Guidelines for reproducible analysis of adaptive immune receptor repertoire sequencing data.

Enhancing the reproducibility and comprehension of adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis is critical for scientific progress. This study presents guidelines for reproducible AIRR-seq data analysis, and a collection of ready-to-use pipelines with comprehensive documentation. To this end, ten common pipelines were implemented using ViaFoundry, a user-friendly interface for pipeline management and automation. This is accompanied by versioned containers, documentation and archiving capabilities. The automation of pre-processing analysis steps and the ability to modify pipeline parameters according to specific research needs are emphasized. AIRR-seq data analysis is highly sensitive to varying parameters and setups; using the guidelines presented here, the ability to reproduce previously published results is demonstrated. This work promotes transparency, reproducibility, and collaboration in AIRR-seq data analysis, serving as a model for handling and documenting bioinformatics pipelines in other research domains.

IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data.

In adaptive immune receptor repertoire analysis, determining the germline variable (V) allele associated with each T- and B-cell receptor sequence is a crucial step. This process is highly impacted by allele annotations. Aligning sequences, assigning them to specific germline alleles, and inferring individual genotypes are challenging when the repertoire is highly mutated, or sequence reads do not cover the whole V region. Here, we propose an alternative naming scheme for the V alleles, as well as a novel method to infer individual genotypes. We demonstrate the strengths of the two by comparing their outcomes to other genotype inference methods. We validate the genotype approach with independent genomic long-read data. The naming scheme is compatible with current annotation tools and pipelines. Analysis results can be converted from the proposed naming scheme to the nomenclature determined by the International Union of Immunological Societies (IUIS). Both the naming scheme and the genotype procedure are implemented in a freely available R package (PIgLET https://bitbucket.org/yaarilab/piglet). To allow researchers to further explore the approach on real data and to adapt it for their uses, we also created an interactive website (https://yaarilab.github.io/IGHV_reference_book).

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

Introduction: The 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. Methods: We 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. Results: In 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. Discussion: These 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.

A somatic hypermutation-based machine learning model stratifies individuals with Crohn's disease and controls.

Crohn's disease (CD) is a chronic relapsing-remitting inflammatory disorder of the gastrointestinal tract that is characterized by altered innate and adaptive immune function. Although massively parallel sequencing studies of the T cell receptor repertoire identified oligoclonal expansion of unique clones, much less is known about the B cell receptor (BCR) repertoire in CD. Here, we present a novel BCR repertoire sequencing data set from ileal biopsies from pediatric patients with CD and controls, and identify CD-specific somatic hypermutation (SHM) patterns, revealed by a machine learning (ML) algorithm trained on BCR repertoire sequences. Moreover, ML classification of a different data set from blood samples of adults with CD versus controls identified that V gene usage, clusters, or mutation frequencies yielded excellent results in classifying the disease (F1 > 90%). In summary, we show that an ML algorithm enables the classification of CD based on unique BCR repertoire features with high accuracy.

Ontogeny of the B Cell Receptor Repertoire and Microbiome in Mice.

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.

T cell receptor beta germline variability is revealed by inference from repertoire data.

BACKGROUND: T and B cell receptor (TCR, BCR) repertoires constitute the foundation of adaptive immunity. Adaptive immune receptor repertoire sequencing (AIRR-seq) is a common approach to study immune system dynamics. Understanding the genetic factors influencing the composition and dynamics of these repertoires is of major scientific and clinical importance. The chromosomal loci encoding for the variable regions of TCRs and BCRs are challenging to decipher due to repetitive elements and undocumented structural variants. METHODS: To confront this challenge, AIRR-seq-based methods have recently been developed for B cells, enabling genotype and haplotype inference and discovery of undocumented alleles. However, this approach relies on complete coverage of the receptors' variable regions, whereas most T cell studies sequence a small fraction of that region. Here, we adapted a B cell pipeline for undocumented alleles, genotype, and haplotype inference for full and partial AIRR-seq TCR data sets. The pipeline also deals with gene assignment ambiguities, which is especially important in the analysis of data sets of partial sequences. RESULTS: From the full and partial AIRR-seq TCR data sets, we identified 39 undocumented polymorphisms in T cell receptor Beta V (TRBV) and 31 undocumented 5 ' UTR sequences. A subset of these inferences was also observed using independent genomic approaches. We found that a single nucleotide polymorphism differentiating between the two documented T cell receptor Beta D2 (TRBD2) alleles is strongly associated with dramatic changes in the expressed repertoire. CONCLUSIONS: We reveal a rich picture of germline variability and demonstrate how a single nucleotide polymorphism dramatically affects the composition of the whole repertoire. Our findings provide a basis for annotation of TCR repertoires for future basic and clinical studies.

Immune2vec: Embedding B/T Cell Receptor Sequences in ℝ N 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.

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.

VDJbase: an adaptive immune receptor genotype and haplotype database.

