Biology subjects

Spiegel, S.

Publications and source records attributed to Spiegel, S..

5 recordsLinked to original sources

bioRxiv2025

Depot-specific roles for sphingosine kinase 1 in conversion of white adipose to thermogenic adipose

BackgroundAs of 2023, approximately 100.1 million adults and 14.7 million children in the USA are obese. Many comorbidities develop with obesity, which impairs quality of life and burdens the health care system. Consequently, there is an urgent need for interventions and treatments to reverse obesity and its comorbidities and restore health. Sphingosine Kinase 1 (SphK1), a key enzyme in sphingolipid metabolism, produces sphingosine-1-phosphate (S1P), a bioactive lipid implicated in obesity and metabolic dysfunction. While global deletion of Sphk1 protects against diet-induced obesity, adipocyte-specific SPHK1 deficiency paradoxically promotes weight gain, glucose intolerance, and adipose inflammation. Given the known role of sphingolipids in adipose thermogenesis, we investigated whether Sphk1 regulates adipocyte beiging and mitochondrial function. MethodsWe assessed thermogenic responses in SphK1-deficient adipocytes and adipocyte-specific Sphk1 knockout (Ad-SphK1{Delta}) mice under basal and {beta}3-adrenergic stimulation using CL 316,243. Thermoneutral housing (30{degrees}C) and room temperature (23{degrees}C) conditions were used to minimize and assess ambient temperature effects on thermogenesis. Molecular, histological, and bioenergetic analyses were conducted across multiple adipose depots. Results{beta}3-adrenergic stimulation upregulated Sphk1 expression in mature white adipocytes, while SphK1-deficient adipocytes exhibited enhanced Ucp1 expression, indicating a suppressive role for SphK1 in beiging. In vivo, adipocyte-specific Sphk1 knockout (Ad-SphK1{Delta}) mice showed elevated Ucp1 expression in inguinal and gonadal white adipose tissue (iWAT, gWAT), both basally and after CL 316,243 treatment. These changes were accompanied by depot-specific alterations in adipocyte size and increased adiposity, independent of ambient temperature. Despite similar elevation of thermogenic markers, Sphk1 deletion had differential effect on mitochondrial function: iWAT showed increased mitochondrial content but reduced complex IV activity and ATP production, whereas gWAT showed reduced mitochondrial abundance without changes in respiration. ConclusionOur work suggests that Sphk1 may act as a negative regulator of thermogenic expression and affect mitochondrial function in a depot-specific manner. Loss of Sphk1 enhances beiging but compromises mitochondrial efficiency, revealing a complex role for the SphK1/S1P axis in adipose plasticity and metabolic regulation. These insights may inform future therapeutic strategies targeting sphingolipid pathways for obesity and metabolic disease.

biochemistry↗

bioRxiv2023

The sterol transporter STARD3 transports sphingosine at ER-lysosome contact sites

Sphingolipids are important structural components of membranes. Additionally, simple sphingolipids such as sphingosine are highly bioactive and participate in complex subcellular signaling. Sphingolipid deregulation is associated with many severe diseases including diabetes, Parkinsons and cancer. Here, we focus on how sphingosine, generated from sphingolipid catabolism in late endosomes/lysosomes, is reintegrated into the biosynthetic machinery at the endoplasmic reticulum (ER). We characterized the sterol transporter STARD3 as a sphingosine transporter acting at lysosome-ER contact sites. Experiments featuring crosslinkable sphingosine probes, supported by unbiased molecular dynamics simulations, exposed how sphingosine binds to the lipid-binding domain of STARD3. Following the metabolic fate of pre-localized lysosomal sphingosine showed the importance of STARD3 and its actions at contact sites for the integration of sphingosine into ceramide in a cellular context. Our findings provide the first example of inter-organellar sphingosine transfer and pave the way for a better understanding of sphingolipid - sterol co-regulation.

cell biology↗

bioRxiv2023

Calorie Restriction activates a gastric Notch-FOXO1 pathway to expand Ghrelin cells

Calorie restriction increases lifespan. While some tissue-specific protective effects of calorie restriction have been described, the impact of calorie restriction on the gastrointestinal tract remains unclear. We found increased abundance of chromogranin A+, including orexigenic ghrelin+, endocrine cells in the stomach of calorie-restricted mice. This effect coincided with increased Notch target Hes1 and Notch ligand Jag1 and was reversed when Notch signaling was blocked using the {gamma}-secretase inhibitor DAPT. Using primary cultures and genetically-modified reporter mice, we determined that increased endocrine cell abundance was due to altered stem and progenitor proliferation. Different from the intestine, calorie restriction decreased gastric Lgr5+ stem cells, while increasing a FOXO1/Neurog3+ subpopulation of endocrine progenitors in a Notch-dependent manner. Further, calorie restriction triggered nuclear localization of FOXO1, which was sufficient to promote endocrine cell differentiation. Taken together, the data indicate that calorie restriction promotes gastric endocrine cell differentiation triggered by active Notch signaling and regulated by FOXO1.

cell biology↗

bioRxiv2022

High-content cellular screen image analysis benchmark study

Recent development of novel methods based on deep neural networks has transformed how high-content microscopy cellular images are analyzed. Nonetheless, it is still a challenge to identify cellular phenotypic changes caused by chemical or genetic treatments and to elucidate the relationships among treatments in an unsupervised manner, due to the large data volume, high phenotypic complexity and the presence of a priori unknown phenotypes. Here we benchmarked five deep neural network methods and two feature engineering methods on a well-characterized public data set. In contrast to previous benchmarking efforts, the manual annotations were not provided to the methods, but rather used as evaluation criteria afterwards. The seven methods individually performed feature extraction or representation learning from cellular images, and were consistently evaluated for downstream phenotype prediction and clustering tasks. We identified the strengths of individual methods across evaluation metrics, and further examined the biological concepts of features automatically learned by deep neural networks.

bioinformatics↗

bioRxiv2019

Metadata-Guided Visual Representation Learning for Biomedical Images

MotivationThe clustering of biomedical images according to their phenotype is an important step in early drug discovery. Modern high-content-screening devices easily produce thousands of cell images, but the resulting data is usually unlabelled and it requires extra effort to construct a visual representation that supports the grouping according to the presented morphological characteristics.\n\nResultsWe introduce a novel approach to visual representation learning that is guided by metadata. In high-context-screening, meta-data can typically be derived from the experimental layout, which links each cell image of a particular assay to the tested chemical compound and corresponding compound concentration. In general, there exists a one-to-many relationship between phenotype and compound, since various molecules and different dosage can lead to one and the same alterations in biological cells.\n\nOur empirical results show that metadata-guided visual representation learning is an effective approach for clustering biomedical images. We have evaluated our proposed approach on both benchmark and real-world biological data. Furthermore, we have juxtaposed implicit and explicit learning techniques, where both loss function and batch construction differ. Our experiments demonstrate that metadata-guided visual representation learning is able to identify commonalities and distinguish differences in visual appearance that lead to meaningful clusters, even without image-level annotations.\n\nNotePlease refer to the supplementary material for implementation details on metadata-guided visual representation learning strategies.

bioinformatics↗