Dual Analysis of Virus-Host Interactions: The Case of  Ostreid herpesvirus  1 and the Cupped Oyster  Crassostrea gigas

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Abstract

Dual analyses of the interactions between Ostreid herpesvirus 1 (OsHV-1) and the bivalve Crassostrea gigas during infection can unveil events critical to the onset and progression of this viral disease and can provide novel strategies for mitigating and preventing oyster mortality. Among the currently used “omics” technologies, dual transcriptomics (dual RNA-seq) coupled with the analysis of viral DNA in the host tissues has greatly advanced the knowledge of genes and pathways mostly contributing to host defense responses, expression profiles of annotated and unknown OsHV-1 open reading frames (ORFs), and viral genome variability. In addition to dual RNA-seq, proteomics and metabolomics analyses have the potential to add complementary information, needed to understand how a malacoherpesvirus can redirect and exploit the vital processes of its host. This review explores our current knowledge of “omics” technologies in the study of host-pathogen interactions and highlights relevant applications of these fields of expertise to the complex case of C gigas infections by OsHV-1, which currently threaten the mollusk production sector worldwide.

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Temporal Proteome and Lipidome Profiles Reveal Hepatitis C Virus-Associated Reprogramming of Hepatocellular Metabolism and Bioenergetics

Deborah L Diamond, Andrew Syder, Jon Jacobs … (2010)

Introduction Persistent infection with hepatitis C virus (HCV), a single-stranded positive RNA virus of the Flaviviridae family, is a major cause of liver disease and a global public health problem. Chronically infected individuals develop variable degrees of hepatic inflammation and fibrosis, and are at increased risk for developing cirrhosis and hepatocellular carcinoma [1]. Current therapy consists of a combination of drugs that target cellular functions, including pegylated-interferon to boost the interferon-mediated antiviral response, and ribavirin, a nucleoside analog that suppresses HCV replication by impairing guanine nucleotide biosynthesis [2]. Unfortunately, this treatment regimen has limited efficacy, especially for certain HCV genotypes and patient populations, and its poor tolerability often leads to discontinuation. All viruses rely on constituents of the host cell to provide the energy, macromolecules and structural organization necessary for their propagation. This dependence on host interactions has led to significant interest in better understanding those pathways/processes crucial to the viral life cycle, as these represent potential targets for new antiviral strategies [2]–[4]. HCV infection has long been associated with abnormalities in lipid metabolism, and lipids have been shown to play important roles in various aspects of the virus life cycle [2],[3],[5]. For example, the biosynthesis of cholesterol, fatty acids, and geranylgeranyl and sphingolipid species is key to HCV replication, presumably by promoting the formation of lipid rafts on which replicase complexes assemble [6]–[10]. The development of a cell culture system that supports not only HCV replication but also the production of infectious virus has revealed additional roles for lipid metabolism in viral particle assembly, secretion and infectivity. Lipid droplets have been shown to function in the assembly of infectious particles, and HCV production is further dependent on apolipoprotein B (apoB) expression and very low density lipoprotein (VLDL) assembly and secretion [11]–[13]. The association of HCV morphogenesis with VLDL production has led to the identification of new cellular targets (e.g. apoB, microsomal triglyceride transfer protein, and long chain acyl –coenzyme A synthetase 3) with the potential to limit both processes [12]–[15]. Lipidomic analyses of mature virions isolated from infected-cell culture supernatants suggest that the HCV membrane is enriched in cholesterol; modulation of the virion-associated cholesterol or sphingomyelin composition alters infectivity by inhibiting virus internalization [16]. Host cell lipid metabolism is therefore critical for multiple stages of the HCV life cycle, and represents an important area for the exploration of new antiviral reagents. Despite the demonstrated importance of lipid components, the extent to which HCV modulates global intracellular metabolism to create an environment for RNA replication and production of progeny particles is currently unknown. Here we use liquid chromatography-mass spectrometry (LC-MS) together with the AMT tag approach to identify alterations in the host cell proteome and lipidome occurring in response to in vitro infection of Huh-7.5 cells with a chimeric HCV genotype 2a virus, J6/JFH-1. Our data reveal a temporal sequence of modifications to the host proteome that were not predicted from our previous gene expression analyses [17], suggesting that HCV dramatically disrupts cellular metabolic homeostasis via post-transcriptional regulatory mechanisms. We also observe global changes in lipid abundance, which are predicted to impact the HCV life cycle and pathogenesis. We further describe a computational modeling approach, which uses these high-throughput datasets to infer regulatory and functional relationship networks that provide information about the systems-level role of key proteins and lipids important for HCV-associated metabolic reprogramming. Materials and Methods Generation of Cell Culture Virus and Experimental Infections Approximately 7×106 Huh-7.5 cells (human hepatoma cell line) were electroporated with 2 µg of in vitro transcribed RNA, representing the chimeric HCV genome J6/JFH-1 [18]. One source of virus used for the infection experiments consisted of a pool of adapted virions obtained from cell culture supernatants that had been passaged multiple times following electroporation (HCVcc ‘pool’; refer to [17]). Alternatively, a J6/JFH-1 chimeric genome containing twelve cell culture adaptive mutations (HCVcc ‘clone’), isolated from the HCVcc ‘pool’, was used to generate virus stocks directly from electroporated cell supernatants by collecting samples every 12 h over 2–5 days post-electroporation. Both sources of virus demonstrated enhanced viral kinetics (>10-fold viral titer compared to parent; manuscript in preparation, Rice CM) that was necessary to infect the large number of cells required for the proteomic analysis. Parallel electroporations were performed in the absence of HCV RNA to generate a control supernatant sample (‘mock’). In addition, the HCV stock was used to generate a UV-inactivated, non-infectious control (UV-HCVcc; refer to [17]). For infection experiments (n = 4), low passage Huh-7.5 cells were seeded at a density of ∼3×106 cells/p150 plate, to ensure that cells would not reach confluency by the time of harvest, and treated for ∼8–12 h with 20 ml of supernatant containing virus (HCVcc), UV-inactivated virus (UV-HCVcc), or conditioned media (mock, CM). Following initial exposure, the supernatant was replaced with fresh media and incubated until harvest at 24, 48, or 72 h post-infection. The multiplicity of infection (MOI) for the experiments was ∼1–2 and resulted in >50% of cells being infected by 24 hours after HCVcc exposure. Harvesting Cells After Virus Infection Following removal of supernatant, cells were washed once with PBS and then scraped from the plate(s) in ice cold PBS. Identically treated cells were pooled from replicate plates, if necessary, to obtain approximately 107 total cells per time point per condition. The cells were subsequently split into several fractions: 10% for RNA isolation for previously reported genomics analyses [17], 10% for lipidomic analysis, and 80% for peptide generation/proteomic analysis. Lipidomic Sample Preparation Cells were pelleted in a siliconized microfuge tube, followed by disruption with chloroform∶methanol (2∶1 ratio), and pre-chilled to −20°C. After several rounds of vortexing on ice, samples were centrifuged at 2,000 RPM for 10 minutes to separate a water-soluble fraction (upper layer) and a lipid soluble fraction (lower layer). Both fractions were dried in a speed vac and stored at −80°C until analysis. Proteomic Sample Preparation Cells were washed in 0.5 x PBS, pelleted, and stored at −80°C until sample preparation. Samples from all time points were prepared on the same day, initially by lysing the thawed cell pellets in hypotonic buffer (5 mM K3PO4) at room temperature. Samples were solubilized and denatured by adding trifluoroethanol (TFE) to achieve a final concentration of 50%, followed by sonication on ice and incubation at 60°C for 1 h along with additional sonication. An equal volume of 500 mM NH4HCO3 (pH 8.0) was added to adjust to pH 8.0, and 5 mM tributylphosphine (TBP) was then added for 1 h at 37°C to allow reduction of disulfide bonds. Following centrifugation, soluble material was transferred to a fresh tube and the volume of sample was reduced to ∼100 µl using a speed vac. Additional NH4HCO3 (50 mM) was then added to reduce the amount of TFE in the samples to 1.5-fold change, P-Value 1.5-fold change, P-Value<0.05) exhibiting statistically significant increases in abundance are highlighted in red and those exhibiting statistically significant decreases in abundance are highlighted in green. Functional categories represented include glycolysis, the pentose phosphate pathway, and the tricarboxylic acid (TCA) cycle whose constituents are listed in their order of appearance in the appropriate pathway. For all other functional categories typically representing a broader spectrum of activities including pyruvate metabolism, oxidative phosphorylation, glutamate metabolism, fatty acid oxidation, nucleotide biosynthesis and homeostasis, lipogenesis, chaperones, and NRF2 stress response the proteins have been arranged in order of the fold-change observed in the HCVcc sample at 24 h post-infection. (0.07 MB XLS) Click here for additional data file. Table S3 Select list of 272 lipid species exhibiting significant differences across conditions and time points (ANOVA P<0.05). Mass to charge ratio (M/Z), normalized elution time (NET), log 2 average abundances (mean) and standard deviations (sd) are presented for each condition (CM: conditioned media, UV-HCVcc: UV-inactivated chimeric HCV 2a, HCVcc: chimeric HCV 2a virus) and time point. Also reported are the overall P-values from ANOVA analysis performed on minimum observation data (a feature was required to be observed in at least two out of three conditions (CM, UV-HCVcc, HCVcc) and there must be duplicate measurements for the two out of three conditions). P-values listed as 0.000 are equivalent to p<0.001. NA indicates where missing values exist. Among the 272 features exhibiting statistically significant differences between treatment conditions and/or time points, 73 lipid species were identified by matching to a lipid AMT tag database or fragmentation information collected via targeted MS/MS analyses. Identity abbreviations were made for phoshatidylcholine (PC; O- fatty acid chain number means that an alkyl acyl linkage to the glycerol chain is present for the respective PC), sphingomyelin (SM), ceramide (Cer), triacylglycerol (TAG), and cholesterol ester (CE). The notation further indicates total number of carbons and double bonds however it does not discern redundancy associated with varying fatty acid composition for the same molecular weight. (0.07 MB XLS) Click here for additional data file. Table S4 Top 5% of the bottlenecks identified from the combined lipidomics, proteomics inferred and protein-protein interaction network. Accompanying notes about the known or implicated roles of the identified bottlenecks in HCV response is also included. (0.03 MB XLS) Click here for additional data file.

