Cene Skubic

Advances in high-throughput sequencing technologies have transformed human genomics, enabling large-scale initiatives to characterise genome structure, function, and population diversity. Following foundational international projects such as the Human Genome Project, HapMap, and the 1000 Genomes Project, many countries have developed national sequencing initiatives to generate population-specific genomic resources and support precision medicine. In this review, we examine the development of human genomic sequencing initiatives with a particular focus on Europe, where national projects differ substantially in their objectives, infrastructure, governance, funding models, cohort design, and degree of integration with healthcare systems. We further consider how pan-European programmes are increasingly linking these heterogeneous national efforts through shared standards, federated data infrastructures, and coordinated population-reference initiatives. Comparisons with selected initiatives from other world regions highlight common challenges, including unequal population representation, long-term sustainability, data governance, interoperability, and translation into clinical practice. We also discuss the growing impact of long-read sequencing and artificial intelligence on population genomics and genomic medicine. Overall, the European landscape illustrates that successful genomic initiatives depend not simply on sequencing scale, but on the alignment of scientific objectives with representative recruitment, sustainable infrastructure and financing, interoperable data systems, and pathways for clinical implementation.

Circadian rhythm disruptions are increasingly recognized in disorders such as obstructive sleep apnea (OSA), yet analysis of 24 h gene expression patterns remains challenging due to the lack of reliable reference genes for normalization. Even commonly used housekeeping genes may exhibit circadian oscillations, which can confound rhythmic gene expression analyses and hinder biomarker identification. To address this limitation, we evaluated the gene expression stability of 11 commonly used housekeeping genes in blood collected every 6 h over 24 h period from 40 adults with varying OSA severity and controls. Stability ranking by analytical tools RefFinder and EndoGeneAnalyzer identified ACTB (β-actin) and RPL13A (ribosomal protein L13a) as the most consistent reference genes, with minimal intra- and inter-individual variability across sampling times and disease groups. Their suitability was assessed by personalized cosinor analysis of core clock genes (BMAL1, PER2, CRY1), demonstrating that appropriate normalization enables detection of circadian oscillations in clinical samples. Using the optimal normalization, CosinorPy analysis of the core clock genes revealed significant circadian oscillations of at least one clock gene in the studied participants. These findings establish ACTB and RPL13A as robust reference genes for blood-based circadian studies of OSA and provide an important methodological framework for future circadian biomarker research.

Circadian rhythm disruptions are increasingly recognized in disorders such as obstructive sleep apnea (OSA), yet analysis of 24-hour gene expression patterns remains challenging due to the lack of reliable reference genes for normalization. Even commonly used housekeeping genes may exhibit circadian oscillations, which can confound rhythmic gene expression analyses and hinder biomarker identification. To address this limitation, we evaluated the gene expression stability of 11 commonly used housekeeping genes in blood collected every 6 hours over 24-hour period from 40 adults with varying OSA severity and controls. Stability ranking by analytical tools RefFinder and EndoGeneAnalyzer identified ACTB (β-actin) and RPL13A (ribosomal protein L13a) as the most consistent reference genes, with minimal intra- and inter-individual variability across sampling times and disease groups. Their suitability was assessed by personalized cosinor analysis of core clock genes (BMAL1, PER2, CRY1), demonstrating that appropriate normalization enables detection of circadian oscillations in clinical samples. Using the optimal normalization, CosinorPy analysis of the core clock genes revealed significant circadian oscillations of at least one clock gene in the studied participants. These findings establish ACTB and RPL13A as robust reference genes for blood-based circadian studies of OSA and provide an important methodological framework for future circadian biomarker research.

Obstructive sleep apnea (OSA), one of the most common breathing disorders during sleep, is associated with circadian rhythm disruption, yet circadian genes expressed in blood remain largely unexplored as biomarkers. Reliable circadian gene expression analysis requires stable reference genes for qRT-PCR normalization, but no clear guidance exists for blood-based circadian studies in OSA. We evaluated the expression of 11 candidate reference genes ( GAPDH, ACTB, RPL13A, PPIB, TBP, HPRT1, PPIA, SDHA, TUBB2A, UBC ) in whole blood collected every 6 hours over 24 hours from 40 participants who underwent overnight respiratory polygraphy. Gene stability was assessed with RefFinder and EndoGeneAnalyzer across OSA severity levels, intra-individual variability, and time-point variability. ACTB and RPL13A consistently emerged as the most stable reference genes under all conditions. Their robustness was confirmed by personalized cosinor analysis of core clock genes ( BMAL1, PER2, CRY1 ), showing that reliable normalization enables detection of circadian oscillations in clinical samples. While population-level analysis revealed no significant rhythmicity, individual profiles showed oscillations in 25 participants, independent of OSA severity. These findings identify ACTB and RPL13A as strong candidates for circadian transcriptomic studies in blood and provide a methodological foundation for biomarker discovery in sleep medicine, molecular medicine, and translational chronobiology.

