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Cancer cells undergo a metabolic reprogramming but little is known about metabolic alterations of other cells within tumors. We use mass spectrometry–based profiling and a metabolic pathway–based systems analysis to compare 21 primary human lung cancer–associated fibroblast lines (CAF) to "normal" fibroblast lines (NF) generated from adjacent nonneoplastic lung tissue. CAFs are protumorigenic, although the mechanisms by which CAFs support tumors have not been elucidated. We have identified several pathways whose metabolite abundance globally distinguished CAFs from NFs, suggesting that metabolic alterations are not limited to cancer cells. In addition, we found metabolic differences between CAFs from high and low glycolytic tumors that might reflect distinct roles of CAFs related to the tumor's glycolytic capacity. One such change was an increase of dipeptides in CAFs. Dipeptides primarily arise from the breakdown of proteins. We found in CAFs an increase in basal macroautophagy which likely accounts for the increase in dipeptides. Furthermore, we show a difference between CAFs and NFs in the induction of autophagy promoted by reduced glucose. In sum, our data suggest that increased autophagy may account for metabolic differences between CAFs and NFs and may play additional as yet undetermined roles in lung cancer. Mol Cancer Res; 11(6); 579–92. ©2013 AACR.
This article is featured in Highlights of This Issue, p. 555
Tumors are composed of malignant cancer cells and stromal cells that constitute the tumor microenvironment. Research efforts have predominantly focused on understanding the biology of the epithelial component of tumors, leading to the identification of genes and signaling pathways dysregulated in cancer cells. The nonepithelial cellular elements of the tumor mass, although not by themselves malignant, have essential roles in carcinogenesis by supporting the cancer cells (1, 2). Cancer-associated fibroblasts (CAF) of the tumor microenvironment are protumorigenic, although the exact functions of CAFs have yet to be fully delineated (3, 4). Proposed functions of CAFs include secretion of growth factors, of proangiogenic VEGF, and/or of proteases that remodel the extracellular matrix (5–9). The distinct protumorigenic activities of CAFs identified in the different studies might reflect tumor type–specific roles for CAFs.
Proliferation requires growth-promoting signaling and an appropriate nutrient milieu. Cancer cells frequently have a predominantly glycolytic metabolism, the "Warburg effect" (10, 11), that is required for the growth of some tumors (e.g., ref. 12). The emerging view is that reliance on a predominantly glycolytic metabolism supports the anabolic needs of cancer cells at the expense of efficient production of ATP from glucose (11). Little is known about possible metabolic alterations in the stroma cells of the tumor microenvironment. Alterations in stromal cell metabolism might be advantageous to the cancer cells, as would be the case if stroma cell metabolism was coupled to that of the cancer cells (e.g., ref. 13).
Here, we use mass spectrometry–based profiling of the abundances of 203 biochemicals of 46 metabolic pathways/groups to compare primary human lung tumor CAFs to "normal" fibroblasts (NF) isolated from nonneoplastic lung tissue located at least 5 cm away from the tumor. We found differences in several metabolic pathways that distinguish CAFs from NFs, suggesting that alterations in cellular metabolism are not limited to epithelial cancer cells of the tumor. Moreover, we found differences between CAFs and NFs that correlate with the glycolytic capacity of the tumor, one of which was a relative increase in CAFs of 17 unrelated dipeptides. The relative difference in dipeptides was most significant between CAFs derived from highly glycolytic tumors and their paired NFs, although dipeptides in CAFs from less glycolytic tumors were also increased compared with paired NFs. Unexpectedly, dipeptides were also increased in NFs isolated from lung tissue of individuals with highly glycolytic tumors compared with those from less glycolytic tumors, suggesting either a tumor-specific field effect extending beyond 5 cm or metabolic alteration in NFs induced by a widespread inflammatory or other protumorigenic parenchymal milieu. Dipeptides arise from the breakdown of proteins. In CAFs, we find evidence for increased autophagy, a lysosome-related catabolic mechanism, which likely explains the increase in dipeptides. We also find that reducing the amount of glucose in the medium from 25 to 5 mmol/L causes an increase of dipeptides and autophagy in NFs but not CAFs, suggesting that CAFs and NFs have different set points for glucose induction of autophagy. There is mounting evidence that increased degradative pathways (e.g., autophagy) in cancer cells, potentially as stress response mechanisms, have roles in tumor maintenance and possibly in resistance to chemotherapy (14–16). Our studies provide evidence that increased autophagy and lysosomal capacity are characteristics of human lung CAFs, alterations that might contribute to the protumorigenic activity of CAFs.
Materials and Methods
Human subjects
The clinical study (Fig. 3) was an Institutional Review Board–approved review of a prospectively assembled thoracic surgery database of patients with non–small cell lung cancer (from February 2000 to December 2009). Patient consent was waived. Patients with surgically resected tumors (stage I–III) and documented SUVmax values by positron emission tomography (PET) were considered eligible for this review (n = 693). Patients were segregated into 2 cohorts those with SUVmax ≥8 (n = 231, 33%) and those with SUVmax ≤5 (n = 350, 50%). Disease-free survival (DFS) for the cohort (from date of surgery to recurrent or death) was evaluated by Kaplan–Meier survival analysis. Log-rank test was used for comparison of DFS between the 2 groups. Median follow-up was 23 months.
Cells were derived from resected tumors and adjacent nonneoplastic tissue in an Institutional Review Board–approved study. Patients consented to use of cells derived from their tissue for this study. Tissue, which was processed within 2 hours of resection, was minced with a razor and incubated in 1 mg/mL collagenase D for 60 minutes with agitation at 37°C. The material was passed through a 70-μm sieve, red blood cells lysed, and extensively washed in HEPES-buffered Dulbeccos' Modified Eagle's Medium (DMEM; 25 mmol/L glucose) before plating in DMEM supplemented with 10% FBS. When the cultures reached about 80% confluence, cells were passaged and expanded further by growth. Cell pellets (≥2.5 × 106 cells) were washed with PBS, flash frozen in a dry ice ethanol bath, and stored at −80°C. In some cases, cells were expanded in 5 mmol/L glucose–DMEM–10% FBS.
Mass spectrometry
Sample accessioning.
Each sample received was accessioned into the Metabolon LIMS system and was assigned by the LIMS a unique identifier that was associated with the original source identifier only. This identifier was used to track all sample handling, tasks, results, etc. The samples (and all derived aliquots) were tracked by the LIMS system. All portions of any sample were automatically assigned their own unique identifiers by the LIMS when a new task is created; the relationship of these samples is also tracked. All samples were maintained at −80ºC until processed.
Sample preparation.
Samples were prepared using the automated MicroLab STAR system from Hamilton Company. A recovery standard was added before the first step in the extraction process for quality control (QC) purposes. Sample preparation was conducted using aqueous methanol extraction process to remove the protein fraction while allowing maximum recovery of small molecules. The resulting extract was divided into 4 fractions: one for analysis by ultrahigh performance liquid chromatography/mass spectroscopy (UPLC/MS/MS; positive mode), one for UPLC/MS/MS (negative mode), one for gas chromatography/mass spectroscopy (GC/MS), and one for backup. Samples were placed briefly on a TurboVap (Zymark) to remove the organic solvent. Each sample was then frozen and dried under vacuum. Samples were then prepared for the appropriate instrument either UPLC/MS/MS or GC/MS.
Ultrahigh performance liquid chromatography/mass spectroscopy.
The liquid chromatography/mass spectrometry (LC/MS) was based on a Waters ACQUITY UPLC and a Thermo-Finnigan linear trap quadrupole (LTQ) mass spectrometer, which consisted of an electrospray ionization (ESI) source and linear ion-trap (LIT) mass analyzer. The sample extract was dried then reconstituted in acidic or basic LC-compatible solvents, each of which contained 8 or more injection standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion–optimized conditions and the other using basic negative ion–optimized conditions in 2 independent injections using separate dedicated columns. Extracts reconstituted in acidic conditions were gradient-eluted using water and methanol containing 0.1% formic acid, whereas the basic extracts, which also used water/methanol, contained 6.5 mmol/L ammonium bicarbonate. The MS analysis alternated between MS and data-dependent MS/MS scans using dynamic exclusion. Raw data files were archived and extracted.
