Fanconi Anemia complementation group C protein in metabolic disorders

Establishment of cells expressing different levels of FANCC, which can modulate tumorigenicity

Metabolomics study has become a powerful aspect of research to further our understanding of the basic, biological mechanisms underlying obesity, diabetes, stroke or cancer [19]. In the recognition of the diabetes-prone phenotype in FANCC knockout mice [18], that is not a typical clinical complication shown in FA, we decided to metabolically probe the function of FANCC to broaden the knowledge learned from FA. To avoid the influence of the FA signaling pathway and to systematically study the unique chemical fingerprints driven by FANCC only, we chose cells that carry an impaired FA pathway and also are easily to be built into the corresponding derivative cells to express FANCC at different levels. As shown in Figure 1A and Figure S1A, B, MDA-MB-231 and its derivative cells were validated with an undetectable level of FANCI expression, agreed with the absence of normal chromosome 15 (ATCC) where FANCI locus is located. Therefore, MDA-MB-231 cells essentially carry a non-functioning FA pathway in regards to the fact that FANCI is a partner of FANCD2 to work at the center of the FA signaling pathway [7,20]. FANCC-high or low expression cells were subsequently established from MDA-MB-231cell line (Figure 1B). Interestingly, the expression level of FANCC protein is relatively low in MDA-MB-231 cells compared to the cell lines detected (Figure 1A). This background of MDA-MB-231 cells may have also conferred the effects driven by the ectopically expressed FANCC. Indeed, those FANCC- high cells proliferated relatively slow, formed less number of colonies in soft agar, and carried a reduced migration capacity (Figure 1C, D and Figure S1A, D), suggesting that FANCC can reduce the tumorigenicity of MDA-MB-231 cells.

FANCC-driven metabolomes

Using targeted (UPLC-MS/MS) and untargeted (GC-TOFMS) metabolomics analysis, we determined the metabolite alteration within the set of MDA-MB-231 cells expressing FANCC at different levels (Figure 1B). A total of 205 metabolites were found to be altered significantly between FANCC-high and FANCC-low cells (Table S1). Principal component analysis (PCA) showed a clear separation between empty vector control/FANCC-low and FANCC-high cell groups, providing the evidence that FANCC changes metabolomes (Figure 2A). Heat map made of 29 metabolites (randomly picked up from 205) shows a vivid difference in the metabolomes of two cell groups (Figure 2B): blue color of metabolites indicates reduced metabolites in the metabolomes of cells; while red color indicates those elevated metabolites. Therefore, FANCC at least performs biologic functions that can ultimately lead to the changes of 205 metabolites, which are a better output to tell the occurred cellular processes initiated by FANCC, in an FA pathway-independent manner.

Involvement of FANCC in multi-aspects of cellular processes

To investigate the roles of metabolite alterations triggered by FANCC in the diseases with metabolite dysfunction such as diabetes, inflammation and cancer, we performed molecular pathway and network analyses using Ingenuity Pathway Analysis (IPA). We found that inosine, hypoxanthine, pyrophosphate, L-asparagine, L-phenylalanine, acetyl-L-carnitine, beta-alanine, glutaryl carnitine, niacinamide and creatinine significantly went up in FANCC-high cells compared to FANCC-low cells. IPA revealed that tRNA (19/43), Superpathway of citrulline Metabolism (9/24), Arginine Biosynthesis IV (7/18), Asparagine Biosynthesis (5/8) and Urea cycle (6/14) were the five most significantly altered pathways in cells expressing FANCC at a higher level compared to the control cells (FANCC-low cells) (Figure S2 and Table S2). Furthermore, we found that different metabolites are involved in different types of cellular functions. Among all metabolites, 34% are involved in cellular compromise, lipid metabolism, small molecular biochemistry, 23% are found to be involved in amino acid metabolism, molecular transport. Similarly, 22% are associated with nucleic acid metabolism, 18% is in cellular movement and 3% is related to cellular growth proliferation, organism development, injury and abnormalities (Figure S3 and Table S3). These results provide information suggesting FANCC is related to different diseases by regulating different molecular and cellular functions.

