extfieldsofstudy:
Biology
Medicine
Computer Science

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Quantitative group testing-based overlapping pool sequencing to identify rare variant carriers

Background Genome-wide association studies have revealed that rare variants are responsible for a large portion of the heritability of some complex human diseases. This highlights the increasing importance of detecting and screening for rare variants. Although the massively parallel sequencing technologies have greatly reduced the cost of DNA sequencing, the identification of rare variant carriers by large-scale re-sequencing remains prohibitively expensive because of the huge challenge of constructing libraries for thousands of samples. Recently, several studies have reported that techniques from group testing theory and compressed sensing could help identify rare variant carriers in large-scale samples with few pooled sequencing experiments and a dramatically reduced cost. Results Based on quantitative group testing, we propose an efficient overlapping pool sequencing strategy that allows the efficient recovery of variant carriers in numerous individuals with much lower costs than conventional methods. We used random k-set pool designs to mix samples, and optimized the design parameters according to an indicative probability. Based on a mathematical model of sequencing depth distribution, an optimal threshold was selected to declare a pool positive or negative. Then, using the quantitative information contained in the sequencing results, we designed a heuristic Bayesian probability decoding algorithm to identify variant carriers. Finally, we conducted in silico experiments to find variant carriers among 200 simulated Escherichia coli strains. With the simulated pools and publicly available Illumina sequencing data, our method correctly identified the variant carriers for 91.5–97.9% variants with the variant frequency ranging from 0.5 to 1.5%. Conclusions Using the number of reads, variant carriers could be identified precisely even though samples were randomly selected and pooled. Our method performed better than the published DNA Sudoku design and compressed sequencing, especially in reducing the required data throughput and cost.

Background
Rare variants are responsible for a large portion of the heritability of some common complex human diseases [1,2]. Genome-wide association studies have focused on the contribution of variants of low minor allele frequency (MAF 0.5-5%), or of rare variants (MAF < 0.5%) [2]. The functional and evolutionary impacts of rare variants have been reported; therefore, large-scale screening for disease-associated rare variants is becoming increasingly important [3,4]. One major application of rare variants genotyping is in screens for rare genetic disorders such as Tay-Sachs disease and thalassemia [5].
Because of the extremely low frequency of rare variants, sample sizes must be large enough to guarantee efficient observations. The cost of DNA sequencing has dropped dramatically with the introduction of the massively parallel sequencing technologies. However, identifying rare variant carriers by sequencing individual samples separately remains prohibitively expensive because of the huge challenge of constructing sequencing libraries for thousands of samples [6,7]. Barcoding has been developed as a powerful approach to cost-effectively determine the genotype of each individual [7]. To further reduce the cost of large-scale screens for rare variant carriers, several techniques based on the group testing theory [8] and compressed sensing [9,10] to construct overlapping pool sequencing strategies have been used. These strategies have helped decrease the sequencing times for rare variant carrier identification and further lower the cost [11][12][13][14].
Because a large number of samples are pooled together and then sequenced, overlapping pool sequencing uses fewer pools to identify rare variant carriers among large numbers of individuals. Thus, overlapping pool sequencing can vastly reduce the time and cost for DNA library preparation. Some overlapping pool sequencing programs return true/false values after testing each pool; this scheme was adopted by Erlich et al. [11], Prabhu and Pe'Er [12], and Cao et al. [14], who used the number of normal and variant reads in each pool to determine whether a pool contained carriers. However, the quantitative information in the sequencing data, which can be used to estimate the percentage of variant chromosomes in a pool, is missed in these methods. Quantitative group testing is an alternative scheme that takes the quantitative information into account, thus rare variant carriers can be identified efficiently [13].
We propose an efficient random overlapping pool sequencing strategy with quantitative group testing for the identification of rare variant carriers using massively parallel sequencing data. Because of the excellent performance of random designs in classic group testing [15,16], we employed a random k-set pool design [17] to mix samples. The parameters of the random k-set pool design can be selected appropriately according to an indicative probability value. Based on a depth model for pooled sequencing, we calculated the optimal cut-off of the number of reads containing variants to distinguish pools containing variant carriers from those that do not. Using the quantitative information contained in the sequencing data, we designed a heuristic Bayesian decoding algorithm to identify variant carriers accurately. Compared with the DNA Sudoku algorithm [11] and compressed sequencing [13], our method required less data throughput. Finally, we applied our method to identify variant carriers among 200 simulated Escherichia coli strains using simulated pools and publicly available Illumina sequencing data. The results showed that our method could successfully identify carriers for 91.5-97.9% of the variants with frequencies ranging from 0.5 to 1.5%.

