Recognizing RNA structural motifs in HT-SELEX data for ribosomal protein S15.

BACKGROUND: Proteins recognize many different aspects of RNA ranging from single stranded regions to discrete secondary or tertiary structures. High-throughput sequencing (HTS) of in vitro selected populations offers a large scale method to study RNA-proteins interactions. However, most existing analysis methods require that the binding motifs are enriched in the population relative to earlier rounds, and that motifs are found in a loop or single stranded region of the potential RNA secondary structure. Such methods do not generalize to all RNA-protein interaction as some RNA binding proteins specifically recognize more complex structures such as double stranded RNA. RESULTS: In this study, we use HT-SELEX derived populations to study the landscape of RNAs that interact with Geobacillus kaustophilus ribosomal protein S15. Our data show high sequence and structure diversity and proved intractable to existing methods. Conventional programs identified some sequence motifs, but these are found in less than 5-10% of the total sequence pool. Therefore, we developed a novel framework to analyze HT-SELEX data. Our process accounts for both sequence and structure components by abstracting the overall secondary structure into smaller substructures composed of a single base-pair stack, which allows us to leverage existing approaches already used in k-mer analysis to identify enriched motifs. By focusing on secondary structure motifs composed of specific two base-pair stacks, we identified significantly enriched or depleted structure motifs relative to earlier rounds. CONCLUSIONS: Discrete substructures are likely to be important to RNA-protein interactions, but they are difficult to elucidate. Substructures can help make highly diverse sequence data more tractable. The structure motifs provide limited accuracy in predicting enrichment suggesting that G. kaustophilus S15 can either recognize many different secondary structure motifs or some aspects of the interaction are not captured by the analysis. This highlights the importance of considering secondary and tertiary structure elements and their role in RNA-protein interactions.

Co-evolution of Bacterial Ribosomal Protein S15 with Diverse mRNA Regulatory Structures.

RNA-protein interactions are critical in many biological processes, yet how such interactions affect the evolution of both partners is still unknown. RNA and protein structures are impacted very differently by mechanisms of genomic change. While most protein families are identifiable at the nucleotide level across large phylogenetic distances, RNA families display far less nucleotide similarity and are often only shared by closely related bacterial species. Ribosomal protein S15 has two RNA binding functions. First, it is a ribosomal protein responsible for organizing the rRNA during ribosome assembly. Second, in many bacterial species S15 also interacts with a structured portion of its own transcript to negatively regulate gene expression. While the first interaction is conserved in most bacteria, the second is not. Four distinct mRNA structures interact with S15 to enable regulation, each of which appears to be independently derived in different groups of bacteria. With the goal of understanding how protein-binding specificity may influence the evolution of such RNA regulatory structures, we examine whether examples of these mRNA structures are able to interact with, and regulate in response to, S15 homologs from organisms containing distinct mRNA structures. We find that despite their shared RNA binding function in the rRNA, S15 homologs have distinct RNA recognition profiles. We present a model to explain the specificity patterns observed, and support this model by with further mutagenesis. After analyzing the patterns of conservation for the S15 protein coding sequences, we also identified amino acid changes that alter the binding specificity of an S15 homolog. In this work we demonstrate that homologous RNA-binding proteins have different specificity profiles, and minor changes to amino acid sequences, or to RNA structural motifs, can have large impacts on RNA-protein recognition.

Sampled ensemble neutrality as a feature to classify potential structured RNAs.

BACKGROUND: Structured RNAs have many biological functions ranging from catalysis of chemical reactions to gene regulation. Yet, many homologous structured RNAs display most of their conservation at the secondary or tertiary structure level. As a result, strategies for structured RNA discovery rely heavily on identification of sequences sharing a common stable secondary structure. However, correctly distinguishing structured RNAs from surrounding genomic sequence remains challenging, especially during de novo discovery. RNA also has a long history as a computational model for evolution due to the direct link between genotype (sequence) and phenotype (structure). From these studies it is clear that evolved RNA structures, like protein structures, can be considered robust to point mutations. In this context, an RNA sequence is considered robust if its neutrality (extent to which single mutant neighbors maintain the same secondary structure) is greater than that expected for an artificial sequence with the same minimum free energy structure. RESULTS: In this work, we bring concepts from evolutionary biology to bear on the structured RNA de novo discovery process. We hypothesize that alignments corresponding to structured RNAs should consist of neutral sequences. We evaluate several measures of neutrality for their ability to distinguish between alignments of structured RNA sequences drawn from Rfam and various decoy alignments. We also introduce a new measure of RNA structural neutrality, the structure ensemble neutrality (SEN). SEN seeks to increase the biological relevance of existing neutrality measures in two ways. First, it uses information from an alignment of homologous sequences to identify a conserved biologically relevant structure for comparison. Second, it only counts base-pairs of the original structure that are absent in the comparison structure and does not penalize the formation of additional base-pairs. CONCLUSION: We find that several measures of neutrality are effective at separating structured RNAs from decoy sequences, including both shuffled alignments and flanking genomic sequence. Furthermore, as an independent feature classifier to identify structured RNAs, SEN yields comparable performance to current approaches that consider a variety of features including stability and sequence identity. Finally, SEN outperforms other measures of neutrality at detecting mutational robustness in bacterial regulatory RNA structures.

Proof of concept study to assess fetal gene expression in amniotic fluid by nanoarray PCR.

Microarray analysis of cell-free RNA in amniotic fluid (AF) supernatant has revealed differential fetal gene expression as a function of gestational age and karyotype. Once informative genes are identified, research moves to a more focused platform such as quantitative reverse transcriptase-PCR. Standardized NanoArray PCR (SNAP) is a recently developed gene profiling technology that enables the measurement of transcripts from samples containing reduced quantities or degraded nucleic acids. We used a previously developed SNAP gene panel as proof of concept to determine whether fetal functional gene expression could be ascertained from AF supernatant. RNA was extracted and converted to cDNA from 19 AF supernatant samples of euploid fetuses between 15 to 20 weeks of gestation, and transcript abundance of 21 genes was measured. Statistically significant differences in expression, as a function of advancing gestational age, were observed for 5 of 21 genes. ANXA5, GUSB, and PPIA showed decreasing gene expression over time, whereas CASC3 and ZNF264 showed increasing gene expression over time. Statistically significantly increased expression of MTOR and STAT2 was seen in female compared with male fetuses. This study demonstrates the feasibility of focused fetal gene expression analysis using SNAP technology. In the future, this technique could be optimized to examine specific genes instrumental in fetal organ system function, which could be a useful addition to prenatal care.