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Cells 2013, 2(2), 224-243; doi:10.3390/cells2020224
Abstract: The gene expression kinetics for human cells exposed to hyperthermic stress are not well characterized. In this study, we identified and characterized the genes that are differentially expressed in human epidermal keratinocyte (HEK) cells exposed to hyperthermic stress. In order to obtain temporal gene expression kinetics, we exposed HEK cells to a heat stress protocol (44 °C for 40 min) and used messenger RNA (mRNA) microarrays at 0 h, 4 h and 24 h post-exposure. Bioinformatics software was employed to characterize the chief biological processes and canonical pathways associated with these heat stress genes. The data shows that the genes encoding for heat shock proteins (HSPs) that function to prevent further protein denaturation and aggregation, such as HSP40, HSP70 and HSP105, exhibit maximal expression immediately after exposure to hyperthermic stress. In contrast, the smaller HSPs, such as HSP10 and HSP27, which function in mitochondrial protein biogenesis and cellular adaptation, exhibit maximal expression during the “recovery phase”, roughly 24 h post-exposure. These data suggest that the temporal expression kinetics for each particular HSP appears to correlate with the cellular function that is required at each time point. In summary, these data provide additional insight regarding the expression kinetics of genes that are triggered in HEK cells exposed to hyperthermic stress.
Human beings frequently encounter a wide variety of hyperthermic stressors. Common examples include exposure to hot conducting objects, elevated environmental temperatures, as well as electromagnetic radiation from the sun and numerous man-made sources, such as lasers, communication devices and imaging systems [1,2,3,4,5,6,7,8,9,10]. The heat generated by hyperthermic stressors can be harmful to organisms, because it can temporarily or permanently alter the conformation and function of vital intracellular biomacromolecules (i.e., lipids, proteins and nucleic acids). Such modifications can result in an array of damaging effects, including cellular morphological alterations (i.e., shape, size, irregularities and roughness), cytoskeleton restructuring, organelle reorganization and/or fragmentation and direct interference with intracellular transport processes .
Hyperthermic effects can interfere with an organism’s normal homeostasis, and thus, if they are not adequately counteracted, they can threaten its survival. In order to survive and minimize the effects of hyperthermic stress, virtually all cells in living organisms have evolved sophisticated sensing, response and adaptation mechanisms. The crux of these mechanisms is the molecular defense reaction, most commonly referred to as the cellular stress response (CSR). The CSR is immediately activated in response to stress stimuli, and a hallmark of this response is the robust transcriptional expression of hundreds of diverse genes. These genes encode for a multitude of proteins involved in many important functions, including cellular survival, deoxyribonucleic acid (DNA) sensing and repair, redox regulation, proteolysis, molecular chaperoning, cell cycle control and apoptosis [12,13]. Furthermore, each CSR gene exhibits unique expression profiles, which differ in magnitude, onset time and duration of expression [4,10].
The epidermis, the outermost layer of an organism’s skin, regularly encounters a wide variety of stressors. In fact, the primary function of the epidermis is to give organisms a physiological protection barrier against environmental stimuli, such as hyperthermic stressors. This protective role is mediated primarily by keratinocytes, the predominant epithelial cell type (~95%). Due to their anatomical location, keratinocytes are habitually exposed to environmental temperature variations, and thus, they are the primary and frequent target of hyperthermic stress. In addition, their frequent exposure to heat stress has also been reported to render them with specialized cellular responses, which make them more resistant to stress than virtually all other cell types [14,15]. Clearly, keratinocytes carry a disproportionate burden in maintaining homeostasis for organisms; thus, they are vital players in sensing, responding and adapting to the burdens of hyperthermic stress.
Over the past decade, numerous microarray experiments have been performed to better understand the stress response of human epidermal keratinocytes (HEK) [15,16,17,18,19]. These studies have been particularly fruitful, because they have resulted in the identification of hundreds of stress-inducible genes. However, previous gene expression studies have failed to provide a comprehensive picture of the cellular stress response for several reasons. First, the microarray gene chips utilized in most of the previous studies were incomplete and, thus, provided expression kinetics for only a fraction of the genes in the human genome. Second, many previous studies made gene expression measurements at a single time point; as a result, temporal gene expression kinetics could not be ascertained. Finally, the majority of past studies examined the response of skin cells to ultraviolet stress; however, few studies have specifically analyzed the transcriptional response of HEK to hyperthermic stress .
