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Selecting and implementing a tissue-clearing protocol is challenging. Established more than 100 years ago, tissue clearing is still a rapidly evolving field of research. There are currently many published protocols to choose from, and each performs better or worse across a range of key evaluation factors (e.g., speed, cost, tissue stability, fluorescence quenching). Additionally, tissue-clearing protocols are often optimized for specific experimental contexts, and applying an existing protocol to a new problem can require a lengthy period of adaptation by trial and error. Although the primary literature and review articles provide a useful starting point for optimization, there is growing recognition that many articles do not provide sufficient detail to replicate or reproduce experimental results. To help address this issue, we have developed a novel, freely available repository of tissue-clearing protocols named T-CLEARE (Tissue CLEAring protocol REpository;https://doryworkspace.org/doryviz). T-CLEARE incorporates community responses to an open survey designed to capture details not commonly found in the scientific literature, including modifications to published protocols required for specific use cases and instances when tissue-clearing protocols did not perform well (negative results). The goal of T-CLEARE is to provide a forum for the community to share evaluations and modifications of tissue-clearing protocols for various tissue types and potentially identify best-in-class methods for a given application.

The National Institutes of Health has established the PhenX Toolkit as a web-based resource containing consensus measures freely available to the research community. The National Institute of Mental Health (NIMH) has introduced the Mental Health Research Core Collection as part of the PhenX Toolkit and recently convened the PhenX Early Psychosis Working Group to generate the PhenX Early Psychosis Specialty Collection. The Working Group consisted of two complementary panels for clinical and translational research. We review the process, deliberations, and products of the translational research panel. The Early Psychosis Specialty Collection rationale for measure selection as well as additional information and protocols for obtaining each measure are available on the PhenX website (https://www.phenxtoolkit.org). The NIMH strongly encourages investigators to use instruments from the PhenX Mental Health Research Collections in NIMH-funded studies and discourages use of alternative measures to collect similar data without justification. We also discuss some of the potential advances that can be achieved by collecting common data elements across large-scale longitudinal studies of early psychosis.

Coordinated specialty care (CSC) is a promising multielement treatment for the care of individuals experiencing the onset of schizophrenia. The community mental health block grant program has increased federal support for CSC programs. In order to maximize the number of sites capable of science-to-service or service-to-science translation, the National Institute of Mental Health funded a supplement to the PhenX toolkit consisting of measures for early psychosis. The early psychosis working group included translational research and clinical services panels. The clinical services panel was charged with identifying low-burden and psychometrically sound measures for use in routine clinical settings. The 19 new clinical measures complement existing measures already in the toolkit. Measures cover a range of domains, including symptoms, social and occupational functioning, well-being, medication adherence and side effects, physical activity, and shared decision making and person-centered care. Several challenges are also discussed. The review process underscored the challenges facing nonacademic sites in collecting even low-burden assessments.

Encouraging and supporting Community Based Governance for Informal Housing and Settlements to achieve a sustainable built environment is essential. However, this will be the greatest challenge facing countries as they become more urbanized. Governance has become difficult to define and measure, much less described as a theory that can be modelled and applied to policy and in making decisions. The difficulty in definition was traced both to the evolving roles of the planner; and ontological and epistemological paradigms that have shaped research. This research defined Community Based Governance as a theory. This was explored using a Grounded Mixed Methods to integrate quantitative and qualitative data. Using intersectionality and structuration, the outcomes were examined. Initial results from Trinidad suggest that the reformulated theory of Community Based Governance has demonstrated failures and unsustainability of the public sector’s squatter regularisation programme where Community Based Governance though essential, is undermined. However, this contradicts the success that the programme claims.

Background Single birth cohort studies have been the basis for many discoveries about early life risk factors for childhood asthma but are limited in scope by sample size and characteristics of the local environment and population. The Children’s Respiratory and Environmental Workgroup (CREW) was established to integrate multiple established asthma birth cohorts and to investigate asthma phenotypes and associated causal pathways (endotypes), focusing on how they are influenced by interactions between genetics, lifestyle, and environmental exposures during the prenatal period and early childhood. Methods and results CREW is funded by the NIH Environmental influences on Child Health Outcomes (ECHO) program, and consists of 12 individual cohorts and three additional scientific centers. The CREW study population is diverse in terms of race, ethnicity, geographical distribution, and year of recruitment. We hypothesize that there are phenotypes in childhood asthma that differ based on clinical characteristics and underlying molecular mechanisms. Furthermore, we propose that asthma endotypes and their defining biomarkers can be identified based on personal and early life environmental risk factors. CREW has three phases: 1) to pool and harmonize existing data from each cohort, 2) to collect new data using standardized procedures, and 3) to enroll new families during the prenatal period to supplement and enrich extant data and enable unified systems approaches for identifying asthma phenotypes and endotypes. Conclusions The overall goal of CREW program is to develop a better understanding of how early life environmental exposures and host factors interact to promote the development of specific asthma endotypes.

