Protein degradation by human 20S proteasomes elucidates the interplay between peptide hydrolysis and splicing

If and how proteasomes catalyze not only peptide hydrolysis but also peptide splicing is an open question that has divided the scientific community. The debate has so far been based on immunopeptidomics, in vitro digestions of synthetic polypeptides as well as ex vivo and in vivo experiments, which could only indirectly describe proteasome-catalyzed peptide splicing of full-length proteins. Here we develop a workflow-and cognate software - to analyze proteasome-generated non-spliced and spliced peptides produced from entire proteins and apply it to in vitro digestions of 15 proteins, including well-known intrinsically disordered proteins such as human tau and α-Synuclein. The results confirm that 20S proteasomes produce a sizeable variety of cis-spliced peptides, whereas trans-spliced peptides are a minority. Both peptide hydrolysis and splicing produce peptides with well-defined characteristics, which hint toward an intricate regulation of both catalytic activities. At protein level, both non-spliced and spliced peptides are not randomly localized within protein sequences, but rather concentrated in hotspots of peptide products, in part driven by protein sequence motifs and proteasomal preferences. At sequence level, the different peptide sequence preference of peptide hydrolysis and peptide splicing suggests a competition between the two catalytic activities of 20S proteasomes during protein degradation.

Mammalian oocytes store proteins for the early embryo on cytoplasmic lattices

Mammalian oocytes are filled with poorly understood structures called cytoplasmic lattices. First discovered in the 1960s and speculated to correspond to mammalian yolk, ribosomal arrays, or intermediate filaments, their function has remained enigmatic to date. Here, we show that cytoplasmic lattices are sites where oocytes store essential proteins for early embryonic development. Using super-resolution light microscopy and cryoelectron tomography, we show that cytoplasmic lattices are composed of filaments with a high surface area, which contain PADI6 and subcortical maternal complex proteins. The lattices associate with many proteins critical for embryonic development, including proteins that control epigenetic reprogramming of the preimplantation embryo. Loss of cytoplasmic lattices by knocking out PADI6 or the subcortical maternal complex prevents the accumulation of these proteins and results in early embryonic arrest. Our work suggests that cytoplasmic lattices enrich maternally provided proteins to prevent their premature degradation and cellular activity, thereby enabling early mammalian development.

InvitroSPI and a large database of proteasome-generated spliced and non-spliced peptides

Noncanonical epitopes presented by Human Leucocyte Antigen class I (HLA-I) complexes to CD8+ T cells attracted the spotlight in the research of novel immunotherapies against cancer, infection and autoimmunity. Proteasomes, which are the main producers of HLA-I-bound antigenic peptides, can catalyze both peptide hydrolysis and peptide splicing. The prediction of proteasome-generated spliced peptides is an objective that still requires a reliable (and large) database of non-spliced and spliced peptides produced by these proteases. Here, we present an extended database of proteasome-generated spliced and non-spliced peptides, which was obtained by analyzing in vitro digestions of 80 unique synthetic polypeptide substrates, measured by different mass spectrometers. Peptides were identified through invitroSPI method, which was validated through in silico and in vitro strategies. The peptide product database contains 16,631 unique peptide products (5,493 non-spliced, 6,453 cis-spliced and 4,685 trans-spliced peptide products), and a substrate sequence variety that is a valuable source for predictors of proteasome-catalyzed peptide hydrolysis and splicing. Potential artefacts and skewed results due to different identification and analysis strategies are discussed.

iBench: A ground truth approach for advanced validation of mass spectrometry identification method

The discovery of many noncanonical peptides detectable with sensitive mass spectrometry inside, outside, and on cells shepherded the development of novel methods for their identification, often not supported by a systematic benchmarking with other methods. We here propose iBench, a bioinformatic tool that can construct ground truth proteomics datasets and cognate databases, thereby generating a training court wherein methods, search engines, and proteomics strategies can be tested, and their performances estimated by the same tool. iBench can be coupled to the main database search engines, allows the selection of customized features of mass spectrometry spectra and peptides, provides standard benchmarking outputs, and is open source. The proof-of-concept application to tryptic proteome digestions, immunopeptidomes, and synthetic peptide libraries dissected the impact that noncanonical peptides could have on the identification of canonical peptides by Mascot search with rescoring via Percolator (Mascot+Percolator).

inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction

Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spectrum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.

