N2 resistance in B2 chickens. Afterward, we explored the H9N2 avian influenza virus (AIV)-induced cellular immune response in B2 haplotype chickens in vivo. We found that ratio of CD8+ T cell and kinetic expression of cytotoxicity genes including Granzyme K, interferon-γ, NK lysin, and poly-(ADP-ribose) polymerase in peripheral blood mononuclear cells were significantly increased in defending against H9N2 AIV infection. Especially, we selected 425 epitopes as candidate epitopes based on the peptide-binding motif and further identified four CD8+ T-cell epitopes on H9N2 AIV including NS198-106, PB2552-560, NP182-190, and NP455-463 via ELI-spot interferon-γ detections after stimulating memory lymphocytes with peptides. More importantly, these epitopes were found to be conserved in H7N9 AIV and H9N2 AIV. These findings provide direction for developing effective T cell epitope vaccines using well-conserved internal viral antigens in chickens.

A Bayesian approach to estimate MHC-peptide binding threshold.

Major histocompatibility complex (MHC)-peptide binding is a critical step in enabling a peptide to serve as an antigen for T-cell recognition. Accurate prediction of this binding can facilitate various applications in immunotherapy. While many existing methods offer good predictive power for the binding affinity of a peptide to a specific MHC, few models attempt to infer the binding threshold that distinguishes binding sequences. These models often rely on experience-based ad hoc criteria, such as 500 or 1000nM. However, different MHCs may have different binding thresholds. As such, there is a need for an automatic, data-driven method to determine an accurate binding threshold. In this study, we proposed a Bayesian model that jointly infers core locations (binding sites), the binding affinity and the binding threshold. Our model provided the posterior distribution of the binding threshold, enabling accurate determination of an appropriate threshold for each MHC. To evaluate the performance of our method under different scenarios, we conducted simulation studies with varying dominant levels of motif distributions and proportions of random sequences. These simulation studies showed desirable estimation accuracy and robustness of our model. Additionally, when applied to real data, our results outperformed commonly used thresholds.

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules.

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Efficient and accurate binding free energy calculation of Aß9-40 protofilament propagation.

Self-assembled aggregation of peptides and proteins into regular amyloid fibrils is associated with several neurodegenerative diseases. In case of Alzheimer's disease proteolytic cleavage products of the amyloid precursor protein form pathological amyloid-beta fibrils in a nucleation and propagation phase. The molecular details and thermodynamic driving forces of amyloid formation are not well understood, but are of high relevance for potential pharmacological interference. We used atomistic binding free energy simulations to calculate the free energy of protofilament propagation by an additional Aß9-40 peptide binding to the protofilament tip. It requires sampling of relevant conformational transitions which is challenging since the monomeric Aß9-40 peptide is intrinsically disordered. However, the convergence of umbrella simulations can be enhanced by applying additional restraining potentials on the axial, orientational and conformational degrees of freedom. The improved convergence leads to a much closer agreement with experimental binding free energy data compared to unrestrained umbrella sampling. Moreover, the restraining approach results in a separation of contributions to the total binding free energy. The calculated contributions indicate that the free energy change associated with the restriction of conformational freedom upon propagation makes a large opposing contribution of higher magnitude than the total binding free energy. Finally, optimization of the approach leads to further significant reduction of the computational demand which is crucial for systematic studies on mutations, denaturants and inhibitors in the fibril propagation step.

Effect of peptide-binding motif on survival of HLA-haploidentical transplantation with post-transplant cyclophosphamide.

Peptide-binding motif (PBM) model, a hierarchical clustering of HLA class I based on their binding specificity, was developed to predict immunopeptidome divergence. The effect of PBM mismatches on outcomes is unknown in HLA-haploidentical haematopoietic cell transplantation with post-transplant cyclophosphamide (PTCy-haplo). We therefore conducted a retrospective study using national registry data in PTCy-haplo. Overall, 1352 patients were included in the study. PBM-A bidirectional mismatch was associated with an increased risk of overall mortality in multivariable analysis (hazard ratio, 1.26; 95% confidence interval, 1.06 to 1.50; p = 0.010). None of relapse, non-relapse mortality (NRM) and graft-versus-host disease showed significant differences according to PBM-A bidirectional mismatch status in the entire cohort. The impact of PBM-A bidirectional mismatch on overall survival (OS) was preserved within the HLA-A genotype bidirectional mismatch population, and their lower OS stemmed from higher relapse rate in this population. The worse OS due to high NRM with PBM-A bidirectional mismatch was prominent in lymphoid malignancies receiving reduced-intensity conditioning. The PBM model may predict outcomes more accurately than HLA genotype mismatches. In conclusion, this study demonstrated that the presence of PBM-A bidirectional mismatch elevated the risk of mortality of PTCy-haplo. Avoiding PBM-A bidirectional mismatch might achieve better outcomes in PTCy-haplo.

