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A systematic analysis of human lipocalin family and its expression in esophageal carcinoma

Abstract

The lipocalin proteins (lipocalins) are a large family of small proteins characterized by low sequence similarity and highly conserved crystal structures. Lipocalins have been found to play important roles in many human diseases. For this reason, a systemic analysis of the molecular properties of human lipocalins is essential. In this study, human lipocalins were found to contain four structurally conserved regions (SCRs) and could be divided into two subgroups. A human lipocalin protein-protein interaction network (PPIN) was constructed and integrated with their expression data in esophageal carcinoma. Many lipocalins showed obvious co-expression patterns in esophageal carcinoma. Their subcellular distributions also suggested these lipocalins may transfer signals from the extracellular space to the nucleus using the pathway-like paths. These analyses also expanded our knowledge about this human ancient protein family in the background of esophageal carcinoma.

Introduction

The lipocalin proteins (lipocalins) are a large family of small extracellular proteins with 100–300 amino acid residues. They are found in both prokaryotes and eukaryotes1. Lipocalins typically transport or store small biological compounds, including vitamins, steroid hormones, odorants and various secondary metabolites. The lipocalin genes show a conserved exon/intron arrangement2. Though the family demonstrates considerable diversity even at the protein sequence level, most lipocalins share one or three conserved sequence motifs, called structurally conserved regions (SCRs)3. Belying their sequence dissimilarity, the crystal structures of lipocalins are highly conserved and similar, comprising an eight-stranded hydrogen-bonded antiparallel beta-barrel, which acts an internal ligand-binding site4. The definition of human lipocalins has been expanded on the basis of sequence or structural similarity5. The lipocalins are conserved during the species evolution and new members have been discovered in plants and bacteria6,7.

After more than 20 years of analyses, it has come to be generally believed that lipocalins play important roles in normal human physiological activities and in diseases8. Because of its antioxidant and anti-inflammatory activity, Apolipoprotein D (APOD) is upregulated in the aging human brain and elevated levels of APOD play a protective role in a large number of neurologic disorders, such as Alzheimer’s disease, schizophrenia and stroke9. LCN13 is expressed in multiple tissues, including those of the liver, pancreas, epididymis and skeletal muscle. Obesity is associated with a downregulation of LCN13 expression and lower levels of circulating LCN13. LCN13 therapy enhances insulin sensitivity in adipocytes and improves glucose intolerance and hepatic steatosis10. LCN1, also called tear lipocalin (TLC), binds to macromolecules, which regulate tear viscosity, the binding and release of lipids and endonuclease inactivation of viral DNA11. Hepatic overexpression of apolipoprotein M (APOM) in low-density-lipoprotein-receptor-deficient mice has been shown to lead to an approximately 70% reduction in atherosclerosis12.

Lipocalins play important role in the innate immune response to bacterial infection13. Bacterial pathogens usually obtain iron from the host by production of siderophores, which are small high-affinity iron chelating compounds, in order to survive and grow. As a defense, the host produces siderocalins, which limit pathogen growth by intercepting siderophores, preventing the delivery of iron to the pathogen. Siderocalins are a siderophore-binding subset of the lipocalins and they include LCN1, LCN2 and FABP. Siderocalins have been identified in humans and many other mammals, linking the crosstalk between inflammation and cancer14.

In recent years, the functional roles of lipocalins in human cancers have been determined and published, which has drawn even more attention15. LCN2 (lipocalin-2), also known as neutrophil gelatinase-associated lipocalin (NGAL), is overexpressed under many other pathologic conditions, including cancer. It is frequently associated with tumor size, stage and invasiveness. Cumulative experimental results have demonstrated that LCN2 has multiple functions in various cancers, including inhibition of apoptosis, stimulation of proliferation and promotion of the epithelial-to-mesenchymal transition (EMT). LCN2 stabilizes the proteolytic enzyme matrix metalloprotease-9 (MMP-9) by forming a heterogeneous complex, thereby preventing autodegradation and promoting metastasis of cancer cells16. Another lipocalin associated with carcinoma is glycodelin, also called PAEP (progestagen-associated endometrial protein). It is involved in cell recognition and epithelial differentiation. Glycodelin reduces carcinoma cells growth both in vitro and in vivo, suggesting it acts as a tumor suppressor in breast cancer17. In breast cancer, APOD inhibits translocation of phosphorylated MAPK into the nucleus, reducing the proliferative activity of cancer cells18.

Though several reports have described the sequence, structure and evolution of lipocalins in the past ten years, these reviews and analyses have specific limitations19,20,21. One weak point is that while the lipocalins have bene collected from different species, they have not included the new lipocalins identified in recent years. The other is that lipocalins have not been analyzed under a specific human pathological condition. In this study, human lipocalin proteins were collected. Their protein-protein interaction network (PPIN) was constructed and their expression levels in esophageal carcinoma, including esophageal adnocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC) were integrated into the PPIN.

