\r\n\tPainful attack commonly occurs in the big toe, the ankle joint, the achilles tendon, the knee joint and the wrist joint. Sometimes gouty nodules can occur even in the auricle. The occurance of urinary stones is a common complication. Administration of anti-inflammatory drugs including steroid or joint injection of steroid are used for the treatment. \r\n\tGout often occurs in patients with renal impairment, we should check renal function before the treatment. \r\n\tIt requires time to improve serum uric acid level and to disappear gout attack, and care of the patients might continue for their lifetime. \r\n\tOur goal is to provide effective treatments and prevention methods.
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\n\t\t\t
1. Introduction
\n\t\t\t
Solution microcalorimetry has entrenched itself as a major technique in laboratories concerned with studying the thermodynamics of chemical systems. Recent developments in the calorimeter marketplace will undoubtedly continue to popularize microcalorimeters as mainstream instruments. The technology of microcalorimetry has in turn benefited from this trend in terms of enhanced sensitivity, signal stability, physical footprint and user-friendliness. As the popularity of solution microcalorimeters has grown, so has an impressive body of literature on various aspects of microcalorimetry, particularly with respect to biophysical characterizations. The focus of this chapter is on experimental and analytical aspects of solution microcalorimetry that are novel or represent potential pitfalls. It is hoped that this information will aid bench scientists in the formulation and numerical analysis of models that describe their particular experimental systems. This is a valuable skill, since frustrations often arise from uninformed reliance on turnkey software that accompany contemporary instruments. This chapter will cover both differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC). It targets physical chemists, biochemists, and chemical engineers who have some experience in calorimetric techniques as well as nonlinear regression (least-square analysis), and are interested in quantitative thermodynamic characterizations of noncovalent interactions in solution.
\n\t\t
\n\t\t
\n\t\t\t
2. Differential scanning calorimetry
\n\t\t\t
DSC measures the heat capacity (Cp) of a sample as the instrument “scans” up or down in temperature. For reversible systems, direct interpretation of the data in terms of thermodynamic parameters requires that chemical equilibrium be re-established much more rapidly than the scan rate. This can be verified by comparing data obtained at different scan rates. For transitions involving a change in molecularity (e.g., self-association/dissociation, ligand binding/unbinding), reversibility can also be confirmed by the lack of hysteresis between heating and cooling experiments. The optimal scan rate is ultimately a compromise between the requirement for reversibility and the desire for reasonable throughput; typically this falls between 0.5 to 1.0 C/min for most systems in dilute aqueous solutions.
\n\t\t\t
\n\t\t\t\t
2.1. Experimental conditions for DSC
\n\t\t\t\t
The observed or apparent thermodynamics of solution systems generally include linked contributions from other solutes in addition to the species of interest. They include buffers, salts, neutral cosolutes, and cosolvents. Of these, the choice of buffer, or any ionizable species in general, must take into account the change in pK\n\t\t\t\t\ta with respect to temperature i.e., the enthalpy of ionization (ΔH\n\t\t\t\t\tion). Unless a buffer’s ΔH\n\t\t\t\t\tion is negligibly small, its pK\n\t\t\t\t\ta will exhibit a temperature dependence, leading to a change in pH of the solution upon heating or cooling. Failure to take this fact into account may introduce significant artifacts into the observed melting behavior. Such changes in pH represent a different issue from any coupled ionization enthalpy arising from the release or uptake of protons associated with the transition of interest.
\n\t\t\t\t
The direction and extent of the temperature of pH for a buffered solution depends on the sign and magnitude of ΔH\n\t\t\t\t\tion as well as the concentration of the buffering species. Consider the ionization of a buffer A\n\t\t\t\t\t\tZ\n\t\t\t\t\t in the direction of deprotonation to produce its conjugate base B\n\t\t\t\t\t\tZ-1\n\t\t\t\t\t:
Thus, for a buffer with a positive (endothermic) ΔH\n\t\t\t\t\tion, its ionization equilibrium shifts towards deprotonation as temperature increases, leading to a drop in pH. Conversely, the pH of a solution buffered by an exothermic buffer rises with increasing pH.
\n\t\t\t\t
\n\t\t\t\t\tTable 1 lists several common buffers for aqueous solutions (King 1969; Disteche 1972; Lo Surdo et al. 1979; Kitamura and Itoh 1987; Goldberg et al. 2002). As a group, substituted ammonium compounds exhibit substantial positive values of ΔH\n\t\t\t\t\tion, making them poor choices for DSC experiments. These compounds include the so-called “Good buffers” (Good et al. 1966) that are prevalent in biochemistry. Among these, Tris, is a particular offender: the pH of a 25 mM solution initially buffered at pH 9.0 drops by more than one pH unit from 0 to 37 C (Poon et al. 2002). In contrast, the ionization of carboxylic acids and their analogues is far less sensitive to temperature. Generally, buffers based on acetate, cacodylate, and phosphate, for example, are preferred choices for DSC experiments.
\n\t\t\t\t
Another important note relates to polyprotic species such as phosphates, citrates, and borates, whose pK\n\t\t\t\t\ta also depends markedly on ionic strength. This relationship is quantitatively given by the volume changes of ionization (ΔV ) and interpreted in terms of electrostriction of solvent water molecules. Thus, the addition of salts such as NaCl or guanidinium salts (the latter commonly used to denature proteins) will systematically reduce the pH of a solution buffered by polyprotic acids. The pH of a 0.1 M phosphate buffer at pH 7.2, for example, can fall by 0.5 pH unit upon addition of 0.5 M of NaCl. Molar concentrations of guanidinium hydrochloride will produce an even greater drop. On the other hand, inorganic cosolvents have the opposite effect by affecting the solution dielectric. Of course, once the pH of these buffers is adjusted to a value that is compatible with the apparent pK\n\t\t\t\t\ta, it will be stable with respect to temperature. As seen in Table 1, it is generally the case that a buffer is either sensitive to temperature or ionic strength in aqueous solution.
\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Buffer
\n\t\t\t\t\t\t\t
pKa\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
ΔH°, kJ mol-1\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
ΔCp°, J K-1 mol-1\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
ΔV°, mL mol-1\n\t\t\t\t\t\t\t\ta\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Acetate
\n\t\t\t\t\t\t\t
4.756
\n\t\t\t\t\t\t\t
-0.41
\n\t\t\t\t\t\t\t
-142
\n\t\t\t\t\t\t\t
-10.6
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Bicine
\n\t\t\t\t\t\t\t
8.334
\n\t\t\t\t\t\t\t
26.34
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
-2.0
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Bis-tris
\n\t\t\t\t\t\t\t
6.484
\n\t\t\t\t\t\t\t
28.4
\n\t\t\t\t\t\t\t
27
\n\t\t\t\t\t\t\t
3.1
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Cacodylate
\n\t\t\t\t\t\t\t
6.28
\n\t\t\t\t\t\t\t
-3
\n\t\t\t\t\t\t\t
-86
\n\t\t\t\t\t\t\t
-13.3
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Citrate
\n\t\t\t\t\t\t\t
3.128 4.761 6.396
\n\t\t\t\t\t\t\t
4.07 2.23 -3.38
\n\t\t\t\t\t\t\t
-131 -178 -254
\n\t\t\t\t\t\t\t
-10.7 -12.3 -22.3
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Glycine
\n\t\t\t\t\t\t\t
2.351 9.780
\n\t\t\t\t\t\t\t
4 44.2
\n\t\t\t\t\t\t\t
-139 -57
\n\t\t\t\t\t\t\t
-6.8
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
HEPES
\n\t\t\t\t\t\t\t
7.564
\n\t\t\t\t\t\t\t
20.4
\n\t\t\t\t\t\t\t
47
\n\t\t\t\t\t\t\t
4.8
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Imidazole
\n\t\t\t\t\t\t\t
6.993
\n\t\t\t\t\t\t\t
36.64
\n\t\t\t\t\t\t\t
-9
\n\t\t\t\t\t\t\t
1.8
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
MES
\n\t\t\t\t\t\t\t
6.27
\n\t\t\t\t\t\t\t
14.8
\n\t\t\t\t\t\t\t
5
\n\t\t\t\t\t\t\t
3.9
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
MOPS
\n\t\t\t\t\t\t\t
7.184
\n\t\t\t\t\t\t\t
21.1
\n\t\t\t\t\t\t\t
25
\n\t\t\t\t\t\t\t
4.7
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Phosphate
\n\t\t\t\t\t\t\t
2.148 7.198 12.35
\n\t\t\t\t\t\t\t
-8 3.6 16
\n\t\t\t\t\t\t\t
-141 -230 -242
\n\t\t\t\t\t\t\t
-16.3 -25.9 -36.0
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Succinate
\n\t\t\t\t\t\t\t
4.207 5.636
\n\t\t\t\t\t\t\t
3.0 -0.5
\n\t\t\t\t\t\t\t
-121 -217
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Tris
\n\t\t\t\t\t\t\t
8.072
\n\t\t\t\t\t\t\t
47.45
\n\t\t\t\t\t\t\t
-142
\n\t\t\t\t\t\t\t
4.3
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\ta Ionization volume at atmospheric pressure at infinite dilution. \n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
Table 1.
Thermodynamic properties of ionization by common aqueous buffers
\n\t\t\t
\n\t\t\t
\n\t\t\t\t
2.2. Analysis of DSC data
\n\t\t\t\t
A complete DSC experiment consists of matched scans of a sample and a sample-free reference solution. Blank-subtracted data can be empirically analyzed to obtain model-independent thermodynamic parameters. The difference between pre- and post-transition baselines gives the change in heat capacity, ΔC\n\t\t\t\t\tp. After subtracting a suitable baseline across the transition range, the arithmetic integration of the C\n\t\t\t\t\tp vs. T trace yields the so-called calorimetric enthalpy:
Thus, a single DSC experiment yields the complete thermodynamics of a transition. Model-free determination of thermodynamics, including the direct measurement of ΔC\n\t\t\t\t\tp, is a unique feature of DSC not possible with optical techniques (such as absorption and fluorescence spectroscopy) which access ΔH via the van\'t Hoff equation. However, model fitting by least-square analysis can extract considerably more useful information and facilitate quantitative hypothesis testing.
\n\t\t\t\t
In general, the reference-subtracted DSC data represent the heat capacity of the initial state 0 (C\n\t\t\t\t\tp,0) as well as the excess heat capacity function, <ΔC\n\t\t\t\t\tp(T)>:
Consider a general model in which the sample undergoes a transition from initial state 0 through intermediates 1, 2,..., i to the final state n. (One can readily envisage extensions of this model in which a heterotypic complex dissociates into subunits which then go on to further, independent transitions.) The excess heat capacity function is (Privalov and Potekhin 1986)
where ΔH\n\t\t\t\t\t\n\t\t\t\t\t\ti\n\t\t\t\t\t and ΔCp,i\n\t\t\t\t\t are the enthalpy and heat capacity change, respectively, from state 0 to state i, and α\n\t\t\t\t\t\ti\n\t\t\t\t\t(T) is the fractional conversion at state i. \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t〈\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tδ\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\ttr\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t〉\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tand \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t〈\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tδ\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tint\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t〉\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tare called "transition" and "intrinsic" heat capacities, respectively. The intrinsic heat capacity, which represents the summed heat capacities of the various species present at T, is the baseline function of the observed DSC trace (Figure 1). Some analytical protocols invite the user to
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Figure 1.
An excess heat capacity function and its constituent transition and intrinsic heat capacities. Integration of the transition heat capacity, \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t〈\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tδ\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ttr\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t〉\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t, gives ΔH\n\t\t\t\t\t\t\tcal at the transition temperature (50 C here).
\n\t\t\t\t
perform manual baseline subtraction before fitting a excess heat capacity function. This is intended to eliminate \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t〈\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tδ\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tint\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t〉\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t from the fitting function. In the transition region, manual baseline subtraction requires either heuristic or semi-empirical criteria to connect the pre- and post-transitional states. This is both unnecessary and questionable practice, since manual editing may (and probably do) bias the data. The most appropriate approach is to fit both the excess and intrinsic heat capacities directly according to Eq (7). Since both terms are functions of α\n\t\t\t\t\t\ti\n\t\t\t\t\t(T), the fitted baseline will objectively track the progress of each transition. Note that C\n\t\t\t\t\tp,0 and ΔC\n\t\t\t\t\tp,i\n\t\t\t\t\t are taken to be constants in Eqs (6) and (7) since their temperature dependence is generally weak over the experimental range. They can be formulated, if desired, as polynomials to define nonlinear baselines. Care must be taken, however, to ensure that such curvature is not masking some low-enthalpy transition such as conformation changes of proteins in the native state (Privalov and Dragan 2007).
