Factors affecting firefighter occupational cancer risk adjustment

Benjamin Caffee; Hao-Che Wu; Benjamin Caffee; Hao-Che Wu

doi:10.48130/EMST-2022-0008

2022 Volume 2

Article Contents

Abstract

References

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ARTICLE Open Access

Factors affecting firefighter occupational cancer risk adjustment

Benjamin Caffee¹,
Hao-Che Wu^2, ,

1.
Alabama Fire College, Tuscaloosa, Alabama 35405, USA
2.
Department of Emergency Management and Disaster Science, University of North Texas, Denton, Texas 76203, USA

More Information

Corresponding author: tristan.wu@unt.edu

Received: 11 June 2022
Accepted: 12 July 2022
Published online: 26 September 2022
Emergency Management Science and Technology 2, Article number: 8 (2022) | Cite this article

Abstract

Recent research has shown firefighters are at a higher risk for cancer diagnosis than the general population. Experts have offered six hazard adjustments that may assist in reducing the level of exposure to carcinogens. This study was conducted to better understand what motivates or deters firefighters from engaging in these hazard adjustments. The sample was firefighters who had attended or were otherwise associated with the Alabama Fire College (Alabama, USA). An internet survey was administered to collect the data. The participant recruitment email was opened by 1,539 individuals, and 358 responses were received, giving a response rate of 23%. The findings suggest that firefighters' occupational cancer risk perceptions are high. Also, response efficacy, self-efficacy, and cost of engaging in the behavior were much more reliable predictors of intention and actual hazard adjustment than risk perception, salience, and exposure. The concept of peer perception is used in this Protection Motivation Theory study, which also affects firefighters’ completion of hazard adjustment. The findings of this study will assist fire service leaders in adapting education programs, policies, and procedures to better protect firefighters from occupational cancer risk.
- Protection motivation theory,
- Firefighter cancer risk,
- Hazard adjustment

INTRODUCTION

Grapevines are among the most widely grown and economically important fruit crops globally. Grapes are used not only for wine making and juice, but also are consumed fresh and as dried fruit^[1]. Additionally, grapes have been increasingly recognized as an important source of resveratrol (trans-3, 5,4'-trihydroxystilbene), a non-flavonoid stilbenoid polyphenol that in grapevine may act as a phytoalexin. In humans, it has been widely reported that dietary resveratrol has beneficial impacts on various aspects of health^{[2, 3]}. Because of the potential value of resveratrol both to grapevine physiology and human medicine, resveratrol biosynthesis and its regulation has become an important avenue of research. Similar to other stilbenoids, resveratrol synthesis utilizes key enzymes of the phenylpropanoid pathway including phenylalanine ammonia lyase (PAL), cinnamate 4-hydroxylase (C4H), and 4-coumarate-CoA ligase (4CL). In the final steps, stilbene synthase (STS), a type II polyketide synthase, produces trans-resveratrol from p-coumaroyl-CoA and malonyl-CoA, while chalcone synthase (CHS) synthesizes flavonoids from the same substrates^{[4, 5]}. Moreover, trans-resveratrol is a precursor for other stilbenoids such as cis-resveratrol, trans-piceid, cis-piceid, ε-viniferin and δ-viniferin^[6]. It has been reported that stilbenoid biosynthesis pathways are targets of artificial selection during grapevine domestication^[7] and resveratrol accumulates in various structures in response to both biotic and abiotic stresses^[8−12]. This stress-related resveratrol synthesis is mediated, at least partialy, through the regulation of members of the STS gene family. Various transcription factors (TFs) participating in regulating STS genes in grapevine have been reported. For instance, MYB14 and MYB15^{[13, 14]} and WRKY24^[15] directly bind to the promoters of specific STS genes to activate transcription. VvWRKY8 physically interacts with VvMYB14 to repress VvSTS15/21 expression^[16], whereas VqERF114 from Vitis quinquangularis accession 'Danfeng-2' promotes expression of four STS genes by interacting with VqMYB35 and binding directly to cis-elements in their promoters^[17]. Aided by the release of the first V. vinifera reference genome assembly^[18], genomic and transcriptional studies have revealed some of the main molecular mechanisms involved in fruit ripening^[19−24] and stilbenoid accumulation^{[8, 25]} in various grapevine cultivars. Recently, it has been reported that a root restriction treatment greatly promoted the accumulation of trans-resveratrol, phenolic acid, flavonol and anthocyanin in 'Summer Black' (Vitis vinifera × Vitis labrusca) berry development during ripening^[12]. However, most of studies mainly focus on a certain grape variety, not to investigate potential distinctions in resveratrol biosynthesis among different Vitis genotypes. In this study, we analyzed the resveratrol content in seven grapevine accessions and three berry structures, at three stages of fruit development. We found that the fruits of two wild, Chinese grapevines, Vitis amurensis 'Tonghua-3' and Vitis davidii 'Tangwei' showed significant difference in resveratrol content during development. These were targeted for transcriptional profiling to gain insight into the molecular aspects underlying this difference. This work provides a theoretical basis for subsequent systematic studies of genes participating in resveratrol biosynthesis and their regulation. Further, the results should be useful in the development of grapevine cultivars exploiting the genetic resources of wild grapevines.

RESULTS

Extractable trans-resveratrol content in developing grape berries

For each of the seven cultivars, we analyzed resveratrol content in the skin, pulp, and seed at three stages of development: Green hard (G), véraison (V), and ripe (R) (Table 1). In general, we observed the highest accumulation in skins at the R stage (0.43−2.99 µg g⁻¹ FW). Lesser amounts were found in the pulp (0.03−0.36 µg g⁻¹ FW) and seed (0.05−0.40 µg g⁻¹ FW) at R, and in the skin at the G (0.12−0.34 µg g⁻¹ FW) or V stages (0.17−1.49 µg g⁻¹ FW). In all three fruit structures, trans-resveratrol showed an increasing trend with development, and this was most obvious in the skin. It is worth noting that trans-resveratrol was not detectable in the skin of 'Tangwei' at the G or V stage, but had accumulated to 2.42 µg g⁻¹ FW by the R stage. The highest amount of extractable trans-resveratrol (2.99 µg g⁻¹ FW) was found in 'Tonghua-3' skin at the R stage.

Table 1. Resveratrol concentrations in the skin, pulp and seed of berries from different grapevine genotypes at green hard, véraison and ripe stages.