HLA peptide-binding pocket diversity modulates immunological complications after cord blood transplant in acute leukaemia.

Pocket motifs and their amino acid positions of HLA molecules are known to govern antigen presentation to effector cells. Our objective was to analyse their influence on the risk of graft-versus-host disease (GVHD) and relapse after umbilical cord blood transplant (UCBT). The transplant characteristics of 849 patients with acute leukaemia were obtained from the Eurocord/EBMT database. Higher acute (a) GVHD was associated with homozygosity of UCB HLA-C amino acid positions 77 and 80 (NN/KK) (p = 0.008). Severe aGVHD was associated with HLA-A pocket B YSAVMENVHY motif (p = 0.002) and NN and RR genotypes of the HLA-C amino acid positions 77 and 156 (p = 0.006 and p = 0.002). Such risk was also increased in case of recipient and UCB mismatches in P4 (p < 0.0001) and P9 (p = 0.003) pockets of HLA-DQB1 alleles. For chronic GVHD, the pocket B YYAVMEISNY motif of the HLA-B*15:01 allele and the absence of mismatch between recipient and UCB in the P6 pocket of HLA-DRB1 were associated with a lower risk (p = 0.0007 and p = 0.0004). In relapse, both UCB pocket B YFAVMENVHY belonging to HLA-A*32:01 and recipient pocket B YDSVGENYQY motif of the HLA-C*07:01 allele were associated with higher risk (p = 0.0026 and p = 0.015). We provide clues on HLA-mediated cellular interactions and their role in the development of GVHD and relapse.

Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism.

Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only.

Discovery and Characterization of Linear Motif Mediated Protein-Protein Complexes.

There are myriads of protein-protein complexes that form within the cell. In addition to classical binding events between globular domains, many protein-protein interactions involve short disordered protein regions. The latter contain so-called linear motifs binding specifically to ordered protein domain surfaces. Linear binding motifs are classified based on their consensus sequence, where only a few amino acids are conserved. In this chapter we will review experimental and in silico techniques that can be used for the discovery and characterization of linear motif mediated protein-protein complexes involved in cellular signaling, protein level and gene expression regulation.

Challenging Ancient DNA Results About Putative HLA Protection or Susceptibility to Yersinia pestis.

In a recent article, Immel et al. (Immel A, Key FM, Szolek A, Barquera R, Robinson MK, Harrison GF, Palmer WH, Spyrou MA, Susat J, Krause-Kyora B, et al. 2021. Analysis of genomic DNA from medieval plague victims suggests long-term effect of Yersinia pestis on human immunity genes. Mol Biol Evol. 38:4059-4076) extracted DNA from 36 individuals dead from plague in Ellwangen, Southern Germany, during the 16th century. By comparing their human leukocyte antigen (HLA) genotypes with those of 50 present-day Ellwangen inhabitants, the authors reported a significant decrease of HLA-B*51:01 and HLA-C*06:02 and a significant increase of HLA-DRB1*13:01/13:02 frequencies from ancient to modern populations. After comparing these frequencies with a larger sample of 8,862 modern Germans and performing simulations of natural selection, they concluded that these changes had been driven by natural selection. In an attempt to provide more evidence on such stimulating results, we explored the HLA frequency patterns over all of Europe, we predicted binding affinities of HLA-B/C/DRB1 alleles to 106,515 Yersinia pestis-derived peptides, and we performed forward simulations of HLA genetic profiles under neutrality. Our analyses do not sustain the conclusions of HLA protection or susceptibility to plague based on ancient DNA.

Comparative analysis of the variability of the human leukocyte antigen peptide-binding pockets in patients with acute leukaemia.

