Clinical, genetic, and immunohistochemical characterization of 70 Ukrainian adult cases with post-Chornobyl papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) exhibits various molecular abnormalities, both when sporadic and radiation-related. PTC is still diagnosed in adult individuals who were younger than 18 years at the time of the Chornobyl accident in 1986 and lived within the contaminated area. The preoperative diagnosis of PTC is based on ultrasound-guided fine needle aspiration cytology (FNAC), which is highly informative in up to 90% of biopsies. FNAC is not informative for the discrimination of follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA). Moreover, FNAC is often unreliable for diagnosis of cystic PTC due to its common presentation as a mural nodule in a cystic mass. In case of cystic PTC, biopsy sometimes reveals a cystic fluid containing insufficient amount of representative cells for cytology. In this work, PTC was characterized in relation to irradiation from radioactivity at childhood. Possible preoperative diagnostic markers for discrimination between PTC and other follicular thyroid neoplasms were identified, and their validity was tested. In Study I molecular, genetic and clinical characteristics in 70 post-Chornobyl PTCs were investigated. A common BRAF 1799T>A mutation was detected in 26 cases, overrepresentation of RET/PTC1 in 20 whereas RET/PTC3 was found in 4 cases. BRAF mutation was observed 3.5 times less frequent in the PTC accompanied by chronic lymphocytic thyroiditis (PTC/CLT) as compared to PTC only (12% vs. 44%). Greater expression of cyclin A was observed in PTC ≥ 2 cm as compared to PTC < 2 cm (1.2% vs. 0.6%). In conclusion, BRAF mutation and RET/PTC1 rearrangement as well as other molecular features of adult post-Chornobyl PTC were partly overlapping with other reported PTC cohorts. In Study II the SELDI-TOF mass spectrometry method was applied for PTC, FTC, FTA and normal thyroid tissue (NT). Significant overexpression of the protein S100A6 was identified in PTC as compared to FTC, FTA and NT (p < 0.05). This result was verified both by Western blot (WB), using the same samples, and by IHC in these and additionally in the PTC samples investigated in Study I. Moreover, the presence of two post-translational modifications of S100A6 was observed and verified by LC-MS/MS. S100A6 expression is strongly associated with PTC, and can therefore be tested for discrimination between follicular thyroid tumors and PTC. In Study III a two dimensional gel electrophoresis followed by MALDI-TOF mass spectrometry for proteomic profiling of PTC, FTC and FTA was performed. 25 protein spots showing significantly different expression between studied groups were identified. Of these, 9 protein spots were selected for further analyses by WB using the initially studied samples and by IHC using these as well as samples from Study I. The findings suggest additional proteins to be deregulated in thyroid tumors, and their clinical significance can now be further studied. In Study IV preoperative diagnostic markers for PTC in cystic lesions were identified by applying LC-MS/MS method. Out of all 1581 identified proteins, annexin A3 (ANXA3), carboxymethylenebutenolidase homolog (CMBL) cytokeratin 19 (CK- 19) and S100A13 were selected for validation by IHC and WB. ANXA3 and CMBL showed overexpression in both controls and PTCs, whereas S100A13 and CK-19 were up-regulated in PTC only (p < 0.05), suggesting their possible role for discrimination between cystic PTC and benign thyroid cysts

Programmed cell death 6 interacting protein (PDCD6IP) and Rabenosyn-5 (ZFYVE20) are potential urinary biomarkers for upper gastrointestinal cancer

PURPOSE: Cancer of the upper digestive tract (uGI) is a major contributor to cancer-related death worldwide. Due to a rise in occurrence, together with poor survival rates and a lack of diagnostic or prognostic clinical assays, there is a clear need to establish molecular biomarkers. EXPERIMENTAL DESIGN: Initial assessment was performed on urine samples from 60 control and 60 uGI cancer patients using MS to establish a peak pattern or fingerprint model, which was validated by a further set of 59 samples. RESULTS: We detected 86 cluster peaks by MS above frequency and detection thresholds. Statistical testing and model building resulted in a peak profiling model of five relevant peaks with 88% overall sensitivity and 91% specificity, and overall correctness of 90%. High-resolution MS of 40 samples in the 2-10 kDa range resulted in 646 identified proteins, and pattern matching identified four of the five model peaks within significant parameters, namely programmed cell death 6 interacting protein (PDCD6IP/Alix/AIP1), Rabenosyn-5 (ZFYVE20), protein S100A8, and protein S100A9, of which the first two were validated by Western blotting. CONCLUSIONS AND CLINICAL RELEVANCE: We demonstrate that MS analysis of human urine can identify lead biomarker candidates in uGI cancers, which makes this technique potentially useful in defining and consolidating biomarker patterns for uGI cancer screening

Afforestation impact on soil temperature in regional climate model simulations over Europe

