Molecular choreography to acute heat exposure in a simulated extreme high temperature environment

doi:10.21203/rs.3.rs-3615487/v1

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Molecular choreography to acute heat exposure in a simulated extreme high temperature environment

https://doi.org/10.21203/rs.3.rs-3615487/v1

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Occupational exposure to extreme high temperature and increasing global temperatures require enhanced understanding of how heat exposure impacts human health. However, the molecular changes underlying the response to heat exposure in humans remain to be elucidated. This study used longitudinal multi-omics profiling to assess the impact of acute heat exposure (50℃ for 30 min) in 24 subjects from a mine rescue team. Intravenous blood samples were collected before acute heat exposure (baseline) and at 5 min, 30 min, 1 h, and 24 h after acute heat exposure (recovery). In-depth multi-omics profiling was performed on each sample, including plasma proteomics (untargeted), metabolomics (untargeted), and gene expression (transcriptomics) of peripheral blood monocytes and neutrophils. After data curation and annotation, the final dataset contained 5616 analytes, including 478 proteins, 1995 metabolites, and 3143 transcripts. Time-series analysis unveiled an orchestrated molecular choreography of changes involving the immune response, coagulation, acid-base balance, oxidative stress, cytoskeleton and energy metabolism. Further analysis through protein-protein interactions and network analysis revealed potential regulators of acute heat exposure. Moreover, novel blood-based analytes that predicted change in cardiopulmonary function after acute heat exposure were identified. This study provides a comprehensive evaluation of the molecular changes that underlie the complex physiological processes that occur in humans who undergo heat exposure. Findings will help health impact assessment of extreme high temperature and inform future mechanistic and clinical studies.

Acute heat exposure

Proteomics

Metabolomics

Transcriptome

Occupational exposure to extreme high temperature, as seen in industries such as steelmaking, tile production processes, mining, and rescuing, can lead to detrimental health effects (1). Especially, several studies showed that moisture and heat were the most commonly perceived adverse factors in the deep underground work environment (2–4). For instance, Hunt et al found that 79% of underground mine workers across three mine sites in northern Australia experienced symptoms of heat illness (5). Additionally, an analysis of hazards faced by mine rescuers revealed that the challenging mine environment led to body overheating and heat stroke, which accounted for 26% of total deaths (6). Furthermore, several studies have demonstrated that the extreme heat environment places significant physiological and thermoregulatory stress on firefighters, leading to stress, overexertion, and even death (7–9). Besides occupational exposure, general population is facing extreme high temperature exposure with global warming being one component of climate great change (10). In 2019, heat waves in China resulted in approximately 26800 deaths (11). Therefore, these studies have raised serious concerns about the impacts of extreme high temperature on heat exposed population’ health.

The previous mainly focused on describing the phenotypic and physiological responses associated with heat exposure. In deep mine, the miners maintained a core body temperature of 38°C for most of the day and experienced a significant increase in heart rate and severe dehydration because of the high ambient temperature (12, 13). Similarly, firefighters working in hot environments experience elevated core body temperature, skin temperature, fluid intake, and heart rate (14–16). Another study showed that prolonged heat overload in firefighters working in hot environments increased arterial stiffness and vasodilation (17). These observations have raised the need to evaluate tissue and organ injury, monitor recovery, and predict long-term complications following heat exposure. Notably, delayed measurement of core temperature or other physiological symptoms can result in unexpected occurrences of severe heat-related illnesses, such as heatstroke, which can cause tissue damage and multiorgan dysfunction.

Molecular responses induced by extreme high temperatures can provide valuable insights into the physiological and metabolic changes that occur under such conditions. By monitoring these molecular responses, we can gain a better understanding of the body's adaptive capacity to extreme high temperature and assess the potential degree of damage. For example, elevated levels of cardiac troponin I have been associated with a higher mortality rate among individuals admitted to the emergency department during heatwaves (18). Detecting these molecular changes before obvious clinical manifestations of thermal injury appear offers an opportunity for early warning and intervention. However, the molecular changes that underlie the physiology of heat exposure are often misinterpreted or overlooked. Knowledge of the response to heat exposure is limited to a small number of molecules and biological processes. This highlights the critical need to explore the underlying molecular changes in individuals exposed to extreme high temperature environments.

The objective of this study was to perform longitudinal multi-omics profiling of blood components from 24 mine rescue team members, before and after a 30 min stay in a simulated extreme high temperature environment (50℃). The goal was to understand the short-term tolerance of humans to extreme high temperature and characterize the detailed series of events that occur in response to acute heat exposure. Findings revealed an orchestrated molecular choreography of changes involving numerous molecules and pathways in response to acute heat exposure, and demonstrate the ability of multi-omics analytes to monitor recovery and predict complications following heat exposure. This study provides an in-depth overview of the complex physiological processes that occur in humans who undergo extreme high temperature exposure and will inform novel prevention strategies to reduce organ damage and long-term sequelae.

Study subjects

This study included 24 subjects recruited from the National Mine Emergency Rescue Furong Team (China) in May 2022. The study protocol was approved by the Ethics Review Committee (IRB number, 1351) of West China Hospital of Sichuan University. All subjects provided written informed consent before participating in any study procedures. Subjects were screened for contraindications and/or comorbidities that may have prohibited exposing them to extreme high temperature using a health questionnaire. Subjects had no history of underlying cardiovascular disease, cancer, or other chronic diseases. No subject underwent heat or other stress training within three months before the start of this study.

Study design

In a systematic review assessing the impact of occupational heat strain, the studies included in the analysis reported a wide range of WBGT (19.3 to 52.0°C) and air temperature (21.2 to 150.0°C, this extreme value was observed in a steel plant worksite) (19). Additionally, the rising global temperatures are pushing the human to endure extreme heat, with temperatures even reaching up to 54 ℃. For a focused investigation to extreme high temperature exposure and ensures security considerations, we specifically chose 50 ℃ as the environmental exposure temperature. The study was conducted in three phases: baseline, exposure, and recovery. The baseline period extended from 09.00 am on the day before exposure until the day of exposure. The exposure period took place between 09:00 and 09:30 am. The recovery period extended from 09:30 am until 09:00 am the next morning.

Baseline period

Subjects’ physical condition was assessed on the day preceding exposure. Oral temperature was measured using a liquid crystal thermometer (WatermarkTM). Grip strength of both hands was assessed using a grip dynamometer (EH 101, CAMRY). Objective fatigue was measured using performance tasks (BD-V-302A, BD-V-509A, Beda Qingniao). Pulmonary function was measured using spirometry. Heart function was examined with Doppler echocardiography. Urine specific gravity and pH were evaluated with urinalysis. Blood samples were collected at 07:30 am on the day of exposure.

Exposure period

At 09:00 am on the day of exposure, subjects were exposed to an extreme high temperature environment of 50 ℃ and 37–40% relative humidity for 30 minutes, using a mine roadway simulating test device designed to simulate fire and facilitate training of the National Mine Emergency Rescue Team. Heat was provided by industrial heaters (JH-H150F, Cameron, China) placed at both ends of the roadway. During acute heat exposure, blood pressure and pulse changes were monitored by an electrocardiogram monitor.

Recovery period

Immediately after the heat exposure, oral temperature, grip strength, objective fatigue, lung function, color Doppler echocardiography were detected. Two hours after heat exposure, subjects were asked to rate their perceived exertion. 24 hours after the heat exposure, urine samples were collected to assess recovery.

Objective fatigue

To measure the effects of heat exposure on fatigue, subjects were asked to perform two tasks. First, subjects participated in a bimanual interaction technique, where they controlled a cursor on a computer screen with a joystick using both hands, and the number of cursor-trajectory errors were recorded. Next, subjects participated in a visual reaction time test where they had to press color buttons as soon as corresponding colored icons appeared on a screen using both hands. Reaction times were recorded.

Cardiopulmonary function tests

To assess the pulmonary function of subjects, we conducted a specific assay which measured Forced Expiratory Volume in One Second (FEV 1), Forced Vital Capacity (FVC), Vital Capacity (VC), Maximum Voluntary Ventilation (MVV), Maximum Mid-Respiratory Flow (MMF), and Maximum Expiratory Flow at 50% of Vital Capacity (V50). To assess the cardiac function of subjects, Echocardiograms were performed by experienced cardiologists using GE Vivid E9 and E9-S5 probes (1.6-3.2MHz). According to international consensus guidelines, post-exposure images were obtained immediately after heat exposure, and imaging was satisfactory for all subjects. Echocardiography was analyzed according to the American Society of Echocardiography (ASE) guidelines (2015). Left ventricular function was quantified with the Simpson method using measurements of left ventricular ejection fraction (LVEF), stroke output (SV) and fractional shortening (FS).