Random k-set pool design
Random k-set pool designs were first proposed by Bruno et al. [15] for efficient DNA clone library screening. In such a design, each clone is pooled in exact k pools that are chosen with equal chance. Random k-set pool designs are easy to specify for any number of pools and are known to be efficient in classic group testing, but they have not been used in real situations, partly because of the presence of different sample sets with identical test sets that are indistinguishable when the test results are qualitative [16]. However, this problem can be overcome by quantitative tests such as sequencing experiments.
For n samples containing d positive samples, the basic objective of a random k-set pool design is to identify all the positive samples with a small number of pooled tests. In such a design, each sample occurs in exact k pools, and a pool is defined as positive only when it contains at least one positive sample; otherwise, it is defined as negative. For a random k-set pool design with t pools, Hwang [17] calculated the probability that a given set of i pools is a negative one (Eq. (1)) and the expected number of negative pools (Eq. (2).
where n_min and n_max are the minimum and maximum number of negative pools, respectively, and h is a temporary variable. n_max = t -k, and To evaluate the performance of random designs, Barillot et al. [18] proposed that the number of unresolved negative samples ( N ) can be taken as a criterion. An unresolved negative sample is defined as a negative sample that occurred only in positive pools, as a result, its status can only be confirmed by testing it separately. Negative samples that are contained in at least one negative pool can confidently be determined as negative; therefore, Hwang [17] calculated the expectation (Eq. (3)) and probability distributions (Eq. (4)) for the number of unresolved negative samples.
For quantitative group testing, negative pools are used to recognize the negative samples and the test results of positive pools are used to distinguish real positive samples from unresolved negative samples. When the number of positive pools is less than the sum of unresolved negative samples and positive samples, numerous solutions are possible. Intuitively, a design that has more positive pools and fewer unresolved negative samples will have a higher probability of identifying all the positive samples correctly. Therefore, we designed an indicative probability (PI; Eq. (5)) to evaluate the performance of random k-set designs in quantitative group testing. PI is the probability that positive pools are more than the sum of unresolved negative samples and real positive samples; therefore, designs with high PIs will perform better than designs with low PIs.
where p_min and p_max are the minimum and maximum number of positive pools, respectively, p_min = t -n_max, and p_max = t -n_min. The derivation of Eq. (5) is given in Appendix 1.

Optimal cut-off value for pooled sequencing
For overlapping pool sequencing, the DNA samples are mixed and then sequenced. Samples from variant carriers are treated as positive and samples from normal individuals are treated as negative. To distinguish positive pools containing variant carriers from negative pools consisting of normal individuals, the cut-off for the number of reads containing variants must be selected carefully to declare whether a pool contains carriers or not. Ideally, the cut-off value must guarantee that the minimum error rates are obtained, including false-positive and false-negative rates. The variation of sequencing depth distribution is significantly greater than the mean [19,20]; therefore, in recent studies, negative binomial distribution rather than Poisson distribution has been used to model sequencing depth. Following the study reported by Miller et al. [21], we used a negative binomial model to estimate the sequencing depth distribution. Given the average sequencing depth D, the depth that represents the number of times a nucleotide is read follows a negative binomial distribution NB D r−1 ; 1 r À Á where r is the variance/mean ratio; r is related to sequencing platforms and genomes and can be estimated from sequencing results (Eq. (6)).
For a pool with N samples, given sequencing depth D and average sequencing error rate p error , the probabilities P nv (N v ) and P pv (N v ) that N v reads containing variants are observed in negative pools and positive pools, respectively, are given by Eqs. (7) and (8). For a locus sequenced i times, the number of sequencing errors follows a binomial distribution Bin(i, p error ). ð8Þ Similarly, the probabilities P nn (N n ) and P pn (N n ) that N n reads without variants are observed in negative pools and positive pools, respectively, are given by Eqs. (9) and (10). Given P nv (N v ) and P pv (N v ), the formula for the falsepositive rate F p and false-negative rate F n in classifying pools with a cut-off value T can be constructed (Eqs. (11) and (12)).
The optimal cut-off T can be defined as the value that minimizes the maximum values of F n and F p .

Decoding algorithm
Our decoding procedure involves two steps. In the first step, we identify negative pools according to the sequencing results and cut-off values for each pool. Samples that participate in any negative pools are classified as from normal individuals. Then, separate the real positive samples from unresolved negative samples according to the quantitative information in the sequencing results. The probability of observing the sequencing results under the exact set of variant carriers should be greater than taking other set of samples as variant carriers. Assuming A is the set of variant carriers, the probability that the sequencing result O is observed is given by Eq. (13).
where O iv and O in are the number of reads with and without variants in the i th pool. Given that A is the set of variant carriers, if the i th pool is negative, then For the second step, we designed a Bayesian decoding algorithm based on Eq. (13). To identify variant carriers among haploid samples, after excluding resolved negative samples, the rest of the samples form a set A 0 = {S 1 ,…,S c }. First, assuming that all the samples in A 0 are variant carriers, we calculate the probability of observing the sequencing results and denote it as Pmax_0. Next, replace one positive sample in set A 0 with a negative sample in turn and repeat the probability calculation. Denote the derived set that results in the maximum probability as A 1 and the corresponding probability as Pmax_1. Replace A 0 with A 1 and repeat this step until A c and Pmax_c are obtained. Finally, the set A i that results in the maximum corresponding probability Pmax_i is defined as the set of variant carriers. These steps are written as Algorithm 1. : Our decoding procedure to identify variant carriers among diploid samples is summarized in Algorithm 3. First, we suppose that only heterozygous variant carriers exist and run Algorithm 1 to find the set of variant carriers C 0 . Then, run Algorithm 2 to recognize heterozygous and homozygous variant carriers..

Results and discussion
Optimal cut-off value To approximate the sequencing depth distribution for data obtained by using Illumina sequencing platforms, Miller et al. [21] found that the negative binomial distribution with the variance/mean ratio r of 3 performed much better than the Poisson distribution. Therefore, r was set to 3 in our simulation unless otherwise stated.
For a pool consisting of 10 diploid samples, we calculated the false-positive and false-negative probabilities with different cut-off values when only one heterozygous variant carrier was allowed in the positive pool. The average sequencing error rate and whole depth were set to 0.01 and 600×, respectively (Figure 1a). The results verified the importance of selecting an appropriate cutoff value. With smaller cut-off values, the probability of misclassifying negative pools as positive is high. With higher cut-off values, some positive pools will be misclassified. Both will lower the speed and accuracy of decoding. From the results we can infer that the optimal cut-off value is 14; here both the false-negative and false-positive probabilities are very low (Figure 1a).
In most studies, because of the rarity of positive samples, the number of positive and negative pools is unequal. Therefore, selecting a cut-off value based on the expected number of errors in classifying pools is a more practical approach. For instance, when there are 30 negative and 10 positive pools mentioned above, the optimal cut-off value is 16 (Figure 1b). In the following simulation experiments, we adopted this kind of scheme unless otherwise stated.
As mentioned, the variance/mean ratio r is related to the sequencing platforms and genomes. Because the observed variation is significantly greater than the mean of the sequencing depth, r is larger than 1. Different values for r yield distinct distributions. The pooling design mentioned above consisting of 30 negative and 10 positive pools was used to estimate the effect of r on our methods. We calculated the least depth required for each pool to make the expected number of errors in classifying pools smaller than 1 by increasing the depth gradually (see Additional file 1: Figure S1). From the results, we can see that our method performed even better for smaller r, which required less data throughput.

