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T cell activation and effector functions are determined by the affinity of the interaction between T cell receptor (TCR) and its antigenic peptide MHC (pMHC) ligand. A better understanding of the quantitative aspects of TCR-pMHC affinity-dependent T cell activation is critical for the development of new immunotherapeutic strategies. However, the role of TCR-pMHC affinity in regulating the kinetics of CD8

Themis is a T cell lineage-specific molecule that is involved in TCR signal transduction. The effects of germline Themis deletion on peripheral CD4+ T cell function have not been described before. In this study, we found that Themis-deficient CD4+ T cells had poor proliferative responses, reduced cytokine production in vitro and weaker inflammatory potential, as measured by their ability to cause colitis in vivo. Resting T cells are quiescent, whereas activated T cells have high metabolic demands. Fulfillment of these metabolic demands depends upon nutrient availability and upregulation of nutrient intake channels after efficient TCR signal transduction, which leads to metabolic reprogramming in T cells. We tested whether defects in effector functions were caused by impaired metabolic shifts in Themis-deficient CD4+ T cells due to inefficient TCR signal transduction, in turn caused by the lack of Themis. We found that upon TCR stimulation, Themis-deficient CD4+ T cells were unable to upregulate the expression of insulin receptor (IR), glucose transporter (GLUT1), the neutral amino acid transporter CD98 and the mTOR pathway, as measured by c-Myc and pS6 expression. Mitochondrial analysis of activated Themis-deficient CD4+ T cells showed more oxidative phosphorylation (OXPHOS) than aerobic glycolysis, indicating defective metabolic reprogramming. Furthermore, we found reduced NFAT translocation in Themis-deficient CD4+ T cells upon TCR stimulation. Using previously reported ChIP-seq and RNA-seq data, we found that NFAT nuclear translocation controls IR gene expression. Together, our results describe an internal circuit between TCR signal transduction, NFAT nuclear translocation, and metabolic signaling in CD4+ T cells.

Objective To develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing. Data Source With respect to the linguistic content, we analyzed 283 216 secure messages sent by 6941 diabetes patients to physicians within an integrated system's electronic portal. Sociodemographic, clinical, and utilization data were obtained via questionnaire and electronic health records. Study Design Retrospective study used natural language processing and machine learning to generate five unique “Literacy Profiles” by employing various sets of linguistic indices: Flesch‐Kincaid (LP_FK); basic indices of writing complexity, including lexical diversity (LP_LD) and writing quality (LP_WQ); and advanced indices related to syntactic complexity, lexical sophistication, and diversity, modeled from self‐reported (LP_SR), and expert‐rated (LP_Exp) health literacy. We first determined the performance of each literacy profile relative to self‐reported and expert‐rated health literacy to discriminate between high and low health literacy and then assessed Literacy Profiles’ relationships with known correlates of health literacy, such as patient sociodemographics and a range of health‐related outcomes, including ratings of physician communication, medication adherence, diabetes control, comorbidities, and utilization. Principal Findings LP_SR and LP_Exp performed best in discriminating between high and low self‐reported (C‐statistics: 0.86 and 0.58, respectively) and expert‐rated health literacy (C‐statistics: 0.71 and 0.87, respectively) and were significantly associated with educational attainment, race/ethnicity, Consumer Assessment of Provider and Systems (CAHPS) scores, adherence, glycemia, comorbidities, and emergency department visits. Conclusions Since health literacy is a potentially remediable explanatory factor in health care disparities, the development of automated health literacy indicators represents a significant accomplishment with broad clinical and population health applications. Health systems could apply literacy profiles to efficiently determine whether quality of care and outcomes vary by patient health literacy; identify at‐risk populations for targeting tailored health communications and self‐management support interventions; and inform clinicians to promote improvements in individual‐level care.

