Search

Can traditional computational analysis and machine learning help compensate for inadequate peer review of drug-repurposing papers in the context of an infodemic? [ABSTRACT FROM AUTHOR]

A letter to the editor is presented related to a study on labeling of unmodified bacterial RNA molecules in live cells.

Background: The patient population that is evaluated for bariatric surgery is characterized by a very high body mass index (BMI). Since obesity is the most important risk factor for obstructive sleep apnea (OSA), sleep disordered breathing is highly prevalent in this population. If undiagnosed before bariatric surgery, untreated OSA can lead to perioperative and postoperative complications. Debate exists whether all patients that are considered for bariatric surgery should undergo polysomnography (PSG) evaluation and screening for OSA as opposed to only those patients with clinical history or examination concerning sleep disordered breathing. We examined the prevalence and severity of OSA in all patients that were considered for bariatric surgery. We hypothesized that, by utilizing preoperative questionnaires (regarding sleepiness and OSA respiratory symptoms) in combination with menopausal status and BMI data, we would be able to predict which subjects did not have sleep apnea without the use of polysomnography. In addition, we hypothesized that we would be able to predict which subjects had severe OSA (apnea-hypopnea index (AHI) > 30). Methods: Three hundred forty-two consecutive subjects, evaluated for bariatric surgery from November 1, 2005 to January 31, 2007 underwent overnight polysomnography and completed questionnaires regarding sleepiness, menopausal status, and respiratory symptoms related to OSA. Apneas and hypopneas were classified as follows: mild apnea 5 ≤ AHI ≤ 15, moderate apnea 15 < AHI ≤ 30, and severe apnea AHI > 30. Results: The overall sample prevalence of OSA was 77.2%. Of these, 30.7% had mild OSA; 19.3% had moderate OSA, and 27.2% had severe OSA. Among men, the prevalence of OSA was 93.6% and 73.5% among women. The mean AHI (events per hour) for men with OSA was 49.2 ± 35.5 and 26.3 ± 28.3 for women with OSA. Separate logistic regression models were developed for the following three outcomes: AHI ≥ 5 events per hour, AHI > 15 events per hour, and AHI > 30 events per hour. When predicting these three levels of OSA severity, the area under the curve (AUC) values were: 0.8, 0.72, and 0.8, respectively. The negative predictive value for the presence of sleep apnea (AHI ≥ 5) was 75% when using the most stringent possible cutoff for the prediction model. Conclusions: The prevalence of OSA in all patients considered for bariatric surgery was greater than 77%, irrespective of OSA symptoms, gender, menopausal status, age, or BMI. The prediction model that we developed for the presence of OSA (AHI ≥ 5 events per hour) has excellent discriminative ability (evidenced by an AUC value of 0.8). However, the negative prediction values for the presence of OSA were too low to be clinically useful due to the high prevalence of OSA in this high-risk group. We demonstrated that, by utilizing even the most stringent possible cutoff values for the prediction model, OSA cannot be predicted with enough certainty. Therefore, we advocate routine PSG testing for all patients that are considered for bariatric surgery. [ABSTRACT FROM AUTHOR]

Bacteria show remarkable adaptability in the face of antibiotic therapeutics. Resistance alleles in drug target-specific sites and general stress responses have been identified in individual end-point isolates. Less is known, however, about the population dynamics during the development of antibiotic-resistant strains. Here we follow a continuous culture of Escherichia coli facing increasing levels of antibiotic and show that the vast majority of isolates are less resistant than the population as a whole. We find that the few highly resistant mutants improve the survival of the population’s less resistant constituents, in part by producing indole, a signalling molecule generated by actively growing, unstressed cells. We show, through transcriptional profiling, that indole serves to turn on drug efflux pumps and oxidative-stress protective mechanisms. The indole production comes at a fitness cost to the highly resistant isolates, and whole-genome sequencing reveals that this bacterial altruism is made possible by drug-resistance mutations unrelated to indole production. This work establishes a population-based resistance mechanism constituting a form of kin selection whereby a small number of resistant mutants can, at some cost to themselves, provide protection to other, more vulnerable, cells, enhancing the survival capacity of the overall population in stressful environments. [ABSTRACT FROM AUTHOR]

We describe a technique for the detection and localization of RNA transcripts in living cells. The method is based on fluorescent-protein complementation regulated by the interaction of a split RNA-binding protein with its corresponding RNA aptamer. In our design, the RNA-binding protein is the eukaryotic initiation factor 4A (eIF4A). eIF4A is dissected into two fragments, and each fragment is fused to split fragments of the enhanced green fluorescent protein (EGFP). Coexpression of the two protein fusions in the presence of a transcript containing eIF4A-interacting RNA aptamer resulted in the restoration of EGFP fluorescence in Escherichia coli cells. We also applied this technique to the visualization of an aptamer-tagged mRNA and 5S ribosomal RNA (rRNA). We observed distinct spatial and temporal changes in fluorescence within single cells, reflecting the nature of the transcript. [ABSTRACT FROM AUTHOR]

Current methods for prenatal diagnosis of chromosomal aneuploidies involve the invasive sampling of fetal materials using procedures such as amniocentesis or chorionic villus sampling and constitute a finite risk to the fetus. Here, we outline a strategy for fetal chromosome dosage assessment that can be performed noninvasively through analysis of placental expressed mRNA in maternal plasma. We achieved noninvasive prenatal diagnosis of fetal trisomy 21 by determining the ratio between alleles of a single-nucleotide polymorphism (SNP) in PLAC4 mRNA, which is transcribed from chromosome 21 and expressed by the placenta, in maternal plasma. PLAC4 mRNA in maternal plasma was fetal derived and cleared after delivery. The allelic ratios in maternal plasma correlated with those in the placenta. Fetal trisomy 21 was detected noninvasively in 90% of cases and excluded in 96.5% of controls. [ABSTRACT FROM AUTHOR]

