The new critical metals database “HTMET”: High tech trace element characteristics of sulphides from base metal provinces in the variscan basement and adjacent sedimentary rocks in Germany

High tech (HT) trace elements such as germanium, gallium and indium gain rising importance in the development of innovative technologies. The database “HTMET” forms the first nationwide metal-ore database for Germany, created to visualise HT metal characteristics of base metal ores from important mining districts. Mineralogical and geochemical investigations on 478 samples and ore concentrates from 109 Pb-Zn-Cu occurrences were carried out using analytical methods with high spatial resolution and bulk sample methods. The database provides aggregated data based on 17,000 geochemical data sets, compiled information on regional infrastructure and environmental risks as well as data on innovative raw material-efficient processing techniques. Evaluation of combined data provides interactive maps revealing new potentials for specific HT metals in Germany. Differences in regional distribution of these trace elements and dependency of their concentration levels in the ore on the genetic deposit type became apparent. Sphalerite from the sediment-hosted massive sulphide (SHMS) deposit “Rammelsberg” and skarn deposits in the Erzgebirge contain elevated indium contents (median 14–119 ppm), whereas the SHMS deposit “Meggen” is poor in HT metals. Germanium forms the predominant HT trace element in colloform sphalerite of Mississippi-Valley-Type (MVT) deposits (median 29–147 ppm); in contrast, crystalline sphalerite is low in germanium in this deposit type. Sphalerite in all hydrothermal vein deposits shares a distinct enrichment in gallium (median 6–81 ppm); however, germanium and indium concentrations vary significantly depending on the metal source and fluid conditions. The Ruhrgebiet and the Schwarzwald ore veins show an enrichment in germanium (median 55–73 ppm), whilst vein sphalerite from the Erzgebirge is specialised in indium (median 33 ppm). The data demonstrate that the HT trace element inventory of the studied base metal sulphides is not only a function of the genetic ore deposit type, but is also triggered by locally variable geology such as source rock and fluid composition and organic content of the rock. Gallium seems to derive from adjacent lithologies, whereas indium and germanium may have more distant sources

CVs of <i>GAPDH</i>, <i>UBC</i> and <i>EEF1A1</i> across ischemia time points and patients in normal and tumor colorectal tissue.

<p>Mean value of <i>GAPDH</i>, <i>UBC</i> and <i>EEF1A1</i> Cq values as well as standard deviation (SD) across all patients and time points were calculated. Furthermore, the CV across all four time points (pre-surgery, 10, 20 and 45 minutes after resection) in normal and tumor colorectal tissue was calculated.</p><p>CVs of <i>GAPDH</i>, <i>UBC</i> and <i>EEF1A1</i> across ischemia time points and patients in normal and tumor colorectal tissue.</p

Identification and Validation of a Potential Marker of Tissue Quality Using Gene Expression Analysis of Human Colorectal Tissue

<div><p>Correlative studies have identified numerous biomarkers that are individualizing therapy across many medical specialties, including oncology. Accurate interpretation of these studies requires the collection of tissue samples of sufficient quality. Tissue quality can be measured by changes in levels of gene expression and can be influenced by many factors including pre-analytical conditions, ischemic effects and the surgical collection procedure itself. However, as yet there are no reliable biomarkers of tissue quality at researchers’ disposal. The aim of the current study was to identify genes with expression patterns that fluctuated reproducibly in response to typical post-surgical stress (ischemia) in order to identify a specific marker of tissue quality. All tissue samples were obtained from patients with primary colorectal carcinoma (CRC) (N = 40) either via colonoscopy prior to surgery, or by surgical resection. Surgically resected tissue samples were divided into three groups and subjected to cold ischemia for 10, 20 or 45 minutes. Normal colorectal tissue and CRC tissue was analyzed using microarray and quantitative real-time PCR (qPCR). Comparing changes in gene expression between pre- and post-surgical tissue using microarray analysis identified a list of potential tissue quality biomarkers and this list was validated using qPCR. Results revealed that post-operative ischemia significantly alters gene expression in normal and CRC tissue samples. Both microarray analysis and qPCR revealed regulator of G-protein signaling 1 (<i>RGS1</i>) as a potential marker of CRC tissue quality and eukaryotic translation elongation factor 1 alpha 1 (<i>EEF1A1</i>) as a potential reference gene of post-operative tissue quality. Larger studies with additional time points and endpoints will be needed to confirm these results.</p></div