Blood Collection and sample preparation

After an overnight fast, blood samples were collected from an upper forearm vein at baseline, 5 minutes, 30 minutes and 1 hour after heat exposure, and the morning after heat exposure. Blood samples were collected in purple top vacutainers (BD), placed on ice, and processed immediately. Blood samples were layered onto Ficoll medium and centrifuged at 2000 rpm for 20 minutes at 4℃. The top layer of EDTA plasma was removed, divided into 8 equal portions and immediately frozen at -80℃. Peripheral blood monocytes (PMBCs) and neutrophils were isolated using a monocyte or neutrophil isolation kit, according to the manufacturer's instructions. PBMCs and neutrophils were divided into 4 equal portions and frozen at -80℃. All blood samples were subjected to multi-level molecular profiling. An untargeted proteomics (proteins) and metabolomics (metabolites) approach was used to analyze EDTA plasma. Gene expression profiling was performed on PBMCs and neutrophils (transcriptomics).

RNA sequencing and data processing

Total RNA was extracted using the TRIzol reagent (Invitrogen, CA, USA) according to the manufacturer’s protocol. Then the libraries were constructed using VAHTS Universal V6 RNA-seq Library Prep Kit according to the manufacturer’s instructions. Pooled libraries were sequenced on a HiSeq 4000 sequencer (Illumina, San Diego, CA, USA) generating 30 million paired-end (100 bp) reads per sample. Gene quantification was performed using the script htseq count in the Python software package HTSeq (v0.9.1). Genomic characteristics were defined using GENCODE v28, where each gene is considered a combination of all its exons. Gene counts were normalized for sequencing depth, and genes with a mean expression less than 10 were removed. Missing values were filled in with the k-nearest neighbors’ method using the "impute. knn" function in the R software package "impute" (v1.52.0). Two data sets were generated, one containing gene counts normalized for sequencing depth in each sample (raw counts), and the other was further processed with variance-stable transformation (VST) using the R software package “DESeq2”.

Untargeted Proteomics and data processing

Untargeted proteomics used Data independent acquisition (DIA). After the preparation of plasma samples, protein concentration was determined using a BCA protein assay kit. Subsequently, 200 micrograms of each sample underwent thorough purification and extensive washing. The purified samples were then subjected to overnight digestion at 37°C, resulting in the collection of peptides as filtrate. To facilitate further analysis, the peptide mixture was labeled using the TMT Tag Labeling kit. For the subsequent database search, ProteomeDiscoverer (v.2.4) was utilized to thoroughly search all raw data against the sample protein database. The database search was performed with Trypsin digestion specificity, considering alkylation on cysteine as fixed modifications. To ensure reliable results, a global false discovery rate (FDR) of 0.01 was set, and protein groups were considered for quantification only if they contained at least 2 peptides.

Untargeted Metabolomics from Plasma by Liquid Chromatography (LC)-MS

Plasma samples were prepared and analyzed in a random sequence. Progenesis QI software (v2.3, Nonlinear Dynamics) was used to independently analyze data from each mode. Main parameters of 5 ppm precursor tolerance, 10 ppm product tolerance, and 5% product ion threshold were applied for qualitative analysis. Compound identification was conducted using databases such as The Human Metabolome Database (HMDB), Lipidmaps (V2.3), Metlin, EMDB, and PMDB, as well as self-built databases. A data matrix was created from positive and negative ion data and imported into R for Principal Component Analysis (PCA) to observe overall sample distribution and analysis stability. Orthogonal Partial Least-Squares-Discriminant Analysis (OPLS-DA) and Partial Least-Squares-Discriminant Analysis (PLS-DA) were used to distinguish metabolites that differed between groups, with 7-fold cross-validation and 200 Response Permutation Testing (RPT) used to prevent overfitting. Variable Importance of Projection (VIP) values from the OPLS-DA model ranked the contribution of each variable to group discrimination, and a two-tailed Student’s T-test verified significant differences in metabolites between groups with VIP values greater than 1.0 and p-values less than 0.05.

Quantification and statistical analysis

Fuzzy c-mean clustering

After log2-transformation and Z-score scaling of data, fuzzy c-mean clustering was conducted using the ‘Mfuzz’ R package (v2.20.0). The ‘elbow’ method was used to calculate the minimum centroid distance or a range of cluster numbers and to select the optimal number. t-distributed stochastic neighbor embedding (tSNE) scatterplots were obtained using the ‘Rtsne’ R package, with the following parameters: perplexity = 5 and theta = 0.05.

Inter-individual variability

To avoid bias, the coefficient of variation (CV, CV = standard deviation/mean*100) was calculated for each analyte using non esti R package matched datasets. The transcriptomic dataset was normalized to sequencing depth. Acute heat exposure-induced inter-individual variability was calculated as the median CV for each analyte across all time points after heat exposure relative to baseline. To determine the technical variability of each assay, CVs were calculated across all QC samples. A box and whisker plot were constructed for the first, median, and third quartiles (lower edge, middle line, and upper edges). The upper whisker represented the maximum value of the data that was within 1.5 times the interquartile range over the 75th percentile. The lower whisker represented the minimum value of the data that was within 1.5 times the interquartile range under the 25th percentile.

Pathway dynamic analysis

Differential expressed genes (FDR < 0.05) were identified, and STRING analysis was used to explore the protein-protein interaction network before and after acute heat exposure. In PPI, proteins are represented as nodes, and edges between nodes represent interactions.

Pathway enrichment analysis

Ingenuity pathway analysis (IPA, QIAGEN) was used to identify differentially expressed plasma proteins and genes to determine enrichment of particular pathways. Hypergeometric probability (one-sided) was used to determine the significance of each pathway. The Fisher exact test was used to determine enrichment for metabolites. The Benjamini-Hochberg method was used to correct P values for multiple comparisons. FDRs of < 0.05 and < 0.20 were considered significant for transcripts and proteins or metabolites, respectively. Fold change was estimated for significant molecules using 1) median of fold change relative to baseline, 2) median of beta coefficients, 3) median of max (if up) or min (if down) fold change relative to baseline.

Co-expression analysis of transcriptome and proteomics

Expression levels of genes and corresponding proteins were explored. Differentially expressed genes (DEGs) and differentially expressed proteins (DAPs) were identified and correlated (cor-DEGs-DAPs) before and after acute heat exposure.

WGCNA analysis

WGCNA was applied to analyze the co-expression modules and key genes related to hub molecules. An adjacency matrix was transformed into a topological overlap matrix (TOM). Modules were identified with hierarchical clustering (minModuleSize = 30). Module eigengenes were calculated. Module eigengenes (ME) and module memberships (MM) were used to determine key modules associated with hub molecules. The ME is defined as the first principal component of a given module and provides a representative expression profile. MM measured the relationship of a gene with the module eigengenes and reflects eigengene connectivity. The functions of metabolites and genes in the key modules were investigated via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.

Enzyme-linked immunosorbent assay

Plasma samples collected at baseline and 5 min, 30 min, 1 h, and 24 h after heat exposure were immediately centrifuged at 3,000 g for 10 minutes. To validate the key regulators identified by multi-omics analysis, serum levels of VWF, PF4, THBS1, HSP90AB1, and MPO were measured using enzyme-linked immunosorbent assay (ELISA) kits, following the manufacturer's instructions.

Cohort characteristics and research design

This study included 24 subjects. All were male, with a mean age of 33.04 years, mean height of 170.83 cm, and mean weight of 68.42 kg. Subjects were exposed to an extreme high temperature environment of 50℃ and 37–40% relative humidity for 30 minutes. During acute heat exposure, there was an observed increase in heart rate, while the subjects' blood pressure and blood oxygen levels remained stable (Figure S1). Immediately after acute heat exposure, oral temperature rose to 37.2 ℃ compared to 36.6 ℃ at baseline. Cardiopulmonary function parameters with significant changes were also shown. The forced vital capacity (FVC), vital capacity (VC), left ventricular ejection fraction (EF), and fractional shortening (FS) decreased after acute heat exposure. For the urinalysis, the urine specific gravity of the participants had no change after acute heat exposure, while the urine PH value decreased (Figure S3). As Figure S4 shown, the fatigue increased after acute heat exposure.

For the multi-omics analysis, intravenous blood samples were collected before acute heat exposure (baseline) and at 5 min, 30 min, 1 h, and 24 h after acute heat exposure (recovery). In-depth multi-omics profiling was performed, including plasma proteomics (untargeted), metabolomics (untargeted), and gene expression (transcriptomics) of peripheral blood monocytes and neutrophils (Fig. 1A). After data curation and annotation, the final dataset contained 5616 analytes, including 478 proteins, 1995 metabolites, and 3143 transcripts.