VDJbase is a publicly available database that offers easy searching of data describing the complete sets of gene sequences (genotypes and haplotypes) inferred from adaptive immune receptor repertoire sequencing datasets. VDJbase is designed to act as a resource that will allow the scientific community to explore the genetic variability of the immunoglobulin (Ig) and T cell receptor (TR) gene loci. It can also assist in the investigation of Ig- and TR-related genetic predispositions to diseases. Our database includes web-based query and online tools to assist in visualization and analysis of the genotype and haplotype data. It enables users to detect those alleles and genes that are significantly over-represented in a particular population, in terms of genotype, haplotype and gene expression. The database website can be freely accessed at https://www.vdjbase.org/, and no login is required. The data and code use creative common licenses and are freely downloadable from https://bitbucket.org/account/user/yaarilab/projects/GPHP.

RAbHIT: R Antibody Haplotype Inference Tool.

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.

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.

Antibody Repertoire Analysis of Hepatitis C Virus Infections Identifies Immune Signatures Associated With Spontaneous Clearance.

Hepatitis C virus (HCV) is a major public health concern, with over 70 million people infected worldwide, who are at risk for developing life-threatening liver disease. No vaccine is available, and immunity against the virus is not well-understood. Following the acute stage, HCV usually causes chronic infections. However, ~30% of infected individuals spontaneously clear the virus. Therefore, using HCV as a model for comparing immune responses between spontaneous clearer (SC) and chronically infected (CI) individuals may empower the identification of mechanisms governing viral infection outcomes. Here, we provide the first in-depth analysis of adaptive immune receptor repertoires in individuals with current or past HCV infection. We demonstrate that SC individuals, in contrast to CI patients, develop clusters of antibodies with distinct properties. These antibodies' characteristics were used in a machine learning framework to accurately predict infection outcome. Using combinatorial antibody phage display library technology, we identified HCV-specific antibody sequences. By integrating these data with the repertoire analysis, we constructed two antibodies characterized by high neutralization breadth, which are associated with clearance. This study provides insight into the nature of effective immune response against HCV and demonstrates an innovative approach for constructing antibodies correlating with successful infection clearance. It may have clinical implications for prognosis of the future status of infection, and the design of effective immunotherapies and a vaccine for HCV.

Selective inactivation of enzymes conjugated to nanoparticles using tuned laser illumination.

We report a novel method for specific deactivation of conjugated enzymes using laser-heated gold nanoparticles. Current methods involve treatment of the entire solution, thereby inactivating all bioactive components. Our method enables inactivation of only a single or subset of targeted enzymes. The selected enzyme is pre-conjugated to gold nanoparticles, which are specifically heated by a laser tuned to their surface plasmon resonance. We demonstrate inactivation of a selected enzyme, glucose oxidase, within a mixture of biomolecules. Illumination of non-conjugated enzymes and nanoparticles demonstrated specificity. We propose a novel method to quantitatively regulate enzyme activity, providing a building block for cellular and cell-free biochemical reactions. © 2016 International Society for Advancement of Cytometry.

Novel role for retinol-binding protein 4 in the regulation of blood pressure.

Elevated levels of serum retinol-binding protein 4 (RBP4) contribute to insulin resistance and correlate with increased prevalence of hypertension and myocardial infarction. We sought to determine whether lowering RBP4 would improve blood pressure (BP) and protect against obesity- or angiotensin (Ang)-II-induced hypertension. Systolic and diastolic BP were lower in the RBP4-knockout (RBP4-KO) mice and higher in the RBP4-overexpressing (RBP4-Tg) mice compared with BP in the wild-type (WT) littermates. Carbachol-induced vasodilatation was increased in arteries from the RBP4-KO compared with the WT mice and was impaired in the RBP4-Tg mice. Aortic eNOS(Ser1177) phosphorylation was enhanced ∼50% in the RBP4-KO mice, with no change in total eNOS protein. Feeding a high-fat diet increased BP in the RBP4-KO mice only to the level in the WT mice fed chow and had no effect on aortic eNOS(Ser1177) phosphorylation. Ang-II infusion resulted in 22 mmHg lower systolic BP in the RBP4-KO than in the WT mice, although the relative BP increase over saline infusion was ∼30% in both. Ang-II treatment decreased aortic eNOS(Ser1177) phosphorylation in the WT and RBP4-KO mice, but phosphorylation remained higher in the RBP4-KO mice. Cardiac hypertrophy with Ang-II treatment was diminished by 56% in the RBP4-KO mice. Thus, elevated serum RBP4 raises BP and lack of RBP4 reduces it, with commensurate changes in aortic eNOS(Ser1177) phosphorylation. Lowering RBP4 may reduce BP through enhanced eNOS-mediated vasodilatation and may be a novel therapeutic approach for hypertension.

Nanometric agents in the service of neuroscience: Manipulation of neuronal growth and activity using nanoparticles.