In this study, we sought to identify and characterize the genes that are differentially expressed in HEK exposed to hyperthermic stress. To obtain temporal gene expression kinetics, we exposed HEK to a heat stress protocol (44 °C for 40 min) and used mRNA microarrays at three time points: 0 h, 4 h and 24 h post-heat treatment, as outlined in Figure 1A. Then, we used bioinformatics software to characterize the chief biological processes and canonical pathways associated with these genes. These data provide valuable new insights that give us a much clearer picture of the genes and intracellular signaling pathways that are triggered in human cells exposed to hyperthermic stress.
2. Results and Discussion
2.1. Keratinocytes Induce an Appreciable Stress Response When Exposed To Hyperthermic Stress
We conducted an initial set of experiments to investigate the impact that our selected hyperthermia stress protocol had on cellular viability. For these studies, HEKs were exposed to hyperthermic stress and cellular metabolic activity was measured at 0, 4 and 24 h post-exposure using MTT assays (Figure 1B). For all cell densities tested, the data shows that greater than 90% of the cells survive after being exposed to our hyperthermic stress protocol. This data confirms that the HEKs cells were viable post-heat exposure.
After confirming that the HEK cells survived post-exposure, we then sought to test whether our selected hyperthermic stress protocol induced an appreciable transcriptional stress response To investigate the transcriptional stress response, we exposed HEK cells to hyperthermic stress and used microarray gene chips to quantify the expression level of genes at 0, 4 and 24 h post-exposure (Figure 1A). Since a signature feature of an appreciable stress response is the rapid and marked upregulation of minimal stress genes , we conducted microarray and PCR analyses to examine the genes that HEKs express when exposed to hyperthermia (Figure 1C,D). Using Ingenuity Pathway Analysis (IPA) software, we filtered the microarray data using the following cut-offs: (i) an absolute value of expression magnitude (log2 ratio relative to control) greater or equal to 1.5; and (ii) a p-value less than or equal to 0.05. Of the 54,675 probe sets tested on each gene chip, we found that 762, 1,422 and 1,478 genes were differentially expressed at the 0, 4 and 24 h time points, respectively (Figure 1C). The number of upregulated genes increased in a logarithmic fashion from 466 at 0 h, 872 at 4 h and up to 1,127 at 24 h. Interestingly, roughly 60% of the transcripts regulated at 0 and 4 h were upregulated, but by 24 h, ~75% of the differentially expressed genes were upregulated.
Specifically, we found that the heat-treated groups expressed transcripts for many heat shock proteins (Hsps). We also found that the transcripts encoding for heat shock protein 70 (HSPA6) exhibited the greatest increase in expression and the highest level of statistical significance. To validate these microarray results, we then conducted qRT-PCR analyses for HSPA6 (Hsp70) (Figure 1D). For all of the samples tested, we found that the heat-treated treatment group exhibited statistically significant increases in HSPA6 expression, ranging between 10- and 25-fold compared to the sham group. These results were consistent with the microarray data, which showed that HSPA6 expression increased between 4- and 32-fold.
2.2. Temporal Gene Expression Profiles for Keratinocytes Exposed To Hyperthermic Stress
In order to visualize the global distribution of the gene expression profiles, we then created volcano plots for all genes at each time point. In these plots, the level of statistical significance (p-value) for each gene is plotted versus the fold-change in expression level relative to untreated sham (Figure 2). Genes with the highest level of statistical significance appear on the right side of the plot. In addition, upregulated genes that display large-magnitude fold-changes appear towards the top of the plot (denoted with green triangles), whereas downregulated genes appear towards the bottom (denoted with red triangles). We found that considerably more genes were differentially expressed at 4 h and 24 h than at the 0 h time point. We also found that the transcripts expressed at the later time points also exhibited lower p-values and larger-magnitude changes in expression level than the genes at 0 h. Collectively, our results show that hyperthermic stress triggers a rapid, robust and extensive transcriptional response in HEK cells. The temporal kinetics expression data also show that this stress response is immediately activated in response to stress and is sustained for many hours after exposure.
