Biology subjects

Haim, Y.

Publications and source records attributed to Haim, Y..

6 recordsLinked to original sources

bioRxiv2026

Time-restricted feeding corrects aggravation of glucose intolerance and circadian disruption induced by weight cycling in obese young mice

Weight cycling (WC), defined as weight gain, loss, and regain, is common in obesity, but its metabolic consequences remain unclear. We tested whether WC-aggravated glucose intolerance in obesity is age-dependent and linked to circadian disruption. Young (7w) and mid-aged (12m) mice underwent a 15-week dietary intervention: Lean and Obese mice fed normal chow (NC) and high-fat diet (HFD) throughout, respectively. WC mice undergone HFD-induced weight gain, NC-induced weight loss, and a second HFD-induced weight regain. Late-onset obese (LO) mice ate HFD only paralleling weight regain of WC. In young, but not mid-aged mice, prior obesity accelerated weight regain upon HFD re-exposure, and aggravated glucose intolerance beyond that observed in Obese mice. This occurred without a worse adipose inflammatory profile. Rather, WC young mice exhibited blunting of light/dark-phase oscillation of feeding and energy metabolism, adipose and hepatic core clock gene oscillation, and increased hepatic expression of clock and gluconeogenic genes during the inactive phase. Restricting food availability to the active phase did not alter final weight regain, but improved glucose tolerance selectively in WC mice, normalized hepatic gluconeogenic and clock-genes expression in both liver and adipose tissue. These findings identify circadian disruption as a modifiable mediator of the adverse metabolic impact of WC in young-adulthood obesity. HighlightsO_LIWeight cycling is common in obesity, but whether it worsens metabolic dysfunction beyond persistent obesity remains unclear. C_LIO_LIWe asked whether weight cycling aggravates glucose intolerance in an age-dependent manner and whether circadian disruption contributes to this effect. C_LIO_LIIn young, but not mid-aged mice, weight cycling accelerated weight regain and worsened glucose intolerance, accompanied by blunted diurnal oscillation of behavioral parameters and core clock gene expression, without exaggerated adipose inflammation. C_LIO_LIActive-phase time-restricted feeding improved WC-induced aggravated glucose tolerance and circadian oscillation, identifying circadian disruption as a modifiable mechanism linking weight cycling adverse metabolic outcomes in young-adulthood obesity. C_LI

biochemistry↗

bioRxiv2026

SETD6-mediated methylation of PPARγ establishes a transcriptional feedback circuit promoting lipid accumulation in liver-derived cells

Peroxisome proliferator-activated receptor gamma (PPAR{gamma}) is a key transcriptional regulator of genes mediating adipogenesis (fat-cell differentiation) and lipid storage in several cell types like hepatocytes. As such, its regulation is crucial for cell and organismal physiology. Indeed, PPAR{gamma}s activity is regulated by multiple mechanisms, including post-transcriptional modifications, which, when dys-coordinated, may contribute to the pathogenesis of various states, including obesity, insulin resistance, and fatty liver disease. Here, we demonstrate that SETD6 binds to, and methylates PPAR{gamma} at lysine 170 (K170) both in vitro and in liver-derived cells. This methylation event, in turn, is required for PPAR{gamma}-mediated activation of SETD6 transcription via promoter binding, forming a positive feedback regulatory loop. RNA sequencing revealed that both SETD6 and PPAR{gamma} methylation at K170 are required for full induction of lipid metabolism genes expression, manifesting functionally in lipid droplet biogenesis in liver-derived cells. Together, our findings uncover a novel role for lysine methylation of PPAR{gamma} in the regulation of lipid synthesis and lipid droplet biogenesis, thereby identifying putative new therapeutic targets for lipid over-production diseases, including MAFLD (Metabolic dysfunction-associated fatty liver disease) and obesity.

cell biology↗

bioRxiv2025

Weight cycling-induced hypothalamic and metabolic tissue immune remodeling is uncoupled from metabolic dysfunctions

BackgroundObesity-induced insulin resistance is associated with white adipose tissue (WAT) and liver inflammation, which are both mitigated by weight loss. However, most individuals undergoing weight loss will regain lost weight, resulting in weight cycling (WC), which may exacerbate metabolic dysfunctions. Here, we studied the immunometabolic impact of WC in mice. MethodsC57BL/6J mice were exposed to two cycles of weight gain and weight loss by alternating between low (LFD) and high-fat diet (HFD) feeding. Animals were sacrificed when WC mice were weight stable for 10 weeks upon weight loss (WC-lean) and after a subsequent exposure to weight regain for 10 weeks (WC-obese), and compared to mice persistently fed LFD (LFD-lean) or HFD (HFD-obese). ResultsBody weight stabilized at a higher level in WC-lean mice after two weight gain/loss cycles compared to LFD-lean controls. While insulin resistance, metabolic tissue inflammation, and hepatic steatosis normalized between the two groups of lean mice, WC-lean mice exhibited features of WAT dysfunction. In the hypothalamus, inflammatory microglia were less abundant in WC-lean mice compared to LFD-lean mice, but mean individual microglial cell volume was larger. WC-obese mice stabilized at slightly lower weight compared with HFD-obese controls. Intriguingly, WC-obese mice exhibited increased WAT macrophages and reduced WAT and liver effector T cells compared to HFD-obese mice, whereas energy intake, body composition, whole-body insulin resistance and hepatic steatosis were similar. ConclusionsOur results suggest that WC in mice differently impacts animals in the weight-stable lean and obese states. WC-lean mice display features of a novel body weight settling point, associated with hypothalamic inflammatory changes. However, metabolic dysfunctions were uncoupled from WC-induced metabolic tissue inflammation in WC-obese mice.

