Perturbations of the T-cell receptor repertoire in response to SARS-CoV-2 in immunocompetent and immunocompromised individuals

BACKGROUND

Functional T-cell responses are essential for virus clearance and long-term protection after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, whereas certain clinical factors, such as older age and immunocompromise, are associated with worse outcome.

OBJECTIVE

We sought to study the breadth and magnitude of T-cell responses in patients with coronavirus disease 2019 (COVID-19) and in individuals with inborn errors of immunity (IEIs) who had received COVID-19 mRNA vaccine.

METHODS

Using high-throughput sequencing and bioinformatics tools to characterize the T-cell receptor β repertoire signatures in 540 individuals after SARS-CoV-2 infection, 31 IEI recipients of COVID-19 mRNA vaccine, and healthy controls, we quantified HLA class I- and class II-restricted SARS-CoV-2-specific responses and also identified several HLA allele-clonotype motif associations in patients with COVID-19, including a subcohort of anti-type 1 interferon (IFN-1)-positive patients.

RESULTS

Our analysis revealed that elderly patients with COVID-19 with critical disease manifested lower SARS-CoV-2 T-cell clonotype diversity as well as T-cell responses with reduced magnitude, whereas the SARS-CoV-2-specific clonotypes targeted a broad range of HLA class I- and class II-restricted epitopes across the viral proteome. The presence of anti-IFN-I antibodies was associated with certain HLA alleles. Finally, COVID-19 mRNA immunization induced an increase in the breadth of SARS-CoV-2-specific clonotypes in patients with IEIs, including those who had failed to seroconvert.

CONCLUSIONS

Elderly individuals have impaired capacity to develop broad and sustained T-cell responses after SARS-CoV-2 infection. Genetic factors may play a role in the production of anti-IFN-1 antibodies. COVID-19 mRNA vaccines are effective in inducing T-cell responses in patients with IEIs.

ImmunoTyper-SR: A computational approach for genotyping immunoglobulin heavy chain variable genes using short-read data

Human immunoglobulin heavy chain (IGH) locus on chromosome 14 includes more than 40 functional copies of the variable gene (IGHV), which are critical for the structure of antibodies that identify and neutralize pathogenic invaders as a part of the adaptive immune system. Because of its highly repetitive sequence composition, the IGH locus has been particularly difficult to assemble or genotype when using standard short-read sequencing technologies. Here, we introduce ImmunoTyper-SR, an algorithmic tool for the genotyping and CNV analysis of the germline IGHV genes on Illumina whole-genome sequencing (WGS) data using a combinatorial optimization formulation that resolves ambiguous read mappings. We have validated ImmunoTyper-SR on 12 individuals, whose IGHV allele composition had been independently validated, as well as concordance between WGS replicates from nine individuals. We then applied ImmunoTyper-SR on 585 COVID patients to investigate the associations between IGHV alleles and anti-type I IFN autoantibodies, which were previously associated with COVID-19 severity.

Clinical Exome Sequencing of 1000 Families with Complex Immune Phenotypes: Towards comprehensive genomic evaluations

BACKGROUND

Prospective genetic evaluation of patients at our referral research hospital presents clinical research challenges.

OBJECTIVE

This study sought not only a single-gene explanation for participants' immune-related presentations, but viewed each participant holistically, with the potential to have multiple genetic contributions to their immune-phenotype and other heritable comorbidities relevant to their presentation and health.

METHODS

We developed a program integrating exome sequencing, chromosomal microarray, phenotyping, results return with genetic counseling, and reanalysis in 1505 individuals from 1000 families with suspected or known inborn errors of immunity.

RESULTS

Probands were 50.8% female, 71.5% >18 years, and had diverse immune presentations. Overall, 327/1000 probands (32.7%) received 361 molecular diagnoses. These included 17 probands with diagnostic copy number variants, 32 probands with secondary findings, and 31 probands with multiple molecular diagnoses. Reanalysis added 22 molecular diagnoses, predominantly due to new disease-gene associations (9/22, 40.9%). One-quarter of the molecular diagnoses (92/361) did not involve immune-associated genes. Molecular diagnosis was correlated with younger age, male sex, and a higher number of organ systems involved. This program also facilitated the discovery of new gene-disease associations such as SASH3-related immunodeficiency. A review of treatment options and ClinGen actionability curations suggest that at least 251/361 (69.5%) of these molecular diagnoses could translate into >1 management option.