Predicting the Success of Fmoc-Based Peptide Synthesis

Synthetic peptides are commonly used in biomedical science for many applications in basic and translational research. While peptide synthesis is generally easy and reliable, the chemical nature of some amino acids as well as the many steps and chemical compounds involved can render the synthesis of some peptide sequences difficult. Identification of these problematic sequences and mitigation of issues they may present can be important for the reliable use of peptide reagents in several contexts. Here, we assembled a large dataset of peptides that were synthesized using standard Fmoc chemistry and whose identity was validated using mass spectrometry. We analyzed the mass spectra to identify errors in peptide syntheses and sought to develop a computational tool to predict the likelihood that any given peptide sequence would be synthesized accurately. Our model, named Peptide Synthesis Score (PepSySco), is able to predict the likelihood that a peptide will be successfully synthesized based on its amino acid sequence.

Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes

Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry (MS) required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post-translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines - that is, Mascot, Mascot+Percolator, and PEAKS DB - as final identification step, as well as the features of target database on the ability to correctly identify non-spliced and cis-spliced peptides. We used ground truth datasets measured by MS to benchmark methods' performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis-spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated.

Identification of a class of non-conventional ER-stress-response-derived immunogenic peptides

Efforts to overcome resistance to immune checkpoint blockade therapy have focused on vaccination strategies using neoepitopes, although they cannot be applied on a large scale due to the "private" nature of cancer mutations. Here, we show that infection of tumor cells with Salmonella induces the opening of membrane hemichannels and the extracellular release of proteasome-generated peptides by the exacerbation of endoplasmic reticulum (ER) stress. Peptides released by cancer cells foster an antitumor response in vivo, both in mice bearing B16F10 melanomas and in dogs suffering from osteosarcoma. Mass spectrometry analysis on the supernatant of human melanoma cells revealed 12 peptides capable of priming healthy-donor CD8+ T cells that recognize and kill human melanoma cells in vitro and when xenotransplanted in vivo. Hence, we identified a class of shared tumor antigens that are generated in ER-stressed cells, such as tumor cells, that do not induce tolerance and are not presented by healthy cells.

Potential Mimicry of Viral and Pancreatic β Cell Antigens Through Non-Spliced and cis-Spliced Zwitter Epitope Candidates in Type 1 Diabetes

Increasing evidence suggests that post-translational peptide splicing can play a role in the immune response under pathological conditions. This seems to be particularly relevant in Type 1 Diabetes (T1D) since post-translationally spliced epitopes derived from T1D-associated antigens have been identified among those peptides bound to Human Leucocyte Antigen (HLA) class I and II complexes. Their immunogenicity has been confirmed through CD4+ and CD8+ T cell-mediated responses in T1D patients. Spliced peptides theoretically have a large sequence variability. This might increase the frequency of viral-human zwitter peptides, i.e. peptides that share a complete sequence homology irrespective of whether they originate from human or viral antigens, thereby impinging upon the discrimination between self and non-self antigens by T cells. This might increase the risk of autoimmune responses triggered by viral infections. Since enteroviruses and other viral infections have historically been associated with T1D, we investigated whether cis-spliced peptides derived from selected viruses might be able to trigger CD8+ T cell-mediated autoimmunity. We computed in silico viral-human non-spliced and cis-spliced zwitter epitope candidates, and prioritized peptide candidates based on: (i) their binding affinity to HLA class I complexes, (ii) human pancreatic β cell and medullary thymic epithelial cell (mTEC) antigens' mRNA expression, (iii) antigen association with T1D, and (iv) potential hotspot regions in those antigens. Neglecting potential T cell receptor (TCR) degeneracy, no viral-human zwitter non-spliced peptide was found to be an optimal candidate to trigger a virus-induced CD8+ T cell response against human pancreatic β cells. Conversely, we identified some zwitter peptide candidates, which may be produced by proteasome-catalyzed peptide splicing, and might increase the likelihood of pancreatic β cells recognition by virus-specific CD8+ T cell clones, therefore promoting β cell destruction in the context of viral infections.