Structures	Species	Accessions or cultivars	Content of trans-resveratrol (μg g⁻¹ FW)
Structures	Species	Accessions or cultivars	Green hard	Véraison	Ripe
Skin	V. davidii	Tangwei	nd	nd	2.415 ± 0.220
	V. amurensis	Tonghua-3	0.216 ± 0.041	0.656 ± 0.043	2.988 ± 0.221
	V. amurensis	Shuangyou	0.233 ± 0.062	0.313 ± 0.017	2.882 ± 0.052
	V. amurensis × V. Vinifera	Beibinghong	0.336 ± 0.076	1.486 ± 0.177	1.665 ± 0.100
	V. vinifera	Red Global	0.252 ± 0.051	0.458 ± 0.057	1.050 ± 0.129
	V. vinifera	Thompson seedless	0.120 ± 0.025	1.770 ± 0.032	0.431 ± 0.006
	V. vinifera × V. labrusca	Jumeigui	0.122 ± 0.016	0.170 ± 0.021	0.708 ± 0.135
Pulp	V. davidii	Tangwei	0.062 ± 0.006	0.088 ± 0.009	nd
	V. amurensis	Tonghua-3	0.151 ± 0.066	0.324 ±0.104	0.032 ± 0.004
	V. amurensis	Shuangyou	0.053 ± 0.008	0.126 ± 0.044	0.041 ± 0.017
	V. amurensis × V. Vinifera	Beibinghong	0.057 ± 0.014	0.495 ± 0.068	0.087 ± 0.021
	V. vinifera	Red Global	0.059 ± 0.018	0.159 ± 0.013	0.027 ± 0.004
	V. vinifera	Thompson seedless	0.112 ± 0.016	0.059 ± 0.020	nd
	V. vinifera × V. labrusca	Jumeigui	0.072 ± 0.010	0.063 ± 0.017	0.359 ± 0.023
Seed	V. davidii	Tangwei	0.096 ± 0.014	0.169 ± 0.028	0.049 ± 0.006
	V. amurensis	Tonghua-3	0.044 ± 0.004	0.221 ± 0.024	0.113 ± 0.027
	V. amurensis	Shuangyou	nd	0.063 ± 0.021	0.116 ± 0.017
	V. amurensis × V. Vinifera	Beibinghong	nd	0.077 ± 0.003	0.400 ± 0.098
	V. vinifera	Red Global	0.035 ± 0.023	0.142 ± 0.036	0.199 ± 0.009
	V. vinifera	Thompson seedless	−	−	−
	V. vinifera × V. labrusca	Jumeigui	0.077 ± 0.025	0.017 ± 0.004	0.284 ± 0.021
'nd' indicates not detected in samples, and '−' shows no samples are collected due to abortion.

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Analysis of RNA-Seq data

To gain insight into gene expression patterns influencing resveratrol biosynthesis in 'Tangwei' and 'Tonghua-3', we profiled the transcriptomes of developing berries at G, V, and R stages, using sequencing libraries representing three biological replicates from each cultivar and stage. A total of 142.49 Gb clean data were obtained with an average of 7.92 Gb per replicate, with average base Q30 > 92.5%. Depending on the sample, between 80.47%−88.86% of reads aligned to the V. vinifera reference genome (Supplemental Table S1), and of these, 78.18%−86.66% mapped to unique positions. After transcript assembly, a total of 23,649 and 23,557 unigenes were identified as expressed in 'Tangwei' and 'Tonghua-3', respectively. Additionally, 1,751 novel transcripts were identified (Supplemental Table S2), and among these, 1,443 could be assigned a potential function by homology. Interestingly, the total number of expressed genes gradually decreased from the G to R stage in 'Tangwei', but increased in 'Tonghua-3'. About 80% of the annotated genes showed fragments per kilobase of transcript per million fragments mapped (FPKM) values > 0.5 in all samples, and of these genes, about 40% showed FPKM values between 10 and 100 (Fig. 1a). Correlation coefficients and principal component analysis of the samples based on FPKM indicated that the biological replicates for each cultivar and stage showed similar properties, indicating that the transcriptome data was reliable for further analyses (Fig. 1b & c).

Figure 1. Properties of transcriptome data of 'Tangwei' (TW) and 'Tonghua-3' (TH) berry at green hard (G), véraison (V), and ripe (R) stages. (a) Total numbers of expressed genes with fragments per kilobase of transcript per million fragments mapped (FPKM) values; (b) Heatmap of the sample correlation analysis; (c) Principal component analysis (PCA) showing clustering pattern among TW and TH at G, V and R samples based on global gene expression profiles.

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GO and KEGG pathway enrichment analysis of DEGs

By comparing the transcriptomes of 'Tangwei' and 'Tonghua-3' at the G, V and R stages, we identified 6,770, 3,353 and 6,699 differentially expressed genes (DEGs), respectively (Fig. 2a). Of these genes, 1,134 were differentially expressed between the two cultivars at all three stages (Fig. 2b). We also compared transcriptional profiles between two adjacent developmental stages (G vs V; V vs R) for each cultivar. Between G and V, we identified 1,761 DEGs that were up-regulated and 2,691 DEGs that were down-regulated in 'Tangwei', and 1,836 and 1,154 DEGs that were up-regulated or down-regulated, respectively, in 'Tonghua-3'. Between V and R, a total of 1,761 DEGs were up-regulated and 1,122 DEGs were down-regulated in 'Tangwei', whereas 2,774 DEGs and 1,287 were up-regulated or down-regulated, respectively, in 'Tonghua-3' (Fig. 2c). Among the 16,822 DEGs between the two cultivars at G, V, and R (Fig. 2a), a total of 4,570, 2,284 and 4,597 had gene ontology (GO) annotations and could be further classified to over 60 functional subcategories. The most significantly represented GO terms between the two cultivars at all three stages were response to metabolic process, catalytic activity, binding, cellular process, single-organism process, cell, cell part and biological regulation (Fig. 2d).

Figure 2. Analysis of differentially expressed genes (DEGs) at the green hard (G), véraison (V), and ripe (R) stages in 'Tangwei' (TW) and 'Tonghua-3' (TH). (a) Number of DEGs and (b) numbers of overlapping DEGs between 'Tangwei' and 'Tonghua-3' at G, V and R; (c) Overlap among DEGs between G and V, and V and R, for 'Tangwei' and 'Tonghua-3'; (d) Gene ontology (GO) functional categorization of DEGs.

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We also identified 57 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were enriched, of which 32, 28, and 31 were enriched at the G, V, and R stages, respectively. Seven of the KEGG pathways were enriched at all three developmental stages: photosynthesis-antenna proteins (ko00196); glycine, serine and threonine metabolism (ko00260); glycolysis/gluconeogenesis (ko00010); carbon metabolism (ko01200); fatty acid degradation (ko00071); cysteine and methionine metabolism (ko00270); and valine, leucine and isoleucine degradation (ko00280) (Supplemental Tables S3−S5). Furthermore, we found that the predominant KEGG pathways were distinct for each developmental stage. For example, phenylpropanoid biosynthesis (ko00940) was enriched only at the R stage. Overall, the GO and KEGG pathway enrichment analysis showed that the DEGs in 'Tangwei' and 'Tonghua-3' were enriched for multiple biological processes during the three stages of fruit development.

DEGs related to phenylalanine metabolism

We then analyzed the expression of genes with potential functions in resveratrol and flavonoid biosynthesis between the two cultivars and three developmental stages (Fig. 3 and Supplemental Table S6). We identified 30 STSs, 13 PALs, two C4Hs and nine 4CLs that were differentially expressed during at least one of the stages of fruit development between 'Tangwei' and 'Tonghua-3'. Interestingly, all of the STS genes showed increasing expression with development in both 'Tangwei' and 'Tonghua-3'. In addition, the expression levels of STS, C4H and 4CL genes at V and R were significantly higher in 'Tonghua-3' than in 'Tangwei'. Moreover, 25 RESVERATROL GLUCOSYLTRANSFERASE (RSGT), 27 LACCASE (LAC) and 21 O-METHYLTRANSFERASE (OMT) DEGs were identified, and most of these showed relatively high expression at the G and V stages in 'Tangwei' or R in 'Tonghua-3'. It is worth noting that the expression of the DEGs related to flavonoid biosynthesis, including CHS, FLAVONOL SYNTHASE (FLS), FLAVONOID 3′-HYDROXYLASE (F3'H), DIHYDROFLAVONOL 4-REDUCTASE (DFR), ANTHOCYANIDIN REDUCTASE (ANR) and LEUCOANTHOCYANIDIN REDUCTASE (LAR) were generally higher in 'Tangwei' than in 'Tonghua-3'at G stage.