CONCLUSION

This program contributes to our understanding of the diagnostic and clinical utility whole exome analysis on a large scale.

CLINICAL

Implication: Comprehensive analysis of exome data has diagnostic and clinical utility for patients with suspected inborn errors of immunity.

PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology

The study aims at developing a neural network model to improve the performance of Human Phenotype Ontology (HPO) concept recognition tools. We used the terms, definitions, and comments about the phenotypic concepts in the HPO database to train our model. The document to be analyzed is first split into sentences and annotated with a base method to generate candidate concepts. The sentences, along with the candidate concepts, are then fed into the pre-trained model for re-ranking. Our model comprises the pre-trained BlueBERT and a feature selection module, followed by a contrastive loss. We re-ranked the results generated by three robust HPO annotation tools and compared the performance against most of the existing approaches. The experimental results show that our model can improve the performance of the existing methods. Significantly, it boosted 3.0% and 5.6% in F1 score on the two evaluated datasets compared with the base methods. It removed more than 80% of the false positives predicted by the base methods, resulting in up to 18% improvement in precision. Our model utilizes the descriptive data in the ontology and the contextual information in the sentences for re-ranking. The results indicate that the additional information and the re-ranking model can significantly enhance the precision of HPO concept recognition compared with the base method.

ImmunoTyper-SR: A Novel Computational Approach for Genotyping Immunoglobulin Heavy Chain Variable Genes using Short Read Data

Human immunoglobulin heavy chain (IGH) locus on chromosome 14 includes more than 40 functional copies of the variable gene (IGHV), which, together with the joining genes (IGHJ), diversity genes (IGHD), constant genes (IGHC) and immunoglobulin light chains, code for antibodies that identify and neutralize pathogenic invaders as a part of the adaptive immune system. Because of its highly repetitive sequence composition, the IGH locus has been particularly difficult to assemble or genotype through the use of standard short read sequencing technologies. Here we introduce ImmunoTyper-SR, an algorithmic method for genotype and CNV analysis of the germline IGHV genes using Illumina whole genome sequencing (WGS) data. ImmunoTyper-SR is based on a novel combinatorial optimization formulation that aims to minimize the total edit distance between reads and their assigned IGHV alleles from a given database, with constraints on the number and distribution of reads across each called allele. We have validated ImmunoTyper-SR on 12 individuals with Illumina WGS data from the 1000 Genomes Project, whose IGHV allele composition have been studied extensively through the use of long read and targeted sequencing platforms, as well as nine individuals from the NIAID COVID Consortium who have been subjected to WGS twice. We have then applied ImmunoTyper-SR on 585 samples from the NIAID COVID Consortium to investigate associations between distinct IGHV alleles and anti-type I IFN autoantibodies which have been linked to COVID-19 severity.

PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology

MOTIVATION

Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dictionary-based matching methods, which can achieve high precision but suffer from lower recall. Recently, machine learning-based methods have been proposed to identify biomedical concepts, which can recognize more unseen concept synonyms by automatic feature learning. However, most methods require large corpora of manually annotated data for model training, which is difficult to obtain due to the high cost of human annotation.

RESULTS

In this article, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text. We first use all concepts and synonyms in HPO to construct a dictionary, which is then used to automatically build a distantly supervised training dataset for machine learning. Next, a cutting-edge deep learning model is trained to classify each candidate phrase (n-gram from input sentence) into a corresponding concept label. Finally, the dictionary and machine learning-based prediction results are combined for improved performance. Our method is validated with two HPO corpora, and the results show that PhenoTagger compares favorably to previous methods. In addition, to demonstrate the generalizability of our method, we retrained PhenoTagger using the disease ontology MEDIC for disease concept recognition to investigate the effect of training on different ontologies. Experimental results on the NCBI disease corpus show that PhenoTagger without requiring manually annotated training data achieves competitive performance as compared with state-of-the-art supervised methods.