International audience; Sickle cell disease (SCD) is a debilitating single gene disorder caused by a single point mutation that results in physical deformation (i.e. sickling) of erythrocytes at reduced oxygen tensions. Up to 75% of SCD in newborns world-wide occurs in sub-Saharan Africa, where neonatal and childhood mortality from sickle cell related complications is high. While SCD research across the globe is tackling the disease on multiple fronts, advances have yet to significantly impact on the health and quality of life of SCD patients, due to lack of coordination of these disparate efforts. Ensuring data across studies is directly comparable through standardization is a necessary step towards realizing this goal. Such a standardization requires the development and implementation of a disease-specific ontology for SCD that is applicable globally. Ontology development is best achieved by bringing together experts in the domain to contribute their knowledge.The SCD community and H3ABioNet members joined forces at a recent SCD Ontology workshop to develop an ontology covering aspects of SCD under the classes: phenotype, diagnostics, therapeutics, quality of life, disease modifiers and disease stage. The aim of the workshop was for participants to contribute their expertise to development of the structure and contents of the SCD ontology. Here we describe the proceedings of the Sickle Cell Disease Ontology Workshop held in Cape Town South Africa in February 2016 and its outcomes. The objective of the workshop was to bring together experts in SCD from around the world to contribute their expertise to the development of various aspects of the SCD ontology.

Standard measures and common data elements for sickle cell disease (SCD) will improve the data quality and comparability necessary for cross-study analyses and the development of guidelines that support effective treatments and interventions. In 2014, the National Institutes of Health, National Heart, Lung, and Blood Institute (NHLBI) funded an Administrative Supplement to the PhenX Toolkit (consensus measures for Phenotypes and eXposures; https://www.phenxtoolkit.org/) to identify common measures to promote data comparability across SCD research. An 11-member Sickle Cell Disease Research and Scientific Panel provided guidance to the project, establishing a core collection of SCD-related measures and defining the scope of 2 specialty collections: (1) cardiovascular, pulmonary, and renal complications, and (2) neurology, quality-of-life, and health services. For each specialty collection, a working group of SCD experts selected high-priority measures using a consensus process that included scientific community input. The SCD measures were released into the Toolkit in August 2015. The 25 measures included in the core collection are recommended for use by all NHLBI-funded investigators performing human-subject SCD research. The 10 neurology, quality-of-life, and health services measures and 14 cardiovascular, pulmonary, and renal measures are recommended for use within these specialized research areas. For SCD and other researchers, PhenX measures will promote collaborations with clinicians and patients, facilitate cross-study analysis, accelerate translational research, and lead to greater understanding of SCD phenotypes and epigenetics. For clinicians, using PhenX measures will help elucidate the etiology, progression, and treatment of SCD, leading to improved patient care and quality of life.

A pseudoknot forms in an RNA when nucleotides in a loop pair with a region outside the helices that close the loop. Pseudoknots occur relatively rarely in RNA but are highly overrepresented in functionally critical motifs in large catalytic RNAs, in riboswitches, and in regulatory elements of viruses. Pseudoknots are usually excluded from RNA structure prediction algorithms. When included, these pairings are difficult to model accurately, especially in large RNAs, because allowing this structure dramatically increases the number of possible incorrect folds and because it is difficult to search the fold space for an optimal structure. We have developed a concise secondary structure modeling approach that combines SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension) experimental chemical probing information and a simple, but robust, energy model for the entropic cost of single pseudoknot formation. Structures are predicted with iterative refinement, using a dynamic programming algorithm. This melded experimental and thermodynamic energy function predicted the secondary structures and the pseudoknots for a set of 21 challenging RNAs of known structure ranging in size from 34 to 530 nt. On average, 93% of known base pairs were predicted, and all pseudoknots in well-folded RNAs were identified.

