Genetic, Genomic and Epigenomic Studies of Balkan Endemic Nephropathy (Ben).

BEN is a primary, chronic tubulointerstitial nephritis characterized with chronic anemia, absence of edema, xantoderma, normal blood pressure and normal findings on the fundus oculi. The disease is distributed in restricted areas in Bulgaria, Romania, Croatia, Bosnia, Former Yugoslavia. Despite numerous studies on genetic and environmental factors and their possible involvement in BEN, its etiopathogenesis still remains elusive. Our recent study aim to elucidate the possible epigenetic component in BEN development. Whole genome DNA array methylation analysis was applied to compare the methylation profiles of male and female BEN patients from endemic regions in Bulgaria and Serbia and healthy controls. All three most prominent candidate genes with aberrations in the epigenetic profile discovered with this study are involved in the inflammatory/immune processes and oncogenesis. These data are in concordance with the reported pathological alterations in BEN. This research supports the role of epigenetic changes in BEN pathology. Exome sequencing of 22.000 genes with Illumina Nextera Exome Enrichment Kit revealed three mutant genes (CELA1, HSPG2, and KCNK5) in BEN patients which encode proteins involved in basement membrane/extracellular matrix and vascular tone, tightly connected to process of angiogenesis. We suggest that an abnormal process of angiogenesis plays a key role in the molecular pathogenesis of BEN.

NGS nominated CELA1, HSPG2, and KCNK5 as candidate genes for predisposition to Balkan endemic nephropathy.

Balkan endemic nephropathy (BEN) is a familial chronic tubulointerstitial disease with insidious onset and slow progression leading to terminal renal failure. The results of molecular biological investigations propose that BEN is a multifactorial disease with genetic predisposition to environmental risk agents. Exome sequencing of 22 000 genes with Illumina Nextera Exome Enrichment Kit was performed on 22 DNA samples (11 Bulgarian patients and 11 Serbian patients). Software analysis was performed via NextGene, Provean, and PolyPhen. The frequency of all annotated genetic variants with deleterious/damaging effect was compared with those of European populations. Then we focused on nonannotated variants (with no data available about them and not found in healthy Bulgarian controls). There is no statistically significant difference between annotated variants in BEN patients and European populations. From nonannotated variants with more than 40% frequency in both patients' groups, we nominated 3 genes with possible deleterious/damaging variants--CELA1, HSPG2, and KCNK5. Mutant genes (CELA1, HSPG2, and KCNK5) in BEN patients encode proteins involved in basement membrane/extracellular matrix and vascular tone, tightly connected to process of angiogenesis. We suggest that an abnormal process of angiogenesis plays a key role in the molecular pathogenesis of BEN.

Surface water quality assessment by the use of combination of multivariate statistical classification and expert information.

The present study deals with the assessment of surface water quality from an industrial-urban region located in northern Poland near to the city of Gdansk. Concentrations of thirteen chemicals including total polycyclic aromatic hydrocarbons (PAHs), halogenated volatile organic compounds (HVOCs) and major ions in the samples collected at five sampling points during six campaigns were used as variables throughout the study. The originality in the monitoring data treatment and interpretation was the combination of a traditional classification approach (self-organizing maps of Kohonen) with PAH diagnostic ratios expertise to achieve a reliable pollution source identification. Thus, sampling points affected by pollution from traffic (petroleum combustion products), from crude oil processing (petroleum release related compounds), and from phosphogypsum disposal site were properly discriminated. Additionally, it is shown that this original assessment approach can be useful in finding specific pollution source tracers.

Multivariate classification and modeling in surface water pollution estimation.

The present study deals with the application of self-organizing maps (SOM) and multiway principal-components analysis to classify, model, and interpret a large monitoring data set for surface water quality. The chemometric methods applied made it possible to reveal specific quality patterns of the chemical and biological parameters used to monitor the water quality (relation between water temperature, turbidity, hardness, colibacteria), seasonal impacts during the long period of observation and the relative independence on the spatial location of the sampling sites (water supply sources for the City of Trieste).

Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets.