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Dynamics of the Cellular Metabolome during Human Cytomegalovirus Infection

Joshua Munger, Sunil Bajad, Hilary Coller … (2006)

Introduction Host cells provide viruses with the metabolic resources necessary for their replication. Viral replication requires bulk macromolecule synthesis, which depends on a substantial supply of biopolymer subunits and energy for their assembly. Anti-viral compounds that target viral utilization of macromolecular precursors, such as nucleotide analogs, have proven to be successful clinical treatments. Notwithstanding these therapeutic successes, the mechanisms through which viruses engage and manipulate the small molecule metabolic network of host cells remain largely a mystery. Human cytomegalovirus (HCMV) is a prototypical β-herpes virus family member. Epidemiological studies have demonstrated that the virus is widespread, infecting more than half of the earth's population [1]. Most healthy adults undergo an asymptomatic infection, during which the virus enters a latent state for the life of its host. However, primary infection or reactivation of latent HCMV causes life-threatening disease in immunologically immature or compromised individuals. In addition, HCMV is the leading known infectious cause of birth defects; congenital infection can result in multiple organ system abnormalities with damage to the auditory system occurring in the majority of symptomatic newborns [1]. Like all herpesviruses, the HCMV particle is enveloped and contains a large double-stranded DNA genome packaged inside a protein capsid. The capsid in turn is surrounded by a protein tegument layer residing between the capsid and the envelope. HCMV infection induces total RNA and protein synthesis in infected cells [2,3]. However, it remains unclear to what extent HCMV infection perturbs small molecule metabolism to accomplish the macromolecular synthesis needed for viral replication. Here we used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to measure the levels of 63 different intracellular metabolites during HCMV infection of cultured fibroblasts. Additionally, to provide insight into potential transcriptional activation of metabolic pathways, we conducted mRNA expression profiling in parallel. We find that as infection progresses, the levels of numerous metabolic components in pathways such as glycolysis, the tricarboxylic acid (TCA) cycle, and pyrimidine biosynthesis markedly increase. Mirroring our metabolomics data, HCMV transcriptionally activates genes encoding components of the TCA cycle, glycolytic pathway, and certain aspects of the pyrimidine biosynthetic pathway. The peak levels of various metabolites during infection far exceed those observed during normal fibroblast growth or quiescence, indicating that HCMV markedly disrupts metabolic homeostasis. Results/Discussion Cell Handling and Metabolite Extraction LC-MS/MS data on 167 different metabolites were collected at various times following infection with HCMV or a mock negative control innoculum. Fibroblasts were grown to confluence and then maintained in serum-free medium for 24 h prior to infection. This treatment served to synchronize the cells in G0 (Figure S1), which reduces cell-to-cell variation in response to infection [4]. Additionally, the use of serum-free medium removes a complex source of extraneous metabolites from the analysis. Cells were assayed at 4, 24, 48, 72, and 96 h post infection (hpi) to provide coverage during the extended replication cycle of HCMV. In cultured fibroblasts, viral gene expression is first detected at about 2 hpi, viral DNA replication begins between 24–30 hpi, and peak virus yields are achieved between 72–96 hpi, after which the cells die. At the appropriate time post infection, medium was aspirated from the cells (a thin film of medium remains covering the cells due to surface tension effects, avoiding alteration of the immediate cellular environment) and −75 °C methanol was immediately added to quench metabolic activity and extract metabolites. Given the speed of metabolic reactions, quickly removing medium and immediately quenching metabolism is essential for obtaining reliable results; we have found that even a single wash step leads to substantial changes in metabolome composition. The selection of cold methanol as an extraction solvent was based on systematic studies in bacteria and yeast, which point to its providing relatively good extraction of a broad spectrum of metabolites [5–7], while avoiding marked metabolite decomposition and associated formation of decomposition products, which can themselves mimic metabolites [8]. A contributor to the efficacy of −75 °C methanol as an extraction solvent is likely its disrupting membranes by inducing formation of microscopic ice crystals. Metabolite Quantitation Extracted metabolites were measured via LC-MS/MS, with the LC separation involving hydrophilic interaction chromatography on an aminopropyl resin at basic pH, an approach tailored to quantitation of water-soluble cellular metabolites [9]. Each metabolite measurement was in the form of a selected reaction monitoring scan event (SRM); formation within the mass spectrometer collision cell of a particular molecular weight product ion from a specific molecule weight parent ion by gas phase collision of the parent ion with argon occurring at a specific chromatographic retention time. These SRMs were designed based on prior knowledge of the retention time and mass spectrometry fragmentation pattern of a purified standard of the metabolite of interest [6], with a one-to-one correspondence between metabolites and SRMs. An exception to the one-to-one relationship between metabolites and SRMs was the co-elution of certain metabolite isomers yielding identical fragmentation patterns (e.g., different isomers of glucose-6-phosphate, which we refer to collectively as hexose-phosphate). In many cases, no useful quantitative data were obtained, due to the metabolite never being present in detectable amounts or being present in the medium at such high levels as to interfere with analysis. For 63 metabolites, however, reliable quantitative data were obtained in the form of ion counts from compound-specific SRM chromatograms (for details on the requirements for metabolite inclusion see Materials and Methods). These 63 metabolites included a variety of key components of central carbon metabolism and nucleotide metabolism, including multiple phosphorylated compounds such as ATP and fructose bisphosphate (FBP). Internal Standards To ensure that experimental artifacts such as ion suppression did not lead to misinterpretation of metabolite levels, isotope-labeled internal standards were added to every sample. As is typical for electrospray ionization-mass spectrometry data, there are occasional fluctuations in the internal standard signals, generally in the range of ~20% (Figure S2), which is much smaller than the effects due to viral infection described below. As the fluctuations generally occurred in parallel for the mock versus viral samples and did not follow any systematic pattern with respect to viral versus mock treatment (Figure S2), there is no evidence of systematic error between mock and viral samples due to ion suppression or other electrospray ionization-mass spectrometry artifacts. Accordingly, experimental error in the LC-MS/MS measurement is very unlikely to have influenced the biological conclusions of the present work. Data Normalization and Presentation To enable cross-sample comparison of metabolite (ion count) signals, the observed signal was normalized with respect to the total protein content of the cells used to generate the sample. Protein content, rather than cell number, was used for normalization because HCMV-infected cells increase approximately 2-fold in volume as compared to mock-infected cells. Normalization with respect to protein content should yield signals in units approximating ion counts/cell volume, which for any given metabolite is linearly proportional to its intracellular concentration [9]. Comparison of ion counts across compounds is complicated by compound-specific ionization efficiencies. Accordingly, signals were further normalized to the initial (t = 4 h) signal in the mock-infected condition. Time course data for compounds or pathways of particular interest are shown in Figures 1–6, and complete data (including absolute ion counts) on the 63 quantifiable metabolites are provided in Table S1. RNA expression data were collected by microarray analysis, and data for key metabolic enzymes that changed significantly upon infection are shown in Figure 7. The primary expression data are provided in full