ILC2-modulated T cell-to-MDSC balance is associated with bladder cancer recurrence.

Non-muscle-invasive bladder cancer (NMIBC) is a highly recurrent tumor despite intravesical immunotherapy instillation with the bacillus Calmette-Guérin (BCG) vaccine. In a prospective longitudinal study, we took advantage of BCG instillations, which increase local immune infiltration, to characterize immune cell populations in the urine of patients with NMIBC as a surrogate for the bladder tumor microenvironment. We observed an infiltration of neutrophils, T cells, monocytic myeloid-derived suppressor cells (M-MDSCs), and group 2 innate lymphoid cells (ILC2). Notably, patients with a T cell-to-MDSC ratio of less than 1 showed dramatically lower recurrence-free survival than did patients with a ratio of greater than 1. Analysis of early and later time points indicated that this patient dichotomy existed prior to BCG treatment. ILC2 frequency was associated with detectable IL-13 in the urine and correlated with the level of recruited M-MDSCs, which highly expressed IL-13 receptor α1. In vitro, ILC2 were increased and potently expressed IL-13 in the presence of BCG or tumor cells. IL-13 induced the preferential recruitment and suppressive function of monocytes. Thus, the T cell-to-MDSC balance, associated with a skewing toward type 2 immunity, may predict bladder tumor recurrence and influence the mortality of patients with muscle-invasive cancer. Moreover, these results underline the ILC2/IL-13 axis as a targetable pathway to curtail the M-MDSC compartment and improve bladder cancer treatment

A Phase Ib Study of the Combination of Personalized Autologous Dendritic Cell Vaccine, Aspirin, and Standard of Care Adjuvant Chemotherapy Followed by Nivolumab for Resected Pancreatic Adenocarcinoma—A Proof of Antigen Discovery Feasibility in Three Patients

Despite the promising therapeutic effects of immune checkpoint blockade (ICB), most patients with solid tumors treated with anti-PD-1/PD-L1 monotherapy do not achieve objective responses, with most tumor regressions being partial rather than complete. It is hypothesized that the absence of pre-existing antitumor immunity and/or the presence of additional tumor immune suppressive factors at the tumor microenvironment are responsible for such therapeutic failures. It is therefore clear that in order to fully exploit the potential of PD-1 blockade therapy, antitumor immune response should be amplified, while tumor immune suppression should be further attenuated. Cancer vaccines may prime patients for treatments with ICB by inducing effective anti-tumor immunity, especially in patients lacking tumor-infiltrating T-cells. These "non-inflamed" non-permissive tumors that are resistant to ICB could be rendered sensitive and transformed into "inflamed" tumor by vaccination. In this article we describe a clinical study where we use pancreatic cancer as a model, and we hypothesize that effective vaccination in pancreatic cancer patients, along with interventions that can reprogram important immunosuppressive factors in the tumor microenvironment, can enhance tumor immune recognition, thus enhancing response to PD-1/PD-L1 blockade. We incorporate into the schedule of standard of care (SOC) chemotherapy adjuvant setting a vaccine platform comprised of autologous dendritic cells loaded with personalized neoantigen peptides (PEP-DC) identified through our own proteo-genomics antigen discovery pipeline. Furthermore, we add nivolumab, an antibody against PD-1, to boost and maintain the vaccine's effect. We also demonstrate the feasibility of identifying personalized neoantigens in three pancreatic ductal adenocarcinoma (PDAC) patients, and we describe their optimal incorporation into long peptides for manufacturing into vaccine products. We finally discuss the advantages as well as the scientific and logistic challenges of such an exploratory vaccine clinical trial, and we highlight its novelty

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Mathematical models and computational methods for the analysis of genome-scale protein synthesis