Lanosterol 14α-demethylase (CYP51) is an enzyme involved in cholesterol synthesis, crucial for the normal liver function. Diminished activity of CYP51 leads to metabolism associated liver disease, ending in hepatocellular carcinoma. It is still not clear which processes are most affected in the hepatocytes and how they communicate with other cells towards the progressive liver pathology. Herein we describe a new inducible, liver-specific Cyp51 knockout mouse model (iLKO), developed to study how acute disruption of cholesterol synthesis is managed in the adult liver. Doxycycline inducible deletion avoided developmental confounders of albumin-Cre models and enabled isolation of viable primary hepatocytes. iLKO hepatocytes and liver tissue showed CYP51 depletion with accumulation of lanosterol and 24,25-dihydrolanosterol, while hepatic cholesterol levels remained largely unchanged, indicating compensatory uptake and/or pathway rerouting. Histological examination and transmission electron microscopy (TEM) revealed hepatomegaly with mild portal inflammation and ductular reaction but no overt fibrosis at the studied time points. iLKO hepatocytes displayed increased nuclear lipid droplets (LD) that might be involved in adaptation to endoplasmic reticulum (ER) stress. Additionally, we discovered crystal-like inclusions, especially in Kupffer cells. MALDI-TOF MSI could not resolve their composition, but their occurrence alongside sterol overload implicates non-cholesterol sterol crystallization as a potential trigger of inflammation. In summary, iLKO is a suitable model for dissection of sterol toxicity, clearly separated from developmental effects of Cyp51 depletion. Future examinations could reveal how toxic sterol intermediates are buffered in adult liver and how they might be connected to inflammation driven pathologies, relevant also to human health.

Circadian rhythms are intrinsic, with roughly 24 h oscillations that coordinate many physiological functions and are increasingly recognized as key determinants of human health. When these rhythms become misaligned, there is an increased risk for neurodegenerative and psychiatric disorders, metabolic syndrome, sleep disturbances, and even certain cancers. The hormones, melatonin that rises in the evening and cortisol that peaks shortly after awakening, represent crucial biochemical markers of the circadian phase. This review systematically evaluates contemporary techniques for quantifying melatonin and cortisol, comparing biological matrices (blood, saliva, urine) alongside analytical platforms. Special focus is placed on two clinically informative markers: Dim Light Melatonin Onset (DLMO) and the Cortisol Awakening Response (CAR). We compared immunoassays with liquid chromatography tandem mass spectrometry (LC MS/MS), highlighting differences in sensitivity, specificity, and laboratory feasibility. Potential confounders, including ambient light, body posture, and exact sampling times—are discussed in detail, to show the capacity of providing the most reliable results. By emphasizing the need for standardized protocols and controlled sampling conditions, this review provides essential guidance for researchers and clinicians aiming to assess the circadian biomarkers melatonin and cortisol with precision since they can be used in clinical practice as diagnostic and prognostic tools for assessing numerous pathologies.

Circadian rhythms are intrinsic, with roughly 24-hour oscillations that coordinate many physiological functions and are increasingly recognized as key determinants of human health. When these rhythms become misaligned, there is an increased risk for neuro-degenerative and psychiatric disorders, metabolic syndrome, sleep disturbances, and even certain cancers. The hormones, melatonin that rises in the evening and cortisol that peaks shortly after awakening, represent crucial biochemical markers of the circadian phase. This review systematically evaluates contemporary techniques for quantifying melatonin and cortisol, comparing biological matrices (blood, saliva, urine) alongside analytical platforms. Special focus is placed on two clinically informative markers: Dim Light Melatonin Onset (DLMO) and the Cortisol Awakening Response (CAR). We com-pared immunoassays with liquid chromatography tandem mass spectrometry (LC MS/MS), highlighting differences in sensitivity, specificity, and laboratory feasibility. Po-tential confounders, including ambient light, body posture, and exact sampling times—are discussed in detail, to show the capacity of providing the most reliable results. By emphasizing the need for standardized protocols and controlled sampling conditions, this review provides essential guidance for researchers and clinicians aiming to assess the circadian biomarkers melatonin and cortisol with precision since they can be used in clinical practice as diagnostic and prognostic tools for assessing numerous pathologies.

