ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction

The solute carrier transporter family 6 (SLC6) is of key interest for their critical role in the transport of small amino acids or amino acid-like molecules. Their dysfunction is strongly associated with human diseases such as including schizophrenia, depression, and Parkinson's disease. Linking single point mutations to disease may support insights into the structure-function relationship of these transporters. This work aimed to develop a computational model for predicting the potential pathogenic effect of single point mutations in the SLC6 family. Missense mutation data was retrieved from UniProt, LitVar, and ClinVar, covering multiple protein-coding transcripts. As encoding approach, amino acid descriptors were used to calculate the average sequence properties for both original and mutated sequences. In addition to the full-sequence calculation, the sequences were cut into twelve domains. The domains are defined according to the transmembrane domains of the SLC6 transporters to analyse the regions' contributions to the pathogenicity prediction. Subsequently, several classification models, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) with the hyperparameters optimized through grid search were built. For estimation of model performance, repeated stratified k-fold cross-validation was used. The accuracy values of the generated models are in the range of 0.72 to 0.80. Analysis of feature importance indicates that mutations in distinct regions of SLC6 transporters are associated with an increased risk for pathogenicity. When applying the model on an independent validation set, the performance in accuracy dropped to averagely 0.6 with high precision but low sensitivity scores.

Experimental and Computational Analysis of Newly Identified Pathogenic Mutations in the Creatine Transporter SLC6A8

Creatine is an essential metabolite for the storage and rapid supply of energy in muscle and nerve cells. In humans, impaired metabolism, transport, and distribution of creatine throughout tissues can cause varying forms of mental disability, also known as creatine deficiency syndrome (CDS). So far, 80 mutations in the creatine transporter (SLC6A8) have been associated to CDS. To better understand the effect of human genetic variants on the physiology of SLC6A8 and their possible impact on CDS, we studied 30 missense variants including 15 variants of unknown significance, two of which are reported here for the first time. We expressed these variants in HEK293 cells and explored their subcellular localization and transport activity. We also applied computational methods to predict variant effect and estimate site-specific changes in thermodynamic stability. To explore variants that might have a differential effect on the transporter's conformers along the transport cycle, we constructed homology models of the inward facing, and outward facing conformations. In addition, we used mass-spectrometry to study proteins that interact with wild type SLC6A8 and five selected variants in HEK293 cells. In silico models of the protein complexes revealed how two variants impact the interaction interface of SLC6A8 with other proteins and how pathogenic variants lead to an enrichment of ER protein partners. Overall, our integrated analysis disambiguates the pathogenicity of 15 variants of unknown significance revealing diverse mechanisms of pathogenicity, including two previously unreported variants obtained from patients suffering from the creatine deficiency syndrome.

Genetic and environmental determinants of diastolic heart function

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets.

Network and pathway expansion of genetic disease associations identifies successful drug targets

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand–receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein–protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence can be used for drug target identification.

A systematic approach integrating genetics, computational biology, chemical genomics and gene editing to identify novel targets in the Type I interferon signaling pathway for autoimmune and infectious diseases

The type I interferon (IFN a/b) signaling pathway plays a critical role in microbial host defense, including mediating the interaction between innate and adaptive immunity. A growing body of evidence suggests that IFN is aberrantly regulated in human autoimmune diseases and chronic infections, such as Systematic Lupus Erythematosus, HIV infection and the rare Mendelian diseases, Aicardi-Goutier Syndrome and the Singleton Merton Syndrome (also called “interferonopathies”). This has raised significant interest in exploring the cellular signaling associated with type I IFNs for potential targets for autoimmunity and infectious disease. In this study, we systematically interrogated the type I IFN signaling pathway to identify and validate potential therapeutic targets based on the following workflow: 1. Perform computational biology analysis using genetics, interaction networks and gene expression in human diseases to generate a list of targets that have an association with increased type I IFN. 2. Establish the THP-1 cell-based type I IFNbeta release assay to screen a focused set of target annotated tool compounds. 3. Integrate the inferred targets from the tool compound hits into the in-silico network analysis. 4. Generate a short list of targets (10–20) with high confidence to perform CRISPR analysis in THP-1 cells. 5. Generate a long list of targets (~200) to perform a pooled CRISPR screen using single cell technology and reporter-based flow sorting and enrichment. Here we present validation data on a few representative targets and a summary of the outcomes and learning from this study.

