The Mla system and its role in maintaining outer membrane barrier function in Stenotrophomonas maltophilia.

Stenotrophomonas maltophilia are ubiquitous Gram-negative bacteria found in both natural and clinical environments. It is a remarkably adaptable species capable of thriving in various environments, thanks to the plasticity of its genome and a diverse array of genes that encode a wide range of functions. Among these functions, one notable trait is its remarkable ability to resist various antimicrobial agents, primarily through mechanisms that regulate the diffusion across cell membranes. We have investigated the Mla ABC transport system of S. maltophilia, which in other Gram-negative bacteria is known to transport phospholipids across the periplasm and is involved in maintaining outer membrane homeostasis. First, we structurally and functionally characterized the periplasmic substrate-binding protein MlaC, which determines the specificity of this system. The predicted structure of the S. maltophilia MlaC protein revealed a hydrophobic cavity of sufficient size to accommodate the phospholipids commonly found in this species. Moreover, recombinant MlaC produced heterologously demonstrated the ability to bind phospholipids. Gene knockout experiments in S. maltophilia K279a revealed that the Mla system is involved in baseline resistance to antimicrobial and antibiofilm agents, especially those with divalent-cation chelating activity. Co-culture experiments with Pseudomonas aeruginosa also showed a significant contribution of this system to the cooperation between both species in the formation of polymicrobial biofilms. As suggested for other Gram-negative pathogenic microorganisms, this system emerges as an appealing target for potential combined antimicrobial therapies.

A quantitative systems pharmacology model for certolizumab pegol treatment in moderate-to-severe psoriasis.

Background: Psoriasis is a chronic immune-mediated inflammatory systemic disease with skin manifestations characterized by erythematous, scaly, itchy and/or painful plaques resulting from hyperproliferation of keratinocytes. Certolizumab pegol [CZP], a PEGylated antigen binding fragment of a humanized monoclonal antibody against TNF-alpha, is approved for the treatment of moderate-to-severe plaque psoriasis. Patients with psoriasis present clinical and molecular variability, affecting response to treatment. Herein, we utilized an in silico approach to model the effects of CZP in a virtual population (vPop) with moderate-to-severe psoriasis. Our proof-of-concept study aims to assess the performance of our model in generating a vPop and defining CZP response variability based on patient profiles. Methods: We built a quantitative systems pharmacology (QSP) model of a clinical trial-like vPop with moderate-to-severe psoriasis treated with two dosing schemes of CZP (200 mg and 400 mg, both every two weeks for 16 weeks, starting with a loading dose of CZP 400 mg at weeks 0, 2, and 4). We applied different modelling approaches: (i) an algorithm to generate vPop according to reference population values and comorbidity frequencies in real-world populations; (ii) physiologically based pharmacokinetic (PBPK) models of CZP dosing schemes in each virtual patient; and (iii) systems biology-based models of the mechanism of action (MoA) of the drug. Results: The combination of our different modelling approaches yielded a vPop distribution and a PBPK model that aligned with existing literature. Our systems biology and QSP models reproduced known biological and clinical activity, presenting outcomes correlating with clinical efficacy measures. We identified distinct clusters of virtual patients based on their psoriasis-related protein predicted activity when treated with CZP, which could help unravel differences in drug efficacy in diverse subpopulations. Moreover, our models revealed clusters of MoA solutions irrespective of the dosing regimen employed. Conclusion: Our study provided patient specific QSP models that reproduced clinical and molecular efficacy features, supporting the use of computational methods as modelling strategy to explore drug response variability. This might shed light on the differences in drug efficacy in diverse subpopulations, especially useful in complex diseases such as psoriasis, through the generation of mechanistically based hypotheses.

Synthesis and evaluation of aromatic BDSF bioisosteres on biofilm formation and colistin sensitivity in pathogenic bacteria.

The diffusible signal factor family (DSF) of molecules play an important role in regulating intercellular communication, or quorum sensing, in several disease-causing bacteria. These messenger molecules, which are comprised of cis-unsaturated fatty acids, are involved in the regulation of biofilm formation, antibiotic tolerance, virulence and the control of bacterial resistance. We have previously demonstrated how olefinic N-acyl sulfonamide bioisosteric analogues of diffusible signal factor can reduce biofilm formation or enhance antibiotic sensitivity in a number of bacterial strains. This work describes the design and synthesis of a second generation of aromatic N-acyl sulfonamide bioisosteres. The impact of these compounds on biofilm production in Acinetobacter baumannii, Escherichia coli, Burkholderia multivorans, Burkholderia cepacia, Burkholderia cenocepacia, Pseudomonas aeruginosa and Stenotrophomonas maltophilia is evaluated, in addition to their effects on antibiotic tolerance. The ability of these molecules to increase survival rates on co-administration with colistin is also investigated using the Galleria infection model.

