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Introduction: H chain rearrangement in B cells is a two-step process where first D H binds J H , and only then V H is joined to the complex. As such, there is no direct rearrangement between V H and J H ., Results: Nevertheless, we here show that the V H JH combinations frequency in humans deviates from the one expected based on each gene usage frequency. This bias is observed mainly in functional rearrangements, and much less in out-of-frame rearrangements. The bias cannot be explained by preferred binding for D H genes or a preferred reading frame. Preferred V H J H combinations are shared between donors., Discussion: These results suggest a common structural mechanism for these biases. Through development, thepreferred V H J H combinations evolve during peripheral selection to become stronger, but less shared. We propose that peripheral Heavy chain V H J H usage is initially shaped by a structural selection before the naive B cellstate, followed by pathogen-induced selection for host specific V H - J H pairs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Levi, Dvorkin and Louzoun.)

Background: Some microbiota compositions are associated with negative outcomes, including among others, obesity, allergies, and the failure to respond to treatment. Microbiota manipulation or supplementation can restore a community associated with a healthy condition. Such interventions are typically probiotics or fecal microbiota transplantation (FMT). FMT donor selection is currently based on donor phenotype, rather than the anticipated microbiota composition in the recipient and associated health benefits. However, the donor and post-transplant recipient conditions differ drastically. We here propose an algorithm to identify ideal donors and predict the expected outcome of FMT based on donor microbiome alone. We also demonstrate how to optimize FMT for different required outcomes., Results: We show, using multiple microbiome properties, that donor and post-transplant recipient microbiota differ widely and propose a tool to predict the recipient post-transplant condition (engraftment success and clinical outcome), using only the donors' microbiome and, when available, demographics for transplantations from humans to either mice or other humans (with or without antibiotic pre-treatment). We validated the predictor using a de novo FMT experiment highlighting the possibility of choosing transplants that optimize an array of required goals. We then extend the method to characterize a best-planned transplant (bacterial cocktail) by combining the predictor and a generative genetic algorithm (GA). We further show that a limited number of taxa is enough for an FMT to produce a desired microbiome or phenotype., Conclusions: Off-the-shelf FMT requires recipient-independent optimized FMT selection. Such a transplant can be from an optimal donor or from a cultured set of microbes. We have here shown the feasibility of both types of manipulations in mouse and human recipients. Video Abstract., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Pre-clinical development and in-human trials of 'off-the-shelf' immune effector cell therapy (IECT) are burgeoning. IECT offers many potential advantages over autologous products. The relevant HLA matching criteria vary from product to product and depend on the strategies employed to reduce the risk of GvHD or to improve allo-IEC persistence, as warranted by different clinical indications, disease kinetics, on-target/off-tumor effects, and therapeutic cell type (T cell subtype, NK, etc.)., Objective: The optimal choice of candidate donors to maximize target patient population coverage and minimize cost and redundant effort in creating off-the-shelf IECT product banks is still an open problem. We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing., Study Design: We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). We then compared the efficiency of different optimization algorithms - a greedy solution, a linear programming (LP) solution, and integer linear programming (ILP) -- as well as random donor selection (average of 5 random trials) to show that an optimization can be performed at the entire population level., Results: The average additional population coverage per donor decrease with the number of donors, and varies with the scenario. The Greedy, LP and ILP algorithms consistently achieve the optimal coverage with far fewer donors than the random choice. In all cases, the number of randomly-selected donors required to achieve a desired coverage increases with increasing population. However, when optimal donors are selected, the number of donors required may counter-intuitively decrease with increasing population size. When comparing recruiting more donors vs gene editing, the latter was generally more expensive. When choosing donors and patients from different populations, the number of random donors required drastically increases, while the number of optimal donors does not change. Random donors fail to cover populations different from their original populations, while a small number of optimal donors from one population can cover a different population., Discussion: Graph-based coverage optimization algorithms can flexibly handle various HLA matching criteria and accommodate additional information such as KIR genotype, when such information becomes routinely available. These algorithms offer a more efficient way to develop off-the-shelf IECT product banks compared to random donor selection and offer some possibility of improved transparency and standardization in product design., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer BM declared a past co-authorship with the author MM to the handling editor., (Copyright © 2023 Israeli, Krakow, Maiers, Summers and Louzoun.)