2.3. Biological Functions of Heat-Shock-Induced Genes
After identifying the genes expressed in response to hyperthermic stress, we then used IPA software to identify each gene’s primary biological functions. Figure 3 contains pie charts of the total number of genes associated with each cellular and molecular function. We found that 23 biological functions were common to all three time points. Interestingly, only nine functions in this group were either unique to one or shared by two time points (Figure 3). In general, the 0 h and 24 h time points share many genes with functions required for free radical scavenging, whereas only the 4 h time point contains genes with functions dedicated primarily to protein regulation (i.e., protein trafficking and protein folding) (Figure 3B).
Next, we identified the biological function groups that had the greatest number of differentially expressed genes. For each time point, we generated color-coded pie charts containing the top five biological functions and the number of genes in each group (Figure 4). We found that two biological functions (i.e., cellular growth and proliferation and cell death) appeared in the top five at each time point. In addition to these functions, the 0 h and 4 h time points also shared two more top functions, which are associated with cellular development and gene expression. The only major difference between these time points was that the 0 h time point had more genes associated with molecular transport, whereas the 4 h group had more genes involved in cellular movement functions. In contrast, despite exhibiting a similar number of regulated genes, the 4 h and 24 h groups only shared three top functions: cellular movement, cell death and cell growth. The primary difference between these time points is that the 4 h group had more genes dedicated to cellular development and gene expression, whereas the 24 h group exhibited genes with cellular function and cell cycle processes.
2.5. Pathway Analysis of Microarray Analyzed Data
We next used IPA to analyze the metabolic and signaling pathways that are affected in response to heat stress following the three post-heat time points. We found that at 0 h, 33 of 247 canonical pathways were expressed with statistical significance, defined as [(−log(p-value) ≥ 1.3 or p-value ≤ 0.05] (Table 1). At 4 h post-heat, 19 out of 198 scored Ingenuity Canonical Pathways had significant likelihood (Table 2). At 24 h post-heat, IPA scored 229 Ingenuity Canonical Pathways, of which 36 had significant p-values (Table 3).
|Ingenuity Canonical Pathways||−log(p-value)||Ratio||# of Molecules|
|IL-12 Signaling and Production in Macrophages||4.66||0.118||16|
|Glucocorticoid Receptor Signaling||1.83||0.061||16|
|Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis||1.41||0.054||16|
|Production of Nitric Oxide and Reactive Oxygen Species in Macrophages||1.85||0.070||13|
|Clathrin-Mediated Endocytosis Signaling||1.80||0.069||13|
|Dopamine-DARPP32 Feedback in cAMP Signaling||1.46||0.067||11|
|Neuropathic Pain Signaling in Dorsal Horn Neurons||2.35||0.094||10|
|Type II Diabetes Mellitus Signaling||2.07||0.079||10|
|Virus Entry via Endocytic Pathways||2.28||0.098||9|
|Synaptic Long-Term Potentiation||1.88||0.087||9|
|fMLP Signaling in Neutrophils||1.78||0.077||9|
|PKCθ Signaling in T-lymphocytes||1.64||0.071||9|
|Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses||2.29||0.104||8|
|T-cell Receptor Signaling||1.61||0.078||8|
|iCOS-iCOSL Signaling in T Helper Cells||1.45||0.071||8|
|Pancreatic Adenocarcinoma Signaling||1.41||0.070||8|
|NF-κB Activation by Viruses||1.76||0.089||7|
|RANK Signaling in Osteoclasts||1.42||0.076||7|
|Calcium-induced T-lymphocyte Apoptosis||1.72||0.098||6|
|Growth Hormone Signaling||1.42||0.085||6|
|MSP-RON Signaling Pathway||1.66||0.109||5|
|Cell Cycle: G1/S Checkpoint Regulation||1.33||0.085||5|
|Role of CHK Proteins in Cell Cycle Checkpoint Control||1.50||0.114||4|
|April Mediated Signaling||1.31||0.095||4|
|Ingenuity Canonical Pathways||−log(p-value)||Ratio||# of Molecules|
|Leukocyte Extravasation Signaling||2.58||0.127||24|
|Huntington’s Disease Signaling||2.39||0.114||26|
|Tight Junction Signaling||2.34||0.127||20|
