Loss of Nmp4 optimizes osteogenic metabolism and secretion to enhance bone quality

Yu Shao; Emily Wichern; Paul J Childress; Michele Adaway; Jagannath Misra; Angela Klunk; David B Burr; Ronald C Wek; Amber L Mosley; Yunlong Liu; Alexander G Robling; Nickolay Brustovetsky; James Hamilton; Kylie Jacobs; Deepak Vashishth; Keith R Stayrook; Matthew R Allen; Joseph M Wallace; Joseph P Bidwell

doi:10.1152/ajpendo.00343.2018

. 2019 Jan 15;316(5):E749–E772. doi: 10.1152/ajpendo.00343.2018

Loss of Nmp4 optimizes osteogenic metabolism and secretion to enhance bone quality

Yu Shao ^1,^*, Emily Wichern ^2,^*, Paul J Childress ^3,⁴, Michele Adaway ², Jagannath Misra ⁵, Angela Klunk ², David B Burr ^2,^4,⁶, Ronald C Wek ⁵, Amber L Mosley ⁵, Yunlong Liu ¹, Alexander G Robling ^2,⁴, Nickolay Brustovetsky ⁷, James Hamilton ⁷, Kylie Jacobs ⁸, Deepak Vashishth ⁹, Keith R Stayrook ¹⁰, Matthew R Allen ^2,^4,¹¹, Joseph M Wallace ^3,^4,^6,^*, Joseph P Bidwell ^1,^2,^4,^*,^✉

PMCID: PMC6580174 PMID: 30645175

Abstract

A goal of osteoporosis therapy is to restore lost bone with structurally sound tissue. Mice lacking the transcription factor nuclear matrix protein 4 (Nmp4, Zfp384, Ciz, ZNF384) respond to several classes of osteoporosis drugs with enhanced bone formation compared with wild-type (WT) animals. Nmp4^−/− mesenchymal stem/progenitor cells (MSPCs) exhibit an accelerated and enhanced mineralization during osteoblast differentiation. To address the mechanisms underlying this hyperanabolic phenotype, we carried out RNA-sequencing and molecular and cellular analyses of WT and Nmp4^−/− MSPCs during osteogenesis to define pathways and mechanisms associated with elevated matrix production. We determined that Nmp4 has a broad impact on the transcriptome during osteogenic differentiation, contributing to the expression of over 5,000 genes. Phenotypic anchoring of transcriptional data was performed for the hypothesis-testing arm through analysis of cell metabolism, protein synthesis and secretion, and bone material properties. Mechanistic studies confirmed that Nmp4^−/− MSPCs exhibited an enhanced capacity for glycolytic conversion: a key step in bone anabolism. Nmp4^−/− cells showed elevated collagen translation and secretion. The expression of matrix genes that contribute to bone material-level mechanical properties was elevated in Nmp4^−/− cells, an observation that was supported by biomechanical testing of bone samples from Nmp4^−/− and WT mice. We conclude that loss of Nmp4 increases the magnitude of glycolysis upon the metabolic switch, which fuels the conversion of the osteoblast into a super-secretor of matrix resulting in more bone with improvements in intrinsic quality.

Keywords: bone biomechanics, metabolism, osteoporosis, parathyroid hormone, transcriptome

INTRODUCTION

Osteoporosis is a disease of attenuated bone mass and strength that significantly increases the risk of fragility fractures (92). Teriparatide (PTH) and abaloparatide (PTHrP) are currently the only Food and Drug Administration (FDA)-approved osteoanabolic therapies for this disease (52, 61). These drugs add new bone to the osteoporotic skeleton whereas the primary effect of anticatabolic drugs is a reduction in the pathologically elevated bone resorption (30). The benefits of PTH treatment include an increase in bone mass through a combination of new bone modeling and the sustained bone remodeling with a positive balance as well as improved bone material properties (13, 18, 29, 32, 59). However, the potency of PTH precipitously declines and there is an FDA-mandated 2-yr limit on treatment (18), emphasizing the need for new strategies that improve the efficacy of the drug, such as by combining hormone treatment with an anticatabolic drug or targeting PTH directly to bone (26, 83). Neutralizing intrinsic pathways that temper PTH-induced osteoblast secretion of bone matrix might improve drug efficacy. Indeed, a similar strategy of “inhibiting the inhibitor” (46) has led to the development of the osteoanabolic romosozumab, a monoclonal antibody that neutralizes the action of the osteoinhibitory protein sclerostin, currently under consideration by the FDA for clinical approval (3, 94).

We reported that the transcription factor nuclear matrix protein 4 (Nmp4, Zfp384, Ciz, ZNF384) suppresses the action of osteoanabolics (15, 16, 41, 70, 90, 95), and thus elucidation of the upstream and downstream effectors in the Nmp4 pathway may provide a map of the innate barriers to PTH-induced bone formation. Indeed, as a transacting protein Nmp4 is well positioned to control multiple aspects of bone formation. Genome-wide chromatin immunoprecipitation followed by high-throughput sequencing [chromatin immunoprecipitation sequencing (ChIP-seq)] analysis in MC3T3-E1 cells suggested that Nmp4 has wide-ranging effects on the transcriptome, with over 15,000 Nmp4 binding sites in the osteoblast genome. Of importance, nearly 70% of these sites are within −5 and +2 kb from a transcription start site (TSS) or within introns, both DNA regions that often harbor regulatory regions (16).

Nmp4^−/− mice exhibit more bone marrow osteoprogenitors than their wild-type (WT) littermates (16, 41, 95). Expanded cultures of Nmp4^−/− mesenchymal stem/progenitor cells (MSPCs) induced with osteogenic medium exhibit elevated mRNA expression of the bone matrix proteins type I collagen (Col1a1), osteocalcin (Bglap2), and osteopontin (Spp1). Additionally, the anabolic process of ribosome biogenesis is elevated in these cells, as is the expression of Gadd34 (PPP1r15a), which helps maintain translation and ultimately contributes to the continued trafficking of secretory protein through the endoplasmic reticulum (ER) despite increased protein loads (16, 20, 114).

To address the cellular pathways by which Nmp4 suppresses osteoblast-mediated bone formation, we performed high-throughput RNA sequencing (RNA-seq) of WT and Nmp4^−/− expanded MSPCs during osteogenesis. Network analyses of the RNA-seq output were used for driving hypothesis testing, i.e., select pathways that were significantly altered in the transcriptome were evaluated experimentally. The results phenotypically anchored bioinformatic predictions to changes in metabolic and biochemical properties of the Nmp4^−/− osteogenic cells. Based on the RNA-seq data, we hypothesized that Nmp4^−/− osteoblasts elaborate a matrix that improves bone material and structural characteristics. Therefore we examined these bone properties from experimental WT and Nmp4^−/− mice that had undergone various osteoporosis therapies. These data reveal new aspects of how loss of Nmp4 alters bone matrix secretion as well as the impact of this single gene on bone quality.

MATERIALS AND METHODS

Cell culture.

MSPCs were derived from individual mice as previously described (16, 109). Briefly, long bone marrow was harvested from euthanized mice 6–8 wk of age, and a Ficoll gradient was used to isolate the mononuclear cells. These cells were seeded in Mesencult Media + Mesencult Stimulatory Supplement (StemCell Technologies, Vancouver, Canada) and sustained for 3–4 wk without passage while being fed every 5–7 days by removing 50% of the old media and adding 50% fresh media, so as not to disturb the cells. Upon reaching 80% confluence, the cells were passaged at 1:3 dilution for 2 additional passages before use in experiments or were frozen for stock vials. Cells were used for study between passages 5–10. To assess the mineralization phenotype of each MSPC preparation, cells were seeded in α-MEM supplemented with 100 IU/ml penicillin, 100 µg/ml streptomycin, 25 µg/ml amphotericin, 2 mM l-glutamine (GIBCO-BRL, Life Technologies, Grand Island, NY), and 10% fetal bovine serum (Sigma-Aldrich, St. Louis, MO). At 48 h postseeding, the medium was refreshed and further supplemented with ascorbic acid (5 µg/ml; Sigma-Aldrich), dexamethasone (10 nM; Sigma-Aldrich), and 10 mM glycerol 2-phosphate disodium salt hydrate (BGP; Sigma-Aldrich). To visualize the mineralization in culture, cells were stained with alizarin red as previously described (16).

RNA-seq analysis.

To compare transcriptome profiles of nondifferentiating and osteogenic-differentiating WT and Nmp4^−/− MSPCs, cells were seeded into 12-well plates at either 10,000 cells/well (25 cells/mm²) or 25,000 cells/well (62 cells/mm²). The cells seeded at the lower density were maintained in Mesencult Media + Mesencult Stimulatory Supplement (nondifferentiating medium) for 3 days postseeding and then harvested for total RNA. Cells plated at the higher density were maintained in α-MEM complete medium throughout the experiment. At 48 h postseeding, the medium was refreshed with the ascorbic acid, dexamethasone, and BGP supplement. These cells were harvested at 7 days postseeding as early osteogenic cells.

Total RNA was harvested using RNeasy (Qiagen, Valencia, CA) and measured for quality using the Agilent 2100 Bioanalyzer and Qubit 2.0 Fluorometer. High RNA integrity is critical for evaluating the transcriptome. The RNA integrity number is an algorithm for assigning integrity values to RNA measurements and assigns an electropherogram a value of 1 to 10, with 10 being the least degraded. All RNA integrity number numbers for our samples ranged between 8.2 and 9.7. A conservative cut-off value in the context of RNA degradation lies between 6.4 and 7.9 (31), well below our values. Four technical replicates were harvested for each time point and genotype. Total RNA samples were submitted to the Beijing Genomics Institute for transcriptome sequencing. In brief, magnetic beads with Oligo (dT) were used to isolate mRNA. The mRNA was fragmented and then constructed into HiSeq 2000 strand-specific libraries. The 2 × 100-nt paired-end reads were generated by Illumina HiSeq 2000. Clean reads filtered from raw sequence reads were returned from Beijing Genomics Institute. Raw reads were filtered into clean reads by employing the following rules: 1) remove reads in which the percentage of bases with quality <10 was >50%; 2) remove reads in which unknown bases were >10%; 3) remove reads with adapters; 4) map the clean reads to Mus musculus reference mm10 using STAR (version 2.4.2a) (23); 5) quantify gene-based expression levels with featureCounts (58); and 6) determine differential expression of genes across different treatments with edgeR (88) (Gene Expression Omnibus accession no. GSE112694).

RNA-seq determines the relative amount of each gene in each RNA sample but does not provide any measure of the total RNA output on a per-cell basis. This can be important when some genes are very highly expressed in one sample but not another (89), which is the case for our Nmp4^−/− phenotype. We have previously shown that the Nmp4^−/− MSPCs express upwards to twofold more RNA/cell than WT cells (114). Therefore, we used GusB as a scaling factor for the present RNA-seq data since our previous work identified GusB as an appropriate normalizer for microarray data (16).

Pathway enrichment analysis was performed using the Ingenuity Pathway Analysis software (IPA; Ingenuity Systems, Redwood City, CA) to distinguish significant canonical pathways in which the differentially expressed genes identified in the WT and Nmp4^−/− samples were enriched. Fisher’s exact test was used to compute a P value that denotes the probability of the differentially expressed genes in the pathway being found together due to random chance. We also applied the Benjamini-Hochberg false discovery rate (q < 0.05) correction to account for multiple comparisons in the IPA.

We define a candidate Nmp4 direct target gene as a gene whose expression is altered with the loss of Nmp4 and also supports Nmp4 occupancy. To identify candidate genes, we performed Venn diagram analysis with the gene lists from the present RNA-seq data set and lists derived from our previous study of the Nmp4 genome-wide occupancy by ChIP-Seq in MC3T3-E1 preosteoblasts (16). This cell line is an established in vitro model for osteoblastogenesis. Genes that were identified as supporting Nmp4 occupancy exhibited ChIP-seq peaks within −5 to +2 kb from a TSS and/or within the range defined by the TSS and the transcription end site and not within the promoter range of the same gene (Supplemental Table S1; all Supplemental material is available at https://doi.org/10.6084/m9.figshare.7496147; Gene Expression Omnibus accession no. GSE112693 for complete ChIP-Seq data set) (16). Additionally, we further refined this definition by using only genes contained in both the ChIP-seq and RNA-seq lists.