In the context of the first phase of the Coordinated Regional Climate Downscaling Experiment in the European domain (EURO-CORDEX) flagship plot study on Land Use and Climate Across Scales (LUCAS), we investigate the biophysical impact of afforestation on the seasonal cycle of soil temperature over the European continent with an ensemble of 10 regional climate models. For this purpose, each ensemble member performed two idealized land cover experiments in which Europe is covered either by forests or grasslands. The multi-model mean exhibits a reduction of the annual amplitude of soil temperature (AAST) due to afforestation over all European regions, although this is not a robust feature among the models. In the Mediterranean, the spread of simulated AAST response to afforestation is between −4 and +2 ∘C at 1 m below the ground, while in Scandinavia the inter-model spread ranges from −7 to +1 ∘C. We show that the large range in the simulated AAST response is due to the representation of the summertime climate processes and is largely explained by inter-model differences in leaf area index (LAI), surface albedo, cloud fraction and soil moisture, when all combined into a multiple linear regression. The changes in these drivers essentially determine the ratio between the increased radiative energy at surface (due to lower albedo in forests) and the increased sum of turbulent heat fluxes (due to mixing-facilitating characteristics of forests), and consequently decide the changes in soil heating with afforestation in each model. Finally, we pair FLUXNET sites to compare the simulated results with observation-based evidence of the impact of forest on soil temperature. In line with models, observations indicate a summer ground cooling in forested areas compared to open lands. The vast majority of models agree with the sign of the observed reduction in AAST, although with a large variation in the magnitude of changes. Overall, we aspire to emphasize the biophysical effects of afforestation on soil temperature profile with this study, given that changes in the seasonal cycle of soil temperature potentially perturb crucial biochemical processes. Robust knowledge on biophysical impacts of afforestation on soil conditions and its feedbacks on local and regional climate is needed in support of effective land-based climate mitigation and adaption policies

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX Flagship Pilot Study Land Use and Climate Across Scales (LUCAS) models – Part 2: The role of changing vegetation

International audienceAbstract. Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest, while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snowmelt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. The greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass–snow and forest–snow interactions during the snowmelt season. Pathways to accomplishing this include (1) a more sophisticated representation of forest structure, (2) kilometre-scale simulations, and (3) more observational studies on vegetation–snow interactions in northern Europe

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX Flagship Pilot Study Land Use and Climate Across Scales (LUCAS) models – Part 2: The role of changing vegetation

Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest, while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snowmelt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. The greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass–snow and forest–snow interactions during the snowmelt season. Pathways to accomplishing this include (1) a more sophisticated representation of forest structure, (2) kilometre-scale simulations, and (3) more observational studies on vegetation–snow interactions in northern Europe

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models – Part 1: Evaluation of the snow-albedo effect

Seasonal snow cover plays a major role in the climate system of the Northern Hemisphere via its effect on land surface albedo and fluxes. In climate models the parameterization of interactions between snow and atmosphere remains a source of uncertainty and biases in the representation of local and global climate. Here, we evaluate the ability of an ensemble of regional climate models (RCMs) coupled with different land surface models to simulate snow–atmosphere interactions over Europe in winter and spring. We use a previously defined index, the snow-albedo sensitivity index (SASI), to quantify the radiative forcing associated with snow cover anomalies. By comparing RCM-derived SASI values with SASI calculated from reanalyses and satellite retrievals, we show that an accurate simulation of snow cover is essential for correctly reproducing the observed forcing over middle and high latitudes in Europe. The choice of parameterizations, and primarily the choice of the land surface model, strongly influences the representation of SASI as it affects the ability of climate models to simulate snow cover accurately. The degree of agreement between the datasets differs between the accumulation and ablation periods, with the latter one presenting the greatest challenge for the RCMs. Given the dominant role of land surface processes in the simulation of snow cover during the ablation period, the results suggest that, during this time period, the choice of the land surface model is more critical for the representation of SASI than the atmospheric model

Ageing-associated changes in transcriptional elongation influence longevity

Physiological homeostasis becomes compromised during ageing, as a result of impairment of cellular processes, including transcription and RNA splicing1-4. However, the molecular mechanisms leading to the loss of transcriptional fidelity are so far elusive, as are ways of preventing it. Here we profiled and analysed genome-wide, ageing-related changes in transcriptional processes across different organisms: nematodes, fruitflies, mice, rats and humans. The average transcriptional elongation speed (RNA polymerase II speed) increased with age in all five species. Along with these changes in elongation speed, we observed changes in splicing, including a reduction of unspliced transcripts and the formation of more circular RNAs. Two lifespan-extending interventions, dietary restriction and lowered insulin-IGF signalling, both reversed most of these ageing-related changes. Genetic variants in RNA polymerase II that reduced its speed in worms5 and flies6 increased their lifespan. Similarly, reducing the speed of RNA polymerase II by overexpressing histone components, to counter age-associated changes in nucleosome positioning, also extended lifespan in flies and the division potential of human cells. Our findings uncover fundamental molecular mechanisms underlying animal ageing and lifespan-extending interventions, and point to possible preventive measures

Building regulatory landscapes reveals that an enhancer can recruit cohesin to create contact domains, engage CTCF sites and activate distant genes

Developmental gene expression is often controlled by distal regulatory DNA elements called enhancers. Distant enhancer action is restricted to structural chromosomal domains that are flanked by CTCF-associated boundaries and formed through cohesin chromatin loop extrusion. To better understand how enhancers, genes and CTCF boundaries together form structural domains and control expression, we used a bottom-up approach, building series of active regulatory landscapes in inactive chromatin. We demonstrate here that gene transcription levels and activity over time reduce with increased enhancer distance. The enhancer recruits cohesin to stimulate domain formation and engage flanking CTCF sites in loop formation. It requires cohesin exclusively for the activation of distant genes, not of proximal genes, with nearby CTCF boundaries supporting efficient long-range enhancer action. Our work supports a dual activity model for enhancers: its classic role of stimulating transcription initiation and elongation from target gene promoters and a role of recruiting cohesin for the creation of chromosomal domains, the engagement of CTCF sites in chromatin looping and the activation of distal target genes