Baseline versus heat exposure-induced individual omic variability

Exposure to an extreme high temperature environment of 50℃ induced extensive changes in 5616 analytes spanning multiple omics layers, indicating system-wide changes (Fig. 1B). Patterns of change varied with molecule layer. Levels of circulating plasma proteins and metabolites were altered compared to baseline across all time points, and a large proportion of plasma proteins and metabolites remained significantly different from baseline after 24 h of recovery. The neutrophil and monocyte response to acute heat exposure was rapid. In neutrophils, changes in the transcriptional profile peaked at 30 min after acute heat exposure. In monocytes, changes in the transcriptional profile peaked at 1 h after acute heat exposure. The inter-individual variability in the levels of plasma proteins and metabolites and neutrophil and monocyte transcripts at baseline and in response to acute heat exposure were shown in Fig. 1C. At baseline and in response to acute heat exposure, the coefficient of variation (CV) was highest for neutrophil and monocyte transcripts, followed by plasma proteins and metabolites. The omics datasets were assessed with principal component analysis, which suggested limited batch effects (Fig. 1D-F).

Time series system-wide proteomics data

Cluster analysis (k-means clustering) identified four clusters of circulating plasma proteins with different longitudinal trajectories. The levels of some plasma proteins decreased after acute heat exposure and did not return to baseline within 24 h (cluster 1). The levels of some plasma proteins increased after acute heat exposure and continued to increase during recovery (cluster 2). There was a delayed increase in the levels of some plasma proteins after acute heat exposure (cluster 3). The levels of some plasma proteins increased after acute heat exposure, and quickly returned to baseline (cluster 4) (Fig. 2A). Pathway enrichment analysis was performed on each cluster (Figure S5). Clusters 1, 2, 3 and 4 included proteins associated with the immune response, blood coagulation, structural constituent of cytoskeleton and hemoglobin complex, respectively (Fig. 2B).

Cluster 1

Cluster 1 was enriched in molecules associated with the innate and humoral immune response (Fig. 2C). There was a sharp decrease in the plasma levels of immunoglobulin related fragments (i.e., IGHV3-73, IGLV6-57, IGHV3-38, IGHV3-15, IGHV3-9, IGHV3-43D, IGHV3-43, IGLL1, IGLV3-25, IGHV1-58, IGKV1-27, IGLV7-46, IGKV1D-33, IGKV1-33, IGHV1-45, IGLV2-11, IGKV1-8, FCGR3B, IGLV3-1) and pattern recognition receptor (i.e., C-type mannose receptor 2). These data imply that immune function was suppressed after acute heat exposure.

Cluster 2

Cluster 2 was enriched in molecules associated with the coagulation cascade (platelet glycoprotein V, platelet factor 4, coagulation factor VIII, thrombospondin 1, coagulation factor XIII A chain, coagulation factor IX, coagulation factor X, coagulation factor XII, coagulation factor XIII B chain, coagulation factor V, von Willebrand factor), stress related proteins (serine/threonine-protein kinase/endoribonuclease, heat stress protein 90-beta), and glyceraldehyde-3-phosphate dehydrogenase, which is a key glycolysis rate limiting enzyme (Fig. 2D). These data imply that blood viscosity, risk of disseminated intravascular coagulation (DIC), anaerobic metabolism and the stress response increased after acute heat exposure.

Cluster 3

Cluster 3 was enriched in proteins of the cytoskeleton and components of intercellular junctions (keratin, type II cytoskeletal 2 epidermal, vimentin, coronin-1A, alpha-actinin-1, keratin, type II, cytoskeletal 1, keratin, type I cytoskeletal 10, keratin, type II cytoskeletal 6B, desmoglein-1, keratin, type II cytoskeletal 6C, matrix Gla protein) (Fig. 2E). These data imply that the cytoskeleton, as a visible component diffusely distributed throughout the cell, was sensitive to acute heat exposure.

Cluster 4

Cluster 4 was enriched in hemoglobin (hemoglobin subunit alpha, hemoglobin subunit beta, hemoglobin subunit delta), carbonic anhydrase (carbonic anhydrase 2, carbonic anhydrase 1), plasminogen, S100A8, the anti-fibrinolytic system (plasminogen activator inhibitor 1) and antioxidants (peroxiredoxin-2, peroxiredoxin-6, kallistatin, superoxide dismutase 1) (Fig. 2F). These data imply that hemoglobin and the maintenance of acid-base balance were mobilized under acute heat exposure and the fibrinolytic system and antifibrinolytic system were activated, possibly due to increased coagulation.

Time series system-wide metabolomics data

Cluster analysis (k-means clustering) identified six clusters of circulating plasma metabolites with different longitudinal trajectories. The levels of some plasma metabolites increased after acute heat exposure and did not return to baseline within 24 h (cluster 1). The levels of some plasma metabolites underwent a transient increase after acute heat exposure but followed by a decrease during recovery (cluster 2). The levels of some plasma metabolites decreased after acute heat exposure and returned to baseline within 24 h (cluster 3, cluster 4). The levels of some plasma metabolites increased after acute heat exposure and returned to baseline within 24 h (cluster 5, cluster 6) (Fig. 3A).

Pathway enrichment analysis was performed on each cluster (Figure S6). Cluster 1 was associated with alanine, aspartic acid, and glutamic acid metabolism. Cluster 2 was associated with steroid hormone biosynthesis. Cluster 3 was associated with amino acid metabolism. Cluster 4 was associated with cholesterol metabolism. Cluster 5 was associated with the biosynthesis of unsaturated fatty acids. Cluster 6 was associated with sphingolipid metabolism.

Cluster 1

Cluster 1 was enriched in alanine, aspartic acid, and glutamic acid metabolites (i.e., threo-hydroxyaspartic acid, aspartate, N-acetylaspartate, fumaric acid, glutaric acid) and carnitine and acyl carnitine metabolites (i.e., 3-hydroxybutyric acid, (10Z)-12-hydroxyhexadecenoylcarnitine, (4E,6E)-undeca-4,6,9-trienoylcarnitine, 3-hydroxybutyrylcarnitine, 5-methylnonanoylcarnitine, 7-methyldodecanoylcarnitine, decanoylcarnitine, nonanoylcarnitine, octanoylcarnitine) (Fig. 3B). Accumulation of carnitine and acyl carnitine metabolites likely reflected disordered fatty acid metabolism. Accumulation of glutathione peroxide reflected oxidative stress. Accumulation of 17-phenyl-18,19,20-trinor-prostaglandin E2 and 15(R)-pinane thromboxane A2 suggested endothelial damage and increased coagulation.

Cluster 2

Cluster 2 was enriched in metabolites associated with tyrosine metabolism (i.e., N-methylamine, 3-fumarylpyruvate), pantothenate and CoA biosynthesis (i.e., 5,6-dihydrouracil, 2-ketoisovalic acid), and lysine decomposition (i.e., succinic acid, pipecolic acid) (Fig. 3C). These data imply acute heat exposure was associated with the use of amino acids as an energy source. Accumulation of mineralocorticoid (i.e., 18-hydroxycorticosterone carbohydrate) suggested alterations in the regulation of water and salt balance.

Cluster 3

Cluster 3 was enriched in amino acids (i.e., L-tyrosine, L-arginino-succinate, threonylglutamic acid, itaconic acid, N-acetyltryptophan, gamma-glutamylalanine, 2,5-dihydroxybenzenesulfonic acid, dimethylglycine, L-arginine, D-proline, isoleucyl-leucine, glutamylmethionine, aspterric acid, L-methionine, N-formylglycine, valyl-gamma-glutamate, N-acetylleucine, L-valine, 4-hydroxybutyric acid, salicylic acid, L-asparagine, L-glutamine, N8-acetylspermidine, isoleucyl-aspartate, L-histidine, L-lysine, L-threonine, leucine, serine, L-alanine, phenylalanine) (Fig. 3D). These data imply acute heat exposure was associated with the use of amino acids as an energy source, suggesting protein supplementation may be helpful after exposure to extreme heat.

Cluster 4

Cluster 4 was enriched in bile acid metabolites, including chenodeoxycholic acid, 3β-hydroxy-5-cholenoic acid, hyodeoxycholic acid, glycoursodeoxycholic acid, cholic acid, deoxycholic acid, deoxycholic acid, glycocholic acid, taurochenodesoxycholic acid, taurocholic acid and taurodeoxycholic acid (Fig. 3E). These data imply acute heat exposure was associated with alterations in bile acid metabolism.

