Benny Chain

A Cardinal Role for Cathepsin D in Co-Ordinating the Host-Mediated Apoptosis of Macrophages and Killing of Pneumococci

The bactericidal function of macrophages against pneumococci is enhanced by their apoptotic demise, which is controlled by the anti-apoptotic protein Mcl-1. Here, we show that lysosomal membrane permeabilization (LMP) and cytosolic translocation of activated cathepsin D occur prior to activation of a mitochondrial pathway of macrophage apoptosis. Pharmacological inhibition or knockout of cathepsin D during pneumococcal infection blocked macrophage apoptosis. As a result of cathepsin D activation, Mcl-1 interacted with its ubiquitin ligase Mule and expression declined. Inhibition of cathepsin D had no effect on early bacterial killing but inhibited the late phase of apoptosis-associated killing of pneumococci in vitro. Mice bearing a cathepsin D−/− hematopoietic system demonstrated reduced macrophage apoptosis in vivo, with decreased clearance of pneumococci and enhanced recruitment of neutrophils to control pulmonary infection. These findings establish an unexpected role for a cathepsin D-mediated lysosomal pathway of apoptosis in pulmonary host defense and underscore the importance of apoptosis-associated microbial killing to macrophage function.

Proteomic Evaluation and Validation of Cathepsin D Regulated Proteins in Macrophages Exposed to Streptococcus pneumoniae

Macrophages are central effectors of innate immune responses to bacteria. We have investigated how activation of the abundant macrophage lysosomal protease, cathepsin D, regulates the macrophage proteome during killing of Streptococcus pneumoniae. Using the cathepsin D inhibitor pepstatin A, we demonstrate that cathepsin D differentially regulates multiple targets out of 679 proteins identified and quantified by eight-plex isobaric tag for relative and absolute quantitation. Our statistical analysis identified 18 differentially expressed proteins that passed all paired t-tests (α = 0.05). This dataset was enriched for proteins regulating the mitochondrial pathway of apoptosis or inhibiting competing death programs. Five proteins were selected for further analysis. Western blotting, followed by pharmacological inhibition or genetic manipulation of cathepsin D, verified cathepsin D-dependent regulation of these proteins, after exposure to S. pneumoniae. Superoxide dismutase-2 up-regulation was temporally related to increased reactive oxygen species generation. Gelsolin, a known regulator of mitochondrial outer membrane permeabilization, was down-regulated in association with cytochrome c release from mitochondria. Eukaryotic elongation factor (eEF2), a regulator of protein translation, was also down-regulated by cathepsin D. Using absence of the negative regulator of eEF2, eEF2 kinase, we confirm that eEF2 function is required to maintain expression of the anti-apoptotic protein Mcl-1, delaying macrophage apoptosis and confirm using a murine model that maintaining eEF2 function is associated with impaired macrophage apoptosis-associated killing of Streptococcus pneumoniae. These findings demonstrate that cathepsin D regulates multiple proteins controlling the mitochondrial pathway of macrophage apoptosis or competing death processes, facilitating intracellular bacterial killing.

Computational analysis of stochastic heterogeneity in PCR amplification efficiency revealed by single molecule barcoding

The polymerase chain reaction (PCR) is one of the most widely used techniques in molecular biology. In combination with High Throughput Sequencing (HTS), PCR is widely used to quantify transcript abundance for RNA-seq, and in the context of analysis of T and B cell receptor repertoires. In this study, we combine DNA barcoding with HTS to quantify PCR output from individual target molecules. We develop computational tools that simulate both the PCR branching process itself, and the subsequent subsampling which typically occurs during HTS sequencing. We explore the influence of different types of heterogeneity on sequencing output, and compare them to experimental results where the efficiency of amplification is measured by barcodes uniquely identifying each molecule of starting template. Our results demonstrate that the PCR process introduces substantial amplification heterogeneity, independent of primer sequence and bulk experimental conditions. This heterogeneity can be attributed both to inherited differences between different template DNA molecules, and the inherent stochasticity of the PCR process. The results demonstrate that PCR heterogeneity arises even when reaction and substrate conditions are kept as constant as possible, and therefore single molecule barcoding is essential in order to derive reproducible quantitative results from any protocol combining PCR with HTS.

