Search

Targeting germline (gl-) precursors of broadly neutralizing antibodies (bNAbs) is acknowledged as an important strategy for HIV-1 vaccines. The VRC01-class of bNAbs is attractive because of its distinct genetic signature. However, VRC01-class bNAbs often require extensive somatic hypermutation, including rare insertions and deletions. We describe a BG505 SOSIP trimer, termed GT1.2, to optimize binding to gl-CH31, the unmutated common precursor of the CH30-34 bNAb lineage that acquired a large CDRH1 insertion. The GT1.2 trimer activates gl-CH31 naive B cells in knock-in mice, and B cell responses could be matured by selected boosting immunogens to generate cross-reactive Ab responses. Next-generation B cell sequencing reveals selection for VRC01-class mutations, including insertions in CDRH1 and FWR3 at positions identical to VRC01-class bNAbs, as well as CDRL1 deletions and/or glycine substitutions to accommodate the N276 glycan. These results provide proof of concept for vaccine-induced affinity maturation of B cell lineages that require rare insertions and deletions.

A major goal of HIV-1 vaccine development is induction of broadly neutralizing antibodies (bnAbs). While success has been achieved in initiating bnAb B cell lineages, design of boosting immunogens that select for bnAb B cell receptors with improbable mutations required for bnAb affinity maturation remains difficult. Here we demonstrate a process for designing boosting immunogens for a V3-glycan bnAb B cell lineage. The immunogens induced affinity-matured antibodies by selecting for functional improbable mutations in bnAb precursor knock-in mice. Moreover, we show similar success in prime and boosting with nucleoside-modified mRNA-encoded HIV-1 envelope trimer immunogens, with improved selection by mRNA immunogens of improbable mutations required for bnAb binding to key envelope glycans. These results demonstrate the ability of both protein and mRNA prime-boost immunogens for selection of rare B cell lineage intermediates with neutralizing breadth after bnAb precursor expansion, a key proof-of concept and milestone towards development of an HIV vaccine.One-Sentence SummaryA vaccine strategy for selecting key rare antibody mutations is shown to induce HIV broadly neutralizing antibodies in mice.

Chromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions. Existing approaches, such as HiCCUPS, FitHiC/FitHiC2 and FastHiC, are all designed for analyzing individual cell types or samples. None of them accounts for unbalanced sequencing depths and heterogeneity among multiple cell types or samples in a unified statistical framework. To fill in the gap, we have developed a novel statistical framework MUNIn (Multiple sample UNifying long-range chromatin Interaction detector) for identifying long-range chromatin interactions from multiple samples. MUNIn adopts a hierarchical hidden Markov random field (H-HMRF) model, in which the status (peak or background) of each interacting chromatin loci pair depends not only on the status of loci pairs in its neighborhood region, but also on the status of the same loci pair in other samples. To benchmark the performance of MUNIn, we performed comprehensive simulation studies and real data analysis, and showed that MUNIn can achieve much lower false positive rates for detecting sample-specific interactions (33.1 - 36.2%), and much enhanced statistical power for detecting shared peaks (up to 74.3%), compared to uni-sample analysis. Our data demonstrated that MUNIn is a useful tool for the integrative analysis of interactomic data from multiple samples.

Genome-wide association studies have identified hundreds of genetic associations for complex psychiatric disorders and cognitive traits. However, interpretation of most of these findings is complicated by the presence of many significant and highly correlated genetic variants located in non-coding regions. Here, we address this issue by creating a high-resolution map of the three-dimensional (3D) genome organization by applying Hi-C to adult and fetal brain cortex with concomitant RNA-seq, open chromatin (ATAC-seq), and ChIP-seq data (H3K27ac, H3K4me3, and CTCF). Extensive analyses established the quality, information content, and salience of these new Hi-C data. We used these data to connect 938 significant genetic loci for schizophrenia, intelligence, ADHD, alcohol dependence, Alzheimer's disease, anorexia nervosa, autism spectrum disorder, bipolar disorder, major depression, and educational attainment to 8,595 genes (with 42.1% of these genes implicated more than once). We show that assigning genes to traits based on proximity provides a limited view of the complexity of GWAS findings and that gene set analyses based on functional genomic data provide an expanded view of the biological processes involved in the etiology of schizophrenia and other complex brain traits.