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\n\t\t\t\t\t
2.2.1. Formulation of DSC models
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The principal task in formulating DSC models is deriving expressions for α\n\t\t\t\t\t\t\ti\n\t\t\t\t\t\t(T) from the relevant equilibrium expressions and equations of state. Implicit in this task is the computation of K\n\t\t\t\t\t\ti from its corresponding thermodynamic parameters. This in turn requires the choice of a reference temperature, the most convenient of which is the characteristic temperature T at which ΔG(T ) = 0:
Again ΔC\n\t\t\t\t\t\tp is taken to be independent of temperature in the experimental range. The simplest DSC model involves the isomeric conversion of a species in a strictly two-state manner (i.e., no intermediate state is populated at equilibrium). The denaturation of many single-domain proteins exemplifies this model. This excess heat capacity function is
The two terms on the right side represent \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t〈\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tδ\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\ttr\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t〉\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tand\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t〈\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tδ\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tint\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t〉\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t, respectively. The DSC traces in Figure 1 are simulated using Eq (9) with ΔH = 50 kcal/mol, ΔC\n\t\t\t\t\t\tp = 500 cal mol-1 K-1, and T = 50 C (1 cal ≡ 4.184 J). In this model, T is the midpoint of the transition (i.e., K = 1 and\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tK\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tK\n\t\t\t\t\t\t\t\t\t\t\t+\n\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t0.5\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t) and marks the maximum of the\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t〈\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tδ\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tint\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t〉\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t function.
\n\t\t\t\t\t
For transitions involving changes in molecularity, α\n\t\t\t\t\t\t\ti\n\t\t\t\t\t\t(T) includes total sample concentration, c\n\t\t\t\t\t\tt in addition to equilibrium constants. While the mechanics of formulating such models is not different, a potential source of inconsistency arises from the choice of unit in thermodynamic parameters. Specifically, every intensive thermodynamic parameter can be defined either per unit of monomer or oligomer. Either choice is correct, of course, but the resultant differences may lead to some confusion. Take for example a two-state homo-oligomeric transition (Privalov and Potekhin 1986; Freire 1989):
Equivalent formulations of a two-state homo-oligomeric transition
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In the author\'s experience (Poon et al. 2007), the choice of per unit monomer is more convenient, particularly when oligomers of different molecularities are compared. In addition, it can be seen that K is a polynomial in α of order n. Even in cases where α can be solved explicitly in terms of K and c\n\t\t\t\t\t\tt (n ≤ 4), it is advisable to use numerical procedures such as Newton\'s method instead to minimize potential algebraic errors and avoid a loss of significance in the fitting procedure.
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Figure 2.
Two-state dissociation of an homo-oligomer. The traces are simulated for a pentamer (n = 5) where ΔH = 50 kcal mol-1, ΔC\n\t\t\t\t\t\t\t\tp = 250 cal mol-1 K-1 and T = 100 C. All thermodynamics parameters are per unit monomer. Note the asymmetry in both heat capacity functions.
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An additional consideration for transitions involving changes in molecularity concerns the choice and interpretation of the reference temperature. In contrast with isomeric transitions, T is neither the midpoint of a transition (in the context of concentrations) nor does it mark the maximum of the transition heat capacity function. Both of the latter temperatures are lower than T . In addition, the midpoint of the transition, T\n\t\t\t\t\t\t50, is below the temperature of the transition heat capacity maximum, T\n\t\t\t\t\t\tm. These relationships are illustrated for the two-state dissociation model in Figure 2. The non-equivalence of T\n\t\t\t\t\t\tm and T\n\t\t\t\t\t\t50 also introduces a systematic difference between the calorimetric and van\'t Hoff enthalpies (ΔH\n\t\t\t\t\t\tvH, reported at Tm) (Freire 1989; Freire 1995). Moreover, the actual values of T\n\t\t\t\t\t\t50 and T\n\t\t\t\t\t\tm are concentration-dependent, and this serves as a diagnostic for a change in molecularity in the transition. For data fitting purposes, T remains the most efficient choice because it is independent of concentration. After data fitting, estimates of T\n\t\t\t\t\t\tm and T\n\t\t\t\t\t\t50 can also be easily obtained from the fitted curve.
\n\t\t\t\t\t
Extension of the foregoing discussion applies readily to multi-state transitions. However, an explicit, statistical thermodynamic approach is generally used to derive the required equations for each state as a function of the partition function (Freire and Biltonen 1978). Details in deriving these models have been discussed extensively by Privalov\'s and Freire\'s groups (Privalov and Potekhin 1986; Freire 1994). From the standpoint of numerical analysis, it is worth noting that the excess enthalpy is the summed contributions from each state:
Depending on the number of states considered, the expansion of the derivative on the right side of Eq (11) can be formidable. Commercial programs such as Mathematica (Wolfram Research, Champaign, IL, USA) are thus recommended for symbolic manipulation for all but the most trivial derivations. Less preferably, one can numerically integrate the raw C\n\t\t\t\t\t\tp vs. T data and fit \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t〈\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t〉\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t directly. There are generally enough data points (at 0.1 C resolution) in an experiment that any loss of resolution should be negligibly small.
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3. Isothermal titration calorimetry
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As its name indicates, ITC measures the heat change accompanying the injection of a titrant into titrate at constant temperature. In contemporary instruments, this is accomplished by compensating for any temperature difference between the sample and reference cells (the latter lacking titrate, usually just water). The raw ITC signal is therefore power P, a time-dependent variable. Integration with respect to time therefore yields heat q which is the primary dependent variable that tracks the progress of the titration of interest:
Typically, ITC is operated in incremental mode in which the titrant is injected in preset aliquots after successive re-equilibration periods. A feature of ITC that distinguishes it from most titration techniques is that the measured heat does not accumulate from one injection to the next, but dissipates as the instrument measures the heat signal by returning the sample and reference cells to isothermal conditions. ITC is therefore a differential technique with respect to the concentration of the titrant (X) i.e., the derivative of q with respect to the total titrant concentration, [X]t:
where [X\n\t\t\t\t\ti\n\t\t\t\t]b is the concentration of X in the i-th bound state. This contrasts with most other physical binding signals (e.g., absorbance, fluorescence intensity or anisotropy, pH) which are integrative in nature.
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3.1. Experimental conditions for ITC
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As a thermodynamic tool for studying molecular interactions, the singular strength of ITC is the direct measurement of binding enthalpies. Model-based analysis of ITC data, the subject of Section 3.2, allows the extraction of binding affinity and additional parameters in complex systems. As a label-free technique, ITC compares favorably with other titration techniques such as fluorescence and radioactivity. However, despite much-improved sensitivity (minimum detectable thermal energy <0.1 µJ), baseline stability, and titrant control found in contemporary instruments, sensitivity of ITC is relatively limited. The actual limit of detection depends primarily on the intrinsic enthalpy of the binding system at the temperature of interest, and to a lesser extent, the physical configuration of the instrument. Roughly speaking, a typical ITC experiment requires at least 10-6 M of titrate in a 1-mL volume and 10-4 M of titrant in a 100-µL syringe. (Recently, so-called "low volume" instruments equipped with 200-µL cells and 50-µL syringes have become available.)
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The sensitivity of ITC is helped considerably by the differential nature of its signal (which is proportional to\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t[X]\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tt\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t) with respect to titrant concentration (Figure 3). In practice, the requirement for sufficient concentration in the sample cell to produce sufficient heat signals poses a direct limit on the tightest binding that may be reliably determined. Specifically, depletion of titrant dominates at titrate concentrations that are high relative to the dissociation constant K. Under this condition, the titration approaches a discontinuity in the first derivative at the equivalence point (Poon 2010). In the case of simple 1:1 binding, an empirical relationship that the product 1 < c\n\t\t\t\t\ttitrate/K< 1000, where c\n\t\t\t\t\ttitrate is the titrate concentration, is optimal for reliable estimation of K (Wiseman et al. 1989). One way of getting around this problem for very tight binding is to lower the apparent value of K by including a suitable, fixed concentration of competitor in the cell. Another possibility is continuous ITC, which will be discussed in Section 3.1.2. It should be noted that even under conditions where K cannot be determined, ΔH can still be determined by integration of the measured heat:
where V is the volume of the (fixed) sample cell. (A volume correction is generally necessary; see Section 3.2.1.3.) A requirement for Eq (14) is, of course, that the titration is complete. Whichever the case, repeating the titration at different temperatures provides an estimate of the change in heat capacity by Kirchoff\'s relation:
If the interaction under investigation occurs in a buffered solution, the earlier discussion on the temperature dependence of pK\n\t\t\t\t\ta would again be relevant. More generally, binding that involves coupled uptake or release of protons will contain the buffer\'s enthalpy of ionization in the apparent binding enthalpy (Fisher and Singh 1995).
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Figure 3.
The differential nature of ITC data. The volume and enthalpy are intentionally omitted to illustrate the differential nature of ITC data with respect to total titrant concentration. Simulated 1:1 binding to 10 µM titrate is shown in Panel A, with the corresponding integrated binding curves in Panel B. The values of K are as follows: a, 10-9 M; b, 10-8 M; c, 10-7 M; d, 10-6 M; and e, 10-5 M. The values of c\n\t\t\t\t\t\t\ttitrate/K range from 104 to 1.
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Finally, ITC has been used as an “analog” of DSC for studying the stability of complexes. A concentrated solution of complex in the syringe is titrated into pure water or buffer in the cell. The resulting dilution drives complex dissociation and the attendant enthalpy is measured. This method has been used to characterize complexes through the spectrum of stoichiometries, from dimers (Burrows et al. 1994; Lovatt et al. 1996) to higher oligomers (Lassalle et al. 1998; Luke et al. 2005) to polymeric species (Stoesser and Gill 1967; Arnaud and Bouteiller 2004). Again, given the sensitivity of ITC, relatively high concentrations are required, so this technique is limited to the measurement of relatively weak complexes. It has been shown (Poon 2010) that the ITC data can be used to diagnose a dimeric or higher-order complex based on the presence of an inflection point in the latter.
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3.1.1. Baseline signals
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Two types of baselines are operative in an ITC experiment. One is instrument noise. Drifts on the order of 0.02 µW/h are routinely achievable in contemporary instruments. Another source of baseline arises from the injection of titrant. At the very least, viscous mixing makes a measurable if small exothermic contribution to the observed heat. This effect can be observed in a blank-to-blank injection (Figure 4), and serves as a casual useful indicator of the cleanliness of the cell and syringe between sample runs. Moreover, any mismatch in the matrices of the titrant and titrate will be manifest as a dilution enthalpy with each injection. For small molecules, solids or lyophilized samples are usually dissolved in water or buffer. To complicate matters, hydrophobic solutes often require a cosolvent such as DMSO or DMF to achieve initial solubility before addition of the aqueous solvent; dilution of the cosolvent will therefore contribute to the observed heat at each injection. In other cases, ionizable solutes can perturb solution pH due to their substantial (>10-3 M) concentration in the syringe. Unless the solvent is adequately buffered, the pH in the cell and syringe will differ significantly and neutralization heats will contribute to the observed signal.
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Figure 4.
Typical water-water control "titration." Inset, integrated areas of the water peaks. Given the low signal-to-noise ratios, the values must be considered very crude. However, the scatter also attests to the cleanliness of the cell and syringe.
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In the case of macromolecular titrant and titrates, the solution matrices can be closely matched by extensive co-dialysis in the same solution. Care must be taken, however, with ionic polymers such as nucleic acids. These solutes can alter the distribution of ions in their compartment during dialysis due to the Donnan effect. Specifically, a non-diffusible polyionic solute excludes diffusible ions of the same charge from their compartment and therefore induces an asymmetric distribution of diffusible ions across the semipermeable membrane at equilibrium. To illustrate, for a simple system consisting only of the non-diffusible polyion M and a monovalent salt AB, the ratio concentrations of A+ or B- across the membrane is (Cantor and Schimmel 1980):
where z is the charge on the polyion (shown simply as M for brevity). At the concentrations required for the syringe, z[M] can be substantial. For example, a duplex oligonucleotide consisting of 20 base-pairs represents at 100 µM contributes 4 mM in total anions (phosphates). The so-called Donnan ratio, r\n\t\t\t\t\t\tD, is significantly above unity even at low polyion:salt ratios (Ψ). Eq (16) shows that a ten-fold excess of salt concentrations (Ψ=0.1) leads to a 10% exclusion of A or B from the compartment occupied by polyion. If AB is a buffer salt, the result is also a change of pH. Such asymmetry will modify the heat observed by ITC. As is well known, and illustrated by Eq (16), Donnan effects can be suppressed by ensuring a sufficient excess of diffusible salt in the dialysate. Unfortunately, the required salt concentrations may interfere with the investigation of interactions mediated substantially by electrostatic effects (as is usually the case for polyions). If possible, therefore, it is advisable to arrange for the polyionic species to be in the cell, where concentrations are lower.