Objective: Gestational diabetes mellitus (GDM) is a condition in which women without diabetes are diagnosed with glucose intolerance during pregnancy, typically in the second or third trimester. Early diagnosis, along with a better understanding of its pathophysiology during the first trimester of pregnancy, may be effective in reducing incidence and associated short-term and long-term morbidities., Design: We comprehensively profiled the gut microbiome, metabolome, inflammatory cytokines, nutrition and clinical records of 394 women during the first trimester of pregnancy, before GDM diagnosis. We then built a model that can predict GDM onset weeks before it is typically diagnosed. Further, we demonstrated the role of the microbiome in disease using faecal microbiota transplant (FMT) of first trimester samples from pregnant women across three unique cohorts., Results: We found elevated levels of proinflammatory cytokines in women who later developed GDM, decreased faecal short-chain fatty acids and altered microbiome. We next confirmed that differences in GDM-associated microbial composition during the first trimester drove inflammation and insulin resistance more than 10 weeks prior to GDM diagnosis using FMT experiments. Following these observations, we used a machine learning approach to predict GDM based on first trimester clinical, microbial and inflammatory markers with high accuracy., Conclusion: GDM onset can be identified in the first trimester of pregnancy, earlier than currently accepted. Furthermore, the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis. Potential GDM markers, including microbiota, can serve as targets for early diagnostics and therapeutic intervention leading to prevention., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background and Objectives: To explore the clinical characteristics and HLA associations of patients with anti-leucine-rich glioma-inactivated 1 encephalitis (LGI1E) from a large single center in Israel. Anti-LGI1E is the most commonly diagnosed antibody-associated encephalitic syndrome in adults. Recent studies of various populations reveal significant associations with specific HLA genes. We examined the clinical characteristics and HLA associations of a cohort of Israeli patients., Methods: Seventeen consecutive patients with anti-LGI1E diagnosed at Tel Aviv Medical Center between the years 2011 and 2018 were included. HLA typing was performed using next-generation sequencing at the tissue typing laboratory of Sheba Medical Center and compared with data from the Ezer Mizion Bone Marrow Donor Registry, containing over 1,000,000 samples., Results: Our cohort displayed a male predominance and median age at onset in the 7th decade, as previously reported. The most common presenting symptom was seizures. Notably, paroxysmal dizziness spells were significantly more common than previously reported (35%), whereas faciobrachial dystonic seizures were found only in 23%. HLA analysis revealed overrepresentation of DRB1*07:01 (OR: 3.18, CI: 20.9 p < 1.e-5) and DRB1*04:02 (OR: 3.8, CI: 20.1 p < 1.e-5), as well as of the DQ allele DQB1*02:02 (OR: 2.8, CI: 14.2 p < 0.0001) as previously reported. A novel overrepresentation observed among our patients was of the DQB1*03:02 allele (OR: 2.3, CI: 6.9 p < 0.008). In addition, we found DR-DQ associations, among patients with anti-LGI1E, that showed complete or near-complete linkage disequilibrium (LD). By applying LD analysis to an unprecedentedly large control cohort, we were able to show that although in the general population, DQB*03:02 is not fully associated with DRB1*04:02, in the patient population, both alleles are always coupled, suggesting the DRB1*04:02 association to be primary to disease predisposition. In silico predictions performed for the overrepresented DQ alleles reveal them to be strong binders of LGI1-derived peptides, similarly to overrepresented DR alleles. These predictions suggest a possible correlation between peptide binding sites of paired DR-DQ alleles., Discussion: Our cohort presents distinct immune characteristics with substantially higher overrepresentation of DRB1*04:02 and slightly lower overrepresentation of DQB1*07:01 compared with previous reports implying differences between different populations. DQ-DR interactions found in our cohort may shed additional light on the complex role of immunogenetics in the pathogenesis of anti-LGI1E, implying a possible relevance of certain DQ alleles and DR-DQ interactions., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Background: Labor progression curves are believed to differ between spontaneous and induced labors. However, data describing labor progression patterns with different modes of induction are insufficient., Objective: This study aimed to compare the progress patterns between labors induced with slow-release prostaglandin E 2 vaginal analogue and those induced with a double-balloon catheter., Study Design: This retrospective cohort study included all nulliparous women who delivered at term and who underwent cervical ripening with prostaglandin E 2 vaginal analogue or a double-balloon catheter from 2013 to 2021 in a tertiary hospital in Israel. Included in the analysis were women who achieved 10 cm cervical dilatation. The time intervals between centimeter-to-centimeter changes were evaluated., Results: A total of 1087 women were included of whom 786 (72.3%) were induced using prostaglandin E 2 vaginal analogue and 301 (27.7%) were induced using a double-balloon catheter. The time from induction to birth was similar between the groups (32.5 hours for the prostaglandin E 2 vaginal analogue group [5th-95th percentiles, 6.5-153.8] vs 29.2 hours for the double-balloon group [5th-95th percentiles, 9.1-157.1]; P =.100). The median time of the latent phase (2-6 cm dilation) was longer for the double-balloon catheter group than for the prostaglandin E 2 vaginal analogue group (7.3 hours [5th-95th percentiles, 5.6-14.5] vs 6.0 hours [5th-95th percentiles, 2.4-18.8]; P =.042). The median time of active labor (6-10 cm dilatation) was similar between groups (1.9 hours [5th-95th percentiles, 0.3-7.4] for the prostaglandin E 2 vaginal analogue group vs 2.3 hours [5th-95th percentiles, 0.3-6.5] for the double-balloon catheter group; P =.307)., Conclusion: Deliveries subjected to cervical ripening with a double-balloon catheter were characterized by a slightly longer latent phase than deliveries induced by prostaglandin E 2 vaginal analogue. After reaching the active phase of labor, the mode of cervical ripening did not influence the labor progress pattern., (© 2023 The Authors.)

The immune memory repertoire encodes the history of present and past infections and immunological attributes of the individual. As such, multiple methods were proposed to use T-cell receptor (TCR) repertoires to detect disease history. We here show that the counting method outperforms two leading algorithms. We then show that the counting can be further improved using a novel attention model to weigh the different TCRs. The attention model is based on the projection of TCRs using a Variational AutoEncoder (VAE). Both counting and attention algorithms predict better than current leading algorithms whether the host had CMV and its HLA alleles. As an intermediate solution between the complex attention model and the very simple counting model, we propose a new Graph Convolutional Network approach that obtains the accuracy of the attention model and the simplicity of the counting model. The code for the models used in the paper is provided at: https://github.com/louzounlab/CountingIsAlmostAllYouNeed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Akerman, Isakov, Levi, Psevkin and Louzoun.)

The human gut microbiome is associated with a large number of disease etiologies. As such, it is a natural candidate for machine-learning-based biomarker development for multiple diseases and conditions. The microbiome is often analyzed using 16S rRNA gene sequencing or shotgun metagenomics. However, several properties of microbial sequence-based studies hinder machine learning (ML), including non-uniform representation, a small number of samples compared with the dimension of each sample, and sparsity of the data, with the majority of taxa present in a small subset of samples. We show here using a graph representation that the cladogram structure is as informative as the taxa frequency. We then suggest a novel method to combine information from different taxa and improve data representation for ML using microbial taxonomy. iMic (image microbiome) translates the microbiome to images through an iterative ordering scheme, and applies convolutional neural networks to the resulting image. We show that iMic has a higher precision in static microbiome gene sequence-based ML than state-of-the-art methods. iMic also facilitates the interpretation of the classifiers through an explainable artificial intelligence (AI) algorithm to iMic to detect taxa relevant to each condition. iMic is then extended to dynamic microbiome samples by translating them to movies.

A large number of association studies have related donor characteristics to survival after bone marrow transplantation, for leukemia in general and specifically for acute myeloid leukemia (AML) patients. However, population-based differences often do not hold at the single transplant level. We test whether transplantation outcomes can be predicted at the single-patient level and whether such predictions can be used to better choose donors. The analysis was performed on a mixture of different diseases or with AML only, and with either patient and donor information or donor information only. We analyzed 3671 8-of-8 HLA-matched AML donor-recipient pairs and tested whether the outcome, including 1-year total and event-free survival, can be predicted from patient and donor-related factors. We used multiple machine learning and survival analysis methods. The best method is a fully connected neural network. Multiple outcomes can be predicted, with area under the specificity-sensitivity curve (AUC) values between 0.54 and 0.67 for the different outcomes. The patient age has a strong impact on prediction. However, for a given patient, when only donor or transplant information is used, limited prediction accuracy of 0.54 to 0.56 AUC for event-free survival and survival is obtained. Graft-versus-host disease and rejection after 1 year have slightly higher AUC values of around 0.59, whereas the relapse prediction accuracy was random. All donors' characteristics have a limited influence on the quality of hematopoietic stem cell transplantation for fully matched donors. Many factors with a population effect on survival have a very limited effect when combined with all other factors in a single-donor predictive model., Competing Interests: Declaration of Competing Interest There are no conflicts of interest to report. Authorship statement: SI performed the analysis and wrote a part of the manuscript. YL wrote a part of the manuscript and supervised the analysis. MM performed the initial data analysis and organized the data and wrote a part of the manuscript., (Copyright © 2022 The American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc. All rights reserved.)

