Biology subjects

Keles, U.

Publications and source records attributed to Keles, U..

3 recordsLinked to original sources

bioRxiv2025

Omics-based insights into human liver reveal GTPase-driven mechanisms of MASLD progression in obesity

Metabolic dysfunction-associated steatotic liver disease (MASLD) is often asymptomatic early on but can progress to irreversible conditions like cirrhosis. Due to limited access to human liver biopsies, systematic and integrative molecular resources remain scarce. In this study, we performed transcriptomic analyses on liver and metabolomic analyses on liver and plasma samples from morbidly obese individuals without liver pathology or at early-stage MASLD. While, the plasma metabolomic profile did not fully mirror liver histological features, dual-omics integration of liver samples revealed significantly remodeled lipid and amino acid metabolism pathways. Integrative network analysis uncoupled metabolic remodeling and gene expression as independent features of hepatic steatosis and fibrosis progression, respectively. Notably, GTPases and their regulators emerged as a novel class of genes linked to early liver fibrosis. This study offers a detailed molecular landscape of early MASLD in obesity and highlights potential targets of obesity-linked liver fibrosis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/632747v3_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1188eborg.highwire.dtl.DTLVardef@1340070org.highwire.dtl.DTLVardef@108d560org.highwire.dtl.DTLVardef@f0adda_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2021

Atypical gaze patterns in autism are heterogeneous across subjects but reliable within individuals

People with autism spectrum disorder (ASD) have atypical gaze onto both static visual images 1,2 and dynamic videos 3,4 that could be leveraged for diagnostic purposes 5,6. Eye tracking is important for characterizing ASD across the lifespan 7 and nowadays feasible at home (e.g., from smartphones 8). Yet gaze-based classification has been difficult to achieve, due to sources of variance both across and within subjects. Here we test three competing hypotheses: (a) that ASD could be successfully classified from the fact that gaze patterns are less reliable or noisier than in controls, (b) that gaze patterns are atypical and heterogeneous across ASD subjects but reliable over time within a subject, or (c) that gaze patterns are individually reliable and also homogenous among individuals with ASD. Leveraging dense eye tracking data from two different full-length television sitcom episodes in a total of over 150 subjects (N = 53 ASD, 107 controls) collected at two different sites, we demonstrate support for the second of these hypotheses. The findings pave the way for the investigation of autism subtypes, and for elucidating the specific visual features that best discriminate gaze patterns -- directions that will also inform neuroimaging and genetic studies of this complex disorder.

neuroscience↗

bioRxiv2019

No strong evidence that social network index is associated with gray matter volume from a data-driven investigation

Recent studies in adult humans have reported correlations between individual differences in peoples Social Network Index (SNI) and gray matter volume (GMV) across multiple regions of the brain. However, the cortical and subcortical loci identified are inconsistent across studies. These discrepancies might arise because different regions of interest were hypothesized and tested in different studies without controlling for multiple comparisons, and/or from insufficiently large sample sizes to fully protect against statistically unreliable findings. Here we took a data-driven approach in a pre-registered study to comprehensively investigate the relationship between SNI and GMV in every cortical and subcortical region, using three predictive modeling frameworks. We also included psychological predictors such as cognitive and emotional intelligence, personality, and mood. In a sample of healthy adults (n = 92), neither multivariate frameworks (e.g., ridge regression with cross-validation) nor univariate frameworks (e.g., univariate linear regression with cross-validation) showed a significant association between SNI and any GMV or psychological feature after multiple comparison corrections (all R-squared values [≤] 0.1). These results emphasize the importance of large sample sizes and hypothesis-driven studies to derive statistically reliable conclusions, and suggest that future meta-analyses will be needed to more accurately estimate the true effect sizes in this field.

neuroscience↗