Results

Analysis of human lipocalin protein sequences and structures

Currently, 37 lipocalins have been found in human genome. Information regarding human lipocalins is shown in Table 1. The alignment of lipocalin protein sequences is shown in Fig. 1A, with four structurally conserved lipocalin regions (SCRs) are clearly indicated (Supplementary Figure S1). AMBP has the longest sequence length with 352 amino acids, while FABP1 has the shortest sequence length (127 amino acids). The protein sequences of lipocalins were found to be less similar to each other than expected, without any obvious large conserved region. Every pair of lipocalins was clustered based on their sequence similarities were shown using a matrix (Fig. 1B). Results indicated that the lipocalins could be divided into two groups based on their protein sequence similarities. The first group contained RABPs (RABP1 and RABP2), RBPs (RBP1, RBP2, RBP5 and RBP7), FABPs (FABP1, FABP5, FABP7, FABPH, FABPI, FBP12 and FB5L3) and PMP2. The second group contained A1AGs (A1AG1 and A1AG2), LCNs (LCN1, LCN2, LCN6, LCN8, LCN9, LCN10, LCN12 and LCN15), OBP2s (OBP2A and OBP2B), LC1L1, PTGDS, LCNL1, APOD, APOM and PAEP. This suggested that lipocalins might have evolved in two different ways, which may be useful in investigations of their biological functions.

Table 1 Human lipocalins.
Figure 1
figure 1

(A) Protein sequence alignment of human lipocalins. The four structurally conserved regions (SCRs) are indicated in four blocks. (B) Cluster of sequence similarities, indicating human lipocalins could be divided into two subgroups.

Lipocalins are characterized by conserved protein tertiary structure22. The three-dimensional (3D) structures of lipocalins for which such information is currently available are listed in Supplementary Table S1. The 3D structures of two grouped lipocalins were compared to assess the classification of lipocalins based on their protein sequence similarities. In the first group, which contains RABPs, RBPs and FABPs, only a half of a β-barrel with four antiparallel β-sheet strands was conserved. In the second lipocalin group, a whole conserved β-barrel made of a cylindrically closed β-sheet of eight antiparallel strands was conserved (Supplementary Figure S2).

Functional enrichment analyses of lipocalins

To gain a full view of their potential functions and other important characteristics, the lipocalins were annotated using the Functional Annotation Chart and visualized using the Enrichment Map plugin in Cytoscape. As shown in Fig. 2, each node represented one functional annotation term. The more significant of the category, the deeper the color of the node. Nodes containing more enriched genes were larger. Edge width was here defined using the overlap coefficient between these categories (overlap coefficient cut-off 0.6). The more shared genes there were between two nodes, the wider the edge. Except 52 terms from Gene Ontology (GO) categories, the Functional Annotation Chart results also included 34 other terms from the following annotation categories, 9 INTERPRO, 18 SP_PIR_KEYWORDS, 6 UP_SEQ_FEATURE and 1 KEGG_PATHWAY. These results provide more information than the GO enrichment alone. The most significant enriched term was an InterPro annotation “IPR012674:Calycin.” InterPro is a database that provides functional analysis of protein sequences by classifying them into families and predicting the presence of domains and important sites23. Calycins form a large protein superfamily and share similar beta-barrel structures. This suggests that the conserved structure is the major reason why the lipocalin protein family has been conserved for so long. The three most enriched entries in Gene Ontology (GO) molecular function were “GO:0008289~lipid binding,” “GO:0005501~retinoid binding,” and “GO:0019840~isoprenoid binding,” which are associated with the three most well-known lipocalin ligands. Four lipocalins (RBP4, FABP3, FABP4, FABP1 and FABP7) were found to be involved in cell growth and these were enriched in “GO:0042127~regulation of cell proliferation.” The top SP_PIR_KEYWORDS enrichment term was “transport,” indicating the main function of lipocalins. Here “hsa03320:PPAR signaling pathway,” the only term found in the KEGG_PATHWAY category, was associated with 6 genes (FABP3, FABP4, FABP1, FABP2, FABP7 and FABP5).

Figure 2
figure 2

Functional categories of the lipocalins were visualized using the Enrichment map plugin of the Cytoscape.

Significant functional terms are represented by one node with its size indicating the significance of the enrichment (P-value). Edges indicate gene overlap between nodes and thickness indicates the number of overlapping enriched genes.

The GO-enriched results from WebGestalt also suggested that the lipocalin family was mostly involved in the metabolism of small molecules, such as lipids, retinoids and vitamins (Supplementary Figure S3).

Description of lipocalin co-expression PPIN and association with changes in esophageal carcinoma

A full screening of the lipocalins’ interactions with other proteins was performed to determine how they affect cellular activity. This may provide important clues of their functions. The PPI dataset from both acknowledged HPRD and BioGRID databases provided credible original data for subsequent analysis. The lipocalin PPIN was generated by mapping the lipocalins to the parental PPI network to extract the proteins that interacted directly and all their interactions, forming a sub-network for lipocalins containing 151 nodes and 569 edges (Fig. 3). Currently, the interactions of 23 human lipocalin proteins have been reported. An esophageal carcinoma expression profile GSE26886, containing clinical samples from normal esophageal squamous epithelium, esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), was analyzed to determine the expression trends of lipocalins and the proteins with which they interact. The fold-changes of these proteins in EAC and ESCC and other important parameters were integrated into the PPIN (Fig. 3A,B). In Fig. 3A,B, the color of each node indicates the level of expression. The gradient from red to green indicates upregulation through downregulation, all relative to normal esophageal tissue. The size of the node indicates the degree of the node (the number of proteins with which it interacts directly). The bigger nodes indicate higher degrees of interactions, connecting with more proteins. Every two interacting nodes are linked by an edge. The correlations in the levels of expression of any two interacting proteins are here treated as edge weight. Red edges indicate positive correlations in the expression of two interacting proteins and green edges indicate the negative expression correlation. The strength of the correlation is indicated by the width of the edge.