via the Princeton University MicroArray database (http://puma.princeton.edu/index.shtml). In certain specific cases, we validated our microarray data using quantitative PCR (qPCR) (Figure 2A). General Strategies for Data Interpretation Before addressing particular experimental observations, it is useful to consider the different potential causes of particular metabolite level changes. Consider the following metabolic pathway: Increased intracellular levels of a given metabolite B can arise from either increased flux from A → B, or decreased flux from B → C. These alternatives can likely best be distinguished using isotope tracers; however, in the absence of such tracer data (which will not be presented here), good estimates of the true cause of a change in the level of metabolite B can often be made based on knowledge of key regulated steps in metabolic pathways. That is, if the reaction A → B but not B → C is known to be a key regulated step, then change in flux through A → B likely accounts for the change in the level of B. For example, in Figure 1 we report an increase in FBP at 48 hpi. As production of FBP via phosphofructose kinase-1(PFK-1) is tightly regulated [10–12], whereas consumption of FBP by aldolase is not, the most likely cause of the increase in FBP is increased PFK-1 flux. This conclusion is reinforced by the observation that levels of the aldolase product, dihydroxyacetone phosphate (DHAP), increase roughly in parallel with those of FBP (if the increase in FBP were due to blockade of aldolase, DHAP levels should drop, not increase). It is confirmed by direct measurement of PFK activity from cell lysates, as described further below (Figure 2B). The above analysis considers only the two most basic causes of changes in the level of metabolite B: altered consumption or production via a linear pathway. It is worth noting that especially in a highly compartmentalized eukaryotic cell, other situations can arise. For example, imagine that the reactions A → B → C normally occur in a subcellular compartment that upon viral infection begins to leak B into the cytosol, which contains no enzyme that consumes B. In this case, increased levels of B may occur independently of flux increases or decreases via the standard A→ B → C reaction pathway, depending instead on the extent of B leakage. Such a case may describe the pyrimidine pathway intermediate carbamoyl-aspartate, as will be discussed later. We wish to emphasize that while transport of metabolites between subcellular compartments may play a key role in regulation of mammalian metabolism, our current analytical approach provides only average metabolite levels throughout the full cellular volume. Accordingly, the pathway diagrams in the figures include only covalent, not transport, reactions. Effect of HCMV Infection on Upper Glycolysis Glycolysis and the TCA cycle oxidize hexose sugars to form ATP and NADH, the major energy currencies of the cell. While HCMV infection has been previously shown to stimulate glucose uptake in fibroblasts [13], its detailed effect on intracellular carbon metabolism has not been previously investigated. We found that early during infection (i.e., 4–24 hpi), there was little difference in measurable glycolytic pathway metabolites between mock- and HCMV-infected fibroblasts (Figure 1). Beginning at 48 hpi, however, differences emerged: FBP, DHAP, 3-phosphogylcerate, and phosphoenolpyruvate (PEP) increased relative to mock infection (Figure 1). The increased levels of glycolytic intermediates during HCMV infection correlated with an increase in the transcript levels of several glycolytic enzymes (Figures 1 and 7). One such enzyme, PFK-1, is the key upstream regulator of glycolytic flow [14–16]. To verify our microarray results regarding this important regulatory enzyme, we analyzed its transcript levels over the course of infection by qPCR. Transcript levels were found to be increased at every time point assayed with a peak 6-fold increase at 48 hpi (Figure 2A). The levels of PFK-1′s product, FBP, also peaked at this time, suggesting increased PFK-1 activity in HCMV-infected cells at 48 hpi. To directly test for such activity, we performed biochemical assays on cell lysates. Addition of fructose-6-phosphate and ATP to lysates of HCMV-infected or mock-infected cells resulted in accumulation of FBP, with the initial rate of accumulation ~4-fold greater for HCMV than mock infection. Since PFK-1 regulates glycolytic flow, the increased levels of downstream glycolytic intermediates during HCMV infection likely reflect, at least in part, this increase in PFK-1 activity. Glycolytic Efflux during HCMV Infection Most of the measured glycolytic intermediates increased at 48 hpi relative to their status in mock-infected cells. However, the increase in FBP was transient, presumably due to its rapid conversion to downstream glycolytic intermediates. The terminal glycolytic metabolite, PEP, increased from 48 hpi onward (Figure 1). This suggested the presence of a bottleneck to glycolytic efflux at the level of pyruvate kinase late in infection. Such a bottleneck could arise from decreased maximum velocity for the pyruvate kinase reaction (e.g., due to decreased pyruvate kinase levels), increased Km of the enzyme for PEP (e.g., due to allosteric regulation or shift to a higher Km isozyme), or failure of pyruvate kinase activity to keep pace with increased flux through upper glycolysis. Our microarray data revealed increased expression of enzymes linking FBP to PEP (triose phosphate isomerase, phosphoglycerate kinase, and enolase) but decreased expression of pyruvate kinase (Figures 1, 2A, and 7). This decrease in pyruvate kinase transcript is consistent with (and may potentially cause) the observed accumulation of PEP. Note that the reactions linking DHAP to PEP are associated with net ΔG0′ ~zero [17]. Thus, irrespective of the activities of the intervening enzymes, DHAP and its products must accumulate in parallel with PEP for glycolysis to continue to run in the net forward direction. The principal carbohydrate product of glycolysis is pyruvate, which was not measured here due to its poor fragmentation in MS/MS. A key metabolite of pyruvate is alanine, which is produced via pyruvate transamination. Levels of alanine increase markedly starting at 48 hpi (Figure 3), in concert with the increase in FBP and other glycolytic intermediates (Figure 1). Much like PEP, alanine levels grow steadily from 48 hpi onward. This suggests that overall glycolytic flux (as reflected in part by alanine production) is increased from 48 hpi onward, with shifting enzymatic activity in lower glycolysis altering the balance of FBP versus PEP during this period. Intriguingly, two common features of human cancers are the activation of phosphofructokinase activity mediated by increased F2,6P accumulation and the expression of a particularly low-activity isozyme of pyruvate kinase [11,18,19]. Thus, increased phosphofructokinase activity and decreased pyruvate kinase activity, which together may favor use of glycolytic intermediates for production of biosynthetic building blocks, appear to be shared between cancer and HCMV infection. Increased Acetyl-CoA during HCMV Infection Acetyl-CoA occupies a central position in metabolism. It is the link between glycolysis and the TCA cycle, as well as a hub for fatty acid and amino acid oxidation and de novo lipid biosynthesis. Like alanine, acetyl-CoA can be formed from pyruvate, and like alanine, acetyl-CoA increased dramatically throughout HCMV infection; however, acetyl-CoA is unique in rising even before glycolytic intermediates and ultimately increasing by ~8-fold in infected as compared to uninfected cells (Figure 3). The strength of the acetyl-CoA response, as well as its initiation prior to evidence of increased glycolytic intermediates, suggests the possibility of increased activity of the key acetyl-CoA producing enzyme complex, pyruvate dehydrogenase. Consistent with this notion, pyruvate dehydrogenase transcript levels were upregulated 2- to 3-fold by the virus both by microarray (Figure 7) and by qPCR (Figure 2). TCA Intermediates during HCMV Infection When acetyl-CoA enters the TCA cycle, it is converted, with the addition of oxaloacetate, into citrate. Throughout infection, the levels of many TCA cycle constituents were increased with respect to mock infection; however, citrate levels were not, although citrate levels did increase in parallel in both mock- and virus-infected samples (Figure 3). Citrate synthase activity is inhibited by increased levels of NADH and succinyl-CoA [20], the levels of which are elevated during HCMV infection (Figure 3), potentially preventing accumulation of citrate (Figure 3). Alternatively, oxaloacetate may be diverted via aspartate into de novo pyrimidine synthesis, which is activated during HCMV infection (Figure 4), thus limiting its availability for citrate synthesis; direct oxaloacetate measurement here was precluded by oxaloacetate instability [9]. More likely, however, citrate synthase flux was increased as a consequence of increased acetyl-CoA levels, but citrate levels were kept steady by increased activity of the citrate-consuming enzyme, aconitase, whose mRNA level increased during HCMV infection (Figure 7). Consistent with increased aconitase expression, aconitate (4-fold increased at 24 and 48 hpi) and its downstream TCA cycle products, including succinyl-CoA and malate, increased during infection (Figure 3). Because succinyl-CoA is the entry point for odd-numbered fatty acid chains into central carbon metabolism, its particular strong induction hints at the possibility of increased fatty acid catabolism induced by the virus. Transcriptionally, the expression levels of malic enzyme and malate dehydrogenase both increased (Figure 7). Malic enzyme converts malate to pyruvate, potentially contributing to increased alanine and acetyl-CoA levels and limiting citrate levels, whereas malate dehydrogenase produces oxaloacetate from malate, thus completing the TCA cycle by providing citrate synthase with oxaloacetate. The primary recipient of the energy released from citric acid cycle oxidations is the reduced electron carrier NADH. Consistent with elevated glycolysis and citric acid cycle activity, NADH levels increased approximately 2- to 4-fold relative to mock infection at every time assayed after 4 h (Figure 3). This increase in NADH was unlikely to be due to reduced NADH consumption, as enzymes of the electron transport chain and the ATP-synthesizing proton pump were markedly transcriptionally upregulated (Table S2). Taken together, these results are most consistent with the hypothesis that HCMV infection stimulates both glycolytic and pyruvate dehydrogenase flux. This could generate the observed high level of acetyl-CoA, which was then used to drive the TCA cycle to produce NADH and eventually ATP, as well as potentially also donating carbon units to lipid biosynthesis, which was not explored here. Dynamics of Central Carbon Metabolites in the Absence of HCMV The levels of certain compounds, e.g., citrate, aconitate, and alanine, increased during mock infection (Figure 3). The levels of other compounds, e.g., FBP and NADH, decreased (Figures 1 and 3). As the cellular medium was not changed during the course of infection, these changes may reflect alteration of the cellular nutrient environment over time, perhaps due to glucose becoming less abundant and glycolytic waste products accumulating. Alternatively, the changes in metabolite levels may reflect long-term adaptations of fibroblasts to tissue culture. One effect of HCMV infection may be to accelerate some of these processes that occur even in its absence; however, acceleration of normal physiological processes is inadequate to account fully for the profound and persistent changes seen in many compounds such as acetyl-CoA and NADH. HCMV Manipulation of Pyrimidine Biosynthesis HCMV infection stimulates total RNA and DNA synthesis and increases the total number of cellular ribosomes without degrading host cell chromosomal DNA [2]. Consequently, the virus must induce substantial de novo nucleotide biosynthesis. The first three steps of pyrimidine biosynthesis are catalyzed in eukaryotes by a homo-trimer of the 230-kDa protein termed carbamoyl phosphate synthetase-aspartate transcarbamoylase-dihydroorotase (CAD), which catalyzes the synthesis of carbamoyl-phosphate, carbamoyl-aspartate, and dihydroorotate [21–23]. Carbamoyl-phosphate levels remained relatively similar in mock-infected cells versus HCMV-infected cells until 72 h when they started to fall in infected fibroblasts (Figure 4). Levels of carbamoyl-aspartate, in contrast, increased dramatically during viral infection starting at 24 hpi (Figure 4). While levels of carbamoyl-aspartate increased substantially, levels of the CAD transcript remain unchanged during the course of infection (Figure 4), suggesting that transcriptional activation of the CAD gene is not responsible for the accumulation of carbamoyl-aspartate. The increased carbamoyl-aspartate levels did not appear to arise from blockade in its utilization, as levels of pyrimidine biosynthetic end products such as the ribonucleotides UTP and CTP and the deoxyribonucleotide TTP were generally increased compared to mock infection (Figure 4), although levels of UMP, which sits in the middle of pyrimidine biosynthesis, were actually slightly decreased. Interestingly, UTP reportedly serves as a feedback inhibitor of CAD activity [24,25] and one would expect that the increased levels of UTP that occur during HCMV infection might inhibit CAD activity, thereby blocking carbamoyl-aspartate accumulation. One possible explanation for the continued high levels of carbamoyl-aspartate in the face of high UTP is that infection might spatially segregate UTP and CAD into different compartments. This might involve interplay between the virus and mitogen-activated protein kinase, which has recently been shown to regulate CAD activity/localization [26]. Another challenge in interpreting the carbamoyl-aspartate accumulation is that the three active sites of CAD are linked by a 96-Å long channel which supposedly localizes carbamoyl-phosphate and carbamoyl-aspartate to the interior of the enzyme [27–29]. Thus, the presence of free carbamoyl-aspartate may indicate profound perturbation of CAD in infected cells, perhaps as the consequence of an interacting virus-coded protein that results in leakage of carbamoyl-aspartate from the enzyme interior. Purine Homeostasis during HCMV Infection In contrast to the aforementioned alterations of the pyrimidine biosynthetic pathway, HCMV infection did not dramatically affect purine metabolite pool levels. AMP, ADP, and ATP metabolite levels did not change significantly with respect to mock infection, with the exception of a slight increase in ADP and ATP at 48 h and a small accumulation of AMP after 48 h in HCMV-infected versus mock-infected fibroblasts (Figure 5). The present study does not provide substantial insight into de novo purine biosynthesis however, because the only purine intermediate for which an SRM was included, IMP, could not be measured due to interference from naturally occurring heavy isotopes of AMP (see Materials and Methods). While the levels of purines do not change dramatically, it is likely that increased turnover of the high-energy phosphate bond of ATP occurs, as the RNAs encoding mitochondrial ATP synthases were significantly upregulated (Table S2). The dramatic increase in pyrimidine versus purine levels may reflect different biological roles of the two families of metabolites. Dramatic concentration changes in ATP and GTP could alter the balance of a multitude of enzymatic reactions and interactions based on their widespread utilization as signaling constituents and energy currency. As such, a pathogen may be forced to maintain purine levels close to normal physiological levels. Pyrimidines, in contrast, do not have equally wide-ranging functions and thus HCMV may have license to more dramatically influence pyrimidine levels. HCMV Infection and Amino Acids With the exception of alanine, HCMV infection did not induce increased levels of other measured amino acids. While the presence of exogenous amino acids in the medium precluded quantification of a number of amino acids, the levels of glutamate were not affected by viral infection (Figure 5), nor were the levels of asparagine, aspartate, histidine, or proline (Table S1). Taken together, these data suggest that while HCMV infection strongly impacts the metabolite levels of certain metabolites, it has little effect on many amino acid levels. HCMV Infection Depletes Cells of a Subset of Metabolites A number of metabolites were found to decrease rapidly during HCMV infection compared to mock infection of fibroblasts (Figure 6). These metabolites could be grouped generically into those, such as adenosine and carnitine, for which HCMV infection seemed to accelerate decreases that also occurred during mock infection, and others, like myo-inositol and glycerol phosphate, which decreased only in the HCMV infection condition. Several of the metabolites decreasing during HCMV infection were nucleosides (adenosine and uridine), consistent with use of these metabolites for nucleotide and eventually RNA or DNA biosynthesis. Others were related to lipid metabolism: carnitine, which plays a key role