Joanna Polanska

Bulky DNA adducts in human sperm: relationship with fertility, semen quality, smoking, and environmental factors

The integrity of DNA of spermatogenic cells can be affected by endogenous and exogenous genotoxic factors. Resulting DNA damage in spermatozoa may significantly contribute to impaired fertility. Here, the 32P-postlabeling method was used to analyze the levels of bulky DNA adducts in sperm cells in a group of 179 males, either healthy donors or patients with an impaired fertility. When all donors were analyzed, the levels of bulky DNA adducts was 1.2-fold higher in smokers than in non-smokers, but the difference was not statistically significant (P=0.054). However, a statistically significant difference existed between current smokers and never smokers among the healthy individuals (1.7-fold increase, P=0.008). No correlation between alcohol or coffee consumption and sperm DNA adducts was found. The levels of DNA adducts in sperm seemed to be unaffected by environmental and occupational factors. On the other hand, groups of healthy persons and patients with male-factor infertility differed significantly with respect to the level of bulky DNA adducts (P=0.012). A significant negative correlation between DNA adducts and sperm concentration or sperm motility existed among patients with an impaired fertility (n=93; P<0.029, r(S)=-0.225). These results suggest that DNA adducts in sperm cells can be applied as potential biomarkers in studies of human infertility.

Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer

BackgroundMass spectrometric analysis of the blood proteome is an emerging method of clinical proteomics. The approach exploiting multi-protein/peptide sets (fingerprints) detected by mass spectrometry that reflect overall features of a specimens proteome, termed proteome pattern analysis, have been already shown in several studies to have applicability in cancer diagnostics. We aimed to identify serum proteome patterns specific for early stage breast cancer patients using MALDI-ToF mass spectrometry.MethodsBlood samples were collected before the start of therapy in a group of 92 patients diagnosed at stages I and II of the disease, and in a group of age-matched healthy controls (104 women). Serum specimens were purified and the low-molecular-weight proteome fraction was examined using MALDI-ToF mass spectrometry after removal of albumin and other high-molecular-weight serum proteins. Protein ions registered in a mass range between 2,000 and 10,000 Da were analyzed using a new bioinformatic tool created in our group, which included modeling spectra as a sum of Gaussian bell-shaped curves.ResultsWe have identified features of serum proteome patterns that were significantly different between blood samples of healthy individuals and early stage breast cancer patients. The classifier built of three spectral components that differentiated controls and cancer patients had 83% sensitivity and 85% specificity. Spectral components (i.e., protein ions) that were the most frequent in such classifiers had approximate m/z values of 2303, 2866 and 3579 Da (a biomarker built from these three components showed 88% sensitivity and 78% specificity). Of note, we did not find a significant correlation between features of serum proteome patterns and established prognostic or predictive factors like tumor size, nodal involvement, histopathological grade, estrogen and progesterone receptor expression. In addition, we observed a significantly (p = 0.0003) increased level of osteopontin in blood of the group of cancer patients studied (however, the plasma level of osteopontin classified cancer samples with 88% sensitivity but only 28% specificity).ConclusionMALDI-ToF spectrometry of serum has an obvious potential to differentiate samples between early breast cancer patients and healthy controls. Importantly, a classifier built on MS-based serum proteome patterns outperforms available protein biomarkers analyzed in blood by immunoassays.

Influence of Polymorphisms in DNA Repair Genes XPD, XRCC1 and MGMT on DNA Damage Induced by Gamma Radiation and its Repair in Lymphocytes In Vitro

Abstract Rzeszowska-Wolny, J., Polanska, J., Pietrowska, M., Palyvoda, O., Jaworska, J., Butkiewicz, D. and Hancock, R. Influence of Polymorphisms in DNA Repair Genes XPD, XRCC1 and MGMT on DNA Damage Induced by Gamma Radiation and its Repair in Lymphocytes In Vitro. Radiat. Res. 164, 132– 140 (2005). DNA single-strand breaks (SSBs) were quantified by single-cell gel electrophoresis and micronucleated and apoptotic cells were quantified by microscopic assays in peripheral blood lymphocytes after irradiation on ice with 2 Gy of 60Co γ radiation, and their association with polymorphisms of genes that encode proteins of different DNA repair pathways and influence cancer risk (XPD codon 312Asp → Asn and 751Lys → Gln, XRCC1 399Arg → Gln, and MGMT 84Leu → Phe) was studied. In unirradiated lymphocytes, SSBs were significantly more frequent in individuals older than the median age (52 years) (P = 0.015; n = 81), and the frequency of apoptotic or micronucleated cells was higher in individuals with alleles coding for Asn at XPD 312 or Gln at 751 (P = 0.030 or 0.023 ANOVA, respectively; n = 54). The only polymorphism associated with the background SSB level was MGMT 84Phe (P = 0.04, ANOVA; n = 66). After irradiation, SSB levels and repair parameters did not differ significantly with age or smoking habit. The SSB level varied more than twofold and the repair rate and level of unrepaired SSBs more than 10-fold between individuals. The presence of variant alleles coding for Asn at XPD 312 was associated with more radiation-induced SSBs (P = 0.014) and fewer unrepaired SSBs (P = 0.008), and the phenotype (>median induced SSBs/<median unrepaired SSBs) was seen in the majority of XPD 312Asn/Asn homozygotes; the odds ratio for variant homozygotes to show this phenotype was 5.2 (95% confidence interval 1.4–19.9). The hypothesis is discussed that XPD could participate in repair of ionizing radiation-induced DNA damage. While it cannot be excluded that the effects observed are due to cosegregating polymorphisms or that the responses of lymphocytes are not typical of other cell types, the results suggest that polymorphism of DNA repair genes, particularly XPD, is one factor implicated in the variability of responses to ionizing radiation between different individuals.