Seahorse assay.

Four independent MSPC preparations were used in the metabolic stress tests. The MSPCs 1957R^WT and 1957N^KO were derived from male littermates obtained from an Nmp4^+/− × Nmp4^+/− cross. The 1584L^WT and 1515RR^KO MSPCs were derived from mice obtained from different litters and different parents. Cells were seeded into an XFe24 well plate and grown for ~24 h in culture. MSPCs were then subjected to mitochondrial stress tests using oligomycin, carbonyl cyanide-4 (trifluoromethoxy)phenylhydrazone, rotenone, and antimycin A per the manufacturer’s instructions (Seahorse Biosciences, Lexington, MA). Glycolysis stress tests were performed using oligomycin and 2-deoxy-d-glucose (Seahorse Biosciences). After each analysis, total cell number was quantified and normalized to O₂ consumption rate or extracellular acidification rate (ECAR), respectively. Glycolytic and mitochondrial stress tests were repeated four to five times each (biological replicates). We pooled all data (each well) obtained from the glycolytic or mitochondrial tests as technical replicates for statistical analysis.

Collagen secretion analysis.

All six independent MSPC preparations were used in the collagen secretion assays including 1957R^WT, 1584L^WT, 2001RL^WT, 1957N^KO, 1515RR^KO, and 1986R^KO. Collagen levels were determined using the Sircol assay (Biocolor, Carrickfergus, Northern Ireland) (1, 62). Nondifferentiating WT and Nmp4^−/− MSPCs, cells were seeded into 12-well plates at 10,000–20,000 cells/well (25–50 cells/mm²). These cells were maintained in Mesencult Media + Mesencult Stimulatory Supplement (nondifferentiating medium) for 4 days postseeding. To harvest the acid soluble fraction, cultures were washed twice with ice-cold PBS and then scraped into PBS containing 0.5 M acetic acid and digested overnight at 4–8°C. The samples were then snap frozen. Collagen was concentrated from these acid-soluble fractions and then analyzed according to the manufacturer’s instructions. The collagen amount was normalized to cell number or presented as collagen/well versus cell number/well. All experiments were repeated at least twice. All the data shown in the assays are an average of at least four to five different wells per group.

Col1a1 polysome analysis.

Preparations from four independent MSPC preparations, designated 1957R^WT, 1957N^KO, 1584L^WT, and 1515RR^KO, were used to measure collagen mRNA in polysomes. Equal amounts of WT and Nmp4^−/− MSPCs were cultured into 10-cm culture plates and maintained in Mesencult Media + Mesencult Stimulatory Supplement for 4 days. On day 4, cycloheximide was added to each culture dish for 10 min before harvesting. Cells were rinsed with ice-cold PBS solution containing 50 µg/ml cycloheximide and then lysed with 500 µl of cold lysis buffer containing 10 mM Tris·HCl (pH 7.4), 300 mM KCl, 10 mM MgCl₂, 1 mM DTT, and 50 µg/ml cycloheximide, followed by centrifugation at 13,000 rpm for 10 min at 4°C. Cell lysates were then applied to the top of 10–50% sucrose gradients and subjected to ultracentrifugation in a Beckman SW41Ti rotor at 40,000 rpm for 2 h at 4°C. With the use of a piston gradient fractionator, polysome profiles of each sample was recorded at 254 nM by a UV monitor with Data Quest software as described previously (103). TRIzol LS reagent (Life Technologies) was used to purify RNA present in each of the sucrose gradient fractions. To ensure that there was uniform RNA preparation between fractions, an equal amount of firefly luciferase mRNA was added to each fraction. RNA prepared from equal volumes of each fraction was then used as a template for cDNA synthesis utilizing the TaqMan RT kit (Life Technologies). The quantitative PCR analyses of firefly luciferase and Col1a1 transcripts were measured as described previously (2). Equal amounts of firefly luciferase mRNA was measured in each of the fractions. Primer sequences for both transcripts were Col1a1 forward: 5′-ACGTCCTGGTGAAGTTGGTC-3′ and reverse: 5′-CAGGGAAGCCTCTTTCTCCT-3′; and firefly luciferase forward: 5′-CCAGGGATTTCAGTCGATGT-3′ and reverse: 5′-AATCTCACGCAGGCAGTTCT-3′. Experiments were carried out two independent times with similar results.

Mice.

WT and Nmp4^−/− mice were generated as previously described and maintained at Indiana University Bioresearch Facility School of Dentistry (90). Briefly, the strategy for preparing the global Nmp4^−/− mice involved removing the region of this gene containing coding exons 4–7 via homologous recombination (90). The correctly targeted embryonic stem cell lines from 129SvEv embryonic stem clones were microinjected into C57BL/6J blastocysts, and the chimeric mice were crossbred with the C57BL/6J mice (The Jackson Laboratory, Bar Harbor, ME) to generate germline transmission. These mice were backcrossed for seven generations on the C57BL/6J background. Their WT littermates were used as the control mice for these experiments. The mice were housed, two to four mice/cage, under a 12:12-h light-dark regimen, and Labdiet Rodent 5001 diet was provided ad libitum. The Indiana University Institutional Animal Care and Use Committee approved all experimental procedures described in the present study.

Therapies.

At 10 wk of age, virgin female mice were randomly sorted into eight treatment groups by weight and genotype. Each mouse received two sequential 100-µl injections/day containing the drugs or vehicle(s) 7 days/wk for 7 wk. Mice in select groups were injected subcutaneously with synthetic hPTH 1–34 acetate salt (Bachem Americas, Torrance, CA) at 30 µg·kg⁻¹·day⁻¹, daily, a dose often used in rodents to evaluate PTH bone anabolic action in vivo (37, 63). The dose of the anticatabolic agent raloxifene (RAL; Sigma-Aldrich) is based on human clinical doses. RAL is normally administered as a 60-mg daily dose; therefore, based on a 60-kg patient, the quantity would be 1 mg·kg⁻¹·day⁻¹. The assumption is 100% absorption; therefore, the full dose was administered as a subcutaneous injection (95). Our euthanasia protocol involves using carbon dioxide inhalation at 20%V/min followed by bilateral pneumothorax or cervical dislocation in compliance with the guidelines of our Animal Care and Use Committee. This is an approved method by the Panel on Euthanasia of the American Veterinary Medical Association.

Microcomputed tomography.

Femurs and L5 vertebra were dissected from the 17-wk-old mice. The femurs were soaked in 0.9% saline, wrapped with gauze, and stored at −20°C. The L5 vertebra were transferred to 10% formalin for 2 days and then stored in 70% ethanol. Left femurs were thawed to room temperature and scanned while being hydrated with a 8.5-μm voxel size using a Skyscan 1172 microcomputed tomography (μCT) system (176 mA, 0.5-mm Aauminum filter). Scans were reconstructed with voxel attenuation coefficients ranging from 0 to 0.11, a beam hardening correction of 40%, and a ring artifact correction of 5. Mineral density was calculated using daily scans of manufacturer-supplied hydroxyapatite phantoms of 0.25 and 0.75 g/cm³. L5 vertebrae were scanned with a 6-μm voxel size using the Skyscan 1172 µCT system (176 mA, 0.5-mm aluminum filter). Scans were reconstructed with voxel attenuation coefficients ranging from 0 to 0.08, a beam hardening correction of 20%, and a ring artifact correction of 10. Three-dimensional reconstructions using Skyscan software provided femur and L5 vertebra trabecular bone volume per total volume (BV/TV; %). Parameters obtained for femoral cortical bone included total cross-sectional area (mm²), marrow area (mm²), cortical thickness (mm), periosteal bone surface (mm), endocortical bone surface (mm), anterior-posterior width (AP; mm), medial-lateral width (ML; mm), AP/ML, moment of inertia about the AP axis (mm⁴), moment of inertia about the ML axis (mm⁴), maximum moment of inertia (mm⁴), minimum moment of inertia (mm⁴), medial extreme (mm), and tissue mineral density (g/cm³ hydroxyapatite).

Mechanical testing.

Left femurs from each animal were thawed to room temperature and monotonically tested to failure in three-point bending at a displacement rate of 0.025 mm/s using a support span of 9 mm (4). The bones were oriented in the anterior-posterior direction with the anterior side in tension. The moment of inertia about the medial-lateral axis and the extreme fiber in the anterior direction were obtained from the μCT images using a seven slice region centered on the failure site and were utilized to map load-displacement to stress-strain, employing standard beam bending equations. Structural-level mechanical and tissue-level material properties were then obtained from the load-displacement and stress-strain curves.

Statistical analysis.

We used the statistical package JMP version 7.0.1 (SAS Institute, Cary, NC) to evaluate osteoporosis treatment response in our experimental mice. We tested three experimental therapies and a vehicle control using two genotypes of mice, yielding a total of eight treatment groups. Outliers in the data sets were identified using the interquartile range (IQR) method to assess statistical dispersion (68). The remaining data were analyzed with a two-way ANOVA for effects of genotype and treatment followed by a Tukey-Kramer post hoc test for comparison of more than two groups or Student’s t-test post hoc test for comparing WT and Nmp4^−/− parameters as two groups. Experimental data were sorted by either treatment or genotype to determine whether either or both influenced the value of the end point parameter and whether genotype affected the response to treatment (genotype × treatment interaction). To assess if the combination treatment provided a synergistic effect over the monotherapies we performed 2-way ANOVA tests using PTH and RAL as the independent variables on both WT and Nmp4^−/− data sets. Statistical significance was set at P ≤ 0.05. To evaluate the metabolic profiles of the MSPCs, we used the Statistical Analysis System version 9.4 (SAS Institute) and JMP to perform Student’s t-test in comparing specific metabolic parameters. Finally, ggplot2 was used to create all the heatmaps, volcano plots, and boxplots (107).

RESULTS

Nmp4 regulates a large portion of the osteogenic transcriptome.

We previously showed that expanded cultures of Nmp4^−/− bone marrow MSPCs exhibited a precocious and enhanced mineralization compared with WT cells (16). For the present study, three independently derived WT MPSCs from individual isogenic mice, along with three Nmp4^−/− preparations confirmed that the null cells exhibited mineralization typically within 1 wk of exposure to osteogenic medium compared with 2–3 wk for the WT cells (Fig. 1A).

Fig. 1. — Loss of nuclear matrix protein 4 (*Nmp4*) accelerates and enhances mesenchymal stem/progenitor cell (MSPC) mineralization and has a broad impact on the transcriptome. A: 6 independently expanded MSPC preparations from individual wild-type (WT) and *Nmp4^−/−* mice were established as described in materials and methods. Cultures were stained with alizarin red when mineralization was first observed. The cells derived from the *Nmp4^−/−* mice consistently exhibited mineral days to weeks before this was observed in the WT cultures. The cell preparations 1957N^KO and 1957R^WT were derived from male littermates. The remaining lines were derived from male or female mice selected from random litters. B: volcano plots of RNA sequencing (RNA-seq) data from MSPCs maintained in nondifferentiating culture medium for 3 days and osteogenic differentiating culture medium for 7 days. The x-axis represents the logarithmic transformation to the base 2 of the mean fold change of mRNA expression in *Nmp4^−/−* cells versus control cells and the y-axis represents the negative logarithm to the base 10 of the false discovery rate (FDR) value. Changes in gene expression were considered significant if the fold change of knockout (KO)/WT greater than or equal to +2 (red circles) and FDR ≤0.05 or KO/WT less than or equal to −2 (green circles) and FDR ≤0.05. The black circles represent genes that did not meet either criterion. The dotted line demarcates FDR = 0.05. C: Venn diagrams showing gene overlap between chromatin immunoprecipitation sequencing (ChIP-seq) and RNA-seq data. The former was derived from MC3T3-E1 cells (16). Genes that supported Nmp4 occupancy were required to exhibit peaks (height ≥10) within 5 to +2 kb from a transcription start site (TSS) and/or located within the range defined by the TSS and the transcription end site.