Cluster 5

Cluster 5 was enriched in free fatty acids, including dihomo-linolenic acid, dihomo-alpha-linolenic acid, 4,7,10,13,16-docosapentaenoic acid, trans-11,cis-15-octadecadienoic acid, 4,5-dehydro docosahexaenoic acid, docosahexaenoic acid, alpha-eleostearic acid, docosapentaenoic acid (22n-3), 9-hydroxylinoleic acid, adrenic acid, 2-hydroxyhexadecanoic acid, (E)-10-hydroxy-8-decenoic acid, eicosadienoic acid, erucic acid, linoleic acid, arachidonic acid, heneicosanoic acid, eicosapentaenoic acid, α-linolenic acid and stearic acid (Fig. 3F). Accumulation of free fatty acids may be due to the inhibition of heat production and reduce of fatty acid digestion.

Cluster 6

Cluster 6 was enriched with products of sphingomyelin metabolism (i.e., sphinganine, sphinganine 1-phosphate, sphingosine 1-phosphate, sphingosine 1-phosphate (d16:1-P) (Fig. 3G). Metabolites of sphingomyelin are important signaling molecules, which may be involved in regulating cell proliferation, immune function, inflammatory reactions, and oxidative stress.

Time series analysis of the transcriptomic profile of peripheral blood monocytes

Considering immune cells play a critical role in acute heat exposure, analysis of the gene expression of monocytes in response to acute heat exposure was performed. Transcript trajectories were categorized in 5 clusters. The levels of some transcripts decreased after acute heat exposure and returned to baseline within 24 h (cluster 1). The levels of some transcripts decreased after acute heat exposure and did not return to baseline within 24 h (cluster 3). The levels of some transcripts increased after acute heat exposure, reached a maximum response at different time points, and returned to baseline within 24 h (cluster 2, cluster 4, cluster 5) (Fig. 4A).

Pathway enrichment analysis was performed on each cluster (Figure S7). Cluster 1 was enriched in adrenergic signaling in cardiomyocytes, cluster 2 was enriched in cytokine-cytokine receptor interaction, cluster 3 was enriched in glycine, serine and threonine metabolism, cluster 4 was enriched in neuroactive ligand-receptor interaction and cluster 5 was enriched in platelet activation (Fig. 4B).

Cluster 1

Cluster 1 was enriched in transcripts associated with cardiovascular function (i.e., ITGB8, CTNNA2, KCNE1B, CACNG8, CACNB2) and arginine and proline metabolism (i.e., ARG1, P4HA2) (Fig. 4C).

Cluster 2

Cluster 2 was enriched in transcripts associated with chemokines and inflammatory factors (i.e., C1QA, CCL20, CCL3, CCL3L3, CXCL1, CXCL2, CXCL3, CXCL8, H2AC11, H2AC12, H2AC13, H2AC15, H2AC17, H2AC6, H2AC8, H2BC13, H2BC15, H2BC17, H2BC21, H2BC6, H2BC8, H2BC9, H3C1, H3C10, H3C2, H4C12, H4C9, IFNG, IL12B, IL1A, IL1B, IL23A, IL6, JUN, TGFB2, TNF; DYNLRB2, HBEGF, MAPK10, NFKBIA, NLRP3, RIPK2, ARG2, CSF3, ICAM1, IDO2, IFNG, LAMB3, PPIF, PTGS2, THBS1, CCL4, CCL4L2, CDKN1A, CXCL10, EGR1, FOSL1, HES1, HES4, PDGFRA, PTGS2, TNFAIP3, ZFP36; CXCL9, F3, FOSL1, KITLG, MARCKS, NFKBIE, P2RY12, PF4V1, PTGS2, RAPGEF4, SERPINB2) (Fig. 4D).

Cluster 3

Cluster 3 was enriched in transcripts associated with amino acid metabolism (i.e., AOC1, HAL, HDC, AOC3, AOC2, CBS), carbohydrate digestion and absorption (i.e., MGAM, MGAM2, ATP1B2) and pantothenate and CoA biosynthesis (i.e., VNN3, VNN2) (Fig. 4E).

Cluster 4

Cluster 4 was enriched in histone subunit related transcripts (i.e., H2AC16, H2AC21) and active ligand receptor interaction related transcripts (i.e., GRIN2C, PTGER3, AVPR2, NPBWR1) (Fig. 4F).

Cluster 5

Cluster 5 was enriched in transcripts associated with platelet activation (i.e., GP1BB, ITGA2B, MYLK, PTGS1, GP1BA, GP6, TBXA2R, GP9, ITGB3, VWF, F2RL3, ADCY5), vascular smooth muscle contraction (i.e., MYLK, CALD1, PPP1R14A, AVPR1A, RAMP1, CALCB, MYL9, ADCY5), the cytoskeleton (i.e., GRIN2C, PTGER3, AVPR2, NPBWR1), and the complement and coagulation cascade (i.e., CLU, PROS1, VWF, F2RL3, PLAU and TFPI) (Fig. 4G).

Time series analysis of the transcriptomic profile of peripheral blood neutrophils

Transcript trajectories in peripheral blood neutrophils were categorized in 5 clusters. The levels of some transcripts increased after acute heat exposure, reached a maximum response at different time points, and returned to baseline within 24 h (cluster 1, cluster 2, cluster 4, cluster 5). The levels of some transcripts decreased after heat exposure and returned to baseline within 24 h (cluster 3) (Fig. 5A).

Pathway enrichment analysis was performed for each cluster (Figure S8). Cluster 1 was enriched in platelet activation. Cluster 2 was enriched in ECM-receptor activation. Cluster 3 was enriched in tryptophan metabolism. Cluster 4 was enriched in cytokine-cytokine receptor interaction. Cluster 5 was enriched in the TNF signaling pathway (Fig. 5B).

Cluster 1

Cluster 1 was enriched in transcripts associated with platelet activation (i.e., GUCY1A1, P2RY12, MYLK, GUCY1B1, ITGA2B, F2RL3, ARHGEF12, GP5, GP1BB, GP9, P2RY1, PRKG1) and cell adhesion (i.e., JAM3, CD226, HLA-DPA1, CNTNAP2, HLA-DQA1, CTLA4, ICOS, PDCD1, ESAM, NEO1, CNTNAP1) (Fig. 5C).

Cluster 2

Cluster 2 was enriched in was enriched in transcripts associated with focal adhesion (i.e., ITGB3, LAMA5, AKT3, CAV2, ITGB8, COL6A3, BIRC3, FLT4) and ECM-receptor interaction (i.e., COL19A1, ATP1B1, CPA3, COL6A3, SLC8A3, COL24A1) (Fig. 5D).

Cluster 3

Cluster 3 was enriched in transcripts associated with tryptophan metabolism (i.e., MAOA, IDO1, CYP1A1) and arginine and proline metabolism (i.e., AMD1, MAOA, P4HA2) (Fig. 5E).

Cluster 4

Cluster 4 was enriched in transcripts associated with chemokines and inflammatory factors (i.e., CCL3L3, CXCL1, CXCL6, CXCL8, H2AB1, H2AC15, H2AC19, H2AC7, H2AC8, H2BC18, H2BC21, H2BC8, H4C2, ICAM1, TNF, FCAR, HBEGF, HSPA2, IL1A, IL1B, JUN, NFKBIA, TUBB2A, ARG2, LAMB3, PPIF, CCL4, CCL4L2, COL1A1, EGR2, EGR3, FOSL1, FZD5, FZD7, GADD45B, HES1, HIF1A, JAG1, NOTCH4, NXT1, TNFAIP3, YWHAG, ZFP36; CCL19, DLL4, FOSB, NFKBIE, PLAU, PLAUR, TFRC) (Fig. 5F).

Cluster 5

Cluster 5 was enriched in transcripts associated with chemokines and inflammatory factors CCL20, CCL3, CXCL2, CXCL3, H2AC18, H2BC6, IFNG, IL23A, IL6, TNFSF11, CFL2, DEFA3, DEFA4, HSPA9, IFNG, IL23A, SOCS1, THBS1, CCL3, CDKN1A, CFL2, EIF2AK3, FZD3, GNGT2, ITGA9, KPNA2, PTGER3, SLC2A1, THBS1, ZNF250, ZNF256, ZNF331, ZNF460, CCL20, F3, GNGT2, ICOSLG, IFNG, IL17RB, LCN2, MAPK6, SERPINB2, TRAF4 (Fig. 5G).

Identification of hub molecules involved in the response to acute heat exposure

The levels of some plasma proteins continued to increase after acute heat exposure, implying they are key regulators of heat exposure. To assess the interaction of these plasma proteins and identify potential plasma proteins modulating the response to acute heat exposure, protein-protein interaction analysis was performed. Several important nodes, corresponding to VWF, PF4, THBS1, HSP90AB1, and MPO, were revealed (Fig. 6A). These proteins were also identified through co-expression analysis of transcriptome and proteomics data (Figure S9), and their patterns of expression were validated using ELISA, which showed their levels increased after acute heat exposure (Fig. 6B).