Proteasome-Generated cis-Spliced Peptides and Their Potential Role in CD8+ T Cell Tolerance

The human immune system relies on the capability of CD8+ T cells to patrol body cells, spot infected cells and eliminate them. This cytotoxic response is supposed to be limited to infected cells to avoid killing of healthy cells. To enable this, CD8+ T cells have T Cell Receptors (TCRs) which should discriminate between self and non-self through the recognition of antigenic peptides bound to Human Leukocyte Antigen class I (HLA-I) complexes-i.e., HLA-I immunopeptidomes-of patrolled cells. The majority of these antigenic peptides are produced by proteasomes through either peptide hydrolysis or peptide splicing. Proteasome-generated cis-spliced peptides derive from a given antigen, are immunogenic and frequently presented by HLA-I complexes. Theoretically, they also have a very large sequence variability, which might impinge upon our model of self/non-self discrimination and central and peripheral CD8+ T cell tolerance. Indeed, a large variety of cis-spliced epitopes might enlarge the pool of viral-human zwitter epitopes, i.e., peptides that may be generated with the exact same sequence from both self (human) and non-self (viral) antigens. Antigenic viral-human zwitter peptides may be recognized by CD8+ thymocytes and T cells, induce clonal deletion or other tolerance processes, thereby restraining CD8+ T cell response against viruses. To test this hypothesis, we computed in silico the theoretical frequency of zwitter non-spliced and cis-spliced epitope candidates derived from human proteome (self) and from the proteomes of a large pool of viruses (non-self). We considered their binding affinity to the representative HLA-A*02:01 complex, self-antigen expression in Medullary Thymic Epithelial cells (mTECs) and the relative frequency of non-spliced and cis-spliced peptides in HLA-I immunopeptidomes. Based on the present knowledge of proteasome-catalyzed peptide splicing and neglecting CD8+ TCR degeneracy, our study suggests that, despite their frequency, the portion of the cis-spliced peptides we investigated could only marginally impinge upon the variety of functional CD8+ cytotoxic T cells (CTLs) involved in anti-viral response.

Large database for the analysis and prediction of spliced and non-spliced peptide generation by proteasomes

Proteasomes are the main producers of antigenic peptides presented to CD8+ T cells. They can cut proteins and release their fragments or recombine non-contiguous fragments thereby generating novel sequences, i.e. spliced peptides. Understanding which are the driving forces and the sequence preferences of both reactions can streamline target discovery in immunotherapies against cancer, infection and autoimmunity. Here, we present a large database of spliced and non-spliced peptides generated by proteasomes in vitro, which is available as simple CSV file and as a MySQL database. To generate the database, we performed in vitro digestions of 55 unique synthetic polypeptide substrates with different proteasome isoforms and experimental conditions. We measured the samples using three mass spectrometers, filtered and validated putative peptides, identified 22,333 peptide product sequences (15,028 spliced and 7,305 non-spliced product sequences). Our database and datasets have been deposited to the Mendeley (doi:10.17632/nr7cs764rc.1) and PRIDE (PXD016782) repositories. We anticipate that this unique database can be a valuable source for predictors of proteasome-catalyzed peptide hydrolysis and splicing, with various future translational applications.

ER-aminopeptidase 1 determines the processing and presentation of an immunotherapy-relevant melanoma epitope

Dissecting the different steps of the processing and presentation of tumor-associated antigens is a key aspect of immunotherapies enabling to tackle the immune response evasion attempts of cancer cells. The immunodominant glycoprotein gp100_209-217 epitope, which is liberated from the melanoma differentiation antigen gp100^PMEL17 , is part of immunotherapy trials. By analyzing different human melanoma cell lines, we here demonstrate that a pool of N-terminal extended peptides sharing the common minimal epitope is generated by melanoma proteasome subtypes. In vitro and in cellulo experiments indicate that ER-resident aminopeptidase 1 (ERAP1)-but not ERAP2-defines the processing of this peptide pool thereby modulating the T-cell recognition of melanoma cells. By combining the outcomes of our studies and others, we can sketch the complex processing and endogenous presentation pathway of the gp100_209-217 -containing epitope/peptides, which are produced by proteasomes and are translocated to the vesicular compartment through different pathways, where the precursor peptides that reach the endoplasmic reticulum are further processed by ERAP1. The latter step enhances the activation of epitope-specific T lymphocytes, which might be a target to improve the efficiency of anti-melanoma immunotherapy.