To address the mechanism for the hyperanabolic phenotype elicited by loss of Nmp4, we performed transcriptome analysis on osteogenic MSPCs as a guide for hypothesis testing. Given that there were some variations in time to mineralization between the individual Nmp4^+/+ and Nmp4^−/− MSPC preparations, we elected to carry out RNA-seq on the MSPCs 1584L^WT versus 1515RR^Nmp4−/− under two distinct culture conditions. These cells exhibited a striking difference in the time to mineralization onset. We then carried out the critical phenotypic anchoring experiments with the other MSPC preparations as well as the WT and Nmp4^−/− mice to show that our findings are broadly applicable.

To perform RNA-seq analysis, RNA was harvested from cells at day 3 postseeding that were maintained in nondifferentiation medium and at day 7 in culture in which the cells had been transferred to osteogenic medium 48 h postseeding. All data obtained from these studies including the differences observed in mRNA expression between the WT and Nmp4^−/− MPSCs at different time points following exposure to osteogenic medium are provided in Supplemental Table S2. A volcano plot shows that Nmp4-deleted cells cultured in nondifferentiating medium for 3 days displayed a significant greater than or equal to twofold change in the expression of 5,032 genes compared with WT. Of these, there was an increase in the expression of 3,468 genes and a decrease in expression of 1,564 genes (Fig. 1B). Following this criterion, the expression profiles of 8,438 genes were not significantly affected by Nmp4 status (Fig. 1B).

Loss of Nmp4 had a similar impact on the transcriptome of MSPCs maintained in the osteogenic differentiating medium and harvested at day 7, which coincided with the initiation of mineralization. At the 7-day time point, the expression profiles of 5,313 genes were significantly altered by greater than or equal to twofold, with 3,925 genes presenting an elevation in expression compared with WT cells and 1,388 genes showing a decrease (Fig. 1B). Nmp4 status did not impact the expression of 8,151 genes in cells maintained in the osteogenic medium (Fig. 1B).

We recently reported a genome-wide ChIP-seq analysis of Nmp4 binding in MC3T3-E1 preosteoblasts that identified over 15,000 binding sites for this transcription factor (16). This cell line is an established in vitro model for early osteoblastogenesis that is similar to our primary MSPCs. To identify genes that are direct targets of Nmp4, we determined the overlap of the gene lists derived from the present MSPC RNA-seq data sets and those lists derived from our previous analysis of Nmp4 genome-wide ChIP-Seq analysis (16). The gene list used from the ChIP-seq data set contained genes that had one or more peaks associated with the TSS within −5 to +2 kb from a TSS and/or within the range defined by the TSS and the transcription end site and not within the promoter range of the same gene (Supplemental Table S1) (16). Additionally, we limited the compilation to 4,786 and 4,787 genes expressed by our MSPCs for days 3 and 7 in culture respectively. The Venn diagrams revealed that ~28% of the genes occupied by Nmp4 exhibited a significant increase in expression upon loss of this transcription factor after 3 and 7 days in culture, indicating gene repression by Nmp4. By contrast, ~9% showed a decrease in expression upon loss of Nmp4 suggesting that Nmp4 functions to directly activate these gene targets (Fig. 1C). Expression of ~63% of the genes that supported Nmp4 with significant occupancy were not strongly impacted by loss of Nmp4, suggesting that Nmp4 status alone is not sufficient to alter the expression of these genes (Fig. 1C). We conclude that in this osteogenic context Nmp4 has an extensive influence on the MSPC and osteogenic transcriptomes consistent with its widespread occupancy in their genomic landscapes.

Loss of Nmp4 alters pathways that exhibit the dual functions of driving osteogenesis and glycolysis.

To identify cellular pathways sensitive to Nmp4 status, we performed, IPA (ingenuity pathway analysis)-based network analyses on the 5032 genes that exhibited a significant change in expression between the Nmp4^−/− and WT cells at day 3 (nondifferentiating medium) and on the 5,313 genes that exhibited a change in expression at day 7 (osteogenic medium) in culture. Supplemental Tables S3 and S4 list the 252 significant canonical pathways derived from transcriptome analysis of day 3 and the 201 significant canonical pathways derived from analysis of day 7 cells, respectively. The large number of affected pathways is consistent with the substantial number of genes whose expression is influenced by Nmp4 status.

Many of the canonical pathways listed in Supplemental Tables S3 and S4 were also identified in previous studies characterizing MSPC transcriptomic changes during osteogenic differentiation (11, 72, 80), thus supporting our experimental approach. For example, transforming growth factor-β signaling, insulin-like growth factor-1 (IGF-1), Wnt/β-catenin signaling, and bone morphogenic protein signaling all appear to support human adipose-derived stem cells and bone marrow stromal cell osteogenesis. Additionally, many pathways related to the triggering of cell cycle, growth, differentiation, and migration, such as axonal guidance signaling, platelet-derived growth factor signaling (signaling), integrin signaling, and actin cytoskeleton signaling, have previously been distinguished in these MSPC preparations (11, 72, 80) and were identified here.

In our hypothesis-generating screen of the IPA outcomes, we identified several pathways predicted to be sensitive to Nmp4 status and drive both osteogenesis and metabolic reprogramming necessary for fueling the development of the professional secretory osteoblast (Supplemental Tables S3 and S4) (56, 79, 81, 96). Several pathways were common to cells harvested on either days 3 or 7, and we present some of these data in graphical form for day 7 (Fig. 2, A and B). The bar graphs in Fig. 2A are color coded to reflect the z-score calculated by the IPA algorithm, which predicts the direction of change for the pathway upon loss of Nmp4. An absolute z-score of 2 or more is considered significant. The activation state of the pathway is predicted to be increased if the z-score is ≥ 2, and these bars are color coded with an orange hue. Conversely, bar graphs with a blue hue indicate a z-score less than or equal to −2 representing canonical pathways with a decreased activity. Those pathways represented with a gray bar (z = NaN) indicate that the z-score algorithm cannot predict whether the pathway activity is increased or decreased in the Nmp4^−/− cells.

The bar graphs in Fig. 2B are color coded to reflect the percentage of genes in a particular pathway whose expressions are upregulated (red) or downregulated (green). For example, the Wnt/Ca⁺² signaling pathway z-scores were +3.00^Day3/+3.77^Day7 (Fig. 2A; Supplemental Tables S3 and S4) indicating that loss of Nmp4 enhances the activity of this pathway. Additionally a high percentage of the genes in this pathway exhibited a significant increase in expression in the Nmp4^−/− MSPCs (Fig. 2B). This is significant to the Nmp4^−/− osteoblast phenotype since Wnt signaling is a major driver of bone anabolism and advances osteogenesis in part through its stimulation of glycolysis (27). The IPA's Molecule Activity Predictor algorithm allowed simulating the effects of disabling Nmp4 on the Wnt signaling pathway, which predicted elevated β-catenin activity, a key driver of osteogenesis (49) and the attenuated activity of Nemo-like kinase, a suppressor of β-catenin transcriptional activity and osteogenesis (9, 44, 74) (Fig. 3). The accompanying Wnt pathway heatmap (Fig. 3) suggests this predicted increase in Wnt signaling activity is based, in part, on the diminished expression of numerous Wnt inhibitors including Wif1, Sfrp1, Sfrp2, and Apc2 (7, 101).

Of interest, loss of Nmp4 significantly enhanced the expression of Dkk2 mRNA (see heatmap in Fig. 3). Depending on the cellular context Dkk2 can stimulate or inhibit Wnt signaling (55, 64). For example, Dkk2 is essential for osteoblast terminal differentiation and mineralization may be a novel mediator of the PTH-induced anabolic response in bone (57, 111). The activities of the Igf1 (z = +4.13^Day3) and the Nrf2 signaling pathways (z = +4.33^Day3_; +4.23^Day7) were predicted to be upregulated in Nmp4^−/− cells, and although loss of Nmp4 was projected to alter the hypoxia-inducible factor-1α (Hif-1α) signaling pathway, the direction of activity could not be ascertained (z = NaN^Day3; z = NaN^Day7) (Fig. 2; Supplemental Tables S3 and S4). Nevertheless all pathways regulate osteogenesis as well as govern cellular metabolic reprogramming (28, 39, 82, 85). Furthermore, the phosphate and tensin homolog deleted on chromosome 10 (PTEN) network was significantly sensitive to Nmp4 status and assigned z-scores −2.50^Day3 and −1.76^Day7 (Fig. 2; Supplemental Tables S3 and S4) suggesting that the activity of this pathway is attenuated with loss of Nmp4. Indeed, depletion of PTEN signaling was reported to enhance osteoprogenitor expansion and glycolytic conversion (35, 110).

Of interest, loss of Nmp4 did not significantly alter the expression of Runx2 and Sp7 (Osterix), master regulators of osteogenesis, but elevated expression of the transcription factors Tcf4, Atf4, and Ddit3 (Chop, Gadd153), which all function downstream of Runx2 and Sp7 (Fig. 4). Additionally Nmp4^−/− cells exhibited decreased mRNA expression of transcription factors that drive adipogenesis or chondrogenesis suggesting that loss of Nmp4 facilitates MSPC differentiation toward osteogenesis and that this predisposition is reinforced by shifts in transcriptional networks regulating the activities of the aforementioned osteogenic/metabolic pathways (Figs. 2 and 4, and Supplemental Tables S3 and S4).

Fig. 4. — Loss of nuclear matrix protein 4 (*Nmp4*) biases the mesenchymal stem/progenitor cells (MSPC) transcriptome toward the osteogenic lineage. Heatmap of RNA sequencing (RNA-seq) data from wild-type (WT) and *Nmp4^−/−* MPSCs at *day 3* (uncommitted) and *day 7* (early osteogenesis) in culture. Red boxes indicate increased expression in the *Nmp4^−/−*cells compared with the WT, with greater color saturation indicating higher expression, and green color indicates reduced expression. The star indicates Nmp4 binds proximal to the transcription start site or within the intron of the gene as determined by chromatin immunoprecipitation sequencing (ChIP-seq) analysis (16). Also shown, Ingenuity Pathway Analysis (IPA) canonical pathways and z-scores that support osteogenesis. Orange ovals indicate pathways that are predicted to be activated, whereas blue ovals predict that the pathways are inhibited. Pten, phosphate and tensin homolog deleted on chromosome 10; TGF-β, transforming growth factor-β; IGF-1, insulin-like growth factor-1; BMP, bone morphogenic protein.

Phenotype anchoring of our transcriptional data confirmed Nmp4^−/− MSPCs exhibited an enhanced capacity for glycolytic conversion.

The glycolytic pathway is predicted to be altered in the Nmp4^−/− cells at both days 3 and 7 in culture (Fig. 2; Supplemental Tables S3 and S4). A heatmap of several genes that comprise the glycolytic pathway showed that loss of Nmp4 greatly elevated the expression of the glucose transporter Slc2a1 (a.k.a Glut1) and increased the transcript levels of both Slc2a3 and Slc2a4 (Glut3 and Glut4; Fig. 5A). The lactate transporter Slc16a3 (a.k.a Mct4) was highly expressed in the Nmp4^−/− MPSCs at both days 3 and 7 in culture (Fig. 5A). A primary function of Slc16a3 is the secretion of lactate and protons from highly glycolytic cells (22) and a recent study determined that increased levels of Slc16a3 is necessary for sustaining high glycolysis in macrophages (102). Several genes mediating the conversion of glucose to pyruvate displayed significantly elevated expression in Nmp4^−/− cells (Fig. 5A). Genes responsible for regulating the switch between aerobic glycolysis and oxidative phosphorylation including Hk2, Pkm, Pdk1, and Ldha showed significantly higher mRNA levels in the Nmp4^−/− cells. Additionally, our ChIP-seq analysis in MC3T3-E1 cells showed that Nmp4 binds to both Pdk1 and Pkm genes (Fig. 5B) indicating that this transacting protein directly targets key genes that regulate the glycolytic switch.