in β-oxidation of long-chain fatty acids [30,31]; glycerol-phosphate, a precursor to the glycerol portion of phospholipids, diacylglycerols, and triglycerides; and myo-inositol, a precursor to inositol-containing lipids such as phosphatidyl-inositol. The results for glycerol-phosphate, while unusually noisy, were striking in that decreases were evident already at 4 hpi, making glycerol-phosphate depletion among the earliest known effects of HCMV infection on cellular metabolism. Taken together, these results demonstrate that HCMV infection depletes a subset of precursor metabolites, some very rapidly, most likely utilizing them for its own macromolecular synthesis. Taurine, an amino acid analog involved in osmoregulation and cell volume control [32], is not an obvious precursor metabolite, but was nevertheless decreased 4-fold by HCMV infection (Figure 6, top left panel). Typically, if a cell is subjected to hypo-osmotic conditions, taurine is rapidly transported out of the cell to compensate for the osmotic imbalance and to prevent cell volume increase [32,33]. The cytomegaloviruses are named for the cytopathic effect they induce, which involves a significant increase in cell volume. It seems likely that taurine is not utilized by the virus, but instead excreted by the host cell in response to the virus-induced increase in cell volume. HCMV-Induced Metabolite Levels Far Exceed Those Seen in Actively Growing Cells HCMV activates a number of cyclin-dependent kinases and induces quiescent G0 cells to enter the G1 compartment of the cell cycle and progress to the G1-S border [34,35]. Up to this point, we tested the effects of HCMV infection in confluent, serum-starved G0 cells. Consequently, we were interested if HCMV-induced metabolic changes mirrored the differences between actively growing and quiescent cells, so we tested how the metabolic changes induced by infection of quiescent G0 cells compared to the differences between quiescent and actively growing cells. Toward this end, we harvested fibroblasts that were actively growing and processed them for LC-MS/MS. Metabolite levels in actively growing fibroblasts were compared to 72-hpi, HCMV-infected fibroblasts. The relative levels of acetyl-CoA, alanine, carbomoyl-aspartate, succinyl-CoA, and to a lesser extent PEP, NADH, CTP, and TTP, were dramatically elevated in HCMV-infected fibroblasts compared to actively growing fibroblasts (Figure 8). Thus, HCMV does not modify the cellular metabolome simply by inducing quiescent cells to enter the G1 phase of the cell cycle. Rather, the virus re-programs the metabolic status of the cell, producing a unique signature of infection. Conclusions and Future Directions The network of chemical reactions involving the derivation of energy currency and biosynthetic building blocks from nutrient inputs consists of ~500 different compounds and 700 interconverting reactions [36]. Remarkably, relatively little is known of how intracellular pathogens modulate these metabolites and their interconversions. We utilized a global approach to address this issue. As commonly occurs with such approaches, we generated many specific biochemical hypotheses that necessitate further inquiry. One such hypothesis, that HCMV infection induces phosphofrucktokinase activity, was verified. Many more remain untested. Beyond specific biochemical hypotheses, we also began the process of tackling larger biological questions regarding the metabolic interplay between pathogens and their hosts. Can pathogens meet their biosynthetic needs simply by activating cellular growth pathways with a coordinate increase in metabolic activity? This does not appear to be the case for HCMV. Our results indicate that the virus institutes its own metabolic program, altering normal metabolic homeostasis maintained in quiescent and growing cells and considerably stimulating aspects of glycolysis, the citric acid cycle, and pyrimidine biosynthetic metabolic pathways. Future experiments are necessary to determine whether the numerous metabolic differences observed during HCMV infection are necessary for efficient viral replication, and, if so, the means by which these metabolic alterations contribute to the virus growth cycle. Materials and Methods Biological reagents. Primary human foreskin fibroblasts (passages 6–10) were cultured in Dulbecco's modified Eagle medium (DMEM) containing 7.5% fetal calf serum and 4.5 g/L glucose. All infections were carried out with BADwt, which is derived from a bacterial artificial chromosome (BAC) clone of the AD169 strain of HCMV [37]. The BAC was inserted into the genome of HCMV without deletion of any viral sequence and was excised by a co-transfected CRE recombinase that mediates recombination at the loxP sites, which flank the BAC, leaving just the loxP site in the viral clone. This clone has been tested in a diversity of assays and has always displayed a wild-type AD169 phenotype. Cell culture. Fibroblasts were grown to confluence in 10-cm dishes and maintained for 3–5 d, at which time serum-containing medium was removed, and cells were washed once with serum-free DMEM. After maintenance in serum-free DMEM for 24 h, cells were mock infected or infected at a multiplicity of 3.0 pfu/ cell, and after a 2-h adsorption period, the inoculum was aspirated and fresh serum-free medium was added. Cells were harvested for metabolic, transcriptional, or total protein analysis at various times after the initiation of infection. Actively growing non-confluent primary fibroblasts were grown in DMEM containing 7.5% dialyzed fetal calf serum and harvested 24 h after splitting. Flow cytometric analysis of cellular DNA content was performed as previously described [38]. Protein analysis. For total protein analysis, cells were washed with PBS, scraped, pelleted by low speed centrifugation, and solubilized in lysis buffer containing 150 mM KCl, 10 mM Hepes, 1% NP40, 0.5% deoxycholate, 0.1% SDS, 10 μg/ml phenylmethylsulphonylfluoride (PMSF), and 1 μg/ml pepstatin. After 10 min on wet ice, lysates were centrifuged at 14,000 × g for 10 min. The protein concentration of supernatant fluids was determined by using the Bradford assay (Bio-Rad Laboratories, http://www.bio-rad.com). Metabolite extraction. The medium was aspirated from plates containing medium only, actively growing fibroblasts, or fibroblasts that were 4, 24, 48, 72, or 96 h post-mock or post-HCMV infection. Methanol:water 80:20 (80% methanol) at −75 °C was immediately added to quench metabolic activity and extract metabolites. After metabolism quenching, cells were scraped from the plastic tissue culture dish with the dish kept on dry ice, and the resulting cell suspension vortexed, centrifuged at 6000 × g for 5 min, and re-extracted twice more with 80% methanol at −75 °C. After pooling the three extractions, the samples were completely dried under nitrogen gas, dissolved in 200 μl 50% methanol, and spun at 13,000 × g for 5 min to remove debris. Internal standard mix (15 μl) was added to an aliquot of the supernatant (135 μl) and the mixture was analyzed by LC-MS/MS as described below with the virally infected and mock-infected samples alternated (to avoid artifacts in the measurement of unstable compounds due to differential processing times), and all analyses completed within 16 h of sample collection (to further mitigate any potential problems with sample stability). The internal standard mix was comprised of 13C-,15N-labeled compounds (uniformly labeled unless otherwise indicated, from Cambridge Isotope Laboratories, http://www.isotope.com; or Medical Isotopes, http://www.medicalisotopes.com) in 50% methanol with 0.1% formic acid: ATP (2 μg/ml), TTP (2 μg/ml), succinate (1,4-13C, 1 μg/ml), threonine (1 μg/ml), glutamate (1 μg/ml), UMP (1 μg/ml), AMP (1 μg/ml), and deoxyadenosine (0.1 μg/ml). Phosphofrucktokinase assay. 