Accurate design of translational output by a neural network model of ribosome distribution

Synonymous codon choice can have dramatic effects on ribosome speed and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs. Here, we have modeled this variation in translation elongation by using a feed-forward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. Our approach revealed sequence features affecting translation elongation and characterized large technical biases in ribosome profiling. We applied our model to design synonymous variants of a fluorescent protein spanning the range of translation speeds predicted with our model. Levels of the fluorescent protein in budding yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo; that this information can be harnessed to design coding sequences; and that control of translation elongation alone is sufficient to produce large quantitative differences in protein output

Accurate design of translational output by a neural network model of ribosome distribution

Synonymous codon choice can have dramatic effects on ribosome speed and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs. Here, we have modeled this variation in translation elongation by using a feed-forward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. Our approach revealed sequence features affecting translation elongation and characterized large technical biases in ribosome profiling. We applied our model to design synonymous variants of a fluorescent protein spanning the range of translation speeds predicted with our model. Levels of the fluorescent protein in budding yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo; that this information can be harnessed to design coding sequences; and that control of translation elongation alone is sufficient to produce large quantitative differences in protein output

Evaluation of the Ability of LL-37 to Neutralise LPS In Vitro and Ex Vivo

BACKGROUND: Human cathelicidin LL-37 is a cationic antimicrobial peptide (AMP) which possesses a variety of activities including the ability to neutralise endotoxin. In this study, we investigated the role of LPS neutralisation in mediating LL-37's ability to inhibit Pseudomonas aeruginosa LPS signalling in human monocytic cells. METHODOLOGY/PRINCIPAL FINDINGS: Pre-treatment of monocytes with LL-37 significantly inhibited LPS-induced IL-8 production and the signalling pathway of associated transcription factors such as NF-κB. However, upon removal of LL-37 from the media prior to LPS stimulation, these inhibitory effects were abolished. These findings suggest that the ability of LL-37 to inhibit LPS signalling is largely dependent on extracellular LPS neutralisation. In addition, LL-37 potently inhibited cytokine production induced by LPS extracted from P. aeruginosa isolated from the lungs of cystic fibrosis (CF) patients. In the CF lung, polyanionic molecules such as glycosaminoglycans (GAGs) and DNA bind LL-37 and impact negatively on its antibacterial activity. In order to determine whether such interactions interfere with the LPS neutralising ability of LL-37, the status of LL-37 and its ability to bind LPS in CF sputum were investigated. Overall our findings suggest that in the CF lung, the ability of LL-37 to bind LPS and inhibit LPS-induced IL-8 production is attenuated as a result of binding to DNA and GAGs. However, LL-37 levels and its concomitant LPS-binding activity can be increased with a combination of DNase and GAG lyase (heparinase II) treatment. CONCLUSIONS/SIGNIFICANCE: Overall, these findings suggest that a deficiency in available LL-37 in the CF lung may contribute to greater LPS-induced inflammation during CF lung disease

Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders

Funder: Kennedy Trust Rheumatology Research Prize StudentshipFunder: DFG Cluster of Excellence “Precision Medicine in Chronic In-flammation” (PMI; ID: EXC2167)Funder: EC | EC Seventh Framework Programm | FP7 Ideas: European Research Council (FP7-IDEAS-ERC - Specific Programme: “Ideas” Implementing the Seventh Framework Programme of the European Community for Research, Technological Development and Demonstration Activities (2007 to 2013)); doi: https://doi.org/10.13039/100011199; Grant(s): 715772Funder: NWO-VIDI grant 016.178.056, the Netherlands Heart Foundation CVON grant 2018-27, and NWO Gravitation grant ExposomeNLFunder: Li Ka Shing Foundation (Li Ka Shing Foundation Limited); doi: https://doi.org/10.13039/100007421Abstract: Irritable bowel syndrome (IBS) results from disordered brain–gut interactions. Identifying susceptibility genes could highlight the underlying pathophysiological mechanisms. We designed a digestive health questionnaire for UK Biobank and combined identified cases with IBS with independent cohorts. We conducted a genome-wide association study with 53,400 cases and 433,201 controls and replicated significant associations in a 23andMe panel (205,252 cases and 1,384,055 controls). Our study identified and confirmed six genetic susceptibility loci for IBS. Implicated genes included NCAM1, CADM2, PHF2/FAM120A, DOCK9, CKAP2/TPTE2P3 and BAG6. The first four are associated with mood and anxiety disorders, expressed in the nervous system, or both. Mirroring this, we also found strong genome-wide correlation between the risk of IBS and anxiety, neuroticism and depression (rg > 0.5). Additional analyses suggested this arises due to shared pathogenic pathways rather than, for example, anxiety causing abdominal symptoms. Implicated mechanisms require further exploration to help understand the altered brain–gut interactions underlying IBS