AVAILABILITYAND IMPLEMENTATION

The source code, API information and data for PhenoTagger are freely available at https://github.com/ncbi-nlp/PhenoTagger.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Papillomavirus Episteme: a major update to the papillomavirus sequence database

The Papillomavirus Episteme (PaVE) is a database of curated papillomavirus genomic sequences, accompanied by web-based sequence analysis tools. This update describes the addition of major new features. The papillomavirus genomes within PaVE have been further annotated, and now includes the major spliced mRNA transcripts. Viral genes and transcripts can be visualized on both linear and circular genome browsers. Evolutionary relationships among PaVE reference protein sequences can be analysed using multiple sequence alignments and phylogenetic trees. To assist in viral discovery, PaVE offers a typing tool; a simplified algorithm to determine whether a newly sequenced virus is novel. PaVE also now contains an image library containing gross clinical and histopathological images of papillomavirus infected lesions. Database URL: https://pave.niaid.nih.gov/.

The Papillomavirus Episteme: a central resource for papillomavirus sequence data and analysis

The goal of the Papillomavirus Episteme (PaVE) is to provide an integrated resource for the analysis of papillomavirus (PV) genome sequences and related information. The PaVE is a freely accessible, web-based tool (http://pave.niaid.nih.gov) created around a relational database, which enables storage, analysis and exchange of sequence information. From a design perspective, the PaVE adopts an Open Source software approach and stresses the integration and reuse of existing tools. Reference PV genome sequences have been extracted from publicly available databases and reannotated using a custom-created tool. To date, the PaVE contains 241 annotated PV genomes, 2245 genes and regions, 2004 protein sequences and 47 protein structures, which users can explore, analyze or download. The PaVE provides scientists with the data and tools needed to accelerate scientific progress for the study and treatment of diseases caused by PVs.

CEBS--Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data

CEBS (Chemical Effects in Biological Systems) is an integrated public repository for toxicogenomics data, including the study design and timeline, clinical chemistry and histopathology findings and microarray and proteomics data. CEBS contains data derived from studies of chemicals and of genetic alterations, and is compatible with clinical and environmental studies. CEBS is designed to permit the user to query the data using the study conditions, the subject responses and then, having identified an appropriate set of subjects, to move to the microarray module of CEBS to carry out gene signature and pathway analysis. Scope of CEBS: CEBS currently holds 22 studies of rats, four studies of mice and one study of Caenorhabditis elegans. CEBS can also accommodate data from studies of human subjects. Toxicogenomics studies currently in CEBS comprise over 4000 microarray hybridizations, and 75 2D gel images annotated with protein identification performed by MALDI and MS/MS. CEBS contains raw microarray data collected in accordance with MIAME guidelines and provides tools for data selection, pre-processing and analysis resulting in annotated lists of genes of interest. Additionally, clinical chemistry and histopathology findings from over 1500 animals are included in CEBS. CEBS/BID: The BID (Biomedical Investigation Database) is another component of the CEBS system. BID is a relational database used to load and curate study data prior to export to CEBS, in addition to capturing and displaying novel data types such as PCR data, or additional fields of interest, including those defined by the HESI Toxicogenomics Committee (in preparation). BID has been shared with Health Canada and the US Environmental Protection Agency. CEBS is available at http://cebs.niehs.nih.gov. BID can be accessed via the user interface from https://dir-apps.niehs.nih.gov/arc/. Requests for a copy of BID and for depositing data into CEBS or BID are available at http://www.niehs.nih.gov/cebs-df/.

Procedure volume of gastric cancer resections versus 5-year survival

AIM

We used nationwide, population-based data to examine associations between hospital and surgeon volumes of gastric cancer resections and their patients' short-term and long-term survival likelihood.

METHODS

The study uses 1997-1999 inpatient claims data from Taiwan's National Health Insurance linked to "cause of death" data for 1997-2004. The total cohort of 6909 gastric cancer resection patients were categorized by their surgeon's/hospital's procedure volume, and examined for differences in 6-month mortality and 5-year mortality (post 6 months), by procedure volume, using Cox proportional hazard regressions, adjusting for surgeon, hospital and patient characteristics. We hypothesized that surgeons' case volume and age but not hospital volume will predict short-term and long-term survival.