Modeling Signaling Networks to Advance New Cancer Therapies

Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

A rapidly reversible chemical dimerizer system to study lipid signaling in living cells

Chemical dimerizers are powerful tools for non-invasive manipulation of enzyme activities in intact cells. Here we introduce the first rapidly reversible small-molecule-based dimerization system and demonstrate a sufficiently fast switch-off to determine kinetics of lipid metabolizing enzymes in living cells. We applied this new method to induce and stop phosphatidylinositol 3-kinase (PI3K) activity, allowing us to quantitatively measure the turnover of phosphatidylinositol 3,4,5-trisphosphate (PIP3) and its downstream effectors by confocal fluorescence microscopy as well as standard biochemical methods.

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics

Background

Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools.

Results

We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods.

Conclusions

MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms

Background

Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce.

Results

Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities.

Conclusions

Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.

State-time spectrum of signal transduction logic models

Despite the current wealth of high-throughput data, our understanding of signal transduction is still incomplete. Mathematical modeling can be a tool to gain an insight into such processes. Detailed biochemical modeling provides deep understanding, but does not scale well above relatively a few proteins. In contrast, logic modeling can be used where the biochemical knowledge of the system is sparse and, because it is parameter free (or, at most, uses relatively a few parameters), it scales well to large networks that can be derived by manual curation or retrieved from public databases. Here, we present an overview of logic modeling formalisms in the context of training logic models to data, and specifically the different approaches to modeling qualitative to quantitative data (state) and dynamics (time) of signal transduction. We use a toy model of signal transduction to illustrate how different logic formalisms (Boolean, fuzzy logic and differential equations) treat state and time. Different formalisms allow for different features of the data to be captured, at the cost of extra requirements in terms of computational power and data quality and quantity. Through this demonstration, the assumptions behind each formalism are discussed, as well as their advantages and disadvantages and possible future developments.

In contrast to HIV, KIR3DS1 does not influence outcome in HTLV-1 retroviral infection

While most carriers of human T-cell leukemia virus type 1 (HTLV-1) remain asymptomatic throughout their lifetime, infection is associated with the development of adult T-cell leukemia (ATL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). The exact parameters that determine these outcomes are unknown but are believed to include host genetic factors that control the immune response to infection. Host response to fellow retroviridae member HIV is influenced by the expression of members of the Killer Immunoglobulin Receptor (KIR) family including KIR3DS1. In this study we examined the association of KIR3DS1 with the outcome of HTLV-1 infection in three geographically distinct cohorts (Jamaican, Japanese and Brazilian). Despite increased prevalence of KIR3DS1 in the HAM/TSP patients of the Jamaican cohort, we found no evidence for a role of KIR3DS1 in influencing control of proviral load or disease outcome. This suggests that unlike HIV, KIR3DS1-mediated regulation of HTLV-1 infection does not occur, or is ineffective.

Short communication an interferon-γ ELISPOT assay with two cytotoxic T cell epitopes derived from HTLV-1 tax region 161-233 discriminates HTLV-1-associated myelopathy/tropical spastic paraparesis patients from asymptomatic HTLV-1 carriers in a Peruvian population

HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a chronic and progressive disorder caused by the human T-lymphotropic virus type 1 (HTLV-1). In HTLV-1 infection, a strong cytotoxic T cell (CTL) response is mounted against the immunodominant protein Tax. Previous studies carried out by our group reported that increased IFN-γ enzyme-linked immunospot (ELISPOT) responses against the region spanning amino acids 161 to 233 of the Tax protein were associated with HAM/TSP and increased HTLV-1 proviral load (PVL). An exploratory study was conducted on 16 subjects with HAM/TSP, 13 asymptomatic carriers (AC), and 10 HTLV-1-seronegative controls (SC) to map the HAM/TSP-associated CTL epitopes within Tax region 161-233. The PVL of the infected subjects was determined and the specific CTL response was evaluated with a 6-h incubation IFN-γ ELISPOT assay using peripheral blood mononuclear cells (PBMCs) stimulated with 16 individual overlapping peptides covering the Tax region 161-233. Other proinflammatory and Th1/Th2 cytokines were also quantified in the supernatants by a flow cytometry multiplex assay. In addition, a set of human leukocyte antigen (HLA) class I alleles that bind with high affinity to the CTL epitopes of interest was determined using computational tools. Univariate analyses identified an association between ELISPOT responses to two new CTL epitopes, Tax 173-185 and Tax 181-193, and the presence of HAM/TSP as well as an increased PVL. The HLA-A*6801 allele, which is predicted to bind to the Tax 181-193 peptide, was overpresented in the HAM/TSP patients tested.