An in silico-in vitro Pipeline Identifying an HLA-A*02:01+ KRAS G12V+ Spliced Epitope Candidate for a Broad Tumor-Immune Response in Cancer Patients

Targeting CD8⁺ T cells to recurrent tumor-specific mutations can profoundly contribute to cancer treatment. Some of these mutations are potential tumor antigens although they can be displayed by non-spliced epitopes only in a few patients, because of the low affinity of the mutated non-spliced peptides for the predominant HLA class I alleles. Here, we describe a pipeline that uses the large sequence variety of proteasome-generated spliced peptides and identifies spliced epitope candidates, which carry the mutations and bind the predominant HLA-I alleles with high affinity. They could be used in adoptive T cell therapy and other anti-cancer immunotherapies for large cohorts of cancer patients. As a proof of principle, the application of this pipeline led to the identification of a KRAS G12V mutation-carrying spliced epitope candidate, which is produced by proteasomes, transported by TAPs and efficiently presented by the most prevalent HLA class I molecules, HLA-A^*02:01 complexes.

Proteolytic dynamics of human 20S thymoproteasome

An efficient immunosurveillance of CD8⁺ T cells in the periphery depends on positive/negative selection of thymocytes and thus on the dynamics of antigen degradation and epitope production by thymoproteasome and immunoproteasome in the thymus. Although studies in mouse systems have shown how thymoproteasome activity differs from that of immunoproteasome and strongly impacts the T cell repertoire, the proteolytic dynamics and the regulation of human thymoproteasome are unknown. By combining biochemical and computational modeling approaches, we show here that human 20S thymoproteasome and immunoproteasome differ not only in the proteolytic activity of the catalytic sites but also in the peptide transport. These differences impinge upon the quantity of peptide products rather than where the substrates are cleaved. The comparison of the two human 20S proteasome isoforms depicts different processing of antigens that are associated to tumors and autoimmune diseases.

Untangling Extracellular Proteasome-Osteopontin Circuit Dynamics in Multiple Sclerosis

The function of proteasomes in extracellular space is still largely unknown. The extracellular proteasome-osteopontin circuit has recently been hypothesized to be part of the inflammatory machinery regulating relapse/remission phase alternation in multiple sclerosis. However, it is still unclear what dynamics there are between the different elements of the circuit, what the role of proteasome isoforms is, and whether these inflammatory circuit dynamics are associated with the clinical severity of multiple sclerosis. To shed light on these aspects of this novel inflammatory circuit, we integrated in vitro proteasome isoform data, cell chemotaxis cell culture data, and clinical data of multiple sclerosis cohorts in a coherent computational inference framework. Thereby, we modeled extracellular osteopontin-proteasome circuit dynamics during relapse/remission alternation in multiple sclerosis. Applying this computational framework to a longitudinal study on single multiple sclerosis patients suggests a complex interaction between extracellular proteasome isoforms and osteopontin with potential clinical implications.

Mapping the MHC Class I-Spliced Immunopeptidome of Cancer Cells

Anticancer immunotherapies demand optimal epitope targets, which could include proteasome-generated spliced peptides if tumor cells were to present them. Here, we show that spliced peptides are widely presented by MHC class I molecules of colon and breast carcinoma cell lines. The peptides derive from hot spots within antigens and enlarge the antigen coverage. Spliced peptides also represent a large number of antigens that would otherwise be neglected by patrolling T cells. These antigens tend to be long, hydrophobic, and basic. Thus, spliced peptides can be a key to identifying targets in an enlarged pool of antigens associated with cancer.

PEITH(Θ): perfecting experiments with information theory in Python with GPU support

Motivation

Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial.

Results

PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions.

Availability and implementation

https://github.com/MichaelPHStumpf/Peitho

An Unexpected Major Role for Proteasome-Catalyzed Peptide Splicing in Generation of T Cell Epitopes: Is There Relevance for Vaccine Development?

Efficient and safe induction of CD8⁺ T cell responses is a desired characteristic of vaccines against intracellular pathogens. To achieve this, a new generation of safe vaccines is being developed accommodating single, dominant antigens of pathogens of interest. In particular, the selection of such antigens is challenging, since due to HLA polymorphism the ligand specificities and immunodominance hierarchies of pathogen-specific CD8⁺ T cell responses differ throughout the human population. A recently discovered mechanism of proteasome-mediated CD8⁺ T cell epitope generation, i.e., by proteasome-catalyzed peptide splicing (PCPS), expands the pool of peptides and antigens, presented by MHC class I HLA molecules. On the cell surface, one-third of the presented self-peptides are generated by PCPS, which coincides with one-fourth in terms of abundance. Spliced epitopes are targeted by CD8⁺ T cell responses during infection and, like non-spliced epitopes, can be identified within antigen sequences using a novel in silico strategy. The existence of spliced epitopes, by enlarging the pool of peptides available for presentation by different HLA variants, opens new opportunities for immunotherapies and vaccine design.