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In practical terms, none of these baselines effects are significant if the heat generated by the interaction of interest is sufficiently strong. This is not always achievable, however, for several reasons. Availability or solubility of the sample, particularly biological samples, may be limiting. It may also be desirable, for example, to perform titrations at a range of cell concentrations for binding to oligomerizing systems. Characterization of binding to a polyion at low salt concentration may require a reduced concentration. Thus, strategies for handling relatively low signal-to-noise situations are helpful in many situations. The most basic of these involve inspection and, where necessary, manual editing of the power baseline to mitigate the occasion excursion due to instrumental noise. To this end, an increase in the time between injection may be indicated to unambiguously identify the restoration of baseline. In addition, the residual heats (which may be up to 10 µJ) after the equivalence point are unlikely to be negligible. Subtraction with data from a titrant-to-blank run would likely introduce more noise into the data and be no more helpful than taking a simple average of the final post-equivalence heats. If the data is to be fitted to models, a more appropriate solution is to add a constant parameter B to the fitting equation:
While incremental titrations most commonly performed in ITC, an alternative mode of operation is a continuous titration (cITC) (Markova and Hallén 2004). In cITC, the titrant is continuously into injected the cell at a low rate (~0.1 µL/s). The primary advantage of cITC is throughput. An incremental ITC experiment typically requiring 20 injections of 5 µL at intervals of 300 s takes (neglecting time for baseline stabilization) 100 min; at 0.1 µL/s, cITC would require approximately 17 min. Another potential motivation for cITC is increased resolution in terms of model-dependent analysis. In incremental ITC, peak-by-peak integration of thermal power is performed to obtain\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t[X]\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tt\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t. This step reduces the number of collected data points (typically 1 s-1 over 103 s, or about one to two hours) for nonlinear regression to the number of injections (usually 10 to 30). By using the thermal power data directly for fitting, cITC can in principle discriminate the curvature required to define tight binding.
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Maintenance of quasi-equilibrium conditions throughout the titration is essential to correct interpretation of derived thermodynamic parameters and is a major concern for cITC. To this end, the stirring rate in cITC must be considerably increased (up to 700 rpm) relative to incremental ITC to facilitate mixing of titrant into the titrate solution. Additionally, the instrument must be able to provide sufficient thermal compensation during the titration to maintain isothermal conditions. Finally, the kinetics of the interaction of interest must be fast relative to the injection rate. Generally speaking, these criteria are most likely met by relatively high-affinity interactions with moderate binding enthalpies.
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3.2. Analysis of ITC data
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The direct measurement of ΔH is considered a significant advantage over non-calorimetric binding methods since the latter access the binding thermodynamics indirectly in terms of the equilibrium constant K via the van’t Hoff equation:
Estimation of ΔC\n\t\t\t\t\tp by non-calorimetric methods, therefore, involves taking a second derivative of the measured data. Another potential source of difficulty is the interpretation of K, which is model-dependent. As in DSC, however, ITC becomes a considerably more useful analytical technique when model fitting is used for parameter estimation and hypothesis testing. Moreover, agreement between the directly-fitted, calorimetric ΔH and the van\'t Hoff ΔH\n\t\t\t\t\tvH obtained from Eq (18) is a strong indication of the physical correctness of the model at hand.
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The most fundamental concept in the analysis of ITC data is the differential nature of the heat signal with respect to titrant concentration (Figure 3). Recalling Eq (13),
The ideal approach to fitting ITC data is to directly fit Eq (13) (Poon 2010). For simple models, it is possible to write analytical expressions for \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t[X\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t[X]\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tt\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\texplicitly in terms of [X]t i.e., the functional form of Eq (19). Take, for example, simple 1:1 binding of X + Y \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t⇌\n\t\t\t\t\t\t\n\t\t\t\t\t XY:
where \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tK\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t[X][Y]\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t[XY]\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t[X][Y]\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t[X]\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is the equilibrium dissociation constant. Its derivative is
where r = K/[X]t and Φ = [X]t/[Y]t. Eq (21) is sometimes referred to as the Wiseman isotherm (Wiseman et al. 1989). As complexity of the model increases, however, the algebra involved rapidly becomes prohibitive. In any case, numerical methods provide the means for generating solutions for Eq (13).
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3.2.1. Numerical aspects of ITC data analysis
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Given the usual practice of formulating models in terms of total or unbound concentrations, rather than their derivatives, conventional ITC data analysis has handled the differential nature of calorimetric data by fitting to a finite-difference version of Eq (13). Thus, for the j-th injection:
where δq\n\t\t\t\t\t\tj is a volume-correction factor. This approach has the apparent advantage that models which have been formulated in terms of [X]b vs. [X]t can be used directly. However, Eq (22) represents poor practice in nonlinear regression. Since each computation of Δq\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tj\n\t\t\t\t\t\t requires evaluation of q corresponding to two consecutive injections, j and j-1, the data points (and their errors, albeit small) are not independent. Independence of observations constitutes a major assumption of nonlinear regression, one which the recursive form of Eq (22) clearly violates. Specifically, the residual in Δq\n\t\t\t\t\t\tj during fitting becomes increasing correlated with increasing j. In addition, the resultant propagation of error likely violates the assumption of homoscedasticity (uniform variance) as well. Unless specialized regression techniques are employed (such as correlated least-squares), violations of these assumptions potentially calls the errors of the parameters, and possibly the parameters themselves, into question.
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From the perspective of numerical analysis, Eq (22) is also entirely unnecessary. As stated previously, the most appropriate approach to handling ITC data is to fit Eq (13) directly. Posed in the form of Eq (13), ITC models (more specifically, the solution of\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t[X]\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t[X]\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tt\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t) represent classic initial value problems (IVPs). IVPs are first-order ordinary differential equations (ODEs) with a specific initial condition ([X]b = 0 at [X]t = 0) for which numerical methods for their solution are well-established. In addition avoiding the statistical pitfalls of Eq (22), formulating ITC models as differential equations simplify the algebra considerably. This is because implicit differentiation offers a welcome shortcut that obviates the need for an explicit solution for [X]b. This is illustrated for X + Y \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t⇌\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t XY with Eq (20):\n\t\t\t\t\t
Recognizing that [X]t, [Y]t are constants (and K being the parameter to be estimated), Eq (23) takes on a noticeable simpler (but equivalent) form compared to the Wiseman isotherm, Eq (21). More importantly, implicit differentiation always yields an explicit ODE even when no analytical expression for [X]b exists e.g., polynomials of order >4. Direct substitution of Eq (23) into Eq (13) directly yields the ODE needed to generate values for nonlinear regression.
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This approach of formulating explicit titration models for ITC has been demonstrated for many empirical models in common use, including the multi-site model, homotropic cooperativity, and two-state self-association (Poon 2010). To further illustrate the this approach, a competitive binding model will be examined. This is a method of measuring tight binding by ITC by adding a competitive species in the cell to reduce the apparent affinity (Sigurskjold 2000). The mechanism is an example of the general multi-site model involving two ligands competing for a single site (Wells 1992). The equilibrium distribution of bound titrate is a function of the affinities of the titrant X1 and competitor X2 for the titrate Y as well as the total concentrations of all three species. For the titrant-titrate complex, X1Y:
and K\n\t\t\t\t\t\t1 and K\n\t\t\t\t\t\t2 are the equilibrium dissociation constants of X1 and X2 for the titrate, respectively. The corresponding expression for the competitor-titrate complex, X2Y, can be obtained from symmetry arguments:
It is possible to solve the cubic equation in Eqs (24) and (25) explicitly, followed by differentiation of the solutions to obtain \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t[X\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tY]\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t[X\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tt\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t and \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t[X\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tY]\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t[X\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tt\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t as was done with the Wiseman isotherm. All of this is avoided, however, by implicit differentiation with respect to [X1]t which directly yields the required derivatives:
As shown in Figure 5, a judicious choice of competitor reduces the apparent affinity of the titrant to a range more amenable for regression. In this case, the initial condition for [X2Y] is not zero because the titrate is essentially pre-equilibrated with competitor before any titrant has been injected. The initial value for numerical integration is supplied by Eq (20):
Thus, the competitive model requires prior knowledge of both the concentration and affinity of the competitor. Since the competitor would therefore require its own characterization, a weak competitor is preferred, which means it would need to be present at significant concentrations (i.e., [Y]t ≥ K). Since binding of the titrate involves the unbinding of the competitor, the enthalpy of competitor unbinding may be substantially convoluted in the observed heat as indicated by Eq (27).
\n\t\t\t\t\t
Figure 5.
Titrant-titrate binding in the presence of a competitor. A, simulated 1:1 titrant-titrate binding in the absence of competitor. The parameters are K\n\t\t\t\t\t\t\t\t1 = 10-10 M, ΔH\n\t\t\t\t\t\t\t\t1 = -10 kcal mol-1, and [Y]t = 10 µM. Data representing this level of affinity would be unfit for model-fitting; K\n\t\t\t\t\t\t\t\t1/[Y]t = 104. B, in the presence a competitor, X2, at 10 µM (K\n\t\t\t\t\t\t\t\t2 = 10-8 M, ΔH\n\t\t\t\t\t\t\t\t2 = -5 kcal mol-1). Inset, titration of the competitor.
\n\t\t\t\t\t
Explicit titration models are also amenable to formulating models for continuous ITC. In the case of cITC, thermal power P is directly used in model fitting, so Eq (19) needs to be differentiated with respect to time:
Applying the chain rule and the relation\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t[X\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\ttot\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tc\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tsyr\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tv\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tinj\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tV\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t, where c\n\t\t\t\t\t\tsyr is the concentration of titrant in the syringe and v\n\t\t\t\t\t\tinj is the injection volume,
(The subscript "tot" has been used to denote total concentration to avoid ambiguity with the variable t for time.) Note that \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tv\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tinj\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\tt\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t is the (constant) injection rate. Substituting into Eq (28) gives
Thus explicit expressions of \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t[X\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t[X\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\ttot\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t can be directly used as in incremental ITC. At a sufficiently low injection rate, Eq (30) has the potential of "flattening out" the titration by transforming it in the time domain. This feature, in addition to the higher density of data points available for regression, may allow cITC to characterize much tighter binding than is practicable with incremental ITC (Markova and Hallén 2004).
\n\t\t\t\t\t
In the foregoing discussion, the need to correct for various displacement and dilution effects due to the injection process has not been considered. In the author\'s experience, this is best handled during preliminary data reduction, before nonlinear regression. This aspect will be discussed in Section 3.2.1.3.
\n\t\t\t\t\t
\n\t\t\t\t\t\t
3.2.1.1. Choice of dependent variable
\n\t\t\t\t\t\t
Another benefit of formulating ITC models as explicit ODEs is the flexibility in the choice of dependent variable for implicit differentiation, as long as it is a function in [X]t. For the 1:1 binding model, formulation in terms of the unbound titrant X gives the (and simple) functional form of the familiar Langmuir isotherm:
The simultaneous equations (32) and (34) represent another formulation of the same model, except now [X] is the explicit dependent variable instead of [X]b. (The initial condition is [X] = 0 at [X]t = 0.) Of course, we have previously derived Eq (23) directly, so this approach is regressive for this simple model. However, The flexibility to use any dependent variable of [X]t is useful, for example, for models that are formulated in terms of the binding polynomial which is based on [L] (Schellman 1975; Freire et al. 2009).