Gas chromatography/mass spectroscopy.
The samples destined for gas chromatography/mass spectroscopy (GC/MS) analysis were redried under vacuum desiccation for a minimum of 24 hours before being derivatized under dried nitrogen using bistrimethyl-silyl-triflouroacetamide. The GC column was 5% phenyl and the temperature ramp was from 40° to 300°C in a 16-minute period. Samples were analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer using electron impact ionization. The instrument was tuned and calibrated for mass resolution and mass accuracy on a daily basis. The information output from the raw data files was automatically extracted as discussed below.
Quality assurance/QC.
For quality assurance (QA)/QC purposes, additional samples were included with the analysis of each day. These samples included extracts of a pool of well-characterized human plasma, extracts of a pool created from a small aliquot of the experimental samples, and process blanks. QC samples were spaced evenly among the injections, and all experimental samples were randomly distributed throughout the run. A selection of QC compounds was added to every sample for chromatographic alignment, including those under test. These compounds were carefully chosen so as not to interfere with the measurement of the endogenous compounds.
Data extraction and compound identification
Raw data were extracted, peak-identified, and QC processed using Metabolon's hardware and software. These systems are built on a web service platform using Microsoft's.NET technologies, which run on high-performance application servers and fiber channel storage arrays in clusters to provide active failover and load balancing. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. More than 2,400 commercially available purified standard compounds have been acquired and registered into LIMS for distribution to both the LC and GC platforms for determination of their analytic characteristics.
Data normalization
Missing values (if any) were assumed to be below the level of detection. However, biochemicals that were detected in all samples from one or more groups but not in samples from other groups were assumed to be near the lower limit of detection in the groups in which they were not detected. In these cases, the missing values were replaced with the minimum value obtained for the biochemical within all groups. Each biochemical value was then normalized to cell number and scaled to their median value. The data were also analyzed after normalizing the individual metabolites in each sample to the median of all metabolites in that sample. This scaling accounts for any difference between samples due to a change in the abundance of all metabolites, as would be the case if there were differences in cell size among groups. Similar results were obtained in all comparisons regardless of whether the data for each cell line were scaled to the median value of that cell line. The data sets without and with scaling to the median of all metabolites in that sample are presented in Supplementary Table SI. The results presented are the significant differences common to the analyses of the data without and with the scaling.
Heatmap data were scaled up for zero mean and unit variance by first subtracting the mean of each row for the matrix needs to be visualized and then dividing each row by its SD. This provides a matrix where each metabolite concentrations have mean 0 and SD 1.
Statistical analysis
An unpaired t test for differential abundance with a standard 2-tailed and 2-sample t test was done using mattes function in MATLAB on each metabolite to calculate the Wilcoxon t statistic for the 2 groups being compared. An unpaired t test was then used to conduct t test over the t scores and fold change (log2) of metabolites grouped as required for the analysis. Resultant P values were then adjusted for false discovery rate as described by Benjamini–Hochberg correction for multiple testing. The same analysis was repeated with random shuffling (104 times) of the group labels (permutation testing). Results that had a probability of less than 0.05 of appearing in this shuffling exercise were selected as significant. Complete analysis was done in MATLAB R2011b with Bioinformatics (ver 4.0) and Statistics (ver 7.6) toolboxes.
To analyze biochemicals grouped into 7 main groups, we compared the distributions of t scores of the metabolites of that group (obtained from t-test as described above) to the distribution of the t scores of the rest of the metabolites. The P values for this comparison for each group were determined after correcting for multiple testing. To analyze biochemicals grouped into 46 subgroups, we compared the distributions of t scores (obtained from t test as described above) and the mean log2 fold change of the metabolites in each group with those of rest of the metabolites combined. Significance was calculated by conducting an unpaired t test assuming normal distributions of unequal variances. P values corrected for multiple testing were obtained for each subgroup as described above. Subgroups having an adjusted P < 0.05 from t scores comparisons (which captures the variance of data) and adjusted P < 0.05 from mean fold change comparison were selected. The data were analyzed in 2 ways, with and without scaling the metabolites of an individual sample to the median value of all metabolites in that sample. There were only minor differences in the results with and without the scaling. The results presented are the significant differences between groups that were common to the 2 different methods of analyses.
pH measurements
Lysosomal pH was measured as described previously (17). Cells were incubated for 18 hours in growth medium supplemented with 2.5 mg/mL fluorescein rhodamine–labeled dextran (Invitrogen, Inc.). Following this incubation, cells were washed and incubated for an additional 3 hours in growth medium to chase the dextran into late endosomes and lysosomes. The cells were transferred to a HEPES-balanced salt solution with 10 mmol/L glucose and imaged live by epifluorescence in both fluorescein and rhodamine channels. To construct a pH calibration curve a set of cells similarly labeled were fixed and incubated in monensin containing buffers of pH 3, 4, 5, 6, 7, and 8. These cells were imaged with the same settings as the live cells. Metamorph software was used to quantify the rhodamine and fluorescein fluorescence of regions of interest (ROI) identified in the rhodamine channel. The rhodamine–fluorescein ratios of the ROIs of the fixed cells were used to create the pH calibration curve that was used to assign pH values to the ROIs of the live cells.
LC3, Lamp-2A, and Caveolin-1 analyses
Paired CAFs and NFs were grown in culture to 100% confluence in 6-well plates (DMEM-Hams F12 1:1 blend, 10% FBS). Cells were either treated with 30 μmol/L chloroquine for 3 or 5 hours or kept in normal growth media. Protein extracts were prepared in 150 μL of 2× Laemmli buffer, boiled, sonicated, and run on a 15% SDS-PAGE gel. Membranes were incubated overnight with primary antibodies: rabbit anti-LC3B 1:1,000 (Invitrogen, Inc); rabbit anti-actin 1:5,000 (Cytoskeleton Inc.); rabbit anti-Caveolin-1 (BD Biosciences); and anti-Lamp-2A (Abcam, Inc) and for 1 hour with secondary antibodies (goat anti-rabbit 680 nm, 1:5,000). Membranes were read on a LiCor imager and quantified using Odyssey software (version 3.0). Alternatively, LC3 autophagosomes were detected using indirect immunofluorescence in epifluorescence microscopy. Caveolin and LAMP-2A blotting were conducted on the non-chloroquine–treated extracts only.
Isolation of paired, nonimmortalized lung CAF and NF cell lines
In an Institutional Review Board–approved study, we generated by long-term culture 21 CAF lines from surgically resected primary lung cancers and matched NFs from nonneoplastic lung tissue obtained at least 5 cm from the tumor, and we used these samples for mass spectrometry metabolomic profiling of CAFs (Fig. 1A). Tumor and nonneoplastic tissues were processed by mechanical and collagenase disruption, and the resultant heterogeneous populations of dispersed cells were plated in growth medium supplemented with 10% FBS. One day after plating, keratin-expressing and vimentin-expressing cells were detected by immunofluorescence (Fig. 1B). Overtime in culture without passage, both keratin- and vimentin-expressing cells proliferated, with the keratin-expressing cells growing in clusters characteristic of epithelial cells. However, upon expansion of the cultures by passage, only the vimentin-expressing cells proliferated in samples from both tumor and nonneoplastic tissue (Fig. 1C). These vimentin-expressing cells were further expanded and prepared for metabolomic analysis. Numerous previous studies have isolated CAF and NF lines by culture without specific enrichment steps. To be consistent with the terminology in the literature, we refer to the expanded vimentin-expressing cultures as CAFs cell lines and NFs cell lines, although they are not clonal lines. There were no consistent differences in growth rates of the expanded CAF and NF cultures. Pertinent clinical data associated with the 21 CAFs/NFs paired lines are presented in Table 1.
Increased expression of α-smooth muscle actin (αSMA) is one commonly reported characteristic of CAFs, presumably reflecting that CAFs are in an "activated state" (3, 4). Consistent with those previous observations, expression of αSMA was generally higher in the CAFs than in matched NFs; however, there was a considerable variation in αSMA expression among the different lines (Fig. 1D). This heterogeneity was observed in the percentage of cells expressing αSMA within any given CAF and NF pair as well as in the amount of αSMA expression per cell. No correlation between αSMA expression and clinical data was noted. Heterogeneity in αSMA expression in CAFs has been previously reported for CAFs (e.g., ref. 9).