Figure 3. Expression of differentially expressed genes (DEGs) associated with phenylalanine metabolism. TW, 'Tangwei'; TH, 'Tonghua-3'. PAL, PHENYLALANINE AMMONIA LYASE; C4H, CINNAMATE 4-HYDROXYLASE; 4CL, 4-COUMARATE-COA LIGASE; STS, STILBENE SYNTHASE; RSGT, RESVERATROL GLUCOSYLTRANSFERASE; OMT, O-METHYLTRANSFERASE; LAC, LACCASE; CHS, CHALCONE SYNTHASE; CHI, CHALCONE ISOMERASE; F3H, FLAVANONE 3-HYDROXYLASE; FLS, FLAVONOL SYNTHASE; F3'H, FLAVONOID 3′-HYDROXYLASE; DFR, DIHYDROFLAVONOL 4-REDUCTASE; LAR, LEUCOANTHOCYANIDIN REDUCTASE; ANR, ANTHOCYANIDIN REDUCTASE; LDOX, LEUCOANTHOCYANIDIN DIOXYGENASE; UFGT, UDP-GLUCOSE: FLAVONOID 3-O-GLUCOSYLTRANSFERASE.

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Differentially expressed TF genes

Among all DEGs identified in this study, 757 encoded potential TFs, and these represented 57 TF families. The most highly represented of these were the AP2/ERF, bHLH, NAC, WRKY, bZIP, HB-HD-ZIP and MYB families with a total of 76 DEGs (Fig. 4a). We found that the number of downregulated TF genes was greater than upregulated TF genes at G and V, and 48 were differentially expressed between the two cultivars at all three stages (Fig. 4b). Several members of the ERF, MYB, WRKY and bHLH families showed a strong increase in expression at the R stage (Fig. 4c). In addition, most of the TF genes showed > 2-fold higher expression in 'Tonghua-3' than in 'Tangwei' at the R stage. In particular, a few members, such as ERF11 (VIT_07s0141g00690), MYB105 (VIT_01s0026g02600), and WRKY70 (VIT_13s0067g03140), showed > 100-fold higher expression in 'Tonghua-3' (Fig. 4d).

Figure 4. Differentially expressed transcription factor (TF) genes. (a) The number of differentially expressed genes (DEGs) in different TF families; (b) Number of differentially expressed TF genes, numbers of overlapping differentially expressed TF genes, and (c) categorization of expression fold change (FC) for members of eight TF families between 'Tangwei' and 'Tonghua-3' at green hard (G), véraison (V), and ripe (R) stages; (d) Heatmap expression profiles of the three most strongly differentially expressed TF genes from each of eight TF families.

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Weighted gene co-expression network construction and identification of TF genes co-expressed with STSs

We constructed a gene co-expression network using the weighted gene co-expression network analysis (WGCNA) package, which uses a systems biology approach focused on understanding networks rather than individual genes. In the network, 17 distinct modules (hereafter referred to by color as portrayed in Fig. 5a), with module sizes ranging from 91 (antiquewhite4) to 1,917 (magenta) were identified (Supplemental Table S7). Of these, three modules (ivory, orange and blue) were significantly correlated with resveratrol content, cultivar ('Tonghua-3'), and developmental stage (R). The blue module showed the strongest correlation with resveratrol content (cor = 0.6, p-value = 0.008) (Fig. 5b). KEGG enrichment analysis was carried out to further analyze the genes in these three modules. Genes in the ivory module were significantly enriched for phenylalanine metabolism (ko00360), stilbenoid, diarylheptanoid and gingerol biosynthesis (ko00945), and flavonoid biosynthesis (ko00941), whereas the most highly enriched terms of the blue and orange modules were plant-pathogen interaction (ko04626), plant hormone signal transduction (ko04075) and circadian rhythm-plant (ko04712) (Supplemental Fig. S1). Additionally, a total of 36 genes encoding TFs including in 15 ERFs, 10 WRKYs, six bHLHs, two MYBs, one MADs-box, one HSF and one TRY were identified as co-expressed with one or more STSs in these three modules (Fig. 5c and Supplemental Table S8), suggesting that these TFs may participate in the STS regulatory network.

Figure 5. Results of weighted gene co-expression network analysis (WGCNA). (a) Hierarchical clustering tree indicating co-expression modules; (b) Module-trait relationship. Each row represents a module eigengene, and each column represents a trait. The corresponding correlation and p-value are indicated within each module. Res, resveratrol; TW, 'Tangwei'; TH, 'Tonghua-3'; (c) Transcription factors and stilbene synthase gene co-expression networks in the orange, blue and ivory modules.

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Validation of RNA-seq data by RT-qPCR

To assess the reliability of the RNA-seq data, 12 genes determined to be differentially expressed by RNA-seq were randomly selected for analysis of expression via real-time quantitative PCR (RT-qPCR). This set comprised two PALs, two 4CLs, two STSs, two WRKYs, two LACs, one OMT, and MYB14. In general, these RT-qPCR results strongly confirmed the RNA-seq-derived expression patterns during fruit development in the two cultivars. The correlation coefficients between RT-qPCR and RNA-seq were > 0.6, except for LAC (VIT_02s0154g00080) (Fig. 6).

Figure 6. Comparison of the expression patterns of 12 randomly selected differentially expressed genes by RT-qPCR (real-time quantitative PCR) and RNA-seq. R-values are correlation coefficients between RT-qPCR and RNA-seq. FPKM, fragments per kilobase of transcript per million fragments mapped; TW, 'Tangwei'; TH, 'Tonghua-3'; G, green hard; V, véraison; R, ripe.

