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Gene Expression Profiling of Osteolytic Lesions Around Total Hip Replacements
Arun Shanbhag, PhD, Mahito Kuwahara, Grant Garrigues, MD, and Harry Rubash, MD
 Biomaterials Lab, Massachusetts General Hospital

Introduction

The macrophage response to particulate wear debris initiates a granulomatous reaction around components of total joint replacements. The released inflammatory mediators facilitate osteoclast recruitment, maturation and stimulation to resorb bony anchors stabilizing the components, leading to osteolysis or aseptic loosening.

Recent technologies using cDNA microarrays that can precisely quantify the expression of tens of thousands of genes, permit a comprehensive overview of the complex cytokine machinations in the granulomatous tissues. As such they offer distinct advantages over traditional techniques such as Northern blots and RNase protection assays. A systems approach to understanding the biology of osteolysis can also facilitate the development of targeted therapies to prevent and treat this important clinical problem. In this ongoing investigation we used Affymetrix gene chip technology to profile tissue samples from osteolytic lesions around total hip replacements (THR) (1).

MATERIALS AND METHODS

Osteolytic tissue samples were collected from patients (n=31) undergoing revision surgery for aseptic loosening. Samples were obtained from lesions identified on preoperative radiographs and immediately flash frozen in liquid nitrogen in the operating room. In the laboratory, samples were stored at (-)80oC awaiting RNA extraction. Approximately 1 g of tissue was homogenized in the presence of 2 mL Trizol reagent (Invitrogen, Paisley, UK). RNA was extracted using optimized, and established procedures and additionally cleansed using Rneasy mini spin columns (Qiagen, Valencia CA).

MICROARRAY PROCEDURE

A cut-off quality of RNA, determined as the ratio of the absorbance at A260/A280 nm of 1.9 – 2.1 was used. Additionally, RNA degradation was detected by identifying ribosomal RNA peaks (Agilent Bioanalyzer, Agilent Inc, Palo Alto, CA). Since microarrays are quantitative in nature, it is important to have no RNA degradation in the sample, particularly because it would not be possible to determine if a selective RNA degradation was taking place, compromising the findings. Thus the RNA quality bar is raised much higher than for other qualitative and semi-quantitative techniques such as Northern blots and even PCR.

We experienced that even minor delays in flash freezing samples, like bringing tissues to the laboratory, typically requiring less than 120 seconds, was a major consideration in causing RNA degradation. Similarly, harvesting tissue samples using a heat generating electric cautery caused mRNA degradation. Samples stored in saline or fixed in formalin or other fixatives were under no circumstances used for microarray analyses. Necrotic portions of samples in patients with a long-term loosening likely also contributed to sub-optimal RNA. Most samples did not meet these exacting conditions and only n=4 samples with exquisite RNA quality were carried forward for Affymetrix gene chip analysis.

Double stranded cDNA was synthesized sequentially by first strand and second strand using established protocols. Labeled and fragmented cDNA were mixed with control oligonucleotides and internal controls to create a hybridization cocktail. A test chip was run to verify sample quality and the hybridization procedure was repeated with Affymetrix HG-U133A arrays representing 25,000 genes, representing the vast majority of the entire human genome (1,2).

DATA ANALYSES

The microarray data was loaded into Resolver, a statistical data mining database (Rosetta Resolver v4.0, Rosetta Biosoftware, Seattle, WA), normalized and error estimated. Gene sequences were annotated using NetAffex (www.affymetrix. com), Ensmart (www.enembl.org), and the Expression Analysis Systematic Explorer (EASE) version 1.21 (david.niaid. nih.gov/david/ease.htm) bioinformatics queries. To attain a global perspective of the gene expression profile, data from all 4 samples, representing 22,283 sequences for each sample that were above the noise cutoff, were globally normalized and combined by calculating mean intensities and p-values. The top 200 most highly expressed sequences, representing the >99th percentile were tabulated. Each gene function was annotated using the OMIM database (On-Line Mendelian Inheritance in Man, www.ncbi.nlm.nih.gov) and sequences were grouped based on clearly defined genetic or functional similarities.

The raw data was filtered through a series of statistical cuts. In the initial filter, error-weighed and normalized intensities were defined as “present” if genes were on all 4 arrays with all 4 p-values <0.01. All other genes were defined as “absent”. Subsequently, a more stringent Bonferroni’s corrected significance cutoff of p<10-6 was used.