Performance of the pipeline
To evaluate the performance of our method, we conducted substantial simulations to identify four heterozygous variant carriers among 100 haploid samples. 1000 replicates were done for each pair of design parameters: pool number t and column weight k. The pooling matrix was designed by collecting random binary vectors with length t and weight k, meaning that each sample was mixed in k of t pools. Identical vectors were deleted and the steps were repeated until 100 vectors were obtained to form the matrix.
We used the random function in Perl to simulate the number of reads with and without variants in pooled sequencing. Sequencing error and mixing bias were added to the simulation procedure to bring it closer to a real situation. Sequencing error follows a binomial distribution in sequencing results, and in the simulation the average sequencing error rate was set as 0.01. Mixing bias is caused by the practical difficulty of mixing exactly equal amounts of DNA samples. Based on the study conducted by Shental et al. [13], a random variable following the Gaussian distribution was added to each non-zero element of the pooling matrix to simulate mixing bias. The standard deviation of the Gaussian distribution was 0.05, reflecting up to 5% average noise in the mixed quantities of each sample.
After simulating the pooled sequencing results containing the sequencing errors and mixing bias, the genotypes of the 100 samples were reconstructed using our decoding algorithm. The correct decoding rates, namely the percentages of simulations that identified all the variant carriers correctly, were determined ( Figure 2). The results showed that either a too large or too small k negatively influenced the correct decoding rates. Moreover, a large k meant more pooling procedures and increased experimental costs. Therefore, a proper column weight k is critical for conducting experiments successfully and efficiently.

Cost-effective overlapping pool sequencing
The column weight k denotes the mixing times for each sample in a random k-set pool design. For a given number of pools, a k that is too large or too small will lower the decoding accuracy. We designed an indicative probability PI, which reflects the performance of random k-set designs that could be used to choose the optimal column weight k.
We calculated the correct decoding rates for different k under the condition that 30 pools were allowed to identify four heterozygous variant carriers among 100 diploid samples by conducting 1000 replicates for each k. Next, PI was computed based on Eq. (5) and the results are shown in Figure 3. A strong correlation was observed between PI and the correct decoding rate (Pearson correlation coefficient = 0.92, p-value = 9.8e-06), especially before the correct decoding rate reached the saturation point (k = 6, Pearson correlation coefficient = 0.98, p-value = 3.4e-3). The PI values and correct decoding rates for identifying variant carriers were also obtained under different scenarios (see Additional file 1: Figure S2). All the scenarios showed strong correlations between PI and the correct decoding rate before the correct decoding rate reached the saturation point.
For a given pool number t, we defined the optimal k as the minimum that obtains the maximum PI value, which could maximize the correct decoding rate. Designs with optimal k require fewer pools or lower sequencing depth. In practice, the optimal k is selected by calculating the PI value without the need to conduct simulations, thereby greatly reducing the computational time required.
Next, we conducted a series of simulated overlapping pool sequencing experiments with 20-90 pools and 10,000-40,000× overall sequencing data throughput ( Figure 4). One thousand replicates were conducted for each scenario, and the column weight was set as the optimal value (see Additional file 1: Table S1). The correct decoding rates were low when few pools or data throughput were used. However, adequate pools and data throughput achieved higher accuracy but increased the cost, which conflicted with our motivation in this study. There is a trade-off between the number of pools and data throughput. Hence, numerous simulations need to be performed to verify whether a pool number and data throughput pair can succeed in achieving high accuracy (e.g., 95%). Clearly, the optimal design parameters should be selected based on the whole cost of the sequencing experiment.
For a given population with 100 diploid individuals containing one heterozygous variant carrier, we generated several candidate designs in which over 95% of the simulations correctly identified the variant carrier ( Table 1). The sequencing region was set to 30 Mb to fit the human exome sequencing project [22]. The cost of a sequencing experiment includes library construction and data production. Using the cost model from our previous work [14], we inferred that the lowest cost design was design II in Table 1. Compared with sequencing separately, which requires sequencing depths of 24.2× for each sample to obtain correct decoding rates over 95%, our method can save at least 50% of the cost. With the same procedure, we generated the most cost-effective designs for variants with different frequencies and different sequencing region sizes ( Table 2). For smaller sequencing regions and variants with lower frequencies, there are greater cost reductions with our method compared with those for larger regions and variants with higher frequencies.