For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is leveraged to predict the difficulty of practice texts used in a reading comprehension intelligent tutoring system, iSTART. Human raters estimated the text difficulty level of 262 texts across two text sets (Set A and Set B) in the iSTART library. NLP tools were used to identify linguistic features predictive of text difficulty and these indices were submitted to both flat and hierarchical machine learning algorithms. Results indicated that including NLP indices and machine learning increased accuracy by more than 10% as compared to classic readability metrics (e.g., Flesch-Kincaid Grade Level). Further, hierarchical outperformed non-hierarchical (flat) machine learning classification for Set B (72%) and the combined set A + B (65%), whereas the non-hierarchical approach performed slightly better than the hierarchical approach for Set A (79%). These findings demonstrate the importance of considering deeper features of language related to text difficulty as well as the potential utility of hierarchical machine learning approaches in the development of meaningful text difficulty classification.

BACKGROUND: Low literacy skills impact important aspects of communication, including health-related information exchanges. Unsuccessful communication on the part of physician or patient contributes to lower quality of care, is associated with poorer chronic disease control, jeopardizes patient safety and can lead to unfavorable healthcare utilization patterns. To date, very little research has focused on digital communication between physicians and patients, such as secure messages sent via electronic patient portals. METHOD: The purpose of the current study is to develop an automated readability formula to better understand what elements of physicians’ digital messages make them more or less difficult to understand. The formula is developed using advanced natural language processing (NLP) to predict human ratings of physician text difficulty. RESULTS: The results indicate that NLP indices that capture a diverse set of linguistic features predict the difficulty of physician messages better than classic readability tools such as Flesch Kincaid Grade Level. Our results also provide information about the textual features that best explain text readability. CONCLUSION: Implications for how the readability formula could provide feedback to physicians to improve digital health communication by promoting linguistic concordance between physician and patient are discussed.

Literacy is a critically important and contemporary issue for educators, scientists, and politicians. Efforts to overcome the challenges associated with illiteracy, and the subsequent development of literate societies, are closely related to those of poverty reduction and sustainable human development. In this paper, the authors examine literacy from the lens of text and discourse theorists who focus on the higher-order comprehension processes involved in literacy. Discourse processing models make the assumption that comprehension emerges from the construction of a mental model of the text, which relies on the reader generating inferences to connect ideas within the text and to what the reader already knows. The article provides a broad overview of the theoretical models that drive research on text comprehension and production, as well as how this research shapes literacy instruction and effective interventions. The authors focus on two interventions with proven success in improving deep comprehension and writing, iSTART and the Writing Pal. Increasing literacy across the world call for a greater focus on theory driven strategy interventions to be integrated within classrooms and community at large.

We have previously demonstrated co‐receptor level‐associated functional heterogeneity in apparently homogeneous naive peripheral CD4 T cells, dependent on MHC‐mediated tonic signals. Maturation pathways can differ between naive CD4 and naive CD8 cells, so we tested whether the latter showed similar co‐receptor level‐associated functional heterogeneity. We report that, when either polyclonal and T‐cell receptor (TCR)‐transgenic monoclonal peripheral naive CD8 T cells from young mice were separated into CD8(hi) and CD8(lo) subsets, CD8(lo) cells responded poorly, but CD8(hi) and CD8(lo) subsets of CD8 single‐positive (SP) thymocytes responded similarly. CD8(lo) naive CD8 T cells were smaller and showed lower levels of some cell‐surface molecules, but higher levels of the negative regulator CD5. In addition to the expected peripheral decline in CD8 levels on transferred naive CD8 T cells in wild‐type (WT) but not in MHC class I‐deficient recipient mice, short‐duration naive T‐cell–dendritic cell (DC) co‐cultures in vitro also caused co‐receptor down‐modulation in CD8 T cells but not in CD4 T cells. Constitutive pZAP70/pSyk and pERK levels ex vivo were lower in CD8(lo) naive CD8 T cells and dual‐specific phosphatase inhibition partially rescued their hypo‐responsiveness. Bulk mRNA sequencing showed major differences in the transcriptional landscapes of CD8(hi) and CD8(lo) naive CD8 T cells. CD8(hi) naive CD8 T cells showed enrichment of genes involved in positive regulation of cell cycle and survival. Our data show that naive CD8 T cells show major differences in their signaling, transcriptional and functional landscapes associated with subtly altered CD8 levels, consistent with the possibility of peripheral cellular aging.