Figure 3
figure 3

(A-B) The human lipocalin protein-protein interaction network and the lipocalins’ changes in expression in esophageal adenocarcinoma and esophageal squamous cell carcinoma are shown. The lipocalins are shown using triangles and their interacting proteins are shown in circles. Red indicates upregulation and green indicates downregulation. The size of the node indicates the degree (the number of its interacting proteins) of the node. Bigger nodes have higher degrees. The connection between two nodes is indicated by an edge. Red edges indicate positive correlations in the expression of two proteins and green edges indicate negative correlations. Correlation strength is shown by the edge width. (C) Expression levels of lipocalins in esophageal adenocarcinoma and esophageal squamous cell carcinoma. (D) The power law distribution of the node degree network and analysis of other network parameters. (E) Functional map of the lipocalins PPIN. Functionally grouped network with GO terms are represented as nodes, which were linked based on their kappa score level (≥0.3), suggesting overlapped enriched genes. The similar GO terms were labeled in the same color.

The expression values of 31 out of 36 lipocalins were found in esophageal carcinoma expression profile GSE26886. The trends in the expression of many lipocalins in both EAC and ESCC remained highly consistent (Fig. 3C). Some lipocalins showed significant changes in this esophageal carcinoma expression profile and even the contrast trends were observed in EAC and ESCC. For example, FABP5 was upregulated 4.23-fold in EAC, but downregulated 1.68-fold in ESCC. CRABP2 was overexpressed in EAC by 6.38-fold and downregulated 4.14-fold in ESCC. LCN2 was significantly decreased 6.78-fold in ESCC.

Lipocalin PPIN topology parameters

Real biological networks (e.g. PPIN) are distinguishable from random networks by their distinguishing topological characteristics. The power law of node degree distribution is one of most important criteria24,25. As shown in Fig. 3D, the distributions of node degree approximately followed power law distributions, with an R2 = 0.771. Like many other co-expression PPI networks, they exhibited scale-free topology with R2 values above 0.626,27. As the node degrees increase, the average clustering coefficients declines continuously, indicating which node degree distributions fit the power line curve best. Other network parameters, including cluster coefficient, network diameter, network centralization and network density, are also shown in Fig. 3D. These results suggested that the lipocalin PPIN characterizes scale-free, small world. The illustration of four other important network topology parameters, including closeness centrality, topological coefficients, neighborhood connectivity distribution and average clustering coefficient distribution, are shown in Supplementary Figure S4.

Functional annotation map of lipocalin PPIN

To assess cellular activities related to lipocalins through their protein interactions within the PPI network, the enriched GO “Biological Process” terms for total proteins in the lipocalin PPIN were also analyzed in network format. A functional annotation map containing 19 GO terms was generated. In this map, proteins are represented as nodes according to their enriched GO terms, with the edges connecting the GO terms indicative of proteins share the same enriched GO terms (Fig. 3E). Several GO terms that had not shown GO enrichment during the previous process were discovered using the functional annotation of lipocalins PPIN, including “negative regulation of lipoprotein oxidation,” “phosphatidylcholine biosynthetic process,” “intestinal absorption,” and “digestive system process.”

Correlations in the expression of lipocalins in esophageal carcinoma

To gain insight into whether there are co-expression pattern for lipocalins in esophageal carcinoma, the expression correlation of lipocalins in EAC and ESCC were analyzed using the Pearson correlation and then were also clustered and visualized (Fig. 4). Several pairs of significant correlations were found in the heatmap (Table 2). These results suggested that some lipocalins are co-expressed in esophageal carcinoma and they might co-operate for certain biological functions.

Table 2 Significant correlations in the expression of lipocalins in esophageal carcinoma.
Figure 4
figure 4

Co-expression patterns of lipocalins in esophageal adenocarcinoma and esophageal squamous cell carcinoma.

Subcellular layers of lipocalins PPIN

The appropriate subcellular localization and translocations of proteins are crucial to their functionality. Their functions include complex formation, signal transduction, protein modification and disease28. In a network, nodes were re-distributed by Cerebral plugin according to their subcellular localization without changing their interactions. The lipocalin PPIN was here divided into 7 layers: secreted, membrane, cytoplasm, secreted/nucleus, membrane/nucleus, cytoplasm/nucleus and nucleus (Fig. 5A). These results suggest that lipocalins can transfer cell signals of themselves or their binding complexes from the extracellular space to the intracellular space and nucleus, possibly through the noncanonical pathways.

Figure 5
figure 5

(A) Subcellular layers illustrating the lipocalins PPIN. (B) 20 possible paths from LCN2 to RB1 with integrated with their subcellular localization.