Comparisons with current methods
In 2009, benefiting from the Chinese remainder theorem, Erlich et al. [11] put forward the DNA Sudoku design for overlapping pool sequencing. A pattern consistency decoding algorithm was also developed by Erlich et al. [11] to identify variant carriers with the DNA Sudoku design. In 2010, Shental et al. [13] developed a method called compressed sequencing to identify rare variants and their carriers by borrowing techniques from compressed sensing. Two designs were proposed in compressed sequencing: one used pools with a random half of the samples and the other used pools with sizes equal to the square root of the number of samples. We compared the performance of our method in identifying rare variant carriers with the performances of these two methods.
To identify variant carriers in 100 diploid samples, the DNA Sudoku design with parameter d 0 = 2 was employed that required 36 pools. To maintain consistency, only 36 pools were allowed for the random k-set pool design and compressed sequencing. Since the expected number of positive and negative pools was not clear for the DNA Sudoku design, the cut-off value for the number of reads containing variants to declare a pool to be positive was set based on the false-negative and false-positive rates, and not on the expected number of errors in the classifying pools.
With 36 pools, we computed the least sequencing data throughput required for all the methods by increasing the depth gradually, until 95% of the simulations identified all the carriers correctly for various percentages of heterozygous variant carriers ( Figure 5, Additional file 1: Table S2). Our method performed better than both the designs in compressed sequencing. The advantages of our method were significant with large numbers of variant carriers. The performance of the DNA Sudoku design was similar to our method when the number of variant carriers was no more than two, but it did not perform well for variants with higher frequencies because of the limited efficiency of the pattern consistency decoding algorithm. For these cases, more pools are required for the DNA Sudoku design than for both our method and compressed sequencing. Figure 4 Performance of overlapping pool sequencing using random k-set pool design. Column weight for each scenario was set to the optimal value to identify four variant carriers among 100 samples. Average value from five simulations. Gb is short for gigabases. Data throughput is the sequencing depth multiplied by the length of the sequencing region. b Sequencing separately is the strategy when each sample is sequenced independently. All the candidate designs can identify the variant carrier correctly in 95% of the simulations. The costs were estimated at $500 for one library preparation and $5300 for 100 Gb of data.
The DNA Sudoku design is hard to specify for any number of pools. Therefore, we compared only the performance of compressed sequencing with that of our method to identify four heterozygous variant carriers among 100 diploid samples by using the same amounts of pools and sequencing throughput ( Figure 6, Additional file 1: Figure S3). Our method performed better for most scenarios, especially when the sequencing throughput was limited.

Simulation experiment
We applied our method to identify variant carriers among 200 simulated E. coli strains. Illumina sequencing reads of two E. coli strains were downloaded from GenBank's Short Read Archive (O157:H7 strain [SRA: ERR018562]) and BGI's FTP site (O104:H4 strain, ftp:// ftp.genomics.org.cn/pub/Ecoli_TY-2482). We treated the O157:H7 strain as the variant carrier and the O104:H4 strain as the normal sample. Bowtie0.12.9 [23] was used to map the O157:H7 reads to the O104:H4 genome, and SAMtools 0.1.19 [24] was used to call single base mutations. Because the mean depth was 134× for O157:H7, mutations with depths lower than 130 or higher than 140 were removed to control the quality; the remaining 1271 mutations were used in the analysis.
We conducted three simulation experiments to validate the ability of our method to identify carriers of variants with frequencies ranging from 0.5 to 1.5%. Based The sequencing region for haploid samples was set as 5 Mb to fit the average length of the bacterial genome. The sequencing region for diploid samples was set as 30 Mb to fit the human exome sequencing. Figure 5 Least sequencing data throughput required to achieve a 95% correct decoding rate. Only 36 pools were allowed to identify heterozygous variant carriers among 100 diploid samples. 'Compressed sequencing (a)' used pools with a random half of the samples, and 'compressed sequencing (b)' used pools with sizes equal to the square root of the number of samples.
on the results in Table 2, we designed the pooling matrix and sequencing depth so that 95% of the simulations correctly identified the variant carriers. Next, pooled sequencing was conducted by selecting reads randomly from the data set and mixing them in silico. Considering up to 5% average noise in the DNA quantities of each sample in the pooling procedure, the number of reads for each sample was revised with a random coefficient following a Gaussian distribution to simulate reality. Bowtie was used to map pooled reads, and Perl scripts were used to count the reads with and without variants that were mapped at the loci of variants. After the decoding procedure, variant carriers could be identified correctly for 91.5-97.9% variants. This result was consistent with the design capability (Table 3).

