ABSTRACT

   Hepatectomy is a common clinical procedure for the treatment of many
   liver diseases, and the successful recovery of a patient’s liver
   metabolism and function after surgery is crucial for a good prognosis.
   The objective of this study was to elucidate the metabolic response to
   hepatectomy using high-throughput sequencing analysis of 16S rRNA gene,
   metabolomics, and proteomics data. Fecal and serum samples from beagle
   dogs were collected on day 0 (LH0), day 7 (LH7), and day 28 (LH28)
   after laparoscopic partial hepatectomy. Liver tissue samples were taken
   on LH0 and LH7. Dysbiosis in the fecal microbiota was explored, and
   host-microbiome interactions based on global metabolic and protein
   profiles and inflammatory processes were determined. Results showed
   that the relative abundance of Allobaculum and Turicibacter was
   decreased and that of Escherichia-Shigella was increased after
   hepatectomy (P < 0.05); the phenylalanine, tyrosine, and tryptophan
   biosynthetic pathway, along with the phenylalanine and aminoacyl-tRNA
   biosynthetic pathway, was significantly associated with liver injury.
   The serum metabolites l-phenylalanine and l-arginine were useful as
   biomarkers, and the fecal metabolite l-threonine was a signature target
   monitor for liver recovery. The proteomics profile revealed 412
   significantly different proteins and further highlighted two key
   signaling pathways (mitogen-activated protein kinase [MAPK] and
   peroxisome proliferator-activated receptor [PPAR]) involved in the
   response to liver injury. We systematically explored the metabolic
   mechanism of liver injury and recovery, providing new insights into
   effective ways to promote recovery after hepatectomy and improve liver
   function and long-term survival. These fundamental studies on
   hepatectomy will provide the basis for future advances in treatment and
   recovery from common liver diseases.

   IMPORTANCE As the largest parenchymal organ, the liver is a target for
   bacterial and viral infections, nonalcoholic fatty liver disease
   (NAFLD), cirrhosis, cancer, and many other diseases, constituting a
   serious worldwide problem. The treatment for many of these diseases
   involves hepatectomy. Here, we show that aberrant inflammatory
   processes after hepatectomy of the liver as reflected in the
   association between liver metabolism and gut microbiota create a grave
   risk. This study investigated the mechanisms of gut microbiota and host
   metabolism involved in liver injury and recovery after hepatectomy,
   using proteomics to reveal the mechanisms of postoperative liver injury
   and a comprehensive multi-omics approach to identify changes in
   metabolism after hepatectomy.

   KEYWORDS: hepatectomy, gut microbiology, metabolomics, proteomics

INTRODUCTION

   Liver disease and its complications are one of the leading causes of
   illness and death worldwide. In past decades, hepatectomy has been
   established as a safe primary treatment for various benign and
   malignant diseases of the liver ([34]1). Until now, accumulated
   advances in research have led to significant achievements in
   understanding the characteristics and progress of hepatectomy, as a
   result of tumors, cirrhosis, and liver failure. Sahay et al. ([35]2)
   established an acute liver failure model in rats by performing 70%
   partial hepatectomy (PHx) and consequently evaluated the therapeutic
   potential of cells in bridging the gap between acute liver failure and
   liver transplantation. The PHx rat model has been widely used since
   then to study the involvement of regeneration in the recovery mechanism
   from acute liver injury ([36]3). Hepatic resection is characterized by
   a catabolic state, in which the body’s metabolism as reflected in serum
   and urine is significantly altered ([37]4). These injury-induced
   metabolic changes may adversely affect liver regeneration as well as
   functional recovery, and even the functions of other organs. Although
   several studies have focused on the central role of the liver in
   metabolic regulation, the mechanisms of interaction of inflammation,
   microbiota, hepatic metabolism, and liver regeneration after
   hepatectomy are poorly understood. It is important, therefore, to
   explore the mechanisms of metabolic changes in response to hepatectomy,
   with the goal of developing novel therapeutic regimens with better
   efficacy and safety for reducing liver injury and disease.

   The intestinal microbiota have widely been considered to play a key
   role in preserving liver homeostasis. Current research has focused on
   the integrative functional and physiological connections between the
   liver and the intestine, which are known as the hepatic-intestinal axis
   ([38]5). Preclinical studies showed that intestinal microbiota, acting
   through the gut-liver axis, could significantly reduce liver injury and
   enhance regeneration through their influence on proliferation and
   metabolism ([39]6). Other studies confirmed the correlation between
   changes in the composition and activity of the gut microbial population
   and the progression of liver diseases such as alcoholic and
   nonalcoholic fatty liver disease (NAFLD), steatohepatitis, cirrhosis,
   and hepatocellular carcinoma ([40]7). Conversely, the liver also exerts
   an influence over intestinal microbial communities via secretion of
   bile acids and IgA antibodies ([41]8). Interestingly, some studies have
   found that liver ischemia-reperfusion injury during surgery can cause
   changes in intestinal communities, resulting in decreased abundance of
   Lactobacillus, Bifidobacterium, and Bacteroides and increased abundance
   of Enterococcus and Enterobacteriaceae ([42]9). Lactobacillus,
   Bifidobacterium, and Bacillus mimicus have been found to be closely
   associated with liver injury and regeneration. Specifically,
   Lactobacillus rhamnosus was found to correct intestinal dysbiosis and
   reduce liver fat accumulation and cellular inflammatory responses in
   mice with alcoholic liver injury ([43]10). Collectively, gut microbes
   play a significant role in the etiology of hepatic diseases and the
   processes of liver injury and repair. However, a comprehensive picture
   of the underlying microbial interactions and the associated
   posthepatectomy mechanisms remains elusive.

   With the development of advanced bioinformatics analysis of the
   microbiome through proteomics and metabolomics, the functional activity
   of the gut microbial community and the mechanisms involved in metabolic
   change can now be more fully understood ([44]11). Thus, this holistic,
   integrative, multi-omics approach can give us a clearer picture of how
   changes in the level of metabolites after hepatectomy can be applied in
   diagnosing liver injury and monitoring the recovery progress. In the
   present study, we integrated data on the microbiota, proteome, and
   metabolome to clarify the underlying mechanisms of hepatectomy and
   establish a cross-sectional framework that will provide a useful
   reference for future exploration of new therapies for liver disease.

RESULTS

Partial hepatectomy modeling.

   In this study, the four-trocar method (two 5-mm trocars and two 10-mm
   trocars) was used to successfully establish a canine model of
   laparoscopic left hemihepatectomy in eight dogs. All the dogs recovered
   from anesthesia within 30 min of the completion of the surgery, and no
   major intraoperative or postoperative complications occurred. The total
   length of the incision was 6.7 ± 0.54 cm, the average operative time
   was 102.53 ± 9.07 min, and the estimated blood loss was 32.10 ± 6.43 mL
   (P < 0.05). No complications occurred during the entire experiment, and
   none of the dogs manifested weight loss by 1 month after surgery.

Changes in liver function and inflammatory indices.