Three classification techniques (loading and score projections based on principal components analysis (PCA), cluster analysis (CA) and self-organizing maps (SOM)) were applied to a large environmental data set of chemical indicators of river water quality. The study was carried out by using long-term water quality monitoring data. The advantages of SOM algorithm and its classification and visualization ability for large environmental data sets are stressed. The results obtained allowed detecting natural clusters of monitoring locations with similar water quality type and identifying important discriminant variables responsible for the clustering. SOM clustering allows simultaneous observation of both spatial and temporal changes in water quality. The chemometric approach revealed different patterns of monitoring sites conditionally named "tributary", "urban", "rural" or "background". This objective separation could lead to an optimization of river monitoring nets and to a better tracing natural and anthropogenic changes along the river stream.

Linear-dichroic infrared spectroscopy - validation and experimental design of the new orientation technique of solid samples as suspension in nematic liquid crystal.

A validation of the developed new orientation method of solid samples as suspension in nematic liquid crystal (NLC), applied in linear-dichroic infrared (IR-LD) spectroscopy has been carried out using a model system DL-isoleucine (DL-isoleu). Accuracy, precision and the influence of the liquid crystal medium on peak positions and integral absorbances of guest molecules have been presented. Optimization of experimental conditions has been performed as well. An experimental design for quantitative evaluation of the impact of four input factors: the number of scans, the rubbing-out of KBr-pellets, the amount of studied compounds included in the liquid crystal medium and the ratios of Lorentzian to Gaussian peak functions in the curve fitting procedure on the spectroscopic signal at five different frequencies, indicating important specifities of the system has been studied.

Open path atmospheric spectroscopy using room temperature operated pulsed quantum cascade laser.

We report the application of a distributed feedback quantum cascade laser for 5.8 km long open path spectroscopic monitoring of ozone, water vapor and CO(2). The thermal chirp during a 140 or 200 ns long excitation pulse is used for fast wavelength scanning. The fast wavelength scanning has the advantage of the measured spectra not being affected by atmospheric turbulence, which is essential for long open path measurements. An almost linear tuning of about 0.6 and 1.2 cm(-1) is achieved, respectively. Lines from the nu(3) vibrational band of the ozone spectra centered at 1,031 and 1,049 cm(-1) is used for ozone detection by differential absorption. The lowest column densities (LCD) for ozone of the order of 0.3 ppmm retrieved from the absorption spectra for averaging times less than 20s are better then the LCD value of 2 ppmm measured with UV DOAS systems. The intrinsic haze immunity of mid-IR laser sources is an additional important advantage of mid-IR open path spectroscopy, compared with standard UV-vis DOAS. The third major advantage of the method is the possibility to measure more inorganic and organic atmospheric species compared to the UV-vis DOAS.

Clinical markers in adult offspring of families with and without Balkan Endemic Nephropathy.

Balkan Endemic Nephropathy (BEN) is a kidney disease that progresses slowly. Only a few studies have investigated renal clinical markers in offspring of BEN families before the onset of the disease. This project aimed to determine whether kidney function and structure are altered in BEN offspring compared with non-BEN offspring. The study population consisted of 102 adult BEN offspring and a control group of 99 non-BEN offspring. We collected urine and blood samples, and conducted face-to-face interviews, physical examinations and ultrasound measurements of the kidney. Total protein, albumin, beta2-microglobulin and creatinine in urine, creatinine and urea in serum, and creatinine clearance (CCR) were determined. Two risk factors were assessed: first, the overall status of being an offspring from a BEN family, and second, the specific status of a mother and/or father with BEN. The data were analyzed using linear regression. After adjusting for confounders, we found that kidney length and minimal cortex width in BEN offspring were significantly decreased. Urine concentrations of total protein, albumin, and beta2-microglobulin were higher in BEN offspring. Regarding parental history, the associations were statistically significant only for the offspring of mothers who had BEN, with the exception of minimal cortex width, which showed no parental difference. For CCR, we did not identify a statistically significant effect for BEN offspring status nor for parental history. In conclusion, adult offspring of BEN families can be characterized by shorter kidney length and an increased excretion of albumin, total protein, and beta2-microglobulin, in particular, when the mother had BEN.

Multivariate statistical assessment of the pollution sources along the stream of Kamchia River, Bulgaria.