Limited health literacy (HL) partially mediates health disparities. Measurement constraints, including lack of validity assessment across racial/ethnic groups and administration challenges, have undermined the field and impeded scaling of HL interventions. We employed computational linguistics to develop an automated and novel HL measure, analyzing >300,000 messages sent by >9,000 diabetes patients via a patient portal to create a Literacy Profiles. We carried out stratified analyses among White/non-Hispanics, Black/non-Hispanics, Hispanics, and Asian/Pacific Islanders to determine if the Literacy Profile has comparable criterion and predictive validities. We discovered that criterion validity was consistently high across all groups (c-statistics 0.82–0.89). We observed consistent relationships across racial/ethnic groups between HL and outcomes, including communication, adherence, hypoglycemia, diabetes control, and ED utilization. While concerns have arisen regarding bias in AI, the automated Literacy Profile appears sufficiently valid across race/ethnicity, enabling HL measurement at a scale that could improve clinical care and population health among diverse populations.

Objective In the National Library of Medicine funded ECLIPPSE Project (Employing Computational Linguistics to Improve Patient-Provider Secure Emails exchange), we attempted to create novel, valid, and scalable measures of both patients’ health literacy (HL) and physicians’ linguistic complexity by employing natural language processing (NLP) techniques and machine learning (ML). We applied these techniques to > 400,000 patients’ and physicians’ secure messages (SMs) exchanged via an electronic patient portal, developing and validating an automated patient literacy profile (LP) and physician complexity profile (CP). Herein, we describe the challenges faced and the solutions implemented during this innovative endeavor. Materials and methods To describe challenges and solutions, we used two data sources: study documents and interviews with study investigators. Over the five years of the project, the team tracked their research process using a combination of Google Docs tools and an online team organization, tracking, and management tool (Asana). In year 5, the team convened a number of times to discuss, categorize, and code primary challenges and solutions. Results We identified 23 challenges and associated approaches that emerged from three overarching process domains: (1) Data Mining related to the SM corpus; (2) Analyses using NLP indices on the SM corpus; and (3) Interdisciplinary Collaboration. With respect to Data Mining, problems included cleaning SMs to enable analyses, removing hidden caregiver proxies (e.g., other family members) and Spanish language SMs, and culling SMs to ensure that only patients’ primary care physicians were included. With respect to Analyses, critical decisions needed to be made as to which computational linguistic indices and ML approaches should be selected; how to enable the NLP-based linguistic indices tools to run smoothly and to extract meaningful data from a large corpus of medical text; and how to best assess content and predictive validities of both the LP and the CP. With respect to the Interdisciplinary Collaboration, because the research required engagement between clinicians, health services researchers, biomedical informaticians, linguists, and cognitive scientists, continual effort was needed to identify and reconcile differences in scientific terminologies and resolve confusion; arrive at common understanding of tasks that needed to be completed and priorities therein; reach compromises regarding what represents “meaningful findings” in health services vs. cognitive science research; and address constraints regarding potential transportability of the final LP and CP to different health care settings. Discussion Our study represents a process evaluation of an innovative research initiative to harness “big linguistic data” to estimate patient HL and physician linguistic complexity. Any of the challenges we identified, if left unaddressed, would have either rendered impossible the effort to generate LPs and CPs, or invalidated analytic results related to the LPs and CPs. Investigators undertaking similar research in HL or using computational linguistic methods to assess patient-clinician exchange will face similar challenges and may find our solutions helpful when designing and executing their health communications research.

Themis is a T cell lineage-specific molecule that is involved in TCR signal transduction. The effects of germline Themis deletion on peripheral CD4