The β chain rearrangement in T cells is a two-step process where first D β and J β bind, and only then V β is joined to the complex. We here show that the frequency of human and mouse V β J β combinations deviates from the one expected based on each gene usage frequency. This bias is observed mainly in functional (F) rearrangements, but also slightly in non-functional (NF) rearrangements. Preferred V β J β combinations in F clones are shared between donors and samples, suggesting a common structural mechanism for these biases in addition to any host-specific antigen-induced peripheral selection. The sharing holds even in clones with J β 1 that share the same D β 1 gene. V β J β usage is correlated with the Molecular Weight and Isoelectric Point in F clones. The pairing is also observed in the Double Positive cells in mice thymocytes, suggesting that the selection leading to such a pairing occurs before thymic selection. These results suggest an additional structural checkpoint in the beta chain development prior to thymic selection during the T cell receptor expression. Understanding this structural selection is important for the distinction between normal and aberrant T cell development, and crucial for the design of engineered TCRs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. t:, (Copyright © 2022 Levi and Louzoun.)

T cells recognize antigens through the interaction of their T cell receptor (TCR) with a peptide-major histocompatibility complex (pMHC) molecule. Following thymic-positive selection, TCRs in peripheral naive T cells are expected to bind MHC alleles of the host. Peripheral clonal selection is expected to further increase the frequency of antigen-specific TCRs that bind to the host MHC alleles. To check for a systematic preference for MHC-binding T cells in TCR repertoires, we developed Natural Language Processing-based methods to predict TCR - MHC binding independently of the peptide presented for Class I MHC alleles. We trained a classifier on published TCR - pMHC binding pairs and obtained a high area under curve (AUC) of over 0.90 on the test set. However, when applied to TCR repertoires, the accuracy of the classifier dropped. We thus developed a two-stage prediction model, based on large-scale naive and memory TCR repertoires, denoted T C R H LA -b i nding p re dictor (CLAIRE). Since each host carries multiple human leukocyte antigen (HLA) alleles, we first computed whether a TCR on a CD8 T cell binds an MHC from any of the host Class-I HLA alleles. We then performed an iteration, where we predict the binding with the most probable allele from the first round. We show that this classifier is more precise for memory than for naïve cells. Moreover, it can be transferred between datasets. Finally, we developed a CD4-CD8 T cell classifier to apply CLAIRE to unsorted bulk sequencing datasets and showed a high AUC of 0.96 and 0.90 on large datasets. CLAIRE is available through a GitHub at: https://github.com/louzounlab/CLAIRE, and as a server at: https://claire.math.biu.ac.il/Home., (© The Author(s) 2022. Published by Oxford University Press.)

An accurate estimate of the number of infected individuals in any disease is crucial. Current estimates are mainly based on the fraction of positive samples or the total number of positive samples. However, both methods are biased and sensitive to the sampling depth. We here propose an alternative method to use the attributes of each sample to estimate the change in the total number of positive patients in the total population. We present a Bayesian estimator assuming a combination of condition and time-dependent probability of being positive, and mixed implicit-explicit solution for the probability of a person with conditions i at time t of being positive. We use this estimate to predict the total probability of being positive at a given day t. We show that these estimate results are smooth and not sensitive to the properties of the samples. Moreover, these results are a better predictor of future mortality., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

HLA haplotype frequencies are estimated from ambiguous unphased HLA genotyping data using Expectation-Maximization (EM) algorithms. Current population genetics methods require independent EM frequency estimates for each population, and assume that each population is in Hardy-Weinberg Equilibrium (HWE). The HWE assumption of EM has thus far resulted in the exclusion of individuals from mixed or unknown ethnic backgrounds from reference datasets. Multi-region populations are currently poorly served by stem cell donor registry HLA imputation and matching implementations due to the inability of such algorithms to incorporate admixture into their population genetics models. To address this unmet need, we have expanded the imputation component of our GRaph IMputation and Matching (GRIMM) framework, where imputation becomes the expectation step in an iterative EM algorithm. Our novel multi-region EM implementation considers region as a Bayesian prior, enabling integration of HLA information from multiple single-region population groups, and for the first time including individuals with ambiguous or mixed ethnic backgrounds. We show that our multi-region EM produces much higher likelihood values and better haplotype recovery as measured by Kullback-Leibler divergence than all evaluated EM implementations when tested on real datasets of US donor registry HLA typings as well as simulated multi-region datasets of ambiguous HLA typings., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.)

HLA haplotypes were found to be associated with increased risk for viral infections or disease severity in various diseases, including SARS. Several genetic variants are associated with COVID-19 severity. Studies have proposed associations, based on a very small sample and a large number of tested HLA alleles, but no clear association between HLA and COVID-19 incidence or severity has been reported. We conducted a large-scale HLA analysis of Israeli individuals who tested positive for SARS-CoV-2 infection by PCR. Overall, 72,912 individuals with known HLA haplotypes were included in the study, of whom 6413 (8.8%) were found to have SARS-CoV-2 by PCR. A total of 20,937 subjects were of Ashkenazi origin (at least 2/4 grandparents). One hundred eighty-one patients (2.8% of the infected) were hospitalized due to the disease. None of the 66 most common HLA loci (within the five HLA subgroups: A, B, C, DQB1, DRB1) was found to be associated with SARS-CoV-2 infection or hospitalization in the general Israeli population. Similarly, no association was detected in the Ashkenazi Jewish subset. Moreover, no association was found between heterozygosity in any of the HLA loci and either infection or hospitalization. We conclude that HLA haplotypes are not a major risk/protecting factor among the Israeli population for SARS-CoV-2 infection or severity. Our results suggest that if any HLA association exists with the disease it is very weak, and of limited effect on the pandemic., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Recent advances in T cell repertoire (TCR) sequencing allow for the characterization of repertoire properties, as well as the frequency and sharing of specific TCR. However, there is no efficient measure for the local density of a given TCR. TCRs are often described either through their Complementary Determining region 3 (CDR3) sequences, or theirV/J usage, or their clone size. We here show that the local repertoire density can be estimated using a combined representation of these components through distance conserving autoencoders and Kernel Density Estimates (KDE). We present ELATE-an Encoder-based LocAl Tcr dEnsity and show that the resulting density of a sample can be used as a novel measure to study repertoire properties. The cross-density between two samples can be used as a similarity matrix to fully characterize samples from the same host. Finally, the same projection in combination with machine learning algorithms can be used to predict TCR-peptide binding through the local density of known TCRs binding a specific target., Competing Interests: The authors have declared that no competing interests exist.