RESULTS

Adjusted estimates show that increasing surgeon volume predicts better 6-month survival (adjusted mortality hazard ratio = 1.3 for low-volume surgeons relative to very high-volume surgeons; p < 0.01) and 5-year survival (adjusted mortality hazard ratios = 1.3; p < 0.001 for low-volume; 1.2 with p < 0.01 for medium volume) and increasing surgeon's age (adjusted hazards ratio = 1.4 for age < 41 years relative to 41-50 years; p < or = 0.001; 0.8 for > or = 51 years relative to 41-50 years; p < 0.05). In hospital volume regressions, surgeon's age is a consistent and significant predictor, not hospital volume. Findings suggest a key role of experience in surgical skill and sensitivity for early stage diagnosis in gastric cancer survival.

CONCLUSIONS

Although a key study limitation is the lack of cancer stage data, the pattern of findings suggests that experienced surgeons have relatively better survival outcomes among gastric cancer patients.

Chemical effects in biological systems (CEBS) object model for toxicology data, SysTox-OM: design and application

MOTIVATION

The CEBS data repository is being developed to promote a systems biology approach to understand the biological effects of environmental stressors. CEBS will house data from multiple gene expression platforms (transcriptomics), protein expression and protein-protein interaction (proteomics), and changes in low molecular weight metabolite levels (metabolomics) aligned by their detailed toxicological context. The system will accommodate extensive complex querying in a user-friendly manner. CEBS will store toxicological contexts including the study design details, treatment protocols, animal characteristics and conventional toxicological endpoints such as histopathology findings and clinical chemistry measures. All of these data types can be integrated in a seamless fashion to enable data query and analysis in a biologically meaningful manner.

RESULTS

An object model, the SysBio-OM (Xirasagar et al., 2004) has been designed to facilitate the integration of microarray gene expression, proteomics and metabolomics data in the CEBS database system. We now report SysTox-OM as an open source systems toxicology model designed to integrate toxicological context into gene expression experiments. The SysTox-OM model is comprehensive and leverages other open source efforts, namely, the Standard for Exchange of Nonclinical Data (http://www.cdisc.org/models/send/v2/index.html) which is a data standard for capturing toxicological information for animal studies and Clinical Data Interchange Standards Consortium (http://www.cdisc.org/models/sdtm/index.html) that serves as a standard for the exchange of clinical data. Such standardization increases the accuracy of data mining, interpretation and exchange. The open source SysTox-OM model, which can be implemented on various software platforms, is presented here.

AVAILABILITY

A universal modeling language (UML) depiction of the entire SysTox-OM is available at http://cebs.niehs.nih.gov and the Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. Currently, the public toxicological data in CEBS can be queried via a web application based on the SysTox-OM at http://cebs.niehs.nih.gov

CONTACT

xirasagars@saic.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Suicide trends following the Taiwan earthquake of 1999: empirical evidence and policy implications

OBJECTIVE

Mental health impact of severe earthquakes on survivors has attracted considerable attention. Suicide represents a terminal outcome of the spectrum of potential major mental health issues spawned by severe earthquakes. This study used time-series analysis to examine the time trends of increased suicide rates after the Chi-Chi earthquake of 1999 in Taiwan in the affected counties.

METHOD

Adult cause of death data were used to study monthly suicide rates per 100,000 adult population in the study and control counties, during January 1995 to December 2001. Box and Tiao's event intervention analysis was used to examine changes in monthly suicide rates before and after the Chi-Chi earthquake.

RESULTS

During the post-quake period, October 1999 to December 2001, the mean monthly suicide rate in the affected counties was 1.567 per 100,000, compared with the control counties' rate of 1.297 per 100,000. Mean monthly suicide rate among the high-exposure group was 42% higher during the 26 months following the earthquake than the average for the entire observation period. Examined by time trends, the increased suicide rate registered in the first month following the quake began a monthly gradual decline by 0.7/100,000 thereafter, accounting for a total reduction of 98% in quake-related suicides by the end of 10 months. Suicide rates fell to the baseline level after 10 months.

CONCLUSION

We found that the mean monthly suicide rate for earthquake victims was higher while the low-exposure group remained stable and consistent throughout the observation period, indicating that the impact on the high-exposure group was attributable to the earthquake. This indicates the need for providing strengthened psychiatric services during the first year following major disasters.