Conclusions
Here, an efficient method that harnesses random k-set pool designs and massively parallel sequencing technologies to identify rare variant carriers is presented. The parameters of the random k-set pool design can be selected appropriately depending on an indicative probability. According to the depth model for pooled sequencing, the optimal cutoff value to separate negative pools from positive pools was designed. Taking advantage of the quantitative information in the sequencing results, a heuristic Bayesian decoding algorithm to identify the variant carriers was developed. Compared with the DNA Sudoku design and compressed sequencing, our method showed potential advantages, especially in decreasing the required data throughput. Finally, we applied our method to identify variant carriers among 200 simulated E. coli strains using simulated pools and Illumina sequencing data. Our method successfully identified variant carriers at reduced experimental costs. For the accurate identification of variant carriers, the sequencing depth and pool number must be adequate to overcome sequencing errors and mixing bias. Considering the trade-off between the pool number and data throughput, substantial simulations need to be performed to verify whether a design is capable of identifying all the variant carriers correctly. Because the overall cost of overlapping pool sequencing stems from the pooling procedure, library construction, and data production, the optimal design depends on the whole cost.
Our decoding algorithm identifies the variant carriers by maximizing the posterior probability, and does not Figure 6 Difference in correct decoding rate between our method and compressed sequencing. The design that harnessed pools with a random half of the samples was used for compressed sequencing. The heat map indicates the correct decoding rates using our method minus that of compressed sequencing. Our method performed much better than compressed sequencing, especially when the data throughput was limited. depend too much on the rarity of variants. Therefore, our approach can succeed even for low frequency variants. Furthermore, the sequencing qualities that indicate the sequencing error probabilities could be integrated into the calculation of the posterior probability in the decoding procedure to improve the accuracy. Compared with compressed sequencing, our decoding procedure was very time-consuming because of the substantial calculation of the posterior probability. This will be improved in future work. Further improvement could be made with a reasonable depth model. Although in many studies negative binomial distribution rather than Poisson distribution has been used to fit the sequencing depth, numerous different models exist. We could not determine which model fit the depth distribution best because, in previous studies, these models have not been compared. Additionally, different sequencing procedures and platforms, such as exome sequencing and whole genome sequencing, produce distinct depth distributions. We aim to employ a better depth model to improve the performance of our method.
Our method has the advantage over compressed sequencing because required data throughput is reduced. However, because each sample is sequenced multiple times, the required data throughput is still substantial. Third-generation sequencing technologies [25,26], which significantly reduce the cost for data production, may help to overcome this drawback. We expect that our method could be applied not only in sequencing experiments but also in other fields as long as the pooled experimental results contain quantitative information about the number of positive samples. N is the number of unresolved negative samples, and d is the number of positive samples, then PI can be written as A(1).
where p_min and p_max are the minimum and maximum number of positive pools, respectively. Because N p =i indicates that there are ti negative pools, P(N p =i) can be formulated as A (2).
Eq. (7) and Eq. (9): These equations define the probabilities that N v reads containing variants are observed in a negative pool (P nv (N v )), and N n reads without variants are observed in a negative pool (P nn (N n )), respectively. Briefly, P nv (N v ) can be written as A (5).
where P(i) is the probability that i reads are obtained, and P e (Nv|i) is the probability that N v errors occur among these i reads. Because the depth follows a negative binomial distribution and sequencing errors follow a binomial distribution, these two probabilities can be formulated as A(6) and A (7). In A(6), D and r are the mean depth of coverage for pooled sequencing and the variance/mean ratio, respectively. In A(7), p error is the mean sequencing error rate. After integrating A(5)-A(7), P nv (N v ) can be formulated as A (8).
The derivation of the formula for P nn (N n ) (A(9)) is similar to the derivation for P nv (N v ).
Eq. (8) and Eq. (10): These equations define the probability that N v reads containing variants are observed in a positive pool (P pv (N v )) and N n reads without variants are observed in a positive pool (P pn (N n )), respectively. The observations of a variant in a positive pool consist of two parts: real variants from variant chromosomes, and false variants resulting from sequencing errors. Briefly, P pv (N v ) can be written as A (10) where P N (x) stands for the probability that x reads containing variants stemming from the sequencing results of normal chromosomes, and P P (Ox) denotes the probability that Ox reads contain variants from variant chromosomes.
By applying a similar procedure to the one used to obtain A(8) and A (9), P N (x) and P P (N vx) can be formulated as A(11) and A (12). The only difference is the mean sequencing depth of coverage. Because the percentages of variant chromosomes and normal chromosomes are p and 1p, respectively, the mean depths of coverage for sequencing variant chromosomes and normal chromosomes are pD and (1p)D, respectively.
In the same way, P pv (N v ) can be obtained by integrating A(10)-A (12), which is shown as A(13).
Similarly, P pn (N n ) can be obtained as shown in A (14).
Additional file Additional file 1: Figure S1. Least depth to make the expected number of errors in classifying pools smaller than 1. Figure S2.
Correlation between the PI value and the correct decoding rate for different scenarios. Figure S3. Comparison of the correct decoding rate between our method and compressed sequencing using the second kind of design. Table S1. Optimal column weight for various numbers of pools to identify four variant carriers among 100 samples. Table S2. Least data throughput required to achieve a 95% correct decoding rate in the identification of heterozygous variant carriers among 100 diploid samples under the condition that only 36 pools are allowed.


extfieldsofstudy: Biology Medicine

text:
An Examination of the Relationship between Hotspots and Recombination Associated with Chromosome 21 Nondisjunction

Trisomy 21, resulting in Down Syndrome (DS), is the most common autosomal trisomy among live-born infants and is caused mainly by nondisjunction of chromosome 21 within oocytes. Risk factors for nondisjunction depend on the parental origin and type of meiotic error. For errors in the oocyte, increased maternal age and altered patterns of recombination are highly associated with nondisjunction. Studies of normal meiotic events in humans have shown that recombination clusters in regions referred to as hotspots. In addition, GC content, CpG fraction, Poly(A)/Poly(T) fraction and gene density have been found to be significant predictors of the placement of sex-averaged recombination in the human genome. These observations led us to ask whether the altered patterns of recombination associated with maternal nondisjunction of chromosome 21 could be explained by differences in the relationship between recombination placement and recombination-related genomic features (i.e., GC content, CpG fraction, Poly(A)/Poly(T) fraction or gene density) on 21q or differential hot-spot usage along the nondisjoined chromosome 21. We found several significant associations between our genomic features of interest and recombination, interestingly, these results were not consistent among recombination types (single and double proximal or distal events). We also found statistically significant relationships between the frequency of hotspots and the distribution of recombination along nondisjoined chromosomes. Collectively, these findings suggest that factors that affect the accessibility of a specific chromosome region to recombination may be altered in at least a proportion of oocytes with MI and MII errors.