Weighted correlation network analysis measured the co-expression relationship between VWF, PF4, THBS1, HSP90AB1, MPO, plasma metabolites and peripheral blood monocyte and neutrophil genes. Hierarchical clustering with dynamic tree cut methods were applied to identify metabolite and gene modules (Fig. 6C-E). Pearson’s correlation coefficient showed HSP90AB1, PF4 and THBS1 were associated with metabolite and gene modules, identifying HSP90AB1, PF4 and THBS1 as hub proteins. HSP90AB1 was correlated with the green metabolite module, the cyan metabolite module, the blue monocyte gene module and the yellow monocyte gene module. PF4 was correlated with the yellow metabolite module, the purple metabolite module, the turquoise metabolite module and the turquoise monocyte gene module. THBS1 was correlated with the yellow metabolite module, the purple metabolite module, the turquoise metabolite module, the green-yellow metabolite module, the turquoise monocyte gene module and the green neutrophil gene module (Fig. 6F-H). KEGG pathway enrichment analysis was performed on the metabolites and genes in the key modules correlated with the hub proteins (Figure S10). These modules were enriched in fructose and mannose metabolism, choline metabolism, cholesterol metabolism, histidine metabolism, carbohydrate digestion and absorption, immune response, cytoskeleton, extracellular matrix, and platelet activation. The data imply a regulatory role of HSP90AB1, PF4 and THBS1 and these associated biological processes in the molecular changes underlying the response to acute heat exposure.

Multi-omics prediction of body temperature and cardiopulmonary parameters

Multi-omics data at the time point immediately after acute heat exposure was used to assess the relevance of clinical data for immediately detecting acute heat exposure. Correlation analysis identified a set of biomarkers that were highly predictive of changes in body temperature, FVC VC, EF, and FS (Fig. 7). Body temperature was correlated with metabolites of ascorbic acid and N-acetyl-D-tryptophan, the monocyte transcripts CYCS and MAPK9 and the neutrophil transcript RIPK2. FVC was correlated with the plasma protein acetylphenol sulfate, the monocyte transcripts GAB1 and UBE2T and the neutrophil transcript UBE2D2. VC was correlated with the plasma protein adhesion G protein-coupled receptor L4 VC, the monocyte transcripts CACNA2D2, CACNA2D4, TRAF2 and CRK and the neutrophil transcripts CTSG. EF and FS were correlated with the plasma proteins integrin alpha-5 and creatine kinase M-type, metabolites of amdoxovir and docosahexaenoic acid, the monocyte transcripts ELMO3 and JAK3 and the neutrophil transcripts BRICD5 and SLC31A1. These biomarkers have been previously associated with body temperature and the cardiopulmonary system, highlighting their clinical value.

Occupational exposure to extreme high temperatures and increasing global temperatures require enhanced understanding of how heat exposure impacts human health (20). This study investigated the short-term tolerance of humans to extreme high temperatures and characterized the detailed series of events that occur in response to acute heat exposure (50℃). Longitudinal multi-omics profiling of blood components revealed acute heat exposure affected physiological processes in a time-dependent manner. These physiological processes included the immune response, coagulation, acid-base balance, oxidative stress, cytoskeleton and energy metabolism. Time series data showed variations in the dynamics of these physiological processes and the molecular changes underlying the response to acute heat exposure. HSP90AB1, PF4 and THBS1 were identified as key regulators of the physiological processes involved in the response to acute heat exposure. A small number of blood-based analytes were predictors of changes in cardiopulmonary function, with potential for use as a clinical tool to evaluate the health risks of heat exposed populations.

In this study, the physiological performance of the subjects was detected. We found that during heat exposure, subjects exhibited typical physiological reactions, such as increased heart rate and maintained blood pressure and oxygen saturation, in order to ensure proper circulation and oxygenation of tissues. However, after heat exposure, subjects suffer some impairment of cardiac function, which may be related with the fluid loss or the impaired vascular function (16, 21). Furthermore, our study revealed a decrease in lung function following heat exposure. This finding aligns with a previous study that found a consistent association between each 1°C increase in lifetime summer mean temperature and a 1.07% decrease in forced vital capacity (FVC) (22). These findings suggested that the subjects included in our study exhibited some adaptive responses to acute heat exposure, but experienced a slight decline in their cardiopulmonary function.

Although there were no significant changes in the vital clinical signs of the subjects after extreme high temperature exposure, the changes in molecular reactions still indicated the potential increased risk after heat exposure. With respect to the immune response, the present study showed that synthesis of immunoglobulin was reduced after heat exposure, indicating the decreased immune function even short time exposure to extreme high temperature. This finding is consistent with previous reports. An investigation of individuals who worked in the foundry showed chronic heat exposure was associated with a decrease in the number of white blood cells (23). Studies of patients hospitalized for heat stroke during sustained heatwaves have reported a high incidence of urinary, blood or lung infections during recovery (24–26). This phenomenon may be related to abnormal development and decreased number of lymphocytes (27). Findings from a mouse model undergo heat exposure showed that whole blood collected 30 days after recovery from heat exposure exhibited an immunosuppressive phenotype (28).

The present study also showed that acute heat exposure induced proinflammatory cytokines, and monocytes and neutrophils may mediate this response. The transcriptome of peripheral blood monocytes exposed to acute heat exposure had an inflammatory signature consisting of chemokines (C-C motif ligand, C-X-C motif ligand, interleukin), complement C1qA chain, IFN-γ, TNF, NLRP3, and colony stimulating factor 3. Accumulating evidence indicates that multiple inflammatory cytokines such as TNF-α, IFN-γ, CCL20, IL1 and IL6 induce inflammation (29–32). C1qA, the initiating molecule of the classical complement cascade, can trigger inflammation, and blocking C1q function reduces chronic glial inflammation and neuron loss (33). NLRP3-inflammasome plays a role in inflammatory signaling (34, 35). CSF3 regulates granulocytes and monocytes and has inflammatory properties (36). Notably, the timing and magnitude of heat exposure may determine the type of inflammatory response. Sustained activation of the host inflammatory response will elicit detrimental effects, which may explain the "systemic inflammatory response syndrome" and multiple organ failure in severe heat-related illness. Anti-inflammatory agents targeting factors involved in the inflammatory response to heat exposure may be beneficial at early time after heat exposure, with potential to prevent progression of heat-related illnesses.

Coagulation disorders accompany heat-related illness, and severe DIC caused by heatstroke can be fatal (37–39). Findings from the present study imply that coagulation, fibrinolytic and anti-fibrinolytic processes are mobilized in the initial phase of heat exposure. In particular, coagulation-related factors, including VWF, PF4, and THBS1, remained elevated 24 hours after acute heat exposure. VWF binds the glycoprotein Iba on platelets causing platelet adhesion, activation and aggregation, which is crucial for hemostasis and thrombosis (40). PF4, also known as chemokine (C-X-C motif) ligand 4, has the ability to bind with negatively charged polyanions such as VWF, resulting in the formation of immune complexes that can contribute to the development of thrombosis (41). THBS1, which is produced by platelets when stimulated by thrombin, plays a role in platelet aggregation (42). The increase in coagulation related factors reflects a hypercoagulant state suggesting that modulating coagulation early after exposure to high temperature may be beneficial for preventing heat-related illness. In previous studies, various anticoagulants, such as antithrombin III, recombinant thrombomodulin and tissue factor pathway inhibitors, had been used to prevent heatstroke (43, 44).

Acid-base balance is regulated by hemoglobin and carbonic anhydrase levels, which increased in response to acute heat exposure in the present study. Hemoglobin in red blood cells binds oxygen and releases hydrogen ions (45). Carbonic anhydrase catalyzes the reversible hydration of carbon dioxide (CO₂) to form carbonic acid (H₂CO₃) in the lungs, kidneys, and red blood cells(46). Previous studies showed that weekly heat training increases elite cyclists or cross-country skiers’ hemoglobin mass, enhancing their ability to use oxygen (47–49). In the present study, the increases in hemoglobin and carbonic anhydrase were temporary and returned to baseline levels the day after acute heat exposure, indicating that repeated heat exposures are needed to maintain increased levels of hemoglobin and carbonic anhydrase.