The association between acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML) and the human leukocyte antigens (HLA) has rarely been studied in terms of diversity of peptide-binding pockets. The objective of this study was to analyse whether motifs of HLA class I and class II peptide-binding pockets and/or their amino acid positions were differentially associated with ALL and AML. We included 849 patients from the Eurocord/European Blood and Marrow Transplant registry. The HLA peptide-binding pockets whose amino acid variability was analysed were B and F for HLA class I, P4, P6, and P9 for HLA-DRB1, and P4 and P9 for HLA-DQB1. The motif RFDRAY in P4 of HLA-DRB1*16:01/02/03/05 alleles and the motif YYVSY in P9 of HLA-DQB1*05:02/04/05 alleles, were statistically associated with ALL (corrected p value [pc ] = 0.001 and pc  = 0.035 respectively). The frequency of serine 57 in the P9 of HLA-DQB1 was higher in ALL (odds ratio 2.09, 95% confidence interval: 1.27-3.44; pc  = 0.037). Our analysis suggests that specific motifs in terms of HLA class II pockets and amino acids might be unique to ALL. The associations identified in this study encourage further investigation oF the role of HLA peptide-binding pockets and their amino acids in immune processes underpinning acute leukaemia and ultimately in immunotherapy settings.

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules.

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

Peptidome-wide association analysis of Epstein-Barr virus identifies epitope repertoires associated with nasopharyngeal carcinoma.

Human leukocyte antigen (HLA) molecules are essential for presenting Epstein-Barr virus (EBV) antigens and are closely related to nasopharyngeal carcinoma (NPC). This study aims to systematically investigate the association between HLA-bound EBV peptides and NPC risk through in silico HLA-peptide binding prediction. A total of 455 NPC patients and 463 healthy individuals in NPC endemic areas were recruited, and HLA-target sequencing was performed. HLA-peptide binding prediction for EBV, followed by peptidome-wide logistic regression and motif analysis, was applied. Binding affinity changes for EBV peptides carrying high-risk mutations were analyzed. We found that NPC-associated EBV peptides were significantly enriched in immunogenic proteins and core linkage disequilibrium (LD) proteins related to evolution, especially those binding HLA-A alleles (p = 3.10 × 10-4 for immunogenic proteins and p = 8.10 × 10-5 for core LD proteins related to evolution). These peptides were clustered and showed binding motifs of HLA supertypes, among which supertype A02 presented an NPC-risk effect (padj = 3.77 × 10-4 ) and supertype A03 presented an NPC-protective effect (padj = 4.89 × 10-4 ). Moreover, a decreased binding affinity toward risk HLA supertype A02 was observed for the peptide carrying the NPC-risk mutation BNRF1 V1222I (p = 0.0078), and an increased binding affinity toward protective HLA supertype A03 was observed for the peptide carrying the NPC-risk mutation BALF2 I613V (p = 0.022). This study revealed the distinct preference of EBV peptides for binding HLA supertypes, which may contribute to shaping EBV population structure and be involved in NPC development.

Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function.

When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.

West Nile virus-associated HLA-DRB1 alleles in the Greek population: A structural perspective.

The HLA system plays a significant role via the regulation of the immune system and contributes to the progression and protection of many diseases. In our previous study, several HLA-DRB1 alleles were found to have a susceptible or protective role toward infection and neuroinvasion of West Nile Virus (WNV) in the Greek population. As expected, the majority of polymorphic positions are located in the peptide-binding region of the molecule. In the present work, the structure of these alleles was studied in silico, to examine the effect of polymorphism on the conformation of DRB1 proteins, with the aspect of WNV association. More specifically, molecular dynamics simulations were used for structural prediction of 23 available alleles. These modeled alleles were evaluated using root-mean-square deviation (RMSD) and root-mean-square fluctuation analysis. Low RMSD values indicate that different alleles have similar structures. Furthermore, low fluctuation was observed in the peptide-binding region between alleles with the higher and the lowest RMSD values. These findings indicate that probably variable residues do not affect the behavior of DRB1 alleles in WNV disease, by causing structural differences between them.

Identification of the First Sulfobetaine Hydrogel-Binding Peptides via Phage Display Assay.

