extfieldsofstudy: Biology Medicine

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Replicative cellular age distributions in compartmentalized tissues

The cellular age distribution of hierarchically organized tissues can reveal important insights into the dynamics of cell differentiation and self-renewal and associated cancer risks. Here, we examine the effect of progenitor compartments with varying differentiation and self-renewal capacities on the resulting observable distributions of replicative cellular ages. We find that strongly amplifying progenitor compartments, i.e. compartments with high self-renewal capacities, substantially broaden the age distributions which become skewed towards younger cells with a long tail of few old cells. For several of these strongly amplifying compartments, the age distribution becomes virtually independent of the influx from the stem cell compartment. By contrast, if tissues are organized into many downstream compartments with low self-renewal capacity, the shape of the replicative cell distribution in more differentiated compartments is dominated by stem cell dynamics with little added variation. In the limiting case of a strict binary differentiation tree without self-renewal, the shape of the output distribution becomes indistinguishable from that of the input distribution. Our results suggest that a comparison of cellular age distributions between healthy and cancerous tissues may inform about dynamical changes within the hierarchical tissue structure, i.e. an acquired increased self-renewal capacity in certain tumours. Furthermore, we compare our theoretical results to telomere length distributions in granulocyte populations of 10 healthy individuals across different ages, highlighting that our theoretical expectations agree with experimental observations.

Introduction
Many tissues in multicellular organisms resemble a compartmentalized structure with a hierarchy of cells at different stages of differentiation and function. This hierarchy is usually fuelled by a few stem cells that ideally can self-renew indefinitely, whereas the majority of the tissues consist of shorter-lived differentiated cells that emerge from these stem cells [1][2][3].
In most tissues it is thought that stem cells divide infrequently, while their progenitors and further differentiated cells divide more frequently to ensure tissue function under homeostasis [4]. Such structures allow both the production of many cells in a short time and the reduction of the risk for the accumulation of somatic mutations within the stem cell compartment [1,[5][6][7][8][9][10].
Owing to these pronounced dynamical disparities in hierarchical tissues, replicative age-the number of divisions a cell has undergone-can be an important observable providing information about the structure and cellular dynamics within these tissues. As many somatic mutations are acquired during cell divisions [11,12], we would expect replicative age also to be strongly correlated with different cancer risks in different hierarchical tissues [13 -15].
In the context of ageing, the focus is typically on changes within the stem cell compartment, as stem cells have the ability to self-renew and persist long enough to become relevant for organismal ageing [16,17]. It is generally assumed that replicative cell age in downstream compartments is a good proxy for replicative stem cell age. For example, some of us previously developed and tested a mathematical model for human haematopoietic stem cell ageing based on replicative ages in human lymphocytes and granulocytes [18]. Here, we do not model the detailed dynamics on the stem cell level. Instead, we regard the cellular age distribution on the stem cell level as a steady influx of progenitor cells into the differentiation hierarchy to keep up homeostasis of the tissue.
Cellular dynamics in hierarchically organized tissue structures can be hard to explore experimentally due to the large scaling differences between differentiation levels [19] and the challenges to correctly assign cells to specific differentiation stages. One possibility to assess the age distribution of a cell population is to measure the telomere length of the cellular chromosomes. Telomeres are the protective, noncoding ends of chromosomes, consisting of the same short DNA sequence repeated thousands of times. Telomeres typically shorten with each cell division [20 -22]. Cells with critically short telomeres enter replicative senescence, which is thought to be a cancer suppression mechanism [23]. Moreover, critically short telomeres are often associated with genome instability and corresponding increased risk of cancer [24]. For our purpose, telomere length distributions can be thought of as a measure for the cellular replicative age distribution. These can be assessed in tissue samples [25,26] which are, for example, especially accessible in differentiated tissue in the haematopoietic system and thus, in principle, would also allow for some time resolution within healthy human individuals [18]. For simplicity, we concentrate solely on replicative ageing, that is the number of cell divisions a cell has undergone, in contrast with temporal ageing which is also commonly explored in models [27].
However, it remains unclear if cellular age distributions in hierarchically organized tissues are dominated by stem cell dynamics or alternatively are determined by the possibility of a multi-step differentiation process with strong intermediate self-renewal of progenitor cells. Here, we develop a mathematical framework that allows us to describe the distribution of replicative cellular ages across several hierarchical levels of differentiation. Thereby, we demonstrate under which conditions the distribution of replicative ages in differentiated cell populations can provide insights into the properties of the dynamics within the underlying tissue.

Model
In the following, we present a mathematical description for the replicative age distributions within compartmentalized tissue structures (figure 1). First, we discuss the simplest case of only two compartments-one stem cell compartment and the focal downstream progenitor compartment. We then ask what is the distribution of replicative ages of cells in the progenitor compartment provided a continuous influx of cells from the stem cell compartment. For example, it is estimated that in haematopoiesis of mice there is a constant production of early progenitor cells from stem cells with a rate of around 150 cells d 21 [4]. However, we do not discuss the time dynamics on the stem cell level explicitly. The temporal change of replicative age distributions in stem cell compartments and the resulting potential influx distributions for progenitor compartments are discussed in detail in [18].
We assume that in the progenitor compartment there are n j cells of each replicative age class j. Progenitor cells divide with proliferation rate r and after each division the replicative age of both daughter cells increases by one j ! j þ 1. Each daughter cell can, in principle, take a different cell fate that contributes differently to the distribution of replicative ages (figure 1a). In general, the following outcomes are possible after a single cell division.
(i) With probability p a cell self-renews symmetrically, both daughter cells stay in the same compartment and increase their cellular age by one (n j ! n j À 1, n jþ1 ! n jþ1 þ 2). (ii) With probability d a cell differentiates symmetrically, effectively removing it from the compartment of differentiated cells (n j ! n j À 1). (iii) With probability 1 2 p 2 d, a cells divides asymmetrically, with one cell staying in the pool of differentiated cells, while the other cell leaves the compartment [28] (n j ! n j À 1, n jþ1 ! n jþ1 þ 1).
We choose the influx of cells from the stem cell compartment to be a constant rate i j that might differ for each cellular age j. Below we give explicit examples for different distributions of i j . We assume the dynamics on the stem cell level to be much slower compared to downstream compartments and hence consider the influx i j into the progenitor compartment to be constant over time.
Using the above, we can formulate differential equations for the change of the number of cells in each age class n j . Thereby, we account for the loss of cells due to proliferation and subsequent differentiation and gain of cells due to symmetric self-renewal and cell influx from the stem cell compartment, where we set a ¼ 1 þ p 2 d to be the self-renewal parameter which critically determines the most relevant results of our model. As p and d are probabilities with p þ d 1, the selfrenewal parameter can be in the range 0 a 2. However, as we are interested in homeostasis and not an exponentially growing tissue, the symmetric division probability p in our case must be smaller than the symmetric differentiation probability d and therefore 0 a , 1.
The above system of ordinary differential equations can be solved analytically (see appendix E). However, as we assume that the dynamics on the level of stem cells is much slower compared to progenitor compartments, we can investigate the equilibrium solutions n* j to equation (2.1) for each age class j. The equilibrium solutions can be obtained via recursion by setting @n j /@t ¼ 0 (see appendix A). The general solution is which is equivalent to a convolution sum of the influx i k and a k /r between zero and j.

Multiple compartments
In reality, most tissues will consist of multiple progenitor cell compartments. It is thus natural to ask how multiple downstream compartments affect cellular age distributions. To answer this question, we can generalize our previous framework (figure 2). Differentiated cells in a downstream compartment are produced either by symmetric differentiation with probability d or by asymmetric division with probability 1 2 p 2 d. If we denote the output of cells per unit of time for each age class as v j , we can write To allow for multiple compartments, we can identify the output distribution of a compartment c and the input distribution of the next downstream compartment c þ 1, For our purpose, it is desirable to compare the effect of different tissue structures, that is a different number of total compartments C, but with the same tissue function, that is the same total outflux of fully differentiated cells. In our model, the total outflux of differentiated cells V ¼ P j v j is determined by the total influx of cells I ¼ P j i j , the number of compartments C and the self-renewal parameter a. We therefore choose a such that the total output of cells remains constant, i.e. assuring certain replenishing needs of a specific tissue. For this, we formulate differential equations for the change of the total number of cells N (c) ¼ P j n (c) j in each of the compartments c with a compartment-specific proliferation rate for each cell r (c) by collecting all influx and outflux terms: Here, I is the total influx into the first compartment (c ¼ 0) (i.e. the sum of all direct stem cell derived progenitors per time unit). The total outflux V is related to the number of cells in the last compartment N (C21) via Under steady-state conditions, the above equations can be solved explicitly for the self-renewal parameter a (see appendix B): :

ð2:4Þ
This allows us to adjust the self-renewal parameter a such that the outflux V remains constant given an influx I for any number of compartments C. However, as the selfrenewal parameter is constrained 0 a , 1 (see above section), the minimum amplification of cell production is given by (V=I) min ¼ 2 C corresponding to a ¼ 0.

Properties of the replicative age distribution 2.2.1. Mean and variance
The mean and variance of the replicative age distribution under steady-state conditions can be calculated analytically, see appendix C. The mean m of the replicative age distribution in the progenitor compartment increases compared to the influx based on the self-renewal a to where kjl n* is the first moment of the replicative age distribution in the focal progenitor compartment and kjl i ¼ m i is the average replicative age of the influx. Note that the average replicative age of the outflux m v ¼ kjl v is increased by one to account for the extra differentiation step The minimal increase of the mean between influx and outflux for no self-renewal (a ¼ 0) is therefore equal to one.   The variance s 2 of the replicative age distribution increases similarly as the mean above ð2:6Þ Here, s 2 i denotes the variance of the replicative age distribution of the influx.
Generally, also the higher moments kj g l n* of the replicative age distribution can be calculated based on the moments of the influx distribution kj b l i with b g. The corresponding calculations and results are shown in appendix C.