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DISCUSSION

Grapevines are among the most important horticultural crops worldwide^[26], and recently have been the focus of studies on the biosynthesis of resveratrol. Resveratrol content has previously been found to vary depending on cultivar as well as environmental stresses^[27]. In a study of 120 grape germplasm cultivars during two consecutive years, the extractable amounts of resveratrol in berry skin were significantly higher in seeded cultivars than in seedless ones, and were higher in both berry skin and seeds in wine grapes relative to table grapes^[28]. Moreover, it was reported that total resveratrol content constantly increased from véraison to complete maturity, and ultraviolet-C (UV-C) irradiation significantly stimulated the accumulation of resveratrol of berry during six different development stages in 'Beihong' (V. vinifera × V. amurensis)^[9]. Intriguingly, a recent study reported that bud sport could lead to earlier accumulation of trans-resveratrol in the grape berries of 'Summer Black' and its bud sport 'Nantaihutezao' from the véraison to ripe stages^[29]. In the present study, resveratrol concentrations in seven accessions were determined by high performance liquid chromatography (HPLC) in the seed, pulp and skin at three developmental stages (G, V and R). Resveratrol content was higher in berry skins than in pulp or seeds, and were higher in the wild Chinese accessions compared with the domesticated cultivars. The highest resveratrol content (2.99 µg g⁻¹ FW) was found in berry skins of 'Tonghua-3' at the R stage (Table 1). This is consistent with a recent study of 50 wild Chinese accessions and 45 cultivars, which reported that resveratrol was significantly higher in berry skins than in leaves^[30]. However, we did not detect trans-resveratrol in the skins of 'Tangwei' during the G or V stages (Table 1). To explore the reason for the difference in resveratrol content between 'Tangwei' and 'Tonghua-3', as well as the regulation mechanism of resveratrol synthesis and accumulation during berry development, we used transcriptional profiling to compare gene expression between these two accessions at the G, V, and R stages. After sequence read alignment and transcript assembly, 23,649 and 23,557 unigenes were documented in 'Tangwei' and 'Tonghua-3', respectively. As anticipated, due to the small number of structures sampled, this was less than that (26,346) annotated in the V. vinifera reference genome^[18]. Depending on the sample, 80.47%−88.86% of sequence reads aligned to a single genomic location (Supplemental Table S1); this is similar to the alignment rate of 85% observed in a previous study of berry development in Vitis vinifera^[19]. Additionally, 1751 novel transcripts were excavated (Supplemental Table S2) after being compared with the V. vinifera reference genome annotation information^{[18, 31]}. A similar result was also reported in a previous study when transcriptome analysis was performed to explore the underlying mechanism of cold stress between Chinese wild Vitis amurensis and Vitis vinifera^[32]. We speculate that these novel transcripts are potentially attributable to unfinished V. vinifera reference genome sequence (For example: quality and depth of sequencing) or species-specific difference between Vitis vinifera and other Vitis. In our study, the distribution of genes based on expression level revealed an inverse trend from G, V to R between 'Tangwei' and 'Tonghua-3' (Fig. 1). Furthermore, analysis of DEGs suggested that various cellular processes including metabolic process and catalytic activity were altered between the two cultivars at all three stages (Fig. 2 and Supplemental Table S3−S5). These results are consistent with a previous report that a large number of DEGs and 100 functional subcategories were identified in 'Tonghua-3' grape berries after exposure to UV-C radiation^[8]. Resveratrol biosynthesis in grapevine is dependent on the function of STSs, which compete with the flavonoid branch in the phenylalanine metabolic pathway. Among the DEGs detected in this investigation, genes directly involved in the resveratrol synthesis pathway, STSs, C4Hs and 4CLs, were expressed to significantly higher levels in 'Tonghua-3' than in 'Tangwei' during V and R. On the other hand, DEGs representing the flavonoid biosynthesis pathway were upregulated in 'Tangwei', but downregulated in 'Tonghua-3' (Fig. 3 and Supplemental Table S6). These expression differences may contribute to the difference in resveratrol content between the two cultivars at these stages. We note that 'Tangwei' and 'Tonghua-3' are from two highly diverged species with different genetic backgrounds. There might be some unknown genetic differences between the two genomes, resulting in more than 60 functional subcategories being enriched (Fig. 2d) and the expression levels of genes with putative roles in resveratrol biosynthesis being significantly higher in 'Tonghua-3' than in 'Tangwei' during V and R (Fig. 3). A previous proteomic study also reported that the expression profiles of several enzymes in the phenylalanine metabolism pathway showed significant differences between V. quinquangularis accession 'Danfeng-2' and V. vinifera cv. 'Cabernet Sauvignon' at the véraison and ripening stages^[33]. In addition, genes such as RSGT, OMT and LAC involved in the production of derivatized products of resveratrol were mostly present at the G and V stages of 'Tangwei', potentially resulting in limited resveratrol accumulation. However, we found that most of these also revealed relatively high expression at R in 'Tonghua-3' (Fig. 3). Despite this situation, which does not seem to be conducive for the accumulation of resveratrol, it still showed the highest content (Table 1). It has been reported that overexpression of two grapevine peroxidase VlPRX21 and VlPRX35 genes from Vitis labruscana in Arabidopsis may be involved in regulating stilbene synthesis^[34], and a VqBGH40a belonging to β-glycoside hydrolase family 1 in Chinese wild Vitis quinquangularis can hydrolyze trans-piceid to enhance trans-resveratrol content^[35]. However, most studies mainly focus on several TFs that participate in regulation of STS gene expression, including ERFs, MYBs and WRKYs^{[13, 15, 17]}. For example, VvWRKY18 activated the transcription of VvSTS1 and VvSTS2 by directly binding the W-box elements within the specific promoters and resulting in the enhancement of stilbene phytoalexin biosynthesis^[36]. VqWRKY53 promotes expression of VqSTS32 and VqSTS41 through participation in a transcriptional regulatory complex with the R2R3-MYB TFs VqMYB14 and VqMYB15^[37]. VqMYB154 can activate VqSTS9/32/42 expression by directly binding to the L5-box and AC-box motifs in their promoters to improve the accumulation of stilbenes^[38]. In this study, we found a total of 757 TF-encoding genes among the DEGs, including representatives of the MYB, AP2/ERF, bHLH, NAC, WRKY, bZIP and HB-HD-ZIP families. The most populous family was MYB, representing 76 DEGs at G, V and R between 'Tangwei' and 'Tonghua-3' (Fig. 4). A recent report indicated that MYB14, MYB15 and MYB13, a third uncharacterized member of Subgroup 2 (S2), could bind to 30 out of 47 STS family genes. Moreover, all three MYBs could also bind to several PAL, C4H and 4CL genes, in addition to shikimate pathway genes, the WRKY03 stilbenoid co-regulator and resveratrol-modifying gene^[39]. VqbZIP1 from Vitis quinquangularis has been shown to promote the expression of VqSTS6, VqSTS16 and VqSTS20 by interacting with VqSnRK2.4 and VqSnRK2.6^[40]. In the present study, we found that a gene encoding a bZIP-type TF (VIT_12s0034g00110) was down-regulated in 'Tangwei', but up-regulated in 'Tonghua-3', at G, V and R (Fig. 4). We also identified 36 TFs that were co-expressed with 17 STSs using WGCNA analysis, suggesting that these TFs may regulate STS gene expression (Fig. 5 and Supplemental Table S8). Among these, a STS (VIT_16s0100g00880) was together co-expressed with MYB14 (VIT_07s0005g03340) and WRKY24 (VIT_06s0004g07500) that had been identified as regulators of STS gene expression^{[13, 15]}. A previous report also indicated that a bHLH TF (VIT_11s0016g02070) had a high level of co-expression with STSs and MYB14/15^[15]. In the current study, six bHLH TFs were identified as being co-expressed with one or more STSs and MYB14 (Fig. 5 and Supplemental Table S8). However, further work needs to be done to determine the potential role of these TFs that could directly target STS genes or indirectly regulate stilbene biosynthesis by formation protein complexes with MYB or others. Taken together, these results identify a small group of TFs that may play important roles in resveratrol biosynthesis in grapevine.

CONCLUSIONS

In summary, we documented the trans-resveratrol content of seven grapevine accessions by HPLC and performed transcriptional analysis of the grape berry in two accessions with distinct patterns of resveratrol accumulation during berry development. We found that the expression levels of genes with putative roles in resveratrol biosynthesis were significantly higher in 'Tonghua-3' than in 'Tangwei' during V and R, consistent with the difference in resveratrol accumulation between these accessions. Moreover, several genes encoding TFs including MYBs, WRKYs, ERFs, bHLHs and bZIPs were implicated as regulators of resveratrol biosynthesis. The results from this study provide insights into the mechanism of different resveratrol accumulation in various grapevine accessions.

MATERIALS AND METHODS

Plant material

V. davidii 'Tangwei', V. amurensis × V. Vinifera 'Beibinghong'; V. amurensis 'Tonghua-3' and 'Shuangyou'; V. vinifera × V. labrusca 'Jumeigui'; V. vinifera 'Red Globe' and 'Thompson Seedless' were maintained in the grapevine germplasm resource at Northwest A&F University, Yangling, Shaanxi, China (34°20' N, 108°24' E). Fruit was collected at the G, V, and R stages, as judged by skin and seed color and soluble solid content. Each biological replicate comprised three fruit clusters randomly chosen from three plants at each stage. About 40−50 representative berries were separated into skin, pulp, and seed, and immediately frozen in liquid nitrogen and stored at −80 °C.

Extraction and analysis of resveratrol content

Resveratrol extraction was carried out as previously reported^[8]. Quantitative analysis of resveratrol content was done using a Waters 600E-2487 HPLC system (Waters Corporation, Milford, MA, USA) equipped with an Agilent ZORBAX SB-C18 column (5 µm, 4.6 × 250 mm). Resveratrol was identified by co-elution with a resveratrol standard, and quantified using a standard curve. Each sample was performed with three biological replicates.