   Compared to before surgery (LH0), the serum aspartate transferase (AST)
   and alanine transaminase (ALT) levels were increased on LH1, LH3, and
   LH7 (P < 0.01) and then gradually returned to preoperative levels by
   LH28 ([45]Table 1). The serum tumor necrosis factor alpha (TNF-α)
   concentration showed similar changes, while serum interleukin-1β
   (IL-1β) and IL-6 concentrations showed significant increases on LH1,
   LH3, and LH7 (P < 0.01) and then decreased on LH14 and LH28
   ([46]Table 1). In summary, serum levels of TNF-α, IL-1β, and IL-6 were
   increased significantly at LH1, and the difference was extremely
   significant compared with LH0 (P < 0.01); levels continued to increase
   and reached their highest point at LH3. In contrast, serum levels of
   the anti-inflammatory factors IL-4 and IL-10 were significantly lower
   on LH1, LH3, and LH7 (P < 0.01) and then tended to increase, gradually
   returning to preoperative levels by LH28.

TABLE 1.

   Serum enzymes of liver function and inflammatory cytokines[47]^a
   Enzyme Preop LH1 LH3 LH7 LH14 LH28
   AST (IU/L) 19.6 ± 2.31 260.61 ± 300.88** 49.49 ± 5.78** 27.59 ± 2.36
   23.54 ± 1.85 19.42 ± 1.95
   ALT (U/L) 28.28 ± 8.42 294.83 ± 730.84** 210.31 ± 410.47**
   41.61 ± 26.29 30.72 ± 15.15 31.99 ± 9.62
   TNF-α (pg/mL) 42.54 ± 4.40 61.69 ± 5.62** 71.14 ± 5.32** 51.78 ± 2.33
   46.03 ± 3.74 40.32 ± 3.79
   IL-1β (pg/mL) 27.01 ± 1.30 34.32 ± 2.42** 45.36 ± 2.55** 31.81 ± 1.44**
   28.30 ± 2.80 29.11 ± 1.41
   IL-6 (pg/mL) 87.33 ± 4.51 131.58 ± 1.30** 155.29 ± 4.34**
   121.25 ± 7.13** 105.38 ± 4.56* 85.63 ± 2.47
   IL-4 (pg/mL) 14.36 ± 1.17 10.28 ± 0.17** 7.82 ± 0.28** 11.94 ± 0.62**
   14.05 ± 0.39* 14.58 ± 2.09
   IL-10 (pg/mL) 22.56 ± 1.24 15.50 ± 0.46** 11.66 ± 0.67** 16.74 ± 0.55**
   18.90 ± 0.38** 23.27 ± 1.74
   [48]Open in a new tab
   ^a

   Data shown as mean ± standard deviation, n = 8. Preop, preoperation;
   POD, postoperative day. Compared with preoperation, *, P < 0.05; **, P
   < 0.01. AST, aspartate transferase; ALT, alanine transaminase; TNF,
   tumor necrosis factor; IL, interleukin.

Gut microbiota altered after hepatectomy.

   To investigate the changes in diversity of gut microbiota in response
   to hepatectomy, we first estimated alpha diversity in fecal samples by
   calculating four important parameters (Sobs, Chao, Shannon, and
   Simpson). Compared to LH0 (n = 8), the Sobs (P < 0.05) and Chao (P <
   0.01) indices were significantly decreased on LH7 ([49]Fig. 1A and
   [50]B), indicating a decrease in the level of intestinal microbiota
   richness after hepatectomy. It is worth noting that the higher Shannon
   index and the lower Simpson index indicated high species diversity; the
   Shannon index ([51]Fig. 1C) was decreased and the Simpson index
   ([52]Fig. 1D) was increased on LH7, indicating a decrease in intestinal
   microbiota diversity after hepatectomy. However, on LH28, all four
   indices show a gradual recovery back to LH0 levels. The changes in the
   above indices indicated that the abundance and diversity of intestinal
   microbiota in beagle dogs declined up to LH7 and recovered by LH28
   ([53]Fig. 1A to [54]D). We used principal-coordinate analysis (PCoA)
   based on the Bray-Curtis metric to examine the community structures of
   the gut microbiota of the dogs before and after hepatectomy
   ([55]Fig. 1E, R = 0.3683, P = 0.001). Bacterial communities at LH0 and
   LH28 were clearly separated from LH7 along principal-coordinate axis 1
   (PC1) (P = 0.001), indicating that the composition of the gut
   microbiota was different ([56]Fig. 1E). This finding suggests that
   hepatectomy had significant effects on the microbiota composition.

FIG 1.

   [57]FIG 1
   [58]Open in a new tab

   (A to D) Alpha diversity indices at different time points before and
   after surgery. (A) Sobs index; (B) Chao index; (C) Shannon index; (D)
   Simpson index. LH0, the morning before surgery; LH7, day 7 after
   surgery; LH28, day 28 after surgery. (E) Principal-coordinate analysis
   (PCoA) of microbial communities. Circle, rhombus, and triangle
   represent the gut microbiota of the morning before surgery (LH0), day 7
   after surgery (LH7), and day 28 after surgery (LH28), respectively.
   Distances between symbols on the ordination plot reflect relative
   dissimilarities in community composition. (F) Relative abundances of
   different bacteria at the phylum level of each group. (G) Relative
   abundances of different bacteria at the genus level of each group. (H)
   LEfSe identified the differences in most abundant taxa between LH0 and
   LH7. The enriched taxa on LH0 are indicated with a positive LDA score
   (red), while the taxa enriched on LH7 have a negative score (blue).
   Only the taxa having a P value of <0.01 and an LDA score of >4.0 are
   shown. (I) Heatmap of the Spearman correlations between the top 10 most
   abundant genera and clinical parameters such as inflammatory cytokines
   TNF-α, IL-1β, IL-4, IL-6, and IL-10 and liver function indices ALT and
   AST. *, 0.01 < P ≤ 0.05; **, 0.001 < P ≤ 0.01; ***, P ≤ 0.001.

   We further investigated the specific changes in the microbiota after
   hepatectomy by assessing the relative abundance of taxa ([59]Fig. 1F).
   For differential abundance comparison analysis at the phylum level, the
   relative abundance of Proteobacteria was significantly increased on LH7
   (P < 0.01) relative to LH0 ([60]Table 2). At the genus level, the
   bacterial taxa that displayed differential abundance on LH7 and LH28
   relative to LH0 are shown in [61]Fig. 1G. The relative abundance of
   Peptoclostridium decreased after hepatectomy, and the difference
   between LH7 and LH0 was very significant (P < 0.01), while the
   difference between LH28 and LH0 was significant (P < 0.05). Compared to
   LH0, the most common opportunistic pathogens in the gut
   (Escherichia-Shigella) were overrepresented on LH7 (P < 0.01);
   conversely, compared to LH0, the relative abundance of Turicibacter and
   Allobaculum decreased on LH7 (P < 0.05) ([62]Table 3).

TABLE 2.