Bystander Effects Induced by Medium From Irradiated Cells: Similar Transcriptome Responses in Irradiated and Bystander K562 Cells

PURPOSE Cells exposed to ionizing radiation release factors that induce deoxyribonucleic acid damage, chromosomal instability, apoptosis, and changes in the proliferation rate of neighboring unexposed cells, phenomena known as bystander effects. This work analyzes and compares changes in global transcript levels induced by direct irradiation and by bystander effects in K562 (human erythroleukemia) cells. METHODS AND MATERIALS Cells were X-irradiated with 4 Gy or transferred into culture medium collected from cells 1 h after irradiation (irradiation-conditioned medium). Global transcript profiles were assessed after 36 h of growth by use of Affymetrix microarrays (Affymetrix, Santa Clara, CA) and the kinetics of change of selected transcripts by quantitative reverse transcriptase-polymerase chain reaction. RESULTS The level of the majority (72%) of transcripts changed similarly (increase, decrease, or no change) in cells grown in irradiation-conditioned medium or irradiated, whereas only 0.6% showed an opposite response. Transcript level changes in bystander and irradiated cells were significantly different from those in untreated cells grown for the same amount of time and were confirmed by quantitative reverse transcriptase-polymerase chain reaction for selected genes. Signaling pathways in which the highest number of transcripts changed in both conditions were found in the following groups: neuroactive ligand-receptor, cytokine-cytokine receptor interaction, Janus Kinase-Signal Transducers and Activators of Transcription (JAK-STAT) and Mitogen-Activated Protein Kinase (MAPK) In control cells more transcripts were downregulated than in irradiated and bystander cells with transcription factors YBX1 and STAT5B, heat shock protein HSPA1A, and ribonucleic acid helicase DDX3X as examples. CONCLUSIONS The transcriptomes of cells grown in medium from X-irradiated cells or directly irradiated show very similar changes. Signals released by irradiated cells may cause changes in the transcriptome of neighboring cells that sustain their survival.

Adaptive filtering of microarray gene expression data based on Gaussian mixture decomposition

BackgroundDNA microarrays are used for discovery of genes expressed differentially between various biological conditions. In microarray experiments the number of analyzed samples is often much lower than the number of genes (probe sets) which leads to many false discoveries. Multiple testing correction methods control the number of false discoveries but decrease the sensitivity of discovering differentially expressed genes. Concerning this problem, filtering methods for improving the power of detection of differentially expressed genes were proposed in earlier papers. These techniques are two-step procedures, where in the first step some pool of non-informative genes is removed and in the second step only the pool of the retained genes is used for searching for differentially expressed genes.ResultsA very important parameter to choose is the proportion between the sizes of the pools of removed and retained genes. A new method, which we propose, allow to determine close to optimal threshold values for sample means and sample variances for gene filtering. The method is adaptive and based on the decomposition of the histogram of gene expression means or variances into mixture of Gaussian components.ConclusionsBy performing analyses of several publicly available datasets and simulated datasets we demonstrate that our adaptive method increases sensitivity of finding differentially expressed genes compared to previous methods of filtering microarray data based on using fixed threshold values.

Comparison of peptide cancer signatures identified by mass spectrometry in serum of patients with head and neck, lung and colorectal cancers: Association with tumor progression

Mass spectrometry-based analyses of the low-molecular-weight fraction of serum proteome allow identifying proteome profiles (signatures) that are potentially useful in detection and diagnostics of cancer. Here we compared serum proteome profiles of healthy donors and patients with three different types of cancer aiming to identify peptide signatures that were either common for all cancer samples or specific for cancer type. Blood samples were collected before start of the therapy from patients with head and neck squamous cell cancer, colorectal adenocarcinoma and non-small cell lung cancer, and from a corresponding group of healthy volunteers. Mass profiles of the serum proteome were recorded in the range between 2 and 13 kDa using MALDI-ToF spectrometry and 131 identified peptide ions were used for statistical analyses. Similar degrees of overall similarities were observed in all intra-group and inter-group analyses when general features of serum proteome profiles were compared between individual samples. However, classifiers built of selected spectral components allowed differentiation between healthy donors and three groups of cancer patients with 69-74% sensitivity and 82-84% specificity. There were two common peptide species (3766 and 5867 Da) with increased levels in all cancer samples. Several spectral components permitted differentiation between lung cancer samples and either head and neck cancer or colorectal cancer samples, but two latter types of samples could not be properly discriminated. Abundance of spectral components that putatively corresponded to fragments of serum amyloid A (11511 and 11667 Da) was highest in lung cancer samples, yet increased levels of these peptides appeared to generally associate with more advanced cancer cases. We concluded that certain components of serum peptide signatures are common for different cancer signatures and putatively reflect general response of organism to the disease, yet other components of such signatures are more specific and most likely correspond to clinical stage of the malignancy.

DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images

We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation. The practicality of current learning-based automated tissue classification approaches is severely impeded by their dependency on manually segmented training databases that need to be recreated for each scenario of application, site, or acquisition setup. The comprehensive annotation of reference datasets can be highly labor-intensive, complex, and error-prone. The proposed method derives high-quality classifiers for the different tissue classes from sparse and unambiguous annotations and employs domain adaptation techniques for effectively correcting sampling selection errors introduced by the sparse sampling. The new approach is validated on labeled, multi-modal MR images of 19 patients with malignant gliomas and by comparative analysis on the BraTS 2013 challenge data sets. Compared to training on fully labeled data, we reduced the time for labeling and training by a factor greater than 70 and 180 respectively without sacrificing accuracy. This dramatically eases the establishment and constant extension of large annotated databases in various scenarios and imaging setups and thus represents an important step towards practical applicability of learning-based approaches in tissue classification.

Mass spectrometry-based analysis of therapy-related changes in serum proteome patterns of patients with early-stage breast cancer

BackgroundThe proteomics approach termed proteome pattern analysis has been shown previously to have potential in the detection and classification of breast cancer. Here we aimed to identify changes in serum proteome patterns related to therapy of breast cancer patients.MethodsBlood samples were collected before the start of therapy, after the surgical resection of tumors and one year after the end of therapy in a group of 70 patients diagnosed at early stages of the disease. Patients were treated with surgery either independently (26) or in combination with neoadjuvant chemotherapy (5) or adjuvant radio/chemotherapy (39). The low-molecular-weight fraction of serum proteome was examined using MALDI-ToF mass spectrometry, and then changes in intensities of peptide ions registered in a mass range between 2,000 and 14,000 Da were identified and correlated with clinical data.ResultsWe found that surgical resection of tumors did not have an immediate effect on the mass profiles of the serum proteome. On the other hand, significant long-term effects were observed in serum proteome patterns one year after the end of basic treatment (we found that about 20 peptides exhibited significant changes in their abundances). Moreover, the significant differences were found primarily in the subgroup of patients treated with adjuvant therapy, but not in the subgroup subjected only to surgery. This suggests that the observed changes reflect overall responses of the patients to the toxic effects of adjuvant radio/chemotherapy. In line with this hypothesis we detected two serum peptides (registered m/z values 2,184 and 5,403 Da) whose changes correlated significantly with the type of treatment employed (their abundances decreased after adjuvant therapy, but increased in patients treated only with surgery). On the other hand, no significant correlation was found between changes in the abundance of any spectral component or clinical features of patients, including staging and grading of tumors.ConclusionsThe study establishes a high potential of MALDI-ToF-based analyses for the detection of dynamic changes in the serum proteome related to therapy of breast cancer patients, which revealed the potential applicability of serum proteome patterns analyses in monitoring the toxicity of therapy.

Epidemiology of type 1 diabetes among Silesian children aged 0–14 years, 1989–2005

The aim of this study was to estimate the present Polish incidence rate of diabetes mellitus type 1 in children aged 0–14. The research was conducted between 1989 and 2005 among the children of Upper Silesia region (Poland), being the part of the EURODIAB program, according to all criteria of this project. During this period, 1,385 new cases (720 boys) of diabetes mellitus type 1 were recognized. The analysis of the standardized incidence rates performed after dividing into shorter periods (1989–1994, 1995–1999, 2000–2005) showed a sharp increase from 5.80/105/year through 9.54/105/year to 15.26/105/year, respectively, in the periods. Analysis of age subgroups showed the greatest increase in the incidence rate among the younger children: 3.59 times for children aged 0–4, 3.40 times for children aged 5–9 and 2.08 times for children aged 10–14. No significant difference of incidence rate between boys and girls was established. Such high increase of incidence rate of diabetes mellitus type 1 (above 260%) noted since 1989 shows a secular trend of an epidemic of diabetes mellitus type 1 in Poland and a conversion from countries with the lower incidence rates in Europe to the countries with the highest incidence rates.

Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry

Mixture - modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, de-noising, feature extraction and spectral signal compression. However, existing algorithms do not allow for automated analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in several papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analyses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteomic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automated partitioning of protein mass spectral signal into fragments. The obtained fragments are separately decomposed into Gaussian mixture models. The parameters of the mixture models of fragments are then aggregated to form the mixture model of the whole spectrum. We compare the elaborated algorithm to existing algorithms for peak detection and we demonstrate improvements of peak detection efficiency obtained by using Gaussian mixture modeling. We also show applications of the elaborated algorithm to real proteomic datasets of low and high resolution.