Time required for commitment to T cell proliferation depends on TCR affinity and cytokine response.

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+ T cell commitment to proliferation and differentiation is unknown. Here, we show that the stronger the TCR-pMHC affinity, the shorter the time of T cell-APC co-culture required to commit CD8+ T cells to proliferation. The time threshold for T cell cytokine production is much lower than that for cell proliferation. There is a strong correlation between affinity-dependent differences in AKT phosphorylation and T cell proliferation. The cytokine IL-15 increases the poor proliferation of T cells stimulated with low affinity pMHC, suggesting that pro-inflammatory cytokines can override the affinity-dependent features of T cell proliferation.

Challenges and solutions to employing natural language processing and machine learning to measure patients' health literacy and physician writing complexity: The ECLIPPSE study.

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.

Developing and Testing Automatic Models of Patient Communicative Health Literacy Using Linguistic Features: Findings from the ECLIPPSE study.

Patients with diabetes and limited health literacy (HL) may have suboptimal communication exchange with their health care providers and be at elevated risk of adverse health outcomes. These difficulties are generally attributed to patients' reduced ability to both communicate and understand health-related ideas as well as physicians' lack of skill in identifying those with limited HL. Understanding and identifying patients with barriers posed by lower HL to improve healthcare delivery and outcomes is an important research avenue. However, doing so using traditional methods has proven difficult and infeasible to scale. This study using corpus analyses, expert human ratings of HL, and natural language processing (NLP) approaches to estimate HL at the individual patient level. The goal of the study is to better understand HL from a linguistic perspective and to open new research areas to enhance population management and individualized care. Specifically, this study examines HL as a function of patients' demonstrated ability to communicate health-related information to their providers via secure messages. The study develops an NLP-based HL model and validates the model by predicting patient-related events such as medical outcomes and hospitalizations. Results indicate that the developed model predicts human ratings of HL with ~80% accuracy. Validation indicates that lower HL patients are more likely to be nonwhite and have lower educational attainment. In addition, patients with lower HL suffered more negative health outcomes and had higher healthcare service utilization.

The Ups and Downs of Metabolism during the Lifespan of a T Cell.

Understanding the various mechanisms that govern the development, activation, differentiation, and functions of T cells is crucial as it could provide opportunities for therapeutic interventions to disrupt immune pathogenesis. Immunometabolism is one such area that has garnered significant interest in the recent past as it has become apparent that cellular metabolism is highly dynamic and has a tremendous impact on the ability of T cells to grow, activate, and differentiate. In each phase of the lifespan of a T-cell, cellular metabolism has to be tailored to match the specific functional requirements of that phase. Resting T cells rely on energy-efficient oxidative metabolism but rapidly shift to a highly glycolytic metabolism upon activation in order to meet the bioenergetically demanding process of growth and proliferation. However, upon antigen clearance, T cells return to a more quiescent oxidative metabolism to support T cell memory generation. In addition, each helper T cell subset engages distinct metabolic pathways to support their functional needs. In this review, we provide an overview of the metabolic changes that occur during the lifespan of a T cell and discuss several important studies that provide insights into the regulation of the metabolic landscape of T cells and how they impact T cell development and function.

Correlation of cell-surface CD8 levels with function, phenotype and transcriptome of naive CD8 T cells.

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 CD8hi and CD8lo subsets, CD8lo cells responded poorly, but CD8hi and CD8lo subsets of CD8 single-positive (SP) thymocytes responded similarly. CD8lo 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 CD8lo 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 CD8hi and CD8lo naive CD8 T cells. CD8hi 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.

Modulation of Naive CD8 T Cell Response Features by Ligand Density, Affinity, and Continued Signaling via Internalized TCRs.

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.

Interaction of CD80 with Neph1: a potential mechanism of podocyte injury.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Amyloid form of ovalbumin evokes native antigen-specific immune response in the host: prospective immuno-prophylactic potential.

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.

Automated Strategy Feedback Can Improve the Readability of Physicians' Electronic Communications to Simulated Patients.

Modern communication between health care professionals and patients increasingly relies upon secure messages (SMs) exchanged through an electronic patient portal. Despite the convenience of secure messaging, challenges include gaps between physician and patient expertise along with the asynchronous nature of such communication. Importantly, less readable SMs from physicians (e.g., too complicated) may result in patient confusion, non-adherence, and ultimately poorer health outcomes. The current simulation trial synthesizes work on patient-physician electronic communication, message readability assessments, and feedback to explore the potential for automated strategy feedback to improve the readability of physicians' SMs to patients. Within a simulated secure messaging portal featuring multiple simulated patient scenarios, computational algorithms assessed the complexity of SMs written by 67 participating physicians to patients. The messaging portal provided strategy feedback for how physician responses might be improved (e.g., adding details and information to reduce complexity). Analyses of changes in SM complexity revealed that automated strategy feedback indeed helped physicians compose and refine more readable messages. Although the effects for any individual SM were slight, the cumulative effects within and across patient scenarios showed trends of decreasing complexity. Physicians appeared to learn how to craft more readable SMs via interactions with the feedback system. Implications for secure messaging systems and physician training are discussed, along with considerations for further investigation of broader physician populations and effects on patient experience.

Validity of a Computational Linguistics-Derived Automated Health Literacy Measure Across Race/Ethnicity: Findings from The ECLIPPSE Project.

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.

Themis regulates metabolic signaling and effector functions in CD4+ T cells by controlling NFAT nuclear translocation.

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.

Precision communication: Physicians' linguistic adaptation to patients' health literacy.

Little quantitative research has explored which clinician skills and behaviors facilitate communication. Mutual understanding is especially challenging when patients have limited health literacy (HL). Two strategies hypothesized to improve communication include matching the complexity of language to patients' HL ("universal tailoring"); or always using simple language ("universal precautions"). Through computational linguistic analysis of 237,126 email exchanges between dyads of 1094 physicians and 4331 English-speaking patients, we assessed matching (concordance/discordance) between physicians' linguistic complexity and patients' HL, and classified physicians' communication strategies. Among low HL patients, discordance was associated with poor understanding (P = 0.046). Physicians' "universal tailoring" strategy was associated with better understanding for all patients (P = 0.01), while "universal precautions" was not. There was an interaction between concordance and communication strategy (P = 0.021): The combination of dyadic concordance and "universal tailoring" eliminated HL-related disparities. Physicians' ability to adapt communication to match their patients' HL promotes shared understanding and equity. The 'Precision Medicine' construct should be expanded to include the domain of 'Precision Communication.'

Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study.

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.

Predicting the readability of physicians' secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study.

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.

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

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.

14-3-3γ Prevents Centrosome Amplification and Neoplastic Progression.

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.