Design and implementation of automatic evaluation methods is an integral part of any scientific research in accelerating the development cycle of the output. This is no less true for automatic machine translation (MT) systems. However, no such global and systematic scheme exists for evaluation of performance of an MT system. The existing evaluation metrics, such as BLEU, METEOR, TER, although used extensively in literature have faced a lot of criticism from users. Moreover, performance of these metrics often varies with the pair of languages under consideration. The above observation is no less pertinent with respect to translations involving languages of the Indian subcontinent. This study aims at developing an evaluation metric for English to Hindi MT outputs. As a part of this process, a set of probable errors have been identified manually as well as automatically. Linear regression has been used for computing weight/penalty for each error, while taking human evaluations into consideration. A sentence score is computed as the weighted sum of the errors. A set of 126 models has been built using different single classifiers and ensemble of classifiers in order to find the most suitable model for allocating appropriate weight/penalty for each error. The outputs of the models have been compared with the state-of-the-art evaluation metrics. The models developed for manually identified errors correlate well with manual evaluation scores, whereas the models for the automatically identified errors have low correlation with the manual scores. This indicates the need for further improvement and development of sophisticated linguistic tools for automatic identification and extraction of errors. Although many automatic machine translation tools are being developed for many different language pairs, there is no such generalized scheme that would lead to designing meaningful metrics for their evaluation. The proposed scheme should help in developing such metrics for different language pairs in the coming days.

T cell response magnitudes increase with increasing antigenic dosage. However, it is unclear whether ligand density only modulates the proportions of responding ligand-specific T cells or also alters responses at the single cell level. Using brief (3 h) exposure of TCR-transgenic mouse CD8 T cells in vitro to varying densities of cognate peptide-MHC ligand followed by ligand-free culture in IL-2, we found that ligand density determined the frequencies of responding cells but not the expression levels of the early activation marker molecule, CD69. Cells with low glucose uptake capacity and low protein synthesis rates were less ligand-sensitive, implicating metabolic competence in the response heterogeneity of CD8 T cell populations. Although most responding cells proliferated, ligand density was associated with time of entry into proliferation and with the extent of cell surface TCR downmodulation. TCR internalization was associated, regardless of the ligand density, with rapidity of c-myc induction, loss of the cell cycle inhibitor p27kip1, metabolic reprogramming, and cell cycle entry. A low affinity peptide ligand behaved, regardless of ligand density, like a low density, high affinity ligand in all these parameters. Inhibition of signaling after ligand exposure selectively delayed proliferation in cells with internalized TCRs. Finally, internalized TCRs continued to signal and genetic modification of TCR internalization and trafficking altered the duration of signaling in a T cell hybridoma. Together, our findings indicate that heterogeneity among responding CD8 T cell populations in their ability to respond to TCR-mediated stimulation and internalize TCRs mediates detection of ligand density or affinity, contributing to graded response magnitudes.

T cell activation is ‘digital’: exhaustive single-cell analysis shows that only the proportion of cells that cross the activation threshold is reduced when the affinity of the TCR for its ligand is reduced.

Intelligent Tutoring Systems (ITSs) focus on promoting knowledge acquisition, while providing relevant feedback during students’ practice. Self-explanation practice is an effective method used to help students understand complex texts by leveraging comprehension. Our aim is to introduce a deep learning neural model for automatically scoring student self-explanations that are targeted at specific sentences. The first stage of the processing pipeline performs an initial text cleaning and applies a set of predefined rules established by human experts in order to identify specific cases (e.g., students who do not understand the text, or students who simply copy and paste their self-explanations from the given input text). The second step uses a Recurrent Neural Network with pre-trained Glove word embeddings to predict self-explanation scores on a scale of 1 to 3. In contrast to previous SVM models trained on the same dataset of 4109 self-explanations, we obtain a significant increase of accuracy from 59% to 73%. Moreover, the new pipeline can be integrated in learning scenarios requiring near real-time responses from the ITS, thus addressing a major limitation in terms of processing speed exhibited by the previous approach.

Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of this study was to develop and validate “literacy profiles” as automated indicators of patients’ health literacy to facilitate a non-intrusive, economic and more comprehensive characterization of health literacy among a health care delivery system’s membership. To this end, three literacy profiles were generated based on natural language processing (combining computational linguistics and machine learning) using a sample of 283,216 secure messages sent from 6,941 patients to their primary care physicians. All patients were participants in Kaiser Permanente Northern California’s DISTANCE Study. Performance of the three literacy profiles were compared against a gold standard of patient self-reported health literacy. Associations were analyzed between each literacy profile and patient demographics, health outcomes and healthcare utilization. T-tests were used for numeric data such as A1C, Charlson comorbidity index and healthcare utilization rates, and chi-square tests for categorical data such as sex, race, poor adherence and severe hypoglycemia. Literacy profiles varied in their test characteristics, with C-statistics ranging from 0.61–0.74. Relations between literacy profiles and health outcomes revealed patterns consistent with previous health literacy research: patients identified via literacy profiles indicative of limited health literacy: (a) were older and more likely of minority status; (b) had poorer medication adherence and glycemic control; and (c) exhibited higher rates of hypoglycemia, comorbidities and healthcare utilization. This represents the first successful attempt to employ natural language processing to estimate health literacy. Literacy profiles can offer an automated and economical way to identify patients with limited health literacy and greater vulnerability to poor health outcomes.

