Since the first mammalian TLR (now known as TLR4) was identified by Charles Janeway and colleagues (17
), 10 human TLRs and 13 murine TLRs have been identified (14
). TLRs 1, 2, 4, 5, and 6 are expressed on the cell surface, while TLRs 3, 7, 8, and 9 are found in intracellular compartments, such as endosomes and lysosomes (15
We reasoned that the TLR system, which has evolved to recognize microbes, could be exploited to detect and identify residual microbial components in plasma-derived or recombinant biologic products more efficiently than available tests which utilize nonhuman systems. To achieve this objective, we started with a human cell line, MM6, the cells of which express multiple TLRs. If they react to the product by secreting a cytokine (IL-6 or IL-8), further testing is performed with human HEK293 cells that have been transfected with a single TLR or multiple other TLR-related genes (MD2, CD14). The pattern of response by the different TLRs can help deduce the identity of the microbial contaminant. This can be valuable information for manufacturers, which can apply this during process development.
To determine whether this system had any utility for detecting microbial contaminants, we asked a manufacturer for a set of blinded samples, one of which had tested negative for bacterial contaminants by standard testing as well as ELISA testing for host cell proteins in nonhuman systems. This product sample was made from an old purification process, process I. The sample made by process I was a highly purified recombinant protein that was derived from E. coli and that had been sterile filtered. Similarly, a sample from the improved purification process, process II, was included as a blinded sample. The remaining samples consisted of PBS as a negative control and diluted LPS as a positive control. The four samples were added to a human cell line, MM6, the cells of which express multiple TLRs. Only samples B and D induced IL-6 and IL-8 cytokine responses at levels above the background level in MM6 cells.
When the product samples were tested with the panel of HEK293 cells transfected with human TLRs 2, 3, 4, 5, 7, 8, and 9 (Fig. and Table ), only sample D stimulated HEK-hTLR4 cells (Fig. ) and was inhibited by polymyxin B. Since LPS is the known ligand for TLR4, it was simple to deduce that this sample contained LPS. Since LPS should be detected by standard LAL testing, it was unlikely that this was the implicated product sample but was probably added to the sample set by the manufacturer as a positive control. Samples A and C were nonstimulatory with all the cells tested. Upon breaking of the code, sample C was listed as a negative control containing PBS. Sample A was revealed to be the sample made by process II, the manufacturing process that was improved to further reduce the possibility of contamination. Sample D was listed as a diluted LPS (0.33 EU/ml).
The response pattern that emerged with sample B was complex, since it stimulated all the TLR-bearing cells except TLR3. This implied that sample B contained multiple TLR ligands and/or stimulated HEK293 cells by TLR-independent pathways. After the information about TLRs and these cell lines was reviewed, it became apparent that HEK293 cells express TLR5 constitutively; therefore, it is possible that all of the cell lines tested, except the TLR3 cell line, were triggered by flagellin, a known TLR5 ligand that was present in sample B. Indeed, as little as 1 to 10 ng/ml of flagellin was able to induce IL-8 production from HEK293 cells and even induced a higher degree of IL-8 production from HEK-hTLR5 cells, which overexpress TLR5. The HEK-hTLR3 cell line that we obtained was not responsive to flagellin (Fig. ) and was found not to express detectable TLR5 by flow cytometry (data not shown).
To further test the possibility that sample B contained flagellin, we showed that anti-hTLR5 antibody (Fig. ) but neither anti-hTLR2 nor anti-hTLR4 antibody could block the flagellin- and sample B-induced IL-8 production in various cell lines that express TLR5. Moreover, sample B was shown to contain flagellin when it was stained with antiflagellin antibody by Western blot analysis (Fig. ). This confirmed the results obtained with TLR-bearing cell lines. In addition, the presence of flagellin in sample B was confirmed by immunoprecipitation and MS analysis. That analysis revealed flagellin sequences, including a peptide unique to the flagellin from E. coli.
In conclusion, we have developed a testing algorithm using a cell-based approach to identify subcellular microbial contaminants. Employing this algorithm, we have shown that a panel of TLR-expressing human cell lines is able to detect a bacterial contaminant missed by standard product testing procedures. Furthermore, the results with the cell lines enabled the molecular nature of the contaminant to be identified. This testing algorithm may aid manufacturers with identifying and tracking the source of the contamination in one or more of the manufacturing steps and, in turn, with devising methods that may be used to avoid such contamination in the future. More importantly, tests based on human cells are more likely than tests based on nonhuman systems to detect microbial contaminants that can cause adverse effects in humans.