Fig. 5. — Loss of nuclear matrix protein 4 (*Nmp4*) perturbs the mesenchymal stem/progenitor cells (MSPC) glycolytic pathway. A: *Nmp4^−/−* osteoprogenitors/osteoblasts exhibit significant elevated expression of several genes that drive glycolysis. Schematic of glycolysis/oxidative phosphorylation (OXPHOS) pathways with overlay of heatmap derived from RNA-sequencing (RNA-seq) data generated from WT and *Nmp4^−/−* MPSCs harvested at *day 3* (uncommitted cells) and *day 7* (early osteogenesis). Red boxes indicate increased expression in the *Nmp4^−/−* cells compared with the WT cells, with greater color saturation indicating higher expression, and green color indicates reduced expression. The star indicates Nmp4 binds proximal to the transcription start site or within the intron of the gene as determined by chromatin immunoprecipitation sequencing (ChIP-seq) analysis (16). B: ChIP-seq reveals Nmp4 binding profiles at specific gene loci in mouse MC3T3-E1 cells [Childress et al. (16); Gene Expression Omnibus accession no. GSE112693 for complete ChIP-Seq data set). The Burrows-Wheeler algorithm was used to align sequences (50-nt reads, single end) to the mouse genome (mm10). Alignments were extended in silico at their 3′-ends to a length of 150 bp, which is the average genomic fragment length in the size-selected library, and assigned to 32-nt bins along the genome. The Model-based Analysis of ChIP-Seq algorithm (v1.4.2) with a cutoff of P = 1e-7 was used to determine Nmp4 (Znf384) peak locations. The genomic loci including the chromosome number and nucleotide interval are indicated. The y-axis indicates the read scales. Arrows indicate the transcriptional start sites and direction of transcription; vertical boxes within the gene indicate exons. The Nmp4 ChIP-seq gene profiles include *Pdk1 Pkm*. The input DNA profiles were devoid of peaks.

We linked our transcriptome/ChIP-seq analyses to functional data via the glycolytic stress tests (Fig. 6). WT versus Nmp4^−/− cells derived from the male littermates (1957^WT/1957^KO) and the WT versus Nmp4^−/− cells derived from the males obtained from random litters (1584L^WT/1515RR^KO) were cultured in nondifferentiating medium using the Seahorse analyzer. Cells were seeded directly into an analyzer well plate and grown for 24 h in culture. Subsequently, cells were incubated in medium devoid of glucose or pyruvate, and the analyzer measured the ECAR before and after a saturating amount of glucose was injected. These experiments quantified glycolytic activity (glycolysis), which was significantly elevated in Nmp4^−/− cells (Fig. 6, A–C). The ECAR value was then obtained after injection of oligomycin, which inhibited oxidative phosphorylation driving the cell to use glycolysis to its maximum capacity (glycolytic capacity). Again the Nmp4^−/− cells exhibited a significantly elevated level for this parameter (Fig. 6, A–C). The final injection of 2-deoxy-d-glucose, a glucose analog that inhibited glycolysis through competitive binding to glucose hexokinase, decreased ECAR confirming that the lowered medium pH was the result of increased glycolysis (Fig. 6, A and B). The glycolytic reserve, defined as the difference between glycolytic capacity and glycolysis rate, was elevated with the loss of Nmp4 (Fig. 6, A–C). We conclude that loss of Nmp4 results in the metabolic reprogramming of the MSPCs enhancing their capacity for glycolysis.

Nmp4^−/− MSPCs exhibited an increased mitochondrial respiratory capacity.

Next, the mitochondrial respiratory capacity was compared in the WT and Nmp4^−/− cells. For the mitochondrial stress test, the Seahorse analyzer was used to measure basal respiration reported as oxygen consumption rate and then the cells were sequentially exposed to various compounds to assess mitochondrial electron transport chain function (Fig. 7, A–C). Our results showed that loss of Nmp4 elevated basal respiration, maximal respiration, and ATP production in MSPCs (Fig. 7, A–C). Spare respiratory capacity and nonmitochondrial respiration were also significantly elevated (data not shown). We conclude that metabolic reprogramming occurs in MSPCs as a consequence of Nmp4 loss, enhancing the capacity of these cells for oxidative phosphorylation.

Fig. 7. — Loss of nuclear matrix protein 4 (*Nmp4*) enhances mitochondrial respiratory capacity. The line graphs show a comparison of wild-type (WT) versus *Nmp4^−/−* mesenchymal stem/progenitor cell (MSPC) oxygen consumption rate (OCR) profiles that have undergone the mitochondrial stress test. A: the MSPCs 1957R^WT and 1957N^KO were derived from male littermates. B: the MSPCs 1584L^WT and 1515RR^KO were derived from a random pair of male WT and *Nmp4^−/−* mice. These graphs are representative of 5 individual tissue culture experiments (biological replicates). C: these graphs represent data from 5 separate experiments with cells from 5 different platings. In each experiment, 10 technical replicates with each cell preparation have been performed. The data are means ± SD. Statistical significance was set at P < 0.05. Basal respiration (BASAL RESP) was first measured, and then, the cells were sequentially exposed to various inhibitors of the mitochondrial electron transport chain. ATP production (ATP PROD) was based on the comparison between the basal OCR and the oligomycin-induced drop in OCR. The subsequent injection of carbonyl cyanide-4(trifluoromethoxy)phenylhydrazone (FCCP) uncoupled the electron transport chain increasing OCR and permitting the calculation of the maximal respiration rate (MAX RESP). Nonmitochondrial respiration was determined from the final injection of rotenone, a complex I inhibitor, and antimycin A, a complex III inhibitor. This parameter was significantly higher in the *Nmp4^−/−* cells (data not shown). Spare respiratory capacity was also significantly elevated (data not shown).

Nmp4^−/− osteoprogenitors exhibit enhanced protein production and secretion.

IPA analysis predicted that loss of Nmp4 elevates the activity of several cellular pathways driving protein production and delivery. Specifically, the activities of the eukaryotic initiation factor (eIF)-2 (z = +3.28^Day3_; +2.72^Day7), mammalian target of rapamycin (mTOR; z = +2.949^Day3_; +2.71^Day7), and the eIF4 and p70SK6 (z = +3.16^Day3_; +2.56^Day7) signaling pathways were predicted to be upregulated in Nmp4^−/− cells (Fig. 2; Supplemental Tables S3 and S4). This suggests that loss of Nmp4 stimulates anabolic processes including protein synthesis, translation initiation, and the regulation of energy production in mitochondria (71, 105). Loss of Nmp4 was projected to alter the tRNA signaling pathway, but the direction of change could not be predicted (z = NaN^Day7; Fig. 2; Supplemental Table S4). Nevertheless, several genes of this pathway were significantly upregulated (Figs. 2B and 8). Indeed, the expression of numerous genes comprising the pathways of amino acid transport, amino acid biosynthesis, ribosome biogenesis, and translation initiation was significantly elevated (Fig. 8). Elevated protein production in Nmp4^−/− MSPCs is also supported by our earlier report that enhanced ribosome biogenesis was sustained during induction of the unfolded protein response (UPR), which serves to expand the processing capacity of the ER for nascent secretory proteins (25, 114). This Nmp4-directed transcriptome program may allow a large protein client load to be processed through the ER without halting global osteoblast translation or inducing apoptosis (114). Indeed, the RNA-seq analysis confirmed that Nmp4^−/− MSPCs exhibited elevated expression of several genes of the UPR pathway (Fig. 9) and IPA/Molecule Activity Predictor analysis predicted that protein-folding activity is elevated and UPR-induced apoptosis is attenuated with loss of Nmp4 (Fig. 9).

Fig. 8. — Genes involved in various aspects of protein synthesis are shown in the heatmaps positioned along this cellular process. These heatmaps were derived from RNA sequencing (RNA-seq) data generated from wild-type (WT) and nuclear matrix protein 4 knockout (*Nmp4^−/−*) mesenchymal stem/progenitor cells (MSPCs) harvested at *day 3* (uncommitted cells) and *day 7* (early osteogenesis). Red boxes indicate increased expression in the *Nmp4^−/−* cells compared with the WT cells, with greater color saturation indicating higher expression, and green color indicates reduced expression. The star indicates Nmp4 binds proximal to the transcription start site or within the intron of the gene as determined by chromatin immunoprecipitation sequencing (ChIP-seq) analysis (16).

Fig. 9. — The Ingenuity Pathway Analysis Molecule Activity Predictor algorithm indicated that loss of nuclear matrix protein 4 (*Nmp4*) elevates protein folding and attenuates endoplasmic reticulum (ER) stress-induced apoptosis. Molecules in pink-red are found in the data set and are upregulated. Molecules that are green are found in the data set and are downregulated. Molecules that are gray are found in the data set but did not pass any of the filter parameters originally established for the analysis. White molecules are not in the data set but part of the pathway. On the *left* side of this pathway is a heatmap of genes comprising the unfolded protein response pathway (UPR) derived from the RNA-seq data of WT and *Nmp4^−/−* mesenchymal stem/progenitor cells (MSPCs) at *day 3* (uncommitted) and *day 7* (early osteogenesis) in culture. Red boxes indicate increased expression in the *Nmp4^−/−* cells compared with the WT, with greater color saturation indicating higher expression, and green color indicates reduced expression. The star indicates Nmp4 binds proximal to the transcription start site or within the intron of the gene as determined by chromatin immunoprecipitation sequencing (ChIP-seq) analysis. AMFR, autocrine motility factor receptor; EDEM, endoplasmic reticulum-degradation-enhancing-α-mannidose-like protein; ERAD, endoplasmic reticulum-associated protein degradation; MBTPS, membrane bound transcription factor peptidase; PDI, protein disulfide isomerase; SCAP, SREBF chaperone; VCP, valosin-containing protein.

We validated the transcriptome data experimentally by measuring bone matrix production and delivery in WT and Nmp4^−/− osteoprogenitors by comparing the levels of Col1a1-mRNA associated with polyribosomes and the levels of collagen protein secretion. WT versus Nmp4^−/− cells derived from the littermates (1957^WT/1957^KO) and the WT versus Nmp4^−/− cells derived from mice obtained from random litters (1584L^WT/1515RR^KO) were cultured in nondifferentiating medium for 4 days. We observed elevated levels of 40S and 60S ribosomal subunits and 80S monosomes and increased polysomes in Nmp4^−/− MSPCs compared with WT (Fig. 10A). The RNA-seq data revealed that total Col1a1 mRNA expression was elevated in the Nmp4^−/− cells (Fig. 10B). To address whether Col1a1 mRNA translation accompanied the enhanced global translation, quantitative RT-PCR analysis was performed to quantify the amount the of Col1a1 mRNA present in the polysome fractions prepared from the WT and Nmp4^−/− cells (Fig. 10C). Col1a1 mRNA was present in heavy polysomes in both WT and Nmp4^−/− MSPCs, suggesting efficient translation. However, there was a reproducible increase in Col1a1 mRNA in the largest fraction 7 in Nmp4^−/− cells, suggesting more robust Col1a1 translation in the Nmp4-depleted cells (Fig. 10C). Thus the combination of more Col1a1 mRNA available for translation, along with increased amounts of ribosomes and more efficient Col1a1 mRNA translation, would culminate in elevated synthesis of Col1A1 protein in the Nmp4^−/− cells.

