Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

The Immune System Computes the State of the Body: Crowd Wisdom, Machine Learning, and Immune Cell Reference Repertoires Help Manage Inflammation

Here, we outline an overview of the mammalian immune system that updates and extends the classical clonal selection paradigm. Rather than focusing on strict self-not-self discrimination, we propose that the system orchestrates variable inflammatory responses that maintain the body and its symbiosis with the microbiome while eliminating the threat from pathogenic infectious agents and from tumors. The paper makes four points: The immune system classifies healthy and pathologic states of the body—including both self and foreign elements—by deploying individual lymphocytes as cellular computing machines; immune cells transform input signals from the body into an output of specific immune reactions.Rather than independent clonal responses, groups of individually activated immune-system cells co-react in lymphoid organs to make collective decisions through a type of self-organizing swarm intelligence or crowd wisdom.Collective choices by swarms of immune cells, like those of schools of fish, are modified by relatively small numbers of individual regulators responding to shifting conditions—such collective inflammatory responses are dynamically responsive.Self-reactive autoantibody and T-cell receptor (TCR) repertoires shared by healthy individuals function in a biological version of experience-based supervised machine learning. Immune system decisions are primed by formative experience with training sets of self-antigens encountered during lymphocyte development; these initially trained T cell and B cell repertoires form a Wellness Profile that then guides immune responses to test sets of antigens encountered later. This experience-based machine learning strategy is analogous to that deployed by supervised machine-learning algorithms.We propose experiments to test these ideas. This overview of the immune system bears clinical implications for monitoring wellness and for treating autoimmune disease, cancer, and allograft reactions

Network Representation of T-Cell Repertoire— A Novel Tool to Analyze Immune Response to Cancer Formation

The T cell repertoire potentially presents complexity compatible, or greater than, that of the human brain. T cell based immune response is involved with practically every part of human physiology, and high-throughput biology needed to follow the T-cell repertoire has made great leaps with the advent of massive parallel sequencing [1]. Nevertheless, tools to handle and observe the dynamics of this complexity have only recently started to emerge [e.g., 2, 3, 4] in parallel with sequencing technologies. Here, we present a network-based view of the dynamics of the T cell repertoire, during the course of mammary tumors development in a mouse model. The transition from the T cell receptor as a feature, to network-based clustering, followed by network-based temporal analyses, provides novel insights to the workings of the system and provides novel tools to observe cancer progression via the perspective of the immune system. The crux of the approach here is at the network-motivated clustering. The purpose of the clustering step is not merely data reduction and exposing structures, but rather to detect hubs, or attractors, within the T cell receptor repertoire that might shed light on the behavior of the immune system as a dynamic network. The Clone-Attractor is in fact an extension of the clone concept, i.e., instead of looking at particular clones we observe the extended clonal network by assigning clusters to graph nodes and edges to adjacent clusters (editing distance metric). Viewing the system as dynamical brings to the fore the notion of an attractors landscape, hence the possibility to chart this space and map the sample state at a given time to a vector in this large space. Based on this representation we applied two different methods to demonstrate its effectiveness in identifying changes in the repertoire that correlate with changes in the phenotype: (1) network analysis of the TCR repertoire in which two measures were calculated and demonstrated the ability to differentiate control from transgenic samples, and, (2) machine learning classifier capable of both stratifying control and trangenic samples, as well as to stratify pre-cancer and cancer samples

Spontaneous Emergence of Hierarchy in Biological Systems

Hierarchy is widely observed in biological systems. In this thesis, evidence from nature is presented to show that protein interactions have became increasingly modular as evolution has proceeded over the last four billion years. The evolution of animal body plan development is considered. Results show the genes that determine the phylum and superphylum characters evolve slowly, while those genes that determine classes, families, and speciation evolve more rapidly. This result furnishes support to the hypothesis that the hierarchical structure of developmental regulatory networks provides an organizing structure that guides the evolution of aspects of the body plan. Next, the world trade network is treated as an evolving system. The theory of modularity predicts that the trade network is more sensitive to recessionary shocks and recovers more slowly from them now than it did 40 years ago, due to structural changes in the world trade network induced by globalization. Economic data show that recession-induced change to the world trade network leads to an increased hierarchical structure of the global trade network for a few years after the recession. In the study of influenza virus evolution, an approach for early detection of new dominant strains is presented. This method is shown to be able to identify a cluster around an incipient dominant strain before it becomes dominant. Recently, CRISPR has been suggested to provide adaptive immune response to bacteria. A population dynamics model is proposed that explains the biological observation that the leader-proximal end of CRISPR is more diversified and the leader-distal end of CRISPR is less diversifed. Finally, the creation of diversity of antibody repertoire is investigated. It is commonly believed that a heavy chain is generated by randomly combining V, D and J gene segments. However, using high throughput sequence data in this study, the naive VDJ repertoire is shown to be strongly correlated between individuals, which suggest VDJ recombination involves regulated mechanisms