We applied whole-genome resequencing of Escherichia coli to monitor the acquisition and fixation of mutations that conveyed a selective growth advantage during adaptation to a glycerol-based growth medium. We identified 13 different de novo mutations in five different E. coli strains and monitored their fixation over a 44-d period of adaptation. We obtained proof that the observed spontaneous mutations were responsible for improved fitness by creating single, double and triple site-directed mutants that had growth rates matching those of the evolved strains. The success of this new genome-scale approach indicates that real-time evolution studies will now be practical in a wide variety of contexts. [ABSTRACT FROM AUTHOR]

The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the ‘bottom up’. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the important enzymatic, structural and regulatory roles of RNA in the cell. Here we present a post-transcriptional regulation system in Escherichia coli that uses RNA to both silence and activate gene expression. We inserted a complementary cis sequence directly upstream of the ribosome binding site in a target gene. Upon transcription, this cis-repressive sequence causes a stem-loop structure to form at the 5'-untranslated region of the mRNA. The stem-loop structure interferes with ribosome binding, silencing gene expression. A small noncoding RNA that is expressed in trans targets the cis-repressed RNA with high specificity, causing an alteration in the stem-loop structure that activates expression. Such engineered riboregulators may lend insight into mechanistic actions of endogenous RNA-based processes and could serve as scalable components of biological networks, able to function with any promoter or gene to directly control gene expression. [ABSTRACT FROM AUTHOR]

Transcription in eukaryotic cells has been described as quantal, with pulses of messenger RNA produced in a probabilistic manner. This description reflects the inherently stochastic nature of gene expression, known to be a major factor in the heterogeneous response of individual cells within a clonal population to an inducing stimulus. Here we show in Saccharomyces cerevisiae that stochasticity (noise) arising from transcription contributes significantly to the level of heterogeneity within a eukaryotic clonal population, in contrast to observations in prokaryotes, and that such noise can be modulated at the translational level. We use a stochastic model of transcription initiation specific to eukaryotes to show that pulsatile mRNA production, through reinitiation, is crucial for the dependence of noise on transcriptional efficiency, highlighting a key difference between eukaryotic and prokaryotic sources of noise. Furthermore, we explore the propagation of noise in a gene cascade network and demonstrate experimentally that increased noise in the transcription of a regulatory protein leads to increased cell-cell variability in the target gene output, resulting in prolonged bistable expression states. This result has implications for the role of noise in phenotypic variation and cellular differentiation. [ABSTRACT FROM AUTHOR]

Presents the construction of a genetic toggle switch in Escherichia coli. Synthetic, bistable gene-regulatory network; Conditions necessary for bistability; Mutually inhibitory network-arrangement of any two repressible promoters; Flipping of toggle switch between stable states; Switching threshold; Practical applications. INSET: The toggle model..

There is an association between smoking and cancer, cardiovascular disease and all-cause mortality. However, currently, there are no affordable and informative tests for assessing the effects of smoking on the rate of biological aging. In this study we demonstrate for the first time that smoking status can be predicted using blood biochemistry and cell count results andthe recent advances in artificial intelligence (AI). By employing age-prediction models developed using supervised deep learning techniques, we found that smokers exhibited higher aging rates than nonsmokers, regardless of their cholesterol ratios and fasting glucose levels. We further used those models to quantify the acceleration of biological aging due to tobacco use. Female smokers were predicted to be twice as old as their chronological age compared to nonsmokers, whereas male smokers were predicted to be one and a half times as old as their chronological age compared to nonsmokers. Our findings suggest that deep learning analysis of routine blood tests could complement or even replace the current error-prone method of self-reporting of smoking status and could be expanded to assess the effect of other lifestyle and environmental factors on aging. [ABSTRACT FROM AUTHOR]

Presents information about various practical challenges related to haplotyping in biomedicine. Information about the recording of the historical patterns of mutation, recombination and population dynamics in the haplotype distribution within each population; Expectation that comprehensive maps describing genome-wide haplotype diversity in major human populations will become available within the next few years; Need to accurately estimate the effect that genetic variations have on disease onset or severity.

Focuses on functional genomics. Identification of protein components of the human splicesome; Analysis of the peptides using mass spectrometry; Dominance of the methods with the highest throughput and degree of informativeness; Detection of protein modifications ranging from hyper-phosphorylated states.

Network models are a fundamental tool for the visualization and analysis of molecular interactions occurring in biological systems. While broadly illuminating the molecular machinery of the cell, graphical representations of protein interaction networks mask complex patterns of interaction that depend on temporal, spatial, or condition-specific contexts. In this paper, we introduce a novel graph construct called a biological context network that explicitly captures these changing patterns of interaction from one biological context to another. We consider known gene ontology biological process and cellular component annotations as a proxy for context, and show that aggregating small process-specific protein interaction sub-networks leads to the emergence of observed scale-free properties. The biological context model also provides the basis for characterizing proteins in terms of several context-specific measures, including ‘interactive promiscuity,’ which identifies proteins whose interacting partners vary from one context to another. We show that such context-sensitive measures are significantly better predictors of knockout lethality than node degree, reaching better than 70% accuracy among the top scoring proteins. [ABSTRACT FROM AUTHOR]