Sixteen sampling sites along the stream of Kamchia River were considered as environmental objects in the multivariate statistical study aimed to identify and apportion patterns of sampling sites, latent factors responsible for the data structure and their relation to the emitter industrial and anthropogenic sources in the vicinity of the sampling sites. As variables 11 surface water parameters monitored for a long time period (up to 11 years) were used. Four main site patterns were revealed by cluster analysis (urban, rural, near-to dam and estuary) and for each site latent factors were identified and apportioned (among them "metallurgical", "food production", "winery", domestic wastes", "natural"). The relative contribution of each identified pollution source to the formation of the total concentration of each chemical species or physicochemical parameter was determined and compared to the real emitters in the region of interest.

Chemometrical exploration of the wet precipitation chemistry from the Austrian Monitoring Network (1988-1999).

The present paper deals with the application of different chemometric methods to an environmental data set derived from the monitoring of wet precipitation in Austria (1988-1999). These methods are: principal component analysis (PCA); projection pursuit (PP); density-based spatial clustering of application with noise (DBSCAN); ordering points to identify the clustering structures (OPTICS); self-organizing maps (SOM), also called the Kohonen network; and the neural gas (NG) network. The aim of the study is to introduce some new approaches into environmetrics and to compare their usefulness with already existing techniques for the classification and interpretation of environmental data. The density-based approaches give information about the occurrence of natural clusters in the studied data set, which, however, do not occur in the case presented here; information about high-density zones (very similar samples) and extreme samples is also obtained. The partitioning techniques (clustering, but also neural gas and Kohonen networks) offer an opportunity to classify the objects of interest into several defined groups, the patterns of ionic concentration of which can be studied in detail. The visual aids, such as the color map and the Kohonen map, for each site are very helpful in understanding the relationships between samples and between samples and variables. All methods, and in particular projection pursuit, give information about samples with extreme characteristics.

Assessment of the surface water quality in Northern Greece.

The application of different multivariate statistical approaches for the interpretation of a large and complex data matrix obtained during a monitoring program of surface waters in Northern Greece is presented in this study. The dataset consists of analytical results from a 3-yr survey conducted in the major river systems (Aliakmon, Axios, Gallikos, Loudias and Strymon) as well as streams, tributaries and ditches. Twenty-seven parameters have been monitored on 25 key sampling sites on monthly basis (total of 22,350 observations). The dataset was treated using cluster analysis (CA), principal component analysis and multiple regression analysis on principal components. CA showed four different groups of similarity between the sampling sites reflecting the different physicochemical characteristics and pollution levels of the studied water systems. Six latent factors were identified as responsible for the data structure explaining 90% of the total variance of the dataset and are conditionally named organic, nutrient, physicochemical, weathering, soil-leaching and toxic-anthropogenic factors. A multivariate receptor model was also applied for source apportionment estimating the contribution of identified sources to the concentration of the physicochemical parameters. This study presents the necessity and usefulness of multivariate statistical assessment of large and complex databases in order to get better information about the quality of surface water, the design of sampling and analytical protocols and the effective pollution control/management of the surface waters.

Multivariate statistical study of simultaneously monitored cloud water, aerosol and rainwater data from different elevation levels in an Alpine valley (Achenkirch, Tyrol, Austria).

The present paper deals with data interpretation of monitoring of various atmospheric events (cloud water, aerosol and rainwater) at three different elevation levels at Achenkirch profile in an Alpine valley, Tyrol, Austria (Christlumkopf-1758 m, Christlumalm-1280 m and Talboden-930 m a.s.l.) by the use of principal components analysis. From October 1995 to September 1996 sampling sessions for all sites from the profile and for all events were performed for the major ions NH(4)(+), Na(+), K(+), Ca(2+), Mg(2+), Cl(-), NO(3)(-), SO(4)(2-)-44 cases with eight variables for rainwater; 117 cases with eight variables for cloud water samples and 50 cases with seven variables for aerosol (the major ions as in rain- and cloud water but without magnesium) at any of the elevations. The aim of the multivariate statistical treatment was to extract information about latent factors determining the data structure in all of the cases in order to compare and interpret similarities and dissimilarities with respect to the elevation or the type of the atmospheric event. Four latent factors seem to explain over 85% of the total variance for almost all sites and events but the factors have different identification for the different events or sites (e.g. 'anthropogenic', 'crustal', 'neutralization', 'salt'). Thus, a comparison between sites and between events becomes possible. It was found that cloud water and aerosol events are much more similar with respect to data structure (relevant to emission sources or processes of formation) than the same events and rainwater. Further, the upper sites of the profile (Christlumkopf and Christlumalm) also reveal data structure similarity differing from that of the lowest site Talboden.