   Relative abundance and diversity test of the top five gut bacterial
   groups at the phylum level before and after surgery[63]^a
       Phylum        LH0 (%)        LH7 (%)       LH28 (%)    P value
   Firmicutes     83.53 ± 14.88 54.56 ± 29.32   71.52 ± 15.97 0.31
   Actinobacteria 10.09 ± 6.99  13.27 ± 9.44    22.47 ± 11.27 0.31
   Proteobacteria 0.11 ± 0.06 b 30.58 ± 25.39 A 0.19 ± 0.31 b 0.03
   Fusobacteria   0.99 ± 1.92   0.74 ± 1.48     4.91 ± 6.91   0.31
   Bacteroidetes  5.25 ± 11.66  0.81 ± 0.84     0.81 ± 0.32   0.66
   [64]Open in a new tab
   ^a

   Data are shown as mean ± standard deviation, n = 6. The values of the
   same data at different time points were compared from high to low. If
   two values have the same following letters (except for case), no
   significant difference was found (P > 0.05). If the letters are not the
   same, there is a significant difference. If the letter is uppercase,
   there is a significant difference (P < 0.05), and if the letter is
   lowercase, there is a very significant difference (P < 0.01).

TABLE 3.

   Relative abundance and diversity test of the top 10 gut bacterial
   groups at the genus and family level before and after surgery[65]^a
   Taxon level LH0 (%) LH7 (%) LH28 (%) P value
   Genus
    Peptoclostridium 22.44 ± 17.13 A 4.34 ± 3.71 b 6.25 ± 3.38 B 0.007
    Collinsella 10.10 ± 7.21 8.20 ± 4.84 16.86 ± 10.39 0.29
    Blautia 11.45 ± 7.61 3.86 ± 2.92 10.25 ± 7.34 0.08
    Escherichia-Shigella 0.05 ± 0.05 b 37.13 ± 22.21 A 0.12 ± 0.18 b 0.007
    Romboutsia (%) 2.83 ± 1.81 10.46 ± 9.36 5.07 ± 2.95 0.26
    Turicibacter 9.11 ± 6.87 A 0.17 ± 0.19 B 6.57 ± 5.76 AB 0.007
    Allobaculum 6.25 ± 2.95 A 0.41 ± 0.87 B 7.26 ± 5.63 A 0.007
    Lactobacillus 0.57 ± 1.03 10.58 ± 12.53 4.68 ± 6.50 0.11
   Family
    Erysipelotrichaceae, no rank 0.59 ± 0.85 b 0.40 ± 0.39 b 6.99 ± 2.34 A
   0.007
    Lachnospiraceae, no rank 1.02 ± 0.69 2.98 ± 3.66 1.20 ± 0.49 0.70
   [66]Open in a new tab
   ^a

   Data are shown as mean ± standard deviation, n = 6. Significance is
   shown similarly to [67]Table 2.

   Next, we applied linear discriminant analysis (LDA) coupled with effect
   size (LEfSe) to explore the significance of changes in, and relative
   enrichment of, bacterial communities on LH0 and LH7. The LEfSe analysis
   revealed a significant increase in Escherichia-Shigella, Ruminococcus,
   and Enterococcus and a substantial reduction in Allobaculum,
   Turicibacter, Prevotella, and Faecalibacterium on LH7 relative to LH0
   ([68]Fig. 1H).

Associations between clinical parameters and gut microbiota.

   We investigated the correlation between the relative abundance of the
   top 10 most abundant genera that showed significant differences in LH7
   and the clinical parameters ([69]Fig. 1I). Allobaculum and Turicibacter
   were significantly negatively correlated with AST, ALT, TNF-α, IL-1β,
   and IL-6, and positively correlated with IL-10 and IL-4, whereas
   Escherichia-Shigella was the opposite. These findings suggest that the
   changes in liver function and inflammatory response caused by
   hepatectomy were closely related to the relative abundance of
   Allobaculum, Turicibacter, and Escherichia-Shigella. When the liver is
   damaged and an inflammatory response occurs, the relative abundance of
   Allobaculum and Turicibacter is decreased, while Escherichia-Shigella
   proliferation is increased ([70]Fig. 1I).

Alteration in metabolomics after hepatectomy.

   Considering the interplay between the gut microbiome and host
   metabolism, we performed untargeted metabolomics on fecal samples. An
   orthogonal correction partial least-squares discriminant analysis
   (OPLS-DA) revealed that the metabolic compositions of LH0 and LH28 were
   clearly different from that of LH7, indicating that the composition of
   fecal metabolites was significantly altered during the 7 days after
   hepatectomy. However, the distribution of fecal samples on LH28 and LH0
   was more aggregated together, indicating that the altered fecal
   metabolome gradually recovered to preoperative levels during the
   28 days postoperatively ([71]Fig. 2A). Next, we identified the first 15
   significantly different metabolites based on the OPLS-DA model with
   variable importance in projection (VIP) of >1 and P of <0.05
   (two-tailed Student’s t test). There were 11 different metabolites
   downregulated and four metabolites upregulated on LH7 compared to LH0
   (see Table S1 in the supplemental material). The heatmap of
   hierarchical clustering ([72]Fig. 2B) shows that at LH7, the
   upregulated metabolites include d-biotin and d-galactarate, and the
   downregulated metabolites include phenylacetylglycine, involved in
   phenylalanine, tyrosine, and tryptophan biosynthesis, and betaine
   aldehyde, involved in glycine, serine, and threonine metabolism.
   Compared to LH0, metabolites such as l-threonine were upregulated and
   riboflavin was downregulated on LH28 ([73]Fig. 2B and Table S2). As
   described above, we identified the significantly different metabolites
   between preoperative and postoperative time points and determined the
   major enrichment pathways for these differentially produced metabolites
   based on KEGG and PubChem online databases. Compared to LH0, the major
   enrichment pathways for the differential metabolites on LH7 included
   phenylalanine, tyrosine, and tryptophan biosynthesis and biotin
   metabolism ([74]Fig. 2C). On LH28, the enrichment pathways included
   aminoacyl-tRNA biosynthesis and riboflavin metabolism, among others
   ([75]Fig. 2D). In the comparison between LH7 and LH28, pathway
   enrichment analysis revealed that the main pathways included glycine,
   serine, and threonine metabolism, among others ([76]Fig. 2E).

FIG 2.

   [77]FIG 2
   [78]Open in a new tab

   Fecal metabolomics at different time points before and after surgery.
   (A) Scatterplot of OPLS-DA model for LH0, LH7, and LH28. (B) Heatmap
   showing differential fecal metabolite changes between the three groups.
   (C to E) Pathway enrichment analysis performed using the significantly
   different fecal metabolites between the three groups: LH0 versus LH7
   (C), LH0 versus LH28 (D), and LH7 versus LH28 (E).