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
3.2.1.2. Practical considerations in implementing explicit ITC models
\n\t\t\t\t\t\t
Successful implementation of Eq (13) requires numerical procedures for solving IVPs. The explicit, closed-form ODEs encountered in most models are typically ratios of polynomials. These functions are generally amenable any of the standard Runge-Kutta methods, which are widely available. A fast CPU is helpful, but not required. To this end, the tolerance for iteration should not be unnecessarily stringent in relation to the concentrations used and the value of K expected. Generally, a value of 10-6 will suffice. Even average single-core CPUs will handle ODEs of practical complexity with reasonable dispatch. Any significant delay is almost always related to input/output issues and can be alleviated by suppressing intermediate output. Numerical failures, if they occur, usually does so when there are two or more very different scales of the independent variable on which the dependent variable is changing e.g., extremely tight binding to one site in the multi-site model. One such example is competitive binding in the presence of a very strong competitor in Eq (27). These pathological scenarios are associated with “stiff” differential equations that require more specialized algorithms for numerical solution. Fortunately, these situations are unlikely to be encountered as they are usually incompatible with experimental data in the first place.
\n\t\t\t\t\t\t
Several technical software suites, such as Mathematica (Wolfram Research, Champaign, IL, USA), MATLAB (the MathWorks, Natick, MA USA), Maple (Maplesoft, Waterloo, Ontario, Canada), and IgorPro (WaveMetrics, OR, USA) which have built-in numerical ODE and least-square minimization capabilities, represent full-featured, integrated solutions. Alternatively, pre-compiled libraries containing optimized algorithms for numerical ODEs and least-square minimization are available commercially (the Numerical Algorithm Group Library; Numerical Algorithms Group [NAG], Oxford, UK) or free (GNU Scientific Library [GSL]) for most computing platforms. Functions from these libraries can be called under standard programming environments (e.g., C/C++, FORTRAN) to perform the required procedures. Some commercial data analysis software such as Origin (Northampton, MA, USA) can interface with external libraries such as the NAG Library or GSL to perform numerical ODEs within their least-squares routines. If access to external libraries is not available, an adequate alternative is to code a numerical ODE algorithm (such as Runge-Kutta-Fehlberg) as part of the target function within the data fitting routines of the analysis program. “Cookbook” recipes for a variety of ODE solvers are straightforward and can be found in most texts of numerical analysis (Press 2007).
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
3.2.1.3. Volume correction
\n\t\t\t\t\t\t
The sample and reference cells are typically overfilled for both DSC and ITC. Overfilling maximizes heat transfer between the solution and the wall of the cell as air is a poor thermal conductor. In the case of ITC, overfilling also minimizes stray signal arising from mechanical agitation of the solution-air-cell interface caused by the stirring paddle. However, the introduction of titrant into an overfilled ITC sample cell leads to displacement effects that need to be taken into account. Specifically, each injected volume of titrant simultaneously displaces an equal volume of titrate and any previously injected titrant out of the sample cell (into the access tube). The accounting for these displaced volumes and their effect on titrant and titrate concentration is made on the assumption that the displaced material is immediately and completely excluded from the titration. This implies that no mixing occurs between the injected and displaced materials at the time injection. The concentrations of the titrant X after the i-th injection in the cell is therefore
There are two ways to handle volume corrections. One is to incorporate Eqs (35) and (36) as additional terms in the fitting equation. In the author’s experience (Poon 2010), it is more efficient instead to perform the volume corrections on the dataset at the outset, and simply treat [Y]t,i\n\t\t\t\t\t\t\t as an additional dependent variable in the least-square procedure.
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
3.2.1.4. Error analysis in ITC
\n\t\t\t\t\t\t
Compared to other titrations, particularly in the biochemical laboratory, that requires extensive manual manipulation e.g., electrophoretic mobility shift, filter binding, ITC instrumentation offers a greatly reduced level of statistical error in the measured data. Nonetheless, detailed theoretical and experimental studies of the nature and magnitude of statistical errors in ITC have offered insight into how instrumental errors can be minimized in fitted parameters. Although such studies have so far only focused on 1:1 binding, it has become clear that at least two régimes of instrumental errors exist (Tellinghuisen 2003; 2005b). Specifically, for titrations associated with relatively large heats (>300 µcal or “high-q”), proportional errors in the injected volumes dominate, and the inclusion of statistical weights is indicated to optimize fitted parameters. For “low-q” titrations, which typify low-concentration titrations needed for tight binding interactions, constant errors in thermal detection and compensation dominate, and unweighted fitting does not adversely affect parameter optimization. In either case, the optimal number of injections is considerably lower than the norm of 20 to 25 injections, especially if variable volume procedures are employed (Tellinghuisen 2005a). This approach could substantially reduce titration time and increase experimental throughput. It may be pointed out, however, that in some applications, sample-to-sample variation may be greater than any statistical error inherent in the analytical technique. In these cases, standard errors in parameters estimated from replicate experiments will be higher but more representative indicators of experimental uncertainty than the fitting error extracted from the variance-covariance matrix.
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
4. Conclusion
\n\t\t\t
Commercial development of microcalorimetry has greatly increased the accessibility of this technique for the thermodynamic characterization of chemical systems in solution. Unfortunately, the "black-box" nature of commercial software has engendered unwarranted reliance by many users on the turnkey software accompanying their instruments, and an attendant tendency to fit data to models of questionable relevance to the actual chemistry. This chapter discusses several novel aspects and potential pitfalls in the experimental practice and analysis of both DSC and ITC. This information should enable users to tailor their experiments and model-dependent analysis to the particular requirements.
\n\t\t
\n\t\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/21875.pdf",chapterXML:"https://mts.intechopen.com/source/xml/21875.xml",downloadPdfUrl:"/chapter/pdf-download/21875",previewPdfUrl:"/chapter/pdf-preview/21875",totalDownloads:1705,totalViews:139,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,dateSubmitted:"November 3rd 2010",dateReviewed:"April 7th 2011",datePrePublished:null,datePublished:"November 2nd 2011",readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/21875",risUrl:"/chapter/ris/21875",book:{slug:"thermodynamics-interaction-studies-solids-liquids-and-gases"},signatures:"Gregory M. K. Poon",authors:[{id:"35320",title:"Dr.",name:"Gregory",middleName:null,surname:"Poon",fullName:"Gregory Poon",slug:"gregory-poon",email:"gpoon@wsu.edu",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Differential scanning calorimetry",level:"1"},{id:"sec_2_2",title:"2.1. Experimental conditions for DSC",level:"2"},{id:"sec_3_2",title:"2.2. Analysis of DSC data",level:"2"},{id:"sec_3_3",title:"Table 2.",level:"3"},{id:"sec_6",title:"3. Isothermal titration calorimetry",level:"1"},{id:"sec_6_2",title:"3.1. Experimental conditions for ITC",level:"2"},{id:"sec_6_3",title:"3.1.1. Baseline signals",level:"3"},{id:"sec_7_3",title:"3.1.2. Continuous ITC titrations",level:"3"},{id:"sec_9_2",title:"3.2. Analysis of ITC data",level:"2"},{id:"sec_9_3",title:"3.2.1. Numerical aspects of ITC data analysis",level:"3"},{id:"sec_9_4",title:"3.2.1.1. Choice of dependent variable",level:"4"},{id:"sec_10_4",title:"3.2.1.2. Practical considerations in implementing explicit ITC models",level:"4"},{id:"sec_11_4",title:"3.2.1.3. Volume correction",level:"4"},{id:"sec_12_4",title:"3.2.1.4. Error analysis in ITC",level:"4"},{id:"sec_16",title:"4. 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Jr\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2002\n\t\t\t\t\tThe sequence-specific association of the ETS domain of murine PU.1 with DNA exhibits unusual energetics.\n\t\t\t\t\tBiochemistry\n\t\t\t\t\t41\n\t\t\t\t\t2361\n\t\t\t\t\t2371\n\t\t\t\t\n\t\t\t'},{id:"B23",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPress\n\t\t\t\t\t\t\tW. H.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2007\n\t\t\t\t\tNumerical recipes : the art of scientific computing, 3rd ed. Cambridge University Press, 0521880688, Cambridge, UK ; New York, USA\n\t\t\t'},{id:"B24",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPrivalov\n\t\t\t\t\t\t\tP. L.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDragan\n\t\t\t\t\t\t\tA. I.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2007\n\t\t\t\t\tMicrocalorimetry of biological macromolecules. Biophys Chem\n\t\t\t\t\t126\n\t\t\t\t\t16\n\t\t\t\t\t24\n\t\t\t\t\n\t\t\t'},{id:"B25",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPrivalov\n\t\t\t\t\t\t\tP. L.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPotekhin\n\t\t\t\t\t\t\tS. A.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1986\n\t\t\t\t\tScanning microcalorimetry in studying temperature-induced changes in proteins.\n\t\t\t\t\tMethods Enzymol\n\t\t\t\t\t131\n\t\t\t\t\t4\n\t\t\t\t\t51\n\t\t\t\t\n\t\t\t'},{id:"B26",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSchellman\n\t\t\t\t\t\t\tJ. A.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1975\n\t\t\t\t\tMacromolecular binding. Biopolymers\n\t\t\t\t\t14\n\t\t\t\t\t999\n\t\t\t\t\t1018\n\t\t\t\t\n\t\t\t'},{id:"B27",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSigurskjold\n\t\t\t\t\t\t\tB. W.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2000\n\t\t\t\t\tExact analysis of competition ligand binding by displacement isothermal titration calorimetry.\n\t\t\t\t\tAnal Biochem\n\t\t\t\t\t277\n\t\t\t\t\t260\n\t\t\t\t\t266\n\t\t\t\t\n\t\t\t'},{id:"B28",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tStoesser\n\t\t\t\t\t\t\tP. 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'}],corrections:null},book:{id:"297",title:"Thermodynamics",subtitle:"Interaction Studies - Solids, Liquids and Gases",fullTitle:"Thermodynamics - Interaction Studies - Solids, Liquids and Gases",slug:"thermodynamics-interaction-studies-solids-liquids-and-gases",publishedDate:"November 2nd 2011",bookSignature:"Juan Carlos Moreno-Pirajan",coverURL:"https://cdn.intechopen.com/books/images_new/297.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"14015",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Moreno Piraján",slug:"juan-carlos-moreno-pirajan",fullName:"Juan Carlos Moreno Piraján"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"21844",title:"Thermodynamics of Ligand-Protein Interactions: Implications for Molecular Design",slug:"thermodynamics-of-ligand-protein-interactions-implications-for-molecular-design",totalDownloads:16271,totalCrossrefCites:4,signatures:"Agnieszka K. 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1. Introduction
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With an increasing incidence each year, cancer represents a major health problem worldwide, ranked second, after cardiovascular disease. According to the last estimation presented by the International Agency for Research on Cancer (IARC) in 2012, more than 14 million new cases of cancers were encountered, while the cancer-related deaths reached around 8.2 million people [1]. Unfortunately, the estimation for 2030 shows an increase in over 21 million new cancer cases and about 13 million cancer deaths [2]. Cancer is characterized by changing the phenotype of the cells in which it occurs, leading to an uncontrolled proliferation, invasion, and metastasis. Albeit the cancers in early stages are treatable, especially by surgery, the major challenge is to treat cancers in advanced stages. Currently, the therapeutic regimens for treating the advanced cancers depend on several factors such as localization, phenotype, and tumor size and are based on a combination of at least two of more approaches, represented by surgery, chemotherapy, radiotherapy, hormone therapy, and target therapy. Nevertheless, this anticancer armamentarium is not very efficient because about 90% of advanced cancers lead to metastasis and death ultimately [3]. Current anticancer therapies target either antiproliferative or proapoptotic pathways of tumor cells or activate immune response against tumors, but none of the currently available antitumor therapies target the molecular pathways involved in invasion and metastasis.
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Tumor invasion and metastasis, as they were pointed out by Hanahan and Weinberg [4], represent one of the most important hallmarks of cancer, and therefore, exploiting these features of tumor cells could bring new data to develop more powerful anticancer therapies. Tumor invasion and metastasis are very complex processes that involve a series of sequential and interrelated steps. In this line, epithelial-to-mesenchymal transition (EMT) represents the most important event underlying the tumor invasion [5]. During EMT, tumor cells lose their epithelial characteristics and adhesion and acquire increased motility by shifting toward a mesenchymal phenotype while also diminishing apoptosis and senescence and gaining stem cell properties. The EMT regulation includes a network of many regulators, inducers, and effector molecules, which sustains tumor cell dissemination to distant organs [6].