Bariatric surgery is often the preferred method to resolve obesity and diabetes, with ∼800,000 cases worldwide yearly and high outcome variability. The ability to predict the long-term body mass index (BMI) change following surgery has important implications for individuals and the health care system in general. Given the tight connection between eating habits, sugar consumption, BMI, and the gut microbiome, we tested whether the microbiome before any treatment is associated with different treatment outcomes, as well as other intakes (high-density lipoproteins [HDL], triglycerides, etc.). A projection of the gut microbiome composition of obese (sampled before and after bariatric surgery) and lean patients into principal components was performed, and the relation between this projection and surgery outcome was studied. The projection revealed three different microbiome profiles belonging to lean, obese, and obese individuals who underwent bariatric surgery, with the postsurgery microbiome more different from the lean microbiome than the obese microbiome. The same projection allowed for a prediction of BMI loss following bariatric surgery, using only the presurgery microbiome. The microbial changes following surgery were an increase in the relative abundance of Proteobacteria and Fusobacteria and a decrease in Firmicutes . The gut microbiome can be decomposed into main components depicting the patient's development and predicting in advance the outcome. Those may be translated into the better clinical management of obese individuals planning to undergo metabolic surgery. IMPORTANCE BMI and diabetes can affect the gut microbiome composition. Bariatric surgery has large variabilities in the outcome. The microbiome was previously shown to be a good predictor for multiple diseases. We analyzed here the gut microbiome before and after bariatric surgery and showed the following. (i) The microbiome before surgery can be used to predict surgery outcomes. (ii) The postsurgery microbiome drifts further away from the lean microbiome than the microbiome of the presurgery obese patients. These results can lead to a microbiome-based presurgery decision whether to perform surgery.

Background: 16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ML., Methods: We checked multiple preprocessing steps and tested the optimal combination for 16S sequencing-based classification tasks. We computed the contribution of each step to the accuracy as measured by the Area Under Curve (AUC) of the classification., Results: We show that the log of the feature counts is much more informative than the relative counts. We further show that merging features associated with the same taxonomy at a given level, through a dimension reduction step for each group of bacteria improves the AUC. Finally, we show that z-scoring has a very limited effect on the results., Conclusions: The prepossessing of microbiome 16S data is crucial for optimal microbiome based Machine Learning. These preprocessing steps are integrated into the MIPMLP - Microbiome Preprocessing Machine Learning Pipeline, which is available as a stand-alone version at: https://github.com/louzounlab/microbiome/tree/master/Preprocess or as a service at http://mip-mlp.math.biu.ac.il/Home Both contain the code, and standard test sets., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Jasner, Belogolovski, Ben-Itzhak, Koren and Louzoun.)

Background: Immune-mediated protection is mediated by T cells expressing pathogen-specific T cell antigen receptors (TCR) that are maintained at diverse sites of infection as tissue-resident memory T cells (TRM) or that disseminate as circulating effector-memory (TEM), central memory (TCM), or terminal effector (TEMRA) subsets in blood and tissues. The relationship between circulating and tissue resident T cell subsets in humans remains elusive, and is important for promoting site-specific protective immunity., Methods: We analyzed the TCR repertoire of the major memory CD4 + and CD8 + T cell subsets (TEM, TCM, TEMRA, and TRM) isolated from blood and/or lymphoid organs (spleen, lymph nodes, bone marrow) and lungs of nine organ donors, and blood of three living individuals spanning five decades of life. High-throughput sequencing of the variable (V) portion of individual TCR genes for each subset, tissue, and individual were analyzed for clonal diversity, expansion and overlap between lineage, T cell subsets, and anatomic sites. TCR repertoires were further analyzed for TRBV gene usage and CDR3 edit distance., Results: Across blood, lymphoid organs, and lungs, human memory, and effector CD8 + T cells exhibit greater clonal expansion and distinct TRBV usage compared to CD4 + T cell subsets. Extensive sharing of clones between tissues was observed for CD8 + T cells; large clones specific to TEMRA cells were present in all sites, while TEM cells contained clones shared between sites and with TRM. For CD4 + T cells, TEM clones exhibited the most sharing between sites, followed by TRM, while TCM clones were diverse with minimal sharing between sites and subsets. Within sites, TRM clones exhibited tissue-specific expansions, and maintained clonal diversity with age, compared to age-associated clonal expansions in circulating memory subsets. Edit distance analysis revealed tissue-specific biases in clonal similarity., Conclusions: Our results show that the human memory T cell repertoire comprises clones which persist across sites and subsets, along with clones that are more restricted to certain subsets and/or tissue sites. We also provide evidence that the tissue plays a key role in maintaining memory T cells over age, bolstering the rationale for site-specific targeting of memory reservoirs in vaccines and immunotherapies.

Empirical studies show that epidemiological models based on an epidemic's initial spread rate often fail to predict the true scale of that epidemic. Most epidemics with a rapid early rise die out before affecting a significant fraction of the population, whereas the early pace of some pandemics is rather modest. Recent models suggest that this could be due to the heterogeneity of the target population's susceptibility. We study a computer malware ecosystem exhibiting spread mechanisms resembling those of biological systems while offering details unavailable for human epidemics. Rather than comparing models, we directly estimate reach from a new and vastly more complete data from a parallel domain, that offers superior details and insight as concerns biological outbreaks. We find a highly heterogeneous distribution of computer susceptibilities, with nearly all outbreaks initially over-affecting the tail of the distribution, then collapsing quickly once this tail is depleted. This mechanism restricts the correlation between an epidemic's initial growth rate and its total reach, thus preventing the majority of epidemics, including initially fast-growing outbreaks, from reaching a macroscopic fraction of the population. The few pervasive malwares distinguish themselves early on via the following key trait: they avoid infecting the tail, while preferentially targeting computers unaffected by typical malware.

We present a generic epidemic model with stochastic parameters in which the dynamics self-organize to a critical state with suppressed exponential growth. More precisely, the dynamics evolve into a quasi-steady state, where the effective reproduction rate fluctuates close to the critical value 1 for a long period, as indeed observed for different epidemics. The main assumptions underlying the model are that the rate at which each individual becomes infected changes stochastically in time with a heavy-tailed steady state. The critical regime is characterized by an extremely long duration of the epidemic. Its stability is analyzed both numerically and analytically in different models.

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Introduction: Predicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element used in TCR-peptide binding prediction was the Complementarity Determining Region 3 (CDR3) of the beta chain. However, recently the contribution of other components, such as the alpha chain and the other V gene CDRs has been suggested. We use a highly accurate novel deep learning-based TCR-peptide binding predictor to assess the contribution of each component to the binding., Methods: We have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a sequence-based T-cell receptor (TCR)-peptide binding predictor that employs natural language processing (NLP) -based methods. We improved it to create ERGO-II by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) as to the predictor. We then estimate the contribution of each component to the prediction., Results and Discussion: ERGO-II provides for the first time high accuracy prediction of TCR-peptide for previously unseen peptides. For most tested peptides and all measures of binding prediction accuracy, the main contribution was from the beta chain CDR3 sequence, followed by the beta chain V and J and the alpha chain, in that order. The MHC allele was the least contributing component. ERGO-II is accessible as a webserver at http://tcr2.cs.biu.ac.il/ and as a standalone code at https://github.com/IdoSpringer/ERGO-II., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Springer, Tickotsky and Louzoun.)