|Bladder Cancer Signaling||2.15||0.144||13|
|Colorectal Cancer Metastasis Signaling||1.90||0.107||26|
|Cyclins and Cell Cycle Regulation||1.76||0.126||11|
|Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis||1.70||0.096||30|
|Nur77 Signaling in T-lymphocytes||1.62||0.140||8|
|IL-17A Signaling in Gastric Cells||1.57||0.200||5|
|Cell Cycle: G1/S Checkpoint Regulation||1.49||0.136||8|
|IL-17A Signaling in Fibroblasts||1.49||0.154||6|
|Production of Nitric Oxide and Reactive Oxygen Species in Macrophages||1.37||0.103||19|
|Aldosterone Signaling in Epithelial Cells||1.35||0.106||17|
|Ingenuity Canonical Pathways||−log(p-value)||Ratio||# of Molecules|
|Aryl Hydrocarbon Receptor Signaling||4.33||0.170||23|
|Linoleic Acid Metabolism||2.78||0.197||11|
|Role of IL-17A in Psoriasis||2.74||0.385||4|
|Bladder Cancer Signaling||2.72||0.167||14|
|Oncostatin M Signaling||2.52||0.235||7|
|Cell Cycle: G1/S Checkpoint Regulation||2.21||0.169||9|
|Fcγ Receptor-mediated Phagocytosis in Macrophages and Monocytes||2.08||0.149||13|
|Role of CHK Proteins in Cell Cycle Checkpoint Control||2.00||0.200||6|
|Glucocorticoid Receptor Signaling||1.94||0.108||29|
|Cell Cycle: G2/M DNA Damage Checkpoint Regulation||1.88||0.167||7|
|Amino sugars Metabolism||1.88||0.159||10|
|Metabolism of Xenobiotics by Cytochrome P450||1.86||0.148||12|
|Airway Pathology in Chronic Obstructive Pulmonary Disease||1.75||0.375||2|
|LPS/IL-1 Mediated Inhibition of RXR Function||1.74||0.112||22|
|Chronic Myeloid Leukemia Signaling||1.71||0.127||12|
|Arachidonic Acid Metabolism||1.71||0.137||13|
|Hereditary Breast Cancer Signaling||1.67||0.125||14|
|Leukocyte Extravasation Signaling||1.66||0.116||21|
|Role of BRCA1 in DNA Damage Response||1.65||0.153||8|
|Pancreatic Adenocarcinoma Signaling||1.61||0.123||13|
|Role of IL-17F in Allergic Inflammatory Airway Diseases||1.56||0.167||6|
|Cellular Effects of Sildenafil (Viagra)||1.47||0.121||15|
|Inhibition of Angiogenesis by TSP1||1.47||0.182||5|
|Role of JAK Family Kinases in IL-6-type Cytokine Signaling||1.45||0.192||4|
Subsequently, in order to highlight the pathways more relevant to cellular stress response, we further refined our analysis by excluding signaling pathway categories, such as cancer, cardiovascular and nervous system signaling, toxicity list and disease-specific pathways, in addition to non-skin cell-type-specific canonical pathways. Figure 10 shows a refined presentation of the top canonical pathways for each post-heat recovery time point. For the cell cycle, the G1/S checkpoint regulation pathway was affected in all three time points. The ceramide signaling pathway was affected in both 0 h and 4 h. The glucocorticoid receptor signaling pathway and role of CHK proteins in cell cycle checkpoint control were affected in both 0 h and 24 h. All the remaining canonical pathways involved were exclusive to a particular time point. Notably, although most of the canonical pathways in each post-heat time point were unique, they actually serve overlapping top cellular functions. The canonical pathways playing a role in cellular function and maintenance that were affected in 0 h post-heat consisted of MSP-RON signaling, Rac signaling pathways and two endocytosis pathways. Both endocytosis pathways, namely, macropinocytosis and clathrin-mediated endocytosis signaling pathways, play important roles in plasma membrane reorganization, internalization of extracellular molecules and in transduction of signals within the cell and between cells. Deregulation of seven and 13 genes in macropinocytosis signaling and clathrin-mediated endocytosis signaling, respectively, starting 0 h post-heat, implicate an immediate effect of heat shock on the plasma membrane and signal transduction. Macropinocytosis depends on signaling to the actin cytoskeleton, such as the Rac signaling pathway, which was also affected at 0 h post-heat, with eight deregulated genes, including the Rac activator, TIAM1 (T-lymphoma invasion and metastasis 1). Immediately, TIAM1 mRNA showed more than a 4-fold increase. Interestingly, TIAM1 has been reported to prevent cell death in heat-stressed wild-type murine keratinocytes, since the TIAM1 knock-out cells displayed increased apoptosis .
