Abstract

   The giant freshwater prawn, Macrobrachium rosenbergii (M. rosenbergii)
   as an important freshwater aquaculture species with high commercial
   value, exhibited unsynchronized growth. However, the potentially
   metabolic mechanism remains unclear. In this study, we used liquid
   chromatography tandem mass spectrometry (LC-MS/MS) to investigate the
   hepatopancreatic metabolic profiles of twenty giant freshwater prawns
   between the fast-growing group and slow-growing group. In the
   metabolomics assay, we isolated 8,293 peaks in positive and negative
   iron mode. Subsequently, 44 significantly differential metabolites were
   identified. Functional pathway analysis revealed that these metabolites
   were significantly enriched in three key metabolic pathways. Further
   integrated analysis indicated that glycerophospholipid metabolism and
   aminoacyl-tRNA biosynthesis have significant impact on growth
   performance in M.rosenbergii. Our findings presented here demonstrated
   the critical metabolites and metabolic pathways involved in growth
   performance, moreover provided strong evidence for elucidating the
   potentially metabolic mechanism of the unsynchronized growth in M.
   rosenbergii.

1. Introduction

   The giant freshwater prawn, Macrobrachium rosenbergii (M. rosenbergii)
   is one of commercial important species around the world due to the
   special characteristic of nutrition-rich, fast-growing and higher
   economic values. In China, its production was up to 133,300 tons in
   2018, which potentially contributed the most to its global production
   [[40]1]. Similar to numerous crustaceans, M. rosenbergii exhibited
   unsynchronized growth pattern: some individuals grow fast, otherwise
   some are slowly growing. Notably, difference in growth rate was a
   crucial factor significantly affected yields of giant freshwater
   prawns. Over the past decades, large progresses have been made to
   understand the various internal and external factors, as well as the
   genetic factors, those influence individual growth variability in M.
   rosenbergii [[41]2–[42]4]. However, little is known regarding the
   metabolic mechanisms of unsynchronized growth.

   Metabolomics as an analytical approach was applied to detect the
   low-molecular-weight metabolites [[43]5]. While this method provides a
   glimpse of metabolic profiles, biomarkers and metabolic mechanism
   linked with human diseases [[44]6,[45]7], economic traits of plants
   [[46]8] and domestic animals [[47]9]. Also, metabolomics has been
   widely used in toxicity [[48]10,[49]11], sex differentiation [[50]12],
   cold stress [[51]13], flesh quality [[52]14] and adaptation [[53]15],
   moreover, it was widely utilized to growth performance [[54]16,[55]17]
   in aquaculture species. Otherwise, limited researches of metabolomics
   were published in M. rosenbergii. Bose et al. conducted untargeted
   metabolomics of the antennal gland (AnG), and identified several
   metabolites and biosynthetic pathway implicated in endogenous and
   exogenous transport [[56]18]. Dong et al. performed muscle metabolomics
   of M. rosenbergii by treating with different concentration of ammonia-N
   (0, 0.108, 0.324, or 0.54 mg L−1) for 20 days. Subsequently, a list of
   metabolomics pathways related to lipid, carbohydrate, and protein
   metabolism were identified, which was contributed to illustrate the
   mechanisms underlying the effects of ammonia stress in M. rosenbergii
   [[57]11]. Until now, the metabolic profiles and metabolites regarding
   the unsynchronized growth of M. rosenbergii was scarce.

   Therefore, the objective of the present study was used the liquid
   chromatography tandem mass spectrometry (LC-MS/MS) to investigate the
   metabolic profiles of M. rosenbergii, and further to detect the
   differential metabolites between the fast-growing and slow-growing
   groups. As expected, the results we obtained could provide a clue for
   illustrating the metabolic mechanism to understand the unsynchronized
   growth of M. rosenbergii.

2. Materials and methods

2.1. Ethics statement

   All procedures were in compliance with the institutional guidelines and
   under a protocol approved by the Animal Experimental Ethical Inspection
   Form of Guangxi Botanical Garden of Medicinal Plants.

