How complex communities assemble through the animal's life, and how predictable the process is remains unexplored. Here, we investigate the forces that drive the assembly of rumen microbiomes ...throughout a cow's life, with emphasis on the balance between stochastic and deterministic processes. We analyse the development of the rumen microbiome from birth to adulthood using 16S-rRNA amplicon sequencing data and find that the animals shared a group of core successional species that invaded early on and persisted until adulthood. Along with deterministic factors, such as age and diet, early arriving species exerted strong priority effects, whereby dynamics of late successional taxa were strongly dependent on microbiome composition at early life stages. Priority effects also manifest as dramatic changes in microbiome development dynamics between animals delivered by C-section vs. natural birth, with the former undergoing much more rapid species invasion and accelerated microbiome development. Overall, our findings show that together with strong deterministic constrains imposed by diet and age, stochastic colonization in early life has long-lasting impacts on the development of animal microbiomes.
T-cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous ...TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages experimentally inferred antigen-associated TCRs to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly quantify functionally similar TCRs in bulk repertoires across individuals. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the SARS-CoV-2 antigen-associated TCRs that have strong evidence of restriction to patients with a specific human leukocyte antigen (HLA) genotype. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (false discovery rate FDR<0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package,
that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.
Dengue is a global public health problem and is caused by four dengue virus (DENV) serotypes (DENV1-4). A major challenge in dengue vaccine development is that cross-reactive anti-DENV Abs can be ...protective or potentially increase disease via Ab-dependent enhancement. DENV nonstructural protein 1 (NS1) has long been considered a vaccine candidate as it avoids Ab-dependent enhancement. In this study, we evaluated survival to challenge in a lethal DENV vascular leak model in mice immunized with NS1 combined with aluminum and magnesium hydroxide, monophosphoryl lipid A + AddaVax, or Sigma adjuvant system+CpG DNA, compared with mice infected with a sublethal dose of DENV2 and mice immunized with OVA (negative control). We characterized Ab responses to DENV1, 2, and 3 NS1 using an Ag microarray tiled with 20-mer peptides overlapping by 15 aa and identified five regions of DENV NS1 with significant levels of Ab reactivity in the NS1 + monophosphoryl lipid A + AddaVax group. Additionally, we profiled the Ab responses to NS1 of humans naturally infected with DENV2 or DENV3 in serum samples from Nicaragua collected at acute, convalescent, and 12-mo timepoints. One region in the wing domain of NS1 was immunodominant in both mouse vaccination and human infection studies, and two regions were identified only in NS1-immunized mice; thus, vaccination can generate Abs to regions that are not targeted in natural infection and could provide additional protection against lethal DENV infection. Overall, we identified a small number of immunodominant regions, which were in functionally important locations on the DENV NS1 protein and are potential correlates of protection.
Obesity is a risk factor for developing severe influenza virus infection, making vaccination of utmost importance for this high-risk population. However, vaccinated obese animals and adults have ...decreased neutralizing antibody responses. In these studies, we tested the hypothesis that the addition of either alum or a squalene-based adjuvant (AS03) to an influenza vaccine would improve neutralizing antibody responses and protect obese mice from challenge. Our studies demonstrate that adjuvanted vaccine does increase both neutralizing and nonneutralizing antibody levels compared to vaccine alone. Although obese mice mount significantly decreased virus-specific antibody responses, both the breadth and the magnitude of the responses against hemagglutinin (HA) and neuraminidase (NA) are decreased compared to the responses in lean mice. Importantly, even with a greater than fourfold increase in neutralizing antibody levels, obese mice are not protected against influenza virus challenge and viral loads remain elevated in the respiratory tract. Increasing the antigen dose affords no added protection, and a decreasing viral dose did not fully mitigate the increased mortality seen in obese mice. Overall, these studies highlight that, while the use of an adjuvant does improve seroconversion, vaccination does not fully protect obese mice from influenza virus challenge, possibly due to the increased sensitivity of obese animals to infection. Given the continued increase in the global obesity epidemic, our findings have important implications for public health.