One of the most encouraging, but also the most challenging, aspects of current research on psychopathology is the diversity of measures used to assess constructs across research studies and programs. Clearly, this diversity reflects the creativity and generativity of our field and the continual growth of our science. At the same time, however, this diversity also makes data harmonization across studies difficult, if not sometimes impossible. The National Human Genome Research Institute recognized this conundrum in the field of genetics and started an initiative referred to as consensus measures for Phenotypes and eXposures (PhenX) to identify and recommend a small number of measures for each of 21 broad research domains that could be used as common assessments to facilitate integration across genomewide association studies (1–4). These measures are made available to the scientific community, at no cost, in the PhenX Toolkit (https://www.phenxtoolkit.org). Subsequently, the PhenX consensus process was used to identify measures in support of substance abuse and addiction (SAA) research, adding depth to the toolkit in this area. This project was funded by the National Institute on Drug Abuse (NIDA) with the participation of the National Institute on Alcohol Abuse and Alcoholism. Perhaps due to a growing awareness of the need to share data across studies to increase statistical power and study impact, a number of other common data element programs have been underway, including the Patient

The PhenX Toolkit provides researchers with recommended, well-established, low-burden measures suitable for human-subjects research. The database of Genotypes and Phenotypes (dbGaP) is the data repository for a variety of studies funded by the National Institutes of Health (NIH), including genome-wide association studies (GWAS). The dbGaP requires that investigators provide a data dictionary of study variables as part of the data submission process. Thus, dbGaP is a unique resource that can help investigators identify studies that share the same or similar variables. As a proof of concept, variables from 16 studies deposited in dbGaP were mapped to PhenX measures. Soon, investigators will be able to search dbGaP using PhenX variable identifiers and find comparable and related variables in these 16 studies. To enhance effective data exchange, PhenX measures, protocols, and variables were modeled in Logical Observation Identifiers Names and Codes (LOINC). PhenX domains and measures are also represented in the Cancer Data Standards Registry and Repository (caDSR). Associating PhenX measures with existing standards (LOINC and caDSR) and mapping to dbGaP study variables extends the utility of these measures by revealing new opportunities for cross-study analysis.

The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies. To facilitate replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project. The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains. PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The selected measures are then made freely available to the scientific community via the PhenX Toolkit. Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies. PhenX measures will have the most impact when included at the experimental design stage. The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data. Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions.

Bacterial tRNAs frequently have 4-thiouridine (s4U) modification at position 8, which is adjacent to the C13-G22-m7G46 base triple in the elbow region of the tRNA tertiary structure. Irradiation with light in the UVA range induces an efficient photocrosslink between s4U8 and C13. The temperature dependence of the rate constants for photocrosslinking between the s4U8 and C13 has been used to investigate the tRNA conformational energy and structure in Escherichia coli tRNAVal, tRNAPhe, and tRNAfMet under different conditions. Corrections have been made in the measured rate constants to compensate for differences in the excited state lifetimes due to tRNA identity, buffer conditions, and temperature. The resulting rate constants are related to the rate at which the s4U8 and C13 come into the alignment needed for photoreaction; this depends on an activation energy, attributable to the conformational potential energy that occurs during the photoreaction, and on the extent of the structural change. Different photocrosslinking rate constants and temperature dependencies occur in the three tRNAs, and these differences are due both to modest differences in the activation energies and in the apparent s4U8–C13 geometries. Analysis of tRNAVal in buffers without Mg2+ indicate a smaller activation energy (∼13 kJ mol−1) and a larger apparent s4U8–C13 distance (∼12 Å) compared to values for the same parameters in buffers with Mg2+ (∼26 kJ mol−1 and 0.36 Å, respectively). These measurements are a quantitative indication of the strong constraint that Mg2+ imposes on the tRNA flexibility and structure.

Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, cataract cases and controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 527,953 and 527,936 single nucleotide polymorphisms (SNPs) for gene-gene and gene-environment analyses, respectively, with minor allele frequency1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 13 statistically significant SNP-SNP models with an interaction with p-value1 × 10(-4), as well as an overall model with p-value0.01 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use;these environmental factors have been previously associated with the formation of cataracts. We found a total of 782 gene-environment models that exhibit an interaction with a p-value1 × 10(-4) associatedwith cataract status. Our results show these approaches enable advanced searches for epistasis and gene-environment interactions beyond GWAS, and that the EHR based approach provides an additional source of data for seeking these advanced explanatory models of the etiology of complex disease/outcome such as cataracts.