Proteins are a ubiquitous and indispensable element for every living organism, from simple bacteria to mammals. Already in the simplest organisms, there exist some thousands of different protein species that take up a great variety of structures, and thus different roles, letting them precisely orchestrate the functioning of each cell. Despite this diversity of functions and shapes, all proteins are emerging from a same root: the DNA that encodes all proteins, in a same way as a dictionary contains the definition of each word. When a cell needs a specific protein, it will therefore "read" this "DNA dictionary" and translate it into another "language": from a nucleotide sequence of the DNA to an amino acids sequence, which is the basis of each protein. This process of "reading" the DNA to form a protein, or in better terms the protein synthesis, lies at the heart of every organism. Indeed 80% of the cellular energy is devoted to protein synthesis. The main mechanisms of this process are the same for all proteins and for all kingdoms of life. A good understanding of this process is therefore essential to biology; any malfunctioning could potentially lead to diseases and, on the other hand, any of the steps of protein synthesis could be a prospective drug target. This is already the case of various antibiotics. In addition to that, a good understanding of this system is also valuable in recombinant vaccine and recombinant drug production, in order to help improve the yield of these proteins. Recombinant proteins technology is used for example to synthesize the hepatitis B vaccine in yeast cells or to synthesize the recombinant human insulin in Escherichia coli cells. This is done by inserting into the organism a DNA plasmid that encodes the given protein so that the transfected organism will then synthesize this protein nearly as if it was from its own DNA. A further benefit from an in -depth knowledge of protein synthesis relates to circuit design in synthetic biology. There, the goal is to design cells that will respond to their environment in a predefined manner, and again, this is done by inserting specific genes into the cells. Understanding protein synthesis can help to estimate the sensibility of such a system as well as help to define characteristics of its response. Nowadays, the many facets of protein synthesis and its regulation are getting increasingly better understood. Nevertheless, it also becomes increasingly more evident that the classical approach of studying every component in isolation should be left aside and the system or cell should be studied as a whole, due to the interconnections of all of its elements: we have entered in the systems biology era. With the recent advances in genomics, transcriptomics, proteomics and other –omics technologies, we are able to measure the state of cells under different conditions in a high -throughput manner, enabling some global, genome -scale view as aimed at by systems biology. The huge amount of data collected by these high -throughput techniques poses a new challenge: how can we efficiently integrate these data to make some sense out of them for gaining deeper understanding and for the design and optimization of novel systems. A general answer is that computational approaches are needed. A model can be built to represent the system, and its outputs can then be compared to the experimental measurements. The great advantage of the modeling and simulation approach is that we can build many different in silico systems to test and to compare which one best represents our current knowledge. This in silico system can then be subjected to different "virtual" conditions, with the goal of observing how the system would behave in response to these conditions, which can be repeated for many cases and conditions in a very cost - and time -effective way in comparison to an in vitro or in vivo experiment. In this thesis, we aim at integrating such high -throughput data into a model for a better understanding of protein synthesis. We mainly focus on the often -neglected steps of translation, to observe their possible influence and regulation on the system. For this, a model incorporating all the steps of translation is built, including the various intermediate translation elongation steps. We then develop a novel, efficient, exact stochastic algorithm, targeted here to simulate translation at the genome‐scale, accounting for the competition between mRNAs for shared resources. This algorithm could easily be adapted to other systems than translation as well. Another novelty is further introduced with a methodology to analyze and estimate polysome sizes from experimental measurements in prokaryotes. Integrating various experimental measurements into our model of translation, we additionally estimate translation characteristics at the genome-scale for prokaryotic and eukaryotic cells, and we observe how the system has been optimized to cope with the cellular needs. We further estimate the sensitivity of protein synthesis on different perturbations like changes in initiation, elongation, termination rates, changes in ribosome availabilities or mRNA copy numbers, or changes following starvation conditions. Taken together, the results from this thesis show that the regulation at the translation steps is stronger than is commonly assumed and it can have many implications on the system

Deciphering translation - a genome-wide analysis of translation in Escherichia coli

Protein synthesis is one of the central elements in every living cell. For this process, mRNAs coding for genes are simultaneously competing for the same translation machinery (ribosomes and amino acids). But the ultimate determinants of cellular functions are the proteins, a good understanding of this process is therefore of crucial importance. For this purpose, we developed a genome-wide, mechanistic, mathematical model of translation, taking into account individual codon kinetics. It is used to quantify the rate limiting steps of translation and gain insight into the mechanism of regulation of expression between different proteins. We observe for each gene in a first low-occupancy phase a nearly linear increase in protein synthesis rate with amount of bound ribosomes to the mRNA, followed by a plateau of maximum synthesis rate. A further increase in the ribosomal coverage of the mRNA leads to a decrease in protein production rate, and might act as sinks for system resources like ribosomes. Native E. coli gene sequences are also compared to randomly obtained synonymous sequences, coding the same protein, and it is found some genes of higher significance for the cell have been better optimized for protein synthesis rate than other