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The “omics” revolution has brought us new data about the complexity of signaling pathways in cancer, the type of molecules that are involved in them, and which alterations are associated with cancer. Moreover, noncoding RNAs, including miRNAs, have proved their crucial role in the regulation of mRNA translation in both physiological and pathological status. Because of their high capacity to modulate mRNA expression, miRNAs are defined as master modulators of the human genome. Therefore, miRNAs are involved in all cancer hallmarks, disrupting the normal function of their targets. By gaining or losing the function, miRNAs lead to the validation of tumor phenotype, its progression, and metastasis as well as to drug resistance.
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Increasing the evidence suggests that the modulation of miRNA expression in cancer cells, through the inhibition of oncogenic miRNAs (oncomiRs) and the substitution of deficient tumor suppressive miRNAs (TS-miRNAs), could represent a reliable tool for improving the cancer therapy. In this chapter, we will present an up-to-date overview about the role of miRNA-based therapeutics in oncology, highlighting their role in cancer management, how these therapies can be used, and which would be the future challenges related to miRNA-based therapies.
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2. Short overview about biogenesis and function of miRNAs
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If deciphering the whole human genome has represented a milestone of modern biology, the identification of its precise functionality is still a great challenge. However, by completing the ENCODE project, many data about how the human genome is functioning were revealed. Such as, it is estimated that about 1.5% of human genome includes coding DNA exons from protein-coding genes (PCGs), while the rest of 98% represents noncoding DNAs including regulatory sequences such as the ones defining noncoding RNAs (ncRNAs), as well as introns, and other DNA sequences with unknown functions [7].
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About 80% of human genome is activated in cell physiology, and an important part of noncoding regulatory elements involved in the regulation of PCGs includes noncoding RNAs. Since their recognition as a distinct class of biological regulators [8], micro-RNAs (miRNAs) have become the most studied species of noncoding RNAs. miRNAs are coded by genes located in almost all regions of the genome, including both PCGs and noncoding transcripts. About a half of miRNA genes are located in both intronic (40%) and exonic (10%) regions of noncoding genes, while the majority of the other miRNA loci are located in intragenic regions of PCGs [9]. The first step of miRNA biogenesis includes the transcription of pri-miRNA, a primary long hairpin transcript with a length of hundreds or thousands of nucleotides (Figure 1). Furthermore, after its processing to a shorter hairpin structure of about 70 nucleotides, the pre-miRNA is exported into the cytoplasm, where under enzymatic processing, it is reduced to a single-stranded RNA (mature miRNA) of about 21–23 nucleotides in length. Afterward, by its incorporation into Argonaute 2 protein and then in the RNA-induced silencing complex (RISC), the mature miRNA will function as a guide molecule for silencing complex, targeting specific mRNA transcripts, usually by base-pairing specific mRNA transcripts, in the 3′ untranslated region (3′ UTR). Targeting the mRNA by a specific miRNA leads to the translational repression of mRNA and its exonucleolytic decay [9].
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Figure 1.
miRNA biogenesis. The miRNA biogenesis starts in the nucleus, with a pri-miRNA transcription. Afterward, the pri-miRNA is processed to a shorter hairpin structure of about 70 nucleotides, by Drosha and DGCR8, and it is exported in the cytoplasm by Exportin 5. After enzymatic processing by DICER, double-stranded miRNAs are reduced to a single-stranded mature RNA (21–23 nucleotides in length). Furthermore, by incorporating into Argonaute 2 and RISC, mature miRNA will target mRNA transcripts, usually in the 3′ UTR, leading to mRNA degradation or repression.
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Because of their capacity to modulate up to 60% of PCGs, miRNAs are defined as “master modulators” of the human genome [10]. An important feature of miRNAs is that a single miRNA can target up to 200 mRNAs, while a single mRNA can be modulated by different miRNAs [11]. Nevertheless, to increase the accuracy of miRNA-mRNA binding, several combinatorial prediction tools based on thermodynamic modeling and machine learning techniques have been developed lately [12, 13], bringing new understanding about how miRNAs can exert their regulatory function through a combinatorial-cooperative activity.
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At the moment, 48,885 mature miRNA products from 271 species, including 2654 mature human miRNAs, have been reported in the latest available miRNA database (miRBase release 22; http://www.mirbase.org/) [14]. In normal phenotype, by their modulatory effects, miRNAs maintain the cell physiology, while by their aberrant expression, miRNAs lead to the validation of many diseases including cancer.
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3. The role of miRNAs in cancer
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Croce’s group established for the first time an association of mRNAs with cancer, indicating an alteration of miR-15a/16-1 cluster, in chronic lymphocytic leukemia [15]. Further functional analyses have demonstrated that miR-15 and miR-16 can target and suppress the expression of BCL2 oncogene, inducing the apoptosis. [16]. Through exploring the role of miRNAs, Croce’s group has demonstrated that miRNA profiling could be taken into consideration for characterizing the malignant phenotype [17], opening a new perspective for identifying new cancer-specific miRNAs. Interestingly, for poorly differentiated tumors, tissue miRNA profiling has revealed better diagnosis than mRNA profiling, highlighting their role as tumor biomarkers [18]. An important feature of miRNAs, given by their high stability in formalin-fixed paraffin embedded (FFPE) tissues, blood including serum and plasma, as well as other biological fluids such as urine, tears, breast milk, saliva, and seminal fluids, makes them important candidates for the discovery of new minimally invasive biomarkers [19, 20]
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Therefore, a myriad of studies describing the role of miRNAs in cancer development have been provided, with more than 21,565 papers that are published in PubMed today, when “miRNA, miR, microRNA, and cancer” are used as a string search.
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Alteration of miRNA expression in cancer is due to genetic and epigenetic events. Genetic alterations include: chromosomal rearrangements or loss of heterozygosis (LOH) (e.g. miR-15a/16-1), gene amplification (e.g. miR-17-92 cluster, miR-155), deletions (e.g. let-7 family member), or mutations (e.g. miR-16) [15]. Moreover, genetic alteration may occur in the PCGs involved in the synthesis of the protein components of the Drosha, DGCR8, Exportin 5, Dicer, and AGO2, the main enzymes that process the biogenesis and activation of miRNAs. Pre- and post-transcriptional controls of not only miRNA biogenesis but also epigenetic events, including methylation and acetylation, were also related to aberrant expression of tumor miRNAs [21, 22]. Not lastly, the presence of the single-nucleotide polymorphism (SNP) mutations in the miRNA-coding genes may lead to the alterations of mature miRNA structure, reducing its specificity to the mRNA target [23].
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Functionality studies have demonstrated that the expression of oncogenes and tumor-suppressor genes in cancer is closely controlled by miRNAs (Figure 2). Such as, miRNAs that target and modulate the oncogenic expression are defined as tumor-suppressor miRNAs (TS-miRNAs), while the miRNAs that modulate the expression of tumor-suppressor genes are known as oncomiRs [24]. Genetic and epigenetic alterations occurring in cancer lead to “gain of function” of oncomiRs and inactivation or “loss of function” of TS-miR (Figure 2), which translate into regulating the expression of their targets through downregulation of tumor-suppressor genes and upregulation of oncogenes, respectively [25]. miRNAs are involved in all hallmarks of cancer, including self-sufficiency in growth signals (let-7 family, miR-21), insensitivity to antigrowth signals (e.g. miR-17-92 cluster, miR-195), evasion from apoptosis (e.g. miR-34a, miR-185, miR-15/miR-16), limitless replicative potential (e.g. miR-372/373 cluster, miR221/222), angiogenesis (e.g. miR-210, miR-26, miR-15b, miR-155), invasion and metastases (e.g. miR-10b, miR-31, miR-200 family, miR-21, miR-15b), reprogramming energy metabolism (e.g. miR-23a/b, miR-378, miR-143, miR-15b), evading immune destruction (e.g. miR-124, miR-155, miR-17-92), tumor-promoting inflammation (miR-23b, miR-155, let-7d), and genomic instability (miR-21, miR-155, miR15b) [22, 26, 27].
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Figure 2.
The role of miRNAs in cancer and their use of miRNA-based therapy. (A) miRNAs that function as oncomiRs and TS-miRs. Tumors are characterized by aberrant upregulation of oncomiRs that lead to downregulation of tumor-suppressor genes and inactivation of TS-miRs reflected in overexpression of oncogenes. All of these contribute to tumor development, invasion, and metastasis as well as decrease cell death. (B) The use of anti-miRNA therapies leads to block the oncomiR activities, resulting in the upregulation of tumor-suppressor genes, while substituting TS-miR therapies increases the cellular level of TS-miRs, leading to the inactivation of oncogenes. The effects of miRNA-based therapy indicate an increase in cell death concomitantly with inactivation of tumor development.
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4. Strategies used for miRNA-based therapies
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4.1 miRNA inhibition therapies for oncomiRs
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The choice to use mRNA-based therapies is based on the fact that the expression of mRNAs in tumor cells is altered, and tumor phenotype can be changed by the modulation of the miRNA expression [28].
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MiRNA inhibition therapy is used to suppress the expression of oncomiRs that are frequently overexpressed in human cancers and reestablish the normal expression of tumor-suppressor genes that are targeting directly (Figure 2). The therapy for miRNA inhibition includes the following agents: antisense anti-miR oligonucleotides (AMOs), locked nucleic acid (LNA) anti-miRs, antagomiRs, miRNA sponges, and small molecule inhibitors of miRNAs (SMIRs) [27]. The principle of this therapy consists of an isolation of the endogenous miRNAs in an unrecognizable configuration, leading to inactivating and excluding the mature miRNAs from the RISC.
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AMOs are single-stranded, chemically modified antisense oligonucleotides of about 17–22 nucleotides that are complementary to a miRNA of interest [28]. These antisense oligonucleotides anneal to the complementary mature miRNAs and inhibit their interaction with specific mRNA targets.
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LNA anti-miRs represent an example of a modified antisense anti-miR oligonucleotide [29]. LNA-modified oligonucleotides present a higher thermal stability and affinity for their miRNA target molecules, as well as a higher aqueous solubility and increased metabolic stability for in vivo delivery [30].
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The antagomiRs are single-stranded RNA molecules of about 23 nucleotides in length complementary to miRNA targets that are chemically modified to increase the stability of the RNA and protect it from degradation [31]. One of the most important aspects of using these agents is due to their lack of inducing any immune response.
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miRNA sponges represent a class of RNAs that include multiple artificial binding sites similar to those found in the endogenous miRNA targets. The expression vectors represent the source of miRNA sponge transcription, thus reducing the miRNA’s effects and increasing the expression of the miRNA’s native targets [32].
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SMIRs are small molecules that suppress the miRNA biogenesis or block the interaction between a miRNA and the target. The inhibition therapy using SMIRs is an encouraging one due to the reduced time of production, approval, and cost [33].
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4.1.1 Discussion
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Krützfeldt et al. [31] demonstrated that intravenous administration of several antagomiRs toward miR-16, miR-122, miR-192, and miR-194 leads to a significant reduction in the corresponding endogenous miRNAs.
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Moreover, an important positive effect observed in this study was that after the administration of antagomiR-122, the cholesterol levels in plasma have decreased. Due to the fact that, so far, the therapy using antagomiRs did not induce a significant immune response, it is worth into consideration the development of a promising antisense therapy based on antagomiRs.
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One of the main advantages of using locked nucleic acid (LNA) anti-miRs is that they present a higher thermal stability, high-affinity Watson-Crick hybridization with their RNA target molecules, higher aqueous solubility, and increased metabolic stability for in vivo delivery. Overexpression of miR-21 is a common place in glioblastomas, and Griveau et al.’s [34] study was able to silence miR-21 in U87MG glioblastoma cell line, using a LNA conjugated to lipid nanocapsules (LNC). Another advantage of using LNA-LNC complexes in combination with external beam radiation is represented by the improvement of cell sensitivity to treatment.
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4.2 miRNA replacement therapies for tumor-suppressor miRNAs
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Also defined as miRNA restoration therapy, the replacement therapy with miRNAs includes the following agents: small molecules, synthetic miRNA mimics, and DNA plasmids encoding a miRNA gene that epigenetically alters endogenous expression of miRNAs [35].
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Small molecules in miRNA replacement therapy are represented by hypomethylating agents (Decitabine or 5-azacytidine) and enoxacin, exerting a role in the nonspecific miRNA expression.
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miRNA mimics are double-stranded synthetic RNAs, which are aimed to compensate the lack of tumor-suppressor miRNAs by replacing the lost miRNAs. These chemically structures are loaded into RISC to provide the downstream inhibition of the target mRNAs [27].