Microarray data of genes with the lowest CV across ischemia time points in normal colorectal tissue.

<p>The 10 probe sets (nine genes) with the lowest CV including rank across all four time points (pre-surgery, 10, 20 and 45 minutes after resection) in normal colorectal tissue as well as the corresponding ranks in tumor colorectal tissue are shown. Additionally, CVs and ranks of <i>GAPDH</i> and <i>UBC</i> are included within this table. Table adapted from David K et al. Oncotarget 2014; 5. <b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133987#pone.0133987.ref005" target="_blank">5</a>]</b>.</p><p>Microarray data of genes with the lowest CV across ischemia time points in normal colorectal tissue.</p

Regulation of <i>RGS1</i> and <i>EEF1A1</i> expression levels in colorectal ischemic samples.

<p>Comparison of <i>RGS1</i> expression changes in normal (<b>A</b>) and tumor <b>(B)</b> ischemic tissue samples and <i>EEF1A1</i> expression changes in normal <b>(C)</b> and tumor <b>(D)</b> ischemic tissue samples of 20 individual patients. Patients pre-surgery samples (0) were set to 1 and individual fold changes of ischemic samples were calculated and displayed as dots. Grey lines indicate a 2-fold change in gene expression compared with individual pre-surgical samples. N = normal tissue; T = tumor tissue; 0 = before surgery; 10, 20, 45 = 10, 20, 45 minutes after resection.</p

Expression levels of <i>RGS1</i> following normalization to <i>UBC</i>, <i>GAPDH</i> and <i>EEF1A1</i> as well as to the combination of <i>GAPDH</i> and <i>UBC</i>.

<p>Comparison of <i>RGS1</i> gene expression changes in normal <b>(A)</b> and tumor <b>(B)</b> ischemic tissue samples of 20 individual patients. Patients pre-surgery samples (0) were set to 1 and individual fold changes of ischemic samples were calculated based on normalization to <i>UBC</i> (white box), <i>GAPDH</i> (light grey box), <i>EEF1A1</i> (grey box) or to a combination of <i>GAPDH</i> and <i>UBC</i> (dark grey box). Results were summarized by Box-Whisker Plots. Boxes represent first and third quartile, whiskers represent minima and maxima, solid lines within boxes indicate medians. Kruskal-Wallis test and Dunn’s multiple comparison test were used for statistical analysis to compare the four normalization approaches within each time point. n.s. = not significant; N = normal tissue; T = tumor tissue; 0 = before surgery; 10, 20, 45 = 10, 20, 45 minutes after resection.</p

Number of regulated genes in colon tissue summarized from microarray data.

<p>Number of genes in 40 individual patients that change expression > 2-fold, comparing different time points of collection: N = normal tissue; T = tumor tissue; pre = before surgery; 10` = 10 minutes after resection; 45` = 45 minutes after resection. Kruskal-Wallis test and Dunn’s multiple comparison test were used for statistical analysis. *** p ≤ 0.001.</p

Microarray data of differentially expressed genes in normal and colorectal tumor tissue.

<p>Gene expression was compared before surgery (pre) and 10 minutes after resection (10’), and Pre and 45 minutes after resection (45’). Table adapted from David K et al. Oncotarget 2014; 5. <b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133987#pone.0133987.ref005" target="_blank">5</a>]</b>.</p><p>Microarray data of differentially expressed genes in normal and colorectal tumor tissue.</p