Fig. 10. — Loss of nuclear matrix protein 4 (*Nmp4*) enhances collagen expression and secretion. Data were derived from mesenchymal stem/progenitor cell (MSPC) preparations 1584L^WT, 1515RR^KO, 1957R^WT, and 1957N^KO. A: polysome profiles of lysates prepared from wild-type (WT) and *Nmp4^−/−* MSPCs at 4 days in culture. Representative profiles from 3 biological replicates. B: *Col1a1* mRNA expression as determine by RNA sequencing (RNA-seq) in MSPCs maintained in nondifferentiation medium for 3 days in culture and 7 days in culture (5 days in osteogenic medium). C: following polysome profiling, fractions 1–7 were collected, and the percentage of *Col1a1* mRNA present in each sucrose gradient fraction were quantified by quantitative RT-PCR and presented as a histogram. Data are representative of 2 biological replicates and 3 technical replicates each. Statistical analyses were performed using one-way ANOVA tests; ***P < 0.0001. D: secretion of collagen protein was measured in the acid-soluble cell-matrix layer of 1584L^WT and 1515RR^KO by using the Sircol assay as described in materials and methods. Loss of *Nmp4* significantly enhanced the amount of collagen protein secreted/cell. * P < 0.0001. Data represent 3 biological replicates and 5–6 technical replicates each. E: secretion of collagen protein was measured in the acid-soluble cell-matrix layer by using the Sircol assay at *day 4* postseeding from all MSPC preparations 1584L^WT, 1957R^WT, 2001RL^WT, 1515RR^KO, 1957N^KO, and 1986R^KO and presented as collagen/well (µg) versus cell number/well. All six preparations were tested independently at least twice (*experiments 1* and 2) and experiments comprised 4–6 wells/preparation. All *Nmp4^−/−* (knockout) preparations produced more collagen during the first four days of culture, regardless of cell number. Data represent average ± SD; n = 4–6 wells/group.

Collagen deposition is coupled to osteogenic proliferation (78), and Nmp4^−/− MSPCs frequently exhibit a modest but significant increase in proliferative activity compared with WT (16). To evaluate changes in collagen production induced by Nmp4 deletion, independent of the confounding effects of proliferation differences, we first measured collagen production in the 1515RR^KO and 1584L^WT preparations that normally do not exhibit a difference in proliferation. 1515RR^KO cells produced approximately three- to fourfold more collagen/cell than the 1584L^WT (Fig. 10D). Next we evaluated the amount of collagen recovered/well as a function of the number of cells/well for all six MSPC preparations (Fig. 10E). All three Nmp4^−/− preparations produced more collagen compared with WT cells regardless of cell number during this proliferative period in culture (Fig. 10E). Moreover, preliminary experiments with shRNA knockdown of Nmp4 in MC3T3-E1 cells yielded a similar collagen/well versus cells/well profile (data not shown). This is consistent with our previous in vivo data showing that the Nmp4^−/− mice harbor more bone marrow osteoprogenitors than WT, which in turn produce more bone when stimulated (16, 41, 95). We conclude that loss of Nmp4 converts osteoprogenitors/osteoblasts into super-secretors of bone matrix while moderately enhancing their proliferative activity.

Nmp4^−/− osteoblasts produce a bone matrix with improved material properties.

Several genes representing multiple protein classes comprising the bone matrix (6, 14, 17, 51, 69) were identified as upregulated in our RNA-seq data set suggesting enhanced matrix material properties in the null animal. The mRNA expression of this collection of genes is represented by a heatmap that displays changes between the Nmp4^−/− and WT MSPCs/osteogenic cells (Fig. 11). Loss of Nmp4 significantly increased or decreased the expressions and relative ratio of several extracellular matrix (ECM) genes including those that support bone mechanical properties, e.g., Col1a1, Col1a2, Bglap2 (osteocalcin), and Spp1 (osteopontin) (Fig. 11). Also, the expression of key genes that control mineralization was altered in the Nmp4^−/− cells consistent with the phenotype observed in culture. For example, the genes phosphoethanolamine/phosphocholine phosphatase (Phospho1) and alkaline phosphatase, tissue-nonspecific isozyme (Alpl), encoding phosphatases responsible for initiating mineralization (5, 43, 67, 112, 113), were highly induced in the Nmp4^−/− cells as was the gene Slc20a1 a sodium-phosphate symporter also involved in the initiation of skeletal mineralization (112) (Fig. 11). Finally, the expression of several small leucine-rich proteoglycans (SLRPs) such as lumican (Lum) and decorin (Dec) was highly elevated in the Nmp4^−/− cells (see Fig. 13). SLRPs play significant structural roles within the ECM and regulate collagen fibril growth, organization, and ECM assembly (12, 47, 76).

Fig. 11. — Nuclear matrix protein 4 knockout (*Nmp4^−/−*) osteoprogenitors/osteoblasts exhibit significant elevated expression of several genes that encode proteins of the bone matrix. The schematic shows family of proteins that comprise the bone matrix. Also the expressions of key genes that control mineralization were altered in the *Nmp4^−/−* cells consistent with the observed phenotype observed in culture. The manually annotated heatmap was derived from RNA sequencing (RNA-seq) data generated from wild-type (WT) and *Nmp4^−/−* mesenchymal stem/progenitor cell (MSPCs) harvested at *day 3* (uncommitted cells) and *day 7* (early osteogenesis). Red boxes indicate increased expression in the *Nmp4^−/−* cells compared with the WT cells, with greater color saturation indicating higher expression, and green color indicates reduced expression. The star indicates Nmp4 binds proximal to the transcription start site or within the intron of the gene as determined by chromatin immunoprecipitation sequencing (ChIP-seq) analysis (16). ECM, extracellular matrix.

Fig. 13. — Hypothesis: nuclear matrix protein 4 (Nmp4) is an apex regulator of bone cell anabolic output. This transcription factor directly and indirectly regulates gene programs that control key stages of matrix production and delivery. It may accomplish this by regulating both the expression of master transcriptional regulators of these pathways in addition to broadly engaging several of their downstream target genes. mTOR, mammalian target of rapamycin; UPR, unfolded protein response; ER, endoplasmic reticulum; PTH, parathryoid hormone.

To test the biological ramifications of the transcriptional changes associated with the bone matrix genes, we evaluated skeletal tissue obtained from healthy virgin mice that had been treated with the osteoporosis therapeutics RAL, PTH, PTH + RAL, and vehicle control for 7 wk as described in materials and methods. Ovariectomized mice were not used in this experiment because ovariectomy does not change the enhanced response to anabolic drugs in the Nmp4^−/− animals (16). Furthermore, all the MSPCs used in this study were derived from healthy, virgin mice. Briefly, µCT analysis showed that the PTH + RAL therapy produced more bone compared with both the PTH and RAL monotherapies at the distal femur and L5 vertebra (Fig. 12, A and B and Tables 1 and 2). There was a synergistic (greater than additive) interaction between PTH and RAL in both the WT and Nmp4^−/− mice for BV/TV of the distal femur and the L5 vertebra (Table 2). However, loss of Nmp4 significantly improved the femoral bone gain and the L5 bone gain in the PTH and PTH + RAL treatments (Fig. 12, A and B, and Table 1). Nmp4 status had no impact on bone response to RAL monotherapy (Fig. 12, A and B, and Table 1). Finally, there was no significant difference between the genotypes under the VEH control treatment with respect to femoral and L5 BV/TV (Fig. 12, A and B, and Table 1). However loss of Nmp4 did significantly impact some aspects of femoral cortical geometry, such as cortical thickness, marrow area, as well as other related parameters (Table 3). Altogether, these results are similar to the data we reported in older ovariectomized mice (95).

Fig. 12. — Loss of nuclear matrix protein 4 (*Nmp4*) enhances therapeutically induced bone formation and femoral material properties. A and B: femoral (A) and L5 vetebral (B) bone volume per total volume (BV/TV) for all the experimental cohorts (age 17 wk) comparing WT and *Nmp4^−/−* mice. We compared the therapies raloxifene (RAL), parathyroid hormone (PTH), and PTH + RAL to each other and to vehicle (VEH). Statistical analyses were performed using two-way ANOVA tests setting genotype and treatment as the independent variables. Statistical significance was set at P ≤ 0.05. There were a strong genotype effect and loss of *Nmp4* enhanced femoral and L5 vertebral BV/TV over the cohorts. There was a strong treatment effect and PTH + RAL was the most efficacious osteoanabolic therapy for both femoral and L5 vertebral BV/TV. The analysis revealed a genotype × treatment interaction (G × T) denoted by an asterisk in the dot plot showing improved response in the PTH monotherapy and PTH + RAL combination therapy with loss of *Nmp4*. C and D: results of 3 point-bending analysis for ultimate stress (C) yield stress (D). There were strong genotype and treatment effects for both ultimate stress and yield stress. Data represent average ± SD; n = 8–15 mice/group.

Table 1.

Femoral and L5 trabecular architecture from WT and Nmp4^−/− mice treated with vehicle, raloxifene, parathyroid hormone, and parathyroid hormone + raloxifene

	Femur BV/TV, %	Femur Tb N, mm⁻¹	Femur Tb Th, mm	Femur Tb Sp, mm
Group
WT VEH	4.57 ± 0.83	0.970 ± 0.163	0.047 ± 0.003	0.282 ± 0.013
Nmp4⁻^/⁻ VEH	6.53 ± 0.93	1.346 ± 0.0.163	0.048 ± 0.002	0.250 ± 0.008
WT RAL	11.13 ± 1.03	1.885 ± 0.146	0.060 ± 0.001	0.245 ± 0.010
Nmp4⁻^/⁻ RAL	13.38 ± 1.37	2.203 ± 0.161	0.061 ± 0.002	0.227 ± 0.011
WT PTH	13.60 ± 3.39	2.265 ± 0.382	0.059 ± 0.005	0.230 ± 0.020
Nmp4⁻^/⁻ PTH	25.30 ± 6.86	3.304 ± 0.592	0.076 ± 0.008	0.190 ± 0.016
WT PTH + RAL	26.37 ± 2.04	3.591 ± 0.355	0.077 ± 0.005	0.194 ± 0.012
Nmp4⁻^/⁻ PTH + RAL	47.82 ± 15.70	5.125 ± 1.316	0.092 ± 0.008	0.139 ± 0.028
Two-way ANOVA genotype	G: P < 0.0001	G: P < 0.0001	G: P < 0.0001	G: P < 0.0001
	Nmp4^-/⁻: 23.26^a	Nmp4^-/⁻: 2.99^a	Nmp4^-/⁻: 0.069^a	WT: 0.238^a
	WT: 13.92^b	WT: 2.18^b	WT: 0.061^b	Nmp4^-/⁻: 0.201^b
Two-way ANOVA treatment	T: P < 0.0001	T: P < 0.0001	T: P < 0.0001	T: P < 0.0001
	P + R: 37.10^a	P + R: 4.36^a	P + R: 0.084^a	V: 0.266^a
	P: 19.45^b	P: 2.78^b	P: 0.067^b	R: 0.236^b
	R: 12.25^c	R: 2.04^c	R: 0.062^c	P: 0.210^c
	V: 5.55^d	V: 1.16^d	V: 0.047^d	P + R: 0.167^d
Two-way ANOVA G × T	G × T: P < 0.0001	G × T: P = 0.0015	G × T: P < 0.0001	G × T: P = 0.0022
	Nmp4⁻^/⁻ P + R: 47.82^a	Nmp4⁻^/⁻ P + R: 5.13^a	Nmp4⁻^/⁻ P + R: 0.092^a	WT V: 0.282^a
	WT P + R: 26.37^b	WT P + R: 3.59^b	WT P + R: 0.077^b	Nmp4⁻^/⁻ V: 0.250^b
	Nmp4⁻^/⁻ P: 25.30^b	Nmp4⁻^/⁻ P: 3.30^b	Nmp4⁻^/⁻ P: 0.076^b	WT R: 0.245^b,^c
	WT P: 13.60^c,^d	WT P: 2.27^c	Nmp4⁻^/⁻ R: 0.061^c	WT P: 0.230^b,^c
	Nmp4⁻^/⁻ R: 13.38^c	Nmp4⁻^/⁻ R: 2.20^c	WT R: 0.060^c	Nmp4⁻^/⁻ R: 0.227^c
	WT R: 11.13^c,^d	WT R: 1.88^c,^d	WT P: 0.059^c	WT P + R: 0.194^d
	Nmp4⁻^/⁻ R: 6.53^c,^d	Nmp4⁻^/⁻ V: 1.35^d,^e	Nmp4⁻^/⁻ V: 0.048^d	Nmp4⁻^/⁻ P: 0.190^d
	WT V: 4.57^d	WT V: 0.97^e	WT V: 0.047^d	Nmp4⁻^/⁻ P + R: 0.139^e
	L5 BV/TV, %	L5 Tb N, mm⁻¹	L5 Tb Th, mm	L5 Tb Sp, mm
Group
WT VEH	24.65 ± 2.05	4.37 ± 0.27	0.056 ± 0.002	0.210 ± 0.017
Nmp4⁻^/⁻ VEH	26.56 ± 2.11	4.37 ± 0.22	0.061 ± 0.003	0.204 ± 0.014
WT RAL	28.62 ± 4.10	5.11 ± 0.71	0.056 ± 0.003	0.201 ± 0.027
Nmp4⁻^/⁻ RAL	31.20 ± 2.83	5.24 ± 0.39	0.059 ± 0.003	0.185 ± 0.008
WT PTH	36.26 ± 2.87	6.60 ± 0.30	0.057 ± 0.003	0.161 ± 0.036
Nmp4⁻^/⁻ PTH	40.62 ± 2.23	6.44 ± 0.18	0.062 ± 0.003	0.153 ± 0.016
WT PTH + RAL	48.28 ± 2.72	8.12 ± 0.24	0.061 ± 0.005	0.129 ± 0.003
Nmp4⁻^/⁻ PTH + RAL	55.76 ± 3.02	8.07 ± 0.47	0.071 ± 0.004	0.120 ± 0.013
Two-way ANOVA genotype	G: P < 0.0001	G: P = 0.8545	G: P < 0.0001	G: P = 0.0324
	Nmp4^-/⁻: 38.54^a	Nmp4⁻^/⁻ = WT	Nmp4^-/⁻: 0.063^a	WT: 0.175^a
	WT: 34.22^b		WT: 0.057^b	Nmp4⁻^/⁻: 0.166^b
Two-way ANOVA treament	T: P < 0.0001	T: P < 0.0001	T: P < 0.0001	T: P < 0.0001
	P + R: 52.02^a	P + R: 8.10^a	P + R: 0.066^a	V: A 0.207^a
	P: 38.44^b	P: 6.52^b	P: 0.059^b	R: A 0.193^a
	R: 29.91^c	R: 5.17^c	R: 0.059^b	P: 0.157^b
	V: 25.61^d	V: 4.37^d	V: 0.0578^b	P + R: 0.124^c
Two-way ANOVA G × T	G × T: P = 0.0133	G × T: P = 0.6648	G × T: P = 0.0154	G × T: P = 0.8336
	Nmp4⁻^/⁻ P + R: 55.76^a		Nmp4⁻^/⁻ P + R: 0.071^a
	WT P + R: 48.28^b		Nmp4⁻^/⁻ P: 0.062^b
	Nmp4⁻^/⁻ P: 40.63^c		Nmp4⁻^/⁻ V: 0.061^b,^c
	WT P: 36.26^d		WT P + R: 0.061^b,^c
	Nmp4⁻^/⁻ R: 31.20^e		Nmp4⁻^/⁻ R: 0.059^b,^c
	WT R: EF 28.62		WT P: 0.057^c
	Nmp4⁻^/⁻ V: 26.56^f		WT V: 0.056^c
	WT V: 24.65^f		WT R: 0.056^c