Functional pathways initiated from FANCC

To analyze the specific relations of metabolites in the cells with FANCC potentially for different diseases, we uploaded the identified 205 metabolites to the IPA for functional analysis. For the robustness of the IPA, we normalized metabolic data as described previously [21] on the basis of a total number of cells prepared for the profiling. The metabolic functions (canonical pathways, disease and Bio-functions, molecular and cellular functions, physiological system development and functions, clinical chemistry and hematology, cardiotoxicity, hepatotoxicity and nephrotoxicity) are summarized in Tables S1 and S2. The most significantly biological functions associated with FANCC were cancer (p=4.39×10 ^-02– 5.93X10 ^-14, 30 metabolites), inflammatory disease (p=3.67$\times$X10 ^-02–3.39$\times$10 ^-07, 7 metabolites) gastrointestinal disease ( p=4.39$\times$10 ^-02– 5.93$\times$10 ^-14, 28 metabolites), organismal injury and abnormalities (p=4.4$\times$10 ^-02– 3.39$\times$10 ^-07, 7 metabolites) amino acid metabolism (p=3.89$\times$10 ^-02 – 2.43$\times$10 ^-10, 21 metabolites) and liver inflammation/ Hepatitis (p=3.03$\times$10 ^-01 – 3.67$\times$10 ^-02 , 3 metabolites). All of these processes relatively went down owing to the higher expression of FANCC in comparison with FANCC-low cells. The five significant biological processes were cellular growth and proliferation (30 metabolites), amino acid metabolism (21 metabolites), molecular transport (33 metabolites), Small Molecule Biochemistry (36 metabolites) and cell cycle (13 metabolites). We also determined the most elevated molecules that are inosine, hypoxanthine, pyrosphosphate, L-asparagine, L-phenylalanine, acetyl-L-carnitine, niacinamide, glutarylcarnitine, and creatinine. These metabolites are involved in the suppression of cancer, inflammation as well as diabetes.

Altered metabolites involved in cancer, inflammation, aging and DM

Continuing our metabolomics studies [19] on the roles of the FA signaling pathway in human tumorigenesis, we here focused on an individual FA protein and studied the pathway-free roles of FANCC in a unique cell system in which the FA pathway is deficient (Figure 1). Using IPA analysis we found that cells with extopically expressed FANCC carried either elevated metabolites that are outcomes of tumor inhibitory forces, or the decreased metabolties that are driven by oncogenic potentials. Overall, the cancer promotion signaling was prohibited as shown in red color in FANCC-high cells compared with FANCC-low cells. Other listed pathways were found to be similar mitigated (Figure 3A). Specifically, we found that 30 metabolites were specifically involved in cancer (Figure 3B). Among these metabolites, docosahexaenoic acid [22] and sphingomyelin [23] were shown at a reduced level in FANCC-high cells, which are indicative of the cellular processes that promote cancer development. While L –methionine [24], acetyl-L-carnitine [25], L-aspartic acid [26], L-glutamic acid [27], niacinamide [28] and phosphorylethanolanine [29] were clearly elevated, which are symbolized for cancer inhibitory processes. Therefore, FANCC, besides joining the FA pathway, can also play important pathway-independent roles in the suppression of cancer formation.