© 2015 Elsevier Inc. Axon regeneration in the CNS requires reactivating injured neurons' intrinsic growth state and enabling growth in an inhibitory environment. Using an inbred mouse neuronal phenotypic screen, we find that CAST/Ei mouse adult dorsal root ganglion neurons extend axons more on CNS myelin than the other eight strains tested, especially when pre-injured. Injury-primed CAST/Ei neurons also regenerate markedly in the spinal cord and optic nerve more than those from C57BL/6 mice and show greater sprouting following ischemic stroke. Heritability estimates indicate that extended growth in CAST/Ei neurons on myelin is genetically determined, and two whole-genome expression screens yield the Activin transcript Inhba as most correlated with this ability. Inhibition of Activin signaling in CAST/Ei mice diminishes their CNS regenerative capacity, whereas its activation in C57BL/6 animals boosts regeneration. This screen demonstrates that mammalian CNS regeneration can occur and reveals a molecular pathway that contributes to this ability. Omura et al. screened for neuronal growth on myelin and find CAST/Ei inbred mice are uniquely able to regenerate axons in the injured CNS through Activin signaling. Enhancing Activin signaling confers a CNS-regenerative capacity in normally non-regenerative mouse strains.

Motivation High throughput chromatin conformation capture (3C) technologies, such as Hi-C and ChIA-PET, have the potential to elucidate the functional roles of non-coding variants. However, most of published genome-wide unbiased chromatin organization studies have used cultured cell lines, limiting their generalizability. Results We developed a web browser, HUGIn, to visualize Hi-C data generated from 21 human primary tissues and cell lines. HUGIn enables assessment of chromatin contacts both constitutive across and specific to tissue(s) and/or cell line(s) at any genomic loci, including GWAS SNPs, eQTLs and cis-regulatory elements, facilitating the understanding of both GWAS and eQTL results and functional genomics data. Availability and implementation HUGIn is available at http://yunliweb.its.unc.edu/HUGIn Supplementary information Supplementary data are available at Bioinformatics online.

Background Schizophrenia is an often devastating psychiatric disorder with substantial morbidity, mortality, and personal and societal costs. Genome-wide association, Copy Number Variant (CNV), and exome studies have demonstrated that schizophrenia is a complex genetic disorder, with likely hundreds of genes contributing to its genetic architecture. For most schizophrenia GWAS loci, we are unable to pinpoint the specific genes that connect with these GWAS signals or the direction of association. This markedly limits the biological, clinical, and therapeutic utility of these findings. Prior studies have demonstrated the importance of chromatin looping as a regulatory mechanism and as a way to connect Single Nucleotide Polymorphisms (SNPs) to genes. We aim to facilitate the interpretation and prioritization of genomic findings from schizophrenia and other psychiatric disorders by generating a catalog of chromatin interactions in fetal and adult brain using easy Hi-C (eHi-C, a variation of Hi-C). Generating CNS-specific functional genomics data is critical for the development of a mechanistic understanding of genetic findings in psychiatric disorders. Methods Chromosome conformation capture methods enable the identification of chromatin interactions in vivo. In this study, we implemented eHi-C to generate a comprehensive map of brain-specific chromatin interactions in fetal (n=3) and adult frontal cortex (n=3) at a genome-wide level (~1 billion reads/sample). We used in-house pipelines to process the Hi-C data in line with established methods. As part of quality control, we examined summary statistics of Hi-C reads, including total number of reads, total number of uniquely mapped reads, total number of intra-chromosomal reads, and total number of intra-chromosomal reads which are >15 kb. HiCNorm was used to normalize raw Hi-C contact matrices. We used the insulation square method to identify the boundary regions of topologically associating domains. We used Fit-Hi-C to detect chromatin interaction peaks for all intra-chromosomal interactions within 2 Mb. We further applied HiCNormCis to identify the frequently interacting regions (FIREs), and performed GREAT analysis to assign biological meanings to the detected FIREs. We used HUGIn to visualize Hi-C data and related genetic and epigenetic features. Results All summary statistics from both fetal and adult samples were comparable to previous Hi-C studies. Biological replicates for each sample, showed high reproducibility (Pearson correlation coefficients > 0.96). We were able to connect several loci to genes for the recent PGC Major Depressive Disorder (MDD) GWAS that identified 44 loci. We are applying our chromatin catalog to emerging genetic findings of psychiatric disorders and to the identification of developmentally relevant chromatin interactions. Discussion We used eHi-C to generate high-resolution chromatin interaction data from human brain, at two developmental time points, to facilitate the interpretation and prioritization of genetic findings from GWAS. Large-scale connection of GWAS loci to specific genes could yield “actionable” findings, which may lead to the development of more targeted therapies.