One group has proposed that reduced caveolin-1 expression drives CAF activation and is responsible for a metabolic reprogramming of CAFs (18). We did not find differences in total caveolin-1 expression between CAFs and paired NFs detected by Western blotting (Fig. 1E). In addition, caveolin-1 was homogeneously expressed at the individual cell level in both CAFs and NFs, labeling both the plasma membranes and internal membranes (Fig. 1F). Thus, reduced caveolin-1 expression is not a feature of lung tumor–derived CAFs.
Metabolite groups distinguish cultured primary lung CAFs from normal lung fibroblasts
Mass spectrometry was used to profile 203 metabolites in the 21 pairs of cultured primary lung CAFs and their matched NFs as previously described (19, 20). Metabolite levels were corrected for cell numbers, and each metabolite was scaled to its median across all samples (Fig. 2A and Supplementary Table SI). There was a slight, albeit significant, increase in all 203 metabolites considered as a group in NFs compared with the CAFs (Fig. 2B). The data are corrected for cell number, and we do not know the reason for this difference. To account for this change, we also analyzed the data after normalizing the metabolites of each individual sample to the median value of all metabolites of each individual sample (Supplementary Table SI). This normalization did not change the results of the CAF to NF comparison. No single metabolite was significantly different between CAFs and NFs in a comparison of the means of the individual metabolites (P ≤ 0.05, two-tailed, unpaired Student t test, Benjamini–Hochberg corrected for multiple testing).
To minimize subject-to-subject variations that are independent of possible CAF to NF differences, we examined the ratio of the individual metabolites in each CAF to the amount of that metabolite in the NF derived from the same individual (Supplementary Fig. S1B). In this analysis, only the abundance of glucose was significantly different between CAFs and NFs at P ≤ 0.05 (2-tailed unpaired Student t test, Benjamini–Hochberg–corrected). The biologic significance of this change in glucose without corresponding changes in glucose metabolites is not clear.
Cellular metabolism is an interconnected network; therefore, steady-state analyses can be confounded because changes in biochemicals of a metabolic pathway will be distributed across individual intermediates of that pathway as well as potentially distributed more broadly over connected pathways. To capture differences in abundance of metabolites of metabolic pathways, and thereby potentially increase the sensitivity of our analysis to detect differences between CAFs and NFs, we assigned the 203 metabolites to 7 groups based on Kyoto Encyclopedia of Genes and Genomes (KEGG; ref. 21) and compared CAFs with NFs based on these groups (Supplementary Table SI). To accomplish this, we used the t test to compare the t scores of the metabolites of each of the 7 groups to the t scores of all the other metabolites excluding those of the pathway being examined. In this analysis, we determined whether differences in any of the 7 groupings of metabolites between CAFs and NFs were more pronounced than the difference in the abundance of all metabolites between CAFs and NFs. Two of the 7 groups were significantly enriched in CAFs: amino acids and peptides (P ≤ 0.05, unpaired 2-tailed Student t test, Benjamini–Hochberg–corrected; Fig. 2C). These data indicate differences in the cellular metabolisms of CAFs and NFs that were not apparent at an individual metabolite/biochemical level.
To refine the comparison, we analyzed the data by further categorizing the metabolites into 46 subgroups based on KEGG designation (ref. 21; Supplementary Table SI). We compared both the t scores and the fold changes (log2) in abundance of the metabolites for each subgroup to the respective t scores or fold changes of all the other metabolites excluding those of the subgroup being examined (Supplementary Fig. S2). Thus, in this analysis, we determined whether differences in any of the 46 subgroupings of metabolites between CAFs and NFs were more pronounced than the difference in the abundance of all metabolites between CAFs and NFs. To consider a subgroup significantly different between CAFs and NFs, we imposed the criteria that the distribution of t scores of the metabolites of a subgroup and the mean log2 fold differences in abundance of the metabolites of a subgroup should both be significantly different (P ≤ 0.05, unpaired 2-tailed Student t test, Benjamini–Hochberg–corrected).
This analysis revealed that 3 subgroups were significantly different between CAFs and NFs (Fig. 2D and E). Metabolites of the glycine/serine/threonine subgroup include these amino acids as well as n-acetlylserine and betaine (Supplementary Table SI). Creatinine subgroup consists of creatine and creatinine. The dipeptides subgroup consists of 17 unrelated dipeptides (Supplementary Table SI). The differences between CAFs and NFs in the glycine/serine/threonine and the creatinine subgroups, which are both part of the KEGG designation amino acid group, likely account for this group distinguishing the CAFs from the NFs (Fig. 2C), whereas the change in the dipeptide subgroup accounted for the peptide group distinguishing CAFs from NFs (Fig. 2C). The metabolites of the 3 subgroups that were different between CAFs and NFs were all enriched in CAFs.
These metabolite profiling data support the hypothesis that there are steady-state metabolic differences between CAFs and NFs.
Different metabolite groups distinguish high and low SUVmax cultured primary lung CAFs from paired NFs
To further interrogate the data, we analyzed the metabolites after subdividing the CAFs and NFs based on the glycolytic activity of the parent primary tumor measured by standardized uptake value (SUVmax) on fluorodeoxyglucose PET (Table 1). SUVmax, a measure of intracellular accumulation of phosphorylated deoxyglucose, is a surrogate measure of the glycolytic metabolism of tumors (22). To determine whether there were differences in the metabolite profiles of CAFs and NFs related to the SUVmax of the tumor, the CAF/NF pairs were segregated into "low" SUVmax (≤5) and "high" SUVmax (≥8) groups. Data collected from our clinic show significant differences in DFS for patients based on these SUVmax groupings, in accord with the potential clinical relevance of SUVmax stratification (Fig. 3; refs. 23, 24). On the basis of these criteria, 7 CAF/NF pairs were categorized as low SUVmax and 11 as high SUVmax (Table 1). The 3 CAF/NF pairs that fell between SUVmax of 5 and 7 were excluded from this analysis.
No individual metabolites were significantly different between CAFs derived from high SUVmax tumors and their corresponding NFs. Three of the 46 KEGG subgroups were significantly different between high SUVmax CAFs and their matched NFs (Fig. 4A). Both the dipeptides and the glycine/serine/threonine subgroups were significantly enriched in CAFs. These subgroups were also identified in the comparison of CAFs to NFs independent of SUVmax categorization (Fig. 2D). In addition, the mean abundance of metabolites of the lysolipids subgroup (16 different lysolipid species) was reduced in high SUVmax CAFs relative to their paired NFs (Fig. 4A).
No individual metabolites were significantly different between CAFs derived from low SUVmax tumors and their paired NFs. Two of the 46 KEGG subgroups were significantly different between low SUVmax CAFs and their matched NFs (Fig. 4B). As was the case for the comparison of high SUVmax CAFs to NFs, the dipeptide subgroup was significantly enriched in the low SUVmax CAFs relative to their paired NFs, although the relative enrichment in the in low SUVmax CAFs was smaller than in the high SUVmax comparison. There was also a significant decrease in the glycolysis/pyruvate metabolites subgroup in the low SUVmax CAFs relative to their paired NFs (Fig. 4B). The metabolite subgroups that distinguish low SUVmax CAFs from their matched NFs were not the same as those that distinguish high SUVmax CAFs from their paired NFs, suggesting a link between the metabolic alterations in CAFs and the glycolytic activity of the tumor (i.e., SUVmax) from which the CAFs were derived.
Primary lung CAFs from high SUVmax tumors have different metabolite profiles than those from low SUVmax tumors
To further explore the possibility of a relationship between CAF metabolism and tumor SUVmax, we compared CAFs from low SUVmax tumors to CAFs from high SUVmax tumors. Two subgroups of metabolites were different between CAFs from low and high SUVmax tumors: lysolipid and dipeptides (Fig. 4C). The abundance of lysolipids was reduced in the CAFs from high SUVmax tumors, whereas the dipeptides were increased in CAFs from high SUVmax tumors. These 2 subgroups also distinguished high SUVmax CAFs from their paired NFs (Fig. 4A). Interestingly, in both the comparisons, the lysolipids were reduced and dipeptides were increased in the high SUVmax CAFs, suggesting these changes are characteristics of CAFs derived from high SUVmax tumors, reinforcing the hypothesis that metabolic changes in CAFs are in some way linked to the glycolytic capacity of the tumor.