CEBS object model for systems biology data, SysBio-OM

MOTIVATION

To promote a systems biology approach to understanding the biological effects of environmental stressors, the Chemical Effects in Biological Systems (CEBS) knowledge base is being developed to house data from multiple complex data streams in a systems friendly manner that will accommodate extensive querying from users. Unified data representation via a single object model will greatly aid in integrating data storage and management, and facilitate reuse of software to analyze and display data resulting from diverse differential expression or differential profile technologies. Data streams include, but are not limited to, gene expression analysis (transcriptomics), protein expression and protein-protein interaction analysis (proteomics) and changes in low molecular weight metabolite levels (metabolomics).

RESULTS

To enable the integration of microarray gene expression, proteomics and metabolomics data in the CEBS system, we designed an object model, Systems Biology Object Model (SysBio-OM). The model is comprehensive and leverages other open source efforts, namely the MicroArray Gene Expression Object Model (MAGE-OM) and the Proteomics Experiment Data Repository (PEDRo) object model. SysBio-OM is designed by extending MAGE-OM to represent protein expression data elements (including those from PEDRo), protein-protein interaction and metabolomics data. SysBio-OM promotes the standardization of data representation and data quality by facilitating the capture of the minimum annotation required for an experiment. Such standardization refines the accuracy of data mining and interpretation. The open source SysBio-OM model, which can be implemented on varied computing platforms is presented here.

AVAILABILITY

A universal modeling language depiction of the entire SysBio-OM is available at http://cebs.niehs.nih.gov/SysBioOM/. The Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. The database and interface are being built to implement the model and will be available for public use at http://cebs.niehs.nih.gov.

Vav1 regulates phospholipase cgamma activation and calcium responses in mast cells

The hematopoietic cell-specific protein Vav1 is a substrate of tyrosine kinases activated following engagement of many receptors, including FcepsilonRI. Vav1-deficient mice contain normal numbers of mast cells but respond more weakly than their normal counterparts to a passive systemic anaphylaxis challenge. Vav1-deficient bone marrow-derived mast cells also exhibited reduced degranulation and cytokine production, although tyrosine phosphorylation of FcepsilonRI, Syk, and LAT (linker for activation of T cells) was normal. In contrast, tyrosine phosphorylation of phospholipase Cgamma1 (PLCgamma1) and PLCgamma2 and calcium mobilization were markedly inhibited. Reconstitution of deficient mast cells with Vav1 restored normal tyrosine phosphorylation of PLCgamma1 and PLCgamma2 and calcium responses. Thus, Vav1 is essential to FcepsilonRI-mediated activation of PLCgamma and calcium mobilization in mast cells. In addition to its known role as an activator of Rac1 GTPases, these findings demonstrate a novel function for Vav1 as a regulator of PLCgamma-activated calcium signals.

A perspective: regulation of IgE receptor-mediated mast cell responses by a LAT-organized plasma membrane-localized signaling complex

BACKGROUND

To understand how the high-affinity IgE receptor (FcepsilonRI) communicates with downstream effectors, we focused on exploring the functional importance of the FcepsilonRI-mediated formation and localization of a signaling complex that contains the hematopoietic cell-specific scaffolding protein linker for activation of T cells (LAT) and the guanine nucleotide exchange factor Vav1.

METHODS

Using the mast cell line RBL-2H3, we explored the localization of these proteins by confocal microscopy and cell fractionation. Additionally, the mechanism of function and the importance of LAT and Vav1 to mast cells was studied in genetically disrupted mice and in mast cells derived from their bone marrow.

RESULTS

We found that LAT, Vav1 and the adapter molecule SLP-76 associated in detergent-resistant microdomains (lipid rafts) found in the plasma membrane upon FcepsilonRI stimulation. In the absence of LAT, mast cells showed a remarkable loss of the secretory response and reduced cytokine responses. Vav1 deficiency also affected secretion, although not to the extent of LAT deficiency, and inhibited IL-2 and IFN-gamma production. LAT- and Vav1-deficient mice showed reduced blood histamine levels after a systemic anaphylaxis challenge as compared to their normal counterparts.

CONCLUSIONS

The results demonstrate that LAT is a central mediator in IgE receptor signaling by regulating multiple signaling pathways that affect mast cell degranulation and cytokine production. Vav1, a component of this LAT-containing signaling complex, regulates a specific subset of these responses.

