Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity

IntroductionThe success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance.MethodsWe report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls.ResultsIn contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients.DiscussionThese features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges

PRAS40 and PRR5-Like Protein Are New mTOR Interactors that Regulate Apoptosis

TOR (Target of Rapamycin) is a highly conserved protein kinase and a central controller of cell growth. TOR is found in two functionally and structurally distinct multiprotein complexes termed TOR complex 1 (TORC1) and TOR complex 2 (TORC2). In the present study, we developed a two-dimensional liquid chromatography tandem mass spectrometry (2D LC-MS/MS) based proteomic strategy to identify new mammalian TOR (mTOR) binding proteins. We report the identification of Proline-rich Akt substrate (PRAS40) and the hypothetical protein Q6MZQ0/FLJ14213/CAE45978 as new mTOR binding proteins. PRAS40 binds mTORC1 via Raptor, and is an mTOR phosphorylation substrate. PRAS40 inhibits mTORC1 autophosphorylation and mTORC1 kinase activity toward eIF-4E binding protein (4E-BP) and PRAS40 itself. HeLa cells in which PRAS40 was knocked down were protected against induction of apoptosis by TNFα and cycloheximide. Rapamycin failed to mimic the pro-apoptotic effect of PRAS40, suggesting that PRAS40 mediates apoptosis independently of its inhibitory effect on mTORC1. Q6MZQ0 is structurally similar to proline rich protein 5 (PRR5) and was therefore named PRR5-Like (PRR5L). PRR5L binds specifically to mTORC2, via Rictor and/or SIN1. Unlike other mTORC2 members, PRR5L is not required for mTORC2 integrity or kinase activity, but dissociates from mTORC2 upon knock down of tuberous sclerosis complex 1 (TSC1) and TSC2. Hyperactivation of mTOR by TSC1/2 knock down enhanced apoptosis whereas PRR5L knock down reduced apoptosis. PRR5L knock down reduced apoptosis also in mTORC2 deficient cells. The above suggests that mTORC2-dissociated PRR5L may promote apoptosis when mTOR is hyperactive. Thus, PRAS40 and PRR5L are novel mTOR-associated proteins that control the balance between cell growth and cell death

Regulation of adipogenesis and adipose maintenance by the mammalian TOR complex 1

mTOR, an atypical serine/threonine kinase, is a central component of a highly conserved signal transduction cascade that controls cell growth. It functions as part of two distinct multiprotein complexes, mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2). mTORC1 contains mTOR, raptor, mLST8, and PRAS40, and is sensitive to the immunosuppressive and anti‐cancer drug rapamycin. mTORC1 controls protein synthesis via phosphorylation of two well characterized effectors, the kinase S6K and the translational repressor 4E‐BP1. mTORC2 contains mTOR, mLST8, rictor and mSin1, and is not directly inhibited by rapamycin, although long term rapamycin treatment can inhibit mTORC2 indirectly in certain cell types. It controls organization of the actin cytoskeleton. Both complexes are conserved in structure and function from yeast to human. The mTOR signaling pathway is controlled by nutrients, cellular energy status, and growth factors such as insulin. Since mTOR is regulated by metabolic signals, we focused our research on the roles of the mTOR signaling pathway in metabolic tissues, in particular adipose tissue. My research project concentrated on studying how mTORC1 signaling affects adipocytes, in tissue culture and in mice. Adipose tissue functions mainly as a long term fat storage depot. However, it is also an important endocrine organ, which secretes hormones, cytokines and complement factors. In this thesis, I first present evidence confirming that mTORC1 is required for the differentiation and maintenance of adipocytes in vitro. In tissue culture, inhibition of mTORC1 caused a decrease in the expression of adipose transcription factors, which led to a decreased expression of genes related to fat metabolism and storage. This resulted in de‐differentiation of the cells, manifested as loss of intracellular triglycerides. I further focused my research on the key adipogenic transcription factor PPARγ, and tried to elucidate the molecular mechanism by which mTORC1 regulates its activity. The results suggested that rapamycin treatment acts to inhibit PPARγ downstream of its ligands. To investigate a role of adipose mTORC1 in regulation of adipose and whole body metabolism, we generated mice with an adipose‐specific knockout of raptor (raptorad‐/‐). Compared to control littermates, raptorad‐/‐ mice had substantially less adipose tissue, were protected against diet‐induced obesity and hypercholesterolemia, and exhibited improved insulin sensitivity. Leanness was despite reduced physical activity and unaffected caloric intake, lipolysis, and absorption of lipids from the food. White adipose tissue of raptorad‐/‐ mice displayed enhanced expression of genes encoding mitochondrial uncoupling proteins characteristic of brown fat. Leanness of the raptorad‐ /‐ mice was attributed to elevated energy expenditure due to mitochondrial uncoupling. These results suggest that adipose mTORC1 is a regulator of adipose metabolism and thereby controls whole body energy homeostasis