Primary normal lung fibroblasts from individuals with high SUVmax tumors have different metabolite profiles than those derived from individuals with low SUVmax tumors
The above data provide direct support for the hypothesis that there are differences between CAFs from high and low SUVmax tumors. We next compared the metabolite profiles of NFs assigned to either high or low SUVmax groups based on the SUVmax of the tumor from which their corresponding CAFs were derived. Two of the KEGG designated subgroups of metabolites were different between NFs associated with low and high SUVmax tumors (Fig. 4D). Dipeptides were enriched in NFs associated with high SUVmax tumors, whereas the carnitine subgroup (acetylcarnitine, carnitine, deoxycarnitine, and hexanoylcarnitine) was enriched in NFs associated with low SUVmax tumors. These data suggest an unexpected link between the glycolytic activity of the tumor (i.e., SUVmax) and the metabolic profile of fibroblasts derived from nonneoplastic tissue of the same lobe as the tumor.
Increased basal autophagy in primary lung CAFs relative to normal lung fibroblasts
Dipeptides were increased in each comparison of CAFs to NFs, suggesting an increase in dipeptides is a characteristic of CAFs (Figs. 2 and 4). In addition, dipeptides were also increased in CAFs from high SUVmax tumors relative to those from low SUVmax tumors, and they were increased in NFs associated with high SUVmax tumors relative to those associated with low SUVmax tumors (Fig. 4D). Thus, an increase in dipeptides is also correlated with the higher SUVmax tumors.
Proteolysis is a source of dipeptides and therefore the differences in the dipeptides could reflect differences in proteolysis among the cell groupings. Alterations in autophagy, a lysosome-related proteolytic-degradative mechanism, have been noted in cancer cells, a change that might reflect a response to energy balance alterations in the cancer cells (25). Two autophagic mechanisms, macroautophagy and chaperone-mediated autophagy, have been linked to cancer (14–16, 26).
We first determined whether there were differences in macroautophagy between CAFs and NFs. Upon induction of macroautophagy (from here on out referred to as autophagy), microtubule-associated protein 1 light chain 3 (LC3I) is processed by lipidation to form LC3II that associates with membranes during the elongation phase of autophagosome formation. An increase in LC3II, evident by altered electrophoretic migration, is a measure of increased autophagy (Fig. 5A; refs. 27–29). Although there was variability in the total amount of LC3 (LC3I + II) among CAF/NF pairs (reflecting individual-to-individual variation), there were no consistent differences in total LC3 between CAFs and NFs, nor was there a correlation in the total LC3 with the tumor SUVmax (Fig. 5B). There was, however, a significant increase in the basal steady-state LC3II as a percent of total LC3 in CAFs, indicating increased autophagy in CAFs (Fig. 5C). To establish that the basal steady-state increase in LC3II was due to increased production of LC3II, rather than a decrease in LC3II turnover, we used chloroquine to inhibit degradation of LC3II (29). Chloroquine induced an increase of LC3II in CAFs and NFs lines; however, the relative increase in LC3II as a percentage of total LC3 in CAFs was maintained, consistent with the higher basal LC3II in CAFs being due to increased autophagy (Fig. 5C). When we segregated the data for the tumor SUVmax, we found that the increase in LC3II in CAFs relative to their paired NFs was significant for pairs associated with high SUVmax tumors but not for those associated with low SUVmax tumors (Fig. 5C). These results are consistent with the more robust difference in dipeptides between high SUVmax CAF/NF pairs (Fig. 4), and the steady-state LC3II measurement may not be of sufficient sensitivity to detect the smaller differences between low SUVmax CAF/NF pairs.
We also investigated whether there were differences between CAFs and NFs in chaperone-mediated autophagy (CMA), a process in which cytosolic proteins are transported into lysosomes for degradation that has been shown to be increased in some cancer cells (14–16, (25, 26). Upregulation of LAMP-2A is a marker for increased CMA. Although there was individual-to-individual variation in LAMP-2A expression, there were no consistent differences between CAFs and NFs (Supplemental Fig. 3). These data establish that CMA was not upregulated in CAFs.
The above results established an increase in basal autophagy in CAFs. Nutrient limitation is a primary inducer of autophagy, prompting us to investigate whether CAFs and NFs differ in the activation of autophagy in response to nutrient reductions. We first determined the effect of acute, stringent nutrient deprivation by incubating cells in medium lacking glucose and glutamine for 3 hours. Induction of autophagy was investigated by measuring the LC3II in Western blotting and by fluorescent microscopy measurement of LC3-stained puncta. Increased basal autophagy in CAFs was evident by an increase in the number and intensity of LC3-positive juxtanuclear structures relative to NFs (Fig. 5D). The LC3 labeling and the concentration in the juxtanuclear region of cells are characteristics of autophagosomes. Acute nutrient deprivation of cells in medium lacking glucose and glutamine resulted in an increase in autophagosomes in CAFs and NFs, although there was some cell-to-cell heterogeneity in both CAFs and NFs (Fig. 5D). Acute nutrient deprivation induced an increase of LC3II, measured by Western blotting, to the same level in CAFs and NFs (Fig. 5E). Thus, despite increased basal autophagy in CAFs, there was no difference between CAFs and NFs in the induction of autophagy in response to acute nutrient deprivation, showing that CAFs further induce autophagy in response to nutrient stress.
We next examined differences in autophagy between CAFs and NFs in response to chronic reduction in glucose availability rather than acute challenge with no glucose. Because of the high-glucose demands of cancer cells, glucose available for metabolism by stroma cells within the tumor microenvironment might be chronically limited. Cells are typically grown in culture in media supplemented with 25 mmol/L glucose, and our metabolomic profiling was conducted in cells expanded in 25 mmol/L glucose. To determine the effect of reduced glucose, cells were grown in media with 25 or 5 mmol/L glucose. We used quantitative fluorescence microscopy of LC3 to detect autophagosomes. The amount of LC3 (increased staining intensity) associated with juxtanuclear puncta, characteristic of lysosomes/autophagosome, was increased in NFs grown in 5 versus 25 mmol/L glucose, although the increase was not as great as when the cells were incubated in medium lacking glucose and glutamine (Fig. 6A). These data establish that reduced glucose in the growth medium–induced autophagy in NFs. However, there was no increase in autophagosomes of CAFs grown in 5 mmol/L glucose relative to growth in 25 mmol/L glucose (Fig. 6A). These data suggest that the set point for induction of autophagy by chronically reduced glucose is different between CAFs and NFs.
To determine whether reduced glucose affects dipeptide production, the abundance of the 17 dipeptides measured in the metabolomic profiling were determined for 7 CAFs/NFs grown in 5 mmol/L glucose (Fig. 6B). As was the case for the 21 CAF/NFs pairs (Fig. 2), the abundance of dipeptides was increased in CAFs of this subset of 7 pairs grown in 25 mmol/L glucose. However, there was no difference in the abundance of dipeptides in CAFs compared to NFs when the cells were grown in 5 mmol/L glucose (Fig. 6B). The abundance of dipeptides was significantly increased in NFs grown in 5 versus 25 mmol/L glucose, whereas the abundance of dipeptides in CAFs was not significantly altered by growth of cells in 5 mmol/L glucose. Thus, the increase in autophagosome formation induced in NFs by growth in reduced glucose was paralleled by an increase of dipeptides.
Growth of CAFs and NFs in media with varying amounts of glucose (25, 5, or 2 mmol/L) did not reveal a proliferative advantage of CAFs in response to reduced glucose (Supplementary Fig. S4), although the ability of NFs to upregulate autophagy upon chronic reduction of glucose might in this in vitro assay mitigate any advantage of the increased basal autophagy in CAFs.