KIR2DL2 enhances protective and detrimental HLA class I-mediated immunity in chronic viral infection

Killer cell immunoglobulin-like receptors (KIRs) influence both innate and adaptive immunity. But while the role of KIRs in NK-mediated innate immunity is well-documented, the impact of KIRs on the T cell response in human disease is not known. Here we test the hypothesis that an individual's KIR genotype affects the efficiency of their HLA class I-mediated antiviral immune response and the outcome of viral infection. We show that, in two unrelated viral infections, hepatitis C virus and human T lymphotropic virus type 1, possession of the KIR2DL2 gene enhanced both protective and detrimental HLA class I-restricted anti-viral immunity. These results reveal a novel role for inhibitory KIRs. We conclude that inhibitory KIRs, in synergy with T cells, are a major determinant of the outcome of persistent viral infection.

Hepatitis C virus (HCV) and human T-cell leukemia virus (HTLV-I) infect millions of people worldwide. Some HCV-infected individuals spontaneously clear the virus and many HTLV-1-infected people remain asymptomatic; however, in both cases the infection can lead to serious illness such as cancer. The factors which determine outcome are still elusive. We have found that a gene that encodes a receptor (KIR2DL2) enhances both protective and detrimental HLA class I-mediated immunity to HCV and HTLV-1. Strikingly, although KIRs are primarily associated with innate immunity, our observations suggest that they also have a major impact on the efficiency of the adaptive immune response. This work helps to explain why one individual infected with a virus remains healthy but another, infected with the same virus develops disease; it also helps to explain why particular HLA class I molecules do not always protect or cause susceptibility as expected. Interestingly, the impact of the KIR is entirely context dependent: if an HLA class I molecule is protective then protection is enhanced, but in the context of a detrimental HLA then susceptibility is enhanced. This study reveals a novel role for inhibitory KIRs in adaptive immunity.

In vivo expression of human T-lymphotropic virus type 1 basic leucine-zipper protein generates specific CD8+ and CD4+ T-lymphocyte responses that correlate with clinical outcome

BACKGROUND

The roles of the human T-lymphotropic virus type 1 (HTLV-1) basic leucine zipper (HBZ) gene are not clearly understood. We examined CD8+ and CD4+ T cell responses to HBZ and compared these with Tax responses.

METHOD

Interferon (IFN)-γ and interleukin (IL)-2-secreting T cells were detected by enzyme-linked immunosorbent spot (ELISpot) assays of freshly isolated peripheral blood mononuclear cells (PBMCs) stimulated with synthetic HBZ or Tax peptides. Ten patients with HTLV-1-associated myelopathy (HAM) and 20 asymptomatic HTLV-1 carriers (ACs), (10 high, 10 low viral load).

RESULTS

Of 30 study participants, 17 had detectable HBZ-specific CD4+ T cells and 12 had HBZ-specific CD8+ T cell responses. Detection of Tax-specific CD4+ T cells (IL-2- or IFN-γ-secreting) did not differ by disease status, but Tax-specific CD8+ T cell responses were more commonly detected in patients with HAM. HBZ-specific CD4+ or CD8+ T cells were less likely to be detected than Tax-specific T cells. IL-2-secreting Tax-specific CD8+ T cells, and IFN-γ-secreting Tax-specific CD4+ T cells were associated with HAM. Low viral load, asymptomatic HTLV-1 carriage was associated with IL-2-secreting CD8+ T cells specific for HBZ.