48 h post-mock or post-HCMV infection, cells were washed once and then scraped into ice- cold PBS. After centrifugation at 1,000 × g for 5 min, cell pellets were resuspended in 200 μl 60 mM Hepes (pH 7.4), 1 mM MgCl2, 5 mM DTT, 0.6 mM PMSF, and 40 mM NaF. Cellular suspensions were freeze-thawed three times and centrifuged at 13,000 × g for 10 min to remove insoluble debris. ATP (final concentration 1.8 mM), iodoacetate (final concentration 150 μM), and fructose 6-phosphate (final concentration 1.8 mM) were added immediately, prior to starting the reaction by incubation at 37 °C, with the iodoacetate serving to inhibit the downstream glycolytic enzyme, glyceraldehyde 3-phosphate dehydrogenase. Reactions were stopped at various times by sampling 20 μl and adding 120 μl 80% methanol. Samples were spun at 13,000 × g for 5 min to remove debris. Internal standard mix (15 μl) was added to an aliquot of the supernatant (120 μl) and the mixture was analyzed for FBP accumulation by LC-MS/MS as described below. The resulting data were fit to a single-exponential curve describing saturable product formation with pseudo first-order kinetics: (This maximally simple curve fit approximates Michaelis-Menten kinetics for a single substrate with Km > [substrate].) The data fit the curve very well for both the infected (R2 = 0.998) and uninfected samples (R2 = 0.993), with Smax values equal to 2.3 ×104 (infected) and 3.5 × 104 (uninfected) and an ~6-fold difference in k (0.0405 ± 0.002 versus 0.0066 ± 0.001), which translates into a corresponding ~4-fold difference in initial velocity given the proportionality of initial velocity to k*Smax. LC-MS/MS instrumentation. LC-MS/MS was performed using an LC-10A HPLC system (Shimadzu, http://www.shimadzu.com) and a Luna aminopropyl column (250 mm × 2 mm with a 5-μm particle size from Phenomenex, http://www.phenomenex.com) coupled to the mass spectrometer. The LC parameters were as follows: autosampler temperature, 4 °C; injection volume, 20 μL; column temperature, 15 °C; and flow rate, 150 μL/min. The LC solvents were Solvent A: 20 mM ammonium acetate + 20 mM ammonium hydroxide in 95:5 water:acetonitrile (pH 9.45); and Solvent B: acetonitrile. The gradients are as follows: positive mode—t = 0, 85% B; t = 15 min, 0% B; t = 28 min, 0% B; t = 30 min, 85% B; t = 40 min, 85% B; and negative mode—t = 0, 85% B; t = 15 min, 0% B; t = 38 min, 0% B; t = 40 min, 85% B; t = 50 min, 85% B. Mass spectrometric analyses were performed on a Finnigan TSQ Quantum Ultra triple-quadrupole mass spectrometer (Thermo Electron Corporation, http://www.thermo.com), equipped with an electrospray ionization (ESI) source. ESI spray voltage was 3,200 V in positive mode and 3,000 V in negative mode. Nitrogen was used as sheath gas at 30 psi and as the auxiliary gas at 10 psi, and argon as the collision gas at 1.5 mTorr, with the capillary temperature 325 °C. Scan time for each SRM transition was 0.1 s with a scan width of 1 m/z. The LC runs were divided into time segments, with the SRM scans within each time segment limited to those compounds eluting during that time interval. For compounds eluting at the boundaries between time segments, the SRM scan corresponding to the compound is conducted in both time segments. The instrument control, data acquisition, and data analysis were performed by the Xcalibar software (Thermo Electron Corporation, version 1.4 SR1), which also controlled the chromatography system. Metabolite data analysis. To quantify metabolite levels, peak heights for 167 different metabolite SRMs were determined for each sample. A metabolite was not analyzed further if: (i) its signal for all viral and mock samples was undetectable; (ii) its maximum signal for all viral or mock samples was less than 5-fold that of the maximum signal in the media-alone plates; or (iii) the observed signal likely resulted from a naturally occurring heavy isotope of another metabolite. The last exclusion was implemented, since, if two compounds have similar retention times and fragmentation patterns, the signal for an abundant compound, e.g., AMP, can be potentially mistaken for a less abundant, slightly heavier compound, e.g., IMP, due to naturally occurring 13C, which makes a proportion of AMP heavier and thus potentially contributes to the signal in the IMP SRM. Of the 167 compounds tested, 63 met criteria for further analysis. Metabolite signal levels represent the average from two different experiments performed in duplicate (n = 4). When signals were undetectable or less than 100 for a given sample, they were assigned a signal level of 100, the approximate level of instrument noise. To approximate intracellular concentrations, metabolite signals from each sample were then normalized to 500 μg of total protein (the approximate amount found in the samples) based on the measured total protein level of the corresponding sample (as determined by the Bradford assay). All four measurements (already protein-normalized) taken from the mock-infected cells at 4 h were then averaged to yield a basal signal for each metabolite (Signalbasal). Fold changes (and their associated SE) relative to this basal signal were then calculated for each sampling time point separately for the viral and mock infection: Array analysis. At 4, 24, 48, or 72 h post-mock or post-HCMV infection, medium was aspirated and RNA was prepared using Trizol as recommended by the manufacturer (Invitrogen, http://www.invitrogen.com) and subsequently purified through an RNAeasy column (Quiagen, http://www1.qiagen.com). Fluorescent cRNA (Cy3 and Cy5) was prepared from all samples, as well as a control RNA set (human universal reference total RNA from Clontech, http://www.clontech.com) using the Low RNA Linear Amplification Kit (Agilent, http://www.agilent.com) as per manufacturer's instructions. Independent duplicate samples were dye reversed and sample cRNA was mixed with alternatively labeled (Cy3 versus Cy5) control cRNA and hybridized to Human Whole Genome Oligo Microarray as per the manufacturer's instructions (Agilent). Slides were scanned with an Agilent Scanner (model number G2505B) and data were extracted with Feature Extractor 7.5 software. The resulting data files were imported into Genespring GX 7.3. The recorded fluorescent values were then subjected to the default per-chip and per-gene Lowess normalization with the cross-gene error model active. Only probe sets whose fluorescent signal was flagged as present or unknown were examined. Probe sets were further filtered by control channel expression; all probes with a cRNA control signal of less than 33 fluorescent units were not analyzed further. Analysis of variance (ANOVA) was then performed on the remaining probes using mock versus HCMV infection as the parameter, and grouping the mock 24-, 48-, and 72-h time points together and the HCMV-infected 24-, 48-, and 72-h time points together. The 4-h time points were left out as there was little change between mock- and HCMV-infected metabolic genes at this time point. The Welsh ANOVA analysis was performed using error model variances and Benjamini/Hochberg multiple testing correction with a p-value cutoff of 0.05. Probe sets as listed in the text and Figure 7 can be linked with corresponding gene information through: http://www.reactome.org. Quantitative real-time PCR. RNA used for qRT-PCR was isolated as described above for array analysis. cDNA synthesis was performed utilizing TaqMan Reverse Transcription reagents with random hexamers as per the manufacturer's protocol (Applied Biosystems, http://www.appliedbiosystems.com). qPCR was performed using the Power Sybr Green PCR Master Mix, 7900HT Sequence Detection, and SDS software version 2.1 (Applied Biosystems) following the manufacturer's instructions. Metabolic transcripts were quantified with specific primer pairs as follows: the phosphofructokinase-1 transcript (A_23_P46928) with 5′ AGCTGTATTCAGAAGAGGGCAAAG 3′ and 5′ CCACCCTGCTGCATGTGA3′; the pyruvate dehydrogenase transcript (A_23_P251095) with 5′ GACGAAAAGGAGGTTGT GCTAAAG 3′ and 5′ CCATTGCCCCCGTAGAAGTT 3′; and the pyruvate kinase transcript (A_32_P147241) with 5′ CCGCCTGGACATTGATTCAC 3′ and 5′ GGGC CAATGGTACAGATGATG 3′. The GAPDH transcript (A_23_P13899) has been shown to remain constant during HCMV infection [39] and its amplification, with the primers: 5′ ACCCACTCCTCCACCTTTGAC 3′ and 5′-CTGTTGCTGTAGCCAAATTCGT 3′ was used for normalization purposes. Relative quantification with a standard curve for each primer pair was performed as described in the ABI prism 7900HT sequence detection system guide (Applied Biosystems). A dissociation curve was generated for each reaction and demonstrated amplification of a single product. Reactions were performed in duplicate, and non-template controls were included for each primer pair. Supporting Information Figure S1 DNA Content of Actively Growing or Quiescent Cells (57 KB PPT) Click here for additional data file. Figure S2 Isotope-Labeled Internal Standard Controls Isotope-labeled internal standards were added to mock- (dotted line) and HCMV-infected (red line) samples prior to measurement as described in Materials and Methods. Average SRM peak heights (n = 4) and the standard error of their means were plotted relative to the mock 4-hr internal standards on a log2 axis. (111 KB PPT) Click here for additional data file. Table S1 Effect of HCMV on Metabolite Levels (49 KB XLS) Click here for additional data file. Table S2 Effect of HCMV on RNA Levels of ATP Syntheses (Mitochondrial Proton Transporting) (46 KB PPT) Click here for additional data file.