Limiting behaviour
For very low self-renewal, a(1, the only age class of influx that significantly contributes to the age distribution n* j in equation (2.2) is i k¼j , as it is in zeroth order of a. The influx of all other age classes is of higher order of self-renewal a and will therefore vanish for a(1 such that Hence, the outflux distribution will look approximately like the influx distribution. To evaluate the impact of the progenitor compartment on the replicative age distribution in the limit of high self-renewal 1 2 a(1, we rewrite equation (2.2) to The limiting behaviour therefore strongly depends on the age distribution of the influx i k . If the influx has an upper bound K on replicative age, such that for all k!K holds i k ( a k , the sum in the above equation is constant and the distribution of replicative age will decline exponentially If, on the other hand, the influx distribution i k is not declining fast enough and is in the same order as a k (i k !a k ), we cannot make a general prediction for this limit.

Results
It seems natural to suspect that the specific distribution of replicative ages in downstream compartments strongly depends on the distribution of cellular ages within the stem cell compartment. In the following, we present the resulting age distributions for various different influx distributions. Additionally, we will compare tissue structures with many subsequent downstream compartments and a low probability for self-renewal against having only very few compartments with a high probability for self-renewal. An important parameter for the age distribution in the progenitor compartment is a ¼ 1 þ p 2 d which depends on the probability for both symmetric splitting and symmetric differentiation and critically defines the total size of the compartment as well as the amount of cells produced (appendix B). For a compartment model of haematopoiesis with many differentiation steps as for example in [1,29], a would be around 0.3, whereas for other models with fewer compartments a would need to be higher to allow for sufficient output of fully differentiated cells per unit time [30][31][32].

A single progenitor compartment
Here, we discuss the distributions of replicative age in the special case of a single progenitor compartment given four different influx distributions from the stem cell compartment. All distributions are calculated analytically and the corresponding calculations can be found in the appendix D. Realizations of the resulting replicative age distributions are shown in figure 3.

Identical replicative cellular age influx
We first discuss the simplest case for a cellular age distribution on the stem cell level that is all stem cells have identical replicative age v. This results in a delta function is the Dirac delta distribution and r s is the rate of cell production. Together with equation (2.2), this implies for the age distribution The resulting distribution is shown in figure 3a. Cellular ages within the single progenitor compartment follow an exponential distribution that approaches zero faster for smaller replicative age freq.
. . Figure 2. Several downstream compartments amplifying the rate of cell production from influx to outflux. In each compartment, there are self-renewal or differentiation processes as described in figure 1. Each cell division thereby leads to an increase of replicative age and changes the age distribution of the corresponding compartment. Self-renewal occurs proportional to the self-renewal parameter a, whereas differentiated cells are produced with 1 2 a and go into the next downstream compartment. The compartment number c is shown as superscript, the total number of compartments is C ¼ 4. (Online version in colour.) rsif.royalsocietypublishing.org J. R. Soc. Interface 15: 20180272 self-renewal parameters a and always has the maximum at the influx replicative age v.

Geometrically distributed replicative cellular age influx
The former section is of course an oversimplification. We expect some form of distributed cellular ages on the stem cell level. We first discuss the possibility of a geometrically distributed replicative age i k ¼ r s l k (1 2 l) with distribution parameter l and total cell influx r s as input from the stem cell level. This replicative age distribution resembles the distribution in the first progenitor compartment for an influx with identical replicative age from the stem cell compartment, as shown in the previous section ( §3.1.1); it would therefore correspond to the second downstream compartment for that specific influx. The resulting age distribution within this progenitor compartment-equation (2.2)-can be solved analytically (see appendix D.1): lÀa for a = l, rs r (1 À l)a j (j þ 1) for a ¼ l: ( These age distributions are shown for different self-renewal parameters a in figure 3b. For low self-renewal, the shape of the replicative age in the progenitor compartment strongly resembles that of the influx distribution, i.e. a monotonically decreasing function of replicative age. For large self-renewal a!0.5, however, the distribution of replicative cellular ages in equilibrium becomes increasingly independent of the influx distribution and very similar to the age distributions resulting from other influx distributions, see figure 3c,d.

Influx from stem cell pool with random stem cell divisions
We previously investigated the dynamics within the stem cell compartment given that stem cell proliferations are independent and cell division times are exponentially distributed [18]. Once a stem cell is picked for division, it either divides symmetrically with probability p s , resulting in two stem cells, or asymmetrically with probability 1 2 p s , resulting in one progenitor and one stem cell. Now, we ask how influx from such a stem cell pool percolates through the hierarchy.

Asymmetric stem cell divisions
Exclusively asymmetric divisions ( p s ¼ 0) on the stem cell level result in a Poisson distribution of replicative age [18] and the corresponding influx into the progenitor compartment is given by The distribution depends on age t, proliferation rate r s , as well as the initial number of cells N 0 in the stem cell compartment. We can set l ¼ r s t/N 0 to see that this is a Poisson distribution multiplied by r s N 0 : with a time-dependent rate parameter l ¼ l(t).
The corresponding sum from equation (2.2) can be solved analytically (see appendix D.2) and the distribution of replicative age becomes symmetric where G(a, x) ¼ Ð 1 x t aÀ1 e Àt dt is the upper incomplete gamma function [33].
The above distribution of replicative age is shown in figure 3c for various values of the self-renewal probability a. The normalization factor r s N 0 is set to one, as this does not change the general shape of the underlying distribution. Similar to our previous observations, the age distribution is heavily skewed towards younger cells. This effect is more pronounced for higher values of a, corresponding to more cells in the compartment.

Symmetric stem cell divisions
The age distribution for a growing stem cell compartment due to occasional symmetric stem cell self-renewals with probability p s . 0 are given by Werner et al. [18] Here, the exact distribution depends explicitly on the initial number of stem cells N 0 and the ageing factor t*, which itself depends on the relative increase of the stem cell pool size during time t given a symmetric division probability p s and a proliferation rate r s . However, the distribution is again a Poisson distribution with a different normalization. This becomes apparent if we substitutẽ l ¼ ((1 þ p s )= p s ) ln (t Ã ) and get The solution of the convolution sum in equation (2.2) is therefore the same as for purely asymmetric stem cell divisions and the corresponding calculations are identical (if we exchange l !l) (Appendix D.2), The shape of the resulting influx distribution therefore varies only slightly from the asymmetric case and differences in the age distribution of the progenitor compartment are minimal ( figure 3c,d). However, the difference in average replicative age on the stem cell level is conserved in the progenitor compartment and still can be used to distinguish between those processes on the stem cell level [18].

Multiple compartments
In most organs, the maturation of functional tissue-specific cells requires multiple stages of differentiation. We therefore generalize our approach above and discuss the impact of multiple subsequent non-stem cell compartments on the replicative age distribution within such hierarchical tissue organizations.

Impact of the number of compartments
In order to deduce the impact of the number of compartments on the age distributions, we vary the number of compartments by simultaneously keeping the final outflux of cells constant. This requires an adjustment of the selfrenewal parameter a accordingly and is motivated by the idea that certain tissues might require a certain constant cell replenishment per unit time, but this could, in principle, be achieved in different tissue architectures. We use the same principal influx distributions from the stem cell compartment discussed above, see figure 3. Solutions in this section were obtained by numerically calculating the sums of equation (2.2). Figure 4 shows the resulting replicative age distributions for a broad range of compartment numbers. Interestingly, the age distribution in the final compartment is very sensitive to the number of compartments, even though the total cell number amplification of the compartments is the same by construction. For a large number of compartments and corresponding small self-renewal a, the shape of the influx distribution is basically conserved through all stages of the hierarchy, especially for the extreme case of a purely binary tree (a ¼ 0) where the shape of the distributions is unchanged, but only shifts towards older replicative age.
For the other extreme case of only one or two downstream compartments (a % 1), the distribution of replicative age is almost flat, such that the frequency of young cells is the same as the frequency of very old cells. Note, however, that in this case the steady-state assumption might be violated as the time to reach homeostasis, i.e. the state where the system does not change anymore, might exceed realistic biological timescales. This is shown in appendix E in more detail.
However, distributions of replicative age become similar already for intermediate, but biologically still high, values of self-renewal a % 0.5. It might therefore be impossible to distinguish between age distributions on the stem cell (influx) level from measurement in the differentiated tissue alone, provided there is considerable self-renewal in nonstem cell compartments. This is especially surprising considering the extreme differences in influx distributions, for example delta distributed (figure 4a) and Poisson distributed ( figure 4c,d), which become seemingly undistinguishable in downstream compartments (at equilibrium). This effect is reminiscent of the law of large numbers for random variables, where the sum of independent random variables tends to a normal distribution regardless of the actual distribution of the random variable. In our case, though, the distribution approached is not a Gaussian.

Mean and variance through multiple compartments
In a system with multiple downstream compartments, it is also interesting to see how mean and variance of replicative age change from compartment to compartment. As shown in equations (2.5) and (2.6) for a single progenitor compartment, mean and variance increase linearly from compartment to compartment with a slope of 1/ (1 2 a) for the mean and a/(1 2 a) 2 for the variance. Strong self-renewal therefore has a more pronounced effect on the variance than the mean due to the quadratic term in the denominator. Figure 5 shows the mean and variance of replicative age for multiple subsequent compartments for different total number of compartments, but as above with the same tissue function, that is the same overall cell production. In this example, the variance for strong self-renewal, a ¼ 0.67, at the second out of 10 compartments is already larger than in the last compartment for the case of lower self-renewal, a ¼ 0.34, even though there are five compartments more in the latter case. The impact on the mean of the distribution throughout the compartments is not nearly as pronounced. As both mean and variance only depend on self-renewal a rsif.royalsocietypublishing.org J. R. Soc. Interface 15: 20180272 and the number of compartments, in principle, stem cell dynamics can be inferred from comparing mean and/or variance of telomere length distributions over time [18,34], as long as the general tissue structure and dynamics does not change.