Towards a cross-cultural assessment of binge-watching: Psychometric evaluation of the “watching TV series motives” and “binge-watching engagement and symptoms” questionnaires across nine languages

In view of the growing interest regarding binge-watching (i.e., watching multiple episodes of television (TV) series in a single sitting) research, two measures were developed and validated to assess binge-watching involvement (“Binge-Watching Engagement and Symptoms Questionnaire”, BWESQ) and related motivations (“Watching TV Series Motives Questionnaire”, WTSMQ). To promote international and cross-cultural binge-watching research, the present article reports on the validation of these questionnaires in nine languages (English, French, Spanish, Italian, German, Hungarian, Persian, Arabic, Chinese). Both questionnaires were disseminated, together with additional self-report measures of happiness, psychopathological symptoms, impulsivity and problematic internet use among TV series viewers from a college/university student population (N = 12,616) in 17 countries. Confirmatory factor, measurement invariance and correlational analyses were conducted to establish structural and construct validity. The two questionnaires had good psychometric properties and fit in each language. Equivalence across languages and gender was supported, while construct validity was evidenced by similar patterns of associations with complementary measures of happiness, psychopathological symptoms, impulsivity and problematic internet use. The results support the psychometric validity and utility of the BWESQ and WTSMQ for conducting cross-cultural research on binge-watching

Regression Modeling and Bias Correction of Ribosome Profiling Data

Translational regulation is an important control point for gene expression, modulating the quantity and isoforms of proteins produced in a cell. The ribosome profiling method measures translation dynamics and output directly, by sampling the distribution of ribosomes across all mRNA transcripts in a sample. This method has demonstrated that ribosomes do not move at a uniform rate across transcripts, and that synonymous codon choice can have large effects on ribosome speed, RNA stability, and protein expression. We present a regression model using a feedforward neural network to predict the ribosome density at each codon in a transcriptome as a function of the local sequence neighborhood around that codon. This approach demonstrated a collection of sequence features that contain substantial predictive information about translation elongation rates. We apply this model to characterize the translation rates of naturally occurring genes, and also to design translation optimized coding sequences for a given protein. We present a novel and efficient algorithm that finds the fastest and slowest predicted coding sequences for a given protein. We validated our regression model and optimization procedure by designing synonymous variants of eCitrine, a yellow fluorescent protein, across a range of predicted translation rates. Our results showed that the levels of expressed protein closely tracked the predicted overall translation rates of the synonymous coding sequences. This demonstrated that our model captures information determining translation dynamics in vivo, that we can harness this information to design coding sequences, and that control of translation elongation alone is sufficient to produce large, quantitative differences in protein output. Analysis of our regression model also demonstrated that the terminal regions of ribosome footprints are important predictors of footprint density at a given codon. This suggests that ligation events in the experimental protocol are differentially recovering footprints based on their terminal sequences. We characterized this recovery bias both computationally and experimentally, and demonstrated that it can have a large impact on the count of footprints recovered at a given codon. To correct for this error, we developed a generative model of ribosome footprint experiments that incorporates both the biological distribution of footprints across transcripts and the experimental steps that introduce recovery bias. We developed a software tool to estimate the parameters of this model, and present a statistical method to correct recovery bias and estimate the biological distribution of ribosomes across transcripts. This method enables improved estimates of translation for the many applications in which ribosome profiling data is used