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Transcribed dark matter: meaning or myth?

Chris P. Ponting, T Grant Belgard (2010)

Genomic tiling arrays, cDNA sequencing and, more recently, RNA-Seq have provided initial insights into the extent and depth of transcribed sequence across human and other genomes. These methods have led to greatly improved annotations of protein-coding genes, but have also identified transcription outside of annotated exons. One resultant issue that has aroused dispute is the balance of transcription of known exons against transcription outside of known exons. While non-genic 'dark matter' transcription was found by tiling arrays to be pervasive, it was seen to contribute only a small percentage of the polyadenylated transcriptome in some RNA-Seq experiments. This apparent contradiction has been compounded by a lack of clarity about what exactly constitutes a protein-coding gene. It remains unclear, for example, whether or not all transcripts that overlap on either strand within a genomic locus should be assigned to a single gene locus, including those that fail to share promoters, exons and splice junctions. The inability of tiling arrays and RNA-Seq to count transcripts, rather than exons or exon pairs, adds to these difficulties. While there is agreement that thousands of apparently non-coding loci are present outside of protein-coding genes in the human genome, there is vigorous debate of what constitutes evidence for their functionality. These issues will only be resolved upon the demonstration, or otherwise, that organismal or cellular phenotypes frequently result when non-coding RNA loci are disrupted.