Nerve regeneration and recovery could provide great therapeutic benefits for individuals suffering from nerve damage post trauma or degenerative diseases. However, manipulation of nerves presents a huge challenge for neuroscientists and is not yet clinically feasible. In recent years, nanoparticles have emerged as novel effective agents for control of neuronal growth and behavior. Nanoparticles may facilitate the needed nerve manipulation abilities for therapeutic and diagnostic purposes including within the brain. This review aims at presenting the currently available literature regarding the interactions between inorganic nanoparticles and neurons. A wide range of nanoparticles are presented, including gold, iron oxide, cerium oxide, nanotubes and quantum-dots. The nanoparticles enhance neuronal differentiation and survival, direct growth and regulate electrical activity. The studies are summarized in a concise table, arranged by the function and type of nanoparticle. The latest studies present a novel interdisciplinary approach, which could be harnessed for clinical applications in nanomedicine. FROM THE CLINICAL EDITOR: Nerve regeneration remains the Holy Grail for patients with neuron loss. Nonetheless, this goal has not been realized in clinical setting thus far. In this article, the authors present a comprehensive review on various nanoparticle-based approaches, in both diagnosis and therapy, which should stimulate and generate more research ideas to the advancement in this field.

The synergistic effect of visible light and gentamycin on Pseudomona aeruginosa microorganisms.

Recently there were several publications on the bactericidal effect of visible light, most of them claiming that blue part of the spectrum (400 nm-500 nm) is responsible for killing various pathogens(1-5). The phototoxic effect of blue light was suggested to be a result of light-induced reactive oxygen species (ROS) formation by endogenous bacterial photosensitizers which mostly absorb light in the blue region(4,6,7). There are also reports of biocidal effect of red and near infra red(8) as well as green light(9). In the present study, we developed a method that allowed us to characterize the effect of high power green (wavelength of 532 nm) continuous (CW) and pulsed Q-switched (Q-S) light on Pseudomonas aeruginosa. Using this method we also studied the effect of green light combined with antibiotic treatment (gentamycin) on the bacteria viability. P. aeruginosa is a common noscomial opportunistic pathogen causing various diseases. The strain is fairly resistant to various antibiotics and contains many predicted AcrB/Mex-type RND multidrug efflux systems(10). The method utilized free-living stationary phase Gram-negative bacteria (P. aeruginosa strain PAO1), grown in Luria Broth (LB) medium exposed to Q-switched and/or CW lasers with and without the addition of the antibiotic gentamycin. Cell viability was determined at different time points. The obtained results showed that laser treatment alone did not reduce cell viability compared to untreated control and that gentamycin treatment alone only resulted in a 0.5 log reduction in the viable count for P. aeruginosa. The combined laser and gentamycin treatment, however, resulted in a synergistic effect and the viability of P. aeruginosa was reduced by 8 log's. The proposed method can further be implemented via the development of catheter like device capable of injecting an antibiotic solution into the infected organ while simultaneously illuminating the area with light.

Thermal degradation of DNA.

In this article, we investigate the thermal degradation of deoxyribonucleic acid (DNA). We find that under dry conditions, complete DNA degradation occurs at above 190°C. In addition, as the boiling temperature of water is pressure dependent, we have investigated the thermal degradation of the DNA in water for different applied partial pressures. This information is important for fundamental understanding of DNA structure and energetics, and can be useful for biomedical applications such as thermal targeting of DNA in cancer cells, as well as for basic research.

Gold nanoparticles-based biosensing of single nucleotide DNA mutations.

Detection of DNA mutations is critical for scientific research and diagnostic procedures. Here, we propose a novel interdisciplinary method for rapid and simple detection of DNA mutations. We show that heating a solution containing DNA and gold nanoparticles results in degradation of the DNA. Surprisingly, we found that the DNA can be protected against degradation, if an oligonucleotide that matches the DNA is added to the solution. Moreover, the level of degradation indicates the presence of mutations in the DNA. The method is sensitive enough to indicate even a single nucleotide difference, and has the potential to ultimately replace initial medical genetic tests. As proof of concept, we demonstrate a clear detection of two of the most common mutations leading to Cystic Fibrosis.

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.

mTOR complex 2 in adipose tissue negatively controls whole-body growth.

Mammalian target of rapamycin (mTOR), a highly conserved protein kinase that controls cell growth and metabolism in response to nutrients and growth factors, is found in 2 structurally and functionally distinct multiprotein complexes termed mTOR complex 1 (mTORC1) and mTORC2. mTORC2, which consists of rictor, mSIN1, mLST8, and mTOR, is activated by insulin/IGF1 and phosphorylates Ser-473 in the hydrophobic motif of Akt/PKB. Though the role of mTOR in single cells is relatively well characterized, the role of mTOR signaling in specific tissues and how this may contribute to overall body growth is poorly understood. To examine the role of mTORC2 in an individual tissue, we generated adipose-specific rictor knockout mice (rictor(ad-/-)). Rictor(ad-/-) mice are increased in body size due to an increase in size of nonadipose organs, including heart, kidney, spleen, and bone. Furthermore, rictor(ad-/-) mice have a disproportionately enlarged pancreas and are hyperinsulinemic, but glucose tolerant, and display elevated levels of insulin-like growth factor 1 (IGF1) and IGF1 binding protein 3 (IGFBP3). These effects are observed in mice on either a high-fat or a normal diet, but are generally more pronounced in mice on a high-fat diet. Our findings suggest that adipose tissue, in particular mTORC2 in adipose tissue, plays an unexpectedly central role in controlling whole-body growth.