We next checked the distinct metabolites associated with other metabolic disorders. As shown in Figure 4A, metabolites- glycine, citrulline, ornithine, L-asparagine, L-tyrosine, L-arginine, L-glutamine, L-leucine, L-isoleucine, L-alanine, L-valine, and L-proline [30] were elevated significantly in FANCC-high cells compared to the FANCC-low cells. Those metabolites are the signatures associated with aging inhibitory cellular processes, and FANCC thus can be directly implicated to also play crucial roles in the protection of humans from aging. More importantly, we also found that the levels of metabolites [the inosine [31] l-leucine, l-isoleucine, l-phenylalanine [32], hypoxanthine [33], and L-arginine [34]] were substantially changed in FANCC-high cells, which were directly related to inflammatory disease periodontitis (Figure 4B). Interestingly the level of inosine is 20 times higher in the cells carrying a high level of FANCC expression. Furthermore, we found the metabolites leading to another metabolite-disorder diabetes-mellitus (DM) (fumaric acid, fatty acid, D-glucose, carbon monoxide, collagens, 2-oxoglutaric acid) were altered in FANCC-high cells (Figure 5 A). Among those metabolites, signature ones (fumaric acid and collagens [35,36] to inhibitory DM processes were elevated (colored) in FANCC-high cells. While those metabolites that were anticipated to promote DM [35,36], however, were decreased (uncolored). Together, these findings suggest that FANCC is involved in the regulation of various disorders from cancer, aging, to inflammation as well as to DM, consistent with DM-prone mice deficient in FANCC expression [18].

Cell culture and chemicals

Human breast cancer cell line MDA-MB-231 was obtained from the America Type Culture Collection (ATCC, Manassas, VA). The cell lines were prepared as previously described [47]. For the experiments, cells were trypsinized, and one million cells were counted and washed with 1X PBS; cell pellets were frozen at −80 °C. A total of three replicates were prepared for each cell types. Ethanol, pyridine, methoxyamine hydrochloride, C8−C30 fatty acid methyl esters (FAMEs), and ammonium acetate were purchased from Sigma-Aldrich (St. Louis, MO). Liquid chromatography−mass spectrometry (LC−MS) Optima-grade methanol and acetonitrile, formic acid, N-methyl-N-trimethyl –silyl-tri-fluoro-acetamide (MSTFA) with 1% TMCS, and hexane were obtained from Fisher Sci. (Fair Lawn, NJ). The ultrapure water was produced by Millipore Advantage A10 system with an LC−MS Polisher filter (Billerica, MA).

Cloning, transfection and Western blotting

For cloning a full-length human FANCC, PCR amplification used primers (Forward: ATGGCTCAAGATTCAGTA, Reverse: CTAGACTTGAGTTCGCA). pcDNA3.1/NT-GFP-TOPO TA cloning and Expression (Kit from Invitrogen) and the building up of stable cell lines were carried out as shown previously [48]. For western blotting, protein samples were prepared from FANCC low or high group’s cells via using 1X SDS sample buffer, and separated in 8-10% SDS-PAGE and then transferred to the nitrocellulose membrane (Bio-Rad). Membranes were blocked with 5% non-fat skim milk in 1XPBS with tween-20 then incubated with FANCC and FANCI antibody diluted 1:1000 in 1XPBST. Horseradish peroxidase-conjugated rabbit or mouse antibodies were used as secondary antibody (1:5000-1:10000 dilution) for 1 hour at room temperature.

Trans-well and soft -Agar assays

Trans-well assay was carried out using Matrigel Invasion Chamber, 24 well plates 8.0 Micron purchased from Coring. Briefly, 2X10⁴ cells were cultured in Matrigel well plates with two types of cells (Control and FANCC) respectively. Cells that were not migrated were swapped; while cells that migrated into the other side were fixed and stained with crystal violet. The images and numbers of migrated cells were used to indicate cell migration capacity. For soft Agar assay, 2X DMEM/F12 media was used to prepare 0.6% of base agar in 35mm dish and kept 20 minuses in 4ᴼC to make agar completely solidified, 2X10³ Cells were plated along with 2XDMEM/F12 in 0.4% of top ager. The dishes were incubated for 2 weeks at 37◦C and stained with crystal violet.

Sample preparation for metabolic profiling

The cell line samples were prepared as previously described with modifications [49,50]. The appropriate weight of homogenizer beads and 50 μL of cold water were added to the cell-line samples for the first step of extraction. An aliquot of 270 μL of the mixture of ethanol and chloroform (3:1 = v/v) was added to the extracts for the second-step extraction. The sample extracts were centrifuged at 4 °C and 14500 rpm for 20 min. The supernatant was used for targeted metabolic profiling of lipids with Ultra-Performance Liquid-Chromatography tandem Mass Spectrometry (UPLC−MS/MS) and for untargeted metabolic profiling with Gas Chromatography -Time-of-Flight Mass Spectrometry (GC−TOFMS).