Natural cathepsin E deficiency in the immune system of C57BL/6J mice

Cathepsin E is an aspartic endosomal proteinase, expressed at high levels in some epithelial and haemopoetic cells. The enzyme has been implicated in a variety of functions, including antigen processing. This study documents strain-specific variation in expression of cathepsin E in mice. The levels of cathepsin E protein and message are profoundly decreased in haemopoetic cells from C57BL/6J mice, compared to levels in 129S2/Sv or Balb/c. The deficiency is cell-type-specific, as protein levels in gut are not affected. Deficiency affects B cell, T cells, macrophages and dendritic cells. The low cathepsin E phenotype cosegregates with the C57BL/6J genotype in a panel of C57BL/6J × 129S2/Sv F2 mice. Analysis of the promoter region of cathepsin E reveals a polymorphism which destroys a previously described functional PU.1 transcription binding consensus sequence in the C57BL/6J genome. Antigen processing of ovalbumin by dendritic cells, which has previously been shown to require cathepsin E, is impaired in C57BL/6J-derived dendritic cells. C57BL/6J mice thus exhibit a profound tissue-specific deficiency in cathepsin E expression, which may have important implications for the immune phenotype of this mouse strain.

High-throughput sequencing of the T-cell receptor repertoire: pitfalls and opportunities

&NA; T‐cell specificity is determined by the T‐cell receptor, a heterodimeric protein coded for by an extremely diverse set of genes produced by imprecise somatic gene recombination. Massively parallel high‐throughput sequencing allows millions of different T‐cell receptor genes to be characterized from a single sample of blood or tissue. However, the extraordinary heterogeneity of the immune repertoire poses significant challenges for subsequent analysis of the data. We outline the major steps in processing of repertoire data, considering low‐level processing of raw sequence files and high‐level algorithms, which seek to extract biological or pathological information. The latest generation of bioinformatics tools allows millions of DNA sequences to be accurately and rapidly assigned to their respective variable V and J gene segments, and to reconstruct an almost error‐free representation of the non‐templated additions and deletions that occur. High‐level processing can measure the diversity of the repertoire in different samples, quantify V and J usage and identify private and public T‐cell receptors. Finally, we discuss the major challenge of linking T‐cell receptor sequence to function, and specifically to antigen recognition. Sophisticated machine learning algorithms are being developed that can combine the paradoxical degeneracy and cross‐reactivity of individual T‐cell receptors with the specificity of the overall T‐cell immune response. Computational analysis will provide the key to unlock the potential of the T‐cell receptor repertoire to give insight into the fundamental biology of the adaptive immune system and to provide powerful biomarkers of disease.

Feature selection using a one dimensional naïve Bayes’ classifier increases the accuracy of support vector machine classification of CDR3 repertoires

Motivation: Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor &bgr; chain complementarity determining region 3 (CDR3&bgr;) sequences following immunization with ovalbumin administered with complete Freunds adjuvant (CFA) or CFA alone. Results: The CDR3&bgr; sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one‐dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave‐one‐out validation test reaching >90% in some cases. Summary: The study describes a novel two‐stage classification strategy combining a one‐dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freunds Adjuvant. Availability and implementation: The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893. The Decombinator package is available at github.com/innate2adaptive/Decombinator. The R package e1071 is available at the CRAN repository https://cran.r‐project.org/web/packages/e1071/index.html. Contact: b.chain@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Exploring Carbon Nanomaterial Diversity for Nucleation of Protein Crystals.

Controlling crystal nucleation is a crucial step in obtaining high quality protein crystals for structure determination by X-ray crystallography. Carbon nanomaterials (CNMs) including carbon nanotubes, graphene oxide, and carbon black provide a range of surface topographies, porosities and length scales; functionalisation with two different approaches, gas phase radical grafting and liquid phase reductive grafting, provide routes to a range of oligomer functionalised products. These grafted materials, combined with a range of controls, were used in a large-scale assessment of the effectiveness for protein crystal nucleation of 20 different carbon nanomaterials on five proteins. This study has allowed a direct comparison of the key characteristics of carbon-based nucleants: appropriate surface chemistry, porosity and/or roughness are required. The most effective solid system tested in this study, carbon black nanoparticles functionalised with poly(ethylene glycol) methyl ether of mean molecular weight 5000, provides a novel highly effective nucleant, that was able to induce crystal nucleation of four out of the five proteins tested at metastable conditions.