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Author and article information

Journal

Journal ID (nlm-ta): Evol Bioinform Online

Journal ID (iso-abbrev): Evol. Bioinform. Online

Journal ID (publisher-id): EVB

Journal ID (hwp): spevb

Title: Evolutionary Bioinformatics Online

Publisher: SAGE Publications (Sage UK: London, England )

ISSN (Electronic): 1176-9343

Publication date (Electronic): 22 February 2019

Publication date Collection: 2019

Volume: 15

Electronic Location Identifier: 1176934319831305

Affiliations

[1 ]Department of Biology, University of Padova, Padova, Italy

[2 ]Aquaculture Biotechnology Research Group, School of Science, Faculty of Health and Environmental Sciences, Auckland University of Technology, Auckland, New Zealand

[3 ]Key Laboratory of Maricultural Organism Disease Control, Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, China

Author notes

[*]Paola Venier, Department of Biology, University of Padova, 35131 Padova, Italy. Email: paola.venier@ 123456unipd.it

[*]

U.R. and T.Y. equally contributed to this work

Author information

Umberto Rosani https://orcid.org/0000-0003-0685-1618

Paola Venier https://orcid.org/0000-0001-9634-3662

Article

Publisher ID: 10.1177_1176934319831305

DOI: 10.1177/1176934319831305

PMC ID: 6388457

PubMed ID: 30828244

SO-VID: 6f6cb15b-b866-4aa4-91be-8c1fede3d41c

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This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).