Of the canonical pathways affected at 4 h post-heat, tight junction signaling plays a role in cellular function and maintenance, whereas those affected at 24 h post-heat included amino sugar metabolism and cAMP-mediated signaling pathways. The latter is also important in controlling cellular assembly and organization in addition to post-translational modification. Notably, tight junction signaling also plays a role in cellular assembly and organization in addition to cell-to-cell signaling and interaction. The 0 h time-point post-heat affected pathways playing roles in the regulation of cellular development and/or in cellular growth and proliferation comprised cell cycle G1/S checkpoint regulation, the glucocorticoid receptor, IL-9, NF-κB, April-mediated and growth hormone signaling. The 24 h post-heat assessment showed that pathways regulating these functions are the cell cycle G1/S checkpoint regulation, the glucocorticoid receptor, IL-10 signaling, the role of JAK family kinases in IL-6-type cytokine signaling and purine metabolism, while affected pathways at 4 h post-heat included cell cycle G1/S checkpoint regulation only. The cell cycle control function is regulated by two pathways at 0 h post-heat, namely cell cycle G1/S checkpoint regulation and growth hormone signaling.
Whereas, four of the canonical pathways affected at 4 h post-heat (ATM signaling, cyclins, cell cycle regulation, nitrogen metabolism and cell cycle G1/S checkpoint regulation) function in the control of cell cycle progression. Six of the 17 canonical pathways affected at 24 h post-heat play a role in cell cycle control. These are aryl hydrocarbon receptor signaling, cell cycle G1/S checkpoint regulation, the role of CHK proteins in cell cycle checkpoint control, cell cycle G2/M DNA damage checkpoint regulation and the two metabolic pathways, linoleic acid metabolism and tryptophan metabolism. Cell death, on the other hand, is controlled by the ceramide signaling pathway that was affected at 0 and 4 h post-heat and by the related sphingosine-1-phosphate signaling pathway, as well as by the aryl hydrocarbon receptor signaling and cell cycle G2/M DNA damage checkpoint regulation, which were affected at 24 h post-heat. Interestingly, the canonical pathways involved in inflammatory response were only affected at 24 h post-heat. They consist of IL-10 and IL-22 signaling. The DNA replication, recombination and repair function involves the role of CHK proteins in cell cycle checkpoint control that was affected at 4 h and 24 h post-heat, the ATM signaling pathway that was affected at 4 h post-heat only and cell cycle G2/M DNA damage checkpoint regulation that was affected at 24 h post-heat only.
3. Experimental Section
3.1. Cell Culture Conditions and Hyperthermia Stress Protocol
HEK were cultured in EpiLife® medium containing supplement S7, as per the manufacturer’s recommendations (Life Technologies). For the heat-shock experiment, cells were put in 1.7 mL tubes (1.0 × 106 cells/tube) that were sealed with Para-film® and placed in a circulating water bath at 44 °C for 40 min. No significant change in pH was observed. Following hyperthermia, cells were either harvested within 5 min or were first allowed to recover in the incubator at 37 °C/95% humidity/5% CO2 for 4 h or for 24 h. Control cells harvested alongside the heat-treated cells were also put in 1.7 mL tubes (1.0 × 106 cells/tube), but were maintained at all time in an incubator at 37 °C/95% humidity/5% CO2.
3.2. Cellular Viability Assays
Viability was evaluated at different post-exposure time points using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assays (ATCC, Manassas, VA, USA). In brief, HEKs were exposed to hyperthermic stress, then were incubated for 5 min (0 h post-heat), 4 h (4 h post-heat) or 24 h (24 h post-heat). Subsequently, MTT reagents and detergents were added, as per the manufacturer’s instructions. Absorbance readings were measured at 570 nm with a Synergy HT Plate Reader (Biotech, Winooski, VT, USA). Absorbance readings were also measured for a ladder of serial dilutions (103–106 cells) in culture medium. Absorbance values were plotted versus the cell number, and these curves were used to determine the number of viable cells in each well.
