Ilona Dudka

Dr. Ilona Dudka

orcid

Research Focus & Metrics

Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 July 2014 in Environment International
Verified
Journal Article
Closed access
IDIlona Dudka
Ilona Dudka
BKBarbara Kossowska
Barbara Kossowska
HSHanna Senhadri
Hanna Senhadri
RLRafał Latajka
Rafał Latajka
JHJulianna Hajek
Julianna Hajek
RGRoman Gancarz
Roman Gancarz

18 May 2020 in BMC Cancer
Verified
Journal Article
Open access
PDF file
IDIlona Dudka
Ilona Dudka
ETElin Thysell
Elin Thysell
KLKristina Lundquist
Kristina Lundquist
HAHenrik Antti
Henrik Antti
DIDiego Iglesias-Gato
Diego Iglesias-Gato
GGGerhard Gröbner
Gerhard Gröbner
Background: Prostate cancer (PC) can display very heterogeneous phenotypes ranging from indolent asymptomatic to aggressive lethal forms. Understanding how these PC subtypes vary in their striving for energy and anabolic molecules is of fundamental importance for developing more effective therapies and diagnostics. Here, we carried out an extensive analysis of prostate tissue samples to reveal metabolic alterations during PC development and disease progression and furthermore betweenTMPRSS2-ERGrearrangement-positive and -negative PC subclasses.Methods: Comprehensive metabolomics analysis of prostate tissue samples was performed by non-destructive high-resolution magic angle spinning nuclear magnetic resonance (1H HR MAS NMR). Subsequently, samples underwent moderate extraction, leaving tissue morphology intact for histopathological characterization. Metabolites in tissue extracts were identified by1H/31P NMR and liquid chromatography-mass spectrometry (LC-MS). These metabolomics profiles were analyzed by chemometric tools and the outcome was further validated using proteomic data from a separate sample cohort.Results: The obtained metabolite patterns significantly differed between PC and benign tissue and between samples with high and low Gleason score (GS). Five key metabolites (phosphocholine, glutamate, hypoxanthine, arginine and α-glucose) were identified, who were sufficient to differentiate between cancer and benign tissue and between high to low GS. In ERG-positive PC, the analysis revealed several acylcarnitines among the increased metabolites together with decreased levels of proteins involved in β-oxidation; indicating decreased acyl-CoAs oxidation in ERG-positive tumors. The ERG-positive group also showed increased levels of metabolites and proteins involved in purine catabolism; a potential sign of increased DNA damage and oxidative stress.Conclusions: Our comprehensive metabolomic analysis strongly indicates that ERG-positive PC and ERG-negative PC should be considered as different subtypes of PC; a fact requiring different, sub-type specific treatment strategies for affected patients.

1 April 2021 in Journal of Pharmaceutical and Biomedical Analysis
Verified
Journal Article
Closed access
IDIlona Dudka
Ilona Dudka
ACAngelika Chachaj
Angelika Chachaj
ASAgata Sebastian
Agata Sebastian
WTWojciech Tański
Wojciech Tański
HSHans Stenlund
Hans Stenlund
ASAndrzej Szuba
Andrzej Szuba

1 January 2013 in International Journal of Hygiene and Environmental Health
Verified
Journal Article
Closed access
BKBarbara Kossowska
Barbara Kossowska
IDIlona Dudka
Ilona Dudka
RGRoman Gancarz
Roman Gancarz
JAJolanta Antonowicz-Juchniewicz
Jolanta Antonowicz-Juchniewicz

1 July 2019 in Molecular and Cellular Neuroscience
Verified
Journal Article
Closed access
AVAna Virel
Ana Virel
IDIlona Dudka
Ilona Dudka
RLRutger Laterveer
Rutger Laterveer
SBSara Af Bjerkén
Sara Af Bjerkén

11 November 2020 in Diagnostics
Verified
Journal Article
Open access
PDF file
ACAngelika Chachaj
Angelika Chachaj
RMRafał Matkowski
Rafał Matkowski
GGGerhard Gröbner
Gerhard Gröbner
ASAndrzej Szuba
Andrzej Szuba
IDIlona Dudka
Ilona Dudka
There is growing evidence that lymphatic system plays a pivotal role in the pathogenesis of hypertension. Here, for the first time, the metabolome of interstitial fluid is analyzed in patients with arterial hypertension. Due to ethical issues to obtain human interstitial fluid samples, this study included only oncological patients after axillary lymph node dissection (ALND). These patients were matched into hypertensive (n = 29) and normotensive (n = 35) groups with similar oncological status. Simultaneous evaluation of interstitial fluid, plasma, and urine was obtained by combining high-resolution proton nuclear magnetic resonance (1H NMR) spectroscopy with chemometric analysis. Orthogonal partial least squares discriminant analysis (OPLS-DA) provided a clear differentiation between the hypertension and normotensive group, with the discrimination visible in each biofluid. In interstitial fluid nine potential metabolomic biomarkers for hypertension could be identified (creatinine, proline, pyroglutamine, glycine, alanine, 1-methylhistidine, the lysyl group of albumin, threonine, lipids), seven distinct markers in plasma (creatinine, mannose, isobutyrate, glycine, alanine, lactate, acetate, ornithine), and seven respectively in urine (methylmalonate, citrulline, phenylacetylglycine, fumarate, citrate, 1-methylnicotinamide, trans-aconitate). Biomarkers in plasma and urine allowed for the identification of specific biochemical pathways involved in hypertension, as previously suggested. Analysis of the interstitial fluid metabolome provided additional biomarkers compared to plasma or urine. Those biomarkers reflected primarily alterations in the metabolism of lipids and amino acids, and indicated increased levels of oxidative stress/inflammation in patients with hypertension.