APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries.

The recognition of peptides bound to class I major histocompatibility complex (MHC-I) receptors by T-cell receptors (TCRs) is a determinant of triggering the adaptive immune response. While the exact molecular features that drive the TCR recognition are still unknown, studies have suggested that the geometry of the joint peptide-MHC (pMHC) structure plays an important role. As such, there is a definite need for methods and tools that accurately predict the structure of the peptide bound to the MHC-I receptor. In the past few years, many pMHC structural modeling tools have emerged that provide high-quality modeled structures in the general case. However, there are numerous instances of non-canonical cases in the immunopeptidome that the majority of pMHC modeling tools do not attend to, most notably, peptides that exhibit non-standard amino acids and post-translational modifications (PTMs) or peptides that assume non-canonical geometries in the MHC binding cleft. Such chemical and structural properties have been shown to be present in neoantigens; therefore, accurate structural modeling of these instances can be vital for cancer immunotherapy. To this end, we have developed APE-Gen2.0, a tool that improves upon its predecessor and other pMHC modeling tools, both in terms of modeling accuracy and the available modeling range of non-canonical peptide cases. Some of the improvements include (i) the ability to model peptides that have different types of PTMs such as phosphorylation, nitration, and citrullination; (ii) a new and improved anchor identification routine in order to identify and model peptides that exhibit a non-canonical anchor conformation; and (iii) a web server that provides a platform for easy and accessible pMHC modeling. We further show that structures predicted by APE-Gen2.0 can be used to assess the effects that PTMs have in binding affinity in a more accurate manner than just using solely the sequence of the peptide. APE-Gen2.0 is freely available at https://apegen.kavrakilab.org.

Variation of modulation and expression of biomarkers associated with inflammation in bariatric surgery patients: A systematic review and meta-analysis.

BACKGROUND: Bariatric surgery is an effective intervention that causes a series of metabolic changes related to inflammatory processes; however, the variation of biomarkers related to these processes is not entirely understood. Our objective was to investigate the variation of modulation and expression of biomarkers associated with inflammation in patients who underwent bariatric surgery. METHODS: We searched the MEDLINE (via PubMed), EMBASE (via Elsevier), Cochrane Central Register of Controlled Trials, Latin American and Caribbean Literature on Health Sciences (via virtual health library), Cumulative Index to Nursing and Allied Health Literature (via EBSCO), Web of Science core collection, and Scopus (via Elsevier) databases, and the gray literature was examined from inception to January 2022. Three pairs of reviewers performed data screening, extraction, and quality assessment independently. Meta-analysis with random effects models was used for general, subgroup, and sensitivity analyses. The I2 statistic was used to assess heterogeneity between studies. RESULTS: In total, 96 articles were included in this systematic review; of these, 87 studies met the criteria for the meta-analysis, involving 3,533 participants. Five biomarkers were included in the meta-analysis (tumor necrosis factor alpha; interleukin 6; leptin; interleukin 1 beta, and lipopolysaccharides). Only leptin showed a significant decrease in the first month after surgery (mean difference -20.71; [95% confidence interval: -28.10 to -13.32, P < .0001; I2 = 66.7%), with moderate heterogeneity. The 12 months after surgery showed a significant decrease in tumor necrosis factor alpha (mean difference -0.89; [95% confidence interval: -1.37 to -0.42], P = .0002; I2 = 94.7%), interleukin 6 (mean difference -1.62; [95% confidence interval: -1.95 to -1.29], P < .0001; I2 = 94.9%), leptin (mean difference -28.63; [95% confidence interval: -34.02 to -23.25], P < .0001; I2 = 92.7%), and interleukin 1 beta (mean difference -2.46; [95% confidence interval: -4.23 to -0.68], P = .006; I2 = 98.3%), all with high heterogeneity. The type of surgery did not show significant differences for the biomarkers at the first month and 12 months, and the results have not changed with high-quality studies. In the 12-month measurement, variations in tumor necrosis factor alpha and leptin were associated with body mass index. CONCLUSION: The findings of this meta-analysis suggest that Roux-en-Y gastric bypass and sleeve gastrectomy bariatric surgeries are associated with a significant reduction in leptin at 1 month after bariatric surgical intervention and tumor necrosis factor alpha, leptin, and interleukin 1 beta after 12 months.

PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure.

Introduction: Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe. Methods: Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs. Results and discussion: We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes.

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy.

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient's own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide-ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide-MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC "hot-spots" for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.

Improved structural method for T-cell cross-reactivity prediction.

Cytotoxic T-lymphocytes (CTLs) are the key players of adaptive cellular immunity, being able to identify and eliminate infected cells through the interaction with peptide-loaded major histocompatibility complexes class I (pMHC-I). Despite the high specificity of this interaction, a given lymphocyte is actually able to recognize more than just one pMHC-I complex, a phenomenon referred as cross-reactivity. In the present work we describe the use of pMHC-I structural features as input for multivariate statistical methods, to perform standardized structure-based predictions of cross-reactivity among viral epitopes. Our improved approach was able to successfully identify cross-reactive targets among 28 naturally occurring hepatitis C virus (HCV) variants and among eight epitopes from the four dengue virus serotypes. In both cases, our results were supported by multiscale bootstrap resampling and by data from previously published in vitro experiments. The combined use of data from charges and accessible surface area (ASA) of selected residues over the pMHC-I surface provided a powerful way of assessing the structural features involved in triggering cross-reactive responses. Moreover, the use of an R package (pvclust) for assessing the uncertainty in the hierarchical cluster analysis provided a statistical support for the interpretation of results. Taken together, these methods can be applied to vaccine design, both for the selection of candidates capable of inducing immunity against different targets, or to identify epitopes that could trigger undesired immunological responses.

S100B secretion is stimulated by IL-1beta in glial cultures and hippocampal slices of rats: Likely involvement of MAPK pathway.

S100B is an astrocyte-derived cytokine implicated in the IL-1beta-triggered cytokine cycle in Alzheimer's disease. However, the secretion of S100B following stimulation by IL-1beta has not been directly demonstrated. We investigated S100B secretion in cortical primary astrocyte cultures, C6 glioma cells and acute hippocampal slices exposed to IL-1beta. S100B secretion was induced by IL-1beta in all preparations, involving MAPK pathway and, apparently, NF-small ka, CyrillicB signaling. Astrocytes and C6 cells exhibited different sensitivities to IL-1beta. These results suggest that IL-1beta-induced S100B secretion is a component of the neuroinflammatory response, which would support the involvement of S100B in the genesis of neurodegenerative diseases.