3.3. RNA Isolation and Normalization
RNA was extracted from the heat-treated and control cells using a RNeasy Mini-Kit (Qiagen), according to the manufactures instructions. RNA extraction was performed at three different time intervals as follows: within 5 min following heat-shock (0 h post-heat), after a 4 h recovery period at 37 °C (4 h post-heat) or after a 24 h recovery period at 37 °C (24 h post-heat). At each time point, the RNA concentration was assessed on a NanoDrop Spectrophotometer (NanoDrop Technologies), and the quality was measured on a 2100 Bioanalyzer™ (Agilent Technologies). RNA samples with a RNA Integrity Number greater than 9.5 were subjected to microarray analysis.
3.4. Microarray Data Analysis
Expression analysis was performed for each time point in triplicates (three control and three heat-shock treated) using the Affymetrix GeneChip® Human Genome U133 (HG-U133) plus 2.0 Array that contains 54,675 probe sets. Briefly, two micrograms of RNA were used for preparation of biotin-labeled targets (cRNA) using MessageAmp™-based protocols (Ambion, Inc.). Labeled cRNA was fragmented (0.5 μg/μL per reaction) and was used for array hybridization and washing. The cRNA was mixed with a hybridization cocktail, heated to 99 °C for 5 min and then incubated at 45 °C for 5 min. Hybridization arrays were conducted for 16 h in an Affymetrix Model 640 hybridization oven (45 °C, 60 rpm). Arrays were washed and stained on an FS450 Fluidics station and were scanned on a GeneChip® Scanner 3000 7G. Image signal data, detection calls and annotations were generated for every gene using the Affymetrix Statistical Algorithm MAS 5.0 (GeneChip® Operating Software v1.3). A log2 transformation was conducted and a Student’s t-test was performed for comparison of the two groups (control and heat-shocked). We conducted multiple testing correction—Benjamini and Hochberg—to determine the false discovery rate, and statistical significant genes were identified using Bonferroni correction procedures (−log10 pcutoff > 6.04) .
For interpretation of the results, the Ingenuity Pathways Analysis tool (IPA version 8.7, Ingenuity® Systems Inc., Redwood City, CA, USA; ) was used. IPA is a web-based software application, which enables filtering and dataset comparisons, to identify biological mechanisms, pathways and functions most relevant to experimental datasets or differentially expressed genes. The cut-off criteria for our IPA analysis were: an absolute value of log2 ratio ≥1.5 and a p-value ≤0.05. Other web-based resources, such as the GeneCards Human Gene Database , The HUGO Gene Nomenclature Committee (HGNC, ) and the Gene Ontology  were also used to further supplement the analysis. The targets identified in the microarray study were validated using qRT-PCR. Runs were performed using TaqMan® RNA-to-CT™ 1-Step Kits and TaqMan® Assays for: HSPA6 and GAPDH (Applied Biosystems). Calibrator RNA was used as the control (Cell Applications Inc.). All assays were performed by Asuragen Services.
In this study, we examined the temporal gene expression kinetics for HEK cells exposed to hyperthermic stress. Collectively, our results demonstrate that hyperthermic stress triggers a rapid, robust and extensive transcriptional response in HEK cells. The temporal kinetics expression data show that this cellular response is immediately activated in response to stress and is sustained for many hours after exposure. Specifically, we show that the number of upregulated genes increased in logarithmic fashion as a function of time, ranging from 466 at 0 h, 872 at 4 h and up to 1,127 at 24 h. This finding is consistent with previous in vivo studies, which show that HSP70 expression is bi-phasic and peaks at both 12 h and 24 h post-exposure [5,7,8]. However, this data is in contrast to previous in vitro reports, which show that the number of regulated genes is maximal roughly 4 to 12 h post-exposure [3,4,5]. Furthermore, in this study, we show that many of the genes activated at 24 h encode for chemokines that play central roles in inflammation (i.e., IL-8, IL-10, IL-22, IL-15 and JAK family kinases). This suggests that the second expression peak at 24 h is likely due to cellular inflammation.