Lipid profiling

Each aliquot of 20 μL of the supernatant was added to a 96-well Biocrates Kit plate (Biocrates Life Sciences, Austria) for lipid quantitation. After samples were dried under nitrogen, each 300 μL of extraction solvent (5 mM ammonium acetate in methanol) was added, and the kit plate was gently shaken at room temperature for 30 min. The sample extracts were filtered through the 0.45 μm membrane of the kit plate, and each aliquot of 20 μL of sample was diluted with 380 μL of methanol with 5 mM ammonium acetate for flow-injection analysis (FIA) of lipids.

Each 10 μL of the sample was directly injected into the mass spectrometer with elution solvent (methanol with 5 mM ammonium acetate) at a varied flow rate from 30 to 200 μL/min within 3 min.

Untargeted metabolomics profiling with GC-TOFMS

Each aliquot of 250 μL above the supernatant was dried with a freeze-dryer. The dried samples were derivatized by methoxyamine hydrochloride in pyridine and subsequently by MSTFA. Retention indices of C8−C30 fatty acid methyl esters (FAMEs) were added for retention-time correction.

Each 1 μL sample was analyzed on an Agilent 7890A gas chromatography coupled to a Leco Pegasus time-off-light mass spectrometer (Leco Corp., St Joseph, MI) for global metabolite profiling. The analytes were introduced with a splitless mode to achieve maximum sensitivity and separated on an Rtx-5 MS capillary column (30 m × 0.25 mm I.D., 0.25 μm) (Restek, Bellefonte, PA). The column temperature was initially set to 80 °C for 2 min, increased to 300 °C for 12 min, and maintained at 300 °C for 5 min. The solvent delay was set to 4.4 min. The front inlet temperature, transferline temperature, and source temperature were set to 260 °C, 270 °C, and 220°C, respectively. The mass spectrometer was operated on a fullscan mode from 50 to 500 at an acquisition rate of 20 spectra/sec.

Statistical data analysis

The raw LC−MS/MS data files were processed with TargetLynx Application Manager (Waters Corp., Milford, MA) to extract peak area and retention time of each metabolite. The raw GC−TOFMS data files were processed with Chroma TOF software (Leco Corp., St Joseph, MI) to extract peak signal and retention time for each metabolite. The detected metabolites were annotated with our internal standard library using an automated mass spectral data processing (AMSDP) software package [51]. IPA was applied to visualize the overall difference between FANCC-expressing high and low cells along with SIMCA-P 12.0.1 (Umetrics, Umeå, Sweden). Nonparametric statistical analysis (i.e., the Mann−Whitney U test) was used for searching the significantly different metabolites between the groups with a critical p-value of 0.05 and 0.01.

Molecular pathway and network analysis in Ingenuity Pathway Analysis (IPA)

To systematically understand the metabolites in cells expressing a high level of FANCC compared to control cells expressing a basal level of FANCC, we uploaded the metabolite lists (with HMDB IDs) and the change folds of the differentially expressed metabolites onto an Ingenuity Pathway Analysis (IPA) server (http://3cr-apps.cc.hawaii.edu/ReserveIPA/). Canonical pathways and chemical−protein interaction networks were generated on the basis of the knowledge sorted in the Ingenuity Pathway Knowledge base. A ratio of the number of metabolites that map to the canonical pathway divided by the total number of molecules that map to the pathway was displayed. Fisher’s exact test was used to calculate the p-value to determine the probability that the association between the metabolites and the canonical pathway was explained by chance alone. The network score was based on the hypergeometric distribution and was calculated with the right-tailed Fisher’s exact test. The higher a score was, the more relevant the eligible submitted molecules were to the network.