To further illustrate the strength of this kind of analysis, the shortest path algorithm was used to find the possible shortest path from LCN2 to RB1 (retinoblastoma 1) and identify the linking proteins between LCN2 and RB1. RB1 is involved in many cellular pathways and acts as a transcriptional regulator. It can bind several transcription factors29. The 20 shortest paths from LCN2 to RB1 were found (Table 3); all were 4 in length. These proteins in the paths were distributed according to their sub-cellular localizations (Fig. 5B). Results confirmed that APOD can move into the nucleus after lipopolysaccharide (LPS) treatment30. The shortest paths from APOD to another transcription factor TP63 were also analyzed and 17 were found (Supplementary Figure S4 and Table S2).

Table 3 Possible signal pathways from LCN2 to RB1.

Discussion

Lipocalins are identified in various organisms, such as bacteria, plants, arthropods and vertebrates2,3,4,5. An increasing number of sequences with lipocalin conserved domain are found in protein databanks. The amino acid sequences of lipocalins are quite diverse and low levels of sequence identity, even below 20%, were found between the overall sequences among some members of the family. Despite the low level of sequence similarity, the tertiary structures of lipocalins are strongly preserved31. Although a great deal of attention has been paid to the lipocalin family across species, the lipocalins in Homo sapiens have not yet been reviewed in a systematic way, nor integrated with their expression data as they relate to human cancer. In this study, the human lipocalins were analyzed alongside their PPIN and their expression trends in esophageal carcinoma.

As in previous reports, the human lipocalins also contains the typical lipocalin domain. It has been reported that lipocalins across species contain three SCRs19. However, four SCRs were here found in human lipocalins21. This suggested that human lipocalins are more conserved than homologous lipocalins from other species. These human lipocalins could be divided into two groups based on the similarities of their protein sequence. This was also confirmed from the structural point of view by comparison of three-dimensional structures of these two grouped lipocalins. This suggested that human lipocalins might have evolved in two different ways, providing raw material for their functional innovation. These results also provided important clues to explore their different expressions and functions32.

To gain a full insight into the functions and characters of lipocalins, the Functional Annotation Chart from DAVID bioinformatics was used to annotate them. More than 40 annotation coverages were performed to increase the analytic power, allowing the investigators to analyze their genes from many different biological perspectives in a single space. Results showed the enrichment of lipocalins to be involved in functions related to binding and transporting. The functional terms not from the GO are an important additional information for GO. Other potential functions may be revealed in the future. These functional terms could be used to explain the multiple molecular mechanisms of lipocalins. For example, the enriched “PPAR signaling pathway” contained 6 lipocalins. It has been suggested that this pathway is essential to the regulation of cellular differentiation, development and metabolism (carbohydrate, lipid, protein) and tumorigenesis of higher organisms33,34,35. These results provide links between lipocalins with human diseases and clues that can be used to assess possible functions of lipocalin.

Accumulated studies have shown that an integrative analysis of gene expression and PPIN can provide deep insights into the molecular mechanisms of diseases, or specific genes36,37. A PPIN was described for human lipocalins based on their direct proteins interactions. This is the first time that a human lipocalin PPIN has been presented showing their all known protein interactions. This lipocalin PPIN contained 151 proteins, including 23 lipocalins. Though this is a small, specific PPIN, the topological parameters, especially the power-law degree distribution, indicated that it is also a true biological network, characterized both small-world and scale-free. Lipocalins are characterized by multiple molecular recognition properties, including binding to their cell surface receptors. Many lipocalin receptors have been identified38. In the PPIN, the lipocalin receptors were easier to find than by searching the references one by one. LCN2 is important gene. It promotes cancer cell metastasis and invasion in esophageal carcinoma, which transfers iron by snatching siderophores through its receptor, LRP2. Our previous study has identified a novel splicing variant of LCN2 receptor in ESCC. Both NGAL and its receptor are overexpressed in ESCC39,40. Esophageal cancer is the sixth most common fatal human cancer in the world and the histological type of squamous cell carcinoma is one of the most common cancers in the Chinese population41,42. In this study, the expression data of lipocalins and their interacting proteins were integrated into lipocalin PPIN, to indicate their possible co-expression in esophageal carcinoma. Several lipocalins showed significant changes in esophageal carcinoma, suggesting that expression of these lipocalins might be correlated to the progression of esophageal cancer. Consistent with the results in this study, many lipocalins have been found to be dysregulated in esophageal carcinoma. FABP5 is related to radiosensitivity of ESCC cell line TE-11, with a high degree of DNA methylation within its promoter region in three ESCC cell lines (TE-1, TE-2 and TE-10)43,44. The expression of LCN2 was visibly decreased in the GSE26886 esophageal carcinoma expression data, which contradicts previous results. It was previously reported that both LCN2 and its receptor are upregulated in the Chinese ESCC clinical samples and can serve as independent prognostic factors for ESCC40. This difference might be attributable to the different sources of esophageal carcinoma clinical samples; the clinical samples of GSE26886 came from Germany. Another GEO dataset GSE45168, was designed to analyze the esophageal cancer clinical samples collected in China. In this case, LCN2 was found to be upregulated with 3.01-fold in GSE45168.