mTORC2 Caught in a SINful Akt

The target of rapamycin (TOR), a central controller of cell growth, is found in two distinct, highly conserved multiprotein complexes. Three recent papers in Cell (Jacinto et al., 2006), Developmental Cell (Shiota et al., 2006; this issue), and Current Biology (Frias et al., 2006) shed light on mTOR complex 2 (mTORC2) composition and in vivo function. An important new finding is that mTORC2 determines Akt/PKB substrate specificity rather than absolute activity

RAbHIT: R Antibody Haplotype Inference Tool

Abstract Summary Antibody haplotype inference (chromosomal phasing) may have clinical implications for the identification of genetic predispositions to diseases. Yet, our knowledge of the genomic loci encoding for the variable regions of the antibody is only partial, mostly due to the challenge of aligning short reads from genome sequencing to these highly repetitive loci. A powerful approach to infer the content of these loci relies on analyzing repertoires of rearranged V(D)J sequences. We present here RAbHIT, an R Haplotype Antibody Inference Tool, that implements a novel algorithm to infer V(D)J haplotypes by adapting a Bayesian framework. RAbHIT offers inference of haplotype and gene deletions. It may be applied to sequences from naïve and non-naïve B-cells, sequenced by different library preparation protocols. Availability and implementation RAbHIT is freely available for academic use from comprehensive R archive network (CRAN) (https://cran.r-project.org/web/packages/rabhit/) under CC BY-SA 4.0 license. Supplementary information Supplementary data are available at Bioinformatics online. </jats:sec

Immune2vec: Embedding B/T Cell Receptor Sequences in ℝN Using Natural Language Processing

The adaptive branch of the immune system learns pathogenic patterns and remembers them for future encounters. It does so through dynamic and diverse repertoires of T- and B- cell receptors (TCR and BCRs, respectively). These huge immune repertoires in each individual present investigators with the challenge of extracting meaningful biological information from multi-dimensional data. The ability to embed these DNA and amino acid textual sequences in a vector-space is an important step towards developing effective analysis methods. Here we present Immune2vec, an adaptation of a natural language processing (NLP)-based embedding technique for BCR repertoire sequencing data. We validate Immune2vec on amino acid 3-gram sequences, continuing to longer BCR sequences, and finally to entire repertoires. Our work demonstrates Immune2vec to be a reliable low-dimensional representation that preserves relevant information of immune sequencing data, such as n-gram properties and IGHV gene family classification. Applying Immune2vec along with machine learning approaches to patient data exemplifies how distinct clinical conditions can be effectively stratified, indicating that the embedding space can be used for feature extraction and exploratory data analysis.</jats:p

Ontogeny of the B Cell Receptor Repertoire and Microbiome in Mice

Abstract The immune system matures throughout childhood to achieve full functionality in protecting our bodies against threats. The immune system has a strong reciprocal symbiosis with the host bacterial population and the two systems co-develop, shaping each other. Despite their fundamental role in health physiology, the ontogeny of these systems is poorly characterized. In this study, we investigated the development of the BCR repertoire by analyzing high-throughput sequencing of their receptors in several time points of young C57BL/6J mice. In parallel, we explored the development of the gut microbiome. We discovered that the gut IgA repertoires change from birth to adolescence, including an increase in CDR3 lengths and somatic hypermutation levels. This contrasts with the spleen IgM repertoires that remain stable and distinct from the IgA repertoires in the gut. We also discovered that large clones that germinate in the gut are initially confined to a specific gut compartment, then expand to nearby compartments and later on expand also to the spleen and remain there. Finally, we explored the associations between diversity indices of the B cell repertoires and the microbiome, as well as associations between bacterial and BCR clusters. Our results shed light on the ontogeny of the adaptive immune system and the microbiome, providing a baseline for future research.</jats:p

Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls

Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naïve T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naïve B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naïve BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naïve BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naïve BCR repertoires, as disease susceptibility markers.</jats:p