A Stenotrophomonas maltophilia TetR-Like Transcriptional Regulator Involved in Fatty Acid Metabolism Is Controlled by Quorum Sensing Signals.

Stenotrophomonas maltophilia is an environmental bacterium as well as an emerging opportunistic multidrug-resistant pathogen. They use the endogenous diffusible signal factor (DSF) quorum sensing (QS) system to coordinate population behavior and regulate virulence processes but can also respond to exogenous N-acyl-homoserine lactone (AHL) signals produced by neighboring bacteria. The effect of these QS signals on the global gene expression of this species remains, however, unknown. Whole-transcriptome sequencing analyses were performed for exponential cultures of S. maltophilia K279a treated with exogenous DSF or AHLs. Addition of DSF and AHLs signals resulted in changes in expression of at least 2-fold for 28 and 82 genes, respectively. Interestingly, 22 of these genes were found upregulated by both QS signals, 14 of which were shown to also be induced during the stationary phase. Gene functions regulated by all conditions included lipid and amino acid metabolism, stress response and signal transduction, nitrogen and iron metabolism, and adaptation to microoxic conditions. Among the common top upregulated QS core genes, a putative TetR-like regulator (locus tag SMLT2053) was selected for functional characterization. This regulator controls its own ß-oxidation operon (Smlt2053-Smlt2051), and it is found to sense long-chain fatty acids (FAs), including the QS signal DSF. Gene knockout experiments reveal that operon Smlt2053-Smlt2051 is involved in biofilm formation. Overall, our findings provide clues on the effect that QS signals have in S. maltophilia QS-related phenotypes and the transition from the exponential to the stationary phase and bacterial fitness under high-density growth. IMPORTANCE The quorum sensing system in Stenotrophomonas maltophilia, in addition to coordinating the bacterial population, controls virulence-associated phenotypes, such as biofilm formation, motility, protease production, and antibiotic resistance mechanisms. Biofilm formation is frequently associated with the persistence and chronic nature of nosocomial infections. In addition, biofilms exhibit high resistance to antibiotics, making treatment of these infections extremely difficult. The importance of studying the metabolic and regulatory systems controlled by quorum sensing autoinducers will make it possible to discover new targets to control pathogenicity mechanisms in S. maltophilia.

A decision support system based on artificial intelligence and systems biology for the simulation of pancreatic cancer patient status.

Oncology treatments require continuous individual adjustment based on the measurement of multiple clinical parameters. Prediction tools exploiting the patterns present in the clinical data could be used to assist decision making and ease the burden associated to the interpretation of all these parameters. The goal of this study was to predict the evolution of patients with pancreatic cancer at their next visit using information routinely recorded in health records, providing a decision-support system for clinicians. We selected hematological variables as the visit's clinical outcomes, under the assumption that they can be predictive of the evolution of the patient. Multivariate models based on regression trees were generated to predict next-visit values for each of the clinical outcomes selected, based on the longitudinal clinical data as well as on molecular data sets streaming from in silico simulations of individual patient status at each visit. The models predict, with a mean prediction score (balanced accuracy) of 0.79, the evolution trends of eosinophils, leukocytes, monocytes, and platelets. Time span between visits and neutropenia were among the most common factors contributing to the predicted evolution. The inclusion of molecular variables from the systems-biology in silico simulations provided a molecular background for the observed variations in the selected outcome variables, mostly in relation to the regulation of hematopoiesis. In spite of its limitations, this study serves as a proof of concept for the application of next-visit prediction tools in real-world settings, even when available data sets are small.

On Quality Thresholds for the Clustering of Molecular Structures.