It was here presumed that the biological effects of lipocalins can become more pronounced through the cascades of protein-protein interactions. The lipocalin PPIN was annotated using GO in a network format, showing that this PPIN involves various biological entities, closely related to the currently known functions of lipocalins. However, it was here believed that this kind network functional annotation for PPIN could be expanded when more lipocalins directly interacting proteins that are identified in future.

Another interesting finding in this study is the co-expression pattern of lipocalins in esophageal carcinoma. For example, LCN2 is significantly co-expressed with RBP4 in both EAC and ESCC. Several reports have used the detection of both LCN2 and RBP4 as biomarkers of risk of disease. It is here suggested that circulating levels of LCN2 and RBP4 are positively correlated with carotid IMT and subclinical atherosclerosis in type 2 diabetes45. Significant elevation in serum concentrations of LCN2 and RBP4 has been observed in pancreatic cancer patients46. These results highlight a possible role of the co-expressed lipocalins in the pathogenesis and their possible interplay in diseases.

Subcellular localization is one type of important information that can indicate the participation of proteins in the cellular activities at the subcellular level47. Usually, cellular signaling is transduced by the certain flows or cascades of PPI, which are distributed in several of subcellular localizations. In this study, subcellular localization information was incorporated into the lipocalin PPIN, generating biologically intuitive pathway-like layouts in the network. It was here presumed that the lipocalins not only transported small molecules into the cell, but also transferred extracellular signals into the cell and even the nucleus through the cascades of protein-protein interactions. Several lipocalins are able to translocate into nucleus. It has suggested that one lipocalin, CRABP2 (cellular retinoic acid-binding protein 2), transports retinoic acid into the nucleus to regulate transcription of target genes with heterodimeric nuclear receptors RAR(α, β, γ)48. Do et al. found that, under normal conditions of NIH/3T3, APOD was mainly perinuclear but it accumulated in the cytoplasm and nucleus under these stress conditions. The nuclear APOD appears to have been derived from the secreted protein. Do et al. supposed that it might act as an extracellular ligand transporter or transcriptional regulator depending on its location30. For this reason, it is credible that the lipocalin PPIN may directly or indirectly affect the signal cascades through the flow of extracellular-membrane-cytoskeleton/cytoplasm-nucleus, causing many different biological effects. To find possible paths from extracellular to nucleus, the possible shortest paths from LCN2 to RB1 were analyzed. A total of 20 paths between LCN2 and RB1 were found. According to their subcellular locations, most of these paths follow the flow from extracellular to cytoplasm to the nucleus. It is possible that the lipocalins can affect signal transduction and subsequent biological effects.

In summary, the current findings may facilitate a more comprehensive understanding of human lipocalins. The PPIN outlined here was integrated with the expression data from human cancer and the functional enrichment annotation. These analyses also expand our knowledge of this ancient protein family. The current study also provided a workflow to analyze a protein family through high-throughput experiments.

Materials and methods

Human lipocalin protein sequence collection

Human lipocalins were collected based on searches in the National Center for Biotechnology Information database (http://www.ncbi.nlm.nih.gov/protein) and the proteins of these genes were confirmed to contain the lipocalin domain through a query in UniProt protein database (http://www.uniprot.org/). The FASTA format of the protein sequences was retrieved from Uniprot database for future analyses.

Protein sequence alignment and comparison of structures

The human lipocalin protein sequence alignment was performed using ClustalW embedded in BioEdit software (http://www.mbio.ncsu.edu/bioedit/bioedit.html) to indicate the conserved regions. The alignment was also carried out at http://www.ebi.ac.uk/Tools/msa/clustalw2/ to obtain the percent identity matrix result, which contained features similar to those of every two protein sequences. To visualize the results, the matrix was log-transformed, clustered using Cluster 3.0 software and viewed in the TreeView49. To further illustrate the conservation of lipocalins at three-dimensional (3D) structure level, the 3D structures of lipocalins available as of May 2015 were retrieved from PDB database (http://www.rcsb.org/pdb/home/home.do) and compared using the PDBeFold program (http://www.ebi.ac.uk/msd-srv/ssm/).

Functional Annotation Chart of lipocalins

To better understand the functional classification and correlation of the lipocalins, an enrichment analysis was performed using Functional Annotation Chart in DAVID bioinformatics (http://david.abcc.ncifcrf.gov/). This system can identify over-represented biological terms associated with a given gene list. Functional Annotation Chart covers more than 40 annotation categories, including Gene Ontology (GO) terms, protein-protein interactions, protein functional domains, disease associations, pathways, sequence features, homology and gene functional summaries. Terms from the Functional Annotation Chart that were significantly enriched were visualized using the Cytoscape Enrichment Map plugin (P < 0.05)50. To compare and confirm the results shown on the Functional Annotation Chart, the lipocalin genes were also enriched in WebGestalt at http://bioinfo.vanderbilt.edu/webgestalt/.