2.2. Animals

   The prawn population was established in the national Macrobrachium
   rosenbergii seed multiplication farm, Nanning, Guangxi, China. A total
   of 20 mating pairs (female: male = 20:20) were constructed to produce
   the progeny stock. In August 2019, family production was finished. The
   subsequent procedure for hatching and rearing was according to Hung et
   al. [[58]19]. Totally, 200 juveniles from each family were randomly
   selected and reared into grow-out ponds. Finally, each of 10 prawns
   from one family with extremely growth performance were chosen as
   fast-growing and slow-growing individuals ([59]Table 1).

Table 1. Growth performance of 20 prawns between fast-growing and
slow-growing groups.

                    Fast-growing group Slow-growing group
   Body weight (g)    10.33±0.93^A        4.87±0.38^B
   Body length (cm)    6.4±0.28^A         5.07±0.15^B
   [60]Open in a new tab

2.3. Sample preparation

   The hepatopancreas of each sample from fast-growing and slow-growing
   groups was immediately dissected, and then stored in liquid nitrogen.
   Sample preparation for LC-MS/MS analysis was conducted as previously
   described by Want et al. [[61]20]. Briefly, samples of 100 mg were
   mixed with 1 mL chilled extraction liquid (methanol: water = 4:1, vol:
   vol) containing 2-Chloro-L-phenylalanine (Shanghai Hanhong Scientific
   Co.,Ltd.) as internal standard, vortexed for 30 s, and homogenized to
   extract the compounds from the hepatopancreas. Then, the homogenate was
   further ultrasonically treated in ice bath for 3 min, and deproteinized
   through centrifugation at 4°C (12,000 rpm, 10 min). The supernatant was
   subsequently transferred into a new microcentrifuge tube and
   lyophilized. The dried samples were reconstructed with chilled
   methanol/water (4:1, v: v) for further process.

2.4. LC–MS/MS analysis

   Briefly, the LC-MS/MS experiments were performed on the Dionex UltiMate
   3000 Uhplc system coupled with Q Exactive mass spectrometer (Thermo
   Fisher Scientific, CA, USA) operating in data-dependent acquisition
   (DDA) mode. Samples were injected onto a Hypersil GOLD HPLC column
   (50×2.1 mm, 1.9 μm). The mobile phase consisted of a gradient system of
   (A) 10 mM ammonium formate in water and (B) 10 mM ammonium formate in
   methanol: 0–2 min, 5% B; 2–5 min, 5–30% B; 5–19 min, 30–99% B; 19–22
   min, 99% B; and 22.1–25 min, 5% B.

   Compound ionization was conducted as the following parameters:
   Q-Exactive mass spectrometer was operated in positive/negative polarity
   mode with spray voltage 3.5 kV/3.2 kV, capillary temperature of 320°C,
   sheath gas flow rate 30 psi and aux gas flow rate 10 arb. Samples were
   analyzed using liquid chromatography-high resolution mass spectrometry
   (LC-HRMS) in full scan + data dependent MS2 mode with a scan range from
   100–1000 m/z at a resolution of 70,000, followed by data dependent
   MS/MS (dd-MS/MS) with a normalized collision energy of 30 and at a
   resolution of 17,500. To avoid instrument drift, fourteen quality
   control (QC) samples were preprocessed as the samples for data quality
   assessment.

2.5. Data analysis

   Data processing including peak alignment, retention time correction,
   and peak area extraction was conducted by commercially available
   software, Compound Discoverer v. 3.0 (Thermo Fisher Scientific, CA,
   USA). The identified metabolites were searched against mzCloud and
   ChemSpider database. For multivariate statistical analysis, principal
   component analysis (PCA) and orthogonal partial least-squares
   discriminant analysis (OPLS–DA) were performed to detect the metabolic
   variations between the two experimental groups through the SIMCA-P
   v.14.1 software (Umetrics, Umeå, Sweden) after pareto (Par) scaling.
   The quality of OPLS-DA model was assessed based on the cumulative
   parameters R^2X, R^2Y, and Q^2 in cross-validation, and applying a
   permutation test with 200 permutations.

   Significantly differential metabolites of the pairwise comparisons were
   identified with VIP score > 1 obtained from OPLS-DA model and p < 0.05
   from Student’s t test. Hierarchical cluster analysis (HCA) was
   performed via TBtools software v.1.046 [[62]21]. Afterward, pathway
   enrichment analysis of identified metabolites was carried out through
   MetaboAnalyst v.4.0 software [[63]22]
   ([64]https://www.metaboanalyst.ca/).

