Longitudinal effects of elexacaftor/tezacaftor/ivacaftor on sputum viscoelastic properties, airway infection and inflammation in patients with cystic fibrosis.

BACKGROUND: Recent studies demonstrated that the triple combination cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy elexacaftor/tezacaftor/ivacaftor (ETI) improves lung function and reduces pulmonary exacerbations in cystic fibrosis (CF) patients with at least one F508del allele. However, effects of ETI on downstream consequences of CFTR dysfunction, i.e. abnormal viscoelastic properties of airway mucus, chronic airway infection and inflammation have not been studied. The aim of this study was to determine the longitudinal effects of ETI on airway mucus rheology, microbiome and inflammation in CF patients with one or two F508del alleles aged ≥12 years throughout the first 12 months of therapy. METHODS: In this prospective observational study, we assessed sputum rheology, the microbiome, inflammation markers and proteome before and 1, 3 and 12 months after initiation of ETI. RESULTS: In total, 79 patients with CF and at least one F508del allele and 10 healthy controls were enrolled in this study. ETI improved the elastic modulus and viscous modulus of CF sputum at 3 and 12 months after initiation (all p<0.01). Furthermore, ETI decreased the relative abundance of Pseudomonas aeruginosa in CF sputum at 3 months and increased the microbiome α-diversity at all time points. In addition, ETI reduced interleukin-8 at 3 months (p<0.05) and free neutrophil elastase activity at all time points (all p<0.001), and shifted the CF sputum proteome towards healthy. CONCLUSIONS: Our data demonstrate that restoration of CFTR function by ETI improves sputum viscoelastic properties, chronic airway infection and inflammation in CF patients with at least one F508del allele over the first 12 months of therapy; however, levels close to healthy were not reached.

Evolutionary origins and interactomes of human, young microproteins and small peptides translated from short open reading frames.

All species continuously evolve short open reading frames (sORFs) that can be templated for protein synthesis and may provide raw materials for evolutionary adaptation. We analyzed the evolutionary origins of 7,264 recently cataloged human sORFs and found that most were evolutionarily young and had emerged de novo. We additionally identified 221 previously missed sORFs potentially translated into peptides of up to 15 amino acids-all of which are smaller than the smallest human microprotein annotated to date. To investigate the bioactivity of sORF-encoded small peptides and young microproteins, we subjected 266 candidates to a mass-spectrometry-based interactome screen with motif resolution. Based on these interactomes and additional cellular assays, we can associate several candidates with mRNA splicing, translational regulation, and endocytosis. Our work provides insights into the evolutionary origins and interaction potential of young and small proteins, thereby helping to elucidate this underexplored territory of the human proteome.

Disease- and sex-specific differences in patients with heart valve disease: a proteome study.

Pressure overload in patients with aortic valve stenosis and volume overload in mitral valve regurgitation trigger specific forms of cardiac remodeling; however, little is known about similarities and differences in myocardial proteome regulation. We performed proteome profiling of 75 human left ventricular myocardial biopsies (aortic stenosis = 41, mitral regurgitation = 17, and controls = 17) using high-resolution tandem mass spectrometry next to clinical and hemodynamic parameter acquisition. In patients of both disease groups, proteins related to ECM and cytoskeleton were more abundant, whereas those related to energy metabolism and proteostasis were less abundant compared with controls. In addition, disease group-specific and sex-specific differences have been observed. Male patients with aortic stenosis showed more proteins related to fibrosis and less to energy metabolism, whereas female patients showed strong reduction in proteostasis-related proteins. Clinical imaging was in line with proteomic findings, showing elevation of fibrosis in both patient groups and sex differences. Disease- and sex-specific proteomic profiles provide insight into cardiac remodeling in patients with heart valve disease and might help improve the understanding of molecular mechanisms and the development of individualized treatment strategies.

Serum dihydrotestosterone levels are associated with adverse myocardial remodeling in patients with severe aortic valve stenosis before and after aortic valve replacement.