UV light-induced RNA photocrosslinks are formed at a limited number of specific sites in the Escherichia coli and in other eubacterial 16 S rRNAs. To determine if unusually favorable internucleotide geometries could explain the restricted crosslinking patterns, parameters describing the internucleotide geometries were calculated from the Thermus thermophilus 30 S subunit X-ray structure and compared to crosslinking frequencies. Significant structural adjustments between the nucleotide pairs usually are needed for crosslinking. Correlations between the crosslinking frequencies and the geometrical parameters indicate that nucleotide pairs closer to the orientation needed for photoreaction have higher crosslinking frequencies. These data are consistent with transient conformational changes during crosslink formation in which the arrangements needed for photochemical reaction are attained during the electronic excitation times. The average structural rearrangement for UVA-4-thiouridine (s 4 U)-induced crosslinking is larger than that for UVB or UVC-induced crosslinking; this is associated with the longer excitation time for s 4 U and is also consistent with transient conformational changes. The geometrical parameters do not completely predict the crosslinking frequencies, implicating other aspects of the tertiary structure or conformational flexibility in determining the frequencies and the locations of the crosslinking sites. The majority of the UVB/C and UVA-s 4 U-induced crosslinks are located in four regions in the 30 S subunit, within or at the ends of RNA helix 34, in the tRNA P-site, in the distal end of helix 28 and in the helix 19/helix 27 region. These regions are implicated in different aspects of tRNA accommodation, translocation and in the termination reaction. These results show that photocrosslinking is an indicator for sites where there is internucleotide conformational flexibility and these sites are largely restricted to parts of the 30 S subunit associated with ribosome function.

Ribosome complexes containing deacyl-tRNA1(Val) or biotinylvalyl-tRNA1(Val) and an mRNA analog have been irradiated with wavelengths specific for activation of the cmo5U nucleoside at position 34 in the tRNA1(Val) anticodon loop. The major product for both types of tRNA is the cross-link between 16S rRNA (C1400) and the tRNA (cmo5U34) characterized already by Ofengand and his collaborators [Prince et al. (1982) Proc. Natl Acad. Sci. USA, 79, 5450-5454]. However, in complexes containing deacyl-tRNA1(Val), an additional product is separated by denaturing PAGE and this is shown to involve C1400 and m5C967 of 16S rRNA and cmo5U34 of the tRNA. Puromycin treatment of the biotinylvalyl-tRNA1(Val) -70S complex followed by irradiation, results in the appearance of the unusual photoproduct, which indicates an immediate change in the tRNA interaction with the ribosome after peptide transfer. These results indicate an altered interaction between the tRNA anticodon and the 30S subunit for the tRNA in the P/E hybrid state compared with its interaction in the classic P/P state.

Background: The purpose of this manuscript is to describe the PhenX RISING network and the site experiences in the implementation of PhenX measures into ongoing population-based genomic studies. Methods. Eighty PhenX measures were implemented across the seven PhenX RISING groups, thirty-three of which were used at more than two sites, allowing for cross-site collaboration. Each site used between four and 37 individual measures and five of the sites are validating the PhenX measures through comparison with other study measures. Self-administered and computer-based administration modes are being evaluated at several sites which required changes to the original PhenX Toolkit protocols. A network-wide data use agreement was developed to facilitate data sharing and collaboration. Results: PhenX Toolkit measures have been collected for more than 17,000 participants across the PhenX RISING network. The process of implementation provided information that was used to improve the PhenX Toolkit. The Toolkit was revised to allow researchers to select self- or interviewer administration when creating the data collection worksheets and ranges of specimens necessary to run biological assays has been added to the Toolkit. Conclusions: The PhenX RISING network has demonstrated that the PhenX Toolkit measures can be implemented successfully in ongoing genomic studies. The next step will be to conduct gene/environment studies. © 2014 McCarty et al.; licensee BioMed Central Ltd.

Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, cataract cases and controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 527,953 and 527,936 single nucleotide polymorphisms (SNPs) for gene-gene and gene-environment analyses, respectively, with minor allele frequency > 1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 13 statistically significant SNP-SNP models with an interaction with p-value < 1 × 10(-4), as well as an overall model with p-value < 0.01 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use;these environmental factors have been previously associated with the formation of cataracts. We found a total of 782 gene-environment models that exhibit an interaction with a p-value < 1 × 10(-4) associatedwith cataract status. Our results show these approaches enable advanced searches for epistasis and gene-environment interactions beyond GWAS, and that the EHR based approach provides an additional source of data for seeking these advanced explanatory models of the etiology of complex disease/outcome such as cataracts.

Pregnancy and childbirth are normal conditions, but complications and adverse outcomes are common. Both genetic and environmental factors influence the course of pregnancy. Genetic epidemiologic research into pregnancy outcomes could be strengthened by the use of common measures, which would allow data from different studies to be combined or compared. Here, we introduce perinatal researchers to the PhenX Toolkit and the Collections related to pregnancy and childbirth.The Pregnancy and Birth Collections were drawn from measures in the PhenX Tooklit. The lead author selected a list of measures for each Collection, which was reviewed by the remaining authors and revised on the basis of their comments. We chose the measures we thought were most relevant for perinatal research and had been linked most strongly to perinatal outcomes.The Pregnancy and Birth Health Conditions Collection includes 24 measures related to pregnancy and fertility history, maternal complications, and infant complications. The Pregnancy and Birth Outcome Risk Factors Collection includes 43 measures of chemical, medical, psychosocial, and personal factors associated with pregnancy outcomes.The biological complexity of pregnancy and its sensitivity to environmental and genomic influences suggest that multidisciplinary approaches are needed to generate new insights or practical interventions. To fully exploit new research methods and resources, we encourage the biomedical research community to adopt standard measures to facilitate pooled or meta-analyses.

Background: Common measures and common data elements (CDEs) for sickle cell disease (SCD) are needed to help improve data quality and data comparability necessary for meta-analyses, guidelines development, and implementation science. In recent years, the National Heart, Lung, and Blood Institute (NHLBI) has undertaken several activities to develop CDEs and measures for SCD. These include developing 1) the Sickle Cell Quality of Life Measurement Information System (ASCQ-Me); 2) clinical practice recommendations for the management of SCD; and 3) a lexicon and consensus definitions for the most frequently occurring complications of SCD. In 2014, the NHLBI funded an Administrative Supplement to the PhenX Toolkit to identify common measures to further promote data comparability across SCD research. The Web-based PhenX Toolkit (consensus measures for Phen otypes and eX posures, https://www.phenxtoolkit.org/) provides a catalog of 449 standard measures and associated interoperability resources to help investigators improve quality of data collection and identify opportunities for collaborative research. The development and maintenance of PhenX Toolkit content is driven by the scientific research community. A Steering Committee, comprised of 11 scientists with a wide range of expertise, provides overarching guidance for the project and Working Groups (WG) of subject matter experts select measures for inclusion the PhenX Toolkit. Methods: An 11 member Sickle Cell Disease Research and Scientific Panel (SRSP) was assembled to provide guidance to the project, establish a Core Collection of SCD-related measures, and to define the scope of two Specialty Collections: 1) Cardiovascular, Pulmonary, and Renal Complications and 2) Neuroglogy, Quality of Life, and Health Services. For each Specialty Collection, a Working Group (WG) of seven SCD experts was convened to select high priority measures for inclusion in the Toolkit. Each WG selected representative measures available in the published literature or already in the Toolkit using a consensus process which included input from the scientific community. For each measure, the Toolkit provides a description of the measure, the rationale for its inclusion, detailed protocol(s), and supporting documentation. The Toolkit also provides data collection worksheets and data dictionaries to help integrate PhenX measures into their study design. To support investigators who want to collect data via the Web, PhenX protocols are available as REDCap Instrument Zip files. Results: The Sickle Cell Disease Core and Specialty Collections were released into the Toolkit in August of 2015. The Core Collection, which includes 25 measures covering demographics, socioeconomic status, anthropometrics, pulse oximetry, hemoglobin characterization, history of transfusion, and SCD-related pain episodes, is recommended for use by all NHLBI-funded investigators performing human subjects SCD research. The Specialty Collections are recommended for use within more specialized research areas. The Cardiovascular, Pulmonary, and Renal Specialty Collection includes 11 measures that address heart disease, lung function, and biomarkers for hemolysis, anemia, and iron overload. The Neurology, Quality of Life, and Health Services Specialty Collection includes 8 measures that address developmental delays, stroke risk factors and outcomes, quality of life, and quality of care. A list of other potentially relevant measures from the Toolkit was also developed and annotated for SCD. These measures, including the process, criteria, and rationale for their selection, will be presented. We now have consensus phenotypes of complications in SCD that are available for use in future clinical and epidemiologic studies and for use in harmonizing data across previous studies Implications: For researchers, adoption and use of PhenX standard measures will promote collaborations with clinicians and patients, facilitate cross-study analysis domestically and internationally, accelerate translational research, and lead to greater understanding of phenotypes and epigenetics in SCD. For clinicians, using PhenX measures will help improve patient care and quality of life. Consistent use of these standard measures will establish a common currency to help better understand the etiology, progression, and treatment of SCD. Funding provided by U01 HG004597 and U41 HG007050. Disclosures Klings: Pfizer: Consultancy; Actelion Pharmaceuticals: Research Funding. Panepinto:HRSA, NIH: Research Funding; NKT Therapeutics, Inc: Consultancy.