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Data represent average ± SD, n = 8–15 mice/group. Nmp4, nuclear matrix protein 4; WT, wild type; V/VEH, vehicle; R/RAL, raloxifene; P/PTH, parathyroid hormone; P/PTH + R/RAL, parathyroid hormone + raloxifene; BV/TV, bone volume per total volume; Tb N, trabecular number; Tb Th, trabecular thickness; Tb Sp, trabecular spacing. Statistical analyses were performed using two-way ANOVA tests setting genotype (G) and treatment (T) as the independent variables. Statistical significance was set at P ≤ 0.05. The statistical results list the cohorts by genotype, treatment, and genotype × treatment. See text for explanation of results.

^{a b c d e f}Cohorts not connected by the same letter are statistically different; the average value of the specific parameter precedes the letter.

Table 2.

PTH and RAL synergy

Therapy	P Value PTH Treatment	P Value RAL Treatment	P Value PTH × RAL Interaction
Femur BV/TV
PTH + RAL (WT)	<0.0001	<0.0001	<0.0001
PTH + RAL (Nmp4^−/−)	<0.0001	<0.0001	0.001
L5 BV/TV
PTH + RAL (WT)	<0.0001	<0.0001	0.0008
PTH + RAL (Nmp4^−/−)	<0.0001	<0.0001	<0.0001
Cortical area
PTH + RAL (WT)	<0.0001	0.0591	0.8056
PTH + RAL (Nmp4^−/−)	<0.0001	0.0166	0.5938

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Identification of synergy between parathyroid hormone (PTH) and the anticatabolic selective estrogen receptor modulators raloxifene (RAL) using a series of two-way ANOVA tests comparing the efficacy of the PTH monotherapy, RAL monotherapy, and the combination of the 2 drugs. Nmp4, nuclear matrix protein 4; WT, wild type; BV/TV, bone volume per total volume. Statistical significance was set at P ≤ 0.05.

Table 3.

Femoral cortical architecture from WT and Nmp4^−/− mice treated with vehicle, raloxifene, parathyroid hormone, and parathyroid hormone + raloxifene

	Marrow Area, mm²	Cortical Area, mm²	Cortical Thickness, mm	Periosteal BS, mm	Endocortical BS, mm
Group
WT VEH	0.940 ± 0.049	0.829 ± 0.052	0.204 ± 0.008	5.393 ± 0.139	4.140 ± 0.114
Nmp4⁻^/⁻ VEH	0.913 ± 0.049	0.838 ± 0.039	0.209 ± 0.009	5.353 ± 0.090	4.078 ± 0.106
WT RAL	0.860 ± 0.022	0.867 ± 0.037	0.216 ± 0.006	5.344 ± 0.090	4.019 ± 0.095
Nmp4⁻^/⁻ RAL	0.879 ± 0.039	0.865 ± 0.047	0.218 ± 0.010	5.332 ± 0.111	4.004 ± 0.103
WT PTH	0.969 ± 0.048	0.949 ± 0.078	0.221 ± 0.005	5.617 ± 0.196	4.213 ± 0.108
Nmp4⁻^/⁻ PTH	0.931 ± 0.064	0.951 ± 0.062	0.226 ± 0.007	5.564 ± 0.087	4.142 ± 0.116
WT PTH + RAL	0.892 ± 0.043	0.995 ± 0.070	0.239 ± 0.007	5.552 ± 0.152	4.084 ± 0.106
Nmp4⁻^/⁻ PTH + RAL	0.852 ± 0.029	1.00 ± 0.067	0.249 ± 0.013	5.478 ± 0.081	3.978 ± 0.038
Two-way ANOVA genoptype	G: P = 0.0295	G: P = 0.3420	G: P = 0.008	G: P = 0.0780	G: P = 0.0034
	WT: 0.915^a		Nmp4⁻^/⁻: 0.225^a		WT: 4.11^a
	Nmp4⁻^/⁻: 0.894^b		WT: 0.220^b		Nmp4⁻^/⁻: 4.05^b
Two-way ANOVA treatment	T: P < 0.0001	T: P < 0.0001	T: P < 0.0001	T: P < 0.0001	T: P < 0.0001
	P: 0.950^a	P + R: 0.990^a	P + R: 0.244^a	P: 5.59^a	P: 4.18^a
	V: 0.927^a	P: 0.954^a	P: 0.223^b	P + R: 5.52^a	V: 4.11^a
	P + R: 0.872^b	R: 0.866^b	R: 0.217^b	V: 5.37^b	P + R: 4.03^b
	R: 0.869^b	V: 0.833^b	V: 0.206 ^c	R: 5.34^b	R: 4.01^b
Two-way ANOVA G × T	G × T: P = 0.1459	G × T: P = 0.88	G × T: P = 0.4695	G × T: P = 0.8513	G × T: P = 0.4973
	I_ap, mm⁴	I_ml, mm⁴	I_max, mm⁴	I_min, mm⁴	TMD, g/cm³ HA
Group
WT VEH	0.228 ± 0.029	0.144 ± 0.015	0.237 ± 0.029	0.135 ± 0.015	1.29 ± 0.03
Nmp4⁻^/⁻ VEH	0.219 ± 0.008	0.143 ± 0.012	0.224 ± 0.013	0.137 ± 0.012	0.89 ± 0.01
WT RAL	0.231 ± 0.016	0.144 ± 0.012	0.237 ± 0.016	0.138 ± 0.010	1.27 ± 0.01
Nmp4⁻^/⁻ RAL	0.228 ± 0.021	0.142 ± 0.012	0.235 ± 0.022	0.138 ± 0.014	0.89 ± 0.01
WT PTH	0.285 ± 0.048	0.179 ± 0.022	0.297 ± 0.053	0.167 ± 0.019	1.28 ± 0.03
Nmp4⁻^/⁻ PTH	0.279 ± 0.025	0.173 ± 0.007	0.281 ± 0.022	0.169 ± 0.010	0.89 ± 0.01
WT PTH + RAL	0.291 ± 0.044	0.173 ± 0.016	0.302 ± 0.043	0.162 ± 0.016	1.28 ± 0.02
Nmp4⁻^/⁻ PTH + RAL	0.270 ± 0.030	0.174 ± 0.019	0.276 ± 0.031	0.167 ± 0.017	0.88 ± 0.01
Two-way ANOVA genotype	G: P = 0.1257	G: P = 0.5224	G: P = 0.0325	G: P = 0.4557	G: P < 0.0001
			WT 0.268^a		WT 1.28^a
			Nmp4⁻^/⁻: 0.254^b		Nmp4⁻^/⁻: 0.886^b
Two-way ANOVA treatment	T: P < 0.0001	T: P < 0.0001	T: P < 0.0001	T: P < 0.0001	T: P = 0.2325
	P: 0.282^a	P A 0.176^a	P + R 0.289^a	P: 0.168^a
	P + R: 0.281^a	P + R: 0.173^a	P: 0.289^a	P + R: 0.165^a
	R: 0.230^b	V: 0.143^b	R: 0.236^b	R: 0.138^b
	V: 0.223^b	R: 0.143^b	V: 0.230^b	V: 0.136^b
Two-way ANOVA G × T	G × T: P = 0.7276	G × T: P = 0.9050	G × T: P = 0.6236	G × T: P = 0.9269	G × T: P = 0.3597

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Data represent average ± SD; n = 7–15 mice/group. Nmp4, nuclear matrix protein 4; WT, wild type; V/VEH, vehicle; R/RAL, raloxifene; P/PTH, parathyroid hormone; P/PTH + R/RAL, and parathyroid hormone + raloxifene; HA hydroxyapatite; I_ap moment of inertia about the femoral anterior–posterior length axis; I_max maximum moment of inertia; I_min minimum moment of inertia; I_ml moment of inertia about the femoral medial-lateral axis; TMD tissue mineral density. Statistical analyses were performed using two-way ANOVA tests setting genotype (G) and treatment (T) as the independent variables. Statistical significance was set at P ≤ 0.05. The statistical results list the cohorts by genotype, treatment, and genotype × treatment. See text for explanation of results.

^{a b c}Cohorts not connected by the same letter are statistically different; the average value of the specific parameter precedes the letter.

A key component of these functional investigations required the measurement of material and structural mechanical properties of the bone. Therefore, the left femurs from each animal were monotonically tested to failure. The Nmp4^−/− bones exhibited a significantly higher ultimate stress, which is the stress necessary to fracture the bone at the material level, normalized for the bone geometry (Fig. 12C and Table 4). Yield stress, the stress applied to the bone after which there is permanent damage, normalized for geometry, was also significantly higher in the Nmp4^−/− femurs (Fig. 12D and Table 4). Additionally, the higher value for the elastic modulus, a measure of the material’s stiffness, in Nmp4^−/− bones approached significance (genotype P < 0.06, Table 4). Interestingly, numerous material properties were sensitive to the osteoporosis therapies. PTH + RAL led to significantly higher ultimate stress over RAL and PTH monotherapies in both genotypes (treatment P < 0.0001; Fig. 12C and Table 4). PTH treatment led to a modest but significantly lower yield stress than vehicle control and RAL cohorts, which were equivalent. The lower yield stress in the PTH cohorts is likely due to the increased amount of new and less mineralized bone. This would make the tissue less stiff, which is consistent with the modulus trending lowest in the PTH-treated mice (Table 4).