We identified a large group of diverse heat-inducible genes in this work. The temporal kinetics data shows that each individual gene exhibits distinct expression profiles, which vary dramatically in magnitude, onset time and duration of expression. From the data, we determined that the heat-inducible genes can be grouped according to their temporal expression phases. The first group belongs to an immediate-expression phase consisting of genes that are rapidly expressed shortly (i.e., 5–20 min) after exposure to hyperthermic stress. The second group consists of genes expressed several minutes up to 4 h post exposure, and the third group can be considered an “adaption or recovery phase” that occurs 4 h to 24 h post exposure.
The majority of genes expressed during the immediate expression phase encode for proteins involved in molecular transport, cellular growth and heat shock response. One notable molecular transport gene is HBA1/HBA2, which functions to facilitate the delivery of oxygen. Another interesting finding is that only the well characterized HSPs (i.e., HSP40 and HSP70) are expressed at the early time points, while the smaller HSPs and chaperonins (HSPE1, HSP60; HSPE1, HSP10; HSPB2, HSP27) are expressed at roughly 24 h post-exposure. Furthermore, other HSPs, such as HSPH1 (HSP105), which function primarily to prevent protein aggregation, exhibit maximal expression at the 4 h time-point. This data strongly supports that each particular type of HSP exhibits expression kinetics that appear to be directly correlated with the function that is required at each time point. In summary, these data provide valuable new insights that give us a much clearer picture of the genes and intracellular signaling pathways that are triggered in human epithelial keratinocytes exposed to hyperthermic stress.
This study was supported by the Air Force Office of Scientific Research LRIR entitled “Coupling of terahertz radiation to intracellular biomolecules for non-contact control and activation of adaptive cellular responses.” The ideas and opinions presented here are those of the authors and not those of the US Air Force.
Conflict of Interest
The authors declare no conflict of interest.
- Schreck, R.; Albermann, K.; Baeuerle, P.A. Nuclear factor kappa B: An oxidative stress-responsive transcription factor of eukaryotic cells (a review). Free Radic. Res. Commun. 1992, 17, 221–237. [Google Scholar] [CrossRef]
- Wilmink, G.J.; Grundt, J.E. Invited review article: Current state of research on biological effects of terahertz radiation. J. Infrared. Millim. Te. 2011, 32, 1074–1122. [Google Scholar] [CrossRef]
- Wilmink, G.J.; Opalenik, S.R.; Beckham, J.T.; Abraham, A.A.; Nanney, L.B.; Mahadevan-Jansen, A.; Davidson, J.M.; Jansen, E.D. Molecular imaging-assisted optimization of hsp70 expression during laser-induced thermal preconditioning for wound repair enhancement. J. Invest. Dermatol. 2009, 129, 205–216. [Google Scholar] [CrossRef]
- Wilmink, G.J.; Opalenik, S.R.; Beckham, J.T.; Davidson, J.M.; Jansen, E.D. Assessing laser-tissue damage with bioluminescent imaging. J. Biomed. Opt. 2006, 11, 041114. [Google Scholar] [CrossRef]
- Wilmink, G.J.; Opalenik, S.R.; Beckham, J.T.; Mackanos, M.A.; Nanney, L.B.; Contag, C.H.; Davidson, J.M.; Jansen, E.D. In-vivo optical imaging of hsp70 expression to assess collateral tissue damage associated with infrared laser ablation of skin. J. Biomed. Opt. 2008, 13, 054066. [Google Scholar] [CrossRef]
- Wilmink, G.J.; Rivest, B.D.; Roth, C.C.; Ibey, B.L.; Payne, J.A.; Cundin, L.X.; Grundt, J.E.; Peralta, X.; Mixon, D.G.; Roach, W.P. In vitro investigation of the biological effects associated with human dermal fibroblasts exposed to 2.52 THz radiation. Laser. Surg. Med. 2011, 43, 152–163. [Google Scholar] [CrossRef]