3. Results

3.1. Overall data set and metabolic profiles

   In total, 5,589 and 2,704 peaks were detected in positive and negative
   ion modes, respectively. Subsequently, after further quality control
   filtering, 1,254 and 222 peaks were retained for parallel analyses.

   To characterize the variations in the metabolic profiles of
   M.rosenbergii between the fast-growing group and slow-growing group,
   PCA and OPLS-DA were conducted.

   As shown in [65]Fig 1A and 1B, apparent separation was observed of the
   20 prawns between fast-growing and slow-growing groups. The percentage
   of explained value in the metabolomics analysis of PC1 and PC2 was
   28.2% and 18.3% (positive ion mode), 45.8% and 16.0% (negative ion
   mode), respectively. Subsequently, a further examination based on the
   OPLS-DA score plot showed a clear separation between the two groups. In
   the positive (negative) ion mode, the parameters considered for
   classification from the software were R^2X (cum) = 0.303, R^2Y (cum) =
   0.860, Q^2 (cum) = 0.528 (R^2X (cum) = 0.608, R^2Y (cum) = 0.704, Q^2
   (cum) = 0.467) ([66]Fig 1C and 1D). Subsequently, model
   cross-validation through permutation tests (200 times) generated
   intercepts of R^2 and Q^2 (positive ion mode, 0.644 and -0.478;
   negative ion mode, 0.573 and -0.433, respectively) ([67]Fig 2). Herein,
   all of which confirmed that the OPLS-DA model was stable and not
   over-fitted. Taken together, multivariate analyses (PCA and OPLS-DA)
   demonstrated a clear and significant separation of the fast-growing
   group versus slow-growing group.

Fig 1. PCA (A and B, in positive and negative modes, respectively) and
OPLS-DA (C and D, positive and negative modes, respectively) scores plots
based on LC-MS/MS data of hepatopancreas samples from Fg (red) and Sg
(green).

   [68]Fig 1
   [69]Open in a new tab

Fig 2. OPLS-DA permutation test for positive (A) and negative ion mode (B).

   [70]Fig 2
   [71]Open in a new tab

3.2. Significantly differential metabolites

   On the basis of the OPLS-DA results, a total of 44 (36 in positive ion
   mode and 8 in negative ion mode) significantly differential metabolites
   (SDMs) were identified (VIP > 1, p < 0.05) between the fast-growing and
   slow-growing groups ([72]Table 2), and MS/MS spectrums of seven
   representative SDMs were listed in additional file 1. Among the 44
   SDMs, 11 and 33 metabolites were significantly up-regulated and
   down-regulated compared with those in slow-growing group. Hierarchical
   clustering analysis also indicated that each type of the two groups
   exhibited a distinct metabolic pattern ([73]Fig 3).

Table 2. Significantly differential metabolites for 20 prawns between
fast-growing and slow-growing groups.