Telomere length data
In order to compare our theoretical expectations to biological data, we use previously published telomere length distributions of human granulocyte cell populations [18] in healthy adults across different ages. Granulocytes are differentiated cells of the myeloid arm of the haematopoietic system. Differentiation from haematopoietic stem cells to fully mature granulocytes requires multiple steps, allowing us to use our multi-compartment model.
The telomere length distributions for 10 healthy humans along with the best parameter fit are shown in figure 6. We use a least-squares fit, varying the number of compartments C and the initial telomere length of cells with a replicative age of zero. However, the initial telomere length is a model parameter that only shifts the full distribution along the x-axis and is therefore of limited interest. On the other hand, the number of compartments determines the shape of the resulting replicative age distribution. The self-renewal a is a function of the total number of compartments C given that we assume a constant total daily output of granulocytes according to equation (2.4).
We compare the fits for all four previously discussed influx distributions (figure 3), but adjust the age t for the stem cell model according to the dataset (see §3.1.3 for details), while assuming a daily total influx of cells from  Figure 5. Mean m and variance s 2 of replicative age distributions per compartment. Influx age distribution is the Poisson distribution with a mean and variance of l ¼ 10 as in figure 3c; the x-axis shows the progression through the compartments c/C. To compare different tissue structures, the self-renewal parameter a is adjusted for the same output of cells as in figure 2. (a) The mean of replicative age increases slightly faster for high self-renewal. (b) The variance of replicative age increases also linearly, however, the impact of the self-renewal parameter a is much more pronounced: for a ¼ 0, there is no change, but for a ¼ 0.67, there is a drastic increase of the standard deviation per compartment. (Online version in colour.) rsif.royalsocietypublishing.org J. R. Soc. Interface 15: 20180272 the stem cell compartment of approximately I ¼ r s ¼ 1 cell d 21 and N 0 ¼ 400 haematopoietic stem cells [1]. The daily outflux of granulocytes is set to V ¼ 2.1 Â 10 10 cells d 21 , as can be estimated from the total number of mature granulocytes in humans N gran % 2.1 Â 10 10 cells [35] and a removal rate of mature granulocytes from circulation with rate g % 1 d 21 [36]. Additionally, we assume a fixed loss of telomeric DNA of 100 base pairs per cell division.
By varying a single parameter for the shape of the distribution, we obtain a good agreement between our model and the granulocyte data of healthy humans, regardless of the specific influx distribution from the stem cell compartment. The results point towards relatively high self-renewal a % 0.85 with around 10 downstream compartments and the fit result is virtually independent of the influx distribution applied. The telomere length distributions could, in principle, also be fitted by Gaussian distributions (e.g. in the upper left panel of figure 6 we find a Gaussian with mean m ¼ 9.65 kbp and standard deviation s ¼ 2.04 kbp with a mean squared error of S ¼ 3.51 Â 10 24 ). In contrast with that approach, we provide a model with a dynamical interpretation of the observed distribution. However, the results presented here rely on rather strong assumptions and experimentally challenging to estimate parameters such as telomeric loss per cell division or the number of haematopoietic stem cell participating in homeostasis. As the replicative age distributions within the stem cell compartment are unknown and hard to assess experimentally, in principle it is also possible that the observed telomere length distributions are caused by much broader influx distributions from the stem cell level with far less self-renewal in the downstream compartments. Nevertheless, our results suggest that it is difficult to infer detailed stem cell dynamics from telomere length data of differentiated tissue in the case of high self-renewal in the downstream compartments.  telomere length (kbp) Figure 6. Comparison of our model to telomere length distributions from human granulocytes. Previously published telomere length distributions from Werner et al. [18], where details of the experimental procedures are given. We compare the fits for four different influx distributions as shown in figure 3a -d: delta influx distribution, geometric influx distribution, and influx according to the stem cell model discussed in §3.1.3 with and without symmetric self-renewal adjusted to the age of the person. We assume a loss of 100 base pairs of telomeric DNA per cell division. We fit our model by varying the total number of compartments C and the initial telomere length, while fixing the self-renewal parameter a according to equation (2.4) such that we obtain the same total outflux from the last compartment V ¼ 2. Chronic myeloid leukaemia (CML) is a cancer of the haematopoietic system that can be characterized by enhanced self-renewal of cancerous cells in the progenitor compartments compared to healthy cells [37]. Here, we compare the replicative age distribution for different self-renewal probabilities in the same tissue structure. For this, the tissue consists of 29 downstream compartments with either self-renewal probability p ¼ 0.15 for healthy cells [1] or self-renewal p ¼ 0.28 for cancerous cells [37] and without asymmetric division (d ¼ 1 2 p), leading to self-renewal parameters a healthy ¼ 0.3 or a CML ¼ 0.56. The resulting distributions are shown in figure 7. For CML both mean and standard deviation are strongly increased compared to healthy haematopoiesis, which can be measured by comparing telomere length distributions during treatment of the disease [38]. We accordingly expect that both mean and standard deviation will decrease under successful treatment, when self-renewal in progenitor compartments normalizes again, which is consistent with available clinical data [39].

Discussion
While the age structure of cells within a tissue is driven by the age structure of the tissue specific stem cells, the progenitor compartments can substantially alter this age distribution. From a perspective of signal processing, they act as a filter that transforms an input signal (in our case a distribution) into an output signal. The properties of this filter are restricted by the biological structure of the tissue. Two limiting cases are of particular interest: (i) Focussing on a compartment that is weakly amplifying (a ( 1), such that the number of output cells is approximately twice the number of input cells, the replicative age distribution in the progenitor compartment resembles that of the influx distribution. Only the average age of the cells is then increasing with the compartment number, even in tissues with many subsequent downstream compartments, such as blood [1]. (ii) For intermediate to high self-renewal (large a), the distributions of cell replicative age in a differentiated tissue with multiple progenitor compartments are virtually indistinguishable from one another, even for influxes with completely different replicative age distributions. Measuring replicative age distributions in differentiated tissue, for example via telomere lengths [18,34], may therefore be more informative about the tissue structure and dynamics than the dynamics within the long-lasting stem cell level.
Cellular age is explored in many experimental studies (e.g. [40] gives a nice overview) and in multiple models [41,42]. Some of these models also take the effect of replicative ageing into account [18,42,43]. Furthermore, some of these models, for example, show that cellular age might be a critical parameter for certain diseases such as sickle cell anaemia and malaria [27,44]. However, replicative ageing in differentiated tissues is often overlooked, because here the cell turnover is very high and mutation accumulation as well as loss of function in these cells might not be as clinically relevant as in stem cells or early progenitor cells. On the other hand, we show that understanding the replicative ageing of differentiated cells and the resulting age distributions in the cell population can lead to a much better understanding of tissue dynamics from measurements.
Previous models of replicative ageing in a tissue hierarchy including stem cells and progenitor cells focussed strongly on the total replication limit of cells [45,46]. However, in our model such a total replication limit would most often leave the largest (and potentially measurable) portion of the replicative age distribution unchanged. However, the question becomes critical for the accumulation of mutations and the risk of cancer initiation, which we only peripherally discuss here. In reality, it is not clear whether or not fully differentiated cells are close to the end of their replicative life span in vivo, but it appears likely that they have sufficiently many cell cycles left and only a significantly increased cell turn over would lead to an exhaustion of possible cell cycles. We therefore did not include an upper replication limit explicitly here.
When comparing the distributions of replicative age between individuals or at different time points (or, for most practical purposes, their average and variance), changes of replicative age in the differentiated tissue might not always point towards changed dynamics on the stem cell level, but towards abnormal dynamics in the progenitor compartments. Accordingly, we would expect to observe these differences in replicative age distributions in certain diseases that change proliferation and differentiation characteristics in the progenitor compartments. Examples of this include CML, acute promyelocytic leukaemia and some other forms of acute myeloid leukaemia where a progenitor cell in the 'middle' of the hierarchy acquires enhanced self-renewal capabilities. For example, increased self-renewal would lead to an increase of average cellular age [30,37,38,47].
In our model, we focus on a homogeneous population of cells that have the same proliferation rates, differentiation probabilities and therefore the same fitness across multiple compartments [48]. However, mutations or epigenetic changes can change the proliferative properties of cells within the tissue structure. Interestingly, these changes can also affect the selfrenewal capacities and thus fitnesses of cells across compartments directly causing selection gradients for different lineages of cells throughout the hierarchy. Examples for both negative and positive selection are known. For example cells might die prematurely, as for example in sickle cell anaemia [44], or die later or not at all, as is observed in many cancers [49]. An important complication that we have not considered here is that real tissues are often found in dynamical regimes that change the cellular age distribution over time. In multicellular organisms, the rates for self-renewal and for symmetric differentiation or cell death are variable and tightly regulated by a variety of feedback mechanisms [50]. In this way, a tissue can respond to environmental conditions such as injury or infection. Also, in the context of tissue reconstitution of the haematopoietic system after stem cell transplants, the tissue structure is initially far from the steady state [41]. It was shown previously that replicative age can give valuable insight into the dynamics of tissue reconstitution [39] and modelling of replicative ageing can potentially contribute towards better understanding of tissue reconstitution. It is important to point out that the steady state results presented here are not directly applicable in this situation.
In conclusion, quantitatively describing replicative age distributions of tissues in multicellular organisms can contribute to our understanding of the complex dynamical processes within such tissues and allows us to describe deviations from healthy and diseased tissue states due to changed cell proliferation properties.