RNA extraction, transcriptome library construction and sequencing

Three biological replicates of each stage (G, V and R) from whole berries of 'Tangwei' and 'Tonghua-3' were used for all RNA-Seq experiments. Total RNA was extracted from 18 samples using the E.Z.N.A. Plant RNA Kit (Omega Bio-tek, Norcross, GA, USA). For each sample, sequencing libraries were constructed from 1 μg RNA using the NEBNext UltraTM RNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA). The library preparations were sequenced on an Illumina HiSeq2500 platform (Illumina, San Diego, CA, USA) at Biomarker Technologies Co., Ltd. (Beijing, China).

RNA-seq data analysis

Raw sequence reads were filtered to remove low-quality reads, and then mapped to the V. vinifera 12X reference genome^{[18, 31]} using TopHat v.2.1.0^[41]. The mapped reads were assembled into transcript models using Stringtie v2.0.4^[42]. Transcript abundance and gene expression levels were estimated as FPKM^[43]. The formula is as follows:

FPKM     = \frac{cDNA Fragments}{Mapped       Fragments (Millions) × Transcript Length (kb)}

$\text{FPKM =}\frac{\text{cDNA Fragments}}{\text{Mapped Fragments}\left(\text{Millions}\right)\text{× Transcript Length (kb)}}$

Biological replicates were evaluated using Pearson's Correlation Coefficient^[44] and principal component analysis. DEGs were identified using the DEGSeq R package v1.12.0^[45]. A false discovery rate (FDR) threshold was used to adjust the raw P values for multiple testing^[46]. Genes with a fold change of ≥ 2 and FDR < 0.05 were assigned as DEGs. GO and KEGG enrichment analyses of DEGs were performed using GOseq R packages v1.24.0^[47] and KOBAS v2.0.12^[48], respectively. Co-expression networks were constructed based on FPKM values ≥ 1 and coefficient of variation ≥ 0.5 using the WGCNA R package v1.47^[49]. The adjacency matrix was generated with a soft thresholding power of 16. Then, a topological overlap matrix (TOM) was constructed using the adjacency matrix, and the dissimilarity TOM was used to construct the hierarchy dendrogram. Modules containing at least 30 genes were detected and merged using the Dynamic Tree Cut algorithm with a cutoff value of 0.25^[50]. The co-expression networks were visualized using Cytoscape v3.7.2^[51].

Validation of RNA-Seq data by RT-qPCR

RT-qPCR was carried out using the SYBR Green Kit (Takara Biotechnology, Beijing, China) and the Step OnePlus Real-Time PCR System (Applied Biosystems, Foster, CA, USA). Gene-specific primers were designed using Primer Premier 5.0 software (PREMIER Biosoft International, Palo Alto, CA, USA). Cycling parameters were 95 °C for 30 s, 42 cycles of 95 °C for 5 s, and 60 °C for 30 s. The grapevine ACTIN1 (GenBank Accession no. AY680701) gene was used as an internal control. Each reaction was performed in triplicate for each of the three biological replicates. Relative expression levels of the selected genes were calculated using the 2^−ΔΔCᴛ method^[52]. Primer sequences are listed in Supplemental Table S9.

ACKNOWLEDGMENTS

This research was supported by the National Key Research and Development Program of China (2019YFD1001401) and the National Natural Science Foundation of China (31872071 and U1903107).

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions
Copyright: © 2022 by the author(s). Published by Maximum Academic Press on behalf of Nanjing Tech University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.

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Caffee B, Wu H. 2022. Factors affecting firefighter occupational cancer risk adjustment. Emergency Management Science and Technology 2:8 doi: 10.48130/EMST-2022-0008

Caffee B, Wu H. 2022. Factors affecting firefighter occupational cancer risk adjustment. Emergency Management Science and Technology 2:8 doi: 10.48130/EMST-2022-0008

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INTRODUCTION

Members of the fire service face many hazards during the course of completing their duties. The fire service operates in a unique environment where it may be perceived that the greater the risk accepted by the firefighter, the less risk there will be to the public^[1]. While firefighters face a diverse number of risks when providing emergency services to the public, a newly discovered problem is growing, cancer. Firefighters are regularly exposed to carcinogens during firefighting activities which are suggested to be causing a higher rate of cancer diagnosis^[2−4]. In fact, studies have documented the higher cancer diagnosis and morbidity rates in firefighters as compared to the general population^{[3, 5]}.

This study seeks to examine the ways in which firefighters perceive their occupational cancer risks as well as what affects their intention and the actual completion of adopting cancer hazard adjustment activities. Protection Motivation Theory (PMT) is used to guide the theoretical aspect of this study^{[6, 7]}. PMT suggests protection motivations are affected by two factors, threat and coping appraisals.

Risk perception has long been used to measure threat appraisal in many studies^[8−11]. Researchers suggest individuals not only examine risks but also that the perceived risk is weighed against the potential benefit of the activity^[12−14]. Studies have found that individuals will accept higher levels of risk when they perceive the outcome of the action will be of greater benefit than the risk^{[15, 16]}. Some health science and earthquake risk studies also found risk perception has an effect on hazard adjustments and its intentions, but findings are not consistent across events^{[10,11,17−19]}. While hazard salience and hazard exposure are not explicitly mentioned in PMT, studies on natural hazard adjustments have found a positive correlation between hazard salience and hazard adjustments^{[11,17, 20, 21]}.

The coping appraisal includes multiple factors^{[6, 7]}. Response efficacy is the individual’s perception of how well the hazard adjustment will protect them. Studies found that response efficacy variables strongly correlated with intended and actual hazard adjustments^{[10, 11, 17]}. Self-efficacy is related to the individual’s perception of their ability to complete the hazard adjustment, such as whether it requires special knowledge or skills. Perry & Lindell found that one’s responsibility to protect oneself was a significant predictor of hazard adjustments^[22]. While the findings in the aforementioned study seem reasonable, they may be challenging to apply in a workplace as unique as the fire service. Previous studies show peers’ perceptions might affect one’s behavior^{[23, 24]}. Therefore, a variable not considered in previous PMT studies is included in this study. That is whether the respondent’s peers would frown upon the action. Response cost relates to the cost to the individual of implementing the hazard adjustment, such as effort, costs, or usefulness. Wang et al. found that influenza adjustments variables such as cost, time restraints, and tools required did not negatively correlate with hazard adjustments^[18]; however, they did find that being useful for other purposes affects hazard adjustments.

While organizational culture is not directly mentioned in PMT, some variables that are used in the coping appraisal for this study pertain to fire service culture. The fire service is steeped in tradition, many of which can be traced back to its origin. Most occupations struggle to balance risk with the desired amount of production; however, as previously discussed, the increased acceptance of risk by firefighters can be perceived as the desired outcome, greater public safety. While not all fire service traditions are considered negative, some are being identified as problematic^[1]. One problem, in particular, is being dirty after a fire and dirty PPE as a badge of experience and honor. For many years, firefighters with dirty PPE have been viewed as seasoned veterans that are skilled and capable on the fire ground^[25]. This view can also affect protection behaviors such as showering and working out after fighting a fire^{[26, 27]}. This traditional view, however, is in direct contrast with the suggested hazard adjustment of gross decontamination on the scene as well as washing personnel and personal protective equipment (PPE). Another problem is using PPE and self-contained breathing apparatus (SCBA) properly. Fent et al. noted that firefighters are exposed to carcinogens through inhalation and absorption through the skin^[25]. When firefighters do not wear their SCBA through the completion of overhaul activities, they are exposed to higher levels of carcinogens. PPE has also been shown to continue off-gassing carcinogens after a fire which, if not cleaned, will continue to expose firefighters to carcinogens, such as in-vehicle cabs and dormitories^[25]. Recent studies of Florida firefighters found that while firefighters had a positive perception of cleaning PPE and its ability to protect them from cancer and other health hazards, many were unlikely to complete the hazard adjustment regularly ^[23]. The study notes this could be due to concerns about time constraints and functioning in wet PPE.