Restoration of T cell repertoire diversity after allogeneic bone marrow transplantation (allo-BMT) is crucial for immune recovery. T cell diversity is produced by rearrangements of germline gene segments (V (D) and J) of the T cell receptor (TCR) α and β chains, and selection induced by binding of TCRs to MHC-peptide complexes. Multiple measures were proposed for this diversity. We here focus on the V-gene usage and the CDR3 sequences of the beta chain. We compared multiple T cell repertoires to follow T cell repertoire changes post-allo-BMT in HLA-matched related donor and recipient pairs. Our analyses of the differences between donor and recipient complementarity determining region 3 (CDR3) beta composition and V-gene profile show that the CDR3 sequence composition does not change during restoration, implying its dependence on the HLA typing. In contrast, V-gene usage followed a time-dependent pattern, initially following the donor profile and then shifting back to the recipients' profile. The final long-term repertoire was more similar to that of the recipient's original one than the donor's; some recipients converged within months, while others took multiple years. Based on the results of our analyses, we propose that donor-recipient V-gene distribution differences may serve as clinical biomarkers for monitoring immune recovery.

In times of outbreaks, an essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals. An accurate estimate of this fraction is crucial for the assessment of the situation and the establishment of protective measures. In most current studies using epidemics models, the total number of infected is either approximated by the number of diagnosed individuals or is dependent on the model parameters and assumptions, which are often debated. We here study the relationship between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected. We show that those two are approximately the same in exponential models and across most models currently used in the study of epidemics, independently of the model parameters. As an application, we compute an estimate of the effective number of infected by the SARS-CoV-2 virus in various countries.

Exposure to antibiotics in the first days of life is thought to affect various physiological aspects of neonatal development. Here, we investigate the long-term impact of antibiotic treatment in the neonatal period and early childhood on child growth in an unselected birth cohort of 12,422 children born at full term. We find significant attenuation of weight and height gain during the first 6 years of life after neonatal antibiotic exposure in boys, but not in girls, after adjusting for potential confounders. In contrast, antibiotic use after the neonatal period but during the first 6 years of life is associated with significantly higher body mass index throughout the study period in both boys and girls. Neonatal antibiotic exposure is associated with significant differences in the gut microbiome, particularly in decreased abundance and diversity of fecal Bifidobacteria until 2 years of age. Finally, we demonstrate that fecal microbiota transplant from antibiotic-exposed children to germ-free male, but not female, mice results in significant growth impairment. Thus, we conclude that neonatal antibiotic exposure is associated with a long-term gut microbiome perturbation and may result in reduced growth in boys during the first six years of life while antibiotic use later in childhood is associated with increased body mass index.

Background: Multiple studies suggest a key role for gut microbiota in IgE-mediated food allergy (FA) development, but to date, none has studied it in the persistent state., Methods: To characterize the gut microbiota composition and short-chain fatty acid (SCFAs) profiles associated with major food allergy groups, we recruited 233 patients with FA including milk (N = 66), sesame (N = 38), peanut (N = 71), and tree nuts (N = 58), and non-allergic controls (N = 58). DNA was isolated from fecal samples, and 16S rRNA gene sequences were analyzed. SCFAs in stool were analyzed from patients with a single allergy (N = 84) and controls (N = 31)., Results: The gut microbiota composition of allergic patients was significantly different compared to age-matched controls both in α-diversity and β-diversity. Distinct microbial signatures were noted for FA to different foods. Prevotella copri (P. copri) was the most overrepresented species in non-allergic controls. SCFAs levels were significantly higher in the non-allergic compared to the FA groups, whereas P. copri significantly correlated with all three SCFAs. We used these microbial differences to distinguish between FA patients and non-allergic healthy controls with an area under the curve of 0.90, and for the classification of FA patients according to their FA types using a supervised learning algorithm. Bacteroides and P. copri were identified as taxa potentially contributing to KEGG acetate-related pathways enriched in non-allergic compared to FA. In addition, overall pathway dissimilarities were found among different FAs., Conclusions: Our results demonstrate a link between IgE-mediated FA and the composition and metabolic activity of the gut microbiota.

Background: Adjuvant chemotherapy induces weight gain, glucose intolerance, and hypertension in about a third of women. The mechanisms underlying these events have not been defined. This study assessed the association between the microbiome and weight gain in patients treated with adjuvant chemotherapy for breast and gynecological cancers., Methods: Patients were recruited before starting adjuvant therapy. Weight and height were measured before treatment and 4-6 weeks after treatment completion. Weight gain was defined as an increase of 3% or more in body weight. A stool sample was collected before treatment, and 16S rRNA gene sequencing was performed. Data regarding oncological therapy, menopausal status, and antibiotic use was prospectively collected. Patients were excluded if they were treated by antibiotics during the study. Fecal transplant experiments from patients were conducted using Swiss Webster germ-free mice., Results: Thirty-three patients were recruited; of them, 9 gained 3.5-10.6% of baseline weight. The pretreatment microbiome of women who gained weight following treatment was significantly different in diversity and taxonomy from that of control women. Fecal microbiota transplantation from pretreatment samples of patients that gained weight induced metabolic changes in germ-free mice compared to mice transplanted with pretreatment fecal samples from the control women., Conclusion: The microbiome composition is predictive of weight gain following adjuvant chemotherapy and induces adverse metabolic changes in germ-free mice, suggesting it contributes to adverse metabolic changes seen in patients. Confirmation of these results in a larger patient cohort is warranted.

Prenatal stress (PS) impacts early postnatal behavioural and cognitive development. This process of 'fetal programming' is mediated by the effects of the prenatal experience on the developing hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system (ANS). We derive a multi-scale multi-species approach to devising preclinical and clinical studies to identify early non-invasively available pre- and postnatal biomarkers of PS. The multiple scales include brain epigenome, metabolome, microbiome and the ANS activity gauged via an array of advanced non-invasively obtainable properties of fetal heart rate fluctuations. The proposed framework has the potential to reveal mechanistic links between maternal stress during pregnancy and changes across these physiological scales. Such biomarkers may hence be useful as early and non-invasive predictors of neurodevelopmental trajectories influenced by the PS as well as follow-up indicators of success of therapeutic interventions to correct such altered neurodevelopmental trajectories. PS studies must be conducted on multiple scales derived from concerted observations in multiple animal models and human cohorts performed in an interactive and iterative manner and deploying machine learning for data synthesis, identification and validation of the best non-invasive detection and follow-up biomarkers, a prerequisite for designing effective therapeutic interventions., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. We employ new Natural Language Processing (NLP) based methods to predict whether any TCR and peptide bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO reaches similar results to state of the art methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO. ERGO is also available through a webserver at: http://tcr.cs.biu.ac.il/., (Copyright © 2020 Springer, Besser, Tickotsky-Moskovitz, Dvorkin and Louzoun.)

