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

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

Mosaic deletion patterns of the human antibody heavy chain gene locus shown by Bayesian haplotyping

Analysis of antibody repertoires by high-throughput sequencing is of major importance in understanding adaptive immune responses. Our knowledge of variations in the genomic loci encoding immunoglobulin genes is incomplete, resulting in conflicting VDJ gene assignments and biased genotype and haplotype inference. Haplotypes can be inferred using IGHJ6 heterozygosity, observed in one third of the people. Here, we propose a robust novel method for determining VDJ haplotypes by adapting a Bayesian framework. Our method extends haplotype inference to IGHD- and IGHV-based analysis, enabling inference of deletions and copy number variations in the entire population. To test this method, we generated a multi-individual data set of naive B-cell repertoires, and found allele usage bias, as well as a mosaic, tiled pattern of deleted IGHD and IGHV genes. The inferred haplotypes may have clinical implications for genetic disease predispositions. Our findings expand the knowledge that can be extracted from antibody repertoire sequencing data

Hepatic mTORC2 activates glycolysis and lipogenesis through akt, glucokinase, and SREBP1c

Mammalian target of rapamycin complex 2 (mTORC2) phosphorylates and activates AGC kinase family members, including Akt, SGK1, and PKC, in response to insulin/IGF1. The liver is a key organ in insulin-mediated regulation of metabolism. To assess the role of hepatic mTORC2, we generated liver-specific rictor knockout (LiRiKO) mice. Fed LiRiKO mice displayed loss of Akt Ser473 phosphorylation and reduced glucokinase and SREBP1c activity in the liver, leading to constitutive gluconeogenesis, and impaired glycolysis and lipogenesis, suggesting that the mTORC2-deficient liver is unable to sense satiety. These liver-specific defects resulted in systemic hyperglycemia, hyperinsulinemia, and hypolipidemia. Expression of constitutively active Akt2 in mTORC2-deficient hepatocytes restored both glucose flux and lipogenesis, whereas glucokinase overexpression rescued glucose flux but not lipogenesis. Thus, mTORC2 regulates hepatic glucose and lipid metabolism via insulin-induced Akt signaling to control whole-body metabolic homeostasis. These findings have implications for emerging drug therapies that target mTORC2