Biology subjects

Tamir, Y.

Publications and source records attributed to Tamir, Y..

2 recordsLinked to original sources

bioRxiv2024

Quantification of beta cell carrying capacity in prediabetes

Prediabetes, a subclinical state of high glucose, carries a risk of transition to diabetes. One cause of prediabetes is insulin resistance, which impairs the ability of insulin to control blood glucose. However, many individuals with high insulin resistance retain normal glucose due to compensation by enhanced insulin secretion by beta cells. Individuals seem to differ in their maximum compensation level, termed beta cell carrying capacity, such that low carrying capacity is associated with a higher risk of prediabetes and diabetes. Carrying capacity has not been quantified using a mathematical model and cannot be estimated directly from measured glucose and insulin levels in patients, unlike insulin resistance and beta cell function which can be estimated using HOMA-IR and HOMA-B formula. Here we present a mathematical model of beta cell compensation and carrying capacity, and develop a new formula called HOMA-C to estimate it from glucose and insulin measurements. HOMA-C estimates the maximal potential beta cell function of an individual, rather than the current beta cell function. We test this approach using longitudinal cohorts of prediabetic people, finding 10-fold variation in carrying capacity. Low carrying capacity is associated with higher risk of transitioning to diabetes. We estimate the timescales of beta cell compensation and insulin resistance using large datasets, showing that, unlike previous mathematical models, the new model can explain the slow rise in glucose over decades. Our mathematical understanding of beta cell carrying capacity may help to assess the risk of prediabetes in each individual.

systems biology↗

bioRxiv2022

The predictive value of double-stranded RNA for A-to-I editing detection

Structured AbstractO_ST_ABSMotivationC_ST_ABSAdenosine-to-inosine (A-to-I) RNA editing, a crucial reaction for many processes that contribute to transcriptome plasticity, is both widely common across the transcriptome and difficult to predict due to a lack of distinctive genomic characteristics that can be obtained and analyzed computationally. An exception to this is the secondary structure of RNA molecules, which has been shown to have a major impact on the selectivity and specificity of the enzymes responsible for A-to-I editing. Yet, this information is rarely used for the task of editing site prediction. ResultsHere, we demonstrated the value of using base-pairing probabilities of RNA nucleotides to classify genomic sites as A-to-I RNA editing sites, using large-scale truth data which we compiled and make available for use in training future models. Our analysis suggests that the span of four bases from -2 (upstream) to +1 (downstream) of a putative editing site is most informative in this regard. A classifier trained on base-pairing probabilities alone performed with a positive predictive value (PPV) of 0.68, a negative predictive value (NPV) of 0.64, and an area under the receiver operating characteristic curve (AUC) of 0.71. By identifying structure-related features that are informative for detecting A-to-I RNA editing sites and quantifying their predictive value, this work advances our understanding of A-to-I editing determinants. AvailabilityAll source codes and data are available at https://github.com/Ally-s-Lab/P-BEP

bioinformatics↗