Environmetric approaches to estimate pollution impacts on a coastal area by sediment and river water studies.

This paper represents an effort to demonstrate the opportunities of some environmetric methods like regression analysis, cluster analysis and principal components analysis. Their role for data modeling is stressed and the basic theoretical principles are given. The application of the multivariate statistical methods is illustrated by two major examples: Assessment of metal pollution based on multivariate statistical modeling of "hot spot" sediments from the Black Sea; and a trend study of Kamchia River water quality. In the first part of the study the environmetric approach makes it possible to separate three zones of the marine environment with different levels of pollution (Bourgas gulf, Varna gulf and lake buffer zone). Further, the extraction of four latent factors offers a specific interpretation of the possible pollution sources and separates the natural factors from the anthropogenic ones, the latter originating from contamination by chemical and steel-works and an oil refinery. In the second part of the study nine sampling sites along Kamchia River were considered as sources for water quality monitoring data. Trends for all parameters are calculated by the use of linear regression analysis and special attention is paid to a specific coastal site. Then five latent factors were extracted from the monitoring data set in order to gain information about some structural characteristics of the set.

Environmetric modeling and interpretation of river water monitoring data.

This environmetric study deals with modeling and interpretation of river water monitoring data from the basin of the Saale river and its tributaries the Ilm and the Unstrut. For a period of one year of observation between September 1993 and August 1994 a data set from twelve campaigns at twenty-nine sampling sites from the Saale river and six campaigns from the river Ilm at seven sampling sites and from river Unstrut at ten sampling sites was collected. Twenty-seven chemical and physicochemical properties were measured to estimate the water quality. The application of cluster analysis, principal components analysis, and apportioning modeling on absolute principal components scores revealed important information about the ecological status of the region of interest:identification of two separate patterns of pollution (upper and lower stream of the rivers);identification of six latent factors responsible for the data structure with different content for the two identified pollution patterns; anddetermination of the contribution of each latent factor (source of emission) to the formation of the total concentration of the chemical burden of the river water. As a result more objective ecological policy and decision making is possible.

Long-term assessment of the wet precipitation chemistry in Austria (1984-1999).

The aim of the present study was to determine the long-time trends in concentrations and depositions of major ions in wet precipitation samples collected at 11 sampling sites from the Austrian precipitation chemistry network in the period 1984-1999. The analytical results were treated by the use of least square linear regression method. It is shown that a serious decrease of sulfate (between 30% and 60% for the period) and hydrogen ion (between 60% and 102% for the period) concentrations and depositions is achieved at almost all sampling sites and in most of these cases the linear trend proves to be statistically significant. Nitrogen containing ions and base cations do not reveal a distinct trend of changing and in the majority of the sites the linear models are not adequate. In principle, an overall slight concentration and deposition decrease for these major ions is observed (up to 30% for the period of observation) but some substantial exceptions are also found (site Haunsberg or site Lobau). The changes in chloride concentration and deposition, too, do not indicate significant linear trend and, in general, are decreasing for the period of monitoring. In order to give some explanation of the exceptional behaviour of some of the major ions in several sites, an additional comparison with Austrian emission data (sulfur dioxide, nitrogen oxides, ammonium) and with data from five EMEP sites from neighbouring countries is performed. A significant West-East trend of acidity increase is found as well as a good correlation with the emission trends. Therefore, both transboundary and specific local factors could be substantial factors in the wet precipitation chemistry in the region.

Is the incidence of Balkan endemic nephropathy decreasing?

Balkan Endemic Nephropathy (BEN) is a non-inflammatory, slowly progressing, familial, primarily tubulo-interstitial, bilateral renal disease that affects rural populations in several Balkan countries. Our study describes a time trend of the incidence of BEN in eight villages of Vratza District, Bulgaria, for the period 1965-1987, based on three various data sets. The data suggest that after the initial peak between 1967 and 1970, the incidence remained quite stable for the period 1970-1984, and declined after 1984. However, the study also demonstrates under-recording of BEN cases and less complete case identification, especially after 1979. Migration of population might also have contributed to an apparent decline in registered cases. We detected cases of BEN in villages that previously were BEN-free. We recommended a rigorous monitoring of BEN in all afflicted countries, before concluding that the incidence of BEN is decreasing.