Principal component analysis (PCA), a mathematical methodology allowing a reduction of the data set matrix to just two dimensions, was performed to reduce the dimensionality of the data set and thus determine the minimum gene components accounting for the greatest sample variability. Hierarchical clustering and a 4x9 node self-organizing map were further calculated with the filtered data set to ease identification of related gene profiles and similarities in tissue samples. Using a variety of bioinformatics toolkits, genes related to important mediators were annotated, cross-referenced with GenBank numbers and Affymetrix sequence codes, and separated into four functional groupings: inflammation, bone turnover, extra-cellular matrix turnover, and angiogenesis. A list of potential inflammatory mediators implicated from our own studies and the aseptic loosening literature, were compiled and used to query the combined data set using a global normalization schema (1).

RESULTS

Each microarray contained 26,855 probe sequences and 68 internal controls, representing approximately 13,560 known genes and about 270 expressed sequence tags (ESTs). ESTs are RNA transcripts, whose genes have not yet been sequenced and may encode a functional protein. After an initial screening utilizing the 68 internal controls, 11,661 genes were considered “present,” with an intensity range from approximately 10 to 10,000 (Figure 1). After an application of Bonferroni’s correction at p <1x10-6, 6,627 genes were identified from the combined array data, representing approximately 25% of the sequences assayed (1).

The 200 gene expressions representing the top 1% of the overall genes on the array were identified for further investigations. Multiple differing probes for each gene reassuringly had very similar expression levels. Additionally, genes that are logically paired and likely to be co-regulated, e.g. different subunits of the same protein, frequently appeared with nearly matched expression levels. The top 200 genes clearly and repeatedly pointed to 14 important functions in the clinical osteolytic tissues (Table I). Important highlights from this list give us insight into the majority activity occurring within the clinical tissues. These include protein synthesis; MHC-associated expression; lysosome and antigen processing; cytoskeletonassociated genes.

Principal Component Analysis (PCA )

When dealing with very large microarray data sets, it is difficult, if not impossible, to discern trends in the data (3). Further, once a trend in the data set has been captured, it is pretty straightforward to identify only those handful of genes which are necessary to mathematically express this trend. Using PCA, 5,798 genes could be used to express all the variation in the data sets. Using reduction software, only 21 genes could describe greater than 90% of the variability of the data set. It is important to note that while these genes define the variability of the data-set, they do not necessarily represent key biochemical pathways in the osteolytic process.

Gene Clust ering and Querying

Clustering permits us to identify genes of interest and others which share a similar profile. In a clustered data set, any gene of interest can be picked and genes with similar response profiles highlighted. Hierarchical and self organizing maps differ in their treatment of the underlying data and the methodology of the iterations. In addition to the gene relationships, clustering also identifies the similarity between samples. For example, in the case presented in Figure 2 samples 2 and 3 had very similar profiles, with 1 as the next most similar and 4 as the most different with respect to response gene expression profile. In a much larger data-set, such a simpler conceptualization permits us to refer back to patient details to identify the reasons for such differences pointing to possible causation. Using our predefined list of genes implicated in osteolysis, and those potentially involved due to their importance in inflammation, yielded 81 unique genes with p-values <10-6 (Table 2). In a large sample set, such a list may be further helpful in identifying combinatorial differences in gene expression response under subtly different conditions.

DISCUSSION

In-vitro studies benefit from a highly simplified scenario of gene expression, usually by a single cell type – the macrophage( 4). As such, gene expression profiles of cell cultures can be tracked back to specific cell responses already identified in the literature. In studies using clinical materials, the number of variables (patient-, device- and technique-associated) increase exponentially, making interpretations tremendously difficult but more representative of the clinical scenario. Unlike a cell culture study, clinical osteolytic tissues are well documented to include a large variety of cell types including macrophages, foreign body giant cells and fibroblasts. The close proximity to remodeling bone and the presence of blood vessels points to the participation of osteoblasts, endothelial cells and cells of the hematopoietic lineage. Histological investigations have also pointed to a small but significant presence of T-cells (5,6). Thus the gene expression profile of osteolytic tissues includes not only what a single cell type is expressing, but the integral expression of all genes of all cells and types present in the tissue, at the time of its harvest. While an analysis and interpretation of this data is not easy, it is satisfying to know that the complex interactions alluded to in the data set are actually ongoing in the clinical patient at the site of the lesion, and thus of particular import.