Animal studies show a pivotal role of dihydrotestosterone (DHT) in pressure overload-induced myocardial hypertrophy and dysfunction. The aim of our study was to evaluate the role of DHT levels and myocardial hypertrophy and myocardial protein expression in patients with severe aortic valve stenosis (AS). Forty-three patients [median age 68 (41-80) yr] with severe AS and indication for surgical aortic valve replacement (SAVR) were prospectively enrolled. Cardiac magnetic resonance imaging including analysis of left ventricular muscle mass (LVM), fibrosis and function, and laboratory tests including serum DHT levels were performed before and after SAVR. During SAVR, left ventricular (LV) biopsies were performed for proteomic profiling. Serum DHT levels correlated positively with indexed LVM (LVMi, R = 0.64, P = 0.0001) and fibrosis (R = 0.49, P = 0.0065) and inversely with LV function (R = -0.42, P = 0.005) in patients with severe AS. DHT levels were associated with higher abundance of the hypertrophy (moesin, R = 0.52, P = 0.0083)- and fibrosis (vimentin, R = 0.41, P = 0.039)-associated proteins from LV myocardial biopsies. Higher serum DHT levels preoperatively were associated with reduced LV function (ejection fraction, R = -0.34, P = 0.035; circulatory efficiency, R = -0.46, P = 0.012; and global longitudinal strain, R = 0.49, P = 0.01) and increased fibrosis (R = 0.55, P = 0.0022) after SAVR. Serum DHT levels were associated with adverse myocardial remodeling and higher abundance in hypertrophy- and fibrosis-associated proteins in patients with severe AS. DHT may be a target to prevent or attenuate adverse myocardial remodeling in patients with pressure overload due to AS.NEW & NOTEWORTHY Serum dihydrotestosterone (DHT) levels correlated positively with the degree of hypertrophy, fibrosis, and dysfunction from cardiac magnetic resonance imaging in female and male patients with aortic valve stenosis. Left ventricular proteome profiling had been performed in this patient cohort and an association between serum DHT levels and the abundance of the hypertrophy-associated protein moesin and the fibrosis-associated protein vimentin was found.

MYCN mediates cysteine addiction and sensitizes neuroblastoma to ferroptosis.

Aberrant expression of MYC transcription factor family members predicts poor clinical outcome in many human cancers. Oncogenic MYC profoundly alters metabolism and mediates an antioxidant response to maintain redox balance. Here we show that MYCN induces massive lipid peroxidation on depletion of cysteine, the rate-limiting amino acid for glutathione (GSH) biosynthesis, and sensitizes cells to ferroptosis, an oxidative, non-apoptotic and iron-dependent type of cell death. The high cysteine demand of MYCN-amplified childhood neuroblastoma is met by uptake and transsulfuration. When uptake is limited, cysteine usage for protein synthesis is maintained at the expense of GSH triggering ferroptosis and potentially contributing to spontaneous tumor regression in low-risk neuroblastomas. Pharmacological inhibition of both cystine uptake and transsulfuration combined with GPX4 inactivation resulted in tumor remission in an orthotopic MYCN-amplified neuroblastoma model. These findings provide a proof of concept of combining multiple ferroptosis targets as a promising therapeutic strategy for aggressive MYCN-amplified tumors.

Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance.

Very high risk neuroblastoma is characterised by increased MAPK signalling, and targeting MAPK signalling is a promising therapeutic strategy. We used a deeply characterised panel of neuroblastoma cell lines and found that the sensitivity to MEK inhibitors varied drastically between these cell lines. By generating quantitative perturbation data and mathematical modelling, we determined potential resistance mechanisms. We found that negative feedbacks within MAPK signalling and via the IGF receptor mediate re-activation of MAPK signalling upon treatment in resistant cell lines. By using cell-line specific models, we predict that combinations of MEK inhibitors with RAF or IGFR inhibitors can overcome resistance, and tested these predictions experimentally. In addition, phospho-proteomic profiling confirmed the cell-specific feedback effects and synergy of MEK and IGFR targeted treatment. Our study shows that a quantitative understanding of signalling and feedback mechanisms facilitated by models can help to develop and optimise therapeutic strategies. Our findings should be considered for the planning of future clinical trials introducing MEKi in the treatment of neuroblastoma.

Comprehensive micro-scaled proteome and phosphoproteome characterization of archived retrospective cancer repositories.

Formalin-fixed paraffin-embedded (FFPE) tissues are a valuable resource for retrospective clinical studies. Here, we evaluate the feasibility of (phospho-)proteomics on FFPE lung tissue regarding protein extraction, quantification, pre-analytics, and sample size. After comparing protein extraction protocols, we use the best-performing protocol for the acquisition of deep (phospho-)proteomes from lung squamous cell and adenocarcinoma with >8,000 quantified proteins and >14,000 phosphosites with a tandem mass tag (TMT) approach. With a microscaled approach, we quantify 7,000 phosphosites, enabling the analysis of FFPE biopsies with limited tissue amounts. We also investigate the influence of pre-analytical variables including fixation time and heat-assisted de-crosslinking on protein extraction efficiency and proteome coverage. Our improved workflows provide quantitative information on protein abundance and phosphosite regulation for the most relevant oncogenes, tumor suppressors, and signaling pathways in lung cancer. Finally, we present general guidelines to which methods are best suited for different applications, highlighting TMT methods for comprehensive (phospho-)proteome profiling for focused clinical studies and label-free methods for large cohorts.

Alternative lengthening of telomeres in childhood neuroblastoma from genome to proteome.