Oral mucositis (OM) is a common debilitating dose-limiting toxicity of cancer treatment, including hematopoietic stem cell transplantation (HSCT). We hypothesized that the oral microbiome is disturbed during allogeneic HSCT, partially accounting for the variability in OM severity. Using 16S ribosomal RNA gene sequence analysis, metabolomic profiling, and computational methods, we characterized the behavior of the salivary microbiome and metabolome of 184 patients pre- and post-HSCT. Transplantation was associated with a decrease in oral α diversity in all patients. In contrast to the gut microbiome, an association with overall survival was not detected. Among 135 patients given methotrexate for graft-versus-host disease prophylaxis pre-HSCT, Kingella and Atopobium abundance correlated with future development of severe OM. Posttransplant, Methylobacterium species were significantly enriched in patients with severe OM. Moreover, the oral microbiome and metabolome of severe OM patients underwent distinct changes post-HSCT, compared with patients with no or mild OM. Changes in specific metabolites were well explained by microbial composition, and the common metabolic pathway was the polyamines pathway, which is essential for epithelial homeostasis. Together, our findings suggest that salivary microbial composition and metabolites are associated with the development of OM, offering new insights on pathophysiology and potential avenues of intervention., (© 2020 by The American Society of Hematology.)

Objective: Pregnancy may affect the disease course of IBD. Both pregnancy and IBD are associated with altered immunology and intestinal microbiology. However, to what extent immunological and microbial profiles are affected by pregnancy in patients with IBD remains unclear., Design: Faecal and serum samples were collected from 46 IBD patients (31 Crohn's disease (CD) and 15 UC) and 179 healthy controls during first, second and third trimester of pregnancy, and prepregnancy and postpartum for patients with IBD. Peripheral blood cytokine profiles were determined by ELISA, and microbiome analysis was performed by sequencing the V4 region of the bacterial 16S rRNA gene., Results: Proinflammatory serum cytokine levels in patients with IBD decrease significantly on conception. Reduced interleukin (IL)-10 and IL-5 levels but increased IL-8 and interferon (IFN)γ levels compared with healthy controls were seen throughout pregnancy, but cytokine patterns remained stable during gestation. Microbial diversity in pregnant patients with IBD was reduced compared with that in healthy women, and significant differences existed between patients with UC and CD in early pregnancy. However, these microbial differences were no longer present during middle and late pregnancy. Dynamic modelling showed considerable interaction between cytokine and microbial composition., Conclusion: Serum proinflammatory cytokine levels markedly improve on conception in pregnant patients with IBD, and intestinal microbiome diversity of patients with IBD normalises during middle and late pregnancy. We thus conclude that pregnancy is safe and even potentially beneficial for patients with IBD., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)

A method for estimating the Shannon differential entropy of multidimensional random variables using independent samples is described. The method is based on decomposing the distribution into a product of marginal distributions and joint dependency, also known as the copula. The entropy of marginals is estimated using one-dimensional methods. The entropy of the copula, which always has a compact support, is estimated recursively by splitting the data along statistically dependent dimensions. The method can be applied both for distributions with compact and non-compact supports, which is imperative when the support is not known or of a mixed type (in different dimensions). At high dimensions (larger than 20), numerical examples demonstrate that our method is not only more accurate, but also significantly more efficient than existing approaches.

Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors.

The human leukocyte antigen (HLA) is the most polymorphic region in humans. Anthropologists use HLA to trace populations' migration and evolution. However, recent admixture between populations can mask the ancestral haplotype frequency distribution. We present a statistical method based on high-resolution HLA haplotype frequencies to resolve population admixture using a non-negative matrix factorization formalism and validated using haplotype frequencies from 56 world populations. The result is a minimal set of source components (SCs) decoding roughly 90% of the total variance in the studied admixtures. These SCs agree with the geographical distribution, phylogenies, and recent admixture events of the studied groups. With the growing population of multi-ethnic individuals, or individuals that do not report race/ethnic information, the HLA matching process for stem-cell and solid organ transplants is becoming more challenging. The presented algorithm provides a framework that facilitates the breakdown of highly admixed populations into SCs, which can be used to better match the rapidly growing population of multi-ethnic individuals worldwide.

Motivation: For over 10 years allele-level HLA matching for bone marrow registries has been performed in a probabilistic context. HLA typing technologies provide ambiguous results in that they could not distinguish among all known HLA alleles equences; therefore registries have implemented matching algorithms that provide lists of donor and cord blood units ordered in terms of the likelihood of allele-level matching at specific HLA loci. With the growth of registry sizes, current match algorithm implementations are unable to provide match results in real time., Results: We present here a novel computationally-efficient open source implementation of an HLA imputation and match algorithm using a graph database platform. Using graph traversal, the matching algorithm runtime is practically not affected by registry size. This implementation generates results that agree with consensus output on a publicly-available match algorithm cross-validation dataset., Availability and Implementation: The Python, Perl and Neo4j code is available at https://github.com/nmdp-bioinformatics/grimm., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The major histocompatibility complex (MHC) is a central component of the vertebrate immune system and hence evolves in the regime of a host-pathogen evolutionary race. The MHC is associated with quantitative traits which directly affect fitness and are subject to selection pressure. The evolution of haplotypes at the MHC HLA (HLA) locus is generally thought to be governed by selection for increased diversity that is manifested in overdominance and/or negative frequency-dependent selection (FDS). However, recently, a model combining purifying selection on haplotypes and balancing selection on alleles has been proposed. We compare the predictions of several population dynamics models of haplotype frequency evolution to the distributions derived from 6.59-million-donor HLA typings from the National Marrow Donor Program registry. We show that models that combine a multiplicative fitness function, extremely high haplotype discovery rates, and exponential fitness decay over time produce the best fit to the data for most of the analyzed populations. In contrast, overdominance is not supported, and population substructure does not explain the observed haplotype frequencies. Furthermore, there is no evidence of negative FDS. Thus, multiplicative fitness, rapid haplotype discovery, and rapid fitness decay appear to be the major factors shaping the HLA haplotype frequency distribution in the human population., Competing Interests: The authors declare no conflict of interest.

