A Theory-based Educational Pamphlet With Low-residue Diet Improves Colonoscopy Attendance and Bowel Preparation Quality.

GOALS/BACKGROUND: Patients who "no-show" for colonoscopy or present with poor bowel preparation waste endoscopic resources and do not receive adequate examinations for colorectal cancer (CRC) screening. Using the Health Belief Model, we modified an existing patient education pamphlet and evaluated its effect on nonattendance rates and bowel preparation quality. STUDY: We implemented a color patient education pamphlet to target individual perceptions about CRC and changed bowel preparation instructions to include a low-residue diet instead of the previous clear liquid diet. We compared the nonattendance rate over a 2-month period before and after the introduction of the pamphlet, allowing for a washout period during which pamphlet use was inconsistent. We compared the Boston Bowel Preparation Scale (BBPS) in 100 consecutive patients who underwent colonoscopy during each of the 2 periods. RESULTS: Baseline characteristics between the 2 groups were similar, although patients who received the pamphlet were younger (P=0.03). The nonattendance rate was significantly lower in patients who received the pamphlet (13% vs. 21%, P=0.01). The percentage of patients with adequate bowel preparation increased from 82% to 86% after introduction of the pamphlet, although this was not statistically significant (P=0.44). The proportion of patients with a BBPS score of 9 was significantly higher in the pamphlet group (41% vs. 27%, P=0.03). There was no difference in adenoma and sessile serrated adenoma detection rates before and after pamphlet implementation. CONCLUSIONS: After implementing a theory-based patient education intervention with a low-residue diet, our absolute rate for colonoscopy nonattendance decreased by 8% and the proportion of patients with a BBPS score of 9 increased by 14%. The Health Belief Model appears to be a useful construct for CRC screening interventions.

Riboswitch Distribution in the Human Gut Microbiome Reveals Common Metabolite Pathways.

Riboswitches are widely distributed, conserved RNAs which regulate metabolite levels in bacterial cells through direct, noncovalent binding of their cognate metabolite. Various riboswitch families are highly enriched in gut bacteria, suggestive of a symbiotic relationship between the host and bacteria. Previous studies of the distribution of riboswitches have examined bacterial taxa broadly. Thus, the distribution of riboswitches associated with bacteria inhabiting the intestines of healthy individuals is not well understood. To address these questions, we survey the gut microbiome for riboswitches by including an international database of prokaryotic genomes from the gut samples. Using Infernal, a program that uses RNA-specific sequence and structural features, we survey this data set using existing riboswitch models. We identify 22 classes of riboswitches with vitamin cofactors making up the majority of riboswitch-associated pathways. Our finding is reproducible in other representative databases from the oral as well as the marine microbiomes, underscoring the importance of thiamine pyrophosphate, cobalamin, and flavin mononucleotide in gene regulation. Interestingly, riboswitches do not vary significantly across microbiome representatives from around the world despite major taxonomic differences; this suggests an underlying conservation. Further studies elucidating the role of bacterial riboswitches in the host metabolome are needed to illuminate the consequences of our finding.

Dynamic energy landscapes of riboswitches help interpret conformational rearrangements and function.

Riboswitches are RNAs that modulate gene expression by ligand-induced conformational changes. However, the way in which sequence dictates alternative folding pathways of gene regulation remains unclear. In this study, we compute energy landscapes, which describe the accessible secondary structures for a range of sequence lengths, to analyze the transcriptional process as a given sequence elongates to full length. In line with experimental evidence, we find that most riboswitch landscapes can be characterized by three broad classes as a function of sequence length in terms of the distribution and barrier type of the conformational clusters: low-barrier landscape with an ensemble of different conformations in equilibrium before encountering a substrate; barrier-free landscape in which a direct, dominant "downhill" pathway to the minimum free energy structure is apparent; and a barrier-dominated landscape with two isolated conformational states, each associated with a different biological function. Sharing concepts with the "new view" of protein folding energy landscapes, we term the three sequence ranges above as the sensing, downhill folding, and functional windows, respectively. We find that these energy landscape patterns are conserved in various riboswitch classes, though the order of the windows may vary. In fact, the order of the three windows suggests either kinetic or thermodynamic control of ligand binding. These findings help understand riboswitch structure/function relationships and open new avenues to riboswitch design.

Analysing the fitness cost of antibiotic resistance to identify targets for combination antimicrobials.

Mutations in the rifampicin (Rif)-binding site of RNA polymerase (RNAP) confer antibiotic resistance and often have global effects on transcription that compromise fitness and stress tolerance of resistant mutants. We suggested that the non-essential genome, through its impact on the bacterial transcription cycle, may represent an untapped source of targets for combination antimicrobial therapies. Using transposon sequencing, we carried out a genome-wide analysis of fitness cost in a clinically common rpoB H526Y mutant. We find that genes whose products enable increased transcription elongation rates compound the fitness costs of resistance whereas genes whose products function in cell wall synthesis and division mitigate it. We validate our findings by showing that the cell wall synthesis and division defects of rpoB H526Y result from an increased transcription elongation rate that is further exacerbated by the activity of the uracil salvage pathway and unresponsiveness of the mutant RNAP to the alarmone ppGpp. We applied our findings to identify drugs that inhibit more readily rpoB H526Y and other RifR alleles from the same phenotypic class. Thus, genome-wide analysis of fitness cost of antibiotic-resistant mutants should expedite the discovery of new combination therapies and delineate cellular pathways that underlie the molecular mechanisms of cost.

Analysis of riboswitch structure and function by an energy landscape framework.

The thiamine pyrophosphate (TPP) riboswitch employs modular domains for binding TPP to form a platform for gene expression regulation. Specifically, TPP binding triggers a conformational switch in the RNA from a transcriptionally active "on" state to an inactive "off" state that concomitantly causes the formation of a terminator hairpin and halting of transcription. Here, clustering analysis of energy landscapes at different nucleotide lengths suggests a novel computational tool for analysis of the mechanics of transcription elongation in the presence or absence of the ligand. Namely, we suggest that the riboswitch's kinetics are tightly governed by a length-dependent switch, whereby the energy landscape has two clusters available during transcription elongation and where TPP's binding shifts the preference to one form. Significantly, the biologically active and inactive structures determined experimentally matched well the structures predominant in each computational set. These clustering/structural analyses combined with modular computational design suggest design principles that exploit the above features to analyze as well as create new functions and structures of RNA systems.

Estimating the fraction of non-coding RNAs in mammalian transcriptomes.

Recent studies of mammalian transcriptomes have identified numerous RNA transcripts that do not code for proteins; their identity, however, is largely unknown. Here we explore an approach based on sequence randomness patterns to discern different RNA classes. The relative z-score we use helps identify the known ncRNA class from the genome, intergene and intron classes. This leads us to a fractional ncRNA measure of putative ncRNA datasets which we model as a mixture of genuine ncRNAs and other transcripts derived from genomic, intergenic and intronic sequences. We use this model to analyze six representative datasets identified by the FANTOM3 project and two computational approaches based on comparative analysis (RNAz and EvoFold). Our analysis suggests fewer ncRNAs than estimated by DNA sequencing and comparative analysis, but the verity of our approach and its prediction requires more extensive experimental RNA data.