   Significantly differential metabolites identified in positive ion mode
   No. Metabolites MW(Da) RT(min) Log[2]FC P-value VIP
   1 LysoPC(0:0/18:2(9Z,12Z)) 519.332 17.303 -1.33 0.0014 13.8337
   2 LysoPC(0:0/18:1(9Z)) 521.348 17.922 -0.78 0.0144 9.9551
   3 LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)) 541.316 16.71 -1.81 0.0010 7.7474
   4 LysoPC(0:0/16:0) 495.332 17.665 -0.99 0.0351 4.7933
   5 LysoPC(18:3(9Z,12Z,15Z)) 517.316 16.704 -1.58 0.0012 4.6793
   6 PC(14:0/18:1(9Z)) 731.545 21.13 0.26 0.0086 3.9071
   7 LysoPC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)) 567.331 17.471 -1.12 0.0004
   3.7332
   8 Methyl eicosapentaenoate 316.240 18.56 -0.66 0.0479 3.4406
   9 Glycerophosphocholine 257.102 0.458 -0.78 0.0324 3.3007
   10 [L]-Arginine 174.112 0.487 -0.53 0.0250 3.2579
   11 LysoPC(16:1(9Z)) 493.316 16.885 -0.64 0.0353 3.1972
   12 15,16-Dihydrobiliverdin 584.263 14.911 -2.56 0.0022 2.9967
   13 4-Methoxycinnamic acid 178.063 4.534 0.23 0.0436 2.7188
   14 Retinal 284.214 18.555 -0.67 0.0390 2.2772
   15 LysoPC(22:5(7Z,10Z,13Z,16Z,19Z)) 569.347 17.58 -1.33 0.0020 2.2174
   16 PE(16:0/22:1(13Z)) 773.592 21.839 0.41 0.0480 2.1821
   17 LysoPC(14:0) 467.301 16.539 -1.28 0.0002 2.0772
   18 LysoPE(0:0/20:5(5Z,8Z,11Z,14Z,17Z)) 499.269 16.673 -1.22 0.0138
   2.0684
   19 Adenosine 267.097 2.057 -1.94 0.0235 2.0605
   20 LysoPC(20:2(11Z,14Z)) 547.363 18.217 -1.47 0.0016 1.9187
   21 [L]-Proline 115.063 0.506 -0.54 0.0468 1.7536
   22 [L]-Palmitoylcarnitine 399.334 17.702 -0.79 0.0435 1.6945
   23 Nupharamine 251.188 13.506 1.14 0.0053 1.6195
   24 Alanyl-Leucine 202.132 4.516 0.38 0.0283 1.6171
   25 LysoPC(20:3(8Z,11Z,14Z)) 545.347 17.707 -1.42 0.0018 1.5545
   26 LysoPE(18:2(9Z,12Z)/0:0) 477.285 17.238 -1.17 0.0242 1.4848
   27 LysoPE(0:0/20:1(11Z)) 507.332 17.434 -0.94 0.0306 1.2884
   28 LysoPE(0:0/18:0) 481.316 17.138 -1.32 0.0021 1.2403
   29 LysoPE(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0) 525.285 17.397 -1.29 0.0095
   1.2357
   30 Primidolol 333.169 6.939 0.45 0.0319 1.2201
   31 trans-Hexadec-2-enoyl carnitine 397.319 16.896 -0.88 0.0491 1.1108
   32 Codonopsine 267.147 9.468 1.52 0.0193 1.0629
   33 Oxidized glutathione 612.151 0.434 -0.82 0.0189 1.0456
   34 Valyl-Valine 216.147 5.706 0.27 0.0229 1.0404
   35 Adenosine monophosphate 347.063 0.629 -3.3 0.0446 1.0208
   36 LysoPC(18:4(6Z,9Z,12Z,15Z)) 515.301 16.117 -1.78 0.0048 1.0113
   Significantly differential metabolites identified in negative ion mode
   No. Metabolites MW(Da) RT(min) Log[2] FC P-value VIP
   1 Neotame 378.217 14.489 1.28 0.0015 5.4073
   2 CP 47,497-C6-Homolog 304.239 17.776 -0.11 0.0301 2.1848
   3 N-Arachidonoyl dopamine 439.304 18.267 -1.05 0.0172 1.7745
   4 Bilirubin 584.261 14.917 -2.29 0.0022 1.7004
   5 [L]-Histidine 155.068 0.505 -0.41 0.0077 1.6767
   6 5-Chloro AB-PINACA 364.165 10.048 0.93 0.0107 1.2014
   7
   3-Methyl-4-[2-(4-morpholinophenyl)hydrazono]-1-phenyl-4,5-dihydro-1H-py
   razol-5-one 363.170 10.633 0.49 0.0161 1.1153
   8 L-Malic acid 134.020 0.455 -0.36 0.0471 1.0751
   [74]Open in a new tab

Fig 3. Heatmap of significantly differential metabolites between the
fast-growing and slow-growing groups.