Telomere maintenance by telomerase activation or alternative lengthening of telomeres (ALT) is a major determinant of poor outcome in neuroblastoma. Here, we screen for ALT in primary and relapsed neuroblastomas (n = 760) and characterize its features using multi-omics profiling. ALT-positive tumors are molecularly distinct from other neuroblastoma subtypes and enriched in a population-based clinical sequencing study cohort for relapsed cases. They display reduced ATRX/DAXX complex abundance, due to either ATRX mutations (55%) or low protein expression. The heterochromatic histone mark H3K9me3 recognized by ATRX is enriched at the telomeres of ALT-positive tumors. Notably, we find a high frequency of telomeric repeat loci with a neuroblastoma ALT-specific hotspot on chr1q42.2 and loss of the adjacent chromosomal segment forming a neo-telomere. ALT-positive neuroblastomas proliferate slowly, which is reflected by a protracted clinical course of disease. Nevertheless, children with an ALT-positive neuroblastoma have dismal outcome.

Drug repurposing for aging research using model organisms.

Many increasingly prevalent diseases share a common risk factor: age. However, little is known about pharmaceutical interventions against aging, despite many genes and pathways shown to be important in the aging process and numerous studies demonstrating that genetic interventions can lead to a healthier aging phenotype. An important challenge is to assess the potential to repurpose existing drugs for initial testing on model organisms, where such experiments are possible. To this end, we present a new approach to rank drug-like compounds with known mammalian targets according to their likelihood to modulate aging in the invertebrates Caenorhabditis elegans and Drosophila. Our approach combines information on genetic effects on aging, orthology relationships and sequence conservation, 3D protein structures, drug binding and bioavailability. Overall, we rank 743 different drug-like compounds for their likelihood to modulate aging. We provide various lines of evidence for the successful enrichment of our ranking for compounds modulating aging, despite sparse public data suitable for validation. The top ranked compounds are thus prime candidates for in vivo testing of their effects on lifespan in C. elegans or Drosophila. As such, these compounds are promising as research tools and ultimately a step towards identifying drugs for a healthier human aging.

Two forms of death in ageing Caenorhabditis elegans.

Ageing generates senescent pathologies, some of which cause death. Interventions that delay or prevent lethal pathologies will extend lifespan. Here we identify life-limiting pathologies in Caenorhabditis elegans with a necropsy analysis of worms that have died of old age. Our results imply the presence of multiple causes of death. Specifically, we identify two classes of corpse: early deaths with a swollen pharynx (which we call 'P deaths'), and later deaths with an atrophied pharynx (termed 'p deaths'). The effects of interventions on lifespan can be broken down into changes in the frequency and/or timing of either form of death. For example, glp-1 mutation only delays p death, while eat-2 mutation reduces P death. Combining pathology and mortality analysis allows mortality profiles to be deconvolved, providing biological meaning to complex survival and mortality profiles.

SurvCurv database and online survival analysis platform update.

UNLABELLED: Understanding the biology of ageing is an important and complex challenge. Survival experiments are one of the primary approaches for measuring changes in ageing. Here, we present a major update to SurvCurv, a database and online resource for survival data in animals. As well as a substantial increase in data and additions to existing graphical and statistical survival analysis features, SurvCurv now includes extended mathematical mortality modelling functions and survival density plots for more advanced representation of groups of survival cohorts. AVAILABILITY AND IMPLEMENTATION: The database is freely available at https://www.ebi.ac.uk/thornton-srv/databases/SurvCurv/. All data are published under the Creative Commons Attribution License. CONTACT: matthias.ziehm@ebi.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Longevity GWAS Using the Drosophila Genetic Reference Panel.

We used 197 Drosophila melanogaster Genetic Reference Panel (DGRP) lines to perform a genome-wide association analysis for virgin female lifespan, using ~2M common single nucleotide polymorphisms (SNPs). We found considerable genetic variation in lifespan in the DGRP, with a broad-sense heritability of 0.413. There was little power to detect signals at a genome-wide level in single-SNP and gene-based analyses. Polygenic score analysis revealed that a small proportion of the variation in lifespan (~4.7%) was explicable in terms of additive effects of common SNPs (≥2% minor allele frequency). However, several of the top associated genes are involved in the processes previously shown to impact ageing (eg, carbohydrate-related metabolism, regulation of cell death, proteolysis). Other top-ranked genes are of unknown function and provide promising candidates for experimental examination. Genes in the target of rapamycin pathway (TOR; Chrb, slif, mipp2, dredd, RpS9, dm) contributed to the significant enrichment of this pathway among the top-ranked 100 genes (p = 4.79×10(-06)). Gene Ontology analysis suggested that genes involved in carbohydrate metabolism are important for lifespan; including the InterPro term DUF227, which has been previously associated with lifespan determination. This analysis suggests that our understanding of the genetic basis of natural variation in lifespan from induced mutations is incomplete.

MDL-1, a growth- and tumor-suppressor, slows aging and prevents germline hyperplasia and hypertrophy in C. elegans.