Many RNA molecules exert their biological function only after folding to unique three-dimensional structures. For long, noncoding RNA molecules, the complexity of finding the native topology can be a major impediment to correct folding to the biologically active structure. An RNA molecule may fold to a near-native structure but not be able to continue to the correct structure due to a topological barrier such as crossed strands or incorrectly stacked helices. Achieving the native conformation thus requires unfolding and refolding, resulting in a long-lived intermediate. We investigate the role of topology in the folding of two phylogenetically related catalytic group I introns, the Twort and Azoarcus group I ribozymes. The kinetic models describing the Mg2+-mediated folding of these ribozymes were previously determined by time-resolved hydroxyl (⋅OH) radical footprinting. Two intermediates formed by parallel intermediates were resolved for each RNA. These data and analytical ultracentrifugation compaction analyses are used herein to constrain coarse-grained models of these folding intermediates as we investigate the role of nonnative topology in dictating the lifetime of the intermediates. Starting from an ensemble of unfolded conformations, we folded the RNA molecules by progressively adding native constraints to subdomains of the RNA defined by the ⋅OH time-progress curves to simulate folding through the different kinetic pathways. We find that nonnative topologies (arrangement of helices) occur frequently in the folding simulations despite using only native constraints to drive the reaction, and that the initial conformation, rather than the folding pathway, is the major determinant of whether the RNA adopts nonnative topology during folding. From these analyses we conclude that biases in the initial conformation likely determine the relative flux through parallel RNA folding pathways.

MicroRNAs (miRNAs) are short non-coding RNA transcripts that have the ability to regulate the expression of target genes, and have been shown to influence the development of various tumors. The purpose of our study is to identify aberrantly expressed miRNAs in retinoblastoma for the discovery of potential therapeutic targets for this disease, and to gain a greater understanding of the mechanisms driving retinoblastoma progression. We report 41 differentially expressed miRNAs (p

One of the least understood aspects of homologous recombination is the process by which the ends of a double-strand break (DSB) search the entire genome for homologous templates that can be used to repair the break. We took advantage of the natural competition between the alternative donors HML and HMR employed during HO endonuclease-induced switching of the budding yeast MAT locus. The strong mating-type-dependent bias in the choice of the donors is enforced by the recombination enhancer (RE), which lies 17 kb proximal to HML. We investigated factors that improve the use of the disfavored donor. We show that the normal heterochromatic state of the donors does not impair donor usage, as donor choice is not affected by removing this epigenetic silencing. In contrast, increasing the length of homology shared by the disfavored donor increases its use. This result shows that donor choice is not irrevocable and implies that there are several encounters between the DSB ends and even the favored donor before recombination is accomplished. The increase by adding more homology is not linear; these results can be explained by a thermodynamic model that determines the energy cost of using one donor over the other. An important inference from this analysis is that when HML is favored as the donor, RE causes a reduction in its effective genomic distance from MAT from 200 kb to ∼20 kb, which we hypothesize occurs after the DSB is created, by epigenetic chromatin modifications around MAT.

The stochasticity of chromosome organization was investigated by fluorescently labeling genetic loci in live Escherichia coli cells. In spite of the common assumption that the chromosome is well modeled by an unstructured polymer, measurements of the locus distributions reveal that the E. coli chromosome is precisely organized into a nucleoid filament with a linear order. Loci in the body of the nucleoid show a precision of positioning within the cell of better than 10% of the cell length. The precision of interlocus distance of genomically-proximate loci was better than 4% of the cell length. The measured dependence of the precision of interlocus distance on genomic distance singles out intranucleoid interactions as the mechanism responsible for chromosome organization. From the magnitude of the variance, we infer the existence of an as-yet uncharacterized higher-order DNA organization in bacteria. We demonstrate that both the stochastic and average structure of the nucleoid is captured by a fluctuating elastic filament model.