It has been recently suggested that diametral (so-called quality) similarity thresholds are superior to radial ones for the clustering of molecular three-dimensional structures (González-Alemán et al., 2020). The argument has been made for two clustering algorithms available in various software packages for the analysis of molecular structures from ensembles generated by computer simulations, attributed to Daura et al. (1999) (radial threshold) and Heyer et al. (1999) (diametral threshold). Here, we compare these two algorithms using the root-mean-squared difference (rmsd) between the Cartesian coordinates of selected atoms as pairwise similarity metric. We discuss formally the relation between these two methods and illustrate their behavior with two examples, a set of points in two dimensions and the coordinates of the tau polypeptide along a trajectory extracted from a replica-exchange molecular-dynamics simulation (Shea and Levine, 2016). We show that the two methods produce equally sized clusters as long as adequate choices are made for the respective thresholds. The real issue is not whether the threshold is radial or diametral but how to choose in either case a threshold value that is physically meaningful. We will argue that, when clustering molecular structures with the rmsd as a metric, the simplest best guess for a threshold is actually radial in nature.

Synthesis and evaluation of novel furanones as biofilm inhibitors in opportunistic human pathogens.

Diseases caused by biofilm-forming pathogens are becoming increasingly prevalent and represent a major threat to human health. This trend has prompted a search for novel inhibitors of microbial biofilms which could, for example, be used to potentiate existing antibiotics. Naturally-occurring, halogenated furanones isolated from marine algae have proven to be effective biofilm inhibitors in several bacterial species. In this work, we report the synthesis of a library of novel furanones and their subsequent evaluation as biofilm inhibitors in several opportunistic human pathogens including S. enterica, S. aureus, E. coli, S. maltophilia, P. aeruginosa and C. albicans. A number of the most potent compounds were subjected to further analysis by confocal laser-scanning microscopy for their effects on P. aeruginosa and C. albicans biofilms individually, in addition to mixed polymicrobial biofilms. Lastly, we investigated the impact of a promising candidate on survival rates in vivo using a Galleria mellonella model.

Methods to Develop an in silico Clinical Trial: Computational Head-to-Head Comparison of Lisdexamfetamine and Methylphenidate.

Regulatory agencies encourage computer modeling and simulation to reduce the time and cost of clinical trials. Although still not classified in formal guidelines, system biology-based models represent a powerful tool for generating hypotheses with great molecular detail. Herein, we have applied a mechanistic head-to-head in silico clinical trial (ISCT) between two treatments for attention-deficit/hyperactivity disorder, to wit lisdexamfetamine (LDX) and methylphenidate (MPH). The ISCT was generated through three phases comprising (i) the molecular characterization of drugs and pathologies, (ii) the generation of adult and children virtual populations (vPOPs) totaling 2,600 individuals and the creation of physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) models, and (iii) data analysis with artificial intelligence methods. The characteristics of our vPOPs were in close agreement with real reference populations extracted from clinical trials, as did our PBPK models with in vivo parameters. The mechanisms of action of LDX and MPH were obtained from QSP models combining PBPK modeling of dosing schemes and systems biology-based modeling technology, i.e., therapeutic performance mapping system. The step-by-step process described here to undertake a head-to-head ISCT would allow obtaining mechanistic conclusions that could be extrapolated or used for predictions to a certain extent at the clinical level. Altogether, these computational techniques are proven an excellent tool for hypothesis-generation and would help reach a personalized medicine.

Antibacterial Activity of T22, a Specific Peptidic Ligand of the Tumoral Marker CXCR4.

CXCR4 is a cytokine receptor used by HIV during cell attachment and infection. Overexpressed in the cancer stem cells of more than 20 human neoplasias, CXCR4 is a convenient antitumoral drug target. T22 is a polyphemusin-derived peptide and an effective CXCR4 ligand. Its highly selective CXCR4 binding can be exploited as an agent for the cell-targeted delivery and internalization of associated antitumor drugs. Sharing chemical and structural traits with antimicrobial peptides (AMPs), the capability of T22 as an antibacterial agent remains unexplored. Here, we have detected T22-associated antimicrobial activity and biofilm formation inhibition over Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa, in a spectrum broader than the reference AMP GWH1. In contrast to GWH1, T22 shows neither cytotoxicity over mammalian cells nor hemolytic activity and is active when displayed on protein-only nanoparticles through genetic fusion. Under the pushing need for novel antimicrobial agents, the discovery of T22 as an AMP is particularly appealing, not only as its mere addition to the expanding catalogue of antibacterial drugs. The recognized clinical uses of T22 might allow its combined and multivalent application in complex clinical conditions, such as colorectal cancer, that might benefit from the synchronous destruction of cancer stem cells and local bacterial biofilms.