How cells respond to a myriad of stimuli with finite signaling machinery is central to immunology. In naive T cells, the inherent effect of ligand strength on activation pathways and endpoints remains controversial, confounded by environmental fluctuations and intercellular variability within populations. Here, we study how ligand potency affects CD8+ T cell activation in vitro using genome-wide RNA, multi-dimensional protein and functional measurements in single cells. Our data reveal that strong ligands drive more efficient and uniform activation than weak ligands, but all activated cells are fully cytolytic. Importantly, activation follows the same transcriptional pathways, regardless of ligand potency. Thus, stimulation strength does not intrinsically dictate T cell activation route or phenotype; instead it controls how rapidly and simultaneously cells initiate activation, allowing limited machinery to elicit wide-ranging responses.

This study assesses the extent to which machine learning techniques can be used to predict question quality. An algorithm based on textual complexity indices was previously developed to assess question quality to provide feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). In this study, 4,575 questions were coded by human raters based on their corresponding depth, classifying questions into four categories: 1-very shallow to 4-very deep. Here we propose a novel approach to assessing question quality within this dataset based on Recurrent Neural Networks (RNNs) and word embeddings. The experiments evaluated multiple RNN architectures using GRU, BiGRU and LSTM cell types of different sizes, and different word embeddings (i.e., FastText and Glove). The most precise model achieved a classification accuracy of 81.22%, which surpasses the previous prediction results using lexical sophistication complexity indices (accuracy = 41.6%). These results are promising and have implications for the future development of automated assessment tools within computer-based learning environments.

Summarization enhances comprehension and is considered an effective strategy to promote and enhance learning and deep understanding of texts. However, summarization is seldom implemented by teachers in classrooms because the manual evaluation requires a lot of effort and time. Although the need for automated support is stringent, there are only a few shallow systems available, most of which rely on basic word/n-gram overlaps. In this paper, we introduce a hybrid model that uses state-of-the-art recurrent neural networks and textual complexity indices to score summaries. Our best model achieves over 55% accuracy for a 3-way classification that measures the degree to which the main ideas from the original text are covered by the summary . Our experiments show that the writing style, represented by the textual complexity indices, together with the semantic content grasped within the summary are the best predictors, when combined. To the best of our knowledge, this is the first work of its kind that uses RNNs for scoring and evaluating summaries.

A hybrid scheme for noun compound translation from English to Hindi.A scheme for semantic relation identification is proposed for 2-word noun compounds.A scheme for generation of translation pattern(s) for 2/3/4-word noun compounds.Bracketing scheme for 4-word noun compounds.Results of the proposed system compared with the state-of-the-art translators. Despite having a research history of more than 20 years, English to Hindi machine translation often suffers badly from incorrect translations of noun compounds. The problems envisaged can be of various types, such as, the absence of proper postpositions, inappropriate word order, incorrect semantics. Different existing English to Hindi machine translation systems show their vulnerability, irrespective of the underlying technique. A potential solution to this problem lies in understanding the semantics of the noun compounds. The present paper proposes a scheme based on semantic relations to address this issue. The scheme works in three steps: identification of the noun compounds in a given text, determination of the semantic relationship(s) between them, and finally, selecting the right translation pattern. The scheme provides translation patterns for different semantic relations for 2-word noun compounds first. These patterns are used recursively to find the semantic relations and the translation patterns for 3-word and 4-word noun compounds. Frequency and probability based adjacency and dependency models are used for bracketing (grouping) the constituent words of 3-word and 4-word noun compounds into 2-word noun compounds. The semantic relations and the translation patterns generated for 2-word, 3-word and 4-word noun compounds are evaluated. The proposed scheme is compared with some well-known English to Hindi translators, viz. AnglaMT, Anuvadaksh, Bing, Google, and also with the Moses baseline system. The results obtained, show significant improvement over the Moses baseline system. Also, it performs better than the other online MT systems in terms of recall and precision.