Background/Objectives: In recent years, interest in studying changes in cancer metabolites has resulted in significant advances in the metabolomics field. Glioblastoma remains the most aggressive and lethal brain malignancy, which presents with notable metabolic reprogramming. Methods: We performed literature research from the PubMed database and considered research articles focused on the key metabolic pathways altered in glioblastoma (e.g., glycolysis, lipid metabolism, TCA cycle), the role of oncometabolites and metabolic plasticity, and the differential expression of metabolites in glioblastoma. Currently used metabolomics approaches can be either targeted, focusing on specific metabolites and pathways, or untargeted, which involves data-driven exploration of the metabolome and also results in the identification of new metabolites. Data processing and analysis is of great importance and can be improved with the integration of machine learning approaches for metabolite identification. Results: Changes in α/β-glucose, lactate, choline, and 2-hydroxyglutarate were detected in glioblastoma compared with non-tumor tissues. Different metabolites such as fumarate, tyrosine, and leucine, as well as citric acid, isocitric acid, shikimate, and GABA were detected in blood and CSF, respectively. Conclusions: Although promising new technological and bioinformatic approaches help us understand glioblastoma better, challenges associated with biomarker availability, tumor heterogeneity, interpatient variability, standardization, and reproducibility still remain. Metabolomics research, either alone or combined with genomics or proteomics (i.e., multiomics) in glioblastoma, can lead to biomarker identification, tracking of metabolic therapy response, discovery of novel metabolites and pathways, and identification of potential therapeutic targets.

Facilitated electrochemical properties of plasma are crucial for improving functional nanomaterials synthesis and design for novel sensor development. Controlled customization and functionality tailoring at multiscale is the targeted challenge for better detection devices performance. In the modern era of optoelectronics, among various probing approaches, plasmonic sensing accompanied by Raman, FTIR or Fluorescent spectroscopy is considered a tool for the efficient investigation of molecular properties. In particular, Raman scattering is often called SERS (surface-enhanced Raman scattering) due to plasmonic enhancers, which are used to improve the probing of analytes at a reduced scale. This feature is extremely useful for biologically relevant analytical tasks when samples alike DNA and similar need to be investigated fast and accurately. DNA/RNA-related investigations are highly important in modern nanomedicine due to the solid progress of bionanotechnology, DNA vaccines, and functional gene engineering. DNA is the most valued substance for microbiologists because it contains information about bio-species' nature.[1] Currently, the number of molecular-sensitive methods for DNA-level study is quite limited, among which PCR (polymer chain reaction) related methods dominate the field. However, it is challenging to investigate it with optical techniques like SERS. Despite being promising, SERS is still under technological and analytical improvement. Better performance comes with sophisticated plasmonic nanomaterials, which provide considerably improved photon scattering due to the strongly localized field confinement effect.^[2] In the current report, a well-performing nanoplasmonic sensor was designed using a plasma-driven electrochemical reduction mechanism. Exploiting atmospheric pressure He-plasma jet setup operating kHz frequency, the reactive oxidative species donated electrons via liquid-gas electrochemistry towards the Ar-delivered Au(3+)-containing microdroplets. Through this method, microscaled aggregates composed of AuNPs were obtained from the nebulized ionic gold liquid precursor. Due to the strong coupling between nanoparticles, a high analytical enhancement factor of about 10⁷ was achieved and the substrate allowed to obtain Raman fingerprints of bacterial DNA fragments (M. luteus and S. aureus, E. coli, J. lividum) at nanovolume sample quantities. In addition, main DNA molecular vibrational signatures associated with nucleobase motions were extracted from spectra collected and used to classify the bacterial strains reliably by employing statistical principal component analysis. References [1] Garcia-Rico, E., Alvarez-Puebla, R. A., & Guerrini, L. (2018). Chemical Society Reviews, 47(13), 4909-4923. [2] Shvalya, V., Filipič, G., Zavašnik, J., Abdulhalim, I., & Cvelbar, U. (2020). Applied Physics Reviews, 7(3), 031307.