Autophagic degradation ultimately depends on an active lysosomal system. Acidification is essential for the hydrolytic function of lysosomes and regulation of lysosomal pH is one mechanism for controlling lysosomal activity (30–32). We investigated whether there were changes in lysosomal acidification between CAFs and NFs. Cells were incubated with dual labeled fluorescein rhodamine dextran for 18 hours, and the endocytosed dextran was chased into lysosomes with a 3-hour incubation in medium without dextran. In both CAFs and NFs, the label accumulated in puncta distributed throughout the cell but with some degree of concentration in the perinuclear region characteristic of lysosomes (Fig. 6C). The rhodamine–fluorescein fluorescence ratio of the puncta was measured and converted into a pH values using a pH standard curve (Supplementary Figs. S5 and S6). There were no consistent differences in the average lysosomal pH between CAFs and NFs when grown in 25 or 5 mmol/L glucose (Fig. 6D). In all conditions, lysosomes had a pH value of 4.5, in line with previous measurements of lysosomal pH.
Many tumors are metabolically distinct from nonneoplastic tissue (e.g., 18F-deoxyglucose PET activity), reflecting the pronounced glycolytic metabolism of cancer cells. It is not known, however, whether the metabolisms of the nonepithelial cells of the tumor are also altered. To begin to explore that question, we used quantitative mass spectrometry profiling of the steady-state abundances of 203 metabolites in primary, nontransformed human lung CAFs and their paired NFs. Although we found no differences in individual metabolites distinguishing CAFs from NFs, we report herein the novel observation of significant differences between CAFs and NFs in the steady-state abundances of metabolites of select metabolic pathways.
In this initial study, because of some practical limitations of working with primary nontransformed cells, we conducted a steady-state metabolite profiling. We analyzed the data using a systems pathway approach by identifying changes in KEGG designation groups of metabolites that differed significantly from the overall differences in the abundance of metabolites between comparison samples. In addition to comparing CAFs with NFs, we also analyzed the data after segregating the samples based on the SUVmax of the tumor: "high" SUVmax (≥8) and "low" SUVmax (≤5). The impetus for this classification was that SUVmax is a measure of the glycolytic activity of the tumor and therefore it was of interest to know whether this is related to the metabolite profile of CAFs. Furthermore, individuals with high SUVmax tumors have a worse outcome on 5-year disease-free survival, so this classification is of potential clinical significance. We detected changes in groups of metabolites that indicate possible changes in metabolic pathways. We followed up one of these changes, increased dipeptides in CAFs, with additional experiments. Additional future studies are required to investigate other metabolic changes identified in this study. Nonetheless, our results establish for the first time metabolic differences between CAFs and their paired NFs that persist in in vitro culture and that are correlated with the glycolytic activity of the parent tumor.
The relative abundance of the 17 unrelated dipeptides was significantly different in the comparisons of the different fibroblast groups. There was an enrichment of dipeptides, relative to other metabolites, in CAFs versus NFs, and in CAFs from high SUVmax tumors compared with CAFs from low SUVmax tumors. Thus, increase in dipeptide abundance is a general characteristic of CAFs that is correlated with the glycolytic activity of the tumor.
The primary source of cellular dipeptides is protein turnover, suggesting differences in protein degradation among the different fibroblast groups. In parallel with the difference in dipepitde abundance, we found an increase in autophagy in CAFs relative to their paired NFs based on increases of LC3-labeled phagosomes as well as an increase in LC3II/I ratio. Autophagy was originally described as a response to nutrient deprivation in which cellular components are degraded to provide substrates for oxidative energy production. However, autophagy is now recognized to have roles in normal basal cellular physiology by mediating the turnover of long-lived proteins, the clearance of damaged mitochondria and other organelles, and the clearance of potentially toxic protein aggregates as a protection against oxidative damage (14, 25). In addition to these prosurvival activities, autophagy can, under certain conditions, result in cell death (14).
Studies of autophagy in tumors have focused on cancer cells (14). Our data support an increase in autophagy in fibroblasts of the tumor microenvironment, suggesting that alterations in autophagy within tumors are not restricted to the cancer cells. We have also discovered a positive correlation between increased dipeptide abundance/increased basal autophagy in CAFs and the tumor SUVmax, revealing a possible link between the glycolytic activity of the tumor and autophagy.
Increased autophagy is believed to promote survival of cancer cells by providing nutrients (amino acids, lipids, and nucleic acids) and/or as a protection against oxidative damage. Increased CAF autophagy might also provide a survival benefit to CAFs in the tumor. Thus, elevated autophagy, by promoting survival of CAFs, could indirectly contribute to the protumorigenic effects of CAFs, as CAFs thriving in the tumor would be able to support cancer cells and tumor growth by one or more of the previously described mechanisms (e.g., paracrine support). An alternative and not mutually exclusive view is that increased autophagy is not limited to cell-autonomous benefits for CAF survival but that increased autophagy in CAFs might be of direct benefit to cancer cells. Some possibilities include increased CAF autophagy as a means of sparing nutrients in the tumor microenvironment for use by the cancer cells or the secretion of nutrients from CAFs generated by autophagy to enrich the nutrient milieu of the tumor microenvironment. The latter is of particular interest with regard to dipeptides, as supplementation of media with certain dipeptides enhances in vitro cell viability (33).
CAFs and NFs similarly induced autophagy when acutely challenged with the severe nutrient restriction of removing glucose and glutamine from the medium. However, only the NFs induced autophagy when challenged with a chronic reduction of glucose in the medium, which indicates that NFs have a lower set point for induction of autophagy controlled by alterations in the nutrient milieu. Because of the high glucose consumption of cancer cells, glucose availability for other cells within the tumor is likely limited. Thus, it is tempting to speculate that the basal upregulation of autophagy in CAFs reflects this difference in set point for induction of autophagy and that it is advantageous for CAF growth/survival in the tumor environment. Although we did not observe consistent differences between growth of CAFs and NFs in normal growth medium (25 mmol/L glucose) or in conditions of "nutrient stress" induced by reduced glucose (5 or 2 mmol/L glucose), the in vitro growth assay does not recapitulate the conditions experienced by cells within the tumor microenvironment. Thus, future studies are required to address the functional consequences of increased CAF autophagy. Regardless, our results establish that autophagy changes in cells of the tumor microenvironment must be taken into account when considering the role of autophagy in solid tumors.
In addition to an increase in the abundance of dipeptides in CAFs from high SUVmax tumors relative to those from low SUVmax tumors, there was an increase, albeit less robust, in the abundance of dipeptides in NFs isolated from individuals with high SUVmax tumors relative to those from individuals with low SUVmax tumors. This link between the SUVmax of the tumor and an increased abundance of dipeptides in fibroblasts outside the tumor microenvironment was unexpected. An explanation for these results is that the possible tumor effects on CAFs are far reaching and that the less robust difference in the fibroblasts from nonneoplastic lung tissue reflects a diminution of the effect due to distance from the tumor. An alternative possibility is that the increased dipeptide abundance of NFs paired with high SUVmax tumors reflects a pre-existing difference independent of the tumor, and that the more robust differences between CAFs from high and low SUVmax tumors is an effect of the tumor re-enforcing this pre-existing difference. Another alternative possibility is that the differences in metabolites between NF derived from high and low SUVmax tumors may reflect differences in host epigenetic alterations in response to exposure to tobacco-related carcinogens.
Here, we have focused on the changes in dipeptides, which led us to consider differences in autophagy/proteolysis between CAFs and NFs. However, other groups of metabolites were significantly different in some of the fibroblast comparisons. In a number of comparisons, there were changes in metabolites of amino acid pathways, and in each case, the change in abundance was in the same direction as that of the dipeptides. These data implicate changes in protein/amino acid metabolism as important differences between CAFs and NFs.
It has been previously suggested that CAFs might undergo alterations in carbohydrate metabolism to support the "Warburg" metabolism of cancer cells. We found a reduction in the steady-state abundance of glycolytic intermediates in CAFs in the comparison of CAFs to NFs from low SUVmax tumors, consistent with this hypothesis. However, this difference was not observed in the comparison between high SUVmax CAFs and NFs. A possible explanation for these results is that the cancer cells of the low SUVmax tumors require alterations in CAF carbohydrate metabolism, whereas glycolytic metabolism of the cancer cells of the high SUVmax tumors is sufficiently robust independent of accommodation in the metabolism of CAFs within the tumor. Regardless, our study reveals the importance of considering the glycolytic nature of the tumor in studies of CAFs.
