Peatland microbial communities as indicators of the extreme atmospheric dust deposition

We investigated a peat profile from the Izery Mountains, located within the so-called Black Triangle, the border area of Poland, Czech Republic, and Germany. This peatland suffered from an extreme atmospheric pollution during the last 50 years, which created an exceptional natural experiment to examine the impact of pollution on peatland microbes. Testate amoebae (TA), Centropyxis aerophila and Phryganella acropodia, were distinguished as a proxy of atmospheric pollution caused by extensive brown coal combustion. We recorded a decline of mixotrophic TA and development of agglutinated taxa as a response for the extreme concentration of Al (30 g kg−1) and Cu (96 mg kg−1) as well as the extreme amount of fly ash particles determined by scanning electron microscopy (SEM) analysis, which were used by TA for shell construction. Titanium (5.9 %), aluminum (4.7 %), and chromium (4.2 %) significantly explained the highest percentage of the variance in TA data. Elements such as Al, Ti, Cr, Ni, and Cu were highly correlated (r>0.7, p<0.01) with pseudostome position/body size ratio and pseudostome position. Changes in the community structure, functional diversity, and mechanisms of shell construction were recognized as the indicators of dust pollution. We strengthen the importance of the TA as the bioindicators of the recent atmospheric pollution

A classification model for distinguishing copy number variants from cancer-related alterations

<p>Abstract</p> <p>Background</p> <p>Both somatic copy number alterations (CNAs) and germline copy number variants (CNVs) that are prevalent in healthy individuals can appear as recurrent changes in comparative genomic hybridization (CGH) analyses of tumors. In order to identify important cancer genes CNAs and CNVs must be distinguished. Although the Database of Genomic Variants (DGV) contains a list of all known CNVs, there is no standard methodology to use the database effectively.</p> <p>Results</p> <p>We develop a prediction model that distinguishes CNVs from CNAs based on the information contained in the DGV and several other variables, including segment's length, height, closeness to a telomere or centromere and occurrence in other patients. The models are fitted on data from glioblastoma and their corresponding normal samples that were collected as part of The Cancer Genome Atlas project and hybridized to Agilent 244 K arrays.</p> <p>Conclusions</p> <p>Using the DGV alone CNVs in the test set can be correctly identified with about 85% accuracy if the outliers are removed before segmentation and with 72% accuracy if the outliers are included, and additional variables improve the prediction by about 2-3% and 12%, respectively. Final models applied to data from ovarian tumors have about 90% accuracy with all the variables and 86% accuracy with the DGV alone.</p

Atypical ductal hyperplasia is a multipotent precursor of breast carcinoma

The current model for breast cancer progression proposes independent “low‐grade (LG) like” and “high‐grade (HG) like” pathways but lacks a known precursor to HG cancer. We applied low coverage whole genome sequencing to atypical ductal hyperplasia (ADH) with and without carcinoma to shed light on breast cancer progression. 14/20 isolated ADH cases harboured at least one copy number alteration (CNA), but had fewer aberrations than LG or HG ductal carcinoma in situ (DCIS). ADH carried more HG‐like CNA than LG DCIS (eg. 8q gain). Correspondingly, 64% (7/11) of ADH cases with synchronous HG carcinoma were clonally related, similar to LG carcinoma (67%, 6/9). This study represents a significant shift in our understanding of breast cancer progression, with ADH as a common precursor lesion to the independent “low‐grade like” and “high‐grade like” pathways. These data suggest that ADH can be a precursor of HG breast cancer and that LG and HG carcinomas can evolve from a similar ancestor lesion. We propose that although LG DCIS may be committed to a LG molecular pathway, ADH may remain multipotent, progressing to either LG or HG carcinoma. This multipotent nature suggests that some ADH could be more clinically significant than LG DCIS, requiring biomarkers for personalising management

Peatland Microbial Communities as Indicators of the Extreme Atmospheric Dust Deposition

We investigated a peat profile from the Izery Mountains, located within the so-called Black Triangle, the border area of Poland, Czech Republic, and Germany. This peatland suffered from an extreme atmospheric pollution during the last 50 years, which created an exceptional natural experiment to examine the impact of pollution on peatland microbes. Testate amoebae (TA), Centropyxis aerophila and Phryganella acropodia, were distinguished as a proxy of atmospheric pollution caused by extensive brown coal combustion. We recorded a decline of mixotrophic TA and development of agglutinated taxa as a response for the extreme concentration of Al (30 g kg−1) and Cu (96 mg kg−1) as well as the extreme amount of fly ash particles determined by scanning electron microscopy (SEM) analysis, which were used by TA for shell construction. Titanium (5.9 %), aluminum (4.7 %), and chromium (4.2 %) significantly explained the highest percentage of the variance in TA data. Elements such as Al, Ti, Cr, Ni, and Cu were highly correlated (r>0.7, p<0.01) with pseudostome position/body size ratio and pseudostome position. Changes in the community structure, functional diversity, and mechanisms of shell construction were recognized as the indicators of dust pollution. We strengthen the importance of the TA as the bioindicators of the recent atmospheric pollution

KingClonalityData6

Copy number log-ratio data for the 31 samples in the manuscript. It is a gzipped text file. This is part 6. Use cat to concatenate all 6 parts