Introduction
Trisomy 21, leading to Down Syndrome (DS), is the most common autosomal trisomy among live-born infants, occurring in approximately 1 in 700 live-births, and is caused mainly by the failure of chromosome 21 to properly segregate during oogenesis [1]. Increased maternal age and altered number and location of recombination events have been found to be associated with maternal meiotic errors involving chromosome 21 [2,3]. Specifically, the absence of recombination [4] or the presence of a single recombinant event near the telomere of 21q [2] are associated with maternal meiosis I (MI) errors and these associations appear to be independent of the age of the oocyte (i.e., maternal age at the time of birth of the infant with trisomy 21) [5]. Meiosis II (MII) errors appear to be driven by different age and recombination traits: MII errors are associated with the placement of a recombinant event near the centromere of 21q [2] and this association increases with increasing age of the oocyte [5].
Studies of normal meiotic events in humans show that the placement of recombination is not a random event. Rather, both cis and trans-acting factors have been found to be associated with the placement of recombination. Specifically, GC content, CpG fraction and Poly(A)/Poly(T) fraction have each been found to be significant predictors of placement of sex-averaged recombination events in the human genome [6]. In addition, sequence variation in the zinc-finger domain of the gene Proline Rich Domain Containing 9 (PRDM9) has a major impact on the location of recombination in humans [7,8,9,10]. Specifically, allelic differences in the zinc finger binding domain of PRDM9 explain approximately 80% of the heritable variation in ''hotspot usage'' '' (i.e. the frequency in which recombination occurs within linkage disequilibrium (LD) or ''historically''-defined hotspots) [8,11,12]. The observation that both cis and trans-acting factors are associated with the placement of recombination led us to question whether the altered patterns of recombination associated with nondisjunction of chromosome 21 could be explained by differences in the relationship between recombination and genomic features (i.e., GC content, CpG fraction, Poly(A)/Poly(T) fraction or gene density) on 21q or differential hot-spot usage. This paper presents the first analyses of the relationship between recombination rate and the quantity of genomic features or LD-defined hotspots specifically along chromosome 21 in oocytes with a normal meiotic outcome, a MI nondisjunction error or a MII nondisjunction error.

Ethics Statement
The work presented in this publication was approved by the Emory Univeristy Institutional Review Board. All participants in provided written consent which indicated that the individual (1) agreed for study personnel to proceed with the interview and (2) consented for biological specimens to be obtained from them and their child. All information obtained during participant interviews and related to sample collection were catalogued electronically and de-identified.

Trisomic Population
Families with an infant with full trisomy 21 were recruited through a multisite study of risk factors associated with chromosome mal-segregation [2,13,14]. Parents and the infant donated a biological sample (either blood or buccal) from which DNA was extracted. Only families in which DNA was available from both biological parents and the child with trisomy 21 were included, leading to a total of 297 maternal MI and 277 maternal MII cases of trisomy 21 (Table 1).

Trisomic Population Genotyping and Quality Control
Samples were genotyped at 1536 SNP loci on 21q by the Center for Inherited Disease Research using the Illumina Golden Gate Assay. The most centromeric SNP was rs2259403 (13,615,252 bp) and the most telomeric was rs7116 (46,909,248 bp). The average number of SNPs per 500 kb bin was 25.56 with a standard deviation of 25.91 with over 70% of cases exhibiting a recombinant having recombination breakpoints smaller than 1 Mb. Mendelian inconsistencies and sample mix-ups were identified using RelCheck among the trios. In addition, parental genotyping data were used to identify poorly performing SNPs.
SNPs that met the following criteria were excluded from our analyses: minor allele frequency (MAF) ,0.01, deviation from Hardy Weinberg Equilibrium (HWE) (p,0.01), heterozygosity . 0.60 or . 10% missingness. We also excluded SNPs on a familyby-family basis if .50% of the genotype data for a proband had low intensity levels. As it relates to our exclusion of SNPs with a heterozygosity rate of .0.60, while we understand that is a very conservative/stringent cutoff, we did indeed examine the distribution of cases by stage and origin upon changing the heterozygosity rate and we did not see any significant changes in stage (data not shown). In addition, for a significant majority of our cases, stage and origin had been previously determined using STR data and compared to what was identified with our SNP only data.

Determining Stage and Origin of Meiotic Chromosome Mal-Segregation
Individuals with trisomy 21 have three copies of chromosome 21 and thus display three alleles for each SNP genotyped on chromosome 21. In instances where trisomy 21 is caused by a maternal meiotic error, for each SNP examined, one of these alleles is inherited from dad, while the other two are inherited from mom. Maternal meiotic errors were confirmed upon determining that trisomic offspring inherited two alleles from mom and one from dad for SNPs genotyped on chromosome 21. Only cases of maternal origin were included in our analyses. Once the maternal origin of the meiotic error was established, markers located in the pericentromeric region (13,615,252 bp -16,784,299 bp) of 21q were used to infer the stage of the meiotic error, MI or MII. If maternal heterozygosity was retained in the trisomic offspring, we concluded a MI error. If maternal heterozygosity was reduced to homozygosity, we concluded a MII error. In this assay, we cannot distinguish between the different types of underlying errors that might lead to an MII error. For example, sister chromatids that fail to separate during anaphase of MII or an error that is initiated in MI and not resolved properly in MII both lead to the contribution of sister chromatids to the gamete. Also, if sister chromatids prematurely separate in MI, some configurations will lead to both sister chromatids segregating to the same pole in MII. Lastly, when all informative markers in the parent of origin were reduced to homozygosity, the origin of nondisjunction was inferred to be a post-zygotic, mitotic error and excluded from the study.

Identifying the Location of Recombination -Trisomic Samples
After genotyping quality control measures were implemented and SNP data were combined with STR data from our previous studies [3], we defined the location of recombinant events. The breakpoints of a single recombinant event were defined by a minimum of either one STR or eight consecutive, informative SNPs flanking the recombination breakpoint. An exception to this rule occurred when the most proximal or most distal informative markers on 21q indicated the presence of recombinant event. In these instances, a minimum of either one STR or four consecutive, informative SNPs were required to define the breakpoints of recombination. The presence of a double recombinant event was defined by a minimum of either one STR or 8 consecutive, informative SNPs flanking the recombination breakpoint on each side for both events.