The profiling was conducted on cells grown in culture. An advantage of this approach over profiling whole tumor tissue is that we were able to specifically examine the nonepithelial cells of the tumor mass. We focused our analysis on vimentin-positive, keratin-negative cells that grew out of dispersed cells from tumor and adjacent nonneoplastic tissues, which is the standard method for isolating CAFs (e.g., ref. 9). Consistent with the literature, the CAF lines as a group expressed more α-smooth muscle actin (α-SMA), a marker for activated fibroblasts, than did paired NFs, but it is important to note that there was considerable heterogeneity among the different lines. However, despite this heterogeneity, our analyses revealed differences between CAFs and NFs that were maintained during in vitro culture. Previous studies have shown that CAFs retain differences from NFs after in vitro culture, including differences in gene expression as well as protumorigenic activities measured in vitro and in vivo (e.g., ref. 9). Thus, our findings of metabolic difference between CAFs and NFs maintained after in vitro culture are in agreement with previous studies.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors' Contributions
Conception and design: G. Salzler, N.K. Altorki, T.E. McGraw
Development of methodology: S. Dick, O. Elemento, T.E. McGraw
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): G. Salzler, M. Buckman, R. Mohney, B. Stiles
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): G. Salzler, S. Dick, M. Buckman, R. Sordella, E.D. Karoly, R. Mohney, O. Elemento, N.K. Altorki, T.E. McGraw
Writing, review, and/or revision of the manuscript: G. Salzler, S. Dick, M. Buckman, B. Stiles, O. Elemento, N.K. Altorki, T.E. McGraw
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): M. Buckman, N.K. Altorki, T.E. McGraw
Study supervision: O. Elemento, N.K. Altorki, T.E. McGraw
Grant Support
The study was supported by grants from Columbia University DIRC P30 DK063608-09 (principal investigator: D. Accili; sub-award to N.K. Altorki and T.E. McGraw), Neuberger Berman Lung Cancer Research Center (N.K. Altorki), Seth Sprague Education and Charitable Foundation Award (N.K. Altorki), WCMC Discovery award (T.E. McGraw), STARR Cancer Foundation (R. Sordella), NSF career award 1054964 (O. Elemento), Starr Cancer Foundation (O. Elemento), and Clinical and Translational Science Center at Weill Cornell Medical College TL1RR02499 (G. Salzler).
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
The authors thank Fred Maxfield, Vivek Mittal, and the members of the McGraw and Altorki group for comments on the manuscript.
Note: Supplementary data for this article are available at Molecular Cancer Research Online (http://mcr.aacrjournals.org/).
N.K. Altorki and T.E. McGraw are co-corresponding and senior authors.
- Received July 11, 2012.
- Revision received February 20, 2013.
- Accepted February 20, 2013.
- ©2013 American Association for Cancer Research.


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Antioch School News
Friday, February 7, 2014
Antioch Artists
Last year, Ms. Carter's students in grades 3 - 7 participated in an art contest
sponsored by
Bank National Marine Sanctuary
. Stellwagen Bank is New England's only marine sanctuary located between Cape Ann and Cape Cod, about 25 miles off the coast of Boston. The contest was entitled 'Amazing Ocean Creatures of Stellwagen Bank National Marine Sanctuary'. The contest received 726 entries from all over the country and 5 Antioch art students were recognized as having outstanding works of art. Judges included professional artists, graphic designers, marine scientists, exhibit and public outreach specialists, government officials and other individuals familiar with sanctuary species. The winning artwork has been posted on the
sanctuary website
, and is currently on tour.
North Atlantic Right Whale by Xander Schenck
Harbor Seal by Sophie Wood
North Atlantic Right Whale by Jack Viveiros
White Shark, Harbor Seal and Dolphins by
Harrison Carter, Nathan Upchurch
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3i Infrastructure says 88 per cent invested
The fund yesterday paid £25 million (US$49m) for 3i Group's stake in Alma Mater, a joint fund with Société Générale Asset Management (SGAM) which invests in the UK's student accommodation market.
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Basics of the HubSpot integration
Previous section: Integrating with HubSpot
Delighted's HubSpot integration syncs your Delighted feedback directly over to your HubSpot CRM. There are three key steps involved with this syncing process:
- Response is received in Delighted
- We search for a matching Contact record in HubSpot
- Feedback is synced to the matching Contact
In this section, we'll provide an overview of how each step works, any nuances associated with the syncing process, and the exact data we'll sync over. The net goal of this section is to ensure you and the team feel well-versed at the basics of the syncing process.
Response is received in Delighted
Let's say a brand new response has just reached your Delighted dashboard. This is where the HubSpot syncing process begins! After that response hits your dashboard, we'll wait 10 minutes (by default) and then sync the feedback over to your HubSpot CRM. We include this 10 minute delay due to the average respondent taking around 10 minutes to provide a comment. With this average time-to-comment in mind, we wait a full 10 minutes prior to starting the syncing process to ensure the entire comment is captured.
Contact is searched in HubSpot
Once that 10 minute timer is up, our integration will run a look-up in your HubSpot CRM. We map responses from Delighted to HubSpot based on the email address. When we find a HubSpot Contact record with a matching email address, we sync over that Delighted feedback.
Feedback is synced to the matching Contact
Now that a Contact record has been found, we'll proceed to pass over key Delighted response detail feedback to the HubSpot Contact record. Here is a recap of the key data points from Delighted that we push through to HubSpot:
- Delighted Comment: The comment (if applicable) provided in the Delighted survey response
- Delighted Group: The score group for the respondent (ex. for NPS, that would be Promoter, Passive, or Detractor)
- Delighted NPS: Rating selected by the customer (ex. a number between 0-10)
- Delighted Permalink: URL to the response in Delighted
- Delighted Responded At: Original date a response was received
- Delighted Response Updated At: Date a response was updated (if applicable)
Contacts and properties are at the core of most workflows and processes that occur in HubSpot. Whether creating a quick report, or generating a workflow based on a specific property, these Contact records serve as a repository for the complete set of information you have about a customer.
As we've covered in this section's overview, the HubSpot integration will feed through critical customer-driven insights and metrics that can help enrich your existing data and contribute towards more educated and impactful engagement with your customers.
But the benefits don't stop there! In our next section, we'll cover some of the key value adds you'll find when connecting the Delighted and HubSpot platforms.


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The Bucks County Audubon Society will hold its annual Earth Day Festival on Saturday April 22nd at the Audubon Society Visitor Center from 11:00am-4:00pm. There will be vendors, exhibits, demonstrations, presentations, hikes around their beautiful property, activities, crafts, live animals, and so much more during this exciting event. Admission in FREE. Click their link above for more information.
Earth Day Activity Schedule:
11am – Doors Open!
11:15 – Bug Hunt (1hr), Bird Blind Walk (45 min), Creek Exploration (45 min)
11:30- Little B Natural Organics Presentation (30 min)
12pm – Nina's Waffles!!! (2 hrs)
12:15 – Eeyore House Building (45 min)
1:30 – Heritage Conservancy Property Tour (45 min)
1:45pm – Creek Exploration (45 min)
2:30pm – Tree Planting Demonstration for 4 groups. First come, first serve! (45 min)
3pm – Pond Bird Walk (45min), Bug Hunt (1hr)
Leave a Reply.