Appendix A. Steady-state distribution
Here, we show the general solution for the steady-state distribution of replicative age inside a downstream compartment for any input distribution i. The differential equations for the number of cells in each replicative age class are: |fflfflfflfflfflfflffl{zfflfflfflfflfflfflffl} ¼a rn jÀ1 j 1:

< :
We then start by setting the equation for n 0 to zero, such that We use this result to solve for n 1 and then continue recursively until we find the general solution for n j : . . . . . .
which can be written in the more compact form i k a jÀk :

Appendix B. Total cell number amplification
Here, we start again by writing down the differential equations for the total number of cells N (c) in each of the compartments c with proliferation rates r (c) . Additionally, we have the total influx I into the first compartment (c ¼ 0), and similarly the total outflux V from the last compartment (c ¼ C 2 1).
As in the calculation above we assume our compartment to be in the steady state and set the above differential equations to zero.
The same calculation can be done for each compartment c: From this follows for the total outflux V By rearranging this for a we get

Appendix C. Mean and variance of replicative age distribution
To calculate the moments of the replicative age j in the progenitor compartment, we need to normalize the replicative age distribution n* j in the steady state by the total number of cells in the progenitor compartment N Ã ¼ P 1 j¼0 n Ã j . We can then write down the mth moment of the age j in the progenitor compartment rsif.royalsocietypublishing.org J. R. Soc. Interface 15: 20180272 By changing the order of summation in the denominator, we get Similarly, we change the order of summation in the nominator to We then rewrite the above binomial to (j þ k) m ¼ P m i¼0 m i À Á k i j mÀi and rearrange the sums to where in the last step we defined the sum which is independent of the influx The next step is to put together the nominator and denominator and to insert the moments of the replicative age distribution of the influx kj m l i : which is the general solution for any moment of the replicative age distribution.
To get an expression for the mean and variance, we have to solve S m for m ¼ 0, 1, 2: Generally, for m . 0 the sum S m is by definition the polylogarithm Li 2m (a) [51] with negative order m and can be written as Li Àm (a) ¼ 1 (1 À a) mþ1 X mÀ1 k¼0 E(m,k)a mÀk , with the Eulerian numbers E(n, k) ¼ P kþ1 j¼0 ( À 1) j nþ1 j (k þ 1 À j) n . By using the general solution equation (C 1) and the above solutions for S n for n ¼ 0, 1, 2 we can calculate the mean m and variance s 2 of the replicative age distribution: where we used the mean m i and the variance s 2 i of the influx distribution.

D.2. Poisson influx
To find the replicative age distribution in the progenitor compartment for Poisson distributed influx, we have to solve the following sum: e Àl l k k! a jÀk : By factoring out all factors independent of k, we arrive at Now, we substitute x ¼ l/a and note that for the upper incomplete gamma function G(a,x) ¼ Ð 1 x t aÀ1 e Àt dt and integer values of j the following equality holds [33]: x k k! : Using this, we get n Ã j ¼ a j e (l=aÀl) j! G j þ 1, l a : as the desired result.
Appendix E. Time to steady state Equation (2.1) can be solved analytically to get an estimate for the time until steady state is reached. In the following, we solve the equations for an initially empty progenitor compartment, which gives us an estimate to the relaxation time until steady state is reached. For this, we start by solving the equation for the first replicative age class j ¼ 0 and then subsequently for all others: dn 0 dt ¼ i 0 À rn 0 ) n (h) 0 (t) ¼ C e Àrt variation of parameter C ! C(t) ) @C @t e Àrt À Cr e Àrt þ rC e Àrt ¼ i 0 ) C ¼ i 0 r (e rt À 1) This we plug into the differential equation for the next age class dn 1 dt ¼ i 1 þ arn 0 À rn 1 ¼ i 1 þ ai 0 (1 À e Àrt ) À rn 1 , which can be solved in the same way n 1 (t) ¼ (i 1 þ i 0 a) r (1 À e Àrt ) þ ai 0 t e Àrt : Now, we use the steady-state values of the main text (equation (2.2)), n Ã j ¼ P j k¼0 (i k a jÀk =r), which allows us to rewrite the above equation to n 1 (t) ¼ n Ã 1 (1 À e Àrt ) þ arn Ã 0 t e Àrt : For the third age class, we get n 2 (t) ¼ n Ã 2 (1 À e Àrt ) þ a 2 r 2 t 2 2! n Ã 1 e Àrt þ arn Ã 0 t 1! e Àrt : From this, we can infer the general solution n j (t) ¼ n Ã j (1 À e Àrt ) þ X jÀ1 m¼0 (art) (jÀm) (j À m)! n Ã m e Àrt , which as expected goes towards the equilibrium solutions n* j for the limit of time t to infinity. Figure 8a shows the time evolution of of n j for different age classes j. Approach to steady state for this specific value of a is relatively fast. To compare the time until steady state is reached for different values of a, we numerically calculated the time until n j reached 99% of the equilibrium value n* j and the results are shown in figure 8b. As expected, for large a the time to reach steady state increases drastically, as many more downstream compartments have to be filled due to the long tail towards old ages in the distribution of replicative age. Also for higher age classes, in this case j ¼ 20 for example, time to steady state is much longer as all changes have to go through the previous age classes first. However, as the steady-state value in this case is close to zero and the influx into this compartment is even smaller, our assumption of a quasi-static process is still valid.


s2fieldsofstudy: Biology Medicine Chemistry

text:
Epidermal growth factor receptor inhibitor AG1478 affects HepG2 cell proliferation, cell cycle, apoptosis and c-Myc protein expression in a dose-dependent manner

ABSTRACT The aim of this study was to observe the effect of AG1478, an inhibitor of the epidermal growth factor receptor, on cell proliferation, cell cycle and apoptosis of human hepatoma HepG2 cells. Cell counting kit-8 assay was employed to examine the survival rates of cultured HepG2 cells after treatments with different concentrations of AG1478 for 24 h. Flow cytometry was performed to determine the effect of AG1478 on cell cycle and apoptosis. Immunohistochemistry was used to measure the expression of c-Myc protein. The survival rates of human hepatoma HepG2 cells after treatments with 5, 10, 20 and 40 μmol/L of AG1478 for 24 h were 76.0%, 59.6%, 51.2% and 42.1%, respectively. The apoptotic rates after treatments with 5, 10, 20 and 40 μmol/L of AG1478 were (12.88 ± 1.91)%, (23.16 ± 2.67)%, (35.36 ± 1.95)% and (47.16 ± 3.78)%, respectively. HepG2 cells were mainly arrested in the G0/G1 phase after treatment with 10, 20 and 40 μmol/L of AG1478. The c-Myc protein was highly expressed in HepG2 cells, whereas treatment with 20 μmol/L AG1478 substantially inhibited its expression. Overall, AG1478 inhibited the proliferation of human hepatoma cells in vitro, arrested the cells in G0/G1 phase, induced apoptosis and reduced the expression of c-Myc protein. These results also indicated that AG1478 blocked the proliferation and induced apoptosis of hepatoma cells in a dose-dependent manner.

Introduction
Primary liver carcinoma (hepatocellular carcinoma) is one of the mostly detectable cancers, with a total of 630,000 new liver cancer cases and 600,000 deaths all over the world every year. Primary hepatocellular carcinoma is a type of highly malignant cancer with easy recurrence and metastasis, resulting in poor 5-year survival rates in patients [1][2][3].
Epidermal growth factor receptor (EGFR) is a member of the EGFR family [4][5][6]. It is the expression product of oncogene c-erbB-1 located on the short arm of human chromosome 7, with a molecular weight of 170-175 kD. EGFR is a transmembrane receptor protein with tyrosine kinase activity. Through binding with its corresponding ligands such as epidermal growth factor (EGF) and transforming growth factor-a (TGF-a), EGFR undergoes molecular conformational changes, and its inner membrane domain binds to ATP for autophosphorylation, finally leading to the activation of the tyrosine kinase domain. Phosphorylated tyrosine kinase mediates the transduction of cytoplasmic signals into the nucleus through multiple pathways, such as Ras/Raf/mitogen-activated protein kinase (MAPK) and phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt) [7][8][9]. This process changes cell cycle-related genes, induces cancer cell proliferation and suppresses apoptosis [10,11]. The levels of EGFR in normal tissues were very low, while EGFR was highly expressed in many tumour tissues [12,13]. In our previous study [14], immunohistochemistry was used to detect the expression of EGFR in hepatocellular carcinoma and adjacent tissues. The results showed that the level of EGFR in liver cancer tissues was significantly higher than that in adjacent noncancerous tissues, and was dependent on cancer stages. Jiang et al. [15] reported that EGFR was highly expressed in human hepatoma tissues, and EGF might induce cancer cell proliferation through MAPK/ extracellular signal-regulated kinase (ERK) pathway. AG1478, an EGFR-specific inhibitor, can suppress this process effectively, suggesting that EGFR can be an ideal target of anti-cancer drugs.
There are a variety of EGFR-targeted drugs that are being used clinically at present. These drugs are mainly classified into two groups: monoclonal antibodies and CONTACT Wenqi Wang xiangxiang12_22@126.com small molecule inhibitors. Monoclonal antibodies compete with physiological ligands for binding to EGFR and thus inhibit the ligand-activated tyrosine kinase activity of EGFR, resulting in anti-tumour effects. Small molecule inhibitors are usually referred to as molecules that bind to ATP-binding sites of the intracellular segment of EGFR to block the EGFR signalling pathway [16]. AG1478 is a synthetic small molecule that is a protein tyrosine kinase inhibitor specific for EGFR [17][18][19]. Through competitive binding to ATP-binding sites to inhibit protein tyrosine kinase activity, AG1478 blocks EGFR-mediated signalling pathway, inhibits cell proliferation of several tumours and induces apoptosis [20]. This study was designed to investigate the effects of AG1478 on hepatoma cell proliferation, cell cycle and apoptosis.