   [75]Fig 3
   [76]Open in a new tab

3.3. Metabolic KEGG pathway analysis

   To explore potentially metabolic pathways affected by different growth
   performances, pathway analysis of 44 SDMs were performed by
   MetaboAnalyst 4.0. Functional analysis revealed that the metabolites
   those significantly difference were involved in glycerophospholipid
   metabolism, aminoacyl-tRNA biosynthesis and linoleic acid metabolism
   ([77]Fig 4). The putative compounds hit LysoPC
   (20:5(5Z,8Z,11Z,14Z,17Z)) (LysoPC), PC (14:0/18:1(9Z)) (PC),
   Glycerophosphocholine (GPC), PE (16:0/22:1(13Z)) (PE), [L]-histidine,
   [L]-arginine and [L]-proline.

Fig 4. Significant metabolic pathways for 44 SDMs.

   Fig 4
   [78]Open in a new tab

4. Discussion

   In the past decades, unsynchronized growth of M. rosenbergii have
   caused severe productive and economic losses, while the potentially
   metabolic mechanism behind the phenomenon remains unclear.
   Hepatopancreas as the major organ, is implicated in carbohydrate and
   energy metabolism, protein and lipid synthesis [[79]23]. Moreover, it
   plays an important role in the synthesis and secretion of digestive
   enzymes, nutrient absorption, digestion, reserve storage and
   mobilization [[80]24]. Accumulating evidence have shown that
   hepatopancreas has significant impact on crustacean growth [[81]25].
   Thus, in the present study, we performed hepatopancreatic metobolomics
   of giant freshwater prawns with different growth performance between
   the fast-growing group and slow-growing group based on the LC-MS/MS. To
   our knowledge, this study was the first investigation to identify the
   key metabolites and pathways implicated in growth performance, which
   will provide novel insights into understanding of metabolic mechanism
   underlying the unsynchronized growth in M. rosenbergii.

   Of note, combining our data and KEGG database, a comprehensive scheme
   that controlling growth performance of M. rosenbergii is proposed, as
   shown in [82]Fig 5. We speculated that in the hepatopancreas,
   glycerophospholipid metabolism might affect the physiological functions
   of cells and membranes, moreover provide energy for aminoacyl-tRNA
   biosynthesis and amino acid biosynthesis via fatty acid degradation and
   oxidation in response of different growth performance in M.
   rosenbergii.

Fig 5. Hypothesized pathway of the different growth performances of M.
rosenbergii.

   [83]Fig 5
   [84]Open in a new tab

   Up-regulated and down-regulated metabolites are shown in red or green
   letters. Three significant pathways are shown in red boxes, two
   pathways not significantly enriched are shown in blue boxes.
   Abbreviations: PE, PE (16:0/22:1(13Z)); PC, PC (14:0/18:1(9Z)); LysoPC,
   LysoPC (20:5(5Z,8Z,11Z,14Z,17Z)); GPC, Glycerophosphocholine.

4.1. Glycerophospholipid metabolism and linoleic acid metabolism

   Amongst, the elements of glycerophospholipid metabolism, including
   LysoPC (20:5(5Z,8Z,11Z,14Z,17Z)), PC (14:0/18:1(9Z)),
   Glycerophosphocholine and PE (16:0/22:1(13Z)) were significantly
   altered between the two groups. It has been reported that PC and PE are
   the main lipid constituents of cell membranes, which play critical
   roles in functioning well of cells [[85]26]. Decreasing the PC content
   affected the integrity of liver cells and mitochondrial membrane, thus
   directly leading to proliferation, differentiation and apoptosis
   [[86]27]. LysoPC is the main component of low density lipoprotein.
   Generally, the content of LysoPC was low occurred in cells or tissues,
   high concentrations could damage the membrane system of cells [[87]28].

   Glycerophosphocholine is produced by lysoPC [[88]29], which was
   response for cell viability and motility [[89]30]. Some studies have
   proposed that higher content of GPC acted as an indicator of cancer
   progression [[90]31]. Notably, PC was involved in linoleic acid
   metabolism. Interestingly, in recent prior studies, researchers have
   observed that glycerophospholipid metabolism and linoleic acid
   metabolism have closed relationship with energy metabolism via
   β-oxidation [[91]32,[92]33]. The present study investigated that PC and
   PE, LysoPC and GPC were shown down-regulated and up-regulated in
   fast-growing group, respectively. Overall, the data could interpret
   that high concentration of PC and PE, low concentration of LysoPC and
   GPC was essential for different growth performance.