Retinoblastoma is the most common intraocular tumor in children. Current management includes broad-based treatments such as chemotherapy, enucleation, laser therapy, or cryotherapy. However, therapies that target specific pathways important for retinoblastoma progression could provide valuable alternatives for treatment. MicroRNAs are short, noncoding RNA transcripts that can regulate the expression of target genes, and their aberrant expression often facilitates disease. The identification of post-transcriptional events that occur after the initiating genetic lesions could further define the rapidly aggressive growth displayed by retinoblastoma tumors. In this study, we used two phenotypically different retinoblastoma cell lines to elucidate the roles of miRNA-31 and miRNA-200a in tumor proliferation. Our approach confirmed that miRNAs-31 and -200a expression is significantly reduced in human retinoblastomas. Moreover, overexpression of these two miRNAs restricts the expansion of a highly proliferative cell line (Y79), but does not restrict the growth rate of a less aggressive cell line (Weri1). Gene expression profiling of miRNA-31 and/or miRNA-200a-overexpressing cells identified differentially expressed mRNAs associated with the divergent response of the two cell lines. This work has the potential to enhance the development of targeted therapeutic approaches for retinoblastoma and improve the efficacy of treatment.

In the current precision medicine era, more and more samples get genotyped and sequenced. Both researchers and commercial companies expend significant time and resources to reduce the error rate. However, it has been reported that there is a sample mix-up rate of between 0.1% and 1%, not to mention the possibly higher mix-up rate during the down-stream genetic reporting processes. Even on the low end of this estimate, this translates to a significant number of mislabeled samples, especially over the projected one billion people that will be sequenced within the next decade. Here, we first describe a method to identify a small set of Single nucleotide polymorphisms (SNPs) that can uniquely identify a personal genome, which utilizes allele frequencies of five major continental populations reported in the 1000 genomes project and the ExAC Consortium. To make this panel more informative, we added four SNPs that are commonly used to predict ABO blood type, and another two SNPs that are capable of predicting sex. We then implement a web interface (http://qrcme.tech), nicknamed QRC (for QR code based Concordance check), which is capable of extracting the relevant ID SNPs from a raw genetic data, coding its genotype as a quick response (QR) code, and comparing QR codes to report the concordance of underlying genetic datasets. The resulting 80 fingerprinting SNPs represent a significant decrease in complexity and the number of markers used for genetic data labelling and tracking. Our method and web tool is easily accessible to both researchers and the general public who consider the accuracy of complex genetic data as a prerequisite towards precision medicine.

The folding of linear polymers into discrete three-dimensional structures is often required for biological function. The formation of long-lived intermediates is a hallmark of the folding of large RNA molecules due to the ruggedness of their energy landscapes. The precise thermodynamic nature of the barriers (whether enthalpic or entropic) that leads to intermediate formation is still poorly characterized in large structured RNA molecules. A classic approach to analyzing kinetic barriers are temperature dependent studies analyzed with Eyring's transition state theory. We applied Eyring's theory to time-resolved hydroxyl radical (•OH) footprinting kinetics progress curves collected at eight temperature from 21.5 °C to 51 °C to characterize the thermodynamic nature of folding intermediate formation for the Mg(2+)-mediated folding of the Tetrahymena thermophila group I ribozyme. A common kinetic model configuration describes this RNA folding reaction over the entire temperature range studied consisting of primary (fast) transitions to misfolded intermediates followed by much slower secondary transitions, consistent with previous studies. Eyring analysis reveals that the primary transitions are moderate in magnitude and primarily enthalpic in nature. In contrast, the secondary transitions are daunting in magnitude and entropic in nature. The entropic character of the secondary transitions is consistent with structural rearrangement of the intermediate species to the final folded form. This segregation of kinetic control reveals distinctly different molecular mechanisms during the two stages of RNA folding and documents the importance of entropic barriers to defining rugged RNA folding landscapes.