Oxidative damage may be induced by chronic and long-term heat exposure, which enhances GST and GPX enzymatic activity and nitric oxide concentration (50, 51). Consistent with this, the present study shows prooxidants such as MPO and S100A8 were upregulated after acute heat exposure. Interestingly, some antioxidants (peroxiredoxin-2, peroxiredoxin-6, kallistatin, superoxide dismutase 1) were induced to resist harmful effects. Peroxiredoxin-2, peroxiredoxin-6 and superoxide dismutase are antioxidant enzymes that protect organisms from oxidative damage caused by reactive oxygen species (ROS) (52, 53). Kallistatin antagonizes TNF-α-induced oxidative stress, and its active site is crucial for stimulating antioxidant enzyme expression(54). Previous reports show the negative effects of heat exposure on oxidative status may be alleviated by dietary antioxidant supplementation (55, 56). The present study adds to this growing body of evidence.

Energy metabolism was altered by acute heat exposure. Levels of circulating plasma metabolites, such as amino acids, free fatty acids, bile acid, carnitine and sphinganine, were changed. Amino acid levels decreased over time as they were catabolized for energy generation. Acylcarnitines and fatty acids accumulated after acute heat exposure. This may be due to the transformation of beige adipocytes into white adipocytes, which can inhibit adipocyte heat production and reduce fatty acid digestion (57). Sphinganine levels increased briefly, confirming the role of sphingosine as a potent regulator of signaling pathways. Levels of the bile acids chenodeoxycholic acid, cholic acid and deoxycholic acid decreased, which may be associated with the decrease in appetite that is observed in high-temperature environments.

Levels of some circulating plasma proteins continued to increase during the recovery phase after acute heat exposure, implying these proteins are potential regulators of important physiological processes involved in the response to acute heat exposure. Protein-protein interaction and WGCNA analyses identified HSP90AB1, PF4 and THBS1 as hub proteins. HSP90AB1 or HSP90β serve as molecular chaperones to maintain protein stability and have protective roles in response to various kinds of cellular stress, such as heat exposure (58, 59). PF4 and THBS1 play a critical role in coagulation and regulate immune cells and inflammatory responses (60–62).

After heat exposure, we found that the changes in the levels of certain plasma molecules were significantly correlated with the changes in clinical parameters. Notably, some of these molecules have previously been associated with clinical parameters, suggesting their potential clinical value in predicting functional changes following heat exposure. One such molecule is N-Acetyl-D-Tryptophan, which showed a positive correlation with body temperature after acute heat exposure. This molecule is a derivative of tryptophan and can be metabolized into 5-hydroxytryptamine (5-HT), and activation of 5-HT receptors can lead to an increase in body temperature (63, 64). Furthermore, we observed that the increase of creatine kinase M-type leptin and the decline in docosahexaenoic acid were found to be correlated with the decline in cardiac function after heat exposure. Creatine kinase, an enzyme involved in ATP production and utilization, has been extensively studied as potential risk marker for cardiovascular events (65). Docosahexaenoic acid is an omega-3 fatty acid, which is found to have positive effects on cardiac function and significantly decreases the overall mortality in patients diagnosed with coronary heart disease (66). Additionally, we found CTSG was negatively associated with pulmonary function after acute heat exposure. CTSG, also known as cathepsin G, is a serine protease that is involved in various physiological processes, including inflammation, immune response and the breakdown of extracellular matrix proteins. CTSG has been implicated in lung diseases such as chronic obstructive pulmonary disease, asthma, and acute respiratory distress syndrome (67).

This study had some limitations. The sample size was small and did not include females or the elderly. Owing to the safety for subjects, the max tolerance time did not to be detected, and the relative humidity were not controlled at a high level, which had obvious effect on human’s tolerance time of extreme high temperature. Therefore, molecular changes associated with life-threatening heatstroke were not explored.

Overall, this study offers insights into key research gaps in health impact assessment of heat exposure. Longitudinal multi-omics profiling identified thousands of molecules that were affected by acute heat exposure, and time-series clustering and network analysis revealed crosstalk between inflammation, coagulation, immunity, acid-base balance, metabolism and oxidative stress. Potential regulators and biomarkers for acute heat exposure were revealed. These findings have significant implications for both occupational populations exposed to heat and the general population during extreme heat environment. It will help to aid the prediction of short-term health outcomes in acute heat exposure and develop prevention strategies to prevent heat-related injuries and mitigate the adverse effects of heat exposure.

ETHICS DECLARATIONS

This study involving human participants was reviewed and approved by ethics committees at West China Hospital, Sichuan University.

CONSENT FOR PUBLICATION

Not applicable.

COMPETING INTERESTS

The authors declare no competing interests.

INFORMED CONSENT

Informed consent was obtained from all subjects involved in the study.

DATA AVAILABILITY

Raw and processed omic data (transcriptome, untargeted proteome, untargeted metabolome) are hosted on public database. The proteomics data have been deposited to the iProX with the dataset identifier PXD044472. The metabolomics data have been deposited to the MetaboLights with the dataset identifier MTBLS8382. The transcriptomics data have been deposited to the GEO with the dataset identifier SUB13722246.

Funding

The authors would like to express their gratitude for the support provided by 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC21048), Foundation of Sichuan Provincial Science and Technology Program (2022JDR0091, 2023NSFSC0004, 2023NSFSC0639, 2023NSFSC1742), 2020 Cooperation project for Sichuan University and Yibin Municipal People's Government (2020CDYB-35), cooperation project for academician＆experts workstation (HXYS20001) and Sichuan University Education Foundation (0040206107011), National Natural Science Foundation of China (No.82371883). We are grateful to OE Biotech, Inc., (Shanghai, China) for assisting in sequencing and/or bioinformatics analysis.

ACKNOWLEDGEMENTS

Not applicable.

AUTHOR CONTRIBUTIONS

JRW, JW, JL contributed to the conceptualization. JRW, LW, JC, YL, QS contributed to the omic data generation and/or processing. TM, QW, JZ, WY, YC, DH, ZY, LL, MG contributed to recruitment of subjects and measurement of clinical parameters. XS, CL and YZ contributed to the data curation and data visualization. XW, MG, HX, JZ contributed to project administration and supervision. JRW LW, JC, JW, JL contributed to writing and preparing the original draft. All authors reviewed the manuscript.

DECLARATION OF INTERESTS

The authors declare they have no actual or potential competing financial interests.

