Biology subjects

Shilo, S.

Publications and source records attributed to Shilo, S..

4 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

DNA Methylation is stable after MMEJ and NHEJ double strand break repair

DNA double strand breaks (DSBs) are a major source of mutations. Both non-homologous-end-joining (NHEJ) and microhomology-mediated-end-joining (MMEJ) DSB repair pathways are error prone and produce deletions, which can lead to cancer. DSBs also lead to epigenetic changes, including demethylation, which is involved in carcinogenesis. Of specific interest is the MMEJ repair pathway, as it requires methylation restoration around the break, as a result of the resection and formation of single stranded (ssDNA) intermediates. While, methylation patterns after homologous recombination (HR) have been partially studied, the methylation status after MMEJ and NHEJ remains poorly reported, and can be relevant for cancer. To study methylation patterns around DSB after NHEJ and MMEJ repair, we used targeted bisulfite-sequencing (BS-seq) to quantify methylation of dozens of single cell clones after induction of DSB by CRISPR. Each single cell clone was classified according to the sequence signature to a specific repair mechanism: NHEJ or MMEJ. Comparison of single cell clones after DSB to control cells, without DSB, demonstrated correct restoration of the methylation levels. No difference in methylation patterns was noticed when comparing NHEJ to MMEJ. Methylation levels in gene body, highly methylated CpGs (n=61, 4000 base pairs around DSB) and in low methylation CpGs (n=19), remained stable after both MMEJ and NHEJ. Gene body methylation persisted even on the background of DNMT3A R882C mutation, the most prevalent preleukemic mutation, in which the de novo methylation machinery is compromised. An exception observed in a single CpG site (ASXL1 995) which demonstrated elevated methylation rate after DSB repair only in the presence of WT DNMT3A. In summary, DNA methylation restoration demonstrated high fidelity after DSB both in methylated and unmethylated gene body, even in cases where DNA resections and deletions occurred. Author SummaryDNA holds the genetic information. Modifications on the DNA molecule such as DNA methylation are crucial for the genetic regulation. DNA damage is harmful to the cell and needs to be repaired. Different repair mechanisms may result in mutations that can be identified according to their sequence signature. In the work presented here we examined changes in DNA methylation after different types of DNA repair, MMEJ and NHEJ. We found that DNA methylation is highly stable after DNA repair regardless to the repair mechanism and genomic context.

genetics↗

bioRxiv2022

Machine learning reveals conserved chromatin patterns determining meiotic recombination sites in plants

Distribution of meiotic recombination events in plants has been associated with local chromatin and DNA characteristics, chromosome landmark proximity, and other features1-7. However, relative importance of these characteristics is unclear and it is unknown if they are sufficient to unambiguously determine recombination landscape8. Here, we analyzed over 40 DNA sequence, chromatin, and chromosome location features of maize and Arabidopsis recombination sites using machine learning9,10. We discovered that a combination of just three features, CG methylation, CHG methylation, and nucleosome occupancy, enabled identification of exact crossover site with 90% accuracy. These results imply redundancy of most recombination site characteristics. Recombination takes place in a small fraction of the genome with chromatin features distinct from those of genome at large. Surprisingly, crossover sites show elevated heterochromatin histone marks despite low DNA methylation. Crossover site features show broad evolutionary conservation, which will enable creating genetic maps in species where conventional mapping is unfeasible.

plant biology↗

bioRxiv2017

T-DNA integration is rapid and influenced by the chromatin state of the host genome.

Agrobacterium tumefaciens mediated T-DNA integration is a common tool for plant genome manipulation. However, there is controversy regarding whether T-DNA integration is biased towards genes or randomly distributed throughout the genome. In order to address this question, we performed high-throughput mapping of T-DNA-genome junctions obtained in the absence of selection at several time points after infection. T-DNA-genome junctions were detected as early as 6 hours post-infection. T-DNA distribution was apparently uniform throughout the chromosomes, yet local biases toward AT-rich motifs and T-DNA border sequence micro-homology were detected. Analysis of the epigenetic landscape of integration showed that selected events reported on previously were associated with extremely low methylation and nucleosome occupancy. Conversely, non-selected events from this study showed chromatin marks, such as high nucleosome occupancy and high H3K27me3 that correspond to 3D-interacting heterochromatin islands embedded within euchromatin. Such structures might play a role in capturing and silencing invading T-DNA.

plant biology↗