Reagents
Dulbecco's modified Eagle's medium (DMEM), fetal calf serum and 0.25% trypsin were products of HyClone (USA). AG1478, dimethyl sulfoxide and Hoechst33258 were purchased from Sigma-Aldrich (Germany). Cell counting kit-8 (CCK-8) was purchased from Dojindo Molecular Technologies, Inc. (Japan). Propidium iodide (PI), cell cycle and apoptosis analysis kit, and annexin V/ PI double staining kit were purchased from Invitrogen (USA). AG1478 was dissolved in DMEM to reach a final concentration of 1 mmol/L, and then aliquoted for storage at ¡20 C. The antibodies against c-Myc, b-actin, histone H1 and goat anti-mouse IgG-HRP (Cat# sc-2005) were purchased from Santa Cruz Biotechnology, Inc.
(Santa Cruz, USA). The ECL system was from Pierce Biotechnology (Rockford, IL, USA).

Cell line and cell culture
Human hepatoma HepG2 cells provided by the General Surgery Key Laboratory of our hospital were cultured in DMEM (Dulbecco's Modified Eagle Medium) supplemented with 10% fetal calf serum. The cells were kept at 37 C in a 10% CO 2 /90% air atmosphere.

Cell morphology observation
HepG2 cells were treated with vehicle control (dimethyl sulfoxide; 0.006%, vol/vol) or AG1478 (20 mmol/L) for 48 h. Cell morphology was observed under a light microscope (Olympus, Japan). Meanwhile, the cells were stained with 10 mg/mL of Hoechst33258 at 37 C for 30 min. Then the stained cells were observed under a fluorescence microscope (Nikon, Japan).

Cell proliferation assay
HepG2 cells were digested using trypsin, diluted and seeded into 96-well microplates (5000 cells/well, 200 mL) in triplicate for both the control group and the experimental group. The experimental group contained four subgroups with 5, 10, 20 and 40 mmol/L AG1478 being added to the culture medium, respectively. After 24 h treatment, 10 mL of CCK-8 was added for further incubation for 3 h. A 96-well microplate reader was used to measure the absorbance at 450 nm (A 450 ), using empty wells as blanks. Every assay was performed in triplicate at least. The cell survival rate was calculated by the formula of (average A 450 of each experimental group/ average A 450 of the control group) £ 100 (%).

Flow cytometry
To detect apoptosis, HepG2 cells were harvested from the plates after 24 h treatment with 0, 5, 10, 20 and 40 mmol/L of AG1478. After being digested by ethylenediaminetetraacetic acid (EDTA)-free trypsin to obtain single cell suspension, cells were centrifuged at 2000 r/min for 5 min. The supernatant was discarded after centrifugation, and the cells were washed twice in precooled phosphate-buffered saline (PBS). The binding buffer, Annexin V-fluorescein isothiocyanate (FITC) and PI were added sequentially according to instructions of the Annexin V-FITC/PI kit manufacturer. Flow cytometry was performed after incubation at 4 C for 30 min in darkness. Cells (1 £ 10 4 ) were counted each time, using 488 nm as excitation wavelength. Cell Quest analysis software was used to calculate the apoptotic rate. Normal living cells were insensitive to Annexin V and PI staining (AnnexinV ¡ PI ¡ ), and were distributed in the lower left area of the flow cytometric diagram. Early apoptotic cells were sensitive to Annexin V but insensitive to PI staining (Annexin V + PI ¡ ), and were located in the upper left area. Late apoptotic cells or dead cells were sensitive to both Annexin V and PI staining (Annexin V + PI + ), and were usually found in the upper right area [21].
To determine the cell cycle distribution, HepG2 cells were harvested after 24 h treatment with 0, 5, 10, 20 and 40 mmol/L of AG1478 followed by trypsin digestion for cell collection. Cells were washed by precooled PBS twice and centrifuged at 2000 r/min for 5 min. The supernatant and cell debris were discarded after centrifugation, and precooled 70% ethanol was then added to fix cells at 4 C overnight. After centrifugation and washing, 600 mL PI and RNase inhibitor were added for 30 min staining at room temperature in dark. The cell density was adjusted to 1 £ 10 6 /mL by filtering through a # 300 sieve. Cell cycle analysis was performed using flow cytometry by measuring cellular DNA content. The experiments were performed in triplicate.

Immunohistochemistry assay
Immunohistochemistry was performed using the streptavidin-peroxidase method. The cells were fixed and washed by PBS, and then incubated in 3% H 2 O 2 for 10 min at room temperature. After washing with PBS, samples were blocked by goat normal serum for 30 min at room temperature. The serum was discarded before adding 1:100 diluted anti-c-Myc monoclonal antibody (primary antibody) into a humidified chamber, followed by incubation at 4 C overnight. Biotin-labelled secondary antibody was then dripped onto the cells at 37 C over a period of 30 min in a humidified chamber before washing with PBS. Samples were incubated in peroxidase-labelled streptavidin for 30 min at room temperature before washing with PBS. Chromogenic reagent 3,3'-diaminobenzidine was then used to develop the immunostains, and the slides were rinsed with water thoroughly before being restained by hematoxylin. Conventional dehydration, transparent treatment and mounting procedures were carried out. Light microscopy was used for observation. PBS was employed instead of primary antibody as control.

Immunoblotting assays
Total proteins were harvested from cells, separated in 10% SDS/PAGE gels, and then subjected to immunoblot analyses. The membrane was incubated with primary antibodies against c-Myc (1:500), b-actin (1:5000) and histone H1 (1:5000) and then with the secondary antibody of the goat anti-mouse IgG-HRP. The bound antibodies were detected using the ECL system. The immunoblot experiments were repeated more than three times. The mean normalized optical density (OD) of the protein bands relative to the OD of the b-actin band from the same conditions was calculated.
Statistical analysis SPSS 17.0 statistical package for Windows was used for data processing. Results were presented as means with standard error ( § SEM), and statistical differences between the control and experimental groups were evaluated using t test. P < 0.05 was considered statistically significant.

AG1478 causes morphological changes in HepG2 cells
To observe the effect of AG1478 on the morphology of HepG2 cells, microscopy was used. Under an inverted microscope, HepG2 cells in the control group were growing adherently with fusiform or polygonal shapes. The cells contained abundant cytoplasm and underwent vigorous growth, with the neighbouring cells converging into a cell layer (Figure 1(A)). After adding AG1478 into the medium, the cultured HepG2 cells showed blurred edges. Black particles were visualized in the nucleus without clear gaps between particles. In addition, more and more cells detached and floated in the medium (Figure 1(B)). To further support this, we also used Hoechst 33258 staining to evaluate the phenotypic characteristics of AG1478-treated cells. The apoptotic nuclei with obvious morphological alterations were observed and dose-dependently (Figures 1(C,D)), showing that AG1478 may induce HepG2 cell apoptosis. These data suggested that AG1478 caused morphological changes in HepG2 cells.

AG1478 exerts inhibitory effect on HepG2 cell proliferation in a dose-dependent manner
To investigate the effect of AG1478 on the proliferation of HepG2 cells, HepG2 cells were treated with 5, 10, 20 or 40 mmol/mL AG1478 for 24 h ( Table 1). The survival rate of HepG2 cells after 24 h treatment with different concentrations of AG1478 decreased with the increase of drug concentrations in a dose-dependent manner (P < 0.05 for all doses). These data demonstrated that AG1478 exerted inhibitory effect on HepG2 cell proliferation in a dose-dependent manner.

All phases of the apoptosis of HepG2 cells were enhanced by AG1478 in a dose-dependent manner
To study the influence of AG1478 on the apoptosis of HepG2 cells, flow cytometry and Annexin V/PI double staining were employed to count the number of apoptotic cells. After treatment with 0, 5, 10, 20 or 40 mmol/mL AG1478, the apoptotic rates of HepG2 cells in the experimental groups were (12.88 § 1. Furthermore, the early apoptotic rates of HepG2 cells in the experimental groups were (8.12 § 1.06)%, (12.47 § 1.37)%, (21.3 § 2.26)% and (27.10 § 2.73)%, respectively, which were significantly higher than that in the control group [(4.18 § 1.35)%] (P < 0.05). In addition, the ratios of the apoptotic cells in the late phase of the death process were also significantly increased in the experimental groups compared with that in the control group (P < 0.05) ( Table 2). These data showed that all phases of the apoptosis of HepG2 cells were enhanced by AG1478 in a dose-dependent manner.

AG1478 increases the percentage of G0/G1-phase HepG2 cells in a dose-dependent manner
To test the impact of AG1478 on HepG2 cell cycle, HepG2 cells were treated with 0, 5, 10, 20 or 40 mmol/ mL AG1478 for 24 h and analyzed using flow cytometry (Figure 2(B)). The results showed that the number of Sphase cells decreased gradually, whereas the number of G0/G1-phase cells increased. In addition, the percentage of G1-phase HepG2 cells was significantly increased in the groups treated with 10, 20 or 40 mmol/mL AG1478, whereas that of S-phase cells was decreased in a dosedependent manner (P < 0.05). However, there were no  significant changes in the percentage of G2-phase cells in any of the experimental groups compared with the control (P > 0.05) ( Table 3). These data suggested that AG1478 increased the percentage of G0/G1-phase HepG2 cells in a dose-dependent manner.

AG1478 treatment reduces expression of c-Myc protein in HepG2 cells
To detect the expression of c-Myc protein in HepG2 cells, immunohistochemistry assay was performed. The microscopic images showed that c-Myc protein was mainly localized in the cytoplasm of HepG2 cells, being positively stained as brown granules (Figure 3(A)). After treatment with AG1478 (20 mmol/L), the number of brown granules in HepG2 cells was reduced and the colour became lighter, indicating that the expression level of c-Myc had declined (Figure 3(B)). These results indicated that AG1478 treatment reduced the expression of c-Myc protein in HepG2 cells. Furthermore, AG1478 -treated HepG2 cells were harvested and separated into nuclear and cytoplasmic fractions. Equivalent amounts of each fraction were analyzed by immunoblotting with anti-c-Myc. Histone H1 and actin were used as the internal controls for nuclear proteins and cytoplasmic proteins, respectively (Figure 3(C)). The results showed that c-Myc protein was mainly expressed in cell cytoplasm.
To further investigate the effects of AG1478 on HepG2 cells, the total proteins were harvested and immunoblot analyses were performed. As indicated in Figure 4(A,B), the levels of c-Myc proteins were significantly reduced. In this experiment, b-actin was used as the control.