A large fraction of HLA class I ligands are proteasome-generated spliced peptides

The proteasome generates the epitopes presented on human leukocyte antigen (HLA) class I molecules that elicit CD8⁺ T cell responses. Reports of proteasome-generated spliced epitopes exist, but they have been regarded as rare events. Here, however, we show that the proteasome-generated spliced peptide pool accounts for one-third of the entire HLA class I immunopeptidome in terms of diversity and one-fourth in terms of abundance. This pool also represents a unique set of antigens, possessing particular and distinguishing features. We validated this observation using a range of complementary experimental and bioinformatics approaches, as well as multiple cell types. The widespread appearance and abundance of proteasome-catalyzed peptide splicing events has implications for immunobiology and autoimmunity theories and may provide a previously untapped source of epitopes for use in vaccines and cancer immunotherapy.

Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data

Spatial structures often constrain the 3D movement of cells or particles in vivo, yet this information is obscured when microscopy data are analyzed using standard approaches. Here, we present methods, called unwrapping and Riemannian manifold learning, for mapping particle-tracking data along unseen and irregularly curved surfaces onto appropriate 2D representations. This is conceptually similar to the problem of reconstructing accurate geography from conventional Mercator maps, but our methods do not require prior knowledge of the environments' physical structure. Unwrapping and Riemannian manifold learning accurately recover the underlying 2D geometry from 3D imaging data without the need for fiducial marks. They outperform standard x-y projections, and unlike standard dimensionality reduction techniques, they also successfully detect both bias and persistence in cell migration modes. We demonstrate these features on simulated data and zebrafish and Drosophila in vivo immune cell trajectory datasets. Software packages that implement unwrapping and Riemannian manifold learning are provided.

Systems Analysis of the Dynamic Inflammatory Response to Tissue Damage Reveals Spatiotemporal Properties of the Wound Attractant Gradient

In the acute inflammatory phase following tissue damage, cells of the innate immune system are rapidly recruited to sites of injury by pro-inflammatory mediators released at the wound site. Although advances in live imaging allow us to directly visualize this process in vivo, the precise identity and properties of the primary immune damage attractants remain unclear, as it is currently impossible to directly observe and accurately measure these signals in tissues. Here, we demonstrate that detailed information about the attractant signals can be extracted directly from the in vivo behavior of the responding immune cells. By applying inference-based computational approaches to analyze the in vivo dynamics of the Drosophila inflammatory response, we gain new detailed insight into the spatiotemporal properties of the attractant gradient. In particular, we show that the wound attractant is released by wound margin cells, rather than by the wounded tissue per se, and that it diffuses away from this source at rates far slower than those of previously implicated signals such as H₂O₂ and ATP, ruling out these fast mediators as the primary chemoattractant. We then predict, and experimentally test, how competing attractant signals might interact in space and time to regulate multi-step cell navigation in the complex environment of a healing wound, revealing a period of receptor desensitization after initial exposure to the damage attractant. Extending our analysis to model much larger wounds, we uncover a dynamic behavioral change in the responding immune cells in vivo that is prognostic of whether a wound will subsequently heal or not.

Video Abstract

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Computational modeling of in vivo inflammatory response to tissue damage is applied

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The model infers novel spatiotemporal properties of the wound attractant gradient

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Wound signal is released from the wound edge for 30 min and diffuses at 200 μm²/min

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Modeling two competing wounds reveals a period of immune cell desensitization

CD8(+) T cells of Listeria monocytogenes-infected mice recognize both linear and spliced proteasome products

CD8(+) T cells responding to infection recognize pathogen-derived epitopes presented by MHC class-I molecules. While most of such epitopes are generated by proteasome-mediated antigen cleavage, analysis of tumor antigen processing has revealed that epitopes may also derive from proteasome-catalyzed peptide splicing (PCPS). To determine whether PCPS contributes to epitope processing during infection, we analyzed the fragments produced by purified proteasomes from a Listeria monocytogenes polypeptide. Mass spectrometry identified a known H-2K(b) -presented linear epitope (LLO296-304 ) in the digests, as well as four spliced peptides that were trimmed by ERAP into peptides with in silico predicted H-2K(b) binding affinity. These spliced peptides, which displayed sequence similarity with LLO296-304 , bound to H-2K(b) molecules in cellular assays and one of the peptides was recognized by CD8(+) T cells of infected mice. This spliced epitope differed by one amino acid from LLO296-304 and double staining with LLO296-304 - and spliced peptide-folded MHC multimers showed that LLO296-304 and its spliced variant were recognized by the same CD8(+) T cells. Thus, PCPS multiplies the variety of peptides that is processed from an antigen and leads to the production of epitope variants that can be recognized by cross-reacting pathogen-specific CD8(+) T cells. Such mechanism may reduce the chances for pathogen immune evasion.