   To comprehensively analyze the mechanism of metabolic changes in liver
   injury and recovery, we also performed serum metabolomics analyses. The
   metabolites in serum samples were identified by ultrahigh-pressure
   liquid chromatography (UHPLC)–quadrupole time of flight mass
   spectrometry (QTOFMS), and the data were normalized to a defined
   OPLS-DA model with a cumulative R2(Y) of 0.972 and a Q2 value of 0.917,
   showing a trend of separation in LH7 and LH28 compared to LH0
   ([79]Fig. 3A). We further screened for differential metabolites by a
   VIP of >1 and a t test, with a P value of <0.05 as the selection
   criterion in the OPLS-DA model. The heatmap shows that, compared to
   LH0, the upregulated metabolites in LH7 include lipids and lipid-like
   molecules such as 3a,7a-dihydroxy-5b-cholestane and l-phenylalanine,
   which is involved in phenylalanine, tyrosine, and tryptophan
   biosynthesis, phenylalanine metabolism, and aminoacyl-tRNA biosynthesis
   ([80]Fig. 3B and Table S3). On LH28, the main differential metabolites
   were l-arginine and riboflavin, both of which were upregulated
   ([81]Fig. 3B and Table S4). However, the downregulation of differential
   serum metabolites that occurred on LH28 was not significant. To further
   clarify the metabolic pathways and functions during regeneration, a
   comprehensive analysis of the significant differential metabolites
   identified was performed using PubChem and KEGG online databases. The
   results showed that, compared to LH0, the differential metabolites were
   mainly enriched in phenylalanine, tyrosine, and tryptophan
   biosynthesis, phenylalanine metabolism, and primary bile acid
   biosynthesis on LH7 ([82]Fig. 3C). On LH28, they were mainly enriched
   in aminoacyl-tRNA biosynthesis, riboflavin metabolism, and nicotinate
   and nicotinamide metabolism ([83]Fig. 3D).

FIG 3.

   [84]FIG 3
   [85]Open in a new tab

   Posthepatectomy serum metabolome. (A) Comparison of serum metabolome
   between LH0 and LH7 and between LH0 and LH28 visualized using OPLS-DA.
   (B) Heatmap of the differential metabolites identified in serum
   samples. (C to E) Pathway enrichment analysis performed using the
   significantly different serum metabolites between the three groups: LH0
   versus LH7 (C), LH0 versus LH28 (D), and LH7 versus LH28 (E). (F and G)
   Venn diagram illustrating the common metabolic pathways of fecal and
   serum samples (F: LH0 versus LH7; G: LH0 versus LH28).

   Summarizing the metabolomic results, compared to the preoperative
   period, the represented pathways involved in the biosynthesis and
   metabolism of phenylalanine were identified in both fecal and serum
   metabolomics on LH7 ([86]Fig. 3E). The enriched pathway for
   aminoacyl-tRNA biosynthesis was present for significantly different
   fecal and serum metabolites on LH28 ([87]Fig. 3G). This finding
   reinforces the significant effects of both metabolic pathways on
   metabolic changes after hepatectomy, and we hypothesized that the fecal
   metabolite phenylacetylglycine and the serum metabolite
   l-phenylalanine, which are part of the phenylalanine biosynthesis and
   metabolism pathway, could be used as biomarkers for liver injury. The
   fecal metabolite l-threonine and the serum metabolite l-arginine from
   the aminoacyl-tRNA biosynthetic pathway could be used as key target
   metabolites for the liver recovery process.

Associations between the metabolome, clinical parameters, and gut microbiome.

   We assessed the association between the serum level of five
   significantly different clinical parameters, TNF-α, IL-6, IL-4, IL-1β,
   and IL-10, and the serum level of metabolites ([88]Fig. 4A and Table
   S5). We found that the serum metabolite l-phenylalanine, which was
   increased at LH7 after hepatectomy, and 3a,7a-dihydroxy-5b-cholestane,
   which is involved in primary bile acid biosynthesis, were positively
   correlated with the proinflammatory factors TNF-α, IL-6, and IL-1β but
   negatively correlated with anti-inflammatory factors IL-4 and IL-10,
   compared to LH0 (P < 0.05). The differences in levels of these serum
   metabolites can be used to assess the degree of liver damage and to
   further explore their role in liver recovery.

FIG 4.

   [89]FIG 4
   [90]Open in a new tab

   Comparing LH0 with LH7, the top 10 most abundant genera in the gut
   microbiome and the top 15 fecal and serum differential metabolites with
   significant differences based on previous analyses, as well as the five
   main inflammatory indicators, were selected for further correlation
   analysis. (A) The heatmap shows the association between the five
   significantly different clinical serum variables and the serum
   metabolites with significant differences (*, P ≤ 0.05). (B and C) The
   heatmap depicts the association between the most abundant genera in the
   gut microbiome and the metabolites in the fecal samples (B) and serum
   samples (C) (*, P ≤ 0.05).

   Since the physiological impact of the gut microbiota on the host is
   often mediated by a complex host-microbe metabolic axis, we next
   analyzed the associations between the abundance of specific bacterial
   genera and metabolites related to liver injury. In the gut microbiota,
   the abundance of the potentially pathogenic Escherichia-Shigella group
   was negatively correlated with downregulated fecal metabolites such as
   betaine aldehyde and phenylacetylglycine but positively correlated with
   the upregulated fecal metabolites d-galactarate and d-biotin and the
   serum metabolites l-phenylalanine and 3a,7a-dihydroxy-5b-cholestane.
   Notably, these upregulated metabolites were negatively correlated (P <
   0.05), or less correlated, with the abundance of Allobaculum and
   Turicibacter species that underwent downregulation due to liver injury
   ([91]Fig. 4B and [92]C and Tables S6 and S7). In addition to the
   previously characterized liver injury-related species, these
   metabolites also exhibited a close association with liver injury
   inflammatory factors. This further supports the hypothesis that changes
   in the metabolic mechanisms of liver injury are orchestrated by a
   complex, interacting host-microbe metabolic axis.

Influence of hepatectomy on proteomics.

   To further elucidate the mechanism of the metabolic changes brought
   about by hepatectomy, we performed proteomic analysis on liver tissue
   from LH0 and LH7. Of the 5,166 proteins that could be quantified in
   this experiment, a total of 412 proteins were significantly
   differentially expressed between LH0 and LH7; the number of upregulated
   proteins was 337, and the number of downregulated proteins was 75 (fold
   change [FC] of >1.2 or <0.83 and P value of <0.05) ([93]Fig. 5A and
   Table S8). The differentially expressed proteins were analyzed by
   searching the Gene Ontology (GO) database to determine their
   participation in biological processes (BPs) or molecular functions
   (MFs) and the type of cellular component (CC) with which they were
   associated. The BP category analysis revealed that the proteins were
   mainly involved in cellular processes, single-organism processes, and
   metabolic processes ([94]Fig. 5B). The classification according to CC
   showed that the proteins were mainly related to cells, cell parts, and
   specific organelles. With regard to MF, the differentially expressed
   proteins were associated with protein binding and catalytic activity
   ([95]Fig. 5B).

FIG 5.

   [96]FIG 5
   [97]Open in a new tab

   Proteomics analysis and comparison between LH0 and LH7. (A) Changes in
   the hepatic proteome. Alterations in hepatic proteins were demonstrated
   by volcano plots showing log[2]-transformed changes in protein
   abundance (versus control) on the x axis with log[10]-transformed P
   values on the y axis (P < 0.05). Red denotes significantly upregulated
   proteins and blue denotes significantly downregulated proteins meeting
   the predefined fold change (FC of >1.2 or <0.83). (B) GO enrichment
   analysis of differential proteins between LH0 and LH7 (P < 0.05). (C
   and D) KEGG pathway analysis of differential proteins between LH0 and
   LH7 (P < 0.05). (C) Upregulated differential proteins. (D)
   Downregulated differential proteins. (E and F) KEGG enrichment analysis
   of differential protein between LH0 and LH7. Column color gradients
   indicate the significance of enrichment, with darker colors by default
   representing more significant enrichment for that KEGG term, where P <
   0.001 is marked as ***, P < 0.01 is marked as **, and P < 0.05 is
   marked as *. (E) Upregulated differential proteins. (F) Downregulated
   differential proteins.