Intratumoral B and T cell receptors: reconstruction and analysis

When cells divide, mistakes happen. However, an intricate surveillance system has evolved to detect and eliminate anomalous cells before they become detrimental to the host organism. In cancer, abnormal cells manage to escape the immune system and grow uncontrollably. In this sense, cancer can be considered as an oversight of the immune system, as immune escape is a defining feature of clinically detectable cancers. The role of the immune system in fighting cancer is becoming increasingly indisputable as our understanding of its underlying mechanisms expand owing to technological advances in genomics, cancer biology, and computational sciences. In particular, significant research effort is undertaken in the field of cancer immunotherapy, where the immune system is stimulated to recognize and attack cancerous cells. In this thesis, I investigate certain aspects of the immune system in the context of cancer by computationally reconstructing and analyzing intratumoral B and T cell receptors. Applying a novel immune cell receptor profiling protocol to original single-cell RNA sequencing (scRNA-seq) data obtained from melanoma patients, I present a complete computational reconstruction of intratumoral immune receptors in this cancer type. The scRNA-seq results are consistent with the presence of an ongoing intratumoral immune response, likely involving tertiary lymphoid structures and the cooperation between B and T cells. Additionally, using a dataset of paired tumor biopsies collected pre- and post-treatment, I show that B cell infiltration increases after immunotherapy in pancreatic and colorectal cancer. This thesis includes the sequences of the most clonally expanded intratumoral antibodies expressed in these biopsies which I computationally reconstructed from bulk RNA sequencing reads. Furthermore, by combining scRNA-seq and immune cell receptor profiling of samples collected from a novel mouse model, I present a comprehensive statistical analysis of gene expression in clonal tumor-reactive T cells. I also show the distribution of tumor-reactive clones across the tumor and spleen. This study forms a first proof-of-principle effort for the in-depth assessment of tumor-reactive T and B cell clones, in-vivo, and paves the way for further, more extensive experiments

Apyrase-mediated amplification of secretory IgA promotes intestinal homeostasis.

Secretory immunoglobulin A (SIgA) interaction with commensal bacteria conditions microbiota composition and function. However, mechanisms regulating reciprocal control of microbiota and SIgA are not defined. Bacteria-derived adenosine triphosphate (ATP) limits T follicular helper (Tfh) cells in the Peyer's patches (PPs) via P2X7 receptor (P2X7R) and thereby SIgA generation. Here we show that hydrolysis of extracellular ATP (eATP) by apyrase results in amplification of the SIgA repertoire. The enhanced breadth of SIgA in mice colonized with apyrase-releasing Escherichia coli influences topographical distribution of bacteria and expression of genes involved in metabolic versus immune functions in the intestinal epithelium. SIgA-mediated conditioning of bacteria and enterocyte function is reflected by differences in nutrient absorption in mice colonized with apyrase-expressing bacteria. Apyrase-induced SIgA improves intestinal homeostasis and attenuates barrier impairment and susceptibility to infection by enteric pathogens in antibiotic-induced dysbiosis. Therefore, amplification of SIgA by apyrase can be leveraged to restore intestinal fitness in dysbiotic conditions

Reference-based comparison of adaptive immune receptor repertoires

B- and T-cell receptor (immune) repertoires can represent an individual’s immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters (e.g., clonal diversity, germline usage). Here, we introduce immuneREF: a quantitative multi-dimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. immuneREF is implemented in an R package and was validated based on detection sensitivity of immune repertoires with known similarities and dissimilarities. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2400 datasets from individuals with varying immune states (healthy, [autoimmune] disease and infection [Covid-19], immune cell population). Importantly we discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF implements population-wide analysis of immune repertoire similarity and thus enables the study of the adaptive immune response across health and disease states.Support was provided from The Helmsley Charitable Trust (#2019PG-T1D011, to VG), UiO WorldLeading Research Community (to VG), UiO:LifeSciences Convergence Environment Immunolingo (to VG and GKS), EU Horizon 2020 iReceptorplus (#825821) (to VG), a Research Council of Norway FRIPRO project (#300740, to VG), a Research Council of Norway IKTPLUSS project (#311341, to VG and GKS), a Norwegian Cancer Society grant (#215817, to VG), and Stiftelsen Kristian Gerhard Jebsen (K.G. Jebsen Coeliac Disease Research Centre) (to GKS), Swiss National Science Foundation (Project 31003A to S.T.R), the Norwegian Research Council, Helse Sør-Øst, and the University of Oslo through the Centre for Molecular Medicine Norway (#187615 to MLK).N

Sox10 regulates enteric neural crest cell migration in the developing gut

Concurrent Sessions 1: 1.3 - Organs to organisms: Models of Human Diseases: abstract no. 1417th ISDB 2013 cum 72nd Annual Meeting of the Society for Developmental Biology, VII Latin American Society of Developmental Biology Meeting and XI Congreso de la Sociedad Mexicana de Biologia del Desarrollo. The Conference's web site is located at http://www.inb.unam.mx/isdb/Sox10 is a HMG-domain containing transcription factor which plays important roles in neural crest cell survival and differentiation. Mutations of Sox10 have been identified in patients with Waardenburg-Hirschsprung syndrome, who suffer from deafness, pigmentation defects and intestinal aganglionosis. Enteric neural crest cells (ENCCs) with Sox10 mutation undergo premature differentiation and fail to colonize the distal hindgut. It is unclear, however, whether Sox10 plays a role in the migration of ENCCs. To visualize the migration behaviour of mutant ENCCs, we generated a Sox10NGFP mouse model where EGFP is fused to the N-terminal domain of Sox10. Using time-lapse imaging, we found that ENCCs in Sox10NGFP/+ mutants displays lower migration speed and altered trajectories compared to normal controls. This behaviour was cell-autonomous, as shown by organotypic grafting of Sox10NGFP/+ gut segments onto control guts and vice versa. ENCCs encounter different extracellular matrix (ECM) molecules along the developing gut. We performed gut explant culture on various ECM and found that Sox10NGFP/+ ENCCs tend to form aggregates, particularly on fibronectin. Time-lapse imaging of single cells in gut explant culture indicated that the tightly-packed Sox10 mutant cells failed to exhibit contact inhibition of locomotion. We determined the expression of adhesion molecule families by qPCR analysis, and found integrin expression unaffected while L1-cam and selected cadherins were altered, suggesting that Sox10 mutation affects cell adhesion properties of ENCCs. Our findings identify a de novo role of Sox10 in regulating the migration behaviour of ENCCs, which has important implications for the treatment of Hirschsprung disease.postprin