Biology subjects

Urban, Z.

Publications and source records attributed to Urban, Z..

3 recordsLinked to original sources

bioRxiv2024

Sphingosine kinase 1 is integral for elastin deficiency-induced arterial hypermuscularization

Defective elastin and smooth muscle cell (SMC) accumulation characterize both arterial diseases (e.g., atherosclerosis, restenosis and supravalvular aortic stenosis [SVAS]), and physiological ductus arteriosus (DA) closure. Elastin deficiency induces SMC hyperproliferation; however, mechanisms underlying this effect are not well elucidated. Elastin (ELN) is expressed from embryonic day (E) 14 in the mouse aorta. Immunostains of Eln(+/+) and Eln(-/-) aortas indicate that SMCs of the Eln null aorta are first hyperproliferative at E15.5, prior to morphological differences. Bulk RNA-seq reveals that sphingosine kinase 1 (Sphk1) is the most upregulated transcript in Eln(-/-) aortic SMCs at E15.5. Reduced ELN increases levels of transcription factor early growth response 1 (EGR1), resulting in increased SPHK1 levels in cultured human aortic SMCs and in the mouse aorta at E15.5 and P0.5. Aortic tissue from Williams-Beuren Syndrome patients, who have elastin insufficiency and SVAS, also has upregulated SPHK1 expression. SMC-specific Sphk1 deletion or pharmacological inhibition of SPHK1 attenuates SMC proliferation and mitigates aortic disease, leading to extended survival of Eln(-/-) mice. In addition, EGR1 and SPHK1 are increased in the wild-type mouse DA compared to adjacent descending aorta. Treatment with a SPHK1 inhibitor attenuates SMC proliferation and reduces SMC accumulation, leading to DA patency. In sum, SPHK1 is a key node in elastin deficiency-induced hypermuscularization, and inhibiting this kinase may be a therapeutic strategy for SVAS and select congenital heart diseases in which a patent DA maintains circulation. One Sentence SummarySphingosine kinase 1-induced by defective elastin promotes muscularization in pathological aortic stenosis and physiological ductus arteriosus occlusion.

developmental biology↗

bioRxiv2022

Targeting default mode network connectivity with mindfulness-based fMRI neurofeedback: A pilot study among adolescents with affective disorder history

Adolescents experience alarmingly high rates of major depressive disorder (MDD), however, gold-standard treatments are only effective for ~50% of youth. Accordingly, there is a critical need to develop novel interventions, particularly ones that target neural mechanisms believed to potentiate depressive symptoms. Directly addressing this gap, we developed a mindfulness-based fMRI neurofeedback (mbNF) for adolescents that targets default mode network (DMN) hyperconnectivity, which has been implicated in the onset and maintenance of MDD. In this proof-of-concept study, adolescents (n = 9) with a lifetime history of depression and/or anxiety were administered clinical interviews and self-report questionnaires, and then, each participants DMN and central executive network (CEN) were personalized using a resting state fMRI localizer. After the localizer scan, adolescents completed a brief mindfulness training followed by a mbNF session in the scanner wherein they were instructed to volitionally reduce DMN relative to CEN activation by practicing mindfulness meditation. Several promising findings emerged. First, mbNF successfully engaged the target brain state during neurofeedback; participants spent more time in the target state with DMN activation lower than CEN activation. Second, in each of the nine adolescents, mbNF led to significantly reduced within-DMN connectivity, which correlated with post-mbNF increases in state mindfulness. Last, a reduction of within-DMN connectivity mediated the association between better mbNF performance and increased state mindfulness. These findings demonstrate that personalized mbNF can effectively and non-invasively modulate the intrinsic networks known to be associated with the emergence and persistence of depressive symptoms during adolescence.

neuroscience↗

bioRxiv2021

Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data

The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from todays state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.

neuroscience↗