Euploid Population
SNP genotyping data for normally segregating chromosomes 21 were taken from families recruited for 1) the Autism Genetic Research Exchange (AGRE) (N = 743) [15], 2) the Framingham Heart Study (FHS) (N = 764) [16] and 3) the GENEVA Dental Caries Study (N = 107) [17] (Table 1). All families were twogeneration families with a minimum of three children. This was necessary to define specific recombination profiles for each parent child transmission.

Euploid Population Genotyping and Quality Control
The AGRE samples were genotyped for SNPs genome-wide using the Infinium(R) HumanHap550-Duo BeadChip. The AGRE data included genotypes at 520,017 markers genomewide, however 11,473 markers were excluded from the analysis due to deviation HWE (p,10 27 ). After quality control measures were completed, there was genotype information for 7,810 SNPs on 21q for the AGRE dataset. The FHS samples were genotyped for SNPs genome-wide using the Genome-Wide Human SNP Array 5.0. The FHS data included genotypes at 500,568 markers. However, 22,000 markers were excluded from the analysis due to deviation from HWE (p,10 27 ). After quality control measures were completed, there was genotype information for 6,705 SNPs on 21q for the FHS dataset. The GENEVA samples were genotyped using the Illumina 610-Quad Array. The GENEVA dataset included genotypes at 620,901 SNPs. 58,610 markers were excluded from the analysis due to deviation from HWE (p,10 25 ), a MAF , 0.02. After quality control measures were completed, there was genotype information for 8,189 SNPs on 21q from the GENEVA population. All SNP locations were based on human NCBI Build 36 (hg18).

Identifying the Location of Recombination -Euploid Samples
For the AGRE, FHS and GENEVA datasets, genotype data from members of two-generation families with three or more children were used to infer the location of recombination along the maternal chromosome 21. Our approach and software are described in Chowdhury et al. [18]. Briefly, parental genotypes were used to identify informative markers. Then, using these markers, genotypes of the children were compared to identify alleles inherited identical-by-descent from the mothers and fathers. Between two sibs, a switch from sharing the same maternal allele to not sharing was scored as a maternal recombination event.

Examining the Relationship Between Genomic Features and Recombination
We used linear regression models to assess the relationship between the quantity of recombination and the quantity of each variable of interest found within regions across 21q. We divided 21q into 500 kb bins and calculated the quantity of each variable within a bin. We chose this bin size based on our level of refinement of recombination break-points. For the genomic features, we quantified the amount of each bin occupied by each genomic feature of interest, GC content, CpG content, Poly(A)/ Poly(T) content were calculated as the proportion of each bin occupied by each feature however the number of genes per bin was calculated for gene density. Data on genomic features were based on the hg18 build of the human genome and retrieved from the following tables within the USCS Genome Browser: gc5Base, CpGIslandExt and rmsk (repeat master), UniGene_3 and RefGene. As for hotspots, we used the number of LD-defined hotspots, as defined by Myers et. al. [19] per bin as the predictor variable ( Figure S1). The outcome variable was defined as the proportion of all chromosome 21 single or double recombinant events that occurred within the bin. As it is well known that single recombination events cluster in the telomeric and centromeric regions of 21q for the MI and MII error groups (Fig. 1), respectively, we included bin location as a variable in our models as it may be a confounding variable. We stratified analyses by chromosomes with single and double recombinant events (Figs. 2 and 3, Table 1) as mechanisms of chromosome 21 nondisjunction may differ based on the number of recombinant events on 21q [2,3,5,14]. Univariate linear regression was then used to determine whether there was significant correlation between the quantity of each predictor variable and the proportion of recombination within a bin (p#0.05).
General linear regression models were used to test for differences in the slopes of the regression models between comparison groups (MI or MII versus Controls) for each predictor. That is, to compare MI error to normal meiotic outcomes, we included the interaction term of comparison group by genomic feature within a bin. This type of model was also used to compare MII errors with controls. Once again, we included bin location as a covariate for the reason stated above.

Data Availability
Data on recombination along normally segregating chromosomes 21 came from three different studies, the AGRE, FHS and GENEVA. Access to data used in this analysis from the AGRE is publically available upon IRB approval or exemption. For more information please logon to https://research.agre.org. Data from the FHS and GENEVA Studies is now available via dbGaP, accession numbers phs000007.v23.p8 and phs000440.v1.p1 respectively. Genotypes used to determine the placement of recombination along nondisjoined chromosomes 21 will also be available via dbGAP.

Association between genomic features along chromosome 21q and the proportion of recombination events
We first examined meiotic events with one detectable recombinant event on 21q (Table 2). In regression models that included both the specific genomic feature (i.e., GC content, CpG fraction, Poly(A)/Poly(T) fraction or gene density) and the location of the bin along 21q, only location, was found to be a significant predictor of the amount of recombination for the vast majority of features. This is consistent with previous work that has established altered placement of recombination as a significant risk factor for chromosome 21 nondisjunction [2,3]. There was one exception to this pattern: among the MII errors with a single recombinant, both location and GC content were significant predictors of the amount of recombination. This suggests that among MII single recombinant events, where the increased risk is associated with a pericentromeric recombinant, there may be a preference for recombination to occur in regions with elevated GC content and close to the centromere.
We then looked at meiotic events with two detectable recombinants and separated the analyses by the proximal and distal event. For proximal recombinant events (Table 3), GC and CpG content as well as bin location were found to be positively correlated with recombination among MI and MII errors; no association for these features was found among normal meiotic control recombinant events of this type (Table 3). Poly(A)/Poly(T) fraction was found to be inversely correlated with the amount of recombination among MI and MII errors and normal outcomes. Collectively these observations suggest that MI and MII proximal recombinant events occur in GC rich regions more often than statistically expected if there was no relationship between the  amount of recombination and GC (or CpG) content. We did not find any associations between genomic features and recombination among our MI and MII distal recombination events (Table 4).