CNN-PepPred: an open-source tool to create convolutional NN models for the discovery of patterns in peptide sets-application to peptide-MHC class II binding prediction.

SUMMARY: The ability to unveil binding patterns in peptide sets has important applications in several biomedical areas, including the development of vaccines. We present an open-source tool, CNN-PepPred, that uses convolutional neural networks to discover such patterns, along with its application to peptide-HLA class II binding prediction. The tool can be used locally on different operating systems, with CPUs or GPUs, to train, evaluate, apply and visualize models. AVAILABILITY AND IMPLEMENTATION: CNN-PepPred is freely available as a Python tool with a detailed User's Guide at https://github.com/ComputBiol-IBB/CNN-PepPred. The site includes the peptide sets used in this study, extracted from IEDB (www.iedb.org). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Pseudomonas aeruginosa substrate-binding protein Ttg2D functions as a general glycerophospholipid transporter across the periplasm.

In Pseudomonas aeruginosa, Ttg2D is the soluble periplasmic phospholipid-binding component of an ABC transport system thought to be involved in maintaining the asymmetry of the outer membrane. Here we use the crystallographic structure of Ttg2D at 2.5 Å resolution to reveal that this protein can accommodate four acyl chains. Analysis of the available structures of Ttg2D orthologs shows that they conform a new substrate-binding-protein structural cluster. Native and denaturing mass spectrometry experiments confirm that Ttg2D, produced both heterologously and homologously and isolated from the periplasm, can carry two diacyl glycerophospholipids as well as one cardiolipin. Binding is notably promiscuous, allowing the transport of various molecular species. In vitro binding assays coupled to native mass spectrometry show that binding of cardiolipin is spontaneous. Gene knockout experiments in P. aeruginosa multidrug-resistant strains reveal that the Ttg2 system is involved in low-level intrinsic resistance against certain antibiotics that use a lipid-mediated pathway to permeate through membranes.

CuBlock: a cross-platform normalization method for gene-expression microarrays.

MOTIVATION: Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups. RESULTS: We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct datasets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these datasets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study. AVAILABILITY AND IMPLEMENTATION: CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Antigen Discovery in Bacterial Panproteomes.

There is still a lack of vaccines for many bacterial infections for which the best treatment option would be a prophylactic one. On the other hand, effectiveness has been questioned for some existing vaccines, prompting new developments. Therapeutic vaccines are also becoming a treatment option in specific cases where antibiotics tend to fail. In this scenario, refinement and extension of the classical reverse vaccinology approach is allowing scientists to find new and more effective antigens. In this chapter, we describe an in silico methodology that integrates pangenomic, immunoinformatic, structural, and evolutionary approaches for the screening of potential antigens in a given bacterial species. The strategy focuses on targeting relatively conserved epitopes in core proteins to design broadly cross-protective vaccines and avoid allele-specific immunity. The proposed methodological steps and computational tools can be easily implemented in a reverse vaccinology approach not only to identify new leads with strong immune response but also to develop diagnostic assays.

On the Effect of the Various Assumptions and Approximations used in Molecular Simulations on the Properties of Bio-Molecular Systems: Overview and Perspective on Issues.

Computer simulations of molecular systems enable structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic or supra-molecular level and plays an increasingly important role in chemistry, biology and physics. To interpret the results of such simulations appropriately, the degree of uncertainty and potential errors affecting the calculated properties must be considered. Uncertainty and errors arise from (1) assumptions underlying the molecular model, force field and simulation algorithms, (2) approximations implicit in the interatomic interaction function (force field), or when integrating the equations of motion, (3) the chosen values of the parameters that determine the accuracy of the approximations used, and (4) the nature of the system and the property of interest. In this overview, advantages and shortcomings of assumptions and approximations commonly used when simulating bio-molecular systems are considered. What the developers of bio-molecular force fields and simulation software can do to facilitate and broaden research involving bio-molecular simulations is also discussed.

Genetic Variants of the DSF Quorum Sensing System in Stenotrophomonas maltophilia Influence Virulence and Resistance Phenotypes Among Genotypically Diverse Clinical Isolates.