Amyloids are highly organized protein aggregates that arise from inappropriately folded versions of proteins or polypeptides under both physiological as well as simulated ambiences. Once thought to be irreversible assemblies, amyloids have begun to expose their more dynamic and reversible attributes depending upon the intrinsic properties of the precursor protein/peptide and experimental conditions such as temperature, pressure, structural modifications in proteins, or presence of chemicals in the reaction mixture. It has been repeatedly proposed that amyloids undergo transformation to the bioactive peptide/protein forms under specific conditions. In the present study, amyloids assembled from the model protein ovalbumin (OVA) were found to release the precursor protein in a slow and steady manner over an extended time period. Interestingly, the released OVA from amyloid depot was found to exhibit biophysical characteristics of native protein and reacted with native-OVA specific monoclonal as well as polyclonal antibodies. Moreover, antibodies generated upon immunization of OVA amyloidal aggregates or fibrils were found to recognize the native form of OVA. The study suggests that amyloids may act as depots for the native form of the protein and therefore can be exploited as vaccine candidates, where slow antigen release over extended time periods is a pre-requisite for the development of desired immune response.

The induction of CD80 on podocytes has been shown in animal models of podocyte injury and in certain cases of nephrotic syndrome. In a lipopolysaccharide (LPS)-induced mouse model of albuminuria, we have recently shown a signalling axis of LPS-myeloid cell activation-TNFα production-podocyte CD80 induction-albuminuria. Therefore, in this report, we investigated the cellular and molecular consequences of TNFα addition and CD80 expression on cultured podocytes. A murine podocyte cell line was used for TNFα treatment and for over-expressing CD80. Expression and localization of various podocyte proteins was analysed by reverse transcriptase-polymerase chain reaction, western blotting and immunofluorescence. HEK293 cells were used to biochemically characterize interactions. Podocytes treated with LPS in vitro did not cause CD80 upregulation but TNFα treatment was associated with an increase in CD80 levels, actin derangement and poor wound healing. Podocytes stably expressing CD80 showed actin derangement and co-localization with Neph1. CD80 and Neph1 interaction was confirmed by pull down assays of CD80 and Neph1 transfected in HEK293 cells. Addition of TNFα to podocytes causes CD80 upregulation, actin reorganization and podocyte injury. Overexpressed CD80 and Neph1 interact via their extracellular domain. This interaction implies a mechanism of slit diaphragm disruption and possible use of small molecules that disrupt CD80-Neph1 interaction as a potential for treatment of nephrotic syndrome associated with CD80 upregulation.

More than 80% of malignant tumors show centrosome amplification and clustering. Centrosome amplification results from aberrations in the centrosome duplication cycle, which is strictly coordinated with DNA-replication-cycle. However, the relationship between cell-cycle regulators and centrosome duplicating factors is not well understood. This report demonstrates that 14-3-3γ localizes to the centrosome and 14-3-3γ loss leads to centrosome amplification. Loss of 14-3-3γ results in the phosphorylation of NPM1 at Thr-199, causing early centriole disjunction and centrosome hyper-duplication. The centrosome amplification led to aneuploidy and increased tumor formation in mice. Importantly, an increase in passage of the 14-3-3γ-knockdown cells led to an increase in the number of cells containing clustered centrosomes leading to the generation of pseudo-bipolar spindles. The increase in pseudo-bipolar spindles was reversed and an increase in the number of multi-polar spindles was observed upon expression of a constitutively active 14-3-3-binding-defective-mutant of cdc25C (S216A) in the 14-3-3γ knockdown cells. The increase in multi-polar spindle formation was associated with decreased cell viability and a decrease in tumor growth. Our findings uncover the molecular basis of regulation of centrosome duplication by 14-3-3γ and inhibition of tumor growth by premature activation of the mitotic program and the disruption of centrosome clustering.