CONCLUSION

HBZ protein is expressed in vivo in patients with HAM and in ACs. Our results are consistent with the idea that the T cell response to HBZ plays an important part in restricting HTLV-1 viral load.

HLA class I binding of HBZ determines outcome in HTLV-1 infection

CD8(+) T cells can exert both protective and harmful effects on the virus-infected host. However, there is no systematic method to identify the attributes of a protective CD8(+) T cell response. Here, we combine theory and experiment to identify and quantify the contribution of all HLA class I alleles to host protection against infection with a given pathogen. In 432 HTLV-1-infected individuals we show that individuals with HLA class I alleles that strongly bind the HTLV-1 protein HBZ had a lower proviral load and were more likely to be asymptomatic. We also show that in general, across all HTLV-1 proteins, CD8(+) T cell effectiveness is strongly determined by protein specificity and produce a ranked list of the proteins targeted by the most effective CD8(+) T cell response through to the least effective CD8(+) T cell response. We conclude that CD8(+) T cells play an important role in the control of HTLV-1 and that CD8(+) cells specific to HBZ, not the immunodominant protein Tax, are the most effective. We suggest that HBZ plays a central role in HTLV-1 persistence. This approach is applicable to all pathogens, even where data are sparse, to identify simultaneously the HLA Class I alleles and the epitopes responsible for a protective CD8(+) T cell response.

The avidity and lytic efficiency of the CTL response to HTLV-1

In human T-lymphotropic virus type 1 (HTLV-1) infection, a high frequency of HTLV-1-specific CTLs can co-exist stably with a high proviral load and the proviral load is strongly correlated with the risk of HTLV-1-associated inflammatory diseases. These observations led to the hypothesis that HTLV-1 specific CTLs are ineffective in controlling HTLV-1 replication but contribute to the pathogenesis of the inflammatory diseases. But evidence from host and viral immunogenetics and gene expression microarrays suggests that a strong CTL response is associated with a low proviral load and a low risk of HAM/TSP. Here, we quantified the frequency, lytic activity and functional avidity of HTLV-1-specific CD8(+) cells in fresh, unstimulated PBMCs from individuals with natural HTLV-1 infection. The lytic efficiency of the CD8(+) T cell response-the fraction of autologous HTLV-1-expressing cells eliminated per CD8(+) cell per day-was inversely correlated with both the proviral load and the rate of spontaneous proviral expression. The functional avidity of HTLV-1-specific CD8(+) cells was strongly correlated with their lytic efficiency. We conclude that efficient control of HTLV-1 in vivo depends on the CTL lytic efficiency, which depends in turn on CTL avidity of Ag recognition. CTL quality determines the position of virus-host equilibrium in persistent HTLV-1 infection.

Use of advanced life support skills

BACKGROUND

The Advanced Life Support (ALS) Provider Course trains healthcare professionals in a standardised approach to the management of a cardiac arrest. In the setting of limited resources for healthcare training, it is important that courses are fit for purpose in addressing the needs of both the individual and healthcare system. This study investigated the use of ALS skills in clinical practice after training on an ALS course amongst members of the cardiac arrest team compared to first responders.

METHODS

Questionnaires measuring skill use after an ALS course were distributed to 130 doctors and nurses.

RESULTS

91 replies were returned. Basic life support, basic airway management, manual defibrillation, rhythm recognition, drug administration, team leadership, peri- and post-arrest management and resuscitation in special circumstances were used significantly more often by cardiac arrest team members than first responders. There was no difference in skill use between medically and nursing qualified first responders or arrest team members.

CONCLUSION

We believe that the ALS course is more appropriately targeted to members of a cardiac arrest team. In our opinion the recently launched Immediate Life Support course, in parallel with training in the recognition and intervention in the early stages of critical illness, are more appropriate for the occasional or first responder to a cardiac arrest.

Dermatobia hominis in the accident and emergency department: "I've got you under my skin"

An unusual form of larval infestation from South America is presented which, in view of increasing tourism to South america's tropical areas, may present to any accident and emergency department. Infestation with Dermatobia hominis is reviewed in terms of clinical recognition and life cycle. Techniques of removal are described.