PD1-Expressing T Cell Subsets Modify the Rejection Risk in Renal Transplant Patients

We tested whether multi-parameter immune phenotyping before or after renal transplantation can predict the risk of rejection episodes. Blood samples collected before and weekly for 3 months after transplantation were analyzed by multi-parameter flow cytometry to define 52 T cell and 13 innate lymphocyte subsets in each sample, producing more than 11,000 data points that defined the immune status of the 28 patients included in this study. Principle component analysis suggested that the patients with histologically confirmed rejection episodes segregated from those without rejection. Protein death 1 (PD-1)-expressing subpopulations of regulatory and conventional T cells had the greatest influence on the principal component segregation. We constructed a statistical tool to predict rejection using a support vector machine algorithm. The algorithm correctly identified 7 out of 9 patients with rejection, and 14 out of 17 patients without rejection. The immune profile before transplantation was most accurate in determining the risk of rejection, while changes of immune parameters after transplantation were less accurate in discriminating rejection from non-rejection. The data indicate that pretransplant immune subset analysis has the potential to identify patients at risk of developing rejection episodes, and suggests that the proportion of PD1-expressing T cell subsets may be a key indicator of rejection risk.

Quantitative Characterization of the T Cell Receptor Repertoire of Naïve and Memory Subsets Using an Integrated Experimental and Computational Pipeline Which Is Robust, Economical, and Versatile

The T cell receptor (TCR) repertoire can provide a personalized biomarker for infectious and non-infectious diseases. We describe a protocol for amplifying, sequencing, and analyzing TCRs which is robust, sensitive, and versatile. The key experimental step is ligation of a single-stranded oligonucleotide to the 3′ end of the TCR cDNA. This allows amplification of all possible rearrangements using a single set of primers per locus. It also introduces a unique molecular identifier to label each starting cDNA molecule. This molecular identifier is used to correct for sequence errors and for effects of differential PCR amplification efficiency, thus producing more accurate measures of the true TCR frequency within the sample. This integrated experimental and computational pipeline is applied to the analysis of human memory and naive subpopulations, and results in consistent measures of diversity and inequality. After error correction, the distribution of TCR sequence abundance in all subpopulations followed a power law over a wide range of values. The power law exponent differed between naïve and memory populations, but was consistent between individuals. The integrated experimental and analysis pipeline we describe is appropriate to studies of T cell responses in a broad range of physiological and pathological contexts.

Directional Migration of Recirculating Lymphocytes through Lymph Nodes via Random Walks

Naive T lymphocytes exhibit extensive antigen-independent recirculation between blood and lymph nodes, where they may encounter dendritic cells carrying cognate antigen. We examine how long different T cells may spend in an individual lymph node by examining data from long term cannulation of blood and efferent lymphatics of a single lymph node in the sheep. We determine empirically the distribution of transit times of migrating T cells by applying the Least Absolute Shrinkage & Selection Operator () or regularised to fit experimental data describing the proportion of labelled infused cells in blood and efferent lymphatics over time. The optimal inferred solution reveals a distribution with high variance and strong skew. The mode transit time is typically between 10 and 20 hours, but a significant number of cells spend more than 70 hours before exiting. We complement the empirical machine learning based approach by modelling lymphocyte passage through the lymph node . On the basis of previous two photon analysis of lymphocyte movement, we optimised distributions which describe the transit times (first passage times) of discrete one dimensional and continuous (Brownian) three dimensional random walks with drift. The optimal fit is obtained when drift is small, i.e. the ratio of probabilities of migrating forward and backward within the node is close to one. These distributions are qualitatively similar to the inferred empirical distribution, with high variance and strong skew. In contrast, an optimised normal distribution of transit times (symmetrical around mean) fitted the data poorly. The results demonstrate that the rapid recirculation of lymphocytes observed at a macro level is compatible with predominantly randomised movement within lymph nodes, and significant probabilities of long transit times. We discuss how this pattern of migration may contribute to facilitating interactions between low frequency T cells and antigen presenting cells carrying cognate antigen.