   The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis
   was carried out to determine which metabolic pathways the
   differentially expressed proteins were part of. Among the upregulated
   differential proteins, the top five authoritative KEGG pathways (P <
   0.05) were signal transduction, transport and catabolism, translation,
   immune system, and the folding, sorting and degradation ([98]Fig. 5C).
   The metabolic pathways involved with the higher number of downregulated
   differential proteins mainly included neurodegenerative diseases, lipid
   metabolism, and endocrine and metabolic diseases ([99]Fig. 5D). Further
   analysis of signaling pathways significantly enriched in KEGG function
   using Fisher’s exact test, with all proteins as background (P < 0.05),
   revealed that the upregulated differential proteins were mainly
   involved in mucin type O-glycan biosynthesis, DNA replication, and
   mitogen-activated protein kinase (MAPK) signaling, among others
   ([100]Fig. 5E). The downregulated differential proteins were mainly
   involved in oxidative phosphorylation, fat digestion and absorption,
   peroxisome proliferator-activated receptor (PPAR) signaling,
   phenylalanine, tyrosine, and tryptophan biosynthesis, and phenylalanine
   metabolism ([101]Fig. 5F). Notably, the proteomics analysis confirmed
   the metabolomics data in that the phenylalanine, tyrosine, and
   tryptophan biosynthesis and phenylalanine metabolic pathways were
   significantly related to liver injury.

DISCUSSION

   In this study, by integrating fecal microbiota, metabolomics, and
   proteomics data, we investigated the mechanisms of metabolic changes in
   response to hepatectomy. The contribution of microbiota and metabolites
   to liver metabolism and the signaling pathways involved in the
   mechanisms of liver injury development were revealed.

   In the laparoscopic approach, the venous oozing can be effectively
   manipulated by elevation of the pneumoperitoneal pressure ([102]12).
   This is particularly important for patients with cirrhosis where venous
   oozing secondary to portal hypertension is a common phenomenon. It has
   been shown that laparoscopic hepatectomy performed within the range of
   10 to 14 mm Hg was safe ([103]13[104]–[105]16). A pneumoperitoneal
   pressure of 1.33 kPa was found to be optimal for laparoscopic surgery
   in dogs; too high a pneumoperitoneal pressure can lead to severe
   changes in circulatory indicators ([106]17). Therefore, we recommend a
   pneumoperitoneal pressure setting of 8 to 10 mm Hg.

   Our study showed that the specificity of gut microbial response to
   hepatectomy was confirmed by 16S rRNA gene high-throughput sequencing.
   At the genus level, the abundance of Allobaculum and Turicibacter was
   significantly decreased, while Escherichia-Shigella increased after
   hepatectomy, accompanied by an increase in inflammatory factors. Thus,
   we hypothesized that Allobaculum and Turicibacter were involved in
   boosting the production of anti-inflammatory factors in the body,
   protecting the intestinal barrier, and reducing liver damage. Previous
   studies reported that Allobaculum had a strong fermentative capacity
   and released short-chain fatty acids (SCFAs) during the degradation of
   dietary fiber. SCFAs are considered potential mediators of intestinal
   immune function, including inhibiting the production of proinflammatory
   factors and maintaining intestinal barrier function ([107]5).
   Nonalcoholic fatty liver disease also causes a decrease in Turicibacter
   abundance, aggravating liver inflammation and decreasing intestinal
   function and serum metabolic index ([108]18). Consistent with these
   results, we found that the relative abundances of Allobaculum and
   Turicibacter were significantly decreased after hepatectomy, suggesting
   that lobectomy had a significant negative effect on the microbiota.

   Escherichia-Shigella is an opportunistic pathogen, and an increase in
   its relative abundance has been found in a variety of diseases with
   liver function abnormalities after liver transplantation, nonalcoholic
   fatty liver, liver cancer, and other diseases. It was reported that the
   enrichment of Escherichia-Shigella is closely related to the production
   of endotoxin, which leads to endotoxemia, increased intestinal
   permeability, and liver damage ([109]19). We found that hepatectomy
   increased the relative abundance of Escherichia-Shigella, and this
   might be the main reason for the observed inflammation. The F/B
   (Firmicutes/Bacteroidetes) value has been used as an intestinal
   evaluation index in the study of a variety of diseases, including
   alcoholic fatty liver, nonalcoholic fatty liver, liver oxidative stress
   injury, Alzheimer’s disease, and atherosclerosis ([110]20). In this
   study, the ratio of F/B decreased at LH7, indicating a negative effect
   on the beagle’s gut after hepatectomy.

   More importantly, we found that Allobaculum and Turicibacter abundance
   was negatively correlated with indicators of liver damage (ALT and AST)
   and proinflammatory factors (TNF-α, IL-1β, and IL-6) and positively
   correlated with anti-inflammatory factors (IL-4 and IL-10). In addition
   to our research, mounting evidence has suggested that intestinal
   microbiota are active in the gut-liver axis, and liver injury can
   change the microbial composition in the intestine, which is manifested
   as a lower diversity of intestinal microbes ([111]21) that was
   consistent with the decrease in alpha diversity on LH7. Therefore, we
   conclude that the above three bacterial groups may be potential targets
   for improving the liver function and metabolism after liver surgery,
   and our findings clearly show that hepatectomy has a significant impact
   on the microbiota; further modulation of the gut microbiota may play an
   important role in recovery after hepatectomy.

   Studies demonstrated that phenylalanine was primarily catabolized in
   the liver by phenylalanine hydroxylase (PAH) to produce tyrosine and
   that the metabolic control of phenylalanine concentrations in body
   fluids was critical for cognitive development and executive capacity
   ([112]22). Our metabolomics results showed that both fecal and serum
   metabolomes were enriched in phenylalanine, tyrosine, and tryptophan
   biosynthesis and phenylalanine metabolism, which indicated that these
   pathways are important in liver recovery. As previously described by Lu
   et al. ([113]23), kaempferol ameliorated the liver damage caused by
   nonalcoholic fatty liver disease (NAFLD) mainly through the regulation
   of the two pathways of phenylalanine, tyrosine and tryptophan
   biosynthesis and phenylalanine metabolism, and the metabolites they
   produce. Disturbances in the metabolism of phenylalanine and tyrosine
   can lead to the development of NAFLD, which is characterized primarily
   by liver damage.

   Remarkably, our proteomics data showed significant downregulation of
   the phenylalanine metabolic pathway as a result of hepatectomy. In
   addition, aminoacyl-tRNA biosynthesis and riboflavin metabolism were
   significantly associated with recovery mechanisms after liver injury.
   It has been demonstrated that aspirin eugenol ester (AEE) protects
   against paraquat-induced acute liver injury (ALI) in rats by regulating
   metabolite production from phenylalanine, tyrosine, and tryptophan
   biosynthesis, aminoacyl-tRNA biosynthesis, phenylalanine metabolism,
   and other metabolic pathways ([114]24). For example, fumaric acid, a
   metabolite of tyrosine, is involved in the tricarboxylic acid cycle,
   thereby affecting the body’s energy level. An imbalance between AMP and
   ATP can inactivate AMP-dependent protein kinase (AMPK), which leads to
   abnormal activation of apoptotic proteins and exacerbates liver damage.
   Thyroid hormone (TH) is a product of the phenylalanine/tyrosine pathway
   and can cause disturbances in hepatic lipid metabolism and further
   deterioration of liver function ([115]25).

