Diesel Exhaust Exposure Alters The Expression Of Networks Implicated in Neurodegeneration in Zebrafish Brains Part 2

Protein sample preparation

To prepare protein samples, the heads from 80 to 100 anesthetized larvae (5 pdf) were carefully isolated and washed with PBS before transferring to a lysis buffer which consisted of 15% precooled TCA/ acetone containing 0.07% beta-mercaptoethanol (ME) and protease inhibitor cocktail (needs manufacturer) in a total volume of 500 μl. 

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After brief homogenization, proteins were precipitated for 12  h at 20 °C, followed by centrifugation at 12,000 rpm for 5 min at 4 °C. The supernatant was removed, and the protein pellet was washed twice with cold acetone (containing 0.07% ME and protease inhibitor cocktails). The final protein pellet was dissolved in a lysis buffer containing 7.0 M urea, and 2.0 M thiourea by sonication on ice. 

The solubilized protein samples were centrifuged at 15,000 rpm for 10 min at 4 °C to precipitate insoluble particles, and the concentration of the final protein samples was measured using the Bradford method.

TMT-based high throughput proteomics analysis

Samples were reduced, alkylated, and digested by the sequential addition of trypsin and lys-C proteases. Peptides were then labeled using 10-plex TMT isobaric tags according to the manufacturer's instructions. Labeled samples were mixed and then fractionated fine using high pH reversed-phase chromatography. 

Individual fractions were then analyzed by LC–MS/MS using online reversed-phase chromatography and tandem mass spectrometry on a Thermofsher Fusion Lumos mass spectrometer. Data were acquired using the synchronous precursor selection-based MS3 method, as previously described (McAlister et al. 2014). Database searching and the extraction of TMT reporter ion information were performed using the MaxQuant software platform (Cox and Mann 2008). 

The comparison of TMT data across samples was performed using MSStats (Choi et al. 2014). Transcriptomic analysis Approximately 80–100 heads were isolated from anesthetized embryos (120  h), washed with PBS, and subjected to total RNA extraction using Trizol (Sigma Aldrich, Saint Louis, USA) reagent following the manufacturer's instructions. 

The quality and quantity of RNA were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Scientific, MA) and further by Agilent 2100 Bioanalyzer to assure the minimum concentration of 50  ng/μl and RNA integrity number (RIN) of 8. Library preparation was then performed using an Illumina HiSeq4000 according to the protocol: "TruSeq Stranded Total RNA Library Prep workflow with Ribo-Zero Gold," and the samples were sequenced at the following conditions: "Paired End run, R1=75 cycles (antisense strand), Index=8 cycles, R2=75 cycles (sense strand)."

Data analysis

Both datasets for proteomic and transcriptomic studies were simultaneously uploaded and analyzed using the online tool Metascape, which allows for gene annotation for various species, including Danio rerio (Zhou et al. 2019). To evaluate the contribution of either proteome or transcriptome profile alteration in specific pathways, data were analyzed through the use of Ingenuity Pathways Analysis (IPA) software (Krämer et al. 2014). 

Data sets containing gene or protein identifiers and corresponding expression values were uploaded into the application. Each identifier was mapped to its corresponding object in Ingenuity's knowledge base. 

An expression alteration cutoff of 1.3-fold was set to identify molecules whose expression was differentially regulated. Functional analysis identified the biological functions and/or diseases that were most significant to the data set. Molecules from the dataset that met the cut-off and were associated with biological functions were considered for the analysis. Right-tailed Fisher's exact test was used to calculate a p-value determining the probability that each biological function and/or disease assigned to that data set is due to chance alone.

Pathway analysis

To evaluate the contribution of either proteome or transcriptome profile alterations in specific pathways, both datasets were uploaded to the IPA software (Krämer et al. 2014). Top significant altered canonical pathways were then further evaluated and interpreted using PCR and western blotting as described below.

Western blotting

A total of 25  µg of proteins was loaded on a 12% NuPAGE (Novex, CA) and transferred onto PVDF membranes (Novex, CA) as described (MahmoudianSani et al. 2017; Rafee et al. 2019). Membranes were blocked with 5% skim milk in Tris-buffered saline and 0.01% tween 20 (TBST buffer) for 30 min at room temperature and then incubated overnight at 4  °C with either rabbit polyclonal anti-Cyp1A1 (Abcam, CA) or mouse monoclonal anti-GAPDH (Abcam, CA), diluted in TBST buffer containing 1% skim milk. 