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4.2.1 Discussion
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One of the main challenges of miRNA replacement therapy is represented by finding the most suitable, efficient, and specific delivery system. The efficacy of this therapy is significantly decreased by an unsuitable size of the vector or by gene expression. Since miRNAs can be introduced into cells using a similar technique to small interference RNAs [36], it is recommended to improve those techniques based on the insertion of synthetic miRNA mimics, DNA plasmids, and small molecules, as well as to improve the quality of molecules used for this type of therapy.
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4.3 miRNA delivery systems
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An important aspect that is worth considering in miRNA cancer therapy aims to use miRNA delivery systems. One of these delivery systems including microvesicles and exosomes aim to block miRNA-entrapped exosomes released by tumors. It is already demonstrated that miRNA-entrapped exosomes secreted by tumor cells can regulate gene expression in the receiving cells by binding to their target mRNAs [37]. The use of some agents that block specific miRNAs (such as LNA anti-miR-21 and LNA anti-miR-29a) in tumor cells could lead to the reduction in miRNA-entrapped exosomes, released by cancer cells [38]. However, an ideal delivery system meets the following criteria: protects the miRNAs from early degradation in the bloodstream, efficient distribution to the target cells, facilitates cellular uptake, does not induce an immune response, and made of biocompatible and biodegradable materials [39].
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The most commonly used vectors for miRNA delivery include viral and nonviral vectors. Previous data demonstrated that viral vectors mainly caused an immune response; therefore, the focus of the actual studies is on developing efficient nonviral vectors. Nonviral vectors are classified into three main groups, including polymeric vectors (polyethyleneimines, atelocollagen, polylactic-co-glycolic acid, polyamidoamine dendrimers), lipid-based carriers (positively, negatively or neutral charged), and inorganic materials (gold, diamond, silica, and ferric oxide) [40].
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The delivery system based on viral vectors transfers the pri-miRNA or mature miRNAs, usually a TS-miR, into a plasmid, which contains a viral promoter, an antibiotic resistance gene, and a restriction enzyme gene, to the tumor cells. After nuclear integration of the miRNA and further transcription, the mature miRNA represses the translation and/or induces the degradation of the target mRNA [41].
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4.3.1 Discussion
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One of the most studied classes of polymeric vectors was represented by polyethylenimines (PEIs) but was removed from clinical studies due to their high toxicity, given by an excessive positive charge, low biological degradation, and inactivation in serum caused by a nonspecific protein. Ibrahim et al. [42] have demonstrated that by using low molecular weight, PEIs as system delivery for miR-145 and miR-33a would decrease the toxicity and increase the antitumor effect, in a model of colon carcinoma. Recent studies [43, 44] have proved that codelivery of miR-200c with chitosan, a cationic polymer with a high specificity for nucleic acid binding, decreased the angiogenesis, invasion, EMT, and metastasis and increased the apoptosis, highlighting the role of miRNA concentration in treatment effectiveness. Hao et al. [45] used miRNA (MiR-15a, miR16-1)/ATE-APT complex formed by atelocollagen (ATE), a type I collagen positively charged polymer, in combination with a RNA aptamer (APT) used as a ligand to target PCa cells that express prostate-specific membrane antigen (PSMA). Their study concluded that miRNA/ATE-APT complex was more efficient than an ATE-miRNA complex and that by using a PSMA-targeted system, the chances for selective killing of prostate cancer cells significantly would increase.
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Moreover, it is worth into consideration the administration methods used for synthetic miRNAs (miRNA mimics) delivery into cells. Previous studies of Trang et al. have shown that both intratumoral and intravenous administration of let-7a mimics lead to the diminishing of non-small-cell lung cancer (NSCLC) tumor size in mouse models [46, 47].
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As a future improvement in miRNA delivery systems, it is recommended to be synthetized proteins or peptides in order to be used as vector polymeric due to their low cytotoxicity and immunogenicity. Finding a suitable delivery system for a specific miR according to tumor cell type and the development of systems to target specific cancer membrane antigens still represent major challenges.
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5. MicroRNAs in cancer therapies
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5.1 miRNA candidates used in preclinical trials
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Increasing the evidence has demonstrated that miRNA expression is modified in cancer, and restoring the level of cellular miRNA could underpin the development of miRNA-based therapies. Below we briefly describe miRNAs that are currently used in preclinical and clinical trials and also represent examples that affect the emerging hallmarks of cancer such as evasion from apoptosis (miR-15/16, miR-34 cluster) [48], enabling replicative immortality (miR-34a) [48], activating invasion and metastasis (miR-10b) [49], tumor-promoting inflammation (miR-155), and genome instability and mutation (miR-155) [50].
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5.1.1 miR-10b
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Guessous et al. [51] observed that miR-10b is overexpressed in human glioblastoma and stem cell lines when compared to healthy tissues or astrocytes. After the modulation of miR-10b, they found out that the inhibition of miR-10b strongly reduced cell proliferation, invasion, and migration of glioblastoma and stem cell lines, whereas its overexpression caused cell migration and invasion. Moreover, in a previous study, Ma’s group [52] has demonstrated that the use of miR-10b antagomiRs was correlated with reduced metastasis both in cell-culture lines and in animal model of breast tumor-bearing mice. Thus, miR-10b inhibition both in vitro and in vivo significantly decreased miR-10b levels and increased levels of Hoxd10 gene, an important miR-10b target. Curiously, the administration of miR-10b antagomiRs in vivo did not reduce primary mammary tumor growth but significantly suppressed the development of lung metastases, highlighting its antimetastatic role.
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5.1.2 miR-221
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Since miR-221 overexpression alters multiple cancer pathways, it becomes a potential target for miRNA-based therapy. In order to validate the role of miR-221 in tumorigenesis, Callegari et al.[53] showed that in vivo delivery of an AMO anti-miR-221 caused a significant decrease in the size and number of tumor nodules. Based on the results from their study, it was highlighted the promoter role of miR-221 in liver carcinogenesis, being also established a valuable animal model to investigate the anti-miRNA-based therapy for liver cancer.
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Moreover, using a colorectal cancer model, Qin et al. [54] showed that miR-221 promotes cell migration and invasion in vitro and metastasis in vivo, identifying tumor-suppressor RECK gene as a direct target of miR-221.
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With regard to the role of miR-221 in tumorigenesis combined with the need to limit its expression, Brognara’s group demonstrated that a peptide nucleic acid conjugate targeted against miR-221 (Rpep-PNA-a221) caused a suppression of miR-221 expression and an upregulation of its target p27Kip1 in two breast cancer cell lines (MCF-7 and MDA-MB-231), respectively [55]. On the other hand, in a recent study, Gallo et al. [56] evaluated the pharmacokinetic and pharmacodynamic properties of a locked nucleic acid anti-miR-221 (LNA-i-miR-221) in the models of mice and monkeys. Their data highlighted that LNA-anti-miR-221 has a short half-life, optimal tissue bioavailability and minimal urine excretion in both species. A very important aspect of their study was that no toxicity was present in the pilot monkey study. This finding defines the potential application of LNA-anti-miRNAs in clinical studies.
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5.1.3 miR-222
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Sometimes developing a miRNA-based therapy is difficult because the same miRNA can act both as an oncogene and as a tumor-suppressor gene, due to its multiple targets and mechanisms of action. Such an example in this way is represented by miR-222, which has a role of oncomiR in liver cancers, by targeting and suppressing the PTEN tumor-suppressor gene, or TS-miRNA, whose downregulation in erythroblastic leukemia leads to the overexpression of c-KIT oncogene [27].
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5.1.4 miR-34
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miR-34 is one of the most important TS-miRs, being positively controlled by TP53 [57], repressed by MYC [58], and silenced by aberrant CpG methylation [59].
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Overexpression of miR-34 was related to apoptosis and cell cycle arrest [60], while its underexpression was linked to different tumor types, including nonsmall-cell lung cancer (NSCLC) [61], breast cancer [62], or ovarian cancer [63]. Several studies have proved that ionizing radiation upregulates the levels of expression from different miR-34 family members in a variety of human cell types: miR-34b in lymphocytes [64], miR-34c in prostate cancer cell lines [65], and miR-34a in thyroid cells [66]. Consequently, to increase the therapeutic efficiency, some of the future studies should focus on the combined use of DNA damage response related to miRNAs and radio- or chemotherapy. By performing a miR-34 modulation, Trang et al. [47] have demonstrated that synthetic miR-34 mimics incorporated in a lipid-based particle was able to block tumor growth in a mouse model of nonsmall cell lung. Likewise, Daige et al. [67] have proved that the use of encapsulating miR-34a mimics into liposomes (MRX34) leads to increase the level of miR34a in liver tumors, followed by significantly reducing several of its mRNA targets, and consequently tumor regression. Based on these results, encapsulating miR-34a mimics into liposomes (MRX34, Mirna Therapeutics Inc.) was later proposed to be investigated in clinical trials [68].
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5.1.5 miR-16
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miR-16 represents another TS-miR whose decreased expression has been observed among different types of cancers, as well as in nonsmall cell lung cancer (NSCLC) [69], prostate cancer [70], or malignant pleural mesothelioma [71], making it a strong candidate for replacement therapy in future studies with potential use in clinical trials. The data presented by Takeshita et al. [72] revealed that systemic delivery of synthetic miR-16, conjugated to atelocollagen, significantly reduced bone metastases and tumor development in a prostate cancer animal model. Moreover, their in vitro data suggest that miR-16 suppresses prostate tumor growth by regulating the expression of genes associated with cell-cycle control and cellular proliferation such as CDK1 and CDK2. The Hao group has also revealed that miR-16-1/atelocollagen-aptamer complex used in a mice model of human prostate cancer with bone metastasis enhanced anticancer efficacy. They also demonstrated that the efficacy of this complex, including aptamers, was higher, both in vitro and in vivo models than the other atelocollagen complexes that do not include aptamers. Re-expression of miR-16 mimic in malignant pleural mesothelioma cell lines and nude mouse models has caused the inhibition of tumor growth, correlated with downregulation of target genes Bcl-2 and CCND1 [71].
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5.1.6 miR-155
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miR-155, one of the first described oncomiRs [73], was identified as highly expressed in a wide range of tumors including chronic lymphocytic leukemia [74], lung cancer [75], breast cancer [76], acute myeloid leukemia [77], solid tumor including stomach, prostate, colon, pancreas [78], and melanoma [79].
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OncomiR-155 was discovered to target RAD51, an important gene in the homologous recombination DNA repair pathway, and the clinical study of Gasparini et al. [80] for triple negative breast cancer revealed that low miR-155 expression level correlated with worse progression-free survival. Moreover, Pouliot et al. [81] reported a reduced expression of miR-155 in human epidermoid carcinoma cisplatin-resistant cell lines. Dysregulated expression of this miRNA sensitizes the cells to cisplatin-induced apoptosis by targeting WEE1 and CHK1 kinases. Based on these results, future studies are encouraged with the focus on the use of exogenous agents, such as mimics or anti-miRs to sensitize cancer cells to chemo- or radiotherapy, thus overcoming resistance to therapy.
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Alexander et al. [82] found that endogenous miR-155, an important microRNA that regulates inflammation, is released from dendritic cells within exosomes and transferred to recipient dendritic cells. Administration of miR-155 containing exosomes enhances inflammatory gene expression as a response to endotoxin-induced inflammation in mice. Their findings provide strong evidence that endogenous microRNAs follow a functional transfer between immune cells and represent a regulatory mechanism for inflammatory response.
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More examples of tumor-suppressor miRNA mimics, which target multiple oncogenic transcripts, were recently presented by Hosseinahli et al. [41].
Given the results provided by in vitro and in vivo studies, several clinical trials including miRNA-based therapy in human cancers were subsequently initiated (Table 1).
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Company
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Drug
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Targeted miRNA
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Therapy type
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Cancer type
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Delivery system
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Mechanism/effect
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Trial status
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Clinical trials. Gov identifier
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Mirna Therapeutics. Inc
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MRX34
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miR-34
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Mimic
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Nonsmall-cell lung carcinoma, small cell lung cancer, primary liver cancer lymphoma, melanoma, multiple myeloma, renal cell carcinoma
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LNPs (Smarticles)
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Reduction in the expression of oncogenes, tumor regression, enhanced the survival, and inhibited the growth of other nonhepatic tumors
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Multicenter phase I terminated
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NCT01829971
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EnGeneIC
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MesomiR-1
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miR-16
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Mimic
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Malignant pleural mesothelioma, nonsmall cell lung cancer
Reduce overexpression and of oncomiR, leading to decreasing aberrant cell proliferation
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Multicenter phase I recruiting
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NCT02580552
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Table 1.