Sequence conservation and co-variation of base pairs are hallmarks of structured RNAs. For certain RNAs (e.g. riboswitches), a single sequence must adopt at least two alternative secondary structures to effectively regulate the message. If alternative secondary structures are important to the function of an RNA, we expect to observe evolutionary co-variation supporting multiple conformations. We set out to characterize the evolutionary co-variation supporting alternative conformations in riboswitches to determine the extent to which alternative secondary structures are conserved. We found strong co-variation support for the terminator, P1, and anti-terminator stems in the purine riboswitch by extending alignments to include terminator sequences. When we performed Boltzmann suboptimal sampling on purine riboswitch sequences with terminators we found that these sequences appear to have evolved to favor specific alternative conformations. We extended our analysis of co-variation to classic alignments of group I/II introns, tRNA, and other classes of riboswitches. In a majority of these RNAs, we found evolutionary evidence for alternative conformations that are compatible with the Boltzmann suboptimal ensemble. Our analyses suggest that alternative conformations are selected for and thus likely play functional roles in even the most structured of RNAs., Author Summary RNA (Ribonucleic Acid) is a messenger of genetic information, master regulator, and catalyst in the cell. To carry out its function, RNA can fold into complex three-dimensional structures. Certain classes of RNAs, called riboswitches, adopt at least two alternative structures to act as a switch. We set out to detect the evolutionary signal for alternative structures in riboswitches as we hypothesize that these RNA sequences must have evolved to allow both conformations. We find that indeed such signals exist when we compare the sequences of riboswitches from multiple species. When we extend this analysis to other RNA regulators in the cell that are not thought of as switches, we detect equivalent evolutionary support for alternative structures. Viewed through the lens of evolutionary structure conservation RNA sequences appear to have adapted to adopt multiple conformations.

The analysis of time-resolved hydroxyl radical (•OH) footprinting data can reveal the complex and rugged folding landscape of an RNA molecule. This analysis requires the identification and subsequent optimization of a kinetic model and its parameters. The number of possible kinetic models increases factorially with the complexity of the molecule, complicating the modeling process. We detail here a computational approach that allows complex models involving up to five kinetic intermediates to be run on a desktop computer. Our approach involves an initial “model-free” analysis of the data, which reduces the computational complexity of the subsequent kinetic parameter optimization. Our method is able to systematically identify the best fitting kinetic model and reveals the underlying folding mechanism of an RNA.

A majority of SNPs (single nucleotide polymorphisms) map to noncoding and intergenic regions of the genome. Noncoding SNPs are often identified in genome-wide association studies (GWAS) as strongly associated with human disease. Two such disease-associated SNPs in the 5′ UTR of the human FTL (Ferritin Light Chain) gene are predicted to alter the ensemble of structures adopted by the mRNA. High-accuracy single nucleotide resolution chemical mapping reveals that these SNPs result in substantial changes in the structural ensemble in agreement with the computational prediction. Furthermore six rescue mutations are correctly predicted to restore the mRNA to its wild-type ensemble. Our data confirm that the FTL 5′ UTR is a “RiboSNitch,” an RNA that changes structure if a particular disease-associated SNP is present. The structural change observed is analogous to that of a bacterial Riboswitch in that it likely regulates translation. These data further suggest that specific pairs of SNPs in high linkage disequilibrium (LD) will form RNA structure-stabilizing haplotypes (SSHs). We identified 484 SNP pairs that form SSHs in UTRs of the human genome, and in eight of the 10 SSH-containing transcripts, SNP pairs stabilize RNA protein binding sites. The ubiquitous nature of SSHs in the transcriptome suggests that certain haplotypes are conserved to avoid RiboSNitch formation.

The use of highly reactive chemical species to probe the structure and dynamics of nucleic acids is greatly simplified by software that enables rapid quantification of the gel images that result from these experiments. Semiautomated footprinting analysis (SAFA) allows a user to quickly and reproducibly quantify a chemical footprinting gel image through a series of steps that rectify, assign, and integrate the relative band intensities. The output of this procedure is raw band intensities that report on the relative reactivity of each nucleotide with the chemical probe. We describe here how to obtain these raw band intensities using SAFA and the subsequent normalization and analysis procedures required to process these data. In particular, we focus on analyzing time-resolved hydroxyl radical ((•)OH) data, which we use to monitor the kinetics of folding of a large RNA (the L-21 T. thermophila group I intron). Exposing the RNA to bursts of (•)OH radicals at specific time points during the folding process monitors the time progress of the reaction. Specifically, we identify protected (nucleotides that become inaccessible to the (•)OH radical probe when folded) and invariant (nucleotides with constant accessibility to the (•)OH probe) residues that we use for monitoring and normalization of the data. With this analysis, we obtain time-progress curves from which we determine kinetic rates of folding. We also report on a data visualization tool implemented in SAFA that allows users to map data onto a secondary structure diagram.