Additionally, a study has shown that peer pressure from senior department members (organizational culture) is a major factor in newer firefighters' decision to implement the suggested hazard adjustments^[24]. Other hazard adjustments and protective action decision studies have used the Emergent Norm Theory to explain this phenomenon^[28−32]. These studies highlight the importance of education and culture change initiatives in the fire service. These can strongly contribute to an improved operational culture that, in that end, will serve to better protect firefighters from cancer.

Based on the literature, this study intends to use PMT to examine firefighters' intention and actual adoption of firefighting related cancer hazard adjustment actions. This study will introduce a new self-efficacy variable related to fire service culture and peer perception based on the Emergent Norm Theory. In addition, this study would like to examine the association between fire service/individual demographics and hazard adjustment. The followings are the research hypotheses (RHs) and questions (RQs).

RH1: Coping appraisal variables explain more variations in firefighters' cancer hazard adjustment intention than threat appraisal variables.

RH2: Coping appraisal variables explain more variations in firefighters' actual cancer hazard adjustment adoption than threat appraisal variables.

RQ1: Does fire service demographics affect firefighters' cancer hazard adjustment intention?

RQ2: Does previous cancer experience affect firefighters' cancer hazard adjustment intention?

RQ3: Do fire service and personal demographics significantly correlate with firefighters' cancer hazard adjustment intentions and actual hazard adjustments?

RESULTS

Linear regression analyses were used to test RH1 (Coping appraisal variables are better predictors of hazard adjustment intention than threat appraisal variables). The results show that RH1 is confirmed (Table 1). Most coping appraisal variables are significant predictors of the hazard adjustment intention with few exceptions; on the other hand, threat appraisal variables only have limited predictability in these models. For example, in Table 1, the model of gross decontamination adjustment intention is significant (F_(11,291) = 8.69; p < 0.05; Adj R² = 0.22); however, the significant predictors are mainly coping appraisal variables; only one threat appraisal variable is a significant predictor in the model. Table 1 also shows that the coefficients of coping appraisal predictors such as protect me effectively, require a lot of effort, and also be useful for other purposes are all significant across all six models.

Table 1. Regression analysis of fire cancer hazard adjustment intentions.

Variables			Gross decon^†	Contaminated PPE out of cab^†	Washing PPE^†	Showering within 1 hr after firefighting^†	Workout within 24 hr after firefighting^†	Wearing SCBA during overhaul^†
Threat appraisal	Hazard salience	How often do you think about occupational cancer?	0.08	0.03	−0.02	0.01	0.01	0.07
	Risk perception	Occupational cancer concern	0.07	0.14	0.05	0.10	−0.07	0.11
		Likelihood of cancer diagnoses	−0.14	−0.12	0.04	−0.02	0.06	−0.05
		Likelihood of cancer being caused by firefighting	0.02	−0.02	−0.06	−0.03	0.08	0.00
	Hazard exposure	Hazard exposure index	0.11	0.10	−0.06	0.01	0.00	0.02
Coping appraisal	Response efficacy	Protect me effectively	0.27	0.30	0.31	0.31	0.31	0.32
	Self-efficacy	Require special knowledge/skills	0.09	0.07	0.09	0.15	0.10	0.23
	Self-efficacy	Be frowned upon by peers	−0.11	−0.17	−0.03	−0.13	−0.09	−0.04
	Response costs	Require a lot of effort	−0.19	−0.12	−0.15	−0.20	−0.17	−0.24
		Cost a lot of money	−0.02	0.02	−0.13	−0.11	−0.01	−0.08
		Also be useful for other purposes	0.17	0.24	0.12	0.29	0.31	0.23
Statistics			F_(11,291) = 8.69 P < 0.05 Adj R² = 0.22	F_(11,283) = 14.08 P < 0.05 Adj R² = 0.33	F_(11,291) = 5.81 P < 0.05 Adj R² = 0.15	F_(11,290) = 14.01 P < 0.05 Adj R² = 0.32	F_(11,283) = 13.60 P < 0.05 Adj R² = 0.32	F_(11,290) = 14.79 P < 0.05 Adj R² = 0.34
^†Standardized coefficients are reported. Bold font indicates the coefficient is significant at the 0.05 level.

Linear regression analyses were used to test RH2 (Coping appraisal variables are better predictors of actual hazard adjustment adoption than threat appraisal variables). Table 2 shows that this hypothesis is also confirmed. While the regression models for actual adjustments identified fewer significant predictors than the hazard adjustment intention models did, coping appraisal variables were much more significant predictors of actual hazard adjustments (see Table 2). For example, there was only one significant threat appraisal variable in the model for washing PPE (likelihood of cancer diagnosis being caused by firefighting), and it was a weak predictor. On the other hand, the coping appraisal variable protect me effectively produced significant results in all six models. In examing other models, the regression model for wearing SCBA during overhaul produced significant results in five of the six coping appraisal variables, and none of the threat appraisal variables was significant.

Table 2. Regression analysis of fire cancer actual hazard adjustment.

Variables			Gross decon^†	Contaminated PPE out of cab^†	Washing PPE^†	Showering within 1hr after firefighting^†	Workout within 24 hr after firefighting^†	Wearing SCBA during overhaul^†
Threat appraisal	Hazard salience	How often do you think about occupational cancer?	−0.05	−0.01	−0.04	−0.01	0.00	−0.10
	Risk perception	Occupational cancer concern	−0.02	−0.08	−0.01	0.01	0.09	−0.06
		Likelihood of cancer diagnoses	0.04	0.10	−0.06	0.00	−0.09	0.04
		Likelihood of cancer being caused by firefighting	0.00	0.08	0.13	0.04	−0.07	0.07
	Hazard exposure	Hazard exposure index	−0.02	0.05	0.09	0.06	0.03	0.01
Coping appraisal	Response efficacy	Protect me effectively	−0.13	−0.17	−0.30	−0.30	−0.29	−0.17
	Self-efficacy	Require special knowledge/skills	−0.12	−0.15	0.05	−0.14	−0.17	−0.27
	Self-efficacy	Be frowned upon by peers	0.09	0.12	0.09	−0.01	0.02	0.06
	Response costs	Require a lot of effort	0.14	0.11	0.09	0.19	0.11	0.29
		Cost a lot of money	0.06	0.11	0.11	0.03	0.07	0.15
		Also be useful for other purposes	−0.17	−0.13	0.06	−0.08	−0.06	−0.18
Statistics			F_(11,291) = 3.09 P < 0.05 Adj R² = 0.07	F_(11,285) = 3.91 P < 0.05 Adj R² = 0.10	F_(11,291) = 5.10 P < 0.05 Adj R² = 0.13	F_(11,290) = 4.27 P < 0.05 Adj R² = 0.11	F_(11,283) = 13.60 P < 0.05 Adj R² = 0.32	F_(11,290) = 8.54 P < 0.05 Adj R² = 0.22
^†Standardized coefficients are reported. Bold font indicates the coefficient is significant at the 0.05 level.

T-test and Analysis of Variance (ANOVA) were used to test RQ1 (Does fire service demographics affect firefighters’ adjustment intention?) & RQ2 (Does previous cancer experience affect firefighters’ adjustment intention?). Five fire service demographic variables were used to test their effects on the hazard adjustment intention index.

(1) Type of department: there was a significant difference in the mean scores for career firefighters' (M = 3.66, SD = 0.71) and volunteer firefighters' (M = 3.45, SD = 0.72) intentions to complete hazard adjustments (t₍₃₁₂₎ = 2.05, p < 0.05).