Comparative analysis
In this study, we found that the number of cultured HepG2 cells was reduced significantly after being treated with different concentrations of AG1478. With the increase of drug concentrations, the morphology of HepG2 cells was changed from the original polygonal shape to fusiform shape. In addition, the detached cells accumulated and became floating in the medium. CCK-8 assay and flow cytometry were used to study the effect of AG1478 on HepG2 cell proliferation and cell cycle, respectively. The results showed that AG1478 effectively inhibited the proliferation of HepG2 cells in a dosedependent manner, with the survival rate of cultured cells being gradually reduced with the increase of drug concentrations. Flow cytometric analysis indicated that AG1478 induced apoptosis in HepG2 cells with the effect being stronger as the concentration increased. Apoptotic cell counts by flow cytometry after Annexin V/PI double staining suggested that the apoptotic rates in all   Note: Light microscopy was used for observation (£ 20). All of the experiments were repeated three times. The purity of the nuclear and cytoplasmic fractions was assayed by immunoblotting with anti-histone H1 and anti-actin, respectively. experimental groups were significantly higher than that in the control group. Furthermore, it was observed that early apoptotic cells began to appear at a low concentration (5 mmol/L), with the apoptotic rates being significantly different between the control group and the experimental groups. Cell cycle analysis showed that the proportion of G1-phase cells was significantly increased, whereas the percentage of S-phase cells was significantly decreased with the increase ofAG1478 concentration. These results are consistent with previous reports [22][23][24], indicating that the main mechanism of action of AG1478 may be through its binding to EGFR, which then inhibits autophosphorylation, blocks EGFR-mediated signalling pathways, hinders cell cycle progression, inhibits cell proliferation and promotes apoptosis. The c-Myc protein, which has been proved to be an essential factor for cell proliferation, is also an accessory factor for reverse transcription that plays important roles in signal transduction, cell proliferation, differentiation, apoptosis and cell cycle control. The c-Myc encoding gene is an essential early gene for cell proliferation, and the c-Myc protein is a critical factor to control G 0 -S phase transition. The c-Myc protein is closely related to the MAPK signalling pathway. Wang et al. [25] found that the activation of the MAPK pathway down-regulated the expression of cell cycle-related genes p21 and p16, upregulated the expression of c-myc, accelerated G1/S phase transition and promoted cell proliferation. Our immunohistochemical data demonstrated that the expression of c-Myc protein in HepG2 cells was significantly decreased after treatment with AG1478 for 24 h, suggesting a close relationship between EGFR and c-Myc expression. This observation indicated that c-Myc might be involved in the EGFR signalling pathway in hepatocellular carcinoma. However, further investigation is needed to elucidate the exact mechanism.
Conclusions EGFR plays a key role in the occurrence and development of a variety of tumours. Anti-tumour treatment targeting EGFR tyrosine kinase has become a research hotspot for cancer adjuvant therapies in recent years. The present study showed that AG1478, as a tyrosine kinase inhibitor, significantly inhibited the growth of human hepatoma cells, arrested the cell cycle and induced apoptosis. Note: The cells were treated with dimethyl sulfoxide or AG1478. Western blot was used to detect the c-Myc level. The cellular b-actin protein was used as a loading control. Error bars show standard deviation of the means § SD (P < 0.05). *, p < 0.05, when compared with dimethyl sulfoxide only.


s2fieldsofstudy: Biology Medicine

text:
The immunological effect of Galectin-9/TIM-3 pathway after low dose Mifepristone treatment in mice at 14.5 day of pregnancy

The abortifacient Mifepristone (RU486) has proven to be a safe, effective, acceptable option for millions of women seeking abortion during the first and second trimester of pregnancy although its precise mechanism of action is not well understood. The main objective of this study was to investigate the impact of low dose Mifepristone administration on placental Galectin-9 (Gal-9) expression, as well as its effect on the cell surface expression of Gal-9, TIM-3 and CD107a molecules by different T and NK cell subsets. A model of Mifepristone-induced immunological changes was established in syngeneic pregnant BALB/c mice. RU486-induced alteration in placental Gal-9 expression was determined by immunohistochemistry. For immunophenotypic analysis, mid-pregnancy decidual lymphocytes and peripheral mononuclear cells were obtained from Mifepristone treated and control mice at the 14.5 day of gestation. TIM-3 and Gal-9 expression by peripheral and decidual immune cells were examined by flow cytometry. Our results revealed a dramatically decreased intracellular Gal-9 expression in the spongiotrophoblast layer of the haemochorial placenta in Mifepristone treated pregnant mice. Although low dose RU486 treatment did not cause considerable change in the phenotypic distribution of decidual and peripheral immune cells, it altered the Gal-9 and TIM-3 expression by different NK and T cell subsets. In addition, the treatment significantly decreased the CD107a expression by decidual TIM-3+ NK cells, but increased its expression by decidual NKT cell compared to the peripheral counterparts. These findings suggest that low dose Mifepristone administration might induce immune alterations in both progesterone dependent and independent way.

Introduction
Unintended pregnancy is a major globe tragedy for millions of women representing significant direct and indirect costs to health care, no matter for individuals or society. The World Health Organization (WHO) estimates that approximately 40-60 million abortions were induced worldwide each year [1]. During the first and second trimester, medical or surgical abortion is one of the oldest, most commonly practiced and most controversial procedure performed worldwide. PLOS  Since its approval in France in 1988, the abortifacient Mifepristone (RU486) has proven to be a safe, effective, acceptable option for millions of women seeking abortion during the first several weeks of pregnancy [2]. Mifepristone also proved to be a safe and effective method for pregnancy termination during the second-trimester (mainly between the 13 and 20 weeks) with a combination of the synthetic prostaglandin E1 analog Misoprostol [3,4]. Second-trimester medical abortions constitute 10-15% of all induced abortions worldwide [3]. Administration of Mifepristone followed by prostaglandin and misoprostol has been used successfully in the medical termination of pregnancy for over 27 years, and the method is registered in 50 countries [5]. Although it is well tolerated, there still remain a few adverse reactions and side effects, like abdominal pain, nausea, vomiting and diarrhea, and it may also cause complications of hemorrhage and sepsis.
Until now, the exact mechanism of action of Mifepristone is not well investigated and has to be fully elucidated, therefore the development of an animal model that captures the effects of Mifepristone-induced immunological changes during pregnancy may help to expand our understanding of the biological and cellular basis of the abortion process.
Previous data reported that RU486 significantly reduced the quantity and function of Treg cells in the fetal-maternal interface before the onset of induced abortion [6]. Li et al. demonstrated that RU486 blocked the Th2 and Treg predominance in pregnancy and promoted Th1 and Th17 skews in mice at the maternal-fetal interface, which contributes to the termination of pregnancy [7]. Similar findings show a high level of Th1 type cytokines (IFN-γ, IL-2) was found in RU486-induced abortion mice, while the Th2 type cytokine (IL-4, IL-10) levels were not influenced significantly [8]. Bogdan et al. showed that in decidual NK cells perforin colocalizes with PIBF in the granules, and that while in RU486-treated mice (0.8 mg/kg) PIBF positive NK cell counts decreased by approximately 50%, the ratio of perforin positive cells increased within the PIBF positive population [9].
Several previous research studies have established that Gal-9/Tim-3 pathway play an essential role in immunoregulation and induction of tolerance [10][11][12][13]. TIM-3 expression was verified in a variety of immune cells, including Th1, Th17, NK and NKT cells, Tregs, and also on antigen presenting immune cells [14]. TIM-3 molecule has been implicated in both activation and inhibition of immune response [15,16]. It was presented that expression of TIM-3 on Th1 cells provides a key checkpoint that serves to dampen proinflammatory Th1-dependent T-cell response and may contribute to the maintenance of healthy pregnancy, but it's in vivo expression and function after Mifepristone treatment is still unknown [12].
Among the several identified receptors of Gal-9, TIM-3 has been studied most extensively. There are evidence that engagement of TIM-3 by its ligand Gal-9 leads to the apoptosis of Th1 and Th17 cells and induce T cell tolerance both in mice and humans [17][18][19]. Thus, engagement of TIM-3 by Gal-9 may function as a negative immune-regulator abrogating the Th1and Th17-driven immune response and therefore modulate the Th1/Th2 cytokine balance. In this regard, it is supposed that Gal-9/TIM-3 interaction could play an important role in the regulation of maternal immune tolerance towards the fetus and may be a potent regulator of the innate and adaptive immune response. Since the influence of Mifepristone treatment on Gal-9/TIM-3 pathway is still unknown, exploring the relationship between Gal-9/TIM-3 pathway and Mifepristone-induced changes during pregnancy may provide a better understanding of the pathogenesis of immunological changes during immune-mediated abortions.
In the present study, we examined the alterations of the Gal-9/TIM-3 pathway that might play an important role in the immunological changes caused by Mifepristone treatment. The aim of our study was to characterize the Mifepristone-induced changes on the placental Gal-9 production and the immunological alterations at the materno-fetal interface and in the periphery by using a syngeneic pregnant mouse model.

Animal model
BALB/c mice were provided by the Experimental Central Animal Laboratory of the University of Pecs. Animal housing, care and application of experimental procedures were in accordance with institutional guidelines under approved protocols (No. BA02/ 2000-7/2015, University of Pecs). All animals were caged in a temperature-and light controlled environment with 12 hours light and 12 hours dark cycle, and received ad libitum water and food for maintenance. 2 month-old female mice were mated with fertile male mice overnight to establish pregnancy. Successful mating was confirmed by the presence of copulatory (vaginal) plug in the next morning and it was designated as the day 0.5 of pregnancy.