Lee J, Lee W, Choi WJ, Kang SK, Ham S. Association between Exposure to Extreme Temperature and Injury at the Workplace. Int J Environ Res Public Health. 2019;16(24). http://doi.org/10.3390/ijerph16244955.
Liu J, Liu Y, Ma T, Gao M, Zhang R, Wu J, et al. Subjective perceptions and psychological distress associated with the deep underground: A cross-sectional study in a deep gold mine in China. Med (Baltim). 2019;98(22):e15571. http://doi.org/10.1097/MD.0000000000015571.
Strzemecka J, Gozdziewska M, Skrodziuk J, Galinska EM, Lachowski S. Factors of work environment hazardous for health in opinions of employees working underground in the 'Bogdanka' coal mine. Ann Agric Environ Med. 2019;26(3):409–14. http://doi.org/10.26444/aaem/106224.
Xie H, Liu J, Gao M, Liu Y, Ma T, Lu Y, et al. Physical symptoms and mental health status in deep underground miners: A cross-sectional study. Med (Baltim). 2020;99(9):e19294. http://doi.org/10.1097/MD.0000000000019294.
Hunt AP, Parker AW, Stewart IB. Symptoms of heat illness in surface mine workers. Int Arch Occup Environ Health. 2013;86(5):519–27. http://doi.org/10.1007/s00420-012-0786-0.
Marszalek A, Bartkowiak G, Dabrowska A, Krzeminska S, Lezak K, Makowski K, et al. Mine rescuers' heat load during the expenditure of physical effort in a hot environment, using ventilated underwear and selected breathing apparatus. Int J Occup Saf Ergon. 2018;24(1):1–13. http://doi.org/10.1080/10803548.2017.1335971.
Guidotti TL. Human factors in firefighting: ergonomic-, cardiopulmonary-, and psychogenic stress-related issues. Int Arch Occup Environ Health. 1992;64(1):1–12. http://doi.org/10.1007/BF00625945.
Wohlgemuth K, Sekiguchi Y, Mota J. Overexertion and heat stress in the fire service: a new conceptual framework. Am J Ind Med. 2023;66(8):705–9. .http://doi.org/10.1002/ajim.23482.
Cheung SS, Petersen SR, McLellan TM. Physiological strain and countermeasures with firefighting. Scand J Med Sci Sports. 2010;20(Suppl 3):103–16. http://doi.org/10.1111/j.1600-0838.2010.01215.x.
Ebi KL. Managing climate change risks is imperative for human health. Nat Rev Nephrol. 2022;18(2):74–. doi.org/10.1038/s41581-021-00523-2. 5.http://.
Cai W, Zhang C, Suen HP, Ai S, Bai Y, Bao J, et al. The 2020 China report of the Lancet Countdown on health and climate change. Lancet Public Health. 2021;6(1):e64–e81. http://doi.org/10.1016/S2468-2667(20)30256-5.
Lutz EA, Reed RJ, Turner D, Littau SR. Occupational heat strain in a hot underground metal mine. J Occup Environ Med. 2014;56(4):388–96. http://doi.org/10.1097/JOM.0000000000000107.
Yeoman K, DuBose W, Bauerle T, Victoroff T, Finley S, Poplin G. Patterns of Heat Strain Among a Sample of US Underground Miners. J Occup Environ Med. 2019;61(3):212–8. http://doi.org/10.1097/JOM.0000000000001518.
Larsen B, Snow R, Aisbett B. Effect of heat on firefighters' work performance and physiology. J Therm Biol. 2015;53:1–8. http://doi.org/10.1016/j.jtherbio.2015.07.008.
Larsen B, Snow R, Vincent G, Tran J, Wolkow A, Aisbett B. Multiple Days of Heat Exposure on Firefighters' Work Performance and Physiology. PLoS ONE. 2015;10(9):e0136413. http://doi.org/10.1371/journal.pone.0136413.
Angerer P, Kadlez-Gebhardt S, Delius M, Raluca P, Nowak D. Comparison of cardiocirculatory and thermal strain of male firefighters during fire suppression to exercise stress test and aerobic exercise testing. Am J Cardiol. 2008;102(11):1551–6. .http://doi.org/10.1016/j.amjcard.2008.07.052.
Fahs CA, Yan H, Ranadive S, Rossow LM, Agiovlasitis S, Echols G, et al. Acute effects of firefighting on arterial stiffness and blood flow. Vasc Med. 2011;16(2):113–8. http://doi.org/10.1177/1358863X11404940.
Hausfater P, Doumenc B, Chopin S, Le Manach Y, Santin A, Dautheville S, et al. Elevation of cardiac troponin I during non-exertional heat-related illnesses in the context of a heatwave. Crit Care. 2010;14(3):R99. http://doi.org/10.1186/cc9034.
Flouris AD, Dinas PC, Ioannou LG, Nybo L, Havenith G, Kenny GP, et al. Workers' health and productivity under occupational heat strain: a systematic review and meta-analysis. Lancet Planet Health. 2018;2(12):e521–e31. http://doi.org/10.1016/S2542-5196(18)30237-7.
Ebi KL, Capon A, Berry P, Broderick C, de Dear R, Havenith G, et al. Hot weather and heat extremes: health risks. Lancet. 2021;398(10301):698–708. http://doi.org/10.1016/S0140-6736(21)01208-3.
Hunter AL, Shah AS, Langrish JP, Raftis JB, Lucking AJ, Brittan M, et al. Fire Simulation and Cardiovascular Health in Firefighters. Circulation. 2017;135(14):1284–95. .http://doi.org/10.1161/CIRCULATIONAHA.116.025711.
Miao J, Feng S, Wang M, Jiang N, Yu P, Wu Y, et al. Life-time summer heat exposure and lung function in young adults: A retrospective cohort study in Shandong China. Environ Int. 2022;160:107058. http://doi.org/10.1016/j.envint.2021.107058.
Jafari MJ, Pirposhteh EA, Dehghan SF, Khodakarim S, Jafari M. Relationship between heat stress exposure and some immunological parameters among foundry workers. Int J Biometeorol. 2020;64(5):853–61. http://doi.org/10.1007/s00484-020-01874-4.
Argaud L, Ferry T, Le QH, Marfisi A, Ciorba D, Achache P, et al. Short- and long-term outcomes of heatstroke following the 2003 heat wave in Lyon, France. Arch Intern Med. 2007;167(20):2177–83. .http://doi.org/10.1001/archinte.167.20.ioi70147.
Davido A, Patzak A, Dart T, Sadier MP, Meraud P, Masmoudi R, et al. Risk factors for heat related death during the August 2003 heat wave in Paris, France, in patients evaluated at the emergency department of the Hopital Europeen Georges Pompidou. Emerg Med J. 2006;23(7):515–8. .http://doi.org/10.1136/emj.2005.028290.
Dematte JE, O'Mara K, Buescher J, Whitney CG, Forsythe S, McNamee T, et al. Near-fatal heat stroke during the 1995 heat wave in Chicago. Ann Intern Med. 1998;129(3):173–81. http://doi.org/10.7326/0003-4819-129-3-199808010-00001.
Hirakawa R, Nurjanah S, Furukawa K, Murai A, Kikusato M, Nochi T, et al. Heat Stress Causes Immune Abnormalities via Massive Damage to Effect Proliferation and Differentiation of Lymphocytes in Broiler Chickens. Front Vet Sci. 2020;7:46. http://doi.org/10.3389/fvets.2020.00046.
Murray KO, Brant JO, Iwaniec JD, Sheikh LH, de Carvalho L, Garcia CK, et al. Exertional heat stroke leads to concurrent long-term epigenetic memory, immunosuppression and altered heat shock response in female mice. J Physiol. 2021;599(1):119–41. http://doi.org/10.1113/JP280518.
Dinarello CA. Interleukin-1 in the pathogenesis and treatment of inflammatory diseases. Blood. 2011;117(14):3720–32. http://doi.org/10.1182/blood-2010-07-273417.
Hanna DN, Smith PM, Novitskiy SV, Washington MK, Zi J, Weaver CJ, et al. SMAD4 Suppresses Colitis-associated Carcinoma Through Inhibition of CCL20/CCR6-mediated Inflammation. Gastroenterology. 2022;163(5):1334–50. e14.http://doi.org/10.1053/j.gastro.2022.07.016.
Jones SA, Jenkins BJ. Recent insights into targeting the IL-6 cytokine family in inflammatory diseases and cancer. Nat Rev Immunol. 2018;18(12):773–89. http://doi.org/10.1038/s41577-018-0066-7.
Karki R, Sharma BR, Tuladhar S, Williams EP, Zalduondo L, Samir P, et al. Synergism of TNF-alpha and IFN-gamma Triggers Inflammatory Cell Death, Tissue Damage, and Mortality in SARS-CoV-2 Infection and Cytokine Shock Syndromes. Cell. 2021;184(1):149–68. e17.http://doi.org/10.1016/j.cell.2020.11.025.
Holden SS, Grandi FC, Aboubakr O, Higashikubo B, Cho FS, Chang AH, et al. Complement factor C1q mediates sleep spindle loss and epileptic spikes after mild brain injury. Science. 2021;373(6560):eabj2685. http://doi.org/10.1126/science.abj2685.
Karasawa T, Takahashi M. Role of NLRP3 Inflammasomes in Atherosclerosis. J Atheroscler Thromb. 2017;24(5):443–51. http://doi.org/10.5551/jat.RV17001.
Anzai F, Karasawa T, Komada T, Yamada N, Miura Y, Sampilvanjil A, et al. Calciprotein Particles Induce IL-1beta/alpha-Mediated Inflammation through NLRP3 Inflammasome-Dependent and -Independent Mechanisms. Immunohorizons. 2021;5(7):602–14. http://doi.org/10.4049/immunohorizons.2100066.
Dougan M, Dranoff G, Dougan SK, GM-CSF. IL-3, and IL-5 Family of Cytokines: Regulators of Inflammation. Immunity. 2019;50(4):796–811. http://doi.org/10.1016/j.immuni.2019.03.022.