Table 4.

Estimated femoral material properties from WT and Nmp4^−/− mice treated with vehicle, raloxifene, parathyroid hormone, and parathyroid hormone + raloxifene

	Ultimate Stress	Yield Stress	Strain to Yield, µε	Total Strain, µε	Modulus, GPa	Resilience, MPa	Toughness, MPa
Group
WT VEH	141.12 ± 6.97	105.73 ± 12.89	16,012 ± 1124	99,029 ± 30,556	7.87 ± 0.30	0.95 ± 0.18	9.19 ± 1.93
Nmp4⁻^/⁻ VEH	150.08 ± 8.71	121.23 ± 21.47	16,982 ± 2858	82,378 ± 26,470	8.55 ± 0.54	1.08 ± 0.31	8.86 ± 2.59
WT RAL	155.49 ± 5.00	117.92 ± 5.13	16,552 ± 371	83,863 ± 19,659	8.06 ± 0.75	1.06 ± 0.12	9.15 ± 1.86
Nmp4⁻^/⁻ RAL	159.48 ± 13.63	127.00 ± 22.37	16,999 ± 2556	75,610 ± 19,939	8.24 ± 1.13	1.20 ± 0.35	8.95 ± 2.10
WT PTH	152.07 ± 4.20	97.09 ± 15.56	16,881 ± 3008	90,210 ± 29,366	7.57 ± 1.23	0.85 ± 0.28	9.46 ± 2.63
Nmp4⁻^/⁻ PTH	157.94 ± 5.37	97.77 ± 9.56	15,021 ± 1932	91,519 ± 26,401	7.93 ± 0.30	0.82 ± 0.23	10.31 ± 2.28
WT PTH + RAL	166.39 ± 3.78	102.76 ± 18.16	15,452 ± 34324	69,998 ± 19,460	8.44 ± 0.38	0.89 ± 0.32	8.16 ± 1.90
Nmp4⁻^/⁻ PTH + RAL	173.39 ± 10.84	114.52 ± 13.66	15,321 ± 708	63,635 ± 16,321	8.64 ± 1.06	0.99 ± 0.20	8.09 ± 1.91
Two-way ANOVA genotype	G: P = 0.0012	G: P = 0.0109	G: P = 0.7849	G: P = 0.1310	G: P = 0.0559	G: P = 0.1304	G: P = 0.8913
	Nmp4⁻^/⁻: 160.22^a	Nmp4⁻^/⁻: 115.13^a
	WT: 153.77^b	WT: 105.87^b
Two-way ANOVA treatment	T: P < 0.0001	T: P < 0.0001	T: P = 0.2740	T: P = 0.0016	T: P = 0.0318	T: P = 0.0031	T: P = 0.0553
	P + R: 169.89^a	R: 122.46^a		P: A 90864^a	P + R: 8.54^a	R: 1.13^a
	R: 157.48^b	V: 113.48^a,^b		V: A 90704^a	V: 8.21^a,^b	V: 1.01^a,^b
	P: 155.00^b	P + R: 108.64^b,^c		R: 79737^a,^b	R: 8.15^a,^b	P + R: 0.94^a,^b
	V: 145.60^c	P: 97.43^c		P + R: 66816^b	P: 7.55^b	P: 0.84^c
Two-way ANOVA G × T	G × T: P = 0.8141	G × T: P = 0.5067	G × T: P = 0.2220	G × T: P = 0.6425	G × T: P = 0.7598	G × T: P = 0.6545	G × T: P = 0.7901

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Data represent average ± SD; n = 7–14 mice/group. Nmp4, nuclear matrix protein 4; WT, wild type; V/VEH, vehicle; R/RAL, raloxifene; P/PTH, parathyroid hormone; P/PTH + R/RAL, and parathyroid hormone + raloxifene; BV/TV, bone volume per total volume. Statistical analyses were performed using two-way ANOVA tests setting genotype (G) and treatment (T) as the independent variables. Statistical significance was set at P ≤ 0.05. The statistical results list the cohorts by genotype, treatment, and genotype × treatment.

^{a b c}Cohorts not connected by the same letter are statistically different; the average value of the specific parameter precedes the letter.

Finally, total strain, elastic modulus, and resilience were all differentially responsive to the various therapies (treatment P < 0.05; Table 4).

Loss of Nmp4 also altered the structural properties of the femur. Yield force was significantly increased in the null bone (genotype P = 0.004; Table 5), and the increase in ultimate force neared significance (genotype P = 0.07). Total displacement, the total amount of deformation the bone undergoes before failure, was significantly lowered in the Nmp4^−/− femurs (genotype P = 0.04; Table 5), and the decrease in post yield displacement, the amount of deformation that occurs after the yield point, approached significance (P = 0.06; Table 5). Finally, work-to-yield, the energy that goes in to deforming the sample, was significantly higher in the Nmp4^−/− bone (genotype P = 0.03; Table 5). These results show that the Nmp4^−/− osteoblast produces more matrix than WT cells and that the composition of the secretome results in improved bone material and structural properties.

Table 5.

Estimated femoral structural mechanical properties from WT and Nmp4^−/− mice treated with vehicle, raloxifene, parathyroid hormone, and parathyroid hormone + raloxifene

	Yield Force, N	Ultimate Force, N	Displacement to yield, µm	Postyield Displacement, µm	Total Displacement, µm
Group
WT VEH	10.08 ± 0.99	13.52 ± 1.16	162.51 ± 12.49	843.05 ± 320.03	1,005.56 ± 312.72
Nmp4⁻^/⁻ VEH	11.65 ± 2.44	14.27 ± 0.92	172.64 ± 26.99	619.27 ± 224.97	792.43 ± 217.81
WT RAL	11.19 ± 0.20	14.67 ± 0.41	168.22 ± 12.96	689.72 ± 208.95	861.14 ± 199.79
Nmp4⁻^/⁻ RAL	15.22 ± 1.20	15.22 ± 1.20	173.53 ± 15.73	594.32 ± 202.51	769.68 ± 193.22
WT PTH	11.04 ± 2.06	16.94 ± 1.67	163.51 ± 28.73	711.57 ± 297.33	875.08 ± 288.88
Nmp4⁻^/⁻ PTH	11.24 ± 1.79	17.06 ± 1.38	146.49 ± 18.81	742 ± 255.98	894.10 ± 252.89
WT PTH + RAL	11.50 ± 1.18	18.38 ± 2.31	152.56 ± 34.07	540.96 ± 205.28	693.53 ± 202.12
Nmp4⁻^/⁻ PTH + RAL	13.66 ± 2.51	19.43 ± 2.18	152.09 ± 8.08	458.30 ± 166.36	578.77 ± 109.62
Two-way ANOVA genotype	G: P = 0.0037	G: P = 0.0680	G: P = 0.9119	G: P = 0.0610	G: P = 0.0379
	Nmp4⁻^/⁻: 12.22^a				WT: 858.83^a
	WT: 10.95^b				Nmp4^-/⁻: 758.74^b
Two-way ANOVA treatment	T: P = 0.0243	T: P < 0.0001	T: P = 0.0137	T: P = 0.0028	T: P = 0.0005
	P + R: 12.58^a	P + R: 18.91^a	R: 170.87^a	V: 731.16^a	V: 898.99^a
	R: 11.76^a,^b	P: 17.00^b	V: 167.57^a,^b	P: 726.78^a	P: 884.59^a
	P: 11.14^a,^b	R: 14.95^c	P: 155.00^a,^b	R: 642.02^a,^b	R: 815.4^a
	V: 10.87^b	V: 13.90^c	P + R: 152.33^b	P + R: 499.64^b	P + R: 636.15^b
Two-way ANOVA G × T	G × T: P = 0.4051	G × T: P = 0.7791	G × T: P = 0.1830	G × T: P = 0.3475	G × T: P = 0.3910
	Stiffness, N/mm	Work to Yield, mJ	Post Yield Work, mJ	Total Work, mJ
Group
WT VEH	95.73 ± 6.19	0.92 ± 0.16	8.01 ± 2.16	8.93 ± 2.05
Nmp4⁻^/⁻ VEH	104.39 ± 10.17	1.05 ± 0.31	7.53 ± 2.63	8.64 ± 2.63
WT RAL	106.07 ± 12.65	1.05 ± 0.08	8.10 ± 1.91	9.15 ± 1.90
Nmp4⁻^/⁻ RAL	105.42 ± 16.10	1.19 ± 0.34	7.63 ± 2.14	8.91 ± 2.17
WT PTH	120.02 ± 22.68	0.94 ± 0.32	9.44 ± 2.92	10.38 ± 2.76
Nmp4⁻^/⁻ PTH	121.91 ± 16.51	0.89 ± 0.23	10.24 ± 2.81	11.18 ± 2.74
WT PTH + RAL	130.49 ± 10.66	0.92 ± 0.29	7.98 ± 1.97	8.97 ± 1.85
Nmp4⁻^/⁻ PTH + RAL	132.83 ± 17.91	1.25 ± 0.45	7.69 ± 2.61	8.94 ± 2.45
Two-way ANOVA genotype	G: P = 0.3316	G: P = 0.0315	G: P = 0.8291	G: P = 0.8989
		Nmp4^-/⁻: 1.10^a
		WT: 0.96^b
Two-way ANOVA treatment	T: P < 0.0001	T: P = 0.0985	T: P = 0.0103	T: P = 0.0159
	P + R: 131.66^a		P: 9.84^a	P: 10.78^a
	P: 120.97^a		R: 7.87^b	R: 9.^a,^b
	R: 105.74^b		P + R: 7.83^b	P + R: 8.95^b
	V: 100.06^b		V: 7.77^b	V: 8.79^b
Two-way ANOVA G × T	G × T: P = 0.7575	G × T: P = 0.2168	G × T: P = 0.7762	G × T: P = 0.8461

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^{a b c}Cohorts not connected by the same letter are statistically different; the average value of the specific parameter precedes the letter.

DISCUSSION

We investigated the mechanisms underlying the hyperanabolic phenotype of Nmp4^−/− MSPCs during osteogenesis. Transcriptomic data predicted that Nmp4^−/− osteogenic cells have 1) a significantly increased capacity for metabolic conversion to glycolysis, 2) increased Col1a1 mRNA translation, 3) elevated collagen secretion, and 4) elaborate a matrix that improves bone material and structural mechanical properties. In each case, we were able to anchor the predicted phenotype with experimental results. Moreover, the derived mechanistic insights on Nmp4 control of bone cell phenotype were remarkably consistent between the multiple model systems used in this investigation including MSPCs, MC3T3-E1 osteoblast-like cells, and the in vivo bone studies. Nevertheless, additional model systems are required to explore Nmp4 control of bone phenotype, e.g., conditional knockout mice and CRISPR cell lines, and we are currently developing these reagents.

The Nmp4^−/− MSPCs have a significantly increased capacity for metabolic conversion to glycolysis, which is a key driver of osteoblast anabolism. Our ChIP-seq and RNA-seq data show that Nmp4 directly targets and regulates key genes that direct the cell toward aerobic glycolysis including Pdk1 and Pkm. Glucose is a key nutrient for osteoblasts, and aerobic glycolysis is the dominant mode of glucose utilization in these cells (49, 53). Thus, while it is a less efficient source of energy, glycolysis can generate both anabolic growth intermediates and ATP very rapidly owing to the much higher speed of glycolysis reactions (97). In vivo, PTH-induced bone anabolism is driven in part by the hormone-mobilizing osteoblast autocrine IGF-1 signaling. This activates mTORC2, which suppresses glucose entry into the TCA cycle and shunts it into the aerobic glycolysis pathway (28). Concomitantly, PTH downregulates Sost/sclerostin expression in osteocytes, unleashing the anabolic WNT signaling pathway (98), driving osteogenesis in part by further stimulating glycolysis via the rapid increase in GLUT1 and HK2 protein expression and escalation in LDHA and PDK1 activities thus increasing glucose consumption and driving lactate over acetyl-CoA production from pyruvate (27).