   Interestingly, we showed specific upregulation of the serum metabolite
   l-phenylalanine, which is involved in three important signaling
   pathways: phenylalanine, tyrosine, and tryptophan biosynthesis,
   phenylalanine metabolism, and aminoacyl-tRNA biosynthesis. Further
   supporting evidence comes from a previous animal study ([116]26) that
   showed ursodeoxycholic acid (UDCA), a by-product of intestinal
   bacterial metabolism, could mitigate liver dysfunction by significantly
   reducing l-phenylalanine and its downstream molecules and modulating
   the phenylalanine/tyrosine pathway. Similarly, another previous study
   revealed that microbial metabolites of aromatic amino acids such as
   phenylalanine, tyrosine, and especially phenylacetic acid were strongly
   associated with hepatic steatosis ([117]27). As phenylalanine is a
   precursor of phenylacetic acid, increased phenylalanine levels could
   raise phenylacetic acid concentrations and promote hepatic disease
   ([118]28). Huang et al. also demonstrated that a specific set of
   metabolites including phenylalanine, inosine, and bilirubin had great
   potential as biomarkers of drug-induced liver injury (DILI) for
   clinical diagnosis ([119]29). Therefore, we concluded that
   phenylalanine, tyrosine, and tryptophan biosyntheses play important
   roles in the metabolic mechanism of liver injury. l-Phenylalanine could
   be a useful monitor and biomarker of the process of liver injury, and
   its regulation might be utilized to limit liver damage.

   In the correlation analysis, we observed a significant association
   between the serum metabolite 3a,7a-dihydroxy-5b-cholestane, which was
   upregulated within 7 days after hepatectomy compared to preoperatively,
   and inflammatory factors and gut microbiota related to the liver
   injury. The KEGG results revealed that 3a,7a-dihydroxy-5b-cholestane
   enrichment was a component of the primary bile acid biosynthesis
   pathway. We speculate that primary bile acid biosynthesis could be an
   important metabolic pathway in the posthepatectomy response.
   Cholesterol is the main target of primary bile acids, and cholesterol,
   in turn, is an essential part of the energy generation process for
   recovery after hepatectomy. It was reported that primary bile acids
   were synthesized directly from cholesterol in hepatocytes but could be
   converted into secondary bile acids by gut microbes ([120]30). Most
   bile acids are retained in the hepatic-intestinal circulation, and only
   a very small amount enters the blood circulation, but when the organism
   is affected by liver disease, the bile acid secretion function of the
   liver is impaired, which could account for the increase in related
   metabolites such as 3a,7a-dihydroxy-5b-cholestane in the serum
   metabolome during the 7-day postoperative period of liver injury.
   Researchers have shown that liver injuries such as NAFLD ([121]31) and
   DILI ([122]32) augment bile acid levels. The results of Jiao et al.
   ([123]33), who studied serum bile acid levels and gut microbial
   composition in a rat model of NAFLD, suggested that future therapeutic
   interventions in the development of NAFLD could be achieved by
   regulating bile acid metabolism. A study by Ma et al. ([124]34)
   reported that primary bile acid metabolism was closely linked to the
   development and prevention of liver tumors. Hence, primary bile acid
   biosynthesis is an important metabolic pathway to focus on during liver
   injury.

   We also found that the metabolic pathway for aminoacyl-tRNA
   biosynthesis was enriched for fecal and serum metabolites at LH28
   compared to LH0, including a significant increase in the fecal
   metabolite l-threonine and the serum metabolite l-arginine. Based on
   this finding, we hypothesized that aminoacyl-tRNA biosynthesis was an
   important metabolic pathway for liver recovery and that the
   upregulation of two metabolites, l-arginine and l-threonine,
   facilitated the recovery process after liver injury. Previous studies
   have reported that l-arginine was the most important metabolite in
   various metabolic models. l-Arginine is a major intermediate in the
   urea cycle and is the precursor of nitric oxide (NO), which has been
   implicated in various liver injuries ([125]35). l-Arginine increased
   the activity of antioxidant enzymes and reduced malondialdehyde levels
   by promoting NO synthesis, thus mitigating tissue damage caused by
   oxidative stress ([126]36). l-Threonine is an essential amino acid in
   mammalian cells and was shown to have beneficial effects on NAFLD by
   reducing the characteristic liver damage through synthesis of
   phospholipids and oxidation of fatty acids ([127]37). Collectively, our
   findings support the conclusion that an increase in l-arginine and
   l-threonine levels is strongly associated with hepatic recovery and
   that manipulation of the biosynthesis of these signature target
   metabolites may be effective in promoting liver recovery after
   hepatectomy.

   To further elucidate the mechanism of liver injury, we determined the
   proteomic profile response to hepatectomy at 7 days postoperation
   (postop). By analyzing the enrichment pathways of upregulated proteins,
   we found a significant increase in proteins associated with the
   mitogen-activated protein kinase (MAPK) signaling pathway. This pathway
   has been shown to regulate almost all hepatocyte physiological
   processes in previous studies, including the expression of genes
   involved in pretranscriptional, posttranscriptional, and translational
   processes. The MAPK pathway has been reported to be closely associated
   with liver injury and liver-related inflammatory responses, liver
   fibrosis, liver cancer, and other pathologies ([128]38). For example,
   Ma et al. ([129]39) showed that the MAPK pathway played a key role in
   the progression of nonalcoholic steatohepatitis. Ci et al. ([130]40)
   confirmed that the MAPK signaling pathway was a potential molecular
   target for the treatment and prognostic evaluation of hepatocellular
   carcinoma. In a previous study, it was also found that blocking p38
   MAPK promoted DNA replication during liver regeneration but maintained
   the hepatocyte cell cycle arrest in adult liver ([131]41). Inhibition
   of the MAPK signaling pathway improved liver fibrosis ([132]42), and
   blocking MAPK phosphorylation was anti-inflammatory and
   hepatoprotective ([133]43). Moreover, it has also been reported that
   MAPK signaling plays an inhibitory role in the development of NAFLD
   ([134]44). Further studies into the molecular mechanism of regulation
   of liver recovery and the role of the MAPK signaling pathway are
   required.