al-Mashhadani SA, Gader AG, al Harthi SS, Kangav D, Shaheen FA, Bogus F. The coagulopathy of heat stroke: alterations in coagulation and fibrinolysis in heat stroke patients during the pilgrimage (Haj) to Makkah. Blood Coagul Fibrinolysis. 1994;5(5):731–6. http://doi.org/10.1097/00001721-199410000-00009.
Bouchama A, Bridey F, Hammami MM, Lacombe C, al-Shail E, al-Ohali Y, et al. Activation of coagulation and fibrinolysis in heatstroke. Thromb Haemost. 1996;76(6):909–15. https://www.ncbi.nlm.nih.gov/pubmed/8972010.
Jilma B, Derhaschnig U. Disseminated intravascular coagulation in heat stroke: a hot topic. Crit Care Med. 2012;40(4):1370–2. http://doi.org/10.1097/CCM.0b013e31823d785d.
Reininger AJ. VWF attributes–impact on thrombus formation. Thromb Res. 2008;122(Suppl 4):9. 13.http://doi.org/10.1016/S0049-3848(08)70028-8.
Liu ZY, Sun MX, Hua MQ, Zhang HX, Mu GY, Zhou S, et al. New perspectives on the induction and acceleration of immune-associated thrombosis by PF4 and VWF. Front Immunol. 2023;14:1098665. http://doi.org/10.3389/fimmu.2023.1098665.
Patsouras M, Tsiki E, Karagianni P, Vlachoyiannopoulos PG. The role of thrombospondin-1 in the pathogenesis of antiphospholipid syndrome. J Autoimmun. 2020;115:102527. http://doi.org/10.1016/j.jaut.2020.102527.
Hagiwara S, Iwasaka H, Goto K, Ochi Y, Mizunaga S, Saikawa T, et al. Recombinant thrombomodulin prevents heatstroke by inhibition of high-mobility group box 1 protein in sera of rats. Shock. 2010;34(4):402–6. .http://doi.org/10.1097/SHK.0b013e3181d492e4.
Hagiwara S, Iwasaka H, Shingu C, Matsumoto S, Uchida T, Noguchi T. High-dose antithrombin III prevents heat stroke by attenuating systemic inflammation in rats. Inflamm Res. 2010;59(7):511–8. .http://doi.org/10.1007/s00011-009-0155-y.
Gell DA. Structure and function of haemoglobins. Blood Cells Mol Dis. 2018;70:13–42. .http://doi.org/10.1016/j.bcmd.2017.10.006.
Lindskog S. Structure and mechanism of carbonic anhydrase. Pharmacol Ther. 1997;74(1):1–20. http://doi.org/10.1016/s0163-7258(96)00198-2.
Ronnestad BR, Hamarsland H, Hansen J, Holen E, Montero D, Whist JE, et al. Five weeks of heat training increases haemoglobin mass in elite cyclists. Exp Physiol. 2021;106(1):316–27. http://doi.org/10.1113/EP088544.
Ronnestad BR, Lid OM, Hansen J, Hamarsland H, Molmen KS, Nygaard H, et al. Heat suit training increases hemoglobin mass in elite cross-country skiers. Scand J Med Sci Sports. 2022;32(7):1089–98. http://doi.org/10.1111/sms.14156.
Ronnestad BR, Urianstad T, Hamarsland H, Hansen J, Nygaard H, Ellefsen S, et al. Heat Training Efficiently Increases and Maintains Hemoglobin Mass and Temperate Endurance Performance in Elite Cyclists. Med Sci Sports Exerc. 2022;54(9):1515–26. http://doi.org/10.1249/MSS.0000000000002928.
Gharibi V, Khanjani N, Heidari H, Ebrahimi MH, Hosseinabadi MB. The effect of heat stress on hematological parameters and oxidative stress among bakery workers. Toxicol Ind Health. 2020;36(1):1–10. http://doi.org/10.1177/0748233719899824.
Cui Y, Hao Y, Li J, Bao W, Li G, Gao Y, et al. Chronic Heat Stress Induces Immune Response, Oxidative Stress Response, and Apoptosis of Finishing Pig Liver: A Proteomic Approach. Int J Mol Sci. 2016;17(5). http://doi.org/10.3390/ijms17050393.
Wu M, Deng C, Lo TH, Chan KY, Li X, Wong CM. Peroxiredoxin, Senescence, and Cancer. Cells. 2022;11(11). http://doi.org/10.3390/cells11111772.
Bhusari SS, Dobosy JR, Fu V, Almassi N, Oberley T, Jarrard DF. Superoxide dismutase 1 knockdown induces oxidative stress and DNA methylation loss in the prostate. Epigenetics. 2010;5(5):402–9. .http://doi.org/10.4161/epi.5.5.11853.
Chao J, Li P, Chao L. Kallistatin: double-edged role in angiogenesis, apoptosis and oxidative stress. Biol Chem. 2017;398(12):1309–17. http://doi.org/10.1515/hsz-2017-0180.
Chauhan SS, Celi P, Leury BJ, Clarke IJ, Dunshea FR. Dietary antioxidants at supranutritional doses improve oxidative status and reduce the negative effects of heat stress in sheep. J Anim Sci. 2014;92(8):3364–74. http://doi.org/10.2527/jas.2014-7714.
Kassem S, Mohamed M, Sayour H, Canfarotta F, Piletsky S, Soliman MAM. Functionalized Core-Shell Yttrium Oxide Nanoparticles as Antioxidants Agents in Heat Stressed Rats. Biol Trace Elem Res. 2020;198(1):189–97. http://doi.org/10.1007/s12011-020-02036-8.
Morigny P, Boucher J, Arner P, Langin D. Lipid and glucose metabolism in white adipocytes: pathways, dysfunction and therapeutics. Nat Rev Endocrinol. 2021;17(5):276–95. http://doi.org/10.1038/s41574-021-00471-8.
Haase M, Fitze G. HSP90AB1: Helping the good and the bad. Gene. 2016;575(2 Pt 1):171–86. http://doi.org/10.1016/j.gene.2015.08.063.
Sajjanar B, Deb R, Singh U, Kumar S, Brahmane M, Nirmale A, et al. Identification of SNP in HSP90AB1 and its association with the relative thermotolerance and milk production traits in Indian dairy cattle. Anim Biotechnol. 2015;26(1):45–50. http://doi.org/10.1080/10495398.2014.882846.
Drakeford C, Aguila S, Roche F, Hokamp K, Fazavana J, Cervantes MP, et al. von Willebrand factor links primary hemostasis to innate immunity. Nat Commun. 2022;13(1):6320. http://doi.org/10.1038/s41467-022-33796-7.
Gajeton J, Krukovets I, Yendamuri R, Verbovetskiy D, Vasanji A, Sul L, et al. miR-467 regulates inflammation and blood insulin and glucose. J Cell Mol Med. 2021;25(5):2549–62. http://doi.org/10.1111/jcmm.16224.
Gray AL, Karlsson R, Roberts ARE, Ridley AJL, Pun N, Khan B, et al. Chemokine CXCL4 interactions with extracellular matrix proteoglycans mediate widespread immune cell recruitment independent of chemokine receptors. Cell Rep. 2023;42(1):111930. http://doi.org/10.1016/j.celrep.2022.111930.
Feduccia AA, Kongovi N, Duvauchelle CL. Heat increases MDMA-enhanced NAcc 5-HT and body temperature, but not MDMA self-administration. Eur Neuropsychopharmacol. 2010;20(12):884–94. http://doi.org/10.1016/j.euroneuro.2010.08.009.
Young SN, Leyton M. The role of serotonin in human mood and social interaction. Insight from altered tryptophan levels. Pharmacol Biochem Behav. 2002;71(4):857–65. http://doi.org/10.1016/s0091-3057(01)00670-0.
Klatte K, Chaitman BR, Theroux P, Gavard JA, Stocke K, Boyce S, et al. Increased mortality after coronary artery bypass graft surgery is associated with increased levels of postoperative creatine kinase-myocardial band isoenzyme release: results from the GUARDIAN trial. J Am Coll Cardiol. 2001;38(4):1070–7. .http://doi.org/10.1016/s0735-1097(01)01481-4.
Jain AP, Aggarwal KK, Zhang PY. Omega-3 fatty acids and cardiovascular disease. Eur Rev Med Pharmacol Sci. 2015;19(3):441–5. https://www.ncbi.nlm.nih.gov/pubmed/25720716.
Korkmaz B, Horwitz MS, Jenne DE, Gauthier F. Neutrophil elastase, proteinase 3, and cathepsin G as therapeutic targets in human diseases. Pharmacol Rev. 2010;62(4):726–59. http://doi.org/10.1124/pr.110.002733.

No competing interests reported.

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Molecular choreography to acute heat exposure in a simulated extreme high temperature environment

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Version 1

Abstract

Figures

INTRODUCTION

METHODS

Study subjects

Study design

Baseline period

Exposure period

Recovery period

Objective fatigue

Cardiopulmonary function tests

Blood Collection and sample preparation

RNA sequencing and data processing

Untargeted Proteomics and data processing

Untargeted Metabolomics from Plasma by Liquid Chromatography (LC)-MS

Quantification and statistical analysis

Fuzzy c-mean clustering

Inter-individual variability

Pathway dynamic analysis

Pathway enrichment analysis

Co-expression analysis of transcriptome and proteomics

WGCNA analysis

Enzyme-linked immunosorbent assay

RESULTS

Cohort characteristics and research design

Baseline versus heat exposure-induced individual omic variability

Time series system-wide proteomics data

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Time series system-wide metabolomics data

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Cluster 6

Time series analysis of the transcriptomic profile of peripheral blood monocytes

Cluster 1

Cluster 2

Cluster 3

DISCUSSION

CONCLUSION

Declarations

References

Additional Declarations

Supplementary Files

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