- Beckham, J.T.; Wilmink, G.J.; Mackanos, M.A.; Takahashi, K.; Contag, C.H.; Takahashi, T.; Jansen, E.D. Role of HSP70 in cellular thermotolerance. Laser. Surg. Med. 2008, 40, 704–715. [Google Scholar] [CrossRef]
- Beckham, J.T.; Wilmink, G.J.; Opalenik, S.R.; Mackanos, M.A.; Abraham, A.A.; Takahashi, K.; Contag, C.H.; Takahashi, T.; Jansen, E.D. Microarray analysis of cellular thermotolerance. Laser. Surg. Med. 2010, 42, 752–765. [Google Scholar] [CrossRef]
- Kabakov, A.E.; Budagova, K.R.; Latchman, D.S.; Kampinga, H.H. Stressful preconditioning and HSP70 overexpression attenuate proteotoxicity of cellular ATP depletion. Am. J. Physiol. Cell Physiol. 2002, 283, C521–C534. [Google Scholar]
- Wilmink, G.J.; Roth, C.C.; Ibey, B.L.; Ketchum, N.; Bernhard, J.; Cerna, C.Z.; Roach, W.P. Identification of microRNAs associated with hyperthermia-induced cellular stress response. Cell Stress Chaperon. 2010, 15, 1027–1038. [Google Scholar] [CrossRef]
- Richter, K.; Haslbeck, M.; Buchner, J. The heat shock response: Life on the verge of death. Mol. Cell 2010, 40, 253–266. [Google Scholar] [CrossRef]
- Kultz, D. Evolution of the cellular stress proteome: From monophyletic origin to ubiquitous function. J. Exp. Biol. 2003, 206, 3119–3124. [Google Scholar] [CrossRef]
- Kultz, D. Molecular and evolutionary basis of the cellular stress response. Annu. Rev. Physiol. 2005, 67, 225–257. [Google Scholar] [CrossRef]
- Otto, A.I.; Riou, L.; Marionnet, C.; Mori, T.; Sarasin, A.; Magnaldo, T. Differential behaviors toward ultraviolet A and B radiation of fibroblasts and keratinocytes from normal and DNA-repair-deficient patients. Cancer Res. 1999, 59, 1212–1218. [Google Scholar]
- Sesto, A. Analysis of the ultraviolet B response in primary human keratinocytes using oligonucleotide microarrays. Proc. Natl. Acad. Sci. USA 2002, 99, 2965–2970. [Google Scholar] [CrossRef]
- Li, D.; Turi, T.G.; Schuck, A.; Freedberg, I.M.; Khitrov, G.; Blumenberg, M. Rays and arrays: The transcriptional program in the response of human epidermal keratinocytes to UVB illumination. FASEB J. 2001, 15, 2533–2535. [Google Scholar]
- Howell, B.G.; Wang, B.; Freed, I.; Mamelak, A.J.; Watanabe, H.; Sauder, D.N. Microarray analysis of UVB-regulated genes in keratinocytes: Downregulation of angiogenesis inhibitor thrombospondin-1. J. Dermatol. Sci. 2004, 34, 185–194. [Google Scholar] [CrossRef]
- Bender, K.; Blattner, C.; Knebel, A.; Iordanov, M.; Herrlich, P.; Rahmsdorf, H.J. UV-induced signal transduction. J. Photochem. Photobiol. B Biol. 1997, 37, 1–17. [Google Scholar] [CrossRef]
- Tyrrell, R.M. Activation of mammalian gene expression by the UV component of sunlight—From models to reality. BioEssays 1996, 18, 139–148. [Google Scholar] [CrossRef]
- Rygiel, T.P.; Mertens, A.E.; Strumane, K.; van der Kammen, R.; Collard, J.G. The Rac activator Tiam1 prevents keratinocytes apoptosis by controlling ROS-mediated ERK phosphorylation. J. Cell Sci. 2008, 121, 1183–1192. [Google Scholar] [CrossRef]
- Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc. Ser. B 1995, 57, 289–300. [Google Scholar]
- Ingenuity Systems, Inc. Home Page. Available online: http://www.ingenuity.com/ (accessed on 1 August 2012).
- GeneCards® Home Page. Available online: http://www.genecards.org/ (accessed on 1 August 2012).
- HGNC, HUGO Gene Nomenclature Committee Home Page. Available online: http://www.genenames.org/ (accessed on 1 August 2012).
- The Gene Ontology Home Page. Available online: http://geneontology.org/ (accessed on 1 August 2012).
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