Motivation: RNA viruses generate a cloud of genetic variants within each host. This cloud contains high-frequency genotypes, and many rare variants. The dynamics of these variants is crucial to understand viral evolution and their effect on their host., Results: We use an experimental evolution system to show that the genetic cloud surrounding the Coxsackie virus master sequence slowly, but steadily, evolves over hundreds of generations. This movement is determined by strong context-dependent mutations, where the frequency and type of mutations are affected by neighboring positions, even in silent mutations. This context-dependent mutation pattern serves as a spearhead for the viral population's movement within the adaptive landscape and affects which new dominant variants will emerge. The non-local mutation patterns affect the mutated dinucleotide distribution, and eventually lead to a non-uniform dinucleotide distribution in the main viral sequence. We tested these results on other RNA viruses with similar conclusions., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Targeting epitopes derived from neo-antigens (or "neo-epitopes") represents a promising immunotherapy approach with limited off-target effects. However, most peptides predicted using MHC binding prediction algorithms do not induce a CD8 + T cell response, and there is a crucial need to refine the predictions to readily identify the best antigens that could mediate T-cell responses. Such a response requires a high enough number of epitopes bound to the target MHC. This number is correlated with both the peptide-MHC binding affinity and the number of peptides reaching the ER. Beyond this, the response may be affected by the properties of the neo-epitope mutated residues., Methods: Herein, we analyzed several experimental datasets from cancer patients to elaborate better predictive algorithms for T-cell reactivity to neo-epitopes., Results: Indeed, potent classifiers for epitopes derived from neo-antigens in melanoma and other tumors can be developed based on biochemical properties of the mutated residue, the antigen expression level and the peptide processing stage. Among MHC binding peptides, the present classifiers can remove half of the peptides falsely predicted to activate T cells while maintaining the absolute majority of reactive peptides., Conclusions: The classifier properties further highlight the contribution of the quantity of peptides reaching the ER and the mutation type to CD8 + T cell responses. These classifiers were then validated on neo-antigens obtained from other datasets, confirming the validity of our prediction. Algorithm Availability: http://peptibase.cs.biu.ac.il/Tcell&#95;predictor/ or by request from the authors as a standalone code.

Gestation is accompanied by alterations in the microbial repertoire; however, the mechanisms driving these changes are unknown. Here, we demonstrate a dramatic shift in the gut microbial composition of women and mice during late pregnancy, including an increase in the relative abundance of Bifidobacterium. Using in-vivo-transplanted pellets, we found that progesterone, the principal gestation hormone, affects the microbial community. The effect of progesterone on the richness of several bacteria species, including Bifidobacterium, was also demonstrated in vitro, indicating a direct effect. Altogether, our results delineate a model in which progesterone promotes Bifidobacterium growth during late pregnancy., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Avian avulavirus-1 (AAvV-1, previously Newcastle Disease Virus) is responsible for poultry and wild birds' disease outbreaks. Numerous whole genome sequencing methods were reported for this virus. These methods included cloning, specific primers amplification, shotgun PCR approaches, Sequence Independent Single Primer Amplification and next generation sequencing platform kits., Methods: Three methods were used to sequence 173 Israeli Avian avulavirus-1 field isolates and one vaccine strain (VH). The sequencing was performed on Proton and Ion Torrent Personal Genome Machine and to a lesser extent, Illumina MiSeq and NextSeq sequencers. Target specific primers (SP) and Sequence Independent Single Primer Amplification (SISPA) products sequenced via the Ion torrent sequencer had a high error rate and truncated genomes. All the next generation sequencing platform sequencing kits generated high sequence accuracy and near-complete genomic size., Results: A high level of mutations was observed in the intergenic regions between the avian avulavirus-1 genes. Within genes, multiple regions are more mutated than the Fusion region currently used for typing., Conclusions: Our findings suggest that the whole genome sequencing by the Ion torrent sequencing kit is sufficient. However, when higher fidelity is desired, the Illumina NextSeq and Proton torrent sequencing kits were found to be preferable., (© 2019 Published by Elsevier B.V.)

Epitopes presented on MHC class I molecules pass multiple processing stages before their presentation on MHC molecules, the main ones being proteasomal cleavage and TAP binding. Transporter associated with antigen processing (TAP) binding is a necessary stage for most, but not all, MHC-I-binding peptides. The molecular determinants of TAP-binding peptides can be experimentally estimated from binding experiments and from the properties of peptides inducing a CD8 T cell response. We here propose novel optimization formalisms to combine binding and activation experimental results to produce a classifier for TAP binding using dual-output kernel and deep learning approaches. The application of these algorithms to the human and murine TAP binding leads to predictors that are much more precise than current state of the art methods. Moreover, the computed score is highly correlated with the observed binding energy. The new predictors show that TAP binding may be much more selective than previously assumed in humans and mice and sensitive to the properties of most positions of the peptides. Beyond the improved precision for TAP binding, we propose that the same approach holds in most molecular binding problems, where functional and binding measures are simultaneously available, and can be used to significantly improve the precision of binding prediction algorithms in general and immune system molecules specifically.