April 2018


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Business Owners
Intellectual property is very important part of any business. Regardless of the field of activity, such IP objects need protection: trade name, trademarks, trade secrets, know-how, results of workers creative activity (e.g. software), new technologies or devices that could be inventions (utility models), and so on. Underestimating the importance of intellectual property and neglecting the registration IP right objects in time can lead to the risk of negative consequences. For example, the unregistered trademark creates the risk that competitors will ...more info
Medium and Small Business
For Medium and Small Business representatives the same rules of Intellectual Property rights treatment as for major corporations are applied: Absence of registered trademark may lead to "raping' you name by the others as soon as your business turns into successful one. Without registered TM it will not be possible to launch a website in ".UA" domain extension. Your advertising may infringe other entities' copyrights, and a copyright holder's legal lawsuit will do serious problems to your business. Your product may infringe other ...more info
Law Firms and Law Societies
Generally legal services market players fall into "law supermarkets" offering services in many practice areas, and "law boutiques" with a small area of expertise. Almost none of law firms in the world can say that its specialists provide services in all practice areas. IPStyle has a substantial experience in cooperating with law firms in Ukraine and other countries for providing IP protection services to their clients. Moreover, we provide interest representation for foreign law firms' clients in Ukraine and CIS countries while having a good ...more info
Non-governmental and Non-profit Organizations
Despite the non-profit nature of activity, non-profit organizations and NGOs (non-governmental organizations) require proper protection of their Intellectual Property and the analysis of their activity in terms of non-infringement of other entities' rights. As to Intellectual Property protection, the attempts of external agencies to use well-known brands of non-profit organizations (charity funds, anti-corruption initiatives, democracy development and environmental protection organizations) for their own profit should be carefully considered. And it ...more info
Universities and Educational Institutions
Educational and scientific institutions are very particular organizations in terms of Intellectual Property protection. On the one hand, they actively produce new IP objects while on the other hand they themselves use other person's IP. Therefore, universities, research institutes, research centers are constantly assigned the task to protect their Intellectual Property rights – either from infringements by other educational institutions and commercial organizations or from their own employees who can assume all the rights to the invention ...more info


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The trusted mine services contractor performing mining service projects and contract mining across North America.
Arizona, California, Nevada, Oregon, Texas, Virginia
General engineering contractor specializing in mass grading and underground utilities based out of San Diego, CA.
118 year old construction materials and services company based in Watsonville, CA.
Engineers, Interns, Laborers, Mechanics, Operators, Truck Drivers
A full service logging company working throughout South Carolina.
Heavy civil contractor and equipment company with 50 years of earthmoving experience in CA.
Over half a century of of delivering high quality Texas civil projects safely and on time.
Phoenix based heavy civil firm with extensive experience in shoring and foundations.


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I made my 300th post earlier tonight,
and on the spur of the moment decided that I would give a little surprise blog candy to the first person who left a comment on my blog after this.
So.... a little while ago, my friend Anita left me a comment (and after reading again about my cousin Don and his health issue, she left a promise to send a card too!)!
So I'll get a some small goodies sent off to her on Monday!
Congrats Anita. Thanks for visiting my blog and leaving a comment, and for the card too! I've sent you an email.
You never know when I will do something like this. I'd been thinking I needed to do something for some time, and this just occurred to me when I realized I had made that 300th post.
Who knows when the next one will happen, or for what reason I'll do it, but I have a few other goodies in my stash that I think might be loved by someone! They may be small at this time as I'm still not working, but I hope it will be some little things that someone will like.
Keep checking back to see what I'll do. It just may be something for followers next time, you never know!


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Seth Knopp, Artistic Director
Seth Knopp was a founding member of the Peabody Trio, recipient of the 1989 Naumburg Award, and has been the Artistic Director of Yellow Barn since 1998. During his tenure Yellow Barn has twice received the ASCAP/Chamber Music America Award for Adventurous Programming. He is also the Artistic Director of Soundings: New Music at the Nasher, a critically acclaimed series of concerts at the Nasher Sculpture Center in Dallas, Texas. His solo and chamber music performances can be heard on the Artek, Koch, and New World Records labels. Seth serves on the piano and chamber music faculties at the Peabody Institute of Johns Hopkins University.
Catherine Stephan, Executive Director
Catherine Stephan started this phase of her Yellow Barn life in 2010. Formerly a cellist and student of David Wells, she spent three summers at Yellow Barn in the mid-90s. Before returning to Putney she was Executive Director of the Boston Modern Orchestra Project and its record label, BMOP/sound, from 2000-2010. She has been a guest lecturer at New England Conservatory, Boston University, and UCLA, serves as a consultant for individual artists and ensembles, and has been a panelist for the Aaron Copland Fund for Music and. She holds degrees from Wellesley College (B.A.) and Brandeis University (Artist Diploma).
Maria Basescu, Managing Director
Maria Basescu joined Yellow Barn in the fall of 2019. She had been the founding Executive Director for Next Stage Arts Project since 2013. Maria has also served as President of Annapurna Concerts, Public Relations Director for Emerson College's Division of Performing Arts, and Associate Director of Media and Public Relations for the Boston Mayor's Office of Business and Cultural Development. She was a senior executive in communications and external affairs for Northern Berkshire Healthcare, the Brattleboro Retreat, and Marlboro College, and was a Senior Advisor with the strategic communications firm Denterlein. She is an alumna of Dartmouth College (B.A.) and a graduate of the Snelling Center for Government's Vermont Leadership Institute.
anna whistler, special projects manager
Anna Whistler first became involved with Yellow Barn as a participant in the summer internship program in 2010. Thereafter, she served briefly as Yellow Barn's interim production manager before pursuing graduate studies. Prior to returning to Yellow Barn, she worked in broadcasting for WNED in Buffalo, NY, and in sales for Steinway & Sons in Los Angeles, CA. She holds degrees from Smith College (B.A.), the University of Massachusetts Amherst (M.M.), and is currently a J.D. candidate at Cornell Law School.
Joohyun Lee, General Administrator email@example.com
Hannah Rushing, Festival Manager email@example.com
Melody Lin, Admissions Coordinator firstname.lastname@example.com
Crystal Fielding, piano technician
William Ballard, piano technician
Travis LaPlante, Big Barn House Manager
Michael Hanish, video recording


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So only 20 days to go until Learning Live and I have to say I'm very excited about speaking there. I've been to a fair number of conferences over the last few years and have listened to people who I look up to, have been inspired by and aspire to be like.
The first conference I went to was Learning Technologies 2008, it was at this conference I listened to Jane Hart give a presentation on using social networks in the workplace. I am not exaggerating when I say this presentation changed my career as a Learning Professional and to some extent my life. I would not be speaking at Learning Live or have done the things I've done in the last four years without sitting in on this presentation. I was inspired to go and do something different, I was inspired to take action and I've not looked back since.
A little known fact was that my first speaking 'gig' would have been at Learning Technologies 2010. I had been asked by Don Taylor to give a talk entitled "Innovating with existing tools" and I had agreed enthusiastically and appeared in the glossy brochure and on the website. Sadly the organisation where I worked at the time didn't agree this was a fantastic opportunity and decided to stop me from being involved at a very late stage. Needless to say this caused great embarrassment for me and I also felt bad for Don as it left him with little time to get a replacement. Professional credibility and reputation is of great importance to me and this whole episode is not something I look back on fondly.
Anyway back to the happy times and two years on the opportunity has arisen to speak again. At least you know I'm consistent, it's just taken me a little bit longer…..for those of you who know me you will know how much I appreciate and the importance I place on the opportunity.
Why though?
Why the desire to speak at a conference or event?
- Is it vanity?
- Is it a desire to be in the spotlight?
- Do I need some sort of acceptance or acknowledgement from the L&D community?
- Is it fame I seek?
Well you could argue it's a little bit of all the above and I wouldn't disagree. The first time I graced the stage was in an amateur dramatics production of a Sound of Music when I was 10yrs old. Many years later I found myself performing in front of 15,000 people at Earls Court as my alter ego MC LayZ. I like performing, even though I'm self-conscious and it makes me nervous as hell, I enjoy doing it.
I'm looking at Learning Live as a performance, I won't be singing or rapping but I will be talking about something that excites me and something I passionately believe in. That is the reason why I want to talk at conferences so I can share the things I've done and hopefully inspire others to go and take action and do things differently. Just like Jane did with me 4 years ago.
I've got a 30 minute slot on the second day to tell a story and I've been thinking a lot about how to approach it prompted by a post from @SukhPabial. You can read it here #HRD11 – Conference planning, the post was written during the 2011 HRD conference but I think some (if not most) of the things discussed are still relevant for any conference.
The bit that has been making me think is this section about style of presentations
*checks calendar* 8th April 2011. What's that? We're no longer restricted to PowerPoint as a delivery format you say? We've moved beyond the flipchart? Pull the other one! I didn't see one Prezi, pecha kucha, or any other style of presentation. And we're meant to be the leaders of information delivery? And we're meant to be the ones who know everything there is to know about presentations? Attending the various sessions, you really wouldn't think so.
Plenty to think about then. Do I steer clear of PowerPoint and use Prezi or do I do a Pecha Kucha? A time slot of 30 minutes isn't long and will fly by so a Pecha Kucha would be useful as I could tell quite a detailed story in only 6 minutes leaving time for plenty of questions and even a brew.