The pathogenicity of Stenotrophomonas maltophilia is regulated in part by its quorum sensing (QS) system. The main QS signaling molecule in S. maltophilia is known as diffusible signal factor (DSF), and the rpf gene cluster is responsible for its synthesis and perception. Two cluster variants have been previously described, rpf-1 and rpf-2, which differ basically in the conditions under which DSF is produced. Here, correlations between the rpf variant and antibiotic susceptibility, LPS electrophoretic profiles and virulence-related phenotypes were evaluated for a collection of 78 geographically and genetically diverse clinical strains of S. maltophilia. In general there were associations between previously established genogroups and the genetic variant of the rpf cluster. However, only few genotype-phenotype correlations could be observed. Resistance to the ß-lactam antibiotics ceftazidime and ticarcillin was associated with strains carrying the rpf-1 variant, whereas strains of variant rpf-2, particularly those of genogroup C, showed higher resistance levels to colistin. Strains of variant rpf-2 were also significantly more virulent to Galleria mellonella larvae than those of rpf-1, most likely due to an increased ability of rpf-2 strains to form biofilms. A comparative genomic analysis revealed the presence of proteins unique to individual genogroups. In particular, the strains of genogroup C share an operon that encodes for a new virulence determinant in S. maltophilia related to the synthesis of an alternative Flp/Tad pilus. Overall, this study establishes a link between the DSF-based QS system and the virulence and resistance phenotypes in this species, and identifies potential high-risk clones circulating in European hospitals.

The phylogenetic landscape and nosocomial spread of the multidrug-resistant opportunist Stenotrophomonas maltophilia.

Recent studies portend a rising global spread and adaptation of human- or healthcare-associated pathogens. Here, we analyse an international collection of the emerging, multidrug-resistant, opportunistic pathogen Stenotrophomonas maltophilia from 22 countries to infer population structure and clonality at a global level. We show that the S. maltophilia complex is divided into 23 monophyletic lineages, most of which harbour strains of all degrees of human virulence. Lineage Sm6 comprises the highest rate of human-associated strains, linked to key virulence and resistance genes. Transmission analysis identifies potential outbreak events of genetically closely related strains isolated within days or weeks in the same hospitals.

Aggregation-prone peptides modulate activity of bovine interferon gamma released from naturally occurring protein nanoparticles.

Efficient protocols for the production of recombinant proteins are indispensable for the development of the biopharmaceutical sector. Accumulation of recombinant proteins in naturally-occurring protein aggregates is detrimental to biopharmaceutical development. In recent years, the view of protein aggregates has changed with the recognition that they are a valuable source of functional recombinant proteins. In this study, bovine interferon-gamma (rBoIFN-γ) was engineered to enhance the formation of protein aggregates, also known as protein nanoparticles (NPs), by the addition of aggregation-prone peptides (APPs) in the generally recognized as safe (GRAS) bacterial Lactococcus lactis expression system. The L6K2, HALRU and CYOB peptides were selected to assess their intrinsic aggregation capability to nucleate protein aggregation. These APPs enhanced the tendency of the resulting protein to aggregate at the expense of total protein yield. However, fine physico-chemical characterization of the resulting intracellular protein NPs, the protein released from them and the protein purified from the soluble cell fraction indicated that the compactability of protein conformations was directly related to the biological activity of variants of IFN-γ, used here as a model protein with therapeutic potential. APPs enhanced the aggregation tendency of fused rBoIFN-γ while increasing compactability of protein species. Biological activity of rBoIFN-γ was favored in more compacted conformations. Naturally-occurring protein aggregates can be produced in GRAS microorganisms as protein depots of releasable active protein. The addition of APPs to enhance the aggregation tendency has a positive impact in overall compactability and functionality of resulting protein conformers.

Interaction of camel Lactoferrin derived peptides with DNA: a molecular dynamics study.