(2) Years in the service: years of fire service experience did not have a significant effect on hazard adjustment intentions for the five conditions (F_(4,309) = 2.07, ns).

(3) Firefighter Rank: rank did not have a significant effect on hazard adjustment intentions for the six conditions (F_(5,307) = 0.57, ns).

(4) Number of total responses: the number of department calls for service had a significant effect on hazard adjustment intentions (F_(4,308) = 3.27, p < 0.05). Table 3 shows that the departments that responded to between 2,500 to 4,999 calls annually had the highest intention to complete hazard adjustments.

Table 3. Number of total responses and hazard adjustment intention.

Number of responses	Mean	SD	N
0−499	3.56	0.72	49
500−1,499	3.59	0.66	41
1,500−2,499	3.47	0.73	47
2,500−4,999	3.89	0.59	69
≥ 5,000	3.57	0.76	107
Total	3.63	0.71	313
F_(4,308) = 3.27, p < 0.05

(5) Number of fire responses: the number of department fire calls did not have a significant effect on hazard adjustment intentions (F_(4,307) = 1.35, ns).

Several t-tests and ANOVA tests were conducted to determine if personal demographic variables affect hazard adjustment intentions. The results show only previous cancer experience has a significant effect on hazard adjustment intentions (F_(2,311) = 3.25, p < 0.05). Table 4 shows that people are more likely to adopt hazard adjustments if their coworkers are diagnosed with cancer.

Table 4. Previous cancer experience and hazard adjustment intention.

Previous cancer experience	Mean	SD	N
Myself	3.55	0.83	25
Coworker	3.70	0.70	200
None	3.47	0.68	89
Total	3.62	0.71	214
F_(2,311) = 3.25, p < 0.05

Correlation Analyses were used to test RQ3 (Do fire service and personal demographics significantly correlate with hazard adjustment intentions and actual hazard adjustments?). Results indicate fire service and personal demographic variables both produced some significant correlations with the hazard adjustment intentions and actual hazard adjustments and the six hazard adjustments. Being a career fighter was negatively correlated with placing contaminate PPE out of the passenger cab (r = −0.15, p < 0.05) but positively correlated with gross decon (r = 0.15, p < 0.05), washing PPE (r = 0.13, p < 0.05), showering within 1 hr (r = 0.12, p < 0.05), and workout within 24 hr (r = 0.17, p < 0.05). Years in the fire service correlated negatively with workout within 24 hr (r = −0.18, p < 0.05). Rank correlated negatively with showering within 1 hr (r = −0.13, p < 0.05) and working out within 24 hr (r = −0.24, p < 0.05) and positively with contaminated PPE out of the passenger cab. Calls for service by the department correlated positively with washing PPE (r = 0.13, p < 0.05), showering within 1 hr (r = 0.12, p < 0.05), and workout within 24 hr (r = 0.14, p < 0.05) and negatively with contaminated gear out of the compartment (r = −0.16, p < 0.05). Number of fire related calls correlated positively with workout within 24 hr (r = 0.15, p < 0.05) and negatively with contaminate PPE out of the passenger cab (r = −0.13, p < 0.05) and wearing SCBA through overhaul (r = −0.17, p < 0.05). Age correlated positively with PPE out of cab (r = 0.18, p < 0.05) and negatively with workout within 24 hr (r = −0.18, p < 0.05). Number of children correlated negatively with workout within 24 hr (r = −0.14, p < 0.05). Lastly, household income correlated positively with washing PPE (r = 0.18, p < 0.05).

Actual completion of adjustments produced a lower amount of significant correlation results. Being a career firefighter negatively correlate with contaminate PPE out of cab (r = −0.11, p < 0.05), but positive correlations with washing PPE (r = 0.14, p < 0.05) and workout within 24 hr (r = 0.15, p < 0.05). Years in the fire service produced a negative correlation to work out within 24 hr (r = −0.14, p < 0.05). Rank produced a negative correlation to work out within 24 hr (r = −0.15, p < 0.05). Calls for service produced a positive correlation to work out within 24 hr (r = 0.21, p < 0.05) and showering within 1 hr (r = 0.12, p < 0.05). Number of fire related calls produced a positive correlation for work out within 24 hr (r = 0.17, p < 0.05) and a negative correlation for wearing SCBA through overhaul (r = −0.14, p < 0.05). Age produced a negative correlation for work out within 24 hr (r = −0.15, p < 0.05). Number of children produced a negative correlation for work out within 24 hr (r = −0.13, p < 0.05). Lastly, household income correlated positively to washing PPE (r = 0.20, p < 0.05).

DISCUSSION

Both regression models show strong support for PMT. Similar to previous studies, coping appraisal variables better explain the variations in adjustment intentions and actual adjustments compared to threat appraisal variables^[33]. Similar to other firefighter cancer risk perception studies^[23], our sample has a considered high level of cancer risk perceptions; however, they proved to be poor predictors of adjustment intentions. Risk perception is an even poorer predictor of actual adjustments. These findings are consistent with some earthquake adjustment studies^[11,17]. Hazard salience was also measured high in this study; however, it was not a significant predictor in the models. These findings contradict those found in Russell et al.^[21]. In addition, although our study participants are engaged in activities with high levels of cancer hazard exposure, the findings suggested that the hazard exposure index was not a significant predictor in any of the regression models, which differs from previous research^{[21,22, 34−36]}. As mentioned in Jackson's study^[34], this result might be due to the ambiguity of how researchers measure hazard exposure in different studies.

In this study, response efficacy was the only variable significantly predicting both adjustment intentions and actual adjustment models. Self-efficacy variables also produced several significant results in the regression models confirming Floyd et al. claims that response efficacy and self-efficacy appear to be the most important aspects to concentrate on in order to change behavior which also coincides with previous research^{[10, 11,17, 18, 37, 38]}. The most intriguing consideration specific to this study is the use of the new self-efficacy variable that was related to fire service organizational culture (be frowned upon by my peers), which proved to be a significant predictor in at least some of the models and confirms previous findings that peer pressure can have an effect on taking suggested hazard adjustments^[24,39]. This finding could prove a valuable addition to coping appraisal evaluation for the fire service and any organization with strong peer cultures. Lastly, the response cost variable with the most significant result is require a lot of effort. This variable was a significant predictor in all adjustment intention models and half of the actual adjustment models, confirming previous research^{[11,17, 38]}. Also useful for other purposes produced significant results in all of the intention models as well as the actual adjustment models, which also coincides with previous research^{[11,17, 18]}.

The analyses for RQ1 and RQ3 produced three significant results. The first and possibly most important is that career firefighters had a greater intention to complete the suggested cancer hazard adjustments than did volunteer firefighters. This could be due to the tradition that career firefighters have typically kept their PPE in the cab with them or possibly due to the fact that many volunteers may keep PPE in the trunk or storage spaces of their personal vehicles. The importance of this is the need for more training in the volunteer fire service on the implementation of the hazard adjustments and their effectiveness. In addition, survey respondents that reported their organization responded to between 2,500−4,999 calls annually had the highest intention to complete the hazard adjustment. One possible explanation for this is that these individuals have enough regular exposure to hazardous activities, yet they have enough time at the station not to make responses for education and training on the suggested hazard adjustments. Lastly, previous cancer experience produced significant results for hazard adjustment intention, which supports previous research^{[9, 11,17, 21, 35, 36]}. Although fire service leaders are not able to directly control this variable, this finding could support the concept that efforts similar to that of the Boston Fire Department, making education efforts personal by sharing real life cases of cancer victims in the fire service, could be an effective educational tool.