The effects of P/P- and P/E-site tRNA(Phe) binding on the 16S rRNA structure in the Escherichia coli 70S ribosome were investigated using UV cross-linking. The identity and frequency of 16S rRNA intramolecular cross-links were determined in the presence of deacyl-tRNA(Phe) or N-acetyl-Phe-tRNA(Phe) using poly(U) or an mRNA analogue containing a single Phe codon. For N-acetyl-Phe-tRNA(Phe) with either poly(U) or the mRNA analogue, the frequency of an intramolecular cross-link C967 x C1400 in the 16S rRNA was decreased in proportion to the binding stoichiometry of the tRNA. A proportional effect was true also for deacyl-tRNA(Phe) with poly(U), but the decrease in the C967 x C1400 frequency was less than the tRNA binding stoichiometry with the mRNA analogue. The inhibition of the C967 x C1400 cross-link was similar in buffers with, or without, polyamines. The exclusive participation of C967 with C1400 in the cross-link was confirmed by RNA sequencing. One intermolecular cross-link, 16S rRNA (C1400) to tRNA(Phe)(U33), was made with either poly(U) or the mRNA analogue. These results indicate a limited structural change in the small subunit around C967 and C1400 during tRNA P-site binding sensitive to the type of mRNA that is used. The absence of the C967 x C1400 cross-link in 70S ribosome complexes with tRNA is consistent with the 30S and 70S crystal structures, which contain tRNA or tRNA analogues; the occurrence of the cross-link indicates an alternative arrangement in this region in empty ribosomes.

Background Conformational flexibility in structured RNA frequently is critical to function. The 30S ribosomal subunit exists in different conformations in different functional states due to changes in the central part of the 16S rRNA. We are interested in evaluating the factors that might be responsible for restricting flexibility to specific parts of the 16S rRNA using biochemical data obtained from the 30S subunit in solution. This problem was approached taking advantage of the observation that there must be a high degree of conformational flexibility at sites where UV photocrosslinking occurs and a lack of flexibility inhibits photoreactivity at many other sites that are otherwise suitable for reaction. Results We used 30S x-ray structures to quantify the properties of the nucleotide pairs at UV- and UVA-s4U-induced photocrosslinking sites in 16S rRNA and compared these to the properties of many hundreds of additional sites that have suitable geometry but do not undergo photocrosslinking. Five factors that might affect RNA flexibility were investigated – RNA interactions with ribosomal proteins, interactions with Mg2+ ions, the presence of long-range A minor motif interactions, hydrogen bonding and the count of neighboring heavy atoms around the center of each nucleobase to estimate the neighbor packing density. The two factors that are very different in the unreactive inflexible pairs compared to the reactive ones are the average number of hydrogen bonds and the average value for the number of neighboring atoms. In both cases, these factors are greater for the unreactive nucleotide pairs at a statistically very significant level. Conclusion The greater extent of hydrogen bonding and neighbor atom density in the unreactive nucleotide pairs is consistent with reduced flexibility at a majority of the unreactive sites. The reactive photocrosslinking sites are clustered in the 30S subunit and this indicates nonuniform patterns of hydrogen bonding and packing density in the 16S rRNA tertiary structure. Because this analysis addresses inter-nucleotide distances and geometry between nucleotides distant in the primary sequence, the results indicate regional and global flexibility of the rRNA.