The T210M Substitution in the HLA-a*02:01 gp100 Epitope Strongly Affects Overall Proteasomal Cleavage Site Usage and Antigen Processing

MHC class I-restricted epitopes, which carry a tumor-specific mutation resulting in improved MHC binding affinity, are preferred T cell receptor targets in innovative adoptive T cell therapies. However, T cell therapy requires efficient generation of the selected epitope. How such mutations may affect proteasome-mediated antigen processing has so far not been studied. Therefore, we analyzed by in vitro experiments the effect on antigen processing and recognition of a T210M exchange, which previously had been introduced into the melanoma gp100209-217 tumor epitope to improve the HLA-A*02:01 binding and its immunogenicity. A quantitative analysis of the main steps of antigen processing shows that the T210M exchange affects proteasomal cleavage site usage within the mutgp100201-230 polypeptide, leading to the generation of an unique set of cleavage products. The T210M substitution qualitatively affects the proteasome-catalyzed generation of spliced and non-spliced peptides predicted to bind HLA-A or -B complexes. The T210M substitution also induces an enhanced production of the mutgp100209-217 epitope and its N-terminally extended peptides. The T210M exchange revealed no effect on ERAP1-mediated N-terminal trimming of the precursor peptides. However, mutant N-terminally extended peptides exhibited significantly increased HLA-A*02:01 binding affinity and elicited CD8(+) T cell stimulation in vitro similar to the wtgp100209-217 epitope. Thus, our experiments demonstrate that amino acid exchanges within an epitope can result in the generation of an altered peptide pool with new antigenic peptides and in a wider CD8(+) T cell response also towards N-terminally extended versions of the minimal epitope.

Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes

Proteasomal protein degradation is a key determinant of protein half-life and hence of cellular processes ranging from basic metabolism to a host of immunological processes. Despite its importance the mechanisms regulating proteasome activity are only incompletely understood. Here we use an iterative and tightly integrated experimental and modelling approach to develop, explore and validate mechanistic models of proteasomal peptide-hydrolysis dynamics. The 20S proteasome is a dynamic enzyme and its activity varies over time because of interactions between substrates and products and the proteolytic and regulatory sites; the locations of these sites and the interactions between them are predicted by the model, and experimentally supported. The analysis suggests that the rate-limiting step of hydrolysis is the transport of the substrates into the proteasome. The transport efficiency varies between human standard- and immuno-proteasomes thereby impinging upon total degradation rate and substrate cleavage-site usage.

DOI:http://dx.doi.org/10.7554/eLife.07545.001

Cells have to be able to reliably destroy or remove molecules from their interior that they no longer need. Structures called proteasomes play a central part in this complex process by cutting up and digesting proteins. Mammals have several different types of proteasomes, each made up of several protein ‘subunits’. For example, when a cell experiences inflammation some proteasomes change some of their subunits and form an immuno-proteasome. These immuno-proteasomes tend to break down proteins more quickly than ‘standard’ proteasomes, but it was not clear how they are able to do so.

Liepe et al. have now combined experiments and mathematical modelling to construct a detailed model of proteasome activity. The model shows that protein transport into and out of the proteasome chamber are the steps that limit how quickly the proteasomes can break down proteins. Furthermore, these transport processes are also to a large extent responsible for the different rates at which standard and immuno-proteasomes process proteins. Liepe et al. were also able to confirm the existence of regulatory sites within the proteasome, and describe how these are arranged.

Problems that alter the rate at which proteasomes break down proteins have been linked to tumors and neurological and autoimmune diseases. Liepe et al.'s model opens up the ability to study how the proteasome's activity is affected by drugs and therefore makes it easier to investigate ways of interfering with this activity for therapeutic purposes.