immunology↗

bioRxiv2025

Mid-aged mice rapidly normalize dysglycemia but aggravate obesity-induced hypothalamic and microglial changes upon dietary obesity reversal

ObjectiveObesity-induced-dysglycemia and hypothalamic-microgliosis coincide, but whether they remain linked upon obesity reversal, and what is the effect of age, remain unclear. Here we hypothesized that rapid normalization of dysglycemia upon obesity-reversal remains linked to microgliosis resolution, but differs between young and mid-aged mice. MethodsYoung (7w) and mid-aged (1y) mice were fed normal chow (NC) or high-fat diet (HFD,8w), then switched to NC (Rev,2w). ResultsCompared to young mice, NC-fed mid-aged mice were heavier and weight-stable, gained weight with HFD comparably, and lost less weight in Rev. HFD-induced dysglycemia was less severe in mid-aged compared to young mice, but similarly normalized by obesity-reversal. However, whole-hypothalamus RNA sequencing revealed 2,419 differentially expressed genes (DEGs) in mid-aged mice, [~]4-times more than in young mice, and in both age-groups [~]80% of DEGs obesity-induced changes were aggravated in Rev. Furthermore, compared with young mice, middle-aged mice showed greater obesity-induced microglial cyto-morphological changes in the arcuate nucleus (ARC), which associated with increased p-NF{kappa}B-(p-p65) nuclear staining. Only in middle-aged mice obesity-induced microglial changes were aggravated by obesity reversal, with cell volume correlating (Rho({rho})=0.691, p=0.001) with adipose tissue crown-like-structures. ConclusionsIn conclusion, rapid dysglycemia normalization is uncoupled to the resolution of hypothalamic microgliosis, more-so in mid-age.

cell biology↗

bioRxiv2023

Human subcutaneous and visceral adipocyte atlases uncover classical and specialized adipocytes and depot-specific patterns

Human adipose depots are functionally distinct. Yet, recent single-nucleus RNA-sequencing (snRNA-seq) analyses largely uncovered overlapping/similar cell-type landscapes. We hypothesized that adipocytes subtypes, differentiation trajectories, and/or intercellular communication patterns could illuminate this depot similarity-difference gap. For this, we performed snRNA-seq of human subcutaneous and visceral adipose tissue. Whereas the majority of adipocytes in both depots were classical, namely enriched in lipid metabolism pathways, we also observed specialized adipocyte subtypes that were enriched in immune-related, extracellular matrix deposition (fibrosis), vascularization/angiogenesis, or ribosomal processes. Pseudo-temporal analysis suggested a developmental trajectory from adipose progenitor cells to classical adipocytes via specialized adipocytes, suggesting that the classical state stems from loss, rather than gain, of specialized functions. Lastly, intercellular communication routes were consistent with the different inflammatory tone of the two depots. Jointly, these findings provide a high-resolution view into the contribution of cellular composition, differentiation, and intercellular communication patterns to human fat depot differences.

systems biology↗

bioRxiv2023

sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human subcutaneous and visceral adipose tissues

Deconvolution algorithms rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to extract information on the cell-types composition and proportions comprising a certain tissue. Adipose tissues cellular composition exhibits enormous plasticity in response to weight changes and high variance at different anatomical locations (depots). However, adipocytes - the functionally unique cell type of adipose tissue, are not amenable to scRNA-seq, a challenge recently met by applying single-nucleus RNA-sequencing (snRNA-seq). Here we aimed to develop a deconvolution method to estimate the cellular composition of human visceral and subcutaneous adipose tissues (hVAT and hSAT, respectively) using snRNA-seq to assess the true cell-type proportions. To correlate deconvolution-estimated cell-type proportions to true (snRNA-seq -derived) proportions, we analyzed seven hVAT and 5 hSAT samples by both bulk RNA-seq and snRNA-seq. snRNA-seq uncovered 15 distinct cell types in hVAT and 13 in hSAT. Deconvolution tools - SCDC, MuSiC, and Scaden exhibited low performance in estimating cell-type proportions (median |R|= 0.12 for estimated vs. true correlations). Notably, estimation accuracy somewhat improved by decreasing the number of cell-types groups, which nevertheless remained low (|R|<0.42). We therefore developed sNuConv, a novel method that employs Scaden, a deep-learning tool, trained using snRNA-seq - based data corrected by i. snRNA-seq/bulk RNA-seq highly-correlated genes, ii. corrected estimated cell-type proportions based on individual cell-type regression models. Applying sNuConv on our bulk RNA-seq data resulted in cell-type proportion estimation accuracy with median R=0.93 (range:0.76-0.97) for hVAT, and median R=0.95 (range:0.92-0.98) for hSAT. The resulting model was depot-specific, reflecting depot-differences in gene expression patterns. Thus, we present sNuConv, a novel, AI-based, method to deduce the cellular landscape of hVAT and hSAT from bulk RNA-seq data, providing proof-of-concept for producing validated deconvolution algorithms for tissues un-amenable to single-cell RNA sequencing.

bioinformatics↗