Mifepristone (RU486) treatment
29 pregnant mice were assigned to the Mifepristone-treated group and 30 non-treated pregnant mice were selected as controls. In our experimental model, Mifepristone was applied at a dosage of 0.8 mg/kg to trigger immunological changes with the minimal impairment of the decidua. RU486 (Sigma-Aldrich) was applied on gestational day (gd) 14.5 by a single intraperitoneal injection at the dosage of 0.8 mg/kg body weight, freshly dissolved in 200μl phosphatebuffered saline (PBS) before use. In the normal pregnancy control group, mice received an intraperitoneal injection of 200μl sterile PBS on day 14.5 of pregnancy. Mifepristone treated and control pregnant females were killed by cervical dislocation next morning (on the gd 15.5), their spleen and the uterine horns were removed aseptically and were processed as indicated below.

Immunohistochemistry
Placental tissue was removed from uterine horns and fixed with 4% paraffin for more than 24hours. Next, the samples were embedded in paraffin and 4μm thick sections were prepared. After deparaffinization in xylol for 3x5 minutes, samples were rehydrated in degraded alcohol series (96%, 80%, 70%, 50%) for 3 minutes each. Then tissue sections were washed in distilled water (dH2O) and antigen retrieval was performed for 20 minutes in boiling 1x Target Retrieval Solution (TRS) (1:10; pH 6.0-6.2; Dako). Next, the samples were cooled down to RT, washed twice with dH 2 O and endogenous peroxidase was blocked for 15 minutes in 3%H 2 O 2 . Then samples were washed 3x5 minutes with 50mM Tris buffer saline (TBS) supplemented with 0.05% Tween pH7.4 (TBST) and pre-blocked with 3%BSA (Sigma-Aldrich) for 20 minutes. Following washing with TBST for 2x5 minutes slides were incubated with biotinylated goat anti-mouse Gal-9 antibody (1:10; R&D Systems) for 1 hour at RT. After washing three times in TBST, samples were incubated with Streptavidin-Biotinylated Horseradish Peroxidase Complex (1:100; GE Healthcare) for 30 minutes at RT. Following the washing procedure, the signal was detected with Liquid DAB+Substrate Chromogen System (Dako) for 5 to 30 minutes at RT. Hematoxylin counterstain was performed for 3 minutes at RT then slides were covered with mounting medium (Dako).

Isolation of mononuclear cells from the spleen
Spleens were homogenized thoroughly with a syringe plunger, and single-cell suspensions were prepared using a 70μm nylon cell strainer (BD-Biosciences). Subsequently, cells were washed in PBS. The supernatant was aspirated and the pellet was resuspended in PBS and filtered again via 40μm nylon cell strainer (BD-Biosciences). Then, mononuclear cells were separated by Ficoll-Paque Premium 1.084 gradient (GE Healthcare). Isolated cells were collected and resuspended in RPMI1640 (Lonza) medium supplemented with penicillin (1x10 5 U⁄L, Lonza), streptomycin (0.05g⁄L, Lonza) and 10% fetal bovine serum (FBS)(Gibco).

Isolation of mononuclear cells from the decidua
Decidual mononuclear cells from the materno-fetal interface were isolated according to the method described previously [20]. Briefly, the endometrial tissue surrounding the conceptuses was pulled away from the uterine horns. Than the decidua was separated from the placenta disc under a dissecting microscope, sliced with scissors and digested with type IV collagenase (Sigma-Aldrich) in 37˚C for 30 minutes. The suspensions were homogenized with a syringe plunger, and single-cell suspensions were prepared using a 70μm nylon cell strainer. Subsequently, cells were washed in RPMI1640 medium supplemented with penicillin, streptomycin and 10% FBS. The supernatant was aspirated, the pellet was resuspended in PBS and filtered via 40μm nylon cell strainer. Finally, the isolated cells were resuspended in RPMI1640 + 10% FBS.

Labeling of lymphocytes and flow cytometric analysis
For surface labeling isolated lymphocytes (10 6 cells/100μl PBS/tube) were incubated for 30 min at room temperature (RT) with the fluorochrome-labelled monoclonal antibodies indicated below. After washing with PBS cells were resuspended in 300μl PBS containing 1% paraformaldehyde and stored at 4˚C in the dark until fluorescence-activated cell sorting (FACS) analysis. Before sample analysis, the setting of the flow cytometer was checked using Cytometer Setup and Tracking beads (CS&T beads, BD-Biosciences) according to the manufacturer's instructions. Compensation beads were used with single stains of each antibody in order to determine the compensation settings, and were applied in FACSDivaTM V6 software (BD-Biosciences) before data collection. Labeled cells were analyzed with FACSCantoII TM flow cytometer by collecting 100,000 events in the lymphogate in the case of the spleen. In the case of decidua 10,000 events were collected in the lymphogate after CD45 staining. FACSDiva TM V6 software was used for data acquisition and analysis.

FoxP3 staining
Following surface labeling, intracellular staining of FoxP3 was performed using the FoxP3 Staining Buffer Set (eBioscience) in accordance with the manufacture's protocol. Briefly, isolated cells were permeabilized in 1ml fixation/permeabilization buffer (Concentrate/Diluent 1:4 eBioscience) for 1 hour at 4˚C. Then the samples were washed twice in the buffer and stained with APC-conjugated anti-mouse FoxP3 monoclonal antibody for 1 hour at 4˚C. FACS analysis was performed by FACSCantoII TM flow cytometer with the BD FACSDiva TM software for data acquisition and analysis.

Detection of CD107a expression
The CD107a assay was set up based on a publication by Alter et al. [21]. To determine CD107a expression by immune cells, mononuclear cells were incubated for 4h at 37˚C with FITC-conjugated anti-mouse CD107a antibody in RPMI1640 containing 10% FBS, penicillin, streptomycin, ionomycin (Sigma-Aldrich) and phorbol myristate acetate (PMA) (Sigma-Aldrich). Next, the cells were washed with PBS and incubated with anti-CD3-BV510, anti-γ/δ-BV510, anti-CD49b-PE, anti-CD45-PerCP and anti-TIM-3-APC antibodies for 30 minutes at RT in the dark. Then the cells were washed in PBS, fixed with 1% paraformaldehyde (PFA) and evaluated by FACS.

Quantification of immunohistochemistry
The stained slides were evaluated by two independent researchers, who were blinded to treatment groups. Sections stained by immunohistochemistry were analyzed for the percentages of Gal-9 positive cells. During analysis, a total of 100 cells/slide were evaluated at 350× magnification.

Statistical analysis
Statistical analysis was performed using statistical software SPSS version 20 package. Multiple comparisons were made using one-way ANOVA with Bonferroni correction. Differences were considered significant if the p value was equal to or less than 0.05.

Immunohistochemistry of pregnant mouse placentae from untreated control and RU486 treated pregnant mice
Immunohistochemistry on isolated healthy pregnant mouse placentae with anti-Gal-9 antibody demonstrated a strong expression of Gal-9 (yellow arrow) at the spongiotrophoblast layer and in the cytoplasm of giant cells (Fig 1A). Histological sections represented a more intense staining by trophoblast giant cells compared to spongiotrophoblast layer ( Fig 1A). Evaluation of the number of positively stained cells showed a significant decrease of Gal-9 positive cells in the placentae of RU486 treated pregnant mice compared to the placentae of healthy, non-treated pregnant mice ( Fig 1B).

Immunophenotypic analysis of decidual and peripheral mononuclear cells from untreated control and RU486 treated pregnant mice
In our study, we investigated the percentage of CD4+T, CD8+T, γ/δT, Treg, NK and NKT cells using the lymphocyte gate in the spleen (periphery) and in the decidua (locally) of untreated and RU486 treated pregnant mice.
RU486 treatment did not cause a statistically significant change in the phenotype distribution of immune cells both in the decidua and in the periphery when compared to untreated mice (Table 1). Albeit previous literature data presented contradictory results about the impact of RU486 on the distribution of decidual NK cells [22][23] our results were in accordance with the earlier human immunohistochemistry results by Milne et al. [24] who found that simultaneous administration of Mifepristone and the prostaglandin E analog did not affect the number of NK cells in the decidua.

Cell surface Gal-9 expression by decidual and peripheral mononuclear cells isolated from untreated control and RU486 treated pregnant mice
Flow cytometry was used to analyze the cell surface expression of Gal-9 by NK, NKT, γ/δT, CD4+T and Treg cells at the materno-fetal interface as well as in the periphery.
In untreated pregnant mice Gal-9 expression by decidual Treg and CD4+T cells was significantly increased (Figs 2D and 3A), while decidual NK cells showed a significantly decreased Gal-9 expression compared to peripheral NK cells (Fig 2A). Gal-9 positivity of decidual NKT and γ/δ T cells showed no significant difference compared to their peripheral counterparts (Fig 2B and 2C). In RU486 treated mice Gal-9 expression was significantly increased by almost all investigated decidual subpopulations when compared to the periphery except NK cells (Figs 2A, 2B, 2C, 2D and 3A). After RU486 administration Gal-9 expression by decidual Treg The immunological effect of Galectin-9/TIM-3 pathway after low dose Mifepristone treatment in pregnant mice and CD4+T cells were significantly increased in comparison to the decidua of untreated control mice (Figs 2D and 3A), while its expression was significantly decreased in the periphery after RU486 treatment by NK cells compared to untreated mice (Fig 2A).

The proportion of Gal-9+ T-helper cell population in decidua and periphery of untreated control and RU486 treated pregnant mice
It was published by Oomizu et al., that CD4+ T-helper (Th) lymphocytes expressing Gal-9 on their cell surface possess immunosuppressive potential by regulating the Th17/Treg balance [25]. In order to examine the impact of Mifepristone administration on the distribution of this T cell-subset, we investigated the percentage of Gal-9+ Th cells in the decidua and in the periphery from untreated control and RU486 treated pregnant mice. We observed a significant increase in the frequency of decidual Gal-9+ Th cells obtained from RU486 treated mice when compared to the periphery of treated mice or to the control untreated decidua (Fig 3B).