Environmetric modeling of emission sources for dry and wet precipitation from an urban area.

Monitoring data from chemical analysis of rainwater and aerosol samples collected in an urban area have been interpreted by the use of environmetric approaches. An attempt was done to compare the data set structures of both type of precipitation and to estimate the contribution of different anthropogenic and naturally occurring emission sources to the total mass of the wet and dry precipitation. It was found that three latent factors explaining over 80% of the total variance of the set are responsible for the rainwater set structure-'sea spray', 'soil dust', and 'anthropogenic'. Only two were the latent factors explaining the dominant part of the variance in the case of aerosol samples-'anthropogenic' and 'natural'. It is shown that the anthropogenic influence for aerosol samples is more complex that that of rainwater samples and represents interaction between typical anthropogenic sources and natural emitters. Additionally, a source apportioning using multiple regression on absolute principal component scores is performed in order to obtain qualitative information about the impact of the different identified emission sources on the urban environment.

Multivariate statistical interpretation of coastal sediment monitoring data.

Multivariate statistical analysis of sediment data (input matrix 122 x 15) collected from 122 sampling sites from the western coastline of the USA and analyzed for 15 analytes indicates that the data structure could be explained by four latent factors. These factors are conditionally named "anthropogenic", "organic", "natural", and "hot spots". They explain over 85% of the total variance of the data system, which is an acceptable value for the PCA model. The receptor models obtained after regression of the mass on the absolute principal components scores ensures reliable estimation of the contribution of each possible natural or anthropogenic source to the mass of each chemical component. It can be concluded that the region of interest reveals a different pattern of pollution compared with the eastern coastline treated statistically in a previous study.

Ozone and water-vapor measurements by Raman lidar in the planetary boundary layer: error sources and field measurements.

A new lidar instrument has been developed to measure tropospheric ozone and water vapor at low altitude. The lidar uses Raman scattering of an UV beam from atmospheric nitrogen, oxygen, and water vapor to retrieve ozone and water-vapor vertical profiles. By numerical simulation we investigate the sensitivity of the method to both atmospheric and device perturbations. The aerosol optical effect in the planetary boundary layer, ozone interference in water-vapor retrieval, statistical error, optical cross talk between Raman-shifted channels, and optical cross talk between an elastically backscattered signal in Raman-shifted signals and an afterpulse effect are studied in detail. In support of the main conclusions of this model study, time series of ozone and water vapor obtained at the Swiss Federal Institute of Technology in Lausanne and during a field campaign in Crete are presented. They are compared with point monitor and balloon sounding measurements for daytime and nighttime conditions.

Balkan endemic nephropathy in Vratza, Bulgaria, 1964-1987: an epidemiologic analysis of population-based disease registers.

Balkan Endemic Nephropathy (BEN), first described in 1956 in Vratza region, Bulgaria, may result from prolonged, chronic exposure to environmental toxicants, but the underlying etiologic factors remain elusive. There has been no recent systematic characterization of the epidemiology of this disease. Recently, it has been suggested that the incidence of the disease is decreasing. We therefore abstracted data from registers of patients in 21 affected villages and the town of Vratza, Bulgaria maintained from 1964 through 1987. In 1964, the prevalence of BEN was 6.0 per 1000 inhabitants; among residents of the affected villages, the prevalence was 12.3 per 1000. From 1965 to 1975 the incidence rate was 0.7 per 1,000 person-years, and from 1976 to 1987 the incidence rate was 0.3 per 1,000 person-years (rate ratio 0.43; p < 0.001). Incidence was much lower in Vratza town; among residents of affected villages, the period-specific rates were 1.7 and 0.8 per 1,000 per year, respectively (rate ratio 0.47; p < 0.01). These trends were consistent across all villages for which registers were maintained. Median survival following registration increased from 2.0 to 5.0 years over the same period (p < 0.001). BEN appears to be decreasing in incidence in this region.