Using the M13 phage display, a series of 7- and 12-mer peptides which interact with new sulfobetaine hydrogels are identified. Two peptides each from the 7- and 12-mer peptide libraries bind to the new sulfobetaine hydrogels with high affinity compared to the wild-type phage lacking a dedicated hydrogel binding peptide. This is the first report of peptides binding to zwitterionic sulfobetaine hydrogels and the study therefore opens up the pathway toward new phage or peptide/hydrogel hybrids with high application potential.

Population-wide genetic analysis of Ovar-DQA1 and DQA2 loci across sheep breeds in India revealed their evolutionary importance and fitness of sheep in a tropical climate.

Genetic variability at the major histocompatibility complex (MHC) is important in any species due to significant role played by MHC for antigen presentation. DQA locus has not been studied for its genetic variability across sheep population in India. In the present study, MHC of sheep at DQA1 and DQA2 loci were evaluated across 17 Indian sheep breeds. Results revealed high degree of heterozygosity (10.34% to 100% for DQA1 and 37.39 to 100% for DQA2). 18 DQA1 alleles and 22 DQA2 alleles were isolated in different breeds. Nucleotide content for DQA region revealed richness of AT content (54.85% for DQA1 and 53.89% for DQA2). DQA1 and DQA2 sequences clustered independently. We could see evidence of divergence of DQA as DQA1 and DQA2 across sheep breeds. Wu-Kabat variability index revealed vast genetic variation across DQA1 and DQA2, specifically at peptide binding sites (PBS) that consisted 21 residues for DQA1 and 17 residues for DQA2. Evolutionary analysis revealed the presence of positive and balancing selection for DQA1 locus, however DQA2 was under purifying selection across sheep breeds. Higher heterozygosity and large diversity at both loci especially at PBS indicated the fitness of the sheep population for evading pathogens and adapt to the harsh tropical climate.

Cross-population genetic analysis revealed genetic variation and selection in the Ovar-DRB1 gene of Indian sheep breeds.

In sheep, MHC variability is studied widely to explore disease association. The aim of the current study was to explore the genetic diversity of Ovar-DRB diversity across sheep breeds of India. Here, Ovar-DRB1 locus was studied across 20 sheep breeds. DRB1 was amplified (301 bp) and sequenced using a PCR-sequence-based typing approach. Results revealed a high degree of heterozygosity across breeds (mean: 73.99%). Overall mean distance for DRB1 was highest in Sangamneri (0.18) and lowest in Madgyal sheep (0.10). There was a higher rate of transition, across breeds. Further, 39 alleles were isolated in different breeds, out of which 10 were new. To allow easy access and use of the immune-polymorphic database, an online database management system was launched (http://www.mhcdbms.in/). Nucleotide content across breeds for the DRB1 region revealed the richness of GC content (59.26%). Wu-Kabat index revealed vast genetic variation across peptide binding sites (PBS) of DRB1. Residues 6, 66, 69, 52, and 81, were polymorphic showing utility for antigen presentation. All breeds were under positive selection for DRB1 locus (dN > dS). Study revealed the importance of DRB locus diversity for beta chain specifically at PBS across sheep breeds of the Indian subcontinent and presented evidence of positive selection for DRB owing to its evolutionary significance.

Assessment of Novel Proteins Triggering Celiac Disease via Docking-Based Approach.

Human leukocyte antigens (HLAs) are pivotal in antigen processing, presenting to CD4+ T cells, and are linked to autoimmune disease susceptibility. In celiac disease, HLA-DQ2.5 and HLA-DQ8.1 bind gluten peptides on APCs, some recognized by CD4+ T cells, prompting inflammation and tissue damage. While extensively studied experimentally, these alleles lack comprehensive in silico analysis. To explore peptide-HLA preferences, we used molecular docking on peptide libraries, deriving quantitative matrices (QMs) for evaluating amino acids at nine-residue peptide binding cores. Our findings tie specific residue preferences to peptide backbone conformations. Validating QMs on known binders and non-binders showed strong predictive power (89-94% accuracy). These QMs excel in screening protein libraries, even whole proteomes, notably reducing time and costs for celiac disease risk assessment in novel proteins. This computational approach aligns with European Food Safety Authority guidance, promising efficient screening for potential celiac disease triggers.