   The protein-enrichment pathway analysis in the present study revealed
   that phenylalanine and tyrosine, and the tryptophan
   biosynthesis/phenylalanine metabolic pathways, which showed significant
   differences in the metabolome results, had the same downregulation
   trend in the proteomic profiling. We found that the peroxisome
   proliferator-activated receptor (PPAR)-mediated metabolic pathways that
   were demonstrated to be associated with liver injury in previous
   studies ([135]45) showed a similar trend in this study. PPARs, which
   belong to the nuclear receptor hormone family, consist of three
   isoforms, PPARα, PPARβ, and PPARγ. Among these, PPARγ has been shown to
   be involved in hepatic ischemia-reperfusion injury. Treatment with a
   synthetic PPARγ ligand attenuated postischemic liver injury, while
   knockout of PPARγ exacerbated postischemic liver injury ([136]46).
   Other studies have verified that the plant compound berberine may
   relieve fibrosis by regulating PPARγ and restoring lipid homeostasis
   via modulation of arachidonic acid metabolism ([137]47). Further
   studies are needed to evaluate the effectiveness of PPARγ intervention
   by comparison with proteomic results. Taken together, we conclude that
   the PPAR signaling pathway is a key player in the process of liver
   injury and recovery, and future studies are warranted for experimental
   verification of our hypothesis.

Conclusions.

   In summary, our investigation integrates detailed clinical and
   multi-omics data to characterize the metabolic response to hepatectomy
   in beagle dogs. We also performed an interactional analysis to decipher
   the mechanism of liver injury and the body’s response. Our experiments
   identified the key molecules involved in the physiology of liver injury
   and recovery and also characterized the dysbiosis resulting from
   hepatectomy. As one outcome of the metabolomics and liver proteomics
   analyses, we determined that the fecal metabolite phenylacetylglycine
   and the serum metabolite l-phenylalanine could potentially be used as
   biomarkers for liver injury. Additionally, the fecal metabolite
   l-threonine and the serum metabolite l-arginine could potentially be
   used as key target metabolites for the liver recovery process.
   Moreover, the MAPK and PPAR signaling pathways significantly
   contributed to enhancing liver recovery. A limitation of this study is
   that further experiments are necessary to corroborate our findings in
   different species. This study does provide the foundation for future
   treatment of and recovery from common clinical diseases such as liver
   cancer and liver tumors and facilitates studies on the microbiota in
   the gut-liver axis.

MATERIALS AND METHODS

Experimental animals.

   Eight healthy adult (7 to 8 months old, 7.1 ± 6 kg) female beagles were
   obtained from Beijing Marshall Biotechnology Co., Ltd., China (no.
   SYXK2019-0047). The experimental protocol was approved by the Animal
   Ethics Committee of Beijing University of Agriculture (BUAEC
   2019-0205). Animal care and handling were performed in accordance with
   the Guide for the Care and Use of Laboratory Animals of the National
   Research Council ([138]48). The dogs were housed individually and fed a
   standard canine diet (Beijing Keao Xieli Feed Co., Ltd., China) with ad
   libitum access to tap water. Animals were acclimated to their new
   environment for 14 days prior to their first fecal collection, during
   which time the health status of each animal was evaluated daily.

Hepatectomy and sampling.

   A purely laparoscopic left hemihepatectomy was performed under aseptic
   conditions on the dogs under general anesthesia as described in our
   previous study ([139]49). Briefly, the left triangular ligament was cut
   first, the base of the left medial lobe was ligated with silk suture
   using a needle, and the liver parenchyma of the left medial lobe
   approximately 0.5 cm above the ligation site was removed with LigaSure
   (10 mm; Covidien Medical). The left lateral lobe was removed in the
   same manner. The resected lobes were placed in a sterile bag and then
   taken out through a minilaparotomy port that was created by extending
   the trocar site. After ensuring hemostasis, the laparoscopic equipment
   was removed and the ports were sutured. All surgical procedures were
   performed under aseptic conditions by the same team of a surgeon and
   two assistants. Buprenorphine, 0.01 mg/kg of body weight
   intramuscularly (i.m.) (Hansen Pharma Co., Ltd., Changsha, China), was
   administered every 8 h for 3 days after surgery for analgesia. All
   animals were clinically examined daily until they recovered to normal
   preoperative state.

   In the morning before surgery (LH0) and after successful modeling by
   laparoscopic hepatectomy (LH) on days 7 (LH7) and 28 (LH28), we
   collected fecal samples and stored them in liquid nitrogen for 16S rRNA
   gene sequencing and metabolomics analysis. Blood samples were collected
   from the forelimb veins before surgery and after the surgery on days 1,
   3, 7, 14, and 28, allowed to clot, and centrifuged for 10 min at
   3,000 × g. The serum was aliquoted into two microcentrifuge tubes and
   immediately stored at −80°C for later measurement of clinical
   indicators. Blood samples were taken at LH0, LH7, and LH28 for serum
   metabolomic analysis. We took appropriate amounts of liver tissue
   before (LH0) and on day 7 after (LH7) surgery and stored the specimens
   at −70°C for subsequent proteomic analysis.

Serum and hepatic biochemical indicators.

   Serum tumor necrosis factor alpha (TNF-α), interleukin-1β (IL-1β),
   interleukin-6 (IL-6), interleukin-4 (IL-4), and interleukin-10 (IL-10)
   were measured using enzyme-linked immunosorbent assay kits (Huaying
   Biological Technology Co., Ltd., Beijing, China).

   The levels of serum aspartate transferase (AST) and alanine
   transaminase (ALT) were determined with a biochemical analyzer (Mindray
   Biomedical Electronics Co., Ltd., Shenzhen, China) and a liver function
   test kit (Yapu Biological Technology Co., Ltd., Shanghai, China). The
   intra- and interassay coefficients of variation were 8.76 and 4.46%,
   respectively.

16S rRNA gene sequencing and analysis.

   Total DNA extraction and PCR amplification were performed as previously
   described ([140]50, [141]51). The sequences of the primers were as
   follows: 338F, 5′-ACTCCTACGGGAGGCAGCAG-3′; 806R,
   5′-GGACTACHVGGGTWTCTAAT-3′.

   PCR products were purified using the AxyPrep DNA gel extraction kit
   (Axygen Biosciences, Union City, CA, USA), and paired-end sequencing
   was performed using an Illumina MiSeq Platform (Illumina, San Diego,
   CA, USA) according to standard protocols by Majorbio Bio-Pharm
   Technology Co. Ltd. (Shanghai, China) ([142]50).

   For bioinformatics analysis of microbial sequencing results, raw fastq
   files were demultiplexed, quality filtered by Trimmomatic ([143]52),
   and merged by FLASH with the reported criteria ([144]51). Operational
   taxonomic units (OTUs) were clustered with a 97% similarity cutoff
   ([145]50) using UPARSE (version 7.1, [146]http://drive5.com/uparse/).
   Chimeric sequences were identified and removed using UCHIME. The
   taxonomy of the acquired OTUs was analyzed using the Ribosomal Database
   Project (RDP) classifier algorithm ([147]http://rdp.cme.msu.edu/)
   against the SILVA (release 123, [148]http://www.arb-silva.de) ([149]53)
   16S rRNA gene database with a confidence threshold of 70%. Alpha
   diversity analyses, including community richness parameters (Chao and
   Sobs) and community diversity parameters (Shannon and Simpson), were
   calculated using the Mothur software ([150]50). Principal-coordinate
   analysis (PCoA) was performed, with unweighted UniFrac as distance
   measure, to evaluate beta diversity. Bacterial taxonomic distributions
   of sample communities were visualized using the R package software.
   Linear discriminant analysis (LDA) coupled with effect size (LEfSe)
   ([151]54) measurements (based on nonparametric factorial Kruskal-Wallis
   rank sum test and the Wilcoxon rank sum test) was used to identify
   significantly different taxa (biomarkers) between groups (LH0 and LH7),
   with a P value of <0.01 and an LDA score threshold of 4 ([152]55).
   Correlations between clinical parameter indices and 16S rRNA gene
   sequence data are presented in the form of a heatmap diagram based on
   Spearman’s correlation coefficient. At the genus level, the correlation
   coefficient of the R value was color mapped onto the gut microbiota,
   showing the correlation of each gut species with the clinical indices.
   R values are presented in different colors, and the color card shows
   the color partition of different R values (P < 0.05, *; P < 0.01, **)
   ([153]56).