Decisions, decisions, decisions….
I'm going to throw some ideas out and discuss some of the themes from the session on this blog over the next three weeks and if you are attending Learning Live on the 13th September you can catch my session at 15.45-16.15
It's got the catchy title of Using Live Online Learning For Organisational Collaboration
I hope to see you there


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I hope you're enjoying the summer weather wherever you are in the world. I also hope you enjoy this week's selection of posts for my Social Media Weekly Roundup.
10 Pintastic tools to grow your business by Lilach Bullock: "Are you using Pinterest to help market your business? If you want to get better results and save time with your social media marketing, read this post for 10 Pintastic tools to help grow your business."
9 Blogging Tools That Will Make Your Job Easier Content Marketing Institute: "Managing a blog is a tough task, especially if it's a multi-author outlet. It involves a lot of editing, relationship building, and task management."
How to Manage Instagram via the Facebook Inbox from Social Media Examiner: "Ever wish you could manage your Instagram and Facebook comments and messages in one place? Have you heard of the Facebook Inbox? Facebook's Inbox allows you to manage Instagram, Facebook, and Messenger comments and messages in a single location. In this article, you'll discover how to use the Facebook Inbox to manage your Instagram account."
Why you MUST give your best stuff away (for FREE) from We Trigger Growth: "Marketing legend Seth Godin has accomplished a lot in his career. He has written 18 books (available in 35+ languages). He has given multiple TED talks. His Kickstarter campaigns are the most successful ever in the book-funding niche. But most importantly, Seth is the inbound marketing pioneer who famously said:
In the digital world, the more free samples you give away, the better you do.The miserly mindset that afflicts the merchant watching inventory walk out the door at the market is counterproductive in the digital world."


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The dark web is kind of like the last season of Game of Thrones; it's not all good, but it's not all bad either. In her session today, Emily Wilson, CFE, VP of Research at Terbium Labs, discussed the dark web fraud economy thriving just below the accessible search engines we visit every day.
According to Wilson, the dark web is not found through a Google search and it requires special technology called TOR for access. The technology is used to encrypt a user's IP address and browsing history to give you an anonymous presence while you search. The dark web lives below the clear web and the deep web, and is booming with activity. But, Wilson emphasized that it isn't a place anti-fraud professionals should fear or see as only a cesspool of crime. She said multiple times, to ensure attendees heard, "The dark web isn't criminal; it's just another part of the internet."
So if it's not all made up of cybercriminals selling the social security cards of all of my nieces and nephews, then what exactly is on the dark web? It is made up of legal activities like Facebook and The New York Times, as well as communities and discussion forums. The only difference between the content found on the dark web versus the content we access on the clear web is that a user's viewing and search are protected by the encryption TOR technology. As Wilson said before, it's important to remember that the dark web is just another part of the internet and the same risks that apply to the clear web apply to the dark web.
And, as anti-fraud professionals, it is vital to understand those risks and how they are illustrated on the dark web fraud economy. Wilson outlined five categories that content on the dark web can be segmented into:
Personal information: this includes information like credentials, physical addresses and an extended personal history (also knowns as "doxing", which is a targeted release of a vast amount of personal information from a highly motivated individual)
Financial information: including items like payment cards, gift cards, bank account information, and processor accounts like PayPal, Venmo and Western Union payments
Corporate data: this is data like employee personal identifiable information (PII), W2s, tax records, invoicing and intellectual property
Guides and tutorials: these are actual manuals on how to commit crime and fraud courses
Services and tools: including tools like exploit kits, ATM skimmers and spam/harassment-as-a-service resources
"The dark web fraud economy includes all the raw materials, resources and services a cybercriminal needs," Wilson said. "Fraudsters value data based on its potential for monetization. They are thinking about three things: 1) Can I make money from it? 2) How much money can I make from it? And 3) Can I use it again?
Wilson closed with an optimistic and proactive idea about what anti-fraud professionals and investigators can do in the future regarding credit card theft. "It's not magic; it's methodology," she said. "Let's ask a 'what if' question about credit card fraud. What if we could somehow flag the cards before they get posted and sold on dark web? What if when the criminals try to use the stolen information, it is always deactivated?"
Wilson said she is working with other organizations on how this could be implemented and encouraged attendees to think about those "what ifs" and their potential to countering the health of the dark web fraud economy.


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Project Details
Reviewing and providing instrumentation services for drill-and-blast operations and standard operating procedures.
Client: Goldcorp
Time Span: 2018
Location: Argentina
Related Service Areas
Related Solutions
Goldcorp's Cerro Negro mining complex consists of three underground mines that use stoping techniques to recover gold ore. RESPEC partnered with our Professional Associates Dr. Braden Lusk and Dr. Jhon Silva to conduct a 10-day evaluation of the drill-and-blast operations. The RESPEC team worked with Cerro Negro personnel to improve their drill-and-blast process from design to execution and focused on the following Scope of Work:
- Review the current drill-and-blast design criteria/standards
- Review the current drill-and-blast operating procedures for development and production
- Present the findings and recommendations
- Develop an improvement process with the required resources
- Upgrade drill-and-blast design standards and operating procedures
- Develop quality assurance/quality control (QA/QC) protocols and checklists.
We focused on providing an exhaustive review of current drill-and-blast standards and practices. We also provided the instrumentation service to evaluate the proficiency of drilling, explosives loading, explosives performance, and blast results for the development and production rounds. Our review included auditing the drilling design process, drilling practices, and drilling results. On the blasting side, the audit included the blast design process, explosives loading practices, explosives selection and application, explosives performance during detonation, blast fragmentation, and dilution. We will also reviewed in-house and third-party audits that had been completed previously.
Related Products
RESPEC's product line is geared towards making businesses and organizations better, faster, smarter, and safer. Our products benefit clients in need of greater efficiency, accuracy, and cost savings.


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The global solution for quality and performance
GE's Enerbus Isolated Phase Busducts connect the turbine generator to the main transformer. Product features are:
- Nominal current up to 52,000 A (world record)
- Power plant capability up to 1,600 MW
- Short-circuit withstand current up to 1,200,000 A
- Easy access to insulators for maintenance
Plant efficiency
Minimizing losses to maximize plant efficiency, Enerbus is engineered to limit loss in the steel structures adjacent to the enclosure and in the concrete reinforcement.
Power availability
Configured to fit the most stringent power plant environments and withstand the highest short circuit current, our technology can adapt to specific generator and grid constraints.
Power quality
GE has been developing its global manufacturing capability for plant busbars to increase power quality with over 50 years of experience; producing over 1,100 connections delivered to date.
Project References
- Merowe (Sudan) - 10 x 140 MVA
- Dai Ninh (Vietnam) - 2 x 153 MW
- Bakun (Malaysia) - 4 x 300 MW
- Afourer (Morocco) - 471.6 MW
- Chicoasen (Mexico) - 3 x 318 MW
- GM Shrum (Canada) - 10 x 273 MW
Want to get the most out of your hydropower assets? Reach out to GE's team to start the conversation.


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Harbor Freight Tools - East Peoria
location on Google Maps.
in new tab.
on social media.
Founded in 1977, Harbor Freight Tools is the leading discount tool retailer in the U.S. selling great quality tools at "ridiculously low prices" in over 600 stores nationwide and on its website www.harborfreight.com. Harbor Freight Tools stocks over 7,000 items in categories including automotive, air and power tools, shop equipment and hand tools. With a commitment to quality and a lifetime guarantee on all hand tools, Harbor Freight Tools is a favorite of automotive and truck repair shops, government agencies, schools, manufacturers, contractors and tool enthusiasts who want top-quality and great selection and value. The results speak for themselves - with over 25 million customers nationwide and thousands of people switching to Harbor Freight Tools every day.
Business Hours
Monday 8:00 AM - 9:00 PM
Tuesday 8:00 AM - 9:00 PM
Wednesday 8:00 AM - 9:00 PM
Thursday 8:00 AM - 9:00 PM
Friday 8:00 AM - 9:00 PM
Saturday 8:00 AM - 9:00 PM
Sunday 9:00 AM - 6:00 PM