Our transcriptome data identify the HIF-1α and PTEN canonical pathways as significantly sensitive to the status of Nmp4, which is consistent with the glycolytic phenotype of the null cells. Like the IGF-1 and WNT pathways, the HIF-1α and PTEN link osteogenesis and metabolic reprogramming. Stabilization of the transcription factor HIF-1α in Sp7-positive cells in postnatal mice significantly stimulated trabecular bone formation via an increase in the number of osteoblasts and also promoted bone glycolysis via the mRNA upregulation of key glycolytic enzymes including Pdk1, Ldha, and Hk2 (85). Mice expressing the stable-oxygen form of HIF-1α in osteoprogenitors exhibited an expanded pool of these cells and elevated trabecular BV/TV, very reminiscent of the Nmp4^−/− skeletal response to PTH (16, 41, 95). Our pathway analysis predicted that PTEN signaling is attenuated in the Nmp4^−/− cells. Interestingly, PTEN signaling antagonizes the phosphatidylinositol 3-kinase-Akt-mTORC2-p70s6k pathway and thus decreases the glycolytic rate and favors oxidative phosphorylation (77). Specifically, PTEN decreases the levels of two key enzymes involved in the Warburg effect, PKM2 and PFKFB3 (33). Therefore, conditional loss of Pten in osteoprogenitors led to increased numbers of osteoblasts and expanded bone matrix (35), whereas conditionally disabling Pten in mature osteoblasts enhanced mTOR activity and increased bone mineral density (60). Again, this Pten-deficient bone phenotype is similar to the Nmp4^−/− skeleton under PTH challenge.

The Nmp4^−/− MSPCs also exhibited an enhanced capacity for oxidative phosphorylation. This is consistent with a recent study showing that nondifferentiated MC3T3-E1 osteoblast-like cells exhibited both spare glycolytic and oxidative capacities (34). Additionally, this study reported that differentiated MC3T3-E1 cells met ATP demand primarily by aerobic glycolysis, whereas nondifferentiated cells generated ATP through oxidative phosphorylation (34). Further studies with our MSPCs are required to elucidate the impact of Nmp4 on metabolic reprogramming during differentiation.

The present study revealed that Nmp4^−/− MSPCs exhibited increased Col1a1 mRNA translation attendant with elevated collagen secretion revealing part of the mechanism by which these cells are converted into super-secretors. Collagen comprises over 90% of the bone protein matrix and our results suggest that a large percent of Col1a1 mRNA transcripts are present in the heavy polysomes in Nmp4^−/− MSPCs implying a high translation of Col1a1 transcript. This is consistent with our observed increase in collagen protein secretion in these cells. Osteoprogenitor loss of Nmp4 not only redirects metabolic programming toward cellular anabolism, but also elevates gene expression for multiple pathways involved in protein synthesis and delivery, a key step in bone formation (27, 48). Our transcriptomic analysis showed a striking increase in the mRNA expression of several genes that promote protein anabolism during osteoblast differentiation including numerous amino acid transporters, the amino acid synthase Asns and several other genes involved in amino acid synthesis, many tRNA-charging enzymes, and multiple genes driving protein translation initiation as part of the eIF4 and eIF2 pathways. The present data are consistent with our previous study showing that Nmp4^−/− MSPCs exhibited significantly elevated ribosome biogenesis, the primary determinant of translational capacity and a key driver of cell growth (114).

Another key finding of the present work is that Nmp4^−/− osteoblasts elaborate a matrix that improves bone material and structural mechanical properties. The expression of matrix genes that contribute to material and structural mechanical properties, e.g., collagen, osteocalcin, and SLRPs, was elevated in Nmp4^−/− cells, and mechanical analysis of femurs from mice treated with osteoporosis therapies confirmed enhanced improvement in several of these key properties in the Nmp4^−/− bone. Enhanced and accelerated mineralization in vitro often does not correlate with positive effects on the skeleton. For example, osteoblasts deficient in the expression of Naca (66), Sox8 (93), or Foxc1 (42) showed an ex vivo accelerated mineralization phenotype; however, animals harboring deficits in any one of these genes exhibited significant defects in bone development, formation, or mineralization (42, 66, 93). Sodium fluoride (NaF) is an osteoanabolic that increases bone mass, but the newly formed bone lacks normal structure and strength (10, 86, 99). Mechanical load is a bone anabolic signal, and the magnitude of the applied strain determines whether the response is adaptive, forming primarily lamellar bone, or injury, producing woven bone (65). Although woven bone forms faster than lamellar bone, it has inferior mechanical and material properties (8, 19). These findings are in stark contrast to our present model in which the precocious and enhanced mineralization of the Nmp4^−/− osteoblast directly translates into an improved skeletal response to osteoanabolics (15, 16, 41, 95).

The molecular mechanisms underlying the improved Nmp4^−/− bone quality remain to be determined, but the enhanced expression of osteocalcin by pharmacologically induced Nmp4^−/− osteogenic cells (15, 16, 95) may improve the quality of the produced bone. Earlier we proposed that noncollagenous proteins act as “glue” at the collagen-mineral interface to resist the separation of the mineralized fibrils and therefore enhance bone toughness (69, 75, 84, 100). Anabolic therapies that induce the formation of osteocalcin/osteopontin-enriched bone may further enhance energy absorption capacity of bone tissue. Therefore, loss of Nmp4 may modestly alter the ratio of collagen to noncollagenous protein matrix composition, which would be enough to improve bone quality. Additionally, the present data suggest that Nmp4^−/− matrix is enriched in SLRPs, which govern ECM assembly via regulation of linear/lateral fibril growth by binding to the collagen fibril surface (12, 47, 76). Thus the collagen maturation may be accelerated in Nmp4^−/− bone.

The present data demonstrate that Nmp4 acts cell autonomously as a barrier to bone matrix production and mineralization. As an apex regulator of bone cell anabolic output Nmp4 directly and indirectly regulates gene programs that control key stages of matrix production and secretion and the metabolic reprogramming necessary to fuel it (Fig. 13). The Nmp4 transcriptional modus operandi is reminiscent of another apex regulator c-Myc, which like Nmp4 controls the expression of large sets of genes involved in ribosome biogenesis, metabolism, and protein synthesis, representing a cell-type-independent genetic program involved in biomass accumulation (38, 45). Furthermore, whereas c-Myc acts a general amplifier of gene expression (50, 73, 108), Nmp4 appears to act as a general attenuator and suppressor of biomass accrual (16, 114). Both the MSPC RNA-seq and Nmp4 MC3T3-E1 ChIP-seq pathway analyses (16) are consistent with this mechanistic profile. Like c-Myc, Nmp4 may directly govern the expression of master transcriptional regulators of these key networks in addition to broadly engaging some of their downstream target genes. Whether there is a functional relationship between c-Myc and Nmp4 remains to be determined. Nmp4 control of protein translation and movement through the bone cell ER is particularly intriguing since this is a potential promising area for drug target discovery. Several therapeutic strategies and multiple drugs are currently being developed to enhance the adaptive capability of the ER in the service of secretion for numerous diseases including diabetes, cancer, Alzheimer’s disease, and osteoporosis (24, 36, 40, 54, 87, 91, 104). Nmp4 control of metabolic reprogramming may also present therapeutic targets for regulating bone anabolism (48, 49, 106).

There is a critical medical need for understanding the intrinsic barriers to pharmacologically inducing bone formation in the osteoporotic skeleton (21). Investigating these pathways may help to alleviate the current limits to osteoanabolic therapy.

GRANTS

This work was supported, in whole or in part, by Department of Defense Grant PR120563, NIH Grant AR073739 and Indiana University School of Medicine Clinical and Translational Science Institute Biomedical Research Grant (to J. P. Bidwell), and NIH Grants AR-067221 (to J. M. Wallace), AR-062002 (to M. R. Allen), GM-049164 (to R. C. Wek), AR-053237 (to A. G. Robling), and NS-098772 (to N. Brustovetsky).

DISCLAIMERS

The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Defense, Indiana Clinical and Translational Science Institute, or NIH.

DISCLOSURES

K. R. Stayrook is an employee of Eli Lilly and Company. None of the other authors has any conflicts of interest, financial or otherwise, to disclose.

AUTHOR CONTRIBUTIONS

Y.S., E.W., P.J.C., R.C.W., A.L.M., Y.L., A.G.R., N.B., J.H., K.R.S., M.R.A., J.M.W., and J.P.B. conceived and designed research; Y.S., E.W., P.J.C., M.A., J.M., A.K., N.B., J.H., K.J., M.R.A., J.M.W., and J.P.B. performed experiments; Y.S., E.W., P.J.C., M.A., J.M., D.B.B., R.C.W., A.L.M., Y.L., A.G.R., N.B., J.H., K.J., D.V., K.R.S., M.R.A., and J.P.B. analyzed data; Y.S., E.W., P.J.C., M.A., J.M., D.B.B., R.C.W., A.L.M., Y.L., A.G.R., N.B., J.H., K.J., D.V., M.R.A., J.M.W., and J.P.B. interpreted results of experiments; Y.S., E.W., J.M., A.L.M., J.H., J.M.W., and J.P.B. prepared figures; Y.S., R.C.W., J.M.W., and J.P.B. drafted manuscript; Y.S., E.W., P.J.C., M.A., J.M., A.K., D.B.B., R.C.W., A.L.M., Y.L., A.G.R., N.B., J.H., D.V., K.R.S., M.R.A., J.M.W., and J.P.B. edited and revised manuscript; Y.S., E.W., P.J.C., R.C.W., A.L.M., Y.L., A.G.R., N.B., J.H., D.V., K.R.S., J.M.W., and J.P.B. approved final version of manuscript.

ACKNOWLEDGMENTS

We thank Selene Hernandez-Buquer, Jeanette N. McClintick, and Chris Wellbrook for technical assistance.

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PERMALINK

Loss of Nmp4 optimizes osteogenic metabolism and secretion to enhance bone quality

Yu Shao

Emily Wichern

Paul J Childress

Michele Adaway

Jagannath Misra

Angela Klunk

David B Burr

Ronald C Wek

Amber L Mosley

Yunlong Liu

Alexander G Robling

Nickolay Brustovetsky

James Hamilton

Kylie Jacobs

Deepak Vashishth

Keith R Stayrook

Matthew R Allen

Joseph M Wallace

Joseph P Bidwell

Abstract

INTRODUCTION

MATERIALS AND METHODS

Cell culture.

RNA-seq analysis.

Seahorse assay.

Collagen secretion analysis.

Col1a1 polysome analysis.

Mice.

Therapies.

Microcomputed tomography.

Mechanical testing.

Statistical analysis.

RESULTS

Nmp4 regulates a large portion of the osteogenic transcriptome.

Fig. 1.

Loss of Nmp4 alters pathways that exhibit the dual functions of driving osteogenesis and glycolysis.

Fig. 2.

Fig. 3.

Fig. 4.

Phenotype anchoring of our transcriptional data confirmed Nmp4−/− MSPCs exhibited an enhanced capacity for glycolytic conversion.

Fig. 5.

Fig. 6.

Nmp4−/− MSPCs exhibited an increased mitochondrial respiratory capacity.

Fig. 7.

Nmp4−/− osteoprogenitors exhibit enhanced protein production and secretion.

Fig. 8.

Fig. 9.

Fig. 10.

Nmp4−/− osteoblasts produce a bone matrix with improved material properties.

Fig. 11.

Fig. 13.

Fig. 12.

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

DISCUSSION

GRANTS

DISCLAIMERS

DISCLOSURES

AUTHOR CONTRIBUTIONS

ACKNOWLEDGMENTS

REFERENCES

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Phenotype anchoring of our transcriptional data confirmed Nmp4^−/− MSPCs exhibited an enhanced capacity for glycolytic conversion.

Nmp4^−/− MSPCs exhibited an increased mitochondrial respiratory capacity.

Nmp4^−/− osteoprogenitors exhibit enhanced protein production and secretion.

Nmp4^−/− osteoblasts produce a bone matrix with improved material properties.