DOI:http://dx.doi.org/10.7554/eLife.07545.002

Goldstein-Kac telegraph processes with random speeds: Path probabilities, likelihoods, and reported Lévy flights

The Goldstein-Kac telegraph process describes the one-dimensional motion of particles with constant speed undergoing random changes in direction. Despite its resemblance to numerous real-world phenomena, the singular nature of the resultant spatial distribution of each particle precludes the possibility of any a posteriori empirical validation of this random-walk model from data. Here we show that by simply allowing for random speeds, the ballistic terms are regularized and that the diffusion component can be well-approximated via the unscented transform. The result is a computationally efficient yet robust evaluation of the full particle path probabilities and, hence, the parameter likelihoods of this generalized telegraph process. We demonstrate how a population diffusing under such a model can lead to non-Gaussian asymptotic spatial distributions, thereby mimicking the behavior of an ensemble of Lévy walkers.

Proteasome isoforms exhibit only quantitative differences in cleavage and epitope generation

Immunoproteasomes are considered to be optimised to process Ags and to alter the peptide repertoire by generating a qualitatively different set of MHC class I epitopes. Whether the immunoproteasome at the biochemical level, influence the quality rather than the quantity of the immuno-genic peptide pool is still unclear. Here, we quantified the cleavage-site usage by human standard- and immunoproteasomes, and proteasomes from immuno-subunit-deficient mice, as well as the peptides generated from model polypeptides. We show in this study that the different proteasome isoforms can exert significant quantitative differences in the cleavage-site usage and MHC class I restricted epitope production. However, independent of the proteasome isoform and substrates studied, no evidence was obtained for the abolishment of the specific cleavage-site usage, or for differences in the quality of the peptides generated. Thus, we conclude that the observed differences in MHC class I restricted Ag presentation between standard- and immunoproteasomes are due to quantitative differences in the proteasome-generated antigenic peptides.

Modelling proteasome and proteasome regulator activities

Proteasomes are key proteases involved in a variety of processes ranging from the clearance of damaged proteins to the presentation of antigens to CD8+ T-lymphocytes. Which cleavage sites are used within the target proteins and how fast these proteins are degraded have a profound impact on immune system function and many cellular metabolic processes. The regulation of proteasome activity involves different mechanisms, such as the substitution of the catalytic subunits, the binding of regulatory complexes to proteasome gates and the proteasome conformational modifications triggered by the target protein itself. Mathematical models are invaluable in the analysis; and potentially allow us to predict the complex interactions of proteasome regulatory mechanisms and the final outcomes of the protein degradation rate and MHC class I epitope generation. The pioneering attempts that have been made to mathematically model proteasome activity, cleavage preference variation and their modification by one of the regulatory mechanisms are reviewed here.

Information theory and signal transduction systems: from molecular information processing to network inference

Sensing and responding to the environment are two essential functions that all biological organisms need to master for survival and successful reproduction. Developmental processes are marshalled by a diverse set of signalling and control systems, ranging from systems with simple chemical inputs and outputs to complex molecular and cellular networks with non-linear dynamics. Information theory provides a powerful and convenient framework in which such systems can be studied; but it also provides the means to reconstruct the structure and dynamics of molecular interaction networks underlying physiological and developmental processes. Here we supply a brief description of its basic concepts and introduce some useful tools for systems and developmental biologists. Along with a brief but thorough theoretical primer, we demonstrate the wide applicability and biological application-specific nuances by way of different illustrative vignettes. In particular, we focus on the characterisation of biological information processing efficiency, examining cell-fate decision making processes, gene regulatory network reconstruction, and efficient signal transduction experimental design.

A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation

As modeling becomes a more widespread practice in the life sciences and biomedical sciences, researchers need reliable tools to calibrate models against ever more complex and detailed data. Here we present an approximate Bayesian computation (ABC) framework and software environment, ABC-SysBio, which is a Python package that runs on Linux and Mac OS X systems and that enables parameter estimation and model selection in the Bayesian formalism by using sequential Monte Carlo (SMC) approaches. We outline the underlying rationale, discuss the computational and practical issues and provide detailed guidance as to how the important tasks of parameter inference and model selection can be performed in practice. Unlike other available packages, ABC-SysBio is highly suited for investigating, in particular, the challenging problem of fitting stochastic models to data. In order to demonstrate the use of ABC-SysBio, in this protocol we postulate the existence of an imaginary reaction network composed of seven interrelated biological reactions (involving a specific mRNA, the protein it encodes and a post-translationally modified version of the protein), a network that is defined by two files containing 'observed' data that we provide as supplementary information. In the first part of the PROCEDURE, ABC-SysBio is used to infer the parameters of this system, whereas in the second part we use ABC-SysBio's relevant functionality to discriminate between two different reaction network models, one of them being the 'true' one. Although computationally expensive, the additional insights gained in the Bayesian formalism more than make up for this cost, especially in complex problems.