The glass ceiling in academe: health administration is no exception

This paper reviews gender issues in academe and presents findings of a limited survey of ACEHSA-accredited health administration graduate programs. The survey shows gender ratios adverse to women at the full, associate, and assistant professor levels. Men to women ratio among faculty was 1.98, among full-time faculty it was 2.24, and among tenured/tenure-track faculty it was 2.69, despite an excess of female students over male students in graduate programs, and despite equal proportions of women and men faculty holding doctoral degrees. Distribution by rank showed 48.5 percent full professors, 27.8 percent associate professors, and, 20.1 percent assistant professors among men, vs. 27.4 percent, 41.1 percent, and 31.5 percent respectively among women. In other academic fields similar gender ratios prevail, and many researchers have documented evidence of continuing gender inequities in tenure, promotion and salary, given comparable performance, despite the enactment of Title IX in 1972. Gender disparities are rooted in a complex web of gender-specific constraints interwoven with secular human capital and structural variables, and confounded by sexist discriminatory factors. In light of these issues, recommendations are made toward creating an equitable academic climate without compromising the ideal of meritocracy, through gender-sensitive initiatives and vigilance mechanisms to bring policies to fruition.

RNA structure inhibits the TRAP (trp RNA-binding attenuation protein)-RNA interaction

TRAP (trp RNA-binding attenuation protein) regulates expression of the tryptophan biosynthetic genes in response to tryptophan in Bacillus subtilis by binding to two sites containing a series of 9 or 11 (G/U)AG triplet repeats that are generally separated by two or three spacer nucleotides. Previous mutagenesis experiments have identified three TRAP residues, Lys-37, Lys-56, and Arg-58 that are essential for RNA binding. The location of these residues on the TRAP oligomer supports the proposal that RNA binds TRAP by encircling the TRAP oligomer. In this work, we show that RNAs containing 11 GAG or UAG repeats separated by CC dinucleotide spacers (((G/U)AGCC)11) form stable structures that inhibit binding to TRAP. This conclusion is based on the effects of temperature and Mg2+ on the affinity of TRAP for RNAs with CC spacers combined with UV hyperchromicity and circular dichroism. Furthermore, introducing the base analogue 7-deazaguanosine in the ((G/U)AGCC)11 RNAs stabilized the TRAP-RNA interaction. This effect was associated with decreased stability of the RNA structure as measured by circular dichroism spectroscopy. The precise nature of the structure of the ((G/U)AGCC)11 RNAs is not known but evidence is presented that it involves noncanonical interactions. We also observed that substitution of Arg-58 with Lys further reduced the ability of TRAP to interact with structured RNAs. Since in vivo function of TRAP may involve binding to structured RNAs, we suggest a potential function for this residue, which is conserved in TRAP from three different bacilli.

The trp RNA-binding attenuation protein (TRAP) from Bacillus subtilis binds to unstacked trp leader RNA

TRAP (trp RNA-binding attenuation protein) is a tryptophan-activated RNA-binding protein that regulates expression of the trp biosynthetic genes by binding to a series of GAG and UAG trinucleotide repeats generally separated by two or three spacer nucleotides. Previously, we showed that TRAP contains 11 identical subunits arranged in a symmetrical ring. Based on this structure, we proposed a model for the TRAP.RNA interaction where the RNA wraps around the protein with each repeat of the RNA contacting one or a combination of two adjacent subunits of the TRAP oligomer. Here, we have shown that RNAs selected in vitro based on their ability to bind tryptophan-activated TRAP contain multiple G/UAG repeats and show a strong bias for pyrimidines as the spacer nucleotides between these repeats. The affinity of the TRAP.RNA interaction displays a nonlinear temperature dependence, increasing between 5 degrees C and 47 degrees C and then decreasing from 47 degrees C to 67 degrees C. Differential scanning calorimetry and circular dichroism spectroscopy demonstrate that TRAP is highly thermostable with few detectable changes in the structure between 25 degrees C and 70 degrees C, suggesting that the temperature dependence of this interaction reflects changes in the RNA. Results from circular dichroism and UV absorbance spectroscopy support this hypothesis, demonstrating that trp leader RNA becomes unstacked upon binding TRAP. We propose that the bias toward pyrimidines in the spacer nucleotides of the in vitro selected RNAs represents the inability of Us and Cs to form stable base stacking interactions, which allows the flexibility needed for the RNA to wrap around the TRAP oligomer.