Amyloids are highly organized protein aggregates that arise from inappropriately folded versions of proteins or polypeptides under both physiological as well as simulated ambiences. Once thought to be irreversible assemblies, amyloids have begun to expose their more dynamic and reversible attributes depending upon the intrinsic properties of the precursor protein/peptide and experimental conditions such as temperature, pressure, structural modifications in proteins, or presence of chemicals in the reaction mixture. It has been repeatedly proposed that amyloids undergo transformation to the bioactive peptide/protein forms under specific conditions. In the present study, amyloids assembled from the model protein ovalbumin (OVA) were found to release the precursor protein in a slow and steady manner over an extended time period. Interestingly, the released OVA from amyloid depot was found to exhibit biophysical characteristics of native protein and reacted with native-OVA specific monoclonal as well as polyclonal antibodies. Moreover, antibodies generated upon immunization of OVA amyloidal aggregates or fibrils were found to recognize the native form of OVA. The study suggests that amyloids may act as depots for the native form of the protein and therefore can be exploited as vaccine candidates, where slow antigen release over extended time periods is a pre-requisite for the development of desired immune response.

Functional heterogeneity in apparently homogenous T cell populations has been well reported. However, what factors lead to such variations within a T cell population is still unclear. Depending upon the time of egress from the thymus and duration of residence in the periphery, individual cells would be of relatively different ages, however, consequences of such cellular aging have not been addressed. We observed that naive CD8 T cells from aged mice have lower coreceptor levels than naive CD8 T cells of young mice. Further, when naive T cells from young mice are separated into coreceptor-hi and coreceptor-lo subsets, we find that coreceptor-lo cells are smaller, respond poorly and are more susceptible to death. Similar coreceptor-lo associated defects were observed even in TCR-transgenic monoclonal CD8 T cell population. However, this phenotype is an outcome of peripheral residence, as coreceptor-lo and coreceptor-hi subsets of single-positive (SP) thymocytes do not show these differences. We show that coreceptor levels reduce over time, as a result of MHC-I mediated tonic signals and lower CD8 levels can be used as a surrogate marker to identify relatively aged cells in a naive CD8 T cell population. Thus, our data show that apparently unimodally distributed coreceptor heterogeneity among naive T cells, simplistically expected to be due to intrinsic noise, is not only correlated with major functional variation but is modulated by extrinsic microenvironmental cues in vivo. These findings indicate that ensemble averages may not represent individual cell function, even for a unimodal normal distribution of single cell measurements, highlighting the importance of distinguishing between population versus individual T cell responses to antigen encounter. At the population level, CD8 T cell response magnitude increases with ligand dose as well as with ligand affinity. However, whether ligand density and affinity only modulate the frequency of responding T cells and/or alters responses at the single cell level is unclear. We observed that the early activation response of individual naive CD8 T cell is binary such that the frequency of CD69 expressing cells increases with increment in ligand density or affinity, but the levels of CD69 expression in individual responding cells remain constant and variations in baseline cellular metabolic status between individual T cells may contribute to the differences in their activation thresholds. We show that ligand density and affinity determine the extent of TCR internalization in responding CD8 T cells, which in turn regulates the time to cell cycle entry. TCR internalization, which is commonly thought of as a mechanism to attenuate TCR-mediated signaling, was found to be associated with sustained TCR signaling via PI3K, leading to rapid metabolic reprogramming and when the degradation of internalized TCR complex is prevented, the duration of signaling is extended possibly from endosomal compartments, leading to better T cell response. Collectively, our findings reveal that heterogeneity among T cells for signal receptivity as well as microenvironmental influences experienced during their life history shape individual T cell responses and understanding the basis of heterogeneity within putatively homogenous T cell populations is significant for understanding of immune T cell responses. Citation Format: Renu Balyan, Rupali Gund, Chitra Ebenezer, Thyagarajan Krishnamurthy, Amanpreet Singh Chawla, Jeannine Durdik, Anna George, Vineeta Bal, Satyajit Rath. Functionally significant heterogeneity in apparently homogenous naive CD8 T cell populations. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr A80.