Maximizing the information content of experiments in systems biology

Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior.

For most biological signalling and regulatory systems we still lack reliable mechanistic models. And where such models exist, e.g. in the form of differential equations, we typically have only rough estimates for the parameters that characterize the biochemical reactions. In order to improve our knowledge of such systems we require better estimates for these parameters and here we show how judicious choice of experiments, based on a combination of simulations and information theoretical analysis, can help us. Our approach builds on the available, frequently rudimentary information, and identifies which experimental set-up provides most additional information about all the parameters, or individual parameters. We will also consider the related but subtly different problem of which experiments need to be performed in order to decrease the uncertainty about the behaviour of the system under altered conditions. We develop the theoretical framework in the necessary detail before illustrating its use and applying it to the repressilator model, the regulation of Hes1 and signal transduction in the Akt pathway.

Calibrating spatio-temporal models of leukocyte dynamics against in vivo live-imaging data using approximate Bayesian computation

In vivo studies allow us to investigate biological processes at the level of the organism. But not all aspects of in vivo systems are amenable to direct experimental measurements. In order to make the most of such data we therefore require statistical tools that allow us to obtain reliable estimates for e.g. kinetic in vivo parameters. Here we show how we can use approximate Bayesian computation approaches in order to analyse leukocyte migration in zebrafish embryos in response to injuries. We track individual leukocytes using live imaging following surgical injury to the embryos' tail-fins. The signalling gradient that leukocytes follow towards the site of the injury cannot be directly measured but we can estimate its shape and how it changes with time from the directly observed patterns of leukocyte migration. By coupling simple models of immune signalling and leukocyte migration with the unknown gradient shape into a single statistical framework we can gain detailed insights into the tissue-wide processes that are involved in the innate immune response to wound injury. In particular we find conclusive evidence for a temporally and spatially changing signalling gradient that modulates the changing activity of the leukocyte population in the embryos. We conclude with a robustness analysis which highlights the most important factors determining the leukocyte dynamics. Our approach relies only on the ability to simulate numerically the process under investigation and is therefore also applicable in other in vivo contexts and studies.

GPU accelerated biochemical network simulation

MOTIVATION

Mathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive. However, in many cases, the simulations are easily parallelizable. Graphics processing units (GPUs) are capable of efficiently running highly parallel programs and outperform CPUs in terms of raw computing power. Despite their computational advantages, their adoption by the systems biology community is relatively slow, since differences in hardware architecture between GPUs and CPUs complicate the porting of existing code.

RESULTS

We present a Python package, cuda-sim, that provides highly parallelized algorithms for the repeated simulation of biochemical network models on NVIDIA CUDA GPUs. Algorithms are implemented for the three popular types of model formalisms: the LSODA algorithm for ODE integration, the Euler-Maruyama algorithm for SDE simulation and the Gillespie algorithm for MJP simulation. No knowledge of GPU computing is required from the user. Models can be specified in SBML format or provided as CUDA code. For running a large number of simulations in parallel, up to 360-fold decrease in simulation runtime is attained when compared to single CPU implementations.

AVAILABILITY

http://cuda-sim.sourceforge.net/

ABC-SysBio--approximate Bayesian computation in Python with GPU support

MOTIVATION

The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions.

RESULTS

Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio.

AVAILABILITY

http://abc-sysbio.sourceforge.net

The 20S proteasome splicing activity discovered by SpliceMet

The identification of proteasome-generated spliced peptides (PSP) revealed a new unpredicted activity of the major cellular protease. However, so far characterization of PSP was entirely dependent on the availability of patient-derived cytotoxic CD8+ T lymphocytes (CTL) thus preventing a systematic investigation of proteasome-catalyzed peptide splicing (PCPS). For an unrestricted PSP identification we here developed SpliceMet, combining the computer-based algorithm ProteaJ with in vitro proteasomal degradation assays and mass spectrometry. By applying SpliceMet for the analysis of proteasomal processing products of four different substrate polypeptides, derived from human tumor as well as viral antigens, we identified fifteen new spliced peptides generated by PCPS either by cis or from two separate substrate molecules, i.e., by trans splicing. Our data suggest that 20S proteasomes represent a molecular machine that, due to its catalytic and structural properties, facilitates the generation of spliced peptides, thereby providing a pool of qualitatively new peptides from which functionally relevant products may be selected.