The correlations of fire service and personal demographics with adjustment intentions and actual adjustments produced several expected results; however, there were two that warrant discussion. The first, previously mentioned, was that volunteer firefighters were more likely to place contaminated PPE outside of the cab. This finding was the only significant negative correlation for the type of department in both intention and actual adjustment correlations. This could be due to the fact that career firefighters have nowhere on the apparatus to store the PPE or that volunteer firefighters could be storing their PPE in personal vehicle cargo areas. Either way, future research should consider storage solutions for career and volunteer firefighters to combat the exposure to contaminated PPE in the cab. This study also found age, rank, and years in the fire service had negative correlations with working out within 24 hr of firefighting activities. This could be due to age, physical ability, lack of education, or belief in the hazard adjustment.

CONCLUSIONS

This study is an attempt to understand what motivates firefighters to take the suggested hazard adjustments that have been set forth by experts. Unfortunately, even though firefighters are well informed about their increased cancer risk, this study and previous studies find that firefighters do not always take the suggested hazard adjustment^[39]. One major finding of this study is the importance of response and self-efficacy. Fire service organizations should begin to focus exposure reducing training efforts on the effectiveness of the hazard adjustment and on finding effective ways individuals can carry them out. One way this could be accomplished is by fire service leaders partnering with the research community and identifying the most effective hazard adjustments. The risk perception results of this study confirm, along with a previous study, that firefighters are aware of their cancer risk^[23]. However, care must be taken not to create a culture of fatalism which can be caused by an oversaturation of awareness and a lack of hazard adjustments. In other words, if firefighters perceive they are going to get cancer no matter when there will be a tendency to not complete the hazard adjustments.

The fire service, as a whole, must collaborate with researchers to discover, through field research, the most effective means of reducing exposure to carcinogens and the most efficient means of completing these activities. Once these have been identified, the data needs to be presented to firefighters in a way that will increase response and self-efficacy. To date, educational programs have offered little in the way of explanation when compared to other protective measures such as hazardous materials decontamination procedures. The time has come for educational programs to become a much more formal effort, possibly even certification level programs similar to technical rescue or hazardous materials response.

Another major finding in this study is the importance of peer perception and pressure. As mentioned earlier, the fire service is ripe with traditions, but these traditions and the traditional view of what makes a good firefighter can stand in the way of safety. This highlights the importance for fire service administrators and officers to create a cultural norm of safety. These hazard adjustments should be something that takes place in every incident that has the potential for exposure and should be mandated by incident commanders and company officers. In conclusion, fire service leaders should use the results of this and other studies to continue evolving firefighter safety and health initiatives to further protect the future of the fire service.

There are some limitations to this study. The sample was firefighters that had previously attended or are in some way affiliated with the AFC. As with any self-reporting study, one limitation is accurate reporting. Although everyone that responded was informed that the study was for firefighters only, one cannot know for sure if that were the case. Another limitation of this study was the narrowness of the sample. A large majority of the sample was male career firefighters with 21 or more years of experience in the fire service. Future studies may benefit from attempting to oversample to achieve a more diversified sample.

MATERIALS AND METHODS

Sampling

This study was conducted in cooperation with the Alabama Fire College (AFC). The sample was career and volunteer firefighters that affiliated with AFC or have attended AFC courses. The internet survey was developed using Survey Monkey (www.momentive.ai). The survey was modeled considering previous surveys^{[10, 11]} and distributed using the method used by Dillman et al.^[40]. The data was collected from 11/28/2017 to 12/26/2017. The participant recruitment email was opened by 1,539 individuals, and 358 responses were received, giving a response rate of 23%.

Coding
The survey instrument consisted of 27 items. The survey instrument can be shared upon request. Six items were asked to measure fire fighters' cancer risk hazard adjustment actions: (1) gross decontamination after a fire, (2) placing contaminated PPE in compartments other than the passenger cab, (3) washing PPE after a fire, (4) showering within 1 hr of firefighting activities, (5) working out within 24 hr of firefighting activities, and (6) wearing self-contained breathing apparatus until the completion of overhaul activities These items were suggested by firefighting reports and previous studies^[5,6,24].

Respondents were asked to rate their threat appraisal in nine items. Three risk perception related questions were asked using 5-point Likert scales to measure them. Hazard salience was measured by having respondents report the frequency of thinking about occupational cancer risk. In addition, respondents were asked to rate different job aspects for their potential to expose them to cancer causing carcinogens. A hazard exposure index was created by using the hazard exposure variables (Cronbach's α = 0.76). Six survey questions were used to measure coping appraisals. These questions asked respondents to rate their views on the hazard adjustment actions in this survey. In order to measure response efficacy, respondents were asked how effectively they felt the adjustment actions would protect them. Next, to measure self-efficacy, respondents were asked to consider whether they felt the adjustment actions would require specialized knowledge or skills to complete. In a second self-efficacy measurement, respondents were asked to consider if any of the adjustment actions would be frowned upon by their peers. In order to measure response cost, respondents were asked if they felt the adjustment actions would require a lot of effort to complete, if they felt the adjustment actions would cost a lot of money and if they thought the adjustment actions would be useful for purposes other than preventing occupational cancer.

The survey also measures individuals' intention to complete each of the six protective actions; respondents were asked if each of the actions would be something they are likely to do. A hazard adjustment intention index for all adjustment actions was created by using the variables (Cronbach's α = 70). In order to measure the actual protective actions, individuals were asked if they take any of the six protective actions after firefighting activities.

Respondents were also asked to answer questions about fire service demographics such as the type of department (Career vs. Volunteer), years of service, current rank, the number of calls for service annually their department responds to, and the number of fire-related responses their department responds to including structure, dumpster, vehicle, and wildland annually. Lastly, respondents report their personal demographics such as age, marital status, number of children, the highest education level, household income, and cancer experience.

Analytical method
Univariate analyses were used to test the research hypotheses and questions. As mentioned in the previous section, most measures are ordinal data; they are treated as continuous data in the analyses. Since a 5-Likert scale is used and the sample size is considered large, possible bias is insufficient to alter the substantive interpretations^[41]. RH1 and RH2 are trying to identify the variables that significantly explain the variations in the dependent variable: firefighters' cancer hazard adjustment intentions and actual adjustments; therefore, linear regression analyses were used for these two hypotheses. RQ2 and RQ3 aim to determine the significance of mean differences in cancer hazard adjustment intentions based on the study participant’s demographic data. Depending on the categories of each demographic variable, a t-test was used when a demographic variable only has two groups; an Analysis of Variance (ANOVA) was used when a demographic variable has three or more groups. Finally, RQ3 is to identify the significant correlations among the variables. Pearson's r was used to identify the significant correlations at the 0.05 level. IBM SPSS ver. 25 were used to conduct the regression, ANOVA, and correlation analyses (www.ibm.com/analytics/spss-statistics-software?mhsrc=ibmsearch_a&mhq=SPSS).

Contents

Policies

Services

Partnerships

Factors affecting firefighter occupational cancer risk adjustment

Abstract

INTRODUCTION

RESULTS

Extractable trans-resveratrol content in developing grape berries

Analysis of RNA-Seq data

GO and KEGG pathway enrichment analysis of DEGs

DEGs related to phenylalanine metabolism

Differentially expressed TF genes

Weighted gene co-expression network construction and identification of TF genes co-expressed with STSs

Validation of RNA-seq data by RT-qPCR

DISCUSSION

CONCLUSIONS

MATERIALS AND METHODS

Plant material

Extraction and analysis of resveratrol content

RNA extraction, transcriptome library construction and sequencing

RNA-seq data analysis

Validation of RNA-Seq data by RT-qPCR

ACKNOWLEDGMENTS

Conflict of interest

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