Clinical trials using miRNA therapy in human cancers (adapted after Christopher et al. [87] and Rupaimoole et al. [88]).
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In April 2013, Mirna Therapeutics, Inc., a publicly traded company based in Carlsbad, California, which primarily focuses on anti-miRNAs technology, announced that their leading product candidate, MRX34, a mimic of miR-34 encapsulated in a liposomal nanoparticle formulation, called NOV40, was the first microRNA mimic to enter clinical development. The multicenter phase I trial of MRX34 included patients diagnosed with primary liver cancer, nonsmall cell lung cancer (NSCLC), lymphoma, melanoma, multiple myeloma, or renal cell carcinoma. The trial aimed to increase the number of intravenously doses with two times per week or five times per day schedule. In June 2016, a total of 99 patients suffering from HCC, NSCLC, or pancreatic cancer had been enrolled in the study [83]. The phase I clinical trial confirmed partial responses in a patient with metastasized hepatocellular carcinoma (HCC), a patient with advanced acral melanoma, and a patient with advanced renal cell carcinoma (RCC), evaluated through Response Evaluation Criteria in Solid Tumors (RECIST). Also, 14 patients were detected with stable disease (median duration 136 days; range 79–386 days). Results from white blood cell analysis indicated a significant reduction in two miR-34 target genes FOXP1 and BCL2. Nonetheless, because of the immune-related adverse responses involving patient deaths, the trial was finished. Since the cause of these immune reactions remains still unclear, preclinical trials will be need in order to better understand the immune-related toxicities. After passing successfully the phase I clinical trial, they intended to advance into two phase II clinical studies: one for patients diagnosed with advanced malignant melanoma and another for patients diagnosed with advanced renal cell carcinoma (RCC). In concordance with the advancements made by MRX34, their lead product candidate, through the clinical development, miR-34 mimics represent a new promising class of replacement therapy used in cancer. Additionally, Cortez et al. [84] reported that p53 regulated PDL1 expression via miR-34 in nonsmall-cell lung cancer. Administration of miR-34a mimics (MRX34), alone or in combination with radiotherapy (XRT), reduced PDL1 expression in the tumor and antagonized T-cell exhaustion.
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In November 2014, EnGeneIC, a privately held Australian company in collaboration with Asbestos Diseases Research Institute, Sydney, Australia, announced the start of phase I clinical trial using miR-16 mimic charged in nanocells, a bacterial-derived transfection system EDV™. The trial included patients suffering from malignant pleural mesothelioma (MPM) and advanced nonsmall cell lung cancer (NSCLC), refractory to standard therapy. miR-16 mimic-based therapy were delivered intravenously, using EnGeneIC Delivery Vehicle (EDV)-Packaging, and were surface conjugated with an EGFR-targeting antibody in order to facilitate the target of tumor site [85]. Preliminary data presented by Van Zandwijk et al. [86] show manageable safety in response to infusion of 5 billion nanocells loaded with 1.5 μg miR-15/16 mimics as a first-dose level in the first five patients that had been enrolled. Because of the fact that this targomiR trial using miR-16 as a replacement therapy did not present adverse immune response and toxic effects, it is expected to continue to phase II study [86].
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In March 2016, MiRagen Therapeutics, Inc., a privately held company based in Boulder, Colorado, announced the initiation of phase I clinical trial to investigate the anticancer product candidate: MRG-106, a synthetic microRNA antagonist of microRNA-155 (LNA anti-miR). The phase I clinical trial is currently tested in patients diagnosed with cutaneous T-cell lymphoma (CTCL) of the mycosis fungoides (MF) subtype [89].
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Despite some promising preclinical results, the outcome of MRX34 translational clinical trial using miR-16 as a replacement therapy, designed to restore the expression of miR-34 in patients diagnosed with different types of cancer, was discouraging due to adverse toxic effects. At present, this clinical trial is finished, and its suitability to further development to phase II study remains under question. However, this drawback can be addressed by optimizing therapeutic doses and applying organ-specific administration routes [41].
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6. Future challenges: new reliable miRNAs for target therapies
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Epithelial-to-mesenchymal transition (EMT) allows tumor cells to enter the metastatic cascade by changing their morphological and molecular characteristics, and it represents a wide spectrum that cancer cells keep transiting. The EMT program relies on an intricate network of signaling pathways that dictate a series of phenotypic changes in epithelial cells. One clearly visible aspect is the loss of apical-basal polarity and cell-to-cell interaction caused by the destabilization of tight junctions, decreased claudin, occludin, and E-cadherin repression by SNAIL, SLUG, ZEB, TWIST, and SMAD, and its interchange with N-cadherin, a process known as the “cadherin switch” [90, 91]. Moreover, the overexpression of vimentin, a cytoskeleton intermediate-filament protein of mesenchymal origin that connects the nucleus to the plasma membrane, enables filopodia formation and a fibroblastic spindle-like morphology. Vimentin was proved to be induced through Slug and Ras signaling, and it promotes cell movement and migration [92]. The next step is the degradation of extracellular matrix components under the activity of matrix metalloproteinases and the invasion of the surrounding stroma (Figure 3).
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Figure 3.
The EMT-MET plasticity of tumor cells, their migration, and invasion as tumor circulating cells (CTCs).
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The EMT is coordinated by a series of signaling pathways triggered by either the tumor microenvironment or the intrinsic factors, and it falls under the incidence of regulatory noncoding RNAs, especially micro-RNAs [93].
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This is the reason why finding potential microRNA targets for blocking EMT could support the efforts already made in blocking metastasis and improving cancer therapy strategies. Right below, we are succinctly reviewing the results of several studies that investigated the role of microRNAs in EMT and metastasis and their potential in becoming microRNA therapy targets (Table 2).
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The influence of cancer-associated fibroblasts (CAFs) secreted exosomes over endometrial cancer progression was questioned in a study by Li et al. [94]. CAFs secreted exosomes contained significantly lower levels of miR-148b than normal fibroblasts, and miR-148b expression was lower in endometrial cancer specimens than in normal adjacent tissues. miR-148b was correlated with improved prognosis, in vitro and in vivo studies suggesting its role as an EMT inhibitor. Downregulation of DNMT1 oncogene was the mechanism proposed for miR-148b-mediated suppression of endometrial cancer progression. Also, relating to the tumor microenvironment, emerging evidence shows the prometastatic effect of a hypoxic microenvironment over tumor cells. Acidic conditions were shown to promote miR-210 overexpression by activating HIF1 [95, 96]. In prostate cancer, high levels of miR-210 were detected in bone metastases, and they were correlated with poor prognosis of prostate cancer patients. Exogenously overexpression of miR-210-3p in cancer cell lines enhanced cell motility and migration as well as bone metastasis in mouse model by inducing NF-κB signaling and EMT. Moreover, miR-210-3p inhibition reversed EMT and impaired the metastatic potential of cancer cells [95]. Another study confirmed the hypoxia-induced EMT activation and metastasis by HIF1-miR-210 axis in breast cancer. Tang et al. [96] identified high expression of miR-210 in hypoxia grown breast cancer stem cells and in isolated human breast cancer stem cells. The overexpression of MiR-210 in poorly metastatic MCF7 cell lines leads to their invasiveness and migration in vitro as well as increasing metastatic potential in vivo. One suggested mechanism is related to the direct binding of miR-210 to the Open Reading Frame (ORF) of the E-cadherin mRNA and its post-transcriptional inhibition in breast cancer cells.
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miR-652 downregulation in the acidic microenvironment of pancreatic cancer promoted EMT by ZEB1 activation, and it was correlated with a progressive stage, lymphatic invasion, vascular infiltration, and distant metastasis. In vitro experiments showed that miR-652 overexpression has an antimetastatic effect by inducing MET in PANC-1-A miR-652-mimic transfected cells, and it reduced their migration and invasion. Also, in vivo studies confirmed the in vitro results with lower tumor mass, fewer metastases, and overexpression of E-cadherin over vimentin/N-cadherin in mouse xenografts PANC-1-A miR-652-mimic transfected cells vs. miR-652 inhibitor [97].
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Partially due to cancer tumors heterogeneity in construction and behavior and the relative novelty of noncoding RNAs as potential targets for cancer therapy, there are still missing bricks in understanding the mechanism that triggers the regulation of cancer-related microRNAs. However, as shown above, tumor microenvironment has a great impact over cancer progression, and probably understanding its role in modulating microRNA expression in tumor cells might bring light to new potential targets for improved therapy.
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From a different perspective, there are many well-known pathways underlying cancer development and progression. Wnt signaling pathway represents one of the fundamental pathways involved in cell proliferation and specialization, as well as cell movement during both embryonic development and tissue homeostasis. The canonical Wnt signaling functions by regulating the amount of the transcriptional coactivator β-catenin, a molecule that controls key developmental gene expression programs [98]. Wnt signaling aberrations have been shown to regulate various processes that are important for cancer progression, including tumor initiation, tumor growth, cell senescence, cell death, differentiation, and metastasis. Wnt signaling molecules and downstream effectors can promote transcriptional changes in order to induce EMT in cancer cells while also being further activated by EMT in a continuous feedback loop [99].
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Several microRNAs were proven to modulate Wnt signaling in EMT and cancer metastasis. In endometrial cancer, Wnt signaling can be activated by miR-652, which targets and inhibits retinoid orphan nuclear receptor alpha (RORA) gene. RORA represents a tumor-suppressor gene that represses the Wnt/β-catenin pathway through attenuating β-catenin transcriptional activity. Expression of miR-652 is frequently increased in human endometrial cancer tissues, its high expression being correlated with poor tumor differentiation, shorter overall survival, and recurrence. Overexpression of miR-652 in endometrial cancer cell promotes their proliferation and migration in vitro and in vivo [100].
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Another metastasis enhancer, miR-374a, acts by activating the Wnt/β-catenin cascade and promoting EMT. miR-374a maintains constitutively activated Wnt/β-catenin signaling by suppressing multiple negative regulators including WIF1, PTEN, and WNT5A. miR-374a was upregulated in primary tumor samples from breast cancer patients with distant metastases, and it was associated with poor metastasis-free survival. miR-374a transfection into poorly metastatic MCF7 cell line promoted its motility and invasiveness in vitro and lung metastasis forming abilities in BALB/c mice, while miR-374a knockdown in highly invasive MDA-MB-231 cells decreased their motility and metastatic potential [101].
\n
On the other hand, microRNA-590-5p was found to function as a tumor-suppressor in breast cancer, inhibiting EMT, cell migration, and invasion by downregulating the Wnt/β-catenin pathway. Quantitative RT-PCR analysis on breast tumor tissues and paired adjacent normal tissues showed that miR-590-5p was downregulated in breast cancer together with E-cadherin, while its target PITX2, b-catenin, Wnt-1, N-cadherin, and vimentin were upregulated. In vitro experiments and mouse xenografts showed that miR-590-5p upregulation or PITX2 silencing inhibits the activation of Wnt/β-catenin signaling pathway and suppresses the EMT of breast cancer [102].
\n
Another metastasis inhibitor, miR-625, was characterized in different pathologies, and surprisingly, it maintained its function, even though it was involved in different pathways. miR-625 was reported as downregulated in hepatocellular carcinoma, gastric cancer, and colorectal cancer, and its low expression was associated with local invasion, lymph node, and distant metastasis. Ectopic expression of miR-625 induces suppression of migration and invasion of hepatocellular carcinoma cells by post-transcriptionally inhibiting IGF2BP1. Loss of IGF2BP1 suppressed F-actin polymerization, inhibiting the formation of cell protrusions, required for cell migration. The same effect was observed in vivo, where miR-625 overexpression decreased intrahepatic and lung metastasis [103]. Moreover, when it was ectopically induced, miR-625 suppressed the migration and invasion of gastric cancer cells as well as metastasis in nude mice by inhibiting ILK protein synthesis [98]. While the exact signaling pathway was not fully elucidated in colorectal cancer, ectopic miR-625 expression inhibited cell migration and invasion and suppressed colorectal cancer cell metastasis in nude mice [99]. The aforementioned findings highlight miR-625 as an interesting candidate for further in vivo studies in order to test its potential for developing a therapeutic microRNA for blocking invasion and metastasis.
