Recently, ReKom Biotech has published a white paper concerning good practices for antigen titration. This may be seen as an old issue. However, from the point of view of a data analyst the approaches in the literature are not really convincing. Titration experiments should be designed to obtain the most information on how to use the antigen at the lowest cost, that is, with the lowest number of experiments. It should be noted that, while the antigen may be used for quantitative or qualitative analysis, the titration experiments should be designed differently in both cases. The published paper is focused on qualitative analysis since the main goal of the titration experiments in ReKom Biotech is to convince our customers of the high quality of our products. We have devoted considerable efforts to develop titration procedures to indicate the customers at which concentration the antigen best discriminates between positive and negative serum of a given disease. Existent and widely used procedures present several drawbacks. For instance, in most standard titration experiments, such as in Chequerboard Titration (CBT), one single positive and negative sera are employed. From a statistical point of view, this is not a good approach since results are very dependent on the specific sera chosen. On the other hand, when several positive and negative sera are included in the experiment, the results are not assessed with the adequate tools. For instance, we could not find a single paper which compared the outcomes of positive and negative sera with a simple test of significance. The white paper we have published is intended to show clients that we use well-grounded methods to measure the quality of our products, and also to give details on these methods.
The possibility to use ammonium hydroxide to control the pH in fed-batch microbial fermentations would have the advantage over sodium hydroxide that it could be used as a nutrient thus avoiding the increase of the media osmolality. However, this reaction equilibrium should be analysed carefully:
NH3 + H2O = NH4+ + OH-
At 25ºC and infinite dilution:
[NH4+][ OH- ]/[ NH3] = 0.000018
And when temperature or pH rises, free ammonia could be given off, polluting the outlet gas of the bioreactor with this extremely toxic component.
NH4+ + OH- = NH3 + H2O
Due to this reason, ammonium hydroxide only could be used as pH corrector in solutions which are slightly acid, thus in the case of pH = 5, where pOH = 14-5 = 9.
[ NH4+](0.000000001)/[ NH3] = 0.000018
[NH4+]/[ NH3] =18000
In this particular case, the main quantity of the ammonium hydroxide appears to be in the form of ammonium ion which will be consume as nitrogen source and therefore it could be used to rise the pH without the production of free ammonia.
For this reason, ammonium hydroxide shouldn´t be used to adjust the pH in fed-batch microbial fermentations of Escherichia coli where pH 7 is necessary, but it would be very convenient in the case of the methylotrophic yeast Pichia pastoris which optimal growth takes places at pH 5.
In fed-batch microbial fermentations, O2 is not only important as a nutrient, but also it has important effects on metabolism and physiology. There are a number of potential oxygen-sensitive steps which could affect recombinant expression: (i) the results of low oxygen, (ii) the results of high oxygen and (iii) the results of oxygen shifts.
- A shift to anaerobic metabolism can produce accumulation of by-products such as acetate and ethanol which have a negative impact on process performance. For example, acetate can limit productivity of recombinant E. coli MC1016 harbouring the plasmid for hGH production (Jensen and Carlsen, 1990). On the contrary, this partial anaerobiosis, often referred as “microaerobic growth” sometimes is beneficial for expression of certain heterologous genes in E. coli (Tseng et al., 1996). There are several effects under anoxic conditions:
- Regulation of amino acid synthesis by oxygen
- Effects in amino acids depletion
- Plasmid replication
- Hemoglobin expression to improve productivity
- Exposure to elevated oxygen partial pressures (due to OTR limitation or in large bioreactors) can lead to: (Konz et al, 1998).
- An oxidative damage to proteins in five different classes: metal-catalyzed oxidation, disulfide formation, methionine sulfoxide formation, oxidation of iron-sulfur centers and glycation and PUFA conjugation
- Degradation of oxidized proteins
- DNA oxidation and mutation
- Oxidation of free aminoacids
- Oxygen fluctuations can be also detrimental to protein expression. If we shift to oxygen-enriched air during expression of our heterologous gene, our product quality may initially decrease due to the oxidative conditions and afterwards the quality may increase to a new state as tress regulons are induced.
To conclude: fluctuating DO in fed-batch microbial fermentations is not a trivial process which can affect not only the amplification of plasmids in E. coli, but also the quality of our final recombinant protein, sometimes with dramatic consequences.
- Jensen, E. B and S. Carlsen. 1990. Biotechnol. Bioeng. 36: 1-11.
- Tseng, C. P., J. Albretch, and L. P. Gunsalus. 1996. J. of Bacteriol. 178: 1094-1098.
- Konz, J. O., J. King, and C. L. Cooney. 1998. Biotechnol. Prog. 14: 393-409.
Recently, I came across a diagnosis ELISA Kit which was claimed to provide a sensitivity of 100% and a specificity of 99%. These statistics were computed from a skewed sample of sera, with only 5 positive cases and more than 300 negative cases. The potential client should be aware that the confidence on these statistics is very poor for such a low number of positive cases considered. Let me support my discussion with a similar simulated example. Imagine 300 negative readings of a diagnosis kit, representing non infected sera, are simulated following a normal distribution with 0.5 mean and 0.25 standard deviation. Also, 5 positive readings are simulated with a normal distribution with 1.5 mean and 0.5 s.d. This may be seen as a very realistic situation, according to our experience. With these simulated data, the optimum cut-off following a given criterion (the Youden index) is 1.01, and the sensibility and specificity of the (simulated) diagnosis kit is 100% and 97%, respectively. These are quite close statistics to those reported by the test. But the question is: are they reliable values for the client? The answer is no. Mainly because, as already commented, five positives are not enough. Another relevant consideration when using a reduced number of samples is that the sensibility and specificity should always be computed from independent data to that used to set the cut-off. Otherwise, we get very optimistic results. As a matter of fact, the less the size of the sample used, the more optimistic the statistics are. To illustrate this, imagine that a client buy the assay, with cut-off 1.01 and sensibility and specificity of 100% and 97%, respectively. I simulated new samples of positive and negative readings, representing the new sera analyzed by the client with the kit, and obtained a sensibility and specificity of 84% and 98%. Why the sensibility was so low for these new readings? Because the number of positive sera used to compute the cut-off (5) was too low to obtain a reliable statistic. To avoid this problem, resampling techniques such as cross-validation provides more realistic estimates. Cross-validation estimates of the sensibility and specificity were of 80% and 97%, much closer to those observed by the client than the original ones. As a conclusion, statistical validation of diagnosis kits should be always supported by sounded statistical procedures and enough data. Otherwise, performance statistics reported are simply not reliable.
Glycosylation is a very intriguing post-transductional modification of the proteins which sometimes seems to be critical for their antigenicity. Glycans appear to lack a discrete, folded structure; they are assembled without a template through a series of individually catalysed reactions. This arrangement can be particularly effective when glycans are used as tags to direct glycoprotein trafficking, adhesion events or other processes. In some cases, the role of a particular glycan is only evident in the context of a specific glycoconjugate. Therefore, obtaining a consistent glycoform profile in production is desired due to narrow concerns because a molecule can be defined by its carbohydrate structures. Glycosylation optimization will improve therapeutic and diagnostic efficacy and is an in progress goal for researchers in academia and industry alike.