Metabolomics.

   For extracting fecal metabolites, we added 25 mg of each fecal sample
   to 500 μL of acetonitrile-methanol-water (2:2:1, vol/vol/vol)
   containing an isotopically labeled internal standard mixture. After
   vortexing for 30 s, the samples were homogenized at 35 Hz for 4 min and
   sonicated for 5 min, three times, in an ice-water bath. The samples
   were then held for 1 h at −40°C and centrifuged at 12,000 × g for
   15 min at 4°C. The supernatants (400 μL) were transferred to EP tubes
   and dried under vacuum and then dissolved in 200 μL of 50%
   acetonitrile, vortexed for 30 s, sonicated for 10 min on ice, and
   centrifuged at 13,000 × g for 15 min at 4°C. Afterward, 75-μL aliquots
   of supernatant were taken for liquid chromatography with tandem mass
   spectrometry analysis (LC-MS/MS). An equal aliquot of each supernatant
   (10 μL) was used as the quality control (QC) sample. Likewise, for the
   serum samples, we pipetted 50-μL aliquots into a 1.5-mL tube, added
   200 μL of methanol-acetonitrile (1:1, vol/vol) containing an
   isotopically labeled internal standard mixture, vortexed and mixed the
   tubes for 30 s, sonicated the tubes for 10 min on ice, and held the
   tubes at −40°C for 1 h. The samples were then centrifuged at 12,000 × g
   for 15 min at 4°C, and the supernatants were collected. The samples
   were analyzed using a Vanquish ultrahigh-pressure liquid chromatography
   (UHPLC) system (Thermo Fisher Scientific) in conjunction with a Thermo
   Q Exactive HFX mass spectrometer.

   The raw metabolomics data were converted into mzXML format with
   ProteoWizard and then processed using a procedure developed with R for
   automatic data analysis for peak detection, extraction, alignment, and
   integration (Biotree, Shanghai, China). Individual peaks were filtered
   to remove noise based on their relative standard deviation. Peak area
   data with vacancies of ≤50% in a single group or null values of ≤50% in
   all groups were retained. Missing raw data points were filled in with a
   minimum value of one-half, and internal standards were used for data
   standardization and normalization. Afterward, the database was searched
   for all metabolites, and they were sorted according to type.
   Multivariate analyses including principal-component analysis (PCA) and
   orthogonal correction partial least-squares discriminant analysis
   (OPLS-DA) were conducted using SIMCA software (V15.0.2; Sartorius
   Stedim Data Analytics AB, Umea, Sweden). The OPLS-DA was used to filter
   out orthogonal variables in metabolites that were not correlated with
   categorical variables, thus ensuring that only accurately annotated
   metabolites were selected for differential analysis. Metabolites
   differentially expressed between two treatments were identified based
   on variable importance in projection (VIP) from OPLS-DA and statistical
   analysis (VIP of >1 and P of <0.05), and further hierarchical cluster
   analysis (HCA) and heatmap construction were performed for the
   different metabolites ([154]57). After mapping the differentially
   produced metabolites to authoritative metabolite databases such as KEGG
   and PubChem and obtaining matches for the differential metabolites, we
   searched the pathway database for Homo sapiens and performed metabolic
   pathway analysis to determine the enrichment pathways corresponding to
   the different metabolites.

Proteomics.

   Protein extraction was performed on eight beagle hepatectomy samples
   pooled before and 7 days after surgery. To remove residual blood cells,
   the pooled liver samples were rinsed twice with ice-cold
   phosphate-buffered saline (PBS, pH 7.4) and then transferred into
   radioimmunoprecipitation assay (RIPA) lysis buffer (Thermo Fisher
   Scientific, MA, USA) containing protease inhibitors at a final
   concentration of 1% (vol/vol). Liver tissues were homogenized and then
   sonicated for 5 times and 10 s using an ultrasonic instrument (Sonics &
   Materials, Inc., Newtown, CT, USA). Homogenates were kept at 4°C for
   40 min and then centrifuged at 12,000 × g for 10 min at 4°C. Protein
   concentration was determined by bicinchoninic acid assay (Thermo Fisher
   Scientific, MA, USA), and samples were prepared for proteomic analysis
   by Tandem Mass Tag (TMT) labeling ([155]58).

   Aliquots of the desalted peptides (5 μg) were injected for nanoflow
   LC-MS/MS analysis using an LTQ Orbitrap Velos mass spectrometer (Thermo
   Fisher Scientific, Waltham, MA, USA). Technical duplication was
   conducted. The iTRAQ project was used to compare the proteins between
   the two groups in terms of upregulated proteins (FC of >1.2) and
   downregulated proteins (FC of <0.83) (P < 0.05). The identified
   differential proteins were then screened with GO and KEGG databases to
   obtain comprehensive functional information about the annotated
   proteins. The KEGG pathway enrichment analysis was also performed on
   the proteins in the protein set according to the Majorbio Cloud
   Platform development process, using the same principle as the GO
   functional enrichment analysis.

Gut microbiome-metabolome correlation analysis.

   Spearman correlation analysis was used to analyze the association
   between selected analytes and other data sets (metabolomics and
   microbiota). The correlations between clinical parameters (TNF-α, IL-6,
   IL-4, IL-1β, and IL-10) and bacterial/serum metabolites were assessed
   in the LH0 and LH7 groups, respectively, as well as for associations
   between the top 10 bacterial species and the top 15 differential
   metabolites, which were screened to show significant differences. The
   results were visualized as heatmap plots. R values indicate the
   correlation between each metabolite and the genus-level gut microbiota
   as different colors. The correlation coefficient and P value for each
   species-metabolite pair were calculated and considered significantly
   associated with a cutoff P value of <0.05. This was performed using the
   Corr.test function in the stats package of R language tools.

Statistical analysis.

   Values were expressed as mean ± standard deviation (SD) for continuous
   variables. Gut microbial domains, phyla, and genera were compared using
   the Wilcoxon rank sum test, with an adjusted false-discovery rate (FDR)
   of P < 0.05 being considered significantly different. Student’s t test
   (two-sided) was used to compare the differences for other multi-omics
   analyses between preoperative and postoperative time points.
   Differential protein expression was screened according to fold change
   (FC of >1.2 or FC of <0.83), using Fisher’s exact test (P < 0.05) for
   pathway enrichment analysis with all proteins as background.

Data availability.

   Due to the confidentiality of data, the data which support the findings
   of this study are available only in a redacted form upon request. All
   raw sequence data were uploaded to NCBI ([156]PRJNA844202).

ACKNOWLEDGMENTS