4.2. Aminoacyl-tRNA biosynthesis

   For the aminoacyl-tRNA biosynthesis, previous researches have observed
   that it is an important metabolic pathway before amino acid
   biosynthesis [[93]34]. In this study, three amino acids including
   [L]-histidine, [L]-arginine, [L]-proline those identified in the
   metabolic pathway showed down-regulated in fast-growing group.

   Zhao et al. demonstrated that dietary histidine level affected the
   growth performance, body composition of juvenile Jian carp [[94]35].
   Similarly, Zehra et al. also confirmed that dietary histidine level has
   positive effects on the growth performance, protein deposition and
   carcass composition [[95]36]. [L]-Arginine (Arg) is synthesized from
   glutamine, glutamate and proline. It has demonstrated that the
   concentration of Arg in hepatocytes was very low [[96]37]. It has been
   found that dietary arginine contributed to the growth performance,
   immunity and health status of broiler chicks [[97]38]. [L]-Proline as
   an essential precursor for the synthesis of proteins most publications
   have paid attention to proline on plants [[98]39,[99]40]. It was
   indicated that proline displays remarkable role on plant growth and
   development under non-stress or stress conditions, but the role of
   proline on growth was varied [[100]41].

   Meanwhile, the three amino acids have favorable roles in formation of
   peptide chains. Moreover, Wang et al. proposed that aminoacyl-tRNA
   biosynthesis was significantly associated with the growth of D. similis
   [[101]34], which was in agreement with previous finding of Yebra et al.
   [[102]42]. All in all, high contents of [L]-histidine, [L]-arginine,
   [L]-proline might disrupt the homeostasis of hepatopancreas, and
   perturb the transport process to the intestine or plasma.

5. Conclusions

   In summary, we investigated the metabolic profiles of hepatopancreas
   between the fast-growing group and slow-growing group in M. rosenbergii
   based on the LC-MS/MS, and identified 44 significantly differential
   metabolites. Integrated analysis of key metabolic pathways showed that
   glycerophospholipid metabolism and aminoacyl-tRNA biosynthesis played
   crucial role in response to unsynchronized growth. Notably, seven
   metabolites, consist of LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)),
   PC(14:0/18:1(9Z)), Glycerophosphocholine, PE(16:0/22:1(13Z)),
   [L]-histidine, [L]-arginine and [L]-proline, were strongly correlated
   with growth performance. The results obtained in our study demonstrated
   the critical pathways and metabolites to decipher the potential
   metabolic mechanism of the unsynchronized growth in M. rosenbergii.

Supporting information

   S1 Fig. MS/MS spectrum of PC(14:0/18:1(9Z)).

   (DOCX)
   [103]Click here for additional data file.^ (19.8KB, docx)
   S2 Fig. MS/MS spectrum of LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)).

   (DOCX)
   [104]Click here for additional data file.^ (21.5KB, docx)
   S3 Fig. MS/MS spectrum of Glycerylphosphorylcholine.

   (DOCX)
   [105]Click here for additional data file.^ (22.6KB, docx)
   S4 Fig. MS/MS spectrum of PE(16:0/22:1(13Z)).

   (DOCX)
   [106]Click here for additional data file.^ (20.9KB, docx)
   S5 Fig. MS/MS spectrum of [L]-Histidine.

   (DOCX)
   [107]Click here for additional data file.^ (21.9KB, docx)
   S6 Fig. MS/MS spectrum of [L]-Arginine.

   (DOCX)
   [108]Click here for additional data file.^ (22.5KB, docx)
   S7 Fig. MS/MS spectrum of [L]-Proline.

   (DOCX)
   [109]Click here for additional data file.^ (25.1KB, docx)

Data Availability

   All relevant data are within the manuscript and its [110]Supporting
   information files.

Funding Statement

   This work was financially supported by the Natural Science Foundation
   of Guangxi, grant number 2018GXNSFBA281209; Science and Technology
   Major Project of Guangxi, grant number Guike AA17204080-6; and
   Postdoctoral Science Foundation of Guangxi, grant number T3340097968.
   The funders had no role in study design, data collection and analysis,
   decision to publish, or preparation of the manuscript.

References