The naïve immunoglobulin (IG) repertoire in the blood differs from the direct output of the rearrangement process. These differences stem from selection that affects the germline gene usage and the junctional nucleotides. A major complication obscuring the details of the selection mechanism in the heavy chain is the failure to properly identify the D germline and determine the nucleotide addition and deletion in the junction region. The selection affecting junctional diversity can, however, be studied in the light chain that has no D gene. We use probabilistic and deterministic models to infer and disentangle generation and selection of the light chain, using large samples of light chains sequenced from healthy donors and transgenic mice. We have previously used similar models for the beta chain of T-cell receptors and the heavy chain of IGs. Selection is observed mainly in the CDR3. The CDR3 length and mass distributions are narrower after selection than before, indicating stabilizing selection for mid-range values. Within the CDR3, proline and cysteine undergo negative selection, while glycine undergoes positive selection. The results presented here suggest structural selection maintaining the size of the CDR3 within a limited range, and preventing turns in the CDR3 region.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease accompanied by production of autoantibodies directed to a variety of self-proteins and nucleic acids. The genetic basis of SLE is also complex with at least 40 susceptibility loci identified. This complexity suggests that there are a variety of SLE manifestations; nevertheless, SLE is treated as a single disease clinically. One unique SLE target is the Smith antigen (Sm), a nuclear ribonucleoprotein complex. Sm response occurs in 25% of patients with SLE. To simplify analysis of the disease and its associated autoantibody repertoire, we focused on this subset [referred to here as "Sm positive", Sm+]. We analyzed the memory B cell repertoire and identified a V region, Vκ4-1, which was significantly overrepresented in the Sm+ SLE subset. Antibodies that express Vκ4-1 are enriched in antinuclear (ANA) positive specificities and often associated with speckled ANA pattern that is a characteristic of Sm binding. In healthy individuals Vκ4-1 B cells are enriched in the unswitched memory population. Unswitched memory B cells resemble mouse marginal zone B cells and this population is decreased in all SLE patients. Moreover, we found a similar decrease in healthy African American donors. African Americans have a significantly higher prevalence of SLE compared to Caucasians. Thus, reduced unswitched memory B cell compartment may represent a new susceptibility marker for SLE., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Inflammatory bowel diseases (IBD) are a group of chronic inflammatory disorders of the intestine, with as-yet-unclear etiologies, affecting over a million people in the United States alone. With the emergence of microbiome research, numerous studies have shown a connection between shifts in the gut microbiota composition (dysbiosis) and patterns of IBD development. In a previous study, we showed that interleukin 1α (IL-1α) deficiency in IL-1α knockout (KO) mice results in moderate dextran sodium sulfate (DSS)-induced colitis compared to that of wild-type (WT) mice, characterized by reduced inflammation and complete healing, as shown by parameters of weight loss, disease activity index (DAI) score, histology, and cytokine expression. In this study, we tested whether the protective effects of IL-1α deficiency on DSS-induced colitis correlate with changes in the gut microbiota and whether manipulation of the microbiota by cohousing can alter patterns of colon inflammation. We analyzed the gut microbiota composition in both control (WT) and IL-1α KO mice under steady-state homeostasis, during acute DSS-induced colitis, and after recovery using 16S rRNA next-generation sequencing. Additionally, we performed cohousing of both mouse groups and tested the effects on the microbiota and clinical outcomes. We demonstrate that host-derived IL-1α has a clear influence on gut microbiota composition, as well as on severity of DSS-induced acute colon inflammation. Cohousing both successfully changed the gut microbiota composition and increased the disease severity of IL-1α-deficient mice to levels similar to those of WT mice. This study shows a strong and novel correlation between IL-1α expression, microbiota composition, and clinical outcomes of DSS-induced colitis. IMPORTANCE Here, we show a connection between IL-1α expression, microbiota composition, and clinical outcomes of DSS-induced colitis. Specifically, we show that the mild colitis symptoms seen in IL-1α-deficient mice following administration of DSS are correlated with the unique gut microbiota compositions of the mice. However, when these mice are exposed to WT microbiota by cohousing, their gut microbiota composition returns to resemble that of WT mice, and their disease severity increases significantly. As inflammatory bowel diseases are such common diseases, with limited effective treatments to date, there is a great need to better understand the interactions between microbiota composition, the immune system, and colitis. This study shows correlation between microbiota composition and DSS resistance; it may potentially lead to the development of improved probiotics for IBD treatment.

Regardless of sampling depth, accurate genotype imputation is limited in regions of high polymorphism which often have a heavy-tailed haplotype frequency distribution. Many rare haplotypes are thus unobserved. Statistical methods to improve imputation by extending reference haplotype distributions using linkage disequilibrium patterns that relate allele and haplotype frequencies have not yet been explored. In the field of unrelated stem cell transplantation, imputation of highly polymorphic human leukocyte antigen (HLA) genes has an important application in identifying the best-matched stem cell donor when searching large registries totaling over 28,000,000 donors worldwide. Despite these large registry sizes, a significant proportion of searched patients present novel HLA haplotypes. Supporting this observation, HLA population genetic models have indicated that many extant HLA haplotypes remain unobserved. The absent haplotypes are a significant cause of error in haplotype matching. We have applied a Bayesian inference methodology for extending haplotype frequency distributions, using a model where new haplotypes are created by recombination of observed alleles. Applications of this joint probability model offer significant improvement in frequency distribution estimates over the best existing alternative methods, as we illustrate using five-locus HLA frequency data from the National Marrow Donor Program registry. Transplant matching algorithms and disease association studies involving phasing and imputation of rare variants may benefit from this statistical inference framework.

RNA-editing mechanisms, which induce nucleotide substitution in the RNA, increase transcript and protein diversities. Editing dysregulation has been shown to lead to grave outcomes, and transcriptome-wide aberrant RNA editing has been found in tumors. However, little is known about the involvement of editing in other diseases. Systemic lupus erythematosus (SLE) is a multisystemic autoimmune disease characterized by a loss of tolerance for autoantigens from various tissues and the production of multiple autoantibodies. Here, we show that blood samples from individuals with SLE have abnormally high levels of RNA editing, some of which affect proteins and potentially generate novel autoantigens. We suggest that elevated RNA editing, either by ADARs or APOBECs, may be involved in the pathophysiology of SLE, as well as in other autoimmune diseases, by generating or increasing the autoantigen load, a key requisite for the progression of autoimmunity., (Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: The existence of specific microbial profiles for different periodontal conditions is still a matter of debate. The aim of this study was to test the hypothesis that 40 bacterial species could be used to classify patients, utilising machine learning, into generalised chronic periodontitis (ChP), generalised aggressive periodontitis (AgP) and periodontal health (PH)., Method: Subgingival biofilm samples were collected from patients with AgP, ChP and PH and analysed for their content of 40 bacterial species using checkerboard DNA-DNA hybridisation. Two stages of machine learning were then performed. First of all, we tested whether there was a difference between the composition of bacterial communities in PH and in disease, and then we tested whether a difference existed in the composition of bacterial communities between ChP and AgP. The data were split in each analysis to 70% train and 30% test. A support vector machine (SVM) classifier was used with a linear kernel and a Box constraint of 1. The analysis was divided into two parts., Results: Overall, 435 patients (3,915 samples) were included in the analysis (PH = 53; ChP = 308; AgP = 74). The variance of the healthy samples in all principal component analysis (PCA) directions was smaller than that of the periodontally diseased samples, suggesting that PH is characterised by a uniform bacterial composition and that the bacterial composition of periodontally diseased samples is much more diverse. The relative bacterial load could distinguish between AgP and ChP., Conclusion: An SVC classifier using a panel of 40 bacterial species was able to distinguish between PH, AgP in young individuals and ChP., (© 2017 FDI World Dental Federation.)

The evolution of humans as a highly social species tuned the brain to the social world; yet the mechanisms by which humans coordinate their brain response online during social interactions remain unclear. Using hyperscanning EEG recordings, we measured brain-to-brain synchrony in 104 adults during a male-female naturalistic social interaction, comparing romantic couples and strangers. Neural synchrony was found for couples, but not for strangers, localized to temporal-parietal structures and expressed in gamma rhythms. Brain coordination was not found during a three-minute rest, pinpointing neural synchrony to social interactions among affiliative partners. Brain-to-brain synchrony was linked with behavioral synchrony. Among couples, neural synchrony was anchored in moments of social gaze and positive affect, whereas among strangers, longer durations of social gaze and positive affect correlated with greater neural synchrony. Brain-to-brain synchrony was unrelated to episodes of speech/no-speech or general content of conversation. Our findings link brain-to-brain synchrony to the degree of social connectedness among interacting partners, ground neural synchrony in key nonverbal social behaviors, and highlight the role of human attachment in providing a template for two-brain coordination.