Biology subjects

Algavi, Y.

Publications and source records attributed to Algavi, Y..

4 recordsLinked to original sources

bioRxiv2024

DNA methylation of transposons pattern aging differences across a diverse cohort of dogs from the Dog Aging Project

Within a species, larger individuals often have shorter lives and higher rates of age-related disease. Despite this well-known link, we still know little about underlying age-related epigenetic differences, which could help us better understand inter-individual variation in aging and the etiology, onset, and progression of age-associated disease. Dogs exhibit this negative correlation between size, health, and longevity and thus represent an excellent system in which to test the underlying mechanisms. Here, we quantified genome-wide DNA methylation in a cohort of 864 dogs in the Dog Aging Project. Age strongly patterned the dog epigenome, with the majority (66% of age-associated loci) of regions associating age-related loss of methylation. These age effects were non-randomly distributed in the genome and differed depending on genomic context. We found the LINE1 (long interspersed elements) class of TEs (transposable elements) were the most frequently hypomethylated with age (FDR < 0.05, 40% of all LINE1 regions). This LINE1 pattern differed in magnitude across breeds of different sizes- the largest dogs lost 0.26% more LINE1 methylation per year than the smallest dogs. This suggests that epigenetic regulation of TEs, particularly LINE1s, may contribute to accelerated age and disease phenotypes within a species. Since our study focused on the methylome of immune cells, we looked at LINE1 methylation changes in golden retrievers, a breed highly susceptible to hematopoietic cancers, and found they have accelerated age-related LINE1 hypomethylation compared to other breeds. We also found many of the LINE1s hypomethylated with age are located on the X chromosome and are, when considering X chromosome inactivation, counter-intuitively more methylated in males. These results have revealed the demethylation of LINE1 transposons as a potential driver of intra-species, demographic-dependent aging variation.

genomics↗

bioRxiv2024

Aging at scale: Younger dogs and larger breeds from the Dog Aging Project show accelerated epigenetic aging.

Dogs exhibit striking within-species variability in lifespan, with smaller breeds often living more than twice as long as larger breeds. This longevity discrepancy also extends to health and aging-larger dogs show higher rates of age-related diseases. Despite this well-established phenomenon, we still know little about the biomarkers and molecular mechanisms that might underlie breed differences in aging and survival. To address this gap, we generated an epigenetic clock using DNA methylation from over 3 million CpG sites in a deeply phenotyped cohort of 864 companion dogs from the Dog Aging Project, including some dogs sampled annually for 2-3 years. We found that the largest breed size tends to have epigenomes that are, on average, 0.37 years older per chronological year compared to the smallest breed size. We also found that higher residual epigenetic age was significantly associated with increased mortality risk, with dogs experiencing a 34% higher risk of death for each year increase in residual epigenetic age. These findings not only broaden our understanding of how aging manifests within a diverse species but also highlight the significant role that demographic factors play in modulating the biological mechanisms underlying aging. Additionally, they highlight the utility of DNA methylation as both a biomarker for healthspan-extending interventions, a mortality predictor, and a mechanism for understanding inter-individual variation in aging in dogs.

genomics↗

bioRxiv2023

Microbial dispersion in the human gut through the lens of fecal transplant

Microorganisms frequently migrate from one ecosystem to another, influencing and shaping their new environment. Yet, despite the potential importance of this process in modulating the environment and the microbial ecosystem, our understanding of the fundamental forces that govern microbial migration and dispersion is still lacking. Moreover, while theoretical studies and in-vitro experimental work have highlighted the contribution of biotic interactions to the assembly of the community, identifying such interactions in vivo, specifically in communities as complex as the human gut, remains challenging. To this end, we developed a new, robust, and compositionally invariant approach, and leveraged data from well-characterized translocation experiments, namely, clinical fecal microbiota transplant (FMT) trials, to rigorously pinpoint dependencies between taxa during the colonization of human gastrointestinal habitat. Our analysis identified numerous pairwise dependencies between co-colonizing microbes during migration between gastrointestinal environments. We further demonstrated that identified dependencies agree with previously reported findings from in-vitro experiments and population-wide distribution patterns. Finally, we characterized the web of metabolic dependencies between these taxa and explored the functional properties that may promote better dispersion. Combined, our findings provide insights into the principles and determinants of community dynamics following ecological translocation, informing potential opportunities for precise community design.

microbiology↗

bioRxiv2022

A data-driven approach for predicting the impact of drugs on the human microbiome

Many medications can negatively impact the bacteria residing in our gut, depleting beneficial species and causing adverse effects. To determine individualized response to pharmaceutical treatment, a comprehensive understanding of the impact of various drugs on the gut microbiome is needed, yet, to date, experimentally challenging to obtain. Towards this end, we developed a data-driven approach, integrating information about the chemical properties of each drug and the genomic content of each microbe, to systematically predicts drug-microbiome interactions. We show that this framework successfully predicts outcomes of in-vitro pairwise drug-microbe experiments, as well as drug-induced microbiome dysbiosis in both animal models and clinical trials. Applying this methodology, we systematically map all interactions between pharmaceuticals and bacteria and demonstrate that medications anti-microbial properties are tightly linked to their adverse effects. This computational framework has the potential to unlock the development of personalized medicine and microbiome-based therapeutic approaches, improving outcomes and minimizing side effects.

microbiology↗