Vaccination is the most effective strategy for preventing influenza virus infection and is a key component for pandemic preparedness. However, vaccines may fail to provide optimal protection in high-risk groups, including overweight and obese individuals. Given the worldwide obesity epidemic, it is imperative that we understand and improve vaccine efficacy. No work to date has investigated whether adjuvants increase the protective capacity of influenza vaccines in the obese host. In these studies, we show that adjuvants increased the neutralizing and nonneutralizing antibody responses during vaccination of lean and obese mice to levels considered "protective," and yet, obese mice still succumbed to infection. This vulnerability is likely due to a combination of factors, including the increased susceptibility of obese animals to develop severe and even lethal disease when infected with very low viral titers. Our studies highlight the critical public health need to translate these findings and better understand vaccination in this increasing population.
Vaccination, especially with multiple doses, provides substantial population-level protection against COVID-19, but emerging variants of concern (VOC) and waning immunity represent significant risks ...at the individual level. Here we identify correlates of protection (COP) in a multicenter prospective study following 607 healthy individuals who received three doses of the Pfizer-BNT162b2 vaccine approximately six months prior to enrollment. We compared 242 individuals who received a fourth dose to 365 who did not. Within 90 days of enrollment, 239 individuals contracted COVID-19, 45% of the 3-dose group and 30% of the four-dose group. The fourth dose elicited a significant rise in antibody binding and neutralizing titers against multiple VOCs reducing the risk of symptomatic infection by 37% 95%CI, 15%-54%. However, a group of individuals, characterized by low baseline titers of binding antibodies, remained susceptible to infection despite significantly increased neutralizing antibody titers upon boosting. A combination of reduced IgG levels to RBD mutants and reduced VOC-recognizing IgA antibodies represented the strongest COP in both the 3-dose group (HR = 6.34, p = 0.008) and four-dose group (HR = 8.14, p = 0.018). We validated our findings in an independent second cohort. In summary combination IgA and IgG baseline binding antibody levels may identify individuals most at risk from future infections.
Motivation: The development of epitope-based vaccines crucially relies on the ability to classify Human Leukocyte Antigen (HLA) molecules into sets that have similar peptide binding specificities, ...termed supertypes. In their seminal work, Sette and Sidney defined nine HLA class I supertypes and claimed that these provide an almost perfect coverage of the entire repertoire of HLA class I molecules. HLA alleles are highly polymorphic and polygenic and therefore experimentally classifying each of these molecules to supertypes is at present an impossible task. Recently, a number of computational methods have been proposed for this task. These methods are based on defining protein similarity measures, derived from analysis of binding peptides or from analysis of the proteins themselves. Results: In this paper we define both peptide derived and protein derived similarity measures, which are based on learning distance functions. The peptide derived measure is defined using a peptide–peptide distance function, which is learned using information about known binding and non-binding peptides. The protein derived similarity measure is defined using a protein–protein distance function, which is learned using information about alleles previously classified to supertypes by Sette and Sidney (1999). We compare the classification obtained by these two complimentary methods to previously suggested classification methods. In general, our results are in excellent agreement with the classifications suggested by Sette and Sidney (1999) and with those reported by Buus et al. (2004). The main important advantage of our proposed distance-based approach is that it makes use of two different and important immunological sources of information—HLA alleles and peptides that are known to bind or not bind to these alleles. Since each of our distance measures is trained using a different source of information, their combination can provide a more confident classification of alleles to supertypes. Contact:tomboy@cs.huji.ac.il; cheny@cs.huji.ac.il
The interferon gamma, enzyme-linked immunospot (IFN-γ ELISpot) assay is widely used to identify viral antigen-specific T cells is frequently employed to quantify T cell responses in HIV vaccine ...studies. It can be used to define T cell epitope specificities using panels of peptide antigens, but with sample and cost constraints there is a critical need to improve the efficiency of epitope mapping for large and variable pathogens. We evaluated two epitope mapping strategies, based on group testing, for their ability to identify vaccine-induced T-cells from participants in the Step HIV-1 vaccine efficacy trial, and compared the findings to an approach of assaying each peptide individually. The group testing strategies reduced the number of assays required by >7-fold without significantly altering the accuracy of T-cell breadth estimates. Assays of small pools containing 7-30 peptides were highly sensitive and effective at detecting single positive peptides as well as summating responses to multiple peptides. Also, assays with a single 15-mer peptide, containing an identified epitope, did not always elicit a response providing validation that 15-mer peptides are not optimal antigens for detecting CD8+ T cells. Our findings further validate pooling-based epitope mapping strategies, which are critical for characterizing vaccine-induced T-cell responses and more broadly for informing iterative vaccine design. We also show ways to improve their application with computational peptide:MHC binding predictors that can accurately identify the optimal epitope within a 15-mer peptide and within a pool of 15-mer peptides.