After washing in TBST, the blots were incubated with secondary donkey anti-rabbit-HRP (Abcam, CA) or goat anti-mouse-HRP (Santa Cruz, CA) antibodies for 2  h, followed by development with Pierce™ ECL Plus western blotting substrate (Thermo Fisher Scientific, USA). The bands were visualized by imaging using a LI-COR Scanner and analyzed via densitometry (LI_COR Biosciences, NE).

Real-time PCR

Total RNA was isolated from the heads using TRIzol reagent (Sigma Aldrich, Saint Louis, USA) following the manufacturer's instructions and then measured using a NanoDrop 2000 spectrophotometer (Thermo Scientific, MA). An amount of 1 μg of each RNA sample was reverse-transcribed using iScript™ reverse transcription supermix (Bio-Rad, CA), and real-time PCR was performed using SsoAdvanced Universal SYBR Green supermix (Bio-Rad, CA), and the primers are listed in Supplementary Table 1. Relative expression levels were calculated using the 2−ΔΔCT method, and the statistical T-test was used to evaluate the significant differences.

Results and discussion

Proteomic and transcriptomic analyses are two major tools for understanding the molecular mechanisms underlying disease processes and response to environmental stimuli (Duan et al. 2017; García-Estrada et al. 2013; Jami et al. 2014a, b, 2015; Kosalková et al. 2012). Here, we performed deep expression analyses at both the transcriptomic and proteomic levels in the heads of zebrafish embryos exposed to DEPe. 

The heads, composed of mostly brain tissue, were isolated to eliminate the expression profiles of other tissues because we are interested in determining intrinsic pathological pathways of the CNS.

The expression profile of the heads of zebrafish embryos

Profile analysis yielded 11,172 detected proteins and 14,748 mRNA targets out of more than 26,000 coding genes (Howe et al. 2013). Among the 11,172 proteins identified from the TMT-labeled samples (Supplementary Table 2), 141 proteins were significantly upregulated, and 607 were downregulated (Supplementary Table 3 and Fig. 1a). Similarly, 367 transcripts were upregulated, and 149 were downregulated among the 14,748 transcripts (Supplementary Table 4) detected in the RNA-seq analysis (Fig. 1b and Supplementary Table 5).
In most cases, the findings from the upregulated proteomic and transcriptomic analyses were consistent. For instance, top highly upregulated proteins include cytochrome P450 Cyp1a, Cyp1c1, Guanine nucleotide-binding protein subunit gamma, Annexin, Plexin B2b short isoform, Dehydrogenase/reductase (SDR family) member 13-like 1, S-antigen of retina/pineal gland (arrestin) b, and Sulfotransferase 6B1. 

Likewise, genes with the highest transcriptomic upregulation include cytochrome P450 (Cyp1a), chemokine (C–C motif) ligand 27a, aryl-hydrocarbon receptor repressor A, cytochrome P450 (Cyp1b), and Rh family C glycoprotein. Downregulated proteins and transcripts, on the other hand, were not always highly correlated. 

For example, Complexin 2, Spectrin alpha, Cardiac myosin light chain-1, SEC23 interacting protein, ATP synthase membrane subunit EA, Cytochrome b, and NAD-dependent protein deacetylase are among the top highly downregulated proteins, while the retinal outer segment membrane protein 1a, solute carrier family 5 (iodide transporter), FBJ murine osteosarcoma viral oncogene homolog B, complexin 4c, FOSlike antigen 1a, opsin 1, and v-fos show the highest downregulation of transcription (Tables 1 and 2). 

Antibodies recognizing zebrafish proteins are limited, but we did confirm a sample of these changes using western blot analysis. The upregulation of Cyp1A protein and downregulation of Complexin 2 (CPLX2) were confirmed using GAPDH as a loading control (Fig. 2a). Higher levels of TAT and UGT1B1 transcription and lower levels of CHNRB and TH2 were also confirmed by qPCR using Elf-alpha as the internal control (Fig. 2b).

Gene annotation

There are several online bioinformatic tools and software that can provide useful information on gene annotation. Among them, Metascape, with the capability of gene annotation for Danio rerio (Zhou et al. 2019), was used in this work. 

Both datasets for proteomic and transcriptomic studies were simultaneously uploaded and analyzed using this online tool. After combining the output of both proteomic and transcriptomic alterations in the software, several processes such as response to xenobiotic stimulus, metabolism of xenobiotics by cytochrome P450, and circadian regulation of gene expression were found induced upon DEPe treatment (Fig. 3a). 