\n
A dual and somewhat contradictory behavior can be observed in the case of miR-409. While low miR-409 expression in breast cancer and nonsmall cell lung carcinoma (NSCLC) was associated with poorer prognosis and its ectopic uptake decreased the invasiveness of cancer cells [104, 105], it also seems to exert the negative effect in the case of prostate cancer, where it promotes tumorigenesis and EMT [106, 107]. Qi Song et al. [104] showed that miR-409 inhibits NSCLC cell migration, growth, and proliferation abilities by inhibiting SPIN1 translation. miR-409 downregulates PI3K/AKT pathway in NSCLC and inhibits its downstream targets such as CREB1, BCL2, and Cyclin D. Overexpression of miR-409 led to fewer lung metastases in nude mice, confirming its antimetastatic potential. Interestingly, miR-409 targeted the same pathway and suppressed cell growth and invasion in breast cancer. miR-409-3p inhibits the proliferation, migration, and invasion of breast cancer by targeting and suppressing the AKT expression. miR-409-3p was downregulated in several human tumors compared to their corresponding nontumor tissues [105]. On the other hand, miR-409-entrapped exosomes secreted by cancer-associated fibroblasts (CAFs) and promoted EMT and prostate tumorigenesis. In their study, Josson et al. demonstrated that miR-409-3p was highly expressed in CAFs derived from human patients, and it was correlated with higher Gleason score in prostatic tissues. Moreover, the ectopic expression of miR-409 in normal prostate stromal fibroblasts conferred them a CAF phenotype in vitro. Exosome-mediated transport of miR-409 into normal prostate stromal cells induced cell growth and EMT in vitro and in vivo. In this case, the predicted targets of miR-409 were the tumor-suppressor proteins STAG2 and RSU1, which appeared downregulated [108]. Considering these, it would be worth exploring if miR-409 has a positive or a negative impact over EMT and metastasis in more human cancers, and if it could be taken into consideration as a future miRNA therapy target.
\n
Table 2 comprises a set of microRNAs that were evaluated both in vitro and in vivo, while also being analyzed in tumor samples, in correlation with the clinical outcome of the patients. As it can be easily observed, the effect of microRNAs in modulating EMT and metastasis-related pathways varies in different cancer types. In certain situations, the same microRNA can induce completely opposite outcomes by targeting multiple signaling pathways. This would be one of the biggest challenges that need to be overcome when designing new microRNA-based therapeutic compounds, and it is, at the same time, an interesting research niche worth exploring, especially for miR-625, that maintained its anti-EMT function in three different cancer types (hepatocellular carcinoma, gastric cancer, and colorectal cancer) and miR-409 that is able to target multiple pathways with opposing effects in NSCLC, breast cancer, and prostate cancer.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
miRNA
\n
Pathology
\n
Effect
\n
Target
\n
Mechanism
\n
Effect in vitro and in vivo
\n
Clinical associations
\n
Ref.
\n
\n\n\n
\n
miR-148b
\n
Endometrial cancer
\n
Metastasis inhibitor
\n
Inhibits DNMT1
\n
Anti-EMT, increased E-cadherin over vimentin, fibronectin, N-cadherin
\n
Decreased motility and invasion in vitro, decreased metastasis in vivo
Blocks E-cadherin mRNA by binding to the ORF region
\n
Upregulated in mammosphere cells, induced by hypoxia, promotes invasion, migration, proliferation, and self-renewal, induces EMT by loss of E-cadherin in vitro and leads to poorly differentiated tumors, high proliferation, more metastases and higher tumor mass in vivo
\n
Upregulated in BCSC CD44+/CD24-sorted from breast cancer tissue samples
Anti-EMT, reduced migration and invasion, promotes MET in vitro, lower tumor mass, fewer metastases, increased expression of E-cadherin vs. vimentin, N-cadherin in vivo
\n
Downregulated in pancreatic cancer, correlated with progressive stage, lymphatic invasion, vascular infiltration, distant metastasis
Increased proliferation, and increased metastasis potential in vitro and in vivo
\n
Upregulated in EC, associated with poor differentiation, poor prognosis, shorter overall survival, and recurrence; not associated with cancer stage, localization, tumor size
Wnt-b-catenin signaling, promoting the transcriptional activity of TCF/LEF
\n
Induces EMT and enhanced motility in MCF7 transfected cells, decreases MDA cells motility in knockout, promotes lung metastases with MCF7 miR-374a transfected cells, impaired MDA-435 metastases by the administration of antagomiR
\n
Upregulated in patients that presented metastases within the 51 months follow up; associated with low WIF1, PTEN, and WNT5A expression in tumor samples; high b-catenin low E-cadherin in miR-374a overexpressing samples
Upregulation of miR-590 inhibits EMT genes, inhibits proliferation after 48-72 h, impairs migration and invasion of cancer cells, and promotes smaller tumor size for miR-590 mimic or PITX2 inhibition and less lung and lymphatic metastases in nude mice
\n
Downregulated in breast cancer tissues vs. normal tissue, together with E-cadherin, while PITX2, b-catenin, Wnt-1, N-cadherin, and vimentin are upregulated
In vitro and in vivo validated miRNAs related to EMT, invasion, and metastasis that could represent future therapy targets.
\n
\n
\n
7. Conclusions
\n
miRNAs represent key modulators of the human genome because of their capacity to affect up to 60% of protein-coding genes. In cancer, genetic and epigenetic events lead to the alteration of miRNA expression and consequently their mRNA target genes. Functional studies have demonstrated that miRNA modulation in tumor cells causes changes in the phenotype, leading to increased apoptosis and cell death, suppression of tumor development, invasion, and metastasis by inhibiting the oncogenic miRNAs (oncomiRs) and/or substituting the deficient tumor suppressive miRNAs (TS-miRNAs). Considering the encouraging preclinical and clinical data, miRNA-based therapy could become a reliable tool in cancer management.
\n
\n
Acknowledgments
\n
The work for this chapter was supported by the UEFISCDI Program-PN-III-P2-2.1-PED-2016-1750.
\n
\n
Conflict of interest
\n
The authors declare no conflict of interest.
\n
\n',keywords:"cancer, invasions, metastasis, miRNA, therapy",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/64396.pdf",chapterXML:"https://mts.intechopen.com/source/xml/64396.xml",downloadPdfUrl:"/chapter/pdf-download/64396",previewPdfUrl:"/chapter/pdf-preview/64396",totalDownloads:468,totalViews:0,totalCrossrefCites:0,dateSubmitted:"July 27th 2018",dateReviewed:"October 4th 2018",datePrePublished:"November 14th 2018",datePublished:"November 20th 2019",readingETA:"0",abstract:"As master modulators of the human genome, miRNAs are involved in all cancer hallmarks, disrupting the normal function of their targets. By gaining or losing the function, miRNAs lead to the validation of tumor phenotype, its progression, and metastasis as well as to drug resistance. Increasing the evidence suggests that the modulation of miRNAs in cancer cells, by suppressing the oncogenic miRNAs (oncomiRs) and substituting the deficient tumor suppressive miRNAs (TS-miRNAs), could become a reliable tool for improving the cancer therapy. In this chapter, we will present an up-to-date overview of the role of miRNA-based therapeutics in oncology, highlighting their role in cancer management, how these therapies can be used, and which would be the future challenges related to miRNA-based therapies.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/64396",risUrl:"/chapter/ris/64396",signatures:"Ovidiu Balacescu, Simona Visan, Oana Baldasici, Loredana Balacescu, Catalin Vlad and Patriciu Achimas-Cadariu",book:{id:"6987",title:"Antisense Therapy",subtitle:null,fullTitle:"Antisense Therapy",slug:"antisense-therapy",publishedDate:"November 20th 2019",bookSignature:"Shashwat Sharad and Suman Kapur",coverURL:"https://cdn.intechopen.com/books/images_new/6987.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"80113",title:"Dr.",name:"Shashwat",middleName:null,surname:"Sharad",slug:"shashwat-sharad",fullName:"Shashwat Sharad"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"195763",title:"Ph.D.",name:"Ovidiu",middleName:null,surname:"Balacescu",fullName:"Ovidiu Balacescu",slug:"ovidiu-balacescu",email:"obalacescu@yahoo.com",position:null,institution:null},{id:"196758",title:"Dr.",name:"Loreadana",middleName:null,surname:"Balacescu",fullName:"Loreadana Balacescu",slug:"loreadana-balacescu",email:"loredana_balacescu@yahoo.com",position:null,institution:null},{id:"196770",title:"Prof.",name:"Patriciu",middleName:null,surname:"Achimas",fullName:"Patriciu Achimas",slug:"patriciu-achimas",email:"patrick.achimas@hotmail.com",position:null,institution:null},{id:"269553",title:"MSc.",name:"Oana",middleName:null,surname:"Baldasici",fullName:"Oana Baldasici",slug:"oana-baldasici",email:"oana_baldasici@yahoo.co",position:null,institution:null},{id:"269554",title:"Dr.",name:"Simona",middleName:null,surname:"Visan",fullName:"Simona Visan",slug:"simona-visan",email:"simona.visan19@gmail.com",position:null,institution:null},{id:"279298",title:"Dr.",name:"Catalin",middleName:null,surname:"Vlad",fullName:"Catalin Vlad",slug:"catalin-vlad",email:"catalinvlad@yahoo.it",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Short overview about biogenesis and function of miRNAs",level:"1"},{id:"sec_3",title:"3. The role of miRNAs in cancer",level:"1"},{id:"sec_4",title:"4. Strategies used for miRNA-based therapies",level:"1"},{id:"sec_4_2",title:"4.1 miRNA inhibition therapies for oncomiRs",level:"2"},{id:"sec_4_3",title:"4.1.1 Discussion",level:"3"},{id:"sec_6_2",title:"4.2 miRNA replacement therapies for tumor-suppressor miRNAs",level:"2"},{id:"sec_6_3",title:"4.2.1 Discussion",level:"3"},{id:"sec_8_2",title:"4.3 miRNA delivery systems",level:"2"},{id:"sec_8_3",title:"4.3.1 Discussion",level:"3"},{id:"sec_11",title:"5. MicroRNAs in cancer therapies",level:"1"},{id:"sec_11_2",title:"5.1 miRNA candidates used in preclinical trials",level:"2"},{id:"sec_11_3",title:"5.1.1 miR-10b",level:"3"},{id:"sec_12_3",title:"5.1.2 miR-221",level:"3"},{id:"sec_13_3",title:"5.1.3 miR-222",level:"3"},{id:"sec_14_3",title:"5.1.4 miR-34",level:"3"},{id:"sec_15_3",title:"5.1.5 miR-16",level:"3"},{id:"sec_16_3",title:"5.1.6 miR-155",level:"3"},{id:"sec_18_2",title:"5.2 Clinical studies involving miRNA-based therapy",level:"2"},{id:"sec_20",title:"6. Future challenges: new reliable miRNAs for target therapies",level:"1"},{id:"sec_21",title:"7. Conclusions",level:"1"},{id:"sec_22",title:"Acknowledgments",level:"1"},{id:"sec_22",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Society AC. Global Cancer Facts & Figures. 3rd ed. Atlanta: American Cancer Society; 2015'},{id:"B2",body:'Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. 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Department of Functional Genomics, Proteomics and Experimental Pathology, The Oncology Institute “Prof. Dr. Ion Chiricuta”, Romania
Department of Medical Oncology, University of Medicine and Pharmacy “Iuliu Hatieganu”, Romania
Department of Surgical and Gynecological Oncology, University of Medicine and Pharmacy “Iuliu Hatieganu”, Romania
Department of Surgery, The Oncology Institute “Prof. Dr. Ion Chiricuta”, Romania
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I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). I am a Reviewer for several refereed journals and international conferences, such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Industrial Electronics, Optic Letters, Measurement Science Review, and also a member of the International Advisory Committee for 2012 IEEE Business Engineering and Industrial Applications and 2012 IEEE Symposium on Business, Engineering and Industrial Applications.",institutionString:null,institution:{name:"Joseph Fourier University",country:{name:"France"}}},{id:"55578",title:"Dr.",name:"Antonio",middleName:null,surname:"Jurado-Navas",slug:"antonio-jurado-navas",fullName:"Antonio Jurado-Navas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/55578/images/4574_n.png",biography:"Antonio Jurado-Navas received the M.S. degree (2002) and the Ph.D. degree (2009) in Telecommunication Engineering, both from the University of Málaga (Spain). He first worked as a consultant at Vodafone-Spain. 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