Structure mapping experiments (using probes such as dimethyl sulfate [DMS], kethoxal, and T1 and V1 RNases) are used to determine the secondary structures of RNA molecules. The process is iterative, combining the results of several probes with constrained minimum free-energy calculations to produce a model of the structure. We aim to evaluate whether particular probes provide more structural information, and specifically, how noise in the data affects the predictions. Our approach involves generating “decoy” RNA structures (using the sFold Boltzmann sampling procedure) and evaluating whether we are able to identify the correct structure from this ensemble of structures. We show that with perfect information, we are always able to identify the optimal structure for five RNAs of known structure. We then collected orthogonal structure mapping data (DMS and RNase T1 digest) under several solution conditions using our high-throughput capillary automated footprinting analysis (CAFA) technique on two group I introns of known structure. Analysis of these data reveals the error rates in the data under optimal (low salt) and suboptimal solution conditions (high MgCl2). We show that despite these errors, our computational approach is less sensitive to experimental noise than traditional constraint-based structure prediction algorithms. Finally, we propose a novel approach for visualizing the interaction of chemical and enzymatic mapping data with RNA structure. We project the data onto the first two dimensions of a multidimensional scaling of the sFold-generated decoy structures. We are able to directly visualize the structural information content of structure mapping data and reconcile multiple data sets.

Genome-wide association studies (GWAS) often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs) from the Human Gene Mutation Database (HGMD) that map to the untranslated regions (UTRs) of a gene. Rather than using minimum free energy approaches (e.g. mFold), we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, beta-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD), and Hypertension), we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5' UTRs of FTL and RB1) SNP-induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a "RiboSNitch," that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble.

Prokaryotic organisms must strike a balance between DNA accessibility and condensation: facilitating the genetic processes of transcription, replication, and DNA repair while simultaneously enabling the structural and physical processes of chromosome condensation and segregation in rapidly dividing cells. Recent studies have revealed that prokaryotic chromosomes are intricately structured but the mechanism for this physical organization is not yet understood. In this study, we describe the construction of a large number of E.coli strains carrying three spectrally distinct, fluorescently labeled genetic loci. The origin and terminus of replication as well as a random position on the chromosome are labeled and tracked simultaneously in live cells. These labeled loci are identified, counted and tracked automatically using custom MATLAB software which enables the analysis of a large number of nucleoid conformations. Positions of the labeled loci are determined with respect to the cell body and used to compute both the distribution of locations of single loci within the cell (thus revealing “chromosome territories”) as well as the correlations between fluctuations in the positioning of different loci. This data is analyzed in the context of a simple polymer model of the E.coli chromosome and it provides quantitative support for a nucleoid-centered mechanism of chromosome organization.

Eukaryotic and prokaryotic cells have intricately structured chromosomes. Their large-scale physical structure is believed to arise from protein-mediated interactions that can form both inter- and intra-chromosomal tethers as well as anchoring the chromosome to the membrane or purported protein scaffolds. While classical molecular biological techniques have yielded information about the structure and chemistry of chromosomes, little is known about their conformations or dynamics in vivo. To study this, we have placed fluorescent markers in Saccharomyces cerevisiae near the HML locus on chromosome III and at the spindle pole body, which serves as a marker of centromere attachment, and measured the cell-to-cell distribution and dynamics of distances between these two loci. The histograms of distances obtained in this way were analyzed using a model of the chromosome as a random-walk polymer. To account for the measured distance distributions, we conclude that the motion of the left arm of chromosome III is constrained by the presence of a tether. This motivates experiments in which known tethering mechanisms for chromosome III are removed and the effect on the distance distributions is measured. We report on such experiments using strains in which the HML locus was deleted, ones in which a known telomere tethering protein was removed, and strains with a deletion of 17kb of DNA between HML and the centromere.