Protein-protein interaction network (PPIN) construction

The newest versions of validated human protein-protein interaction datasets were downloaded from both HPRD (http://www.hprd.org/) (Release 9) and BioGRID (http://thebiogrid.org/) (Release 3.2.107), which are derived from studies of both low- and high-throughput experimental results51,52. These two datasets have been widely used in studies of disease involving human PPI networks and their reliability has been assessed. In this study, the non-redundant interactions of Homo sapiens species from these two datasets were integrated manually. They contained 18,595 unique proteins and 174,552 interactions and were used as the parental PPIN. Cytoscape software was used for construction, visualization and analysis of PPIN53. In Cytoscape, a PPIN is illustrated as a graph with the nodes as the proteins and the edges representing their interactions. A lipocalin PPIN was constructed. It contains both the lipocalins and their direct PPI neighbors and the interactions between these proteins. The details of the steps in the construction of the PPIN were performed as described previously54. Briefly, lipocalins were used as seed proteins and mapped to the parental PPIN. Cytoscape menus of “Select → Nodes → First Neighbors of Selected Nodes” and “New → Network → From Selected Nodes, All Edges” were used to extract the PPIN. Only the first level of interactions was extracted, generating the specific lipocalin PPIN. Duplicated edges, single nodes and self-interactions in the lipocalin PPIN were considered redundant and removed.

Gene ontology (GO) annotation was integrated into the lipocalin PPIN by mining for enriched GO “biological process” terms of proteins using the ClueGO plugin, which facilitates the annotation and visualization of enriched GO terms in the form of network. In this analysis, only the enriched GO terms with P-values < 0.01 were considered significant. A kappa score was set to 0.3 as the threshold, indicating the relationships between the terms based the number of same genes55.

Expression of lipocalins in esophageal carcinoma

The esophageal carcinoma expression profile GSE26886 is available at GEO (http://www.ncbi.nlm.nih.gov/geo/), which detects the expression data from 19 normal esophageal squamous epithelial samples, 21 esophageal adenocarcinoma (EAC) samples and 9 esophageal squamous cell carcinoma samples (ESCC)56. The fold changes of the expression of genes in EAC and ESCC compared to normal esophageal squamous epithelium and analyzed using the GEO2R program57. The fold-changes of lipocalins and their interacting proteins in EAC and ESCC served as parameters were displayed in PPIN.

Network topological parameters analyses

The topological parameters of lipocalins PPIN were analyzed using NetworkAnalyzer, which can compute computing network diameter, density, centralization, heterogeneity and clustering coefficient, providing insight into the organization and structure of complex networks58. The degree of a node is the number of proteins in the network to which it is directly connected. In this study, the power law of distribution of node degrees, one of most important network topological characteristics, was analyzed as in our previous works54. Briefly, distribution of node degree P(k) is defined as the number of nodes with a degree k for k = 0, 1, 2, … The pattern of their dependencies was determined by fitting a line on the node degree distribution data. NetworkAnalyzer calculates the fitting the line where the power law curve of the forms y = βxa. The R2 value is used to quantify the fit to the power line, which is very close to 1 when the fit is good.

Subcellular layers of the lipocalin PPIN

The subcellular localization classification of each protein in the lipocalins PPIN was retrieved from the HPRD GENE-ONTOLOGY annotation file, which was imported into Cytoscape as a node attribute of lipocalin PPIN. Cerebral (http://www.pathogenomics.ca/cerebral/) was used to re-distribute the nodes in lipocalins PPIN into different subcellular localizations without changing their interactions, which looks like a pathway diagram59. The igraph R program was used to find the shortest path between LCN2 and RB1 (retinoblastoma 1) in the lipocalin PPIN. The shortest path algorithm was able to find the shortest connection between two nodes in the graph60. The protein members in these shortest paths were also displayed in different layouts according to their subcellular localizations, showing the possible pathways from LCN2 to RB1.

Expression correlation of lipocalin and their interacting proteins in esophageal carcinoma

The expression correlations between every two lipocalins in esophageal carcinoma, including EAC and ESCC, were analyzed by a customized R program using Pearson correlation method. The correlation coefficient was log-transformed and clustered using Cluster 3.0 software and viewed in TreeView49. Moreover, to gain a full view of their correlation patterns, the expression correlation for every two interacting protein was integrated into the lipocalin PPIN and served as edge weight.

Additional Information

How to cite this article: Du, Z.-P. et al. A systematic analysis of human lipocalin family and its expression in esophageal carcinoma. Sci. Rep. 5, 12010; doi: 10.1038/srep12010 (2015).

References

  • Goetz, D. H. et al. Ligand preference inferred from the structure of neutrophil gelatinase associated lipocalin. Biochemistry 39, 1935–41 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Salier, J. P. Chromosomal location, exon/intron organization and evolution of lipocalin genes. Biochim. Biophys. Acta 1482, 25–34 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Grzyb, J., Latowski, D. & Strzałka, K. Lipocalins – a family portrait. J. Plant Physiol. 163, 895–915 (2006).

    CAS  PubMed  Article  Google Scholar 

  • Flower, D. R. The lipocalin protein family: structure and function. Biochem. J. 318, 1–14 (1996).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Breustedt, D. A., Schönfeld, D. L. & Skerra, A. Comparative ligand-binding analysis of ten human lipocalins. Biochim. Biophys. Acta 1764, 161–73 (2006).