This analysis also suggested suppressed levels of biological processes such as "Visual perception," "Phototransduction," and "G protein-coupled receptor internalization." The alterations in vision-related expression profiles were not unexpected since DEPe treatment during early development resulted in smaller eyes (data not shown).

Xenobiotic metabolism signaling

Xenobiotics, which are foreign natural or synthetic chemical compounds, can trigger the cellular stress response, leading to differentiation, proliferation, apoptosis, or necrosis. Indeed, the body needs to actively protect itself against xenobiotics, and also toxic endogenous compounds and their metabolites, via the expression of enzymes and transporters involved in their elimination and detoxification. 

These enzymes are classified into three groups: Phase I enzymes (CYP, ALDH, FMO) which introduce polarity into the xenobiotics; Phase II enzymes (UGT, GST, SULT) which introduce hydrophilicity via conjugation of hydrophilic molecules such as sulfate, glucuronic acid, and glutathione to the xenobiotics; and Phase III enzymes (MDR1, OATP2, MRP) that transport the xenobiotics or conjugates formed during Phase II to the extracellular area. 

These enzymes are induced through signaling cascades involving specific receptors (CAR, PXR, AHR) and MAPK-mediated activation of transcription factors (NRF2, MAF) (Omiecinski et al. 2011).

In the absence of activators, the constitutively active receptor (CAR) is located in the cytoplasm as a complex with CCRP and HSP90. But when an activator is present, CAR translocates into the nucleus and binds to RXRα to form a heterodimer and further binds to several variants of the repeat motif, such as DR3, DR4, ER6, and ER8, and contributes to gene expression regulation (Supplementary Table 2). 

Our proteomics analysis revealed the activation of xenobiotic metabolism. Indeed, the clear upregulation of CYP1A1, CYP3A7, HMOX1 (heme oxygenase 1), CAT (catalase), and CES1 (carboxylesterase 1) show the induction of Phase I metabolism, while increased expression of UGT1A1 (UDP glucuronosyltransferase family 1 member A1) and GSTP1 (glutathione S-transferase pi 1) indicates the activation of Phase II metabolism. 

Interestingly, downregulation of sulfate transferases such as SULT1A1 (sulfotransferase family 1A member 1), SULT1C2 (sulfotransferase family 1C member 2), and SULT2B1 (sulfotransferase family 2B member 1) suggests that increasing hydrophilicity in Phase II metabolism tends to preferably occur via conjugation with glucuronic acid and glutathione, but not sulfate conjugation (Fig. 4).

The results of the transcriptomic study are quite consistent, as CYP1A, CYP1B, CYP3A7, GSTO1, GSTP1, and UGT1A1 are all upregulated at the RNA levels. However, SULT2B1 shows upregulation at the RNA level (while underrepresented at the protein levels). 

This may be due to the involvement of other mechanisms that induce the degradation or inactivation of sulfate transferase enzymes upon treatment with DEPe. What is particularly surprising is that these changes in xenobiotic metabolism occurred in the heads (presumably brains) of the fish. Expression of xenobiotic genes has been described in the nervous system of mammals, but this is the 1st  report of them in zebrafish brains (McMillan and Tyndale 2018). 

These findings are consistent with a similar study conducted by Shankar and co-workers who tested the effects of different groups of environmental polycyclic aromatic hydrocarbons (PAHs) on the development of embryos and further evaluated the impact of each treatment regimen on the transcriptome profile. 

On the whole body of 48  h post-fertilization (hpf) embryos, they showed the upregulation of Cyp1a at both transcription and protein levels to be an early reliable biomarker of xenobiotic AHR activation and downstream transcriptomic changes (Shankar et al. 2019).

The NRF2-mediated oxidative stress response

Oxidative stress can trigger apoptosis and necrosis and is believed to be involved in neurodegeneration (Ahmadinejad et al. 2017; Jami et al. 2014b, 2015). A major cellular defense response to oxidative stress is the induction of antioxidant and detoxifying enzymes. 

Nuclear factor-erythroid 2-related factor 2 (Nrf2) binds to promoter antioxidant response elements (ARE) and activates their transcription. Inactive Nrf2 is associated with an actin-binding protein Keap1 and is retained in the cytoplasm. 

Triggered by oxidative stress, Nrf2 is phosphorylated in response to the protein kinase C, phosphatidylinositol 3-kinase, and MAP kinase pathways. The phosphorylated Nrf2 can then translocate to the nucleus and bind AREs to transactivate detoxifying and antioxidant enzymes (Supplementary Table 3) (Ma 2013).

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