The analysis of the top 200 genes actively expressed in interfacial tissues yields a quick glance at the proverbial ‘tip of the iceberg’ (Table 1). The startling feature of this list is the presence of a large number of genes (forty one), associated with putative roles in antigen processing, presentation and its sequelae. This group largely consists of many MHC (major histocompatibility complex) proteins, and confirming that the machinery for macrophage interaction with T and B cells, key drivers of the adaptive immune response has been activated (7). Macrophages, sentinels of the immune response, phagocytize and process antigen, and present it to circulating T-cells in the context of the MHC proteins to decide if the antigen is derived from host protein or not. If the antigen is foreign, the T-cells begin to formulate a antigen specific, adaptive immune response in close coordination with B-cells. At local sites of inflammation, as well as at remote sites of immune response maturation (spleen and lymph nodes), specific responses include antibody formation and cytotoxic T-lymphocyte activation to destroy both extracellular and intracellular invaders (7). Thus expression of genes associated with antigen presentation is not surprising given the histology of the granuloma and the presence of these cell types in clinical materials. Further, in recent studies using protein chips to better identify mediators present in osteolytic tissues, we have demonstrated the significant presence of chemotactic factors recruiting activated T-cells: interferon-?-inducible protein of 10KDa (IP-10) and monokine induced by interferon-? (MIG) (8). In pilot studies just completed in the laboratory, we also reported that T-cell costimulators, CD28, B7-1 and B7-2, all crucial for facilitating and stabilizing the immunological synapse, are also expressed (9).

High expression of inflammation-related genes were expected, considering the macrophage involvement and the overwhelming findings from in-vitro models. But very few inflammatory proteins appeared to be upregulated in the gene expression profile. Rather than a simmering inflammatory response, the gene expression profile paints a vivid picture of a spirited T-cell macrophage interaction. Instead of high levels of interleukin-1 (IL-1) and tumor necrosis factor (TNF-a), we find early lymphocyte activators and lymphocyte chemotactic agents. Co-stimulators of a T-cell response are also upregulated.

While the paucity of acute inflammatory mediators in clinical osteolytic tissues is surprising, it is supported by our recent studies using high-throughput protein chip analysis. We reported that as-harvested tissue samples have minimal levels of IL-1 and TNF-a, while very high levels of T-cell chemotactic agents such as IP-10 and MIG are present (8). Other findings in the gene expression were also found to have correlation with and supported by protein chip investigations. Earlier studies of osteolytic tissues traditionally cultured samples for up to 72 h before analyzing the supernatants. It is this culture period, along with the necessary mincing of samples and associated cellular trauma, that is likely causative of the release of the acute inflammatory mediators. Tissues associated with a chronic, long simmering pathology such as osteolysis have miniscule levels of the acute inflammatory mediators.

SUMMARY

Investigating diseases using cDNA microarrays affords significant advantages over traditional methods of analyzing mRNA. Most notably, such a wholistic approach can identify biochemical molecules, and thus pathways, that were hitherto unrecognized as playing an important role in the development and/or progression of the disease. In understanding osteolysis, we have identified the importance of T-cell chemotactic agents and associated co-stimulators, but the absence of a full-blown adaptive immune response. This finding has also been confirmed using high-throughput protein chips – a technology that was developed after the success of the gene arrays. A Tcell mediated macrophage activation is a potent pathway that results in peri-prosthetic bone resorption. The identification of a large number of associated molecules also points to potential targets for therapeutic intervention.



Arun S. Shanbhag Ph.D., MBA is Director of the Biomaterials Lab at Massachusetts General Hospital and Assistant Professor of Orthopedic Surgery at Harvard Medical School.
Mahito Kuwahara is a member of the Biomaterials Lab at Massachusetts General Hospital.
Harry E. Rubash M.D. is Chief of the Orthopedic Department at Massachusetts General Hospital.

Address correspondence to:

Arun Shanbhag, PhD, MBA
GRJ 1115, 55 Fruit St
Boston, MA 02114

References:

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