    CAS  PubMed  Article  Google Scholar 

  • Hieber, A. D., Bugos, R. C. & Yamamoto, H. Y. Plant lipocalins: violaxanthin de-epoxidase and zeaxanthin epoxidase. Biochim. Biophys. Acta 1482, 84–91 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Bishop, R. E. The bacterial lipocalins. Biochim. Biophys. Acta 1482, 73–83 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Xu, S. & Venge, P. Lipocalins as biochemical markers of disease. Biochim. Biophys. Acta 1482, 298–307 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Dassati, S., Waldner, A. & Schweigreiter, R. Apolipoprotein D takes center stage in the stress response of the aging and degenerative brain. Neurobiol. Aging 35, 1632–42 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Zhou, Y. & Rui, L. Lipocalin 13 regulation of glucose and lipid metabolism in obesity. Vitam. Horm. 91, 369–83 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Dartt, D. A. Tear lipocalin: structure and function. Ocul. Surf. 9, 126–38 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  • Dahlbäck, B. & Nielsen, L. B. Apolipoprotein M--a novel player in high-density lipoprotein metabolism and atherosclerosis. Curr. Opin. Lipidol. 17, 291–5 (2006).

    PubMed  Article  CAS  Google Scholar 

  • Sia, A. K., Allred, B. E. & Raymond, K. N. Siderocalins: Siderophore binding proteins evolved for primary pathogen host defense. Curr. Opin. Chem. Biol. 17, 150–7 (2013).

    CAS  PubMed  Article  Google Scholar 

  • Correnti, C. & Strong, R. K. Mammalian siderophores, siderophore-binding lipocalins and the labile iron pool. J. Biol. Chem. 287, 13524–31 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Bratt, T. Lipocalins and cancer. Biochim Biophys Acta 1482, 318–26 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Lippi, G. et al. Neutrophil gelatinase-associated lipocalin in cancer. Adv. Clin. Chem. 64, 179–219 (2014).

    CAS  PubMed  Article  Google Scholar 

  • Koistinen, H. et al. The role of glycodelin in cell differentiation and tumor growth. Scand. J. Clin. Lab. Invest. 69, 452–9 (2009).

    CAS  PubMed  Article  Google Scholar 

  • Søiland, H. et al. Emerging concepts of apolipoprotein D with possible implications for breast cancer. Cell Oncol. 29, 195–209 (2007).

    PubMed  PubMed Central  Google Scholar 

  • Flower, D. R., North, A. C. & Sansom, C. E. The lipocalin protein family: structural and sequence overview. Biochim. Biophys. Acta 1482, 9–24 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Gutiérrez, G., Ganfornina, M. D. & Sánchez, D. Evolution of the lipocalin family as inferred from a protein sequence phylogeny. Biochim. Biophys. Acta 1482, 35–45 (2000).

    PubMed  Article  Google Scholar 

  • Flower, D. R. Experimentally determined lipocalin structures. Biochim. Biophys. Acta 1482, 46–56 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Skerra, A. Lipocalins as a scaffold. Biochim Biophys Acta 1482, 337–50 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Hunter, S. et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 40, D306–12 (2012).

    CAS  Article  PubMed  Google Scholar 

  • Maslov, S. & Sneppen, K. Specificity and stability in topology of protein networks. Science 296, 910–3 (2002).

    ADS  CAS  PubMed  Article  Google Scholar 

  • Zhu, X., Gerstein, M. & Snyder, M. Getting connected: analysis and principles of biological networks. Genes Dev. 21, 1010–24 (2007).

    CAS  PubMed  Article  Google Scholar 

  • Carlson, M. R. et al. Gene connectivity, function and sequence conservation: predictions from modular yeast co-expression networks. BMC Genomics 7, 40 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Sengupta, U. et al. Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications. PLoS One 4, e8100 (2009).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Hung, M. C. & Link, W. Protein localization in disease and therapy. J. Cell Sci. 124, 3381–92 (2011).

    CAS  PubMed  Article  Google Scholar 

  • Indovina, P. et al. Emerging roles of RB family: new defense mechanisms against tumor progression. J. Cell Physiol. 228, 525–35 (2013).

    CAS  PubMed  Article  Google Scholar 

  • Do, Carmo. S., Levros, L. C. Jr. & Rassart, E. Modulation of apolipoprotein D expression and translocation under specific stress conditions. Biochim. Biophys. Acta 1773, 954–69 (2007).

  • Ganfornina, M. D. et al. A phylogenetic analysis of the lipocalin protein family. Mol. Biol. Evol. 17, 114–26 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Zhang, J. Evolution by gene duplication: an update. Trends in ecology & evolution 18, 292–298 (2003).

    Article  Google Scholar 

  • Belfiore, A., Genua, M. & Malaguarnera, R. PPAR-γ agonists and their effects on IGF-I receptor signaling: Implications for cancer. PPAR Res. 2009, 830501 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Berger, J. & Moller, D. E. The mechanisms of action of PPARs. Annu. Rev. Med. 53, 409–35 (2002).

    CAS  PubMed  Article  Google Scholar 

  • Feige, J. N. et al. From molecular action to physiological outputs: peroxisome proliferator-activated receptors are nuclear receptors at the crossroads of key cellular functions. Prog. Lipid. Res. 45, 120–59 (2006).