In C. elegans, increased lifespan in daf-2 insulin/IGF-1 receptor mutants is accompanied by up-regulation of the MDL-1 Mad basic helix-loop-helix leucine zipper transcription factor. Here we describe the role of mdl-1 in C. elegans germline proliferation and aging. The deletion allele mdl-1(tm311) shortened lifespan, and did so significantly more so in long-lived daf-2 mutants implying that mdl-1(+) contributes to effects of daf-2 on lifespan. mdl-1 mutant hermaphrodites also lay increased numbers of unfertilized oocytes. During aging, unfertilized oocytes in the uterus develop into tumors, whose development was accelerated by mdl-1(tm311). Opposite phenotypes were seen in daf-2 mutants, i.e. mdl-1 and daf-2 mutant germlines are hyperplastic and hypoplastic, respectively. Thus, MDL-1, like its mammalian orthologs, is an inhibitor of cell proliferation and growth that slows progression of an age-related pathology in C. elegans (uterine tumors). In addition, intestine-limited rescue of mdl-1 increased lifespan but not to wild type levels. Thus, mdl-1 likely acts both in the intestine and the germline to influence age-related mortality.

Unlocking the potential of survival data for model organisms through a new database and online analysis platform: SurvCurv.

Lifespan measurements, also called survival records, are a key phenotype in research on aging. If external hazards are excluded, aging alone determines the mortality in a population of model organisms. Understanding the biology of aging is highly desirable because of the benefits for the wide range of aging-related diseases. However, it is also extremely challenging because of the underlying complexity. Here, we describe SurvCurv, a new database and online resource focused on model organisms collating survival data for storage and analysis. All data in SurvCurv are manually curated and annotated. The database, available at www.ebi.ac.uk/thornton-srv/databases/SurvCurv/, offers various functions including plotting, Cox proportional hazards analysis, mathematical mortality models and statistical tests. It facilitates reanalysis and allows users to analyse their own data and compare it with the largest repository of model-organism data from published experiments, thus unlocking the potential of survival data and demographics in model organisms.

Analysing variation in Drosophila aging across independent experimental studies: a meta-analysis of survival data.

Survival records of longevity experiments are a key component in research on aging. However, surprisingly there have been very few cross-study analyses, besides comparisons of median lifespans or similar summary information. Here, we use a large set of full survival data from various studies to address questions in aging, which are beyond the scope of individual studies. We characterize survival differences between female and male flies of different genetic Drosophila strains, showing significant differences between strains. We further analyse the variation in survival of control cohorts recorded under highly similar conditions within different Drosophila strains. We found that overall transgenic constructs of the UAS/GAL4 expression system which should have no effect (e.g. a GAL4 construct alone) extend lifespan significantly in the w1118 strain. Using a large data set comprised of various studies, we found no evidence for larger lifespan extensions being associated with shorter lifespans of the control in Drosophila. This demonstrates that lifespan extending treatments are not purely rescuing weak backgrounds.

Transcriptional feedback in the insulin signalling pathway modulates ageing in both Caenorhabditis elegans and Drosophila melanogaster.

Several components have been previously identified, that modulate longevity in several species, including the target of rapamycin (TOR) and the Insulin/IGF-1 (IIS) signalling pathways. In order to infer paths and transcriptional feedback loops that are likely to modulate ageing, we manually built a comprehensive and computationally efficient signalling network model of the IIS and TOR pathways in worms. The core insulin transduction is signalling from the sole insulin receptor daf-2 to ultimately inhibit the translocation of the transcription factor daf-16 into the nucleus. Reduction in this core signalling is thought to increase longevity in several species. In addition to this core insulin signalling, we have also recorded in our worm model the transcription factors skn-1 and hif-1, those are also thought to modulate ageing in a daf-16 independent manner. Several paths that are likely to modulate ageing were inferred via a web-based service NetEffects, by utilising perturbed components (rheb-1, let-363, aak-2, daf-2;daf-16 and InR;foxo in worms and flies respectively) from freely available gene expression microarrays. These included "routes" from TOR pathway to transcription factors daf-16, skn-1, hif-1 and daf-16 independent paths via skn-1/hif-1. Paths that could be tested by experimental hypotheses, with respect to relative contribution to longevity, are also discussed. Direct comparison of the IIS and TOR pathways in both worm and fly suggest a remarkable similarity. While similarities in the paths that could modulate ageing in both organisms were noted, differences are also discussed. This approach can also be extended to other pathways and processes.

Using answer set programming to integrate RNA expression with signalling pathway information to infer how mutations affect ageing.

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.

POPISK: T-cell reactivity prediction using support vector machines and string kernels.

BACKGROUND: Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. RESULTS: This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. CONCLUSIONS: A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK.

Computational biology for ageing.

High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions.