Biology subjects

Korkmaz, G.

Publications and source records attributed to Korkmaz, G..

4 recordsLinked to original sources

bioRxiv2025

Causal inference and functional dynamics of a visuomotor network demonstrate excitatory/inhibitory alterations in Multiple Sclerosis

Balanced excitation and inhibition are essential for brain dynamics, and their disruption can lead to network dysfunction in neurological diseases. Here, we present a conceptually unified multiscale brain modelling framework combining Dynamic Causal Modelling (DCM) applied to task and resting-state functional Magnetic Resonance Imaging (fMRI) data and The Virtual Brain (TVB) to characterise the excitatory/inhibitory balance of the brain. We applied the framework to a visuomotor brain subnetwork in a cohort of 9 healthy controls and 17 people with multiple sclerosis (pwMS). Acquired data included an event-related task fMRI experiment with variable grip force, resting-state fMRI, and diffusion-weighted imaging. The visuomotor network comprised the bilateral primary visual cortex (V1), left primary motor cortex (M1), supplementary motor and premotor cortex (SMAPMC), cingulate cortex (CC), superior parietal lobule (SPL), and right cerebellar lobule VI (CR). Results from DCM showed that while the overall network architecture was preserved in MS, there were significant alterations in the excitatory/inhibitory nature of effective connectivity: at rest, a statistical change was observed in CR-to-V1 connectivity, which was inhibitory in healthy volunteers but excitatory in MS. During task, effective connectivity feedback, including cerebellar self-connection, was positive in healthy volunteers but negative in MS and became increasingly dysregulated with higher motor demand. Alterations in functional and effective connectivity were associated with behavioural performance (task reaction time) and clinical measures (disability severity). At the overall group level, TVB parameters linked reduced NMDA-mediated excitatory gain to slower task responses. Moreover, integrating DCM and TVB demonstrated that higher global excitatory gain was associated with stronger task-engaged effective connectivity across sensorimotor and visuomotor pathways, linking network-level excitability captured by TVB to context-dependent reconfiguration of directed interactions and to connection-level strength revealed by DCM.

neuroscience↗

bioRxiv2024

Cerebellar control over inter-regional excitatory/inhibitory dynamics discriminates execution from observation of an action

The motor learning theory anticipates that cerebro-cerebellar loops perform sensorimotor prediction thereby regulating motor control. This operation has been identified during action execution (AE) and observation (AO) but the causal interaction between the cerebellum and cerebral cortex remained unclear. Here we used Dynamic Causal Modelling (DCM) to study functional MRI (fMRI) data obtained during a squeeze ball task in either the AE or AO conditions. In both cases, active regions included bilateral primary visual cortex (V1), left primary motor cortex (M1), left supplementary motor and premotor cortex (SMAPMC), left cingulate cortex (CC), left superior parietal lobule (SPL), and right cerebellum (CRBL). AE and AO networks showed the same fixed effective connectivity, with pathways between V1, CRBL, SMAPMC and CC wired in a closed loop. However, the cerebellar communication towards the cerebral cortex switched from excitatory in AE to inhibitory in AO. Moreover, in AE only, signal modulation was non-linear from SMAPMC to CRBL and within the CRBL self-connection, supporting the role of the CRBL in elaborating motor plans received from SMAPMC. Thus, the need for motor planning and the presence of a sensorimotor feedback in AE discriminate the modality of forward control operated by the CRBL on SMAPMC. While the underlying circuit mechanisms remain to be determined, these results reveal that the CRBL differentially controls the excitatory/inhibitory dynamics of inter-regional effective connectivity depending on its functional engagement, opening new prospective for the design of artificial sensorimotor controllers and for the investigation of neurological diseases.

neuroscience↗

bioRxiv2023

Breast cancer risk SNPs converge on estrogen receptor binding sites commonly shared between breast tumors to locally alter estrogen signalling output

Estrogen Receptor alpha (ER) is the main driver and prime drug target in luminal breast. ER chromatin binding is extensively studied in cell lines and a limited number of human tumors, using consensi of peaks shared among samples. However, little is known about inter-tumor heterogeneity of ER chromatin action, along with its biological implications. Here, we use a large set of ER ChIP-seq data from 70 ER+ breast cancers to explore inter-patient heterogeneity in ER DNA binding, to reveal a striking inter-tumor heterogeneity of ER action. Interestingly, commonly-shared ER sites showed the highest estrogen-driven enhancer activity and were most-engaged in long-range chromatin interactions. In addition, the most-commonly shared ER-occupied enhancers were enriched for breast cancer risk SNP loci. We experimentally confirm SNVs to impact chromatin binding potential for ER and its pioneer factor FOXA1. Finally, in the TCGA breast cancer cohort, we could confirm these variations to associate with differences in expression for the target gene. Cumulatively, we reveal a natural hierarchy of ER-chromatin interactions in breast cancers within a highly heterogeneous inter-tumor ER landscape, with the most-common shared regions being most active and affected by germline functional risk SNPs for breast cancer development.

genomics↗

bioRxiv2018

Methylation of E. coli Class I Release Factors Increases the Accuracy of Translation Termination in vitro

Ribosomal protein synthesis (translation) is a highly accurate process. Translation termination, in particular, must be accurate to prevent truncated proteins. How this accuracy is achieved is not fully understood in all its details. Using an E. coli in vitro system, I explore novel mechanisms that contribute to the high accuracy of translation termination. By comparing the Michaelis-Menten parameters of methylated and non-methylated release factors on cognate and non-cognate codons. Post-translational methylation of a strictly conserved GGQ motif in class I release factors increases the accuracy of termination by up to 5-fold. This happens by increasing both the maximum rate of peptide release (kcat) and Michaelis-Menten constant (KM). Further, I demonstrate here that a non-methylated release factor acts like an uncompetitive inhibitor of enzyme reactions. Overall, this study shows that the methylation of class I release factors is a novel mechanism contributing to highly accurate translation termination.\n\nAbbreviations

biochemistry↗