    CAS  PubMed  Article  Google Scholar 

  • Zhu, W., Yang, L. & Du, Z. Layered functional network analysis of gene expression in human heart failure. PLoS One 4, e6288 (2009).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Chen, J., Aronow, B. J. & Jegga, A. G. Disease candidate gene identification and prioritization using protein interaction networks. BMC Bioinformatics 10, 73 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Flower, D. R. Beyond the superfamily: the lipocalin receptors. Biochim. Biophys. Acta 1482, 327–36 (2000).

    CAS  PubMed  Article  Google Scholar 

  • Fang, W. K. et al. A novel alternative spliced variant of neutrophil gelatinase-associated lipocalin receptor in oesophageal carcinoma cells. Biochem. J. 403, 297–303 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Du, Z. P. et al. Neutrophil gelatinase-associated lipocalin and its receptor: independent prognostic factors of oesophageal squamous cell carcinoma. J. Clin. Pathol. 64, 69–74 (2011).

    PubMed  Article  Google Scholar 

  • Ferlay, J. et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer 127, 2893–917 (2010).

    CAS  PubMed  Article  Google Scholar 

  • Yang, L. et al. Estimates of cancer incidence in China for 2000 and projections for 2005. Cancer Epidemiol Biomarkers Prev. 14, 243–50 (2005).

    PubMed  Article  Google Scholar 

  • Ogawa, R. et al. Identification of candidate genes involved in the radiosensitivity of esophageal cancer cells by microarray analysis. Dis. Esophagus 21, 288–97 (2008).

    CAS  PubMed  Article  Google Scholar 

  • Arai, M. et al. Analysis of the methylation status of genes up-regulated by the demethylating agent, 5-aza-2’-deoxycytidine, in esophageal squamous cell carcinoma. Oncol. Rep. 20, 405–12 (2008).

    CAS  PubMed  Google Scholar 

  • Xiao, Y. et al. Circulating lipocalin-2 and retinol-binding protein 4 are associated with intima-media thickness and subclinical atherosclerosis in patients with type 2 diabetes. PLoS One 8, e66607 (2013).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  • El-Mesallamy, H. O. et al. Serum retinol binding protein-4 and neutrophil gelatinase-associated lipocalin are interrelated in pancreatic cancer patients. Scand J. Clin. Lab. Invest. 72, 602–7 (2012).

    CAS  PubMed  Article  Google Scholar 

  • Chou, K. C. & Shen, H. B. Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Nat. Protoc. 3, 153–62 (2008).

    CAS  PubMed  Article  Google Scholar 

  • Manolescu, D. C. et al. Newborn serum retinoic acid level is associated with variants of genes in the retinol metabolism pathway. Pediatr. Res. 67, 598–602 (2010).

    CAS  PubMed  Article  Google Scholar 

  • de Hoon, M. J. et al. Open source clustering software. Bioinformatics 20, 1453–4 (2004).

    CAS  PubMed  Article  Google Scholar 

  • Merico, D. et al. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5, e13984 (2010).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Goel, R. et al. Human protein reference database and human proteinpedia as discovery resources for molecular biotechnology. Mol. Biotechnol. 48, 87–95 (2011).

    CAS  PubMed  Article  Google Scholar 

  • Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41, D816–23 (2013).

    CAS  PubMed  Article  Google Scholar 

  • Smoot, M. E. et al. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431–2 (2011).

    ADS  CAS  Article  PubMed  Google Scholar 

  • Wu, B. et al. Network based analyses of gene expression profile of LCN2 overexpression in esophageal squamous cell carcinoma. Sci. Rep. 4, 5403 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–3 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Wang, Q., Ma, C. & Kemmner, W. Wdr66 is a novel marker for risk stratification and involved in epithelial-mesenchymal transition of esophageal squamous cell carcinoma. BMC Cancer 13, 137 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Barrett, T. et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 41, D991–5 (2013).

    CAS  Article  PubMed  Google Scholar 

  • Assenov, Y. et al. Computing topological parameters of biological networks. Bioinformatics 24, 282–4 (2008).

    CAS  PubMed  Article  Google Scholar 

  • Barsky, A. et al. Cerebral: a Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Bioinformatics 23, 1040–2 (2007).

    CAS  PubMed  Article  Google Scholar 

  • Csardi, G. & Nepusz, T. The igraph software package for complex network research. Inter Journal, Complex Systems 5, 1695 (2006).

    Google Scholar 

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (No. 81172264 and No. 81472613), the National High Technology Research and Development Program of China (No. 2012AA02A503 and No. 2012AA02A209), the Natural Science Foundation of China-Guangdong Joint Fund (No. U1301227) and the Science and Technology Program of Guangdong (2011B060300025). Natural Science Foundation of Guangdong Province, China (No. 2014A030310390).

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Z.D. and B.W. performed most of the analyses and composed the manuscript. X.W., X.L., X.Q., X.Z., D.W., S.W., J.S., C.Z. and Z.W. carried out the gene information collection and R programming, helped in Cytoscape analyses and drafted the manuscript. L.X. and E.L. participated in study design and coordination, provided material support, obtained funding and supervised the study. All authors reviewed and approved the final manuscript.

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Du, ZP., Wu, BL., Wu, X. et al. A systematic analysis of human lipocalin family and its expression in esophageal carcinoma. Sci Rep 5, 12010 (2015). https://doi.org/10.1038/srep12010

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