Retinoblastoma, the most common pediatric cancer of the eye, is a devastating and sometimes fatal pediatric cancer. Within the United States, the majority of retinoblastoma patients are diagnosed before their 2nd birthday, and many lose their sight due to this disease. Approximately 75% of the retinoblastoma patients in the United States have unilateral disease, and treatment for many unilateral patients with advanced disease involves enucleation (removal of the eye). Therefore, new more effective targeted therapies for retinoblastoma could save a child's quality of life by preserving their sight. Outside of the United States, advanced retinoblastoma is an even greater clinical challenge in low-income countries, where the mortality rate among children diagnosed with advanced retinoblastoma is as high as 70%. Currently, the chemotherapeutic treatments for retinoblastoma involve the use of broad-based drugs. However, therapies targeted directly to aberrant signaling pathways may provide more effective therapy for this disease. MicroRNAs, short noncoding RNA transcripts that can regulate the expression of target genes, are known to affect the progression of many different tumor types. MicroRNAs (miRNAs) are also ideal therapeutic targets because they are amenable to therapeutic manipulation. To identify miRNAs associated with retinoblastoma progression, we have conducted miRNA profiling studies on 12 late-stage human retinoblastoma tumors. Our analysis has led to the identification of 19 differentially expressed miRNAs (p Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5037. doi:1538-7445.AM2012-5037

Untranslated Regions (UTRs) are ubiquitous controllers of genetic networks and are a key component of the cell's regulatory machinery. The molecular mechanism by which these UTRs achieve their regulatory efficacy is still poorly understood. We postulate that the RNA structure of these UTRs plays a central role in regulation. We compare the statistical properties of over 21,000 5′ and 3′ UTRs from the human genome of less then 1,000 nucleotides to two sets of randomly generated sequences. One set of random sequences the sequences of the human UTRs are scrambled to keep the same distribution of GC content and length. The second set of random sequences contains 1,000 completely random sequences for each combination of GC content from 0 to 1 and lengths from 25 nucleotides to 1,000 nucleotides. The scrambled sequences allow us to compare distributions of the statistical properties such as the Shannon entropy and the Helix entropy of the RNA partition function. The second set of random sequences allowed us to obtain empirical rules for predicting the average properties such as number of bases paired and average helix length of a sequence given the GC content and the length. With these comparisons it is possible to make predictions about the resilience of a given UTR to mutation and how stable a particular UTR is compared to a random sequence.

Misinterpretations of entropy and conflation with additional misunderstandings of the second law of thermodynamics are ubiquitous among scientists and non-scientists alike and have been used by creationists as the basis of unfounded arguments against evolutionary theory. Entropy is not disorder or chaos or complexity or progress towards those states. Entropy is a metric, a measure of the number of different ways that a set of objects can be arranged. Herein, we review the history of the concept of entropy from its conception by Clausius in 1867 to its more recent application to macroevolutionary theory. We provide teachable examples of (correctly defined) entropy that are appropriate for high school or introductory college level courses in biology and evolution. Finally, we discuss the association of these traditionally physics-related concepts to evolution. Clarification of the interactions between entropy, the second law of thermodynamics, and evolution has the potential for immediate benefit to both students and teachers.

Large RNA molecules are known to fold through multiple parallel pathways to achieve their functional conformation. These pathways include stable intermediate structures that we identify using our KinFold algorithm (http://simtk.org/home/kinfold) for the analysis of time-resolved hydroxyl radical footprinting experiments. KinFold enumerates kinetic model topologies to determine the structure, lifetimes, and abundance of intermediates along the folding pathways of RNA. We are therefore able to identify the best fitting kinetic model for each folding reaction studied. We used this approach to study the folding of the L-21 T. thermophila group I intron, a catalytic ribozyme, at 6 temperatures ranging from 25° to 51° C. Our analysis of this data reveals that the rates for a majority of the transitions (between intermediate, folded and unfolded states) obey the Arrhenius equation, allowing us to estimate the relative energies of activation for inter conversion between the states of an RNA. Furthermore, we are able to estimate the lifetimes of the different intermediate structures. These results reveal a simple landscape, where folding of the peripheral elements of the RNA create significant barriers to folding. Our approach is unique in that it allows us to simultaneously estimate the activation energies for all transitions in an RNA folding reaction.