Browsing by Person "Franjic, Domenic"

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PKC regulates αKlotho gene expression in MDCK and NRK-52E cells
(2024) Wolf, Lisa; Vogt, Julia; Alber, Jana; Franjic, Domenic; Feger, Martina; Föller, Michael
Particularly expressed in the kidney, αKlotho is a transmembrane protein that acts together with bone hormone fibroblast growth factor 23 (FGF23) to regulate renal phosphate and vitamin D homeostasis. Soluble Klotho (sKL) is released from the transmembrane form and controls various cellular functions as a paracrine and endocrine factor. αKlotho deficiency accelerates aging, whereas its overexpression favors longevity. Higher αKlotho abundance confers a better prognosis in cardiovascular and renal disease owing to anti-inflammatory, antifibrotic, or antioxidant effects and tumor suppression. Serine/threonine protein kinase C (PKC) is ubiquitously expressed, affects several cellular responses, and is also implicated in heart or kidney disease as well as cancer. We explored whether PKC is a regulator of αKlotho. Experiments were performed in renal MDCK or NRK-52E cells and PKC isoform and αKlotho expression determined by qRT-PCR and Western Blotting. In both cell lines, PKC activation with phorbol ester phorbol-12-myristate-13-acetate (PMA) downregulated, while PKC inhibitor staurosporine enhanced αKlotho mRNA abundance. Further experiments with PKC inhibitor Gö6976 and RNA interference suggested that PKCγ is the major isoform for the regulation of αKlotho gene expression in the two cell lines. In conclusion, PKC is a negative regulator of αKlotho gene expression, an effect which may be relevant for the unfavorable effect of PKC on heart or kidney disease and tumorigenesis.
Predictor preselection for mixed‐frequency dynamic factor models: a simulation study with an empirical application to GDP nowcasting
(2025) Franjic, Domenic; Schweikert, Karsten
We investigate the performance of dynamic factor model nowcasting with preselected predictors in a mixed‐frequency setting. The predictors are selected via the elastic net as it is common in the targeted predictor literature. A simulation study and an application to empirical data are used to evaluate different strategies for variable selection, the influence of tuning parameters, and to determine the optimal way to handle mixed‐frequency data. We propose a novel cross‐validation approach that connects the preselection and nowcasting step. In general, we find that preselecting provides more accurate nowcasts compared with the benchmark dynamic factor model using all variables. Our newly proposed cross‐validation method outperforms the other specifications in most cases.