TIM-3 expression by decidual and peripheral mononuclear cells from untreated control and RU486 treated pregnant mice
Next, we analyzed, how the expression of TIM-3 receptor is changed by NK, NKT, γ/δT and CD4+T cells at the materno-fetal interface as well as in the periphery of untreated control and Mifepristone treated pregnant mice.
We found that both in control and RU486 treated mice group TIM-3 expression by CD4+T cells was significantly increased in the decidua compared to the periphery (Fig 4D). Contrary, TIM-3 expression by decidual NKT cells of untreated mice was significantly decreased when compared to the periphery (Fig 4B).
Following RU486 administration TIM-3 receptor expression by CD4+T cells was significantly increased at the materno-fetal interface compared to untreated mice (Fig 4D). In RU486 treated mice TIM-3 expression by NK cells was significantly increased in the decidua compared to the periphery (Fig 4A).

CD107a expression by NK, NKT and γ/δ T cells in the decidua and in the periphery from untreated control and RU486 treated pregnant mice
Investigating the expression of CD107a, which is known as a marker of cytotoxic potential by decidual immune cells obtained from the materno-fetal interface we found a significantly higher CD107a expression by decidual γ/δT cells compared to the periphery of untreated control mice (Fig 5C). In all other examined NK and T cell populations the CD107a positivity did not change.
In RU486 treated mice only decidual γ/δT cells showed significantly higher cytotoxic potential than their peripheral counterparts (Fig 5C).
In addition, we analyzed the CD107a expression within the TIM-3+ lymphocyte subsets. In untreated pregnant mice our results demonstrated a significantly decreased CD107a The immunological effect of Galectin-9/TIM-3 pathway after low dose Mifepristone treatment in pregnant mice expression by decidual TIM-3+ γ/δT cells together with a significant increase in CD107a expression by NKT cells compared to the periphery (Fig 5F and 5E). Furthermore, in RU486 treated mice TIM-3+ decidual NKT cells showed significantly higher while NK cells showed significantly lower cytotoxic potential than their peripheral counterparts (Fig 5E and 5D).

Discussion
Accumulating evidence has demonstrated that subpopulations of NK and T cells play a central role in the establishment and maintenance of materno-fetal immunotolerance and alteration in their physiological distribution or function might be associated with pregnancy complications. The exact mechanism underlying the fine regulation of these cells during Mifepristone (RU486) treatment is still under investigation.
Mifepristone has high affinity both for progesterone and glucocorticoid receptors. A number of studies suggest that Mifepristone may be involved in modulation of materno-fetal immune response. In vitro human experiments demonstrated that RU486 significantly inhibits the proliferation of lymphocytes [26], increases the cytotoxicity of peripheral NK cells [27] and blocks the immunosuppressive effect of progesterone mediated by Progesterone Induced Blocking Factor (PIBF) [28]. In addition it was shown that at a dose of 0.3-2 mg/kg RU486 could terminate early pregnancy in mice with an abortion rate of 60-100% [29,30] causing progesterone withdrawal, decreasing the number of PIBF expressing cells, enhancing the perforin expression of decidual NK cells and inducing increased peripheral NK activity [9,31]. On the other hand, late administration of Mifepristone (on gd [14][15][16][17][18][19] is able to induce preterm labor in mice at a dose of 0.4-12.5 mg/kg with a pregnancy termination rate of 66-100%. The immunological effect of Galectin-9/TIM-3 pathway after low dose Mifepristone treatment in pregnant mice Recent publications show that Gal-9/TIM-3 interaction plays an important role in the regulation of maternal immune tolerance and may be a potent regulator of the innate and adaptive immune response [12][20] [32]. Therefore we aimed to investigate the role of Gal-9/Tim-3 pathway after low dose Mifepristone treatment using BALB/c syngeneic mouse pregnancy model at a gd of 14.5. Previously it was found that Gal-9 is widely expressed in the female reproductive tract at the feto-maternal interface [12,[33][34][35][36][37] and by Treg or CD4+T (Th) cells [20] [38]. It was also confirmed that Gal-9+Th cells are veritable immunosuppressors, which play a vital role in the regulation of Th17/Treg balance by producing IL-10/TGF-β [25]. The immunological effect of Galectin-9/TIM-3 pathway after low dose Mifepristone treatment in pregnant mice Our previous human experiments revealed that serum Gal-9 levels and cell-surface expression of Gal-9 by peripheral CD4+T cells were increased throughout pregnancy [12]. Here we demonstrated that a relatively high proportion of peripheral NK, NKT and γ/δ T cells also showed Gal-9 positivity. Although Gal-9 expression by peripheral Treg cells was almost negligible, significantly higher percentage of Gal-9 positivity by decidual Treg cells was found in the decidua. Furthermore, we found that the expression of Gal-9 by NK cells was significantly decreased in the normal pregnant decidua compared to the periphery. 0.8mg/kg RU486-treatment resulted in a nearly complete disappearance of Gal-9 from the junctional zone of the placenta. Furthermore, the treatment significantly decreased the Gal-9 positivity of peripheral NK cells but significantly increased its expression by decidual Treg and CD4+T cells. In addition, the proportion of decidual Gal-9+Th cells with known suppressive capacity was significantly increased after Mifepristone administration.  (Fig 5A-5C). The expression of CD107a by TIM-3 positive NK cells, NKT cells and γ/δT cells in the periphery and in the decidua of untreated control and RU486 treated pregnant mice (Fig 5D-5F). The middle line within the box represents the median, the middle dot within the box represents the mean, the boxes indicate the interquartile ranges and the whiskers show the most extreme observations. Differences were considered statistically significant for p-values 0.05. https://doi.org/10.1371/journal.pone.0194870.g005 The immunological effect of Galectin-9/TIM-3 pathway after low dose Mifepristone treatment in pregnant mice Our data indicate that even a low dose Mifepristone treatment was effective enough to abrogate Gal-9 production of the placenta. The observed, increased Gal-9 expression by decidual Treg and CD4+Th cells suggest that local immunosuppressive mechanisms are also triggered 24 hours after the treatment, possibly to sustain impaired placental function. In addition, we suppose, that the accumulation of the Gal-9 secreting Gal-9+Th cells might be also involved to partly compensate the decreased placental Gal-9 expression at the materno-fetal interface. These mechanisms might inhibit the pro-inflammatory cytokine production of Th1 and Th17 cell by a Gal-9/TIM-3 dependent fashion [13] and aid the maintenance of the whole embryoplacenta unit.
On the basis of the above-mentioned results we hypothesize that beside the trophoblast and decidual cells, Gal-9+Th and Treg cells could serve as an alternative source of the locally secreted Gal-9 at the materno-fetal interface. During physiological conditions, the high local Gal-9 level could aid the development and suppressive function of Treg and Gal-9+Th cells, which might be involved in the inhibition of Th17 cell differentiation, downregulate the local Th1/Th17 cytokine production and induce apoptosis of Th1 CD4+T and activated CD8+T cells in the decidua. Additionally-to a lower extent-cytotoxic effector cells could be also able to produce this lectin both at the periphery and locally. We suggest that the membrane bound or secreted Gal-9 can engage the TIM-3 receptor on the surface of the immune effector cells and therefore it might directly or indirectly affect the innate and adaptive arm of the immune system at the materno-fetal interface.
After low dose Mifepristone treatment, the placental expression of Gal-9 might be seriously impaired. Since local Gal-9 level could be important for placental development and for the regulation of the maternal anti-fetal immune response, the disruption of Gal-9-regulated tolerance mechanisms can contribute to pregnancy complications [39] and lead to pregnancy termination or preterm labor. Harmful effects caused by low dose Mifepristone treatment could be physiologically compensated via the upregulation of Gal-9 expression by decidual Treg and CD4+Th cells and by the local accumulation and/or differentiation of Gal-9 secreting Treg and Gal-9+ Th cells. We suggest that these compensatory mechanisms might sustain impaired placental function when only partial progesterone withdrawal is induced, but might become ineffective when higher dose of Mifepristone would be used. Administration of 2 mg/ kg or higher dose of RU486 can cause complete blocking of progesterone receptors causing estrogen dominance, sensitizing the uterus to the activity of the prostaglandin, resulting in intrauterine fetal death or a definite premature labor at a late stage (gd 14-19) of murine pregnancy.
Beside the above-mentioned actions, it is well established, that during pregnancy RU486 can induce an immunological alteration in a Gal-9/TIM-3 independent way. The regulation of Th1/Th2/Th17/Treg paradigm is one of the most important mechanisms by which progesterone exerts its pregnancy-protective effect [40]. Progesterone induces a Th1 to Th2-type immunity skew by inducing the expression of PIBF and Th2-type cytokines and suppressing the production of Th1 cytokines [41]. As a receptor antagonist, Mifepristone can inhibit the pregnancy-protective immunological effects of progesterone in a TIM-3 independent way by inhibiting the proliferation of lymphocytes [26] and inducing the production of prostaglandins, cyclooxygenases and proinflammatory cytokines in vitro. These effects-together with the dysregulated Gal-9/TIM-3 pathway-might alter the local cytokine milieu from an immunosuppressive/angiogenic environment towards a proinflammatory condition. This can disturb the precise regulation of the Th1/Th2/Th17/Treg balance and induce the activation of local inflammatory and cytotoxic immune response leading to pregnancy loss [42][43] [44].
The limitations of our study is that (1) we used only one dose of Mifepristone (0.8 mg/kg) instead of a several different doses from low to high, (2) we tested the in vivo effect of RU486 only on gestational day 14.5 but not earlier (3) and analysis was performed just 24h after treatment. Based on the literature higher doses of Mifepristone (e.g. >2 mg/kg) would cause complete abortion after 48h as indicated in a paper by Lv et al. [30]. In our case these setting were not applicable since after complete abortion tissue destruction strongly limit to obtain enough decidual lymphocytes for flow cytometric analyses.