BACKGROUND: Lactoferrampin (LFampin), Lactoferricin (LFcin), and LFchimera are three well-known antimicrobial peptides derived from Lactoferrin and proposed as alternatives for antibiotics. Although the intracellular activity of these peptides has been previously demonstrated, their mode of action is not yet fully understood. Here, we performed a molecular dynamics simulation study to understand the molecular interactions between camel Lactoferrin derived peptides, including CLFampin, CLFcin, and CLFchimera, and DNA as an important intracellular target. RESULTS: Our results indicate that all three peptides bind to DNA, albeit with different propensities, with CLFchimera showing the highest binding affinity. The secondary structures of the peptides, modeled on Lactoferrin, did not undergo significant changes during simulation, supporting their functional relevance. Main residues involved in the peptide-DNA interaction were identified based on binding free energy estimates calculated over 200 ns, which, as expected, confirmed strong electrostatic interactions between DNA phosphate groups and positively charged peptide side chains. Interaction between the different concentrations of CLFchimera and DNA revealed that after binding of four copies of CLFchimera to DNA, hydrogen bonds between the two strands of DNA start to break from one of the termini. CONCLUSIONS: Importantly, our results revealed that there is no DNA-sequence preference for peptide binding, in line with a broad antimicrobial activity. Moreover, the results showed that the strength of the interaction between DNA and CLFchimera is concentration dependent. The insight provided by these results can be used for the rational redesign of natural antimicrobial peptides targeting the bacterial DNA.

Advances in the Computational Identification of Allosteric Sites and Pathways in Proteins.

With the increasing difficulty to develop new drugs and the emergence of resistance to traditional orthosteric-site inhibitors, the search for alternatives is finally approaching the focus on allosteric sites. Allosteric sites offer opportunities to regulate many pharmacologically targeted pathways by inhibition or activation. In addition, allosteric sites tend to be less conserved than the functional site, which may facilitate the design of specific effectors in the protein families for which specific orthosteric inhibitors have proved difficult to design. Furthermore, recent evidence suggests that all proteins might be susceptible of allosteric regulation, increasing the space of druggable targets. Computational identification of allosteric sites has therefore become an active field of research. The problem can be approached from two sides: (1) the identification of allosteric-communication pathways between the functional site and potential allosteric sites and (2) the functional-site-independent identification of allosteric sites. While the first approach tends to be more laborious and thus restricted to a single protein, the second tends to be more amenable to larger-scale analysis, thus providing tools for the two drug discovery scenarios: the analysis of known targets and the screening for new potential targets. Here, I show some basic concepts and methods useful to the identification of allosteric sites and pathways, in line with these two approaches. I describe them in some detail to build a clear framework, at the risk of losing the interest of experts. Examples of recent studies involving these methods are also illustrated, focusing on the techniques rather than on their findings on allosterism.

HLA and microtubule-associated protein tau H1 haplotype associations in anti-IgLON5 disease.

OBJECTIVES: We investigated the associations with HLA and microtubule-associated protein tau (MAPT) H1 haplotype in anti-IgLON5 disease, a recently identified disorder characterized by gait instability, brainstem dysfunction, and a prominent sleep disorder in association with IgLON5 antibodies and pathologic findings of a novel neuronal-specific tauopathy. METHODS: We compared the HLA alleles and MAPT H1/H1 genotype of 35 patients with anti-IgLON5 with healthy controls. The on-line server tool NetMHCIIpan 3.1 was used to predict the IgLON5 peptide binding to HLA Class II molecules. RESULTS: The HLA-DRB1*10:01-DQB1*05:01 haplotype was overrepresented in patients with anti-IgLON5 disease (OR = 54.5; 95% CI: 22.2-133.9, p < 0.0001). In addition, HLA-DQA was genotyped in 27 patients, and 25 (92.6%) of them had DQ molecules composed by DQA1*01 and DQB1*05 chains compared with 148/542 (27.3%) controls (OR = 43.9; 95% CI: 10.4-185.5, p < 0.0001). Patients DRB1*10:01 positive developed more frequently sleep or bulbar symptoms than those carrying other HLA alleles (70.0% vs 26.7%; p = 0.011). Prediction algorithms identified 2 IgLON5 peptides (1 located in the signal sequence) that showed strong binding to HLA-DRB1*10:01 and other HLA-DRB1, but not to HLA-DQA and HLA-DQB molecules. The MAPT H1/H1 homozygous genotype was present in 20/24 (83.3%) anti-IgLON5 Caucasian patients compared with 54/116 (46.5%) healthy controls (p = 0.0007). CONCLUSIONS: The robust association of anti-IgLON5 disease with distinct HLA Class II molecules supports a primary autoimmune origin. The significant association of MAPT H1 haplotype also suggests that an underlying neurodegenerative process could be involved in anti-IgLON5 disease.