Protein synthesis - computational modeling and experimental integration

Proteins are among the central elements in every living cell as these ultimately determine how the cell functions. The protein abundances can be regulated at many levels, such as transcription, translation and degradation. As the complexity increases when considering the large number of components involved in the system (eg. mRNA, tRNA, ribosomes and their interactions), some computational study is needed for the analysis of the global regulation related to protein synthesis. Here we will present three projects that demonstrate how mathematical modeling and computational analysis can be used for fundamental analysis and analysis of omics data: • a study of genome-scale properties of Escherichia coli; • an analysis of translatome data of a bacteria, Lactococcus lactis; • an analysis of proteomics data related to drug target identification

Stochastic noise in translation

Translation lies at the heart of every living organism, it is the process synthesizing the proteins, a main component in the cells. Earlier studies have considered protein synthesis as mainly initiation limited, modeling it as a first-order reaction with respect to free ribosomes and mRNA. In such studies, the noise is therefore coming from initiation phase only. We have previously studied a continuous deterministic model for translation, taking into account the ribosome elongation and a possible behavior similar to a “traffic jam” of ribosomes on the mRNA chain. This modeling allowed us to observe that there was a trade-off between ribosome density (fraction of an mRNA chain bound by ribosomes) and protein synthesis rate, with the optimal value in a region of elongation- and initiation-limited synthesis rate. In the present study we implemented an exact stochastic model for translation, based on a modified Gillespie’s algorithm with additional Monte-Carlo steps to follow individual stochastic behavior of all molecules. Based on this, we investigated the relation of initiation noise to the noise in protein synthesis. Additionally we examine if there is also a trade-off between optimal synthesis rate and noise. The study is performed for different ribosome densities and various mRNA lengths

A computational framework for the design of optimal protein synthesis

Despite the establishment of design principles to optimize codon choice for heterologous expression vector design, the relationship between codon sequence and final protein yield remains poorly understood. In this work, we present a computational framework for the identification of a set of mutant codon sequences for optimized heterologous protein production, which uses a codon-sequence mechanistic model of protein synthesis. Through a sensitivity analysis on the optimal steady state configuration of protein synthesis we are able to identify the set of codons, that are the most rate limiting with respect to steady state protein synthesis rate, and we replace them with synonymous codons recognized by charged tRNAs more efficient for translation, so that the resulting codon-elongation rate is higher. Repeating this procedure, we iteratively optimize the codon sequence for higher protein synthesis rate taking into account multiple constraints of various types. We determine a small set of optimized synonymous codon sequences that are very close to each other in sequence space, but they have an impact on properties such as ribosomal utilization or secondary structure. This limited number of sequences can then be offered for further experimental study. Overall, the proposed method is very valuable in understanding the effects of the different properties of mRNA sequences on the final protein yield in heterologous protein production and it can find applications in synthetic biology and biotechnology

Analysis of the dynamics and competition for resources in the system of protein translation using stochastic simulations

Protein synthesis plays an important role in biological systems since its products, proteins and enzymes, constitute most of the molecular machinery required for cell regulation, growth and function in the tissue. Understanding the mechanisms of decoding and its rate limiting steps is crucial for the development of target drugs. A deterministic model based on kinetic description of the ribosomal pathway showed that competitive tRNA behavior was a determinant of codon elongation rates, whereas a recent work proposes Watson-Crick and non-Watson-Crick types of decoding as the main determinant. We use a stochastic algorithm to model the translation process using a mechanistic model for ribosomal kinetics that accounts for both competitive tRNA behavior and Watson-Crick and non-Watson-Crick types of decoding. With this framework we simulate an E. coli translating gene pool and analyze the effects of codon usage in the exploitation of ribosomes and tRNA resources