Cytokines and chemokines are key signaling molecules of the immune system. Recent technological advances enable measurement of multiplexed cytokine profiles in biological samples. These profiles can ...then be used to identify potential biomarkers of a variety of clinical phenotypes. However, testing for such associations for each cytokine separately ignores the highly context-dependent covariation in cytokine secretion and decreases statistical power to detect associations due to multiple hypothesis testing. Here we present CytoMod-a novel data-driven approach for analysis of cytokine profiles that uses unsupervised clustering and regression to identify putative functional modules of co-signaling cytokines. Each module represents a biosignature of co-signaling cytokines. We applied this approach to three independent clinical cohorts of subjects naturally infected with influenza in which cytokine profiles and clinical phenotypes were collected. We found that in two out of three cohorts, cytokine modules were significantly associated with clinical phenotypes, and in many cases these associations were stronger than the associations of the individual cytokines within them. By comparing cytokine modules across datasets, we identified cytokine "cores"-specific subsets of co-expressed cytokines that clustered together across the three cohorts. Cytokine cores were also associated with clinical phenotypes. Interestingly, most of these cores were also co-expressed in a cohort of healthy controls, suggesting that in part, patterns of cytokine co-signaling may be generalizable. CytoMod can be readily applied to any cytokine profile dataset regardless of measurement technology, increases the statistical power to detect associations with clinical phenotypes and may help shed light on the complex co-signaling networks of cytokines in both health and infection.
Machine learning (ML) has become an essential asset for the life sciences and medicine. We selected 250 articles describing ML applications from 17 journals sampling 26 different fields between 2011 ...and 2016. Independent evaluation by two readers highlighted three results. First, only half of the articles shared software, 64% shared data and 81% applied any kind of evaluation. Although crucial for ensuring the validity of ML applications, these aspects were met more by publications in lower-ranked journals. Second, the authors’ scientific backgrounds highly influenced how technical aspects were addressed: reproducibility and computational evaluation methods were more prominent with computational co-authors; experimental proofs more with experimentalists. Third, 73% of the ML applications resulted from interdisciplinary collaborations comprising authors from at least two of the three disciplines: computational sciences, biology, and medicine. The results suggested collaborations between computational and experimental scientists to generate more scientifically sound and impactful work integrating knowledge from both domains. Although scientifically more valid solutions and collaborations involving diverse expertise did not correlate with impact factors, such collaborations provide opportunities to both sides: computational scientists are given access to novel and challenging real-world biological data, increasing the scientific impact of their research, and experimentalists benefit from more in-depth computational analyses improving the technical correctness of work.Applications of machine learning in the life sciences and medicine require expertise in computational methods and in scientific subject matter. The authors surveyed articles in the life sciences that included machine learning applications, and found that interdisciplinary collaborations increased the scientific validity of published research.
Experimental and computational evidence suggests that HLAs preferentially bind conserved regions of viral proteins, a concept we term “targeting efficiency,” and that this preference may provide ...improved clearance of infection in several viral systems. To test this hypothesis, T-cell responses to A/H1N1 (2009) were measured from peripheral blood mononuclear cells obtained from a household cohort study performed during the 2009–2010 influenza season. We found that HLA targeting efficiency scores significantly correlated with IFN-γ enzyme-linked immunosorbent spot responses (P = 0.042, multiple regression). A further population-based analysis found that the carriage frequencies of the alleles with the lowest targeting efficiencies, A*24, were associated with pH1N1 mortality (r = 0.37, P = 0.031) and are common in certain indigenous populations in which increased pH1N1 morbidity has been reported. HLA efficiency scores and HLA use are associated with CD8 T-cell magnitude in humans after influenza infection. The computational tools used in this study may be useful predictors of potential morbidity and identify immunologic differences of new variant influenza strains more accurately than evolutionary sequence comparisons. Population-based studies of the relative frequency of these alleles in severe vs. mild influenza cases might advance clinical practices for severe H1N1 infections among genetically susceptible populations.
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