Hotspot usage among normally disjoined chromosome 21 events
We examined LD-defined hotspots first among normally disjoining chromosomes (controls). We looked separately at those with one recombinant event and those with two recombinant events. Among those with one detectable event, we found a significant positive association between the number of hotspots per bin and the proportion of recombination per bin (p,.0001) (Table 5). Similarly, among those with two detectable events, we found that the proportion of proximal and distal recombinant events within a bin was significantly associated with LD-defined hotspots density (p = 0.001 and ,.0001, respectively, Table 5). Thus, as expected, the amount of recombination per bin is positively correlated with historical hotspot density suggesting that historical hotspots are used for recombination along normally segregating chromosomes 21.  We first examined single recombinants along 21q. Similar to normally segregating chromosomes, we found hotspot density to be a positively correlated with the proportion of recombination within a bin (p = 0.0006, Table 5). In order to determine whether the strength of the relationship between recombination and hotspot density differed between control and MI single recombinant events, we next tested whether the strength of the association between the proportion of recombination and hotspot density among MI errors significantly differed from that of controls and found no evidence for different patterns (Fig. 4A, p = 0.43).
Among nondisjoining chromosomes with two detectable recombinants we separated analyses by the proximal and distal event. We did not detect a significant relationship between hotspot density and the proportion of recombination per bin for proximal recombinants ( fig. 5A, table 5). For distal recombinant events, we found that recombination was significantly associated with LDdefined hotspot density (Table 5, p = 0.02), however for the patterns of association did not differ between MI and controls (Fig. 6A, p = 0.21).
Hotspot usage among nondisjoined chromosome 21events due to MII errors As for MII, we detected a significant positive correlation between hotspot density and the proportion of recombination across 21q for single recombinants. The association patterns differed significantly from that of controls (Fig. 4B, p = 0.01), with MII single recombinant events being less correlated with hotspot density than controls. Among MII errors with two recombinant events, as with MI errors, we did not detect a significant correlation between the proportion of recombination per bin and the density of LD-defined hotspots in the proximal region ( fig 5B, table 5). For MII distal events, there was a significant positive association between LD-defined hotspot density and the proportion of recombination per bin (Table 5, p = 0.02). The association patterns did not differ significantly between MII versus control events (Fig. 6B, p = 0.69).

Discussion
Association between genomic features along chromosome 21 and the proportion of recombination events along 21q In our analysis of the relationships between our genomic features of interest and the proportion of recombination per bin, we found several genomic features to be associated with recombination, although these results were not consistent among recombination types (single, double proximal or distal event). Based on the lack of patterns, we were unable to draw any significant conclusions. We do note that our large sample of normal maternal meiotic events (n = 1,272) for 21q did not show many of the relationships found in the study of Kong et al. [20].  We attribute this to a difference in the study design, not to the sample size, as both studies had comparable numbers of meiotic events. First, we restricted our analysis to 21q, whereas the original associations were found through the analysis of the entire genome. Second, the Kong et al. study the sex-averaged associations based on 628 paternal and 629 maternal meiotic outcomes; we only examined maternal recombination events. Taken together, a study of sex-specific, chromosome-specific associations of genomic features and recombination may provide further insights into the control of recombination.

Hotspot usage among nondisjoined chromosome 21 events
Our findings with regard to LD-defined historical hotspots differ between our meiotic outcomes groups and provide some insight into recombination-associated nondisjunction. First, we gain confidence that our analyses are able to identify associations with hotspot usage, as our findings from normally disjoining chromosomes 21 are consistent with expectation. That is, using our sample of normal meiotic events, our statistical analysis showed the expected pattern of increased recombination in the LD-defined hotspots for single events and double recombinant events on 21q.
As it relates to MI errors, our analysis of single recombinants indicated an association of recombination with the distribution of LD-defined hotspots along 21q, similar to controls, suggesting that these events occur preferentially near or within LD-defined hotspots. This is interesting as our previous studies have shown that the average location of MI single recombinant events is approximately 10 Mb closer to the telomere of 21q than normal single recombinant events [21]. As a result, it does not appear that the altered patterns of recombination associated with MI errors can be explained by differential hotspot usage.
We found different patterns of association for MII single recombinant events compared with those for MI-single recombinants events and controls. Specifically, we found that the proportion of single recombinants across 21q per bin is significantly correlated with LD-defined hotspots; however, this association is not as strong as it is in controls. From our most  recent work, recombination along 21q among MII errors is more proximally located: average location 22.60 Mb on 21q compared with 27.53 Mb on 21q among normal events [21]. Potentially factors characteristic of pericentromeric DNA such as chromatin structure or epigenetic modifications may affect the accessibility of a specific chromosome region to recombination in at least a proportion of oocytes with meiotic errors.
In our analysis of double recombinants events, we found similar results with respect LD-defined hotspots among MI and MII errors. We detected a significant relationship between LD-defined hotspots for the distal recombinant events among doubles, but not the proximal events. Furthermore, the lack of evidence for an association in the proximal region differed from that in controls where an association was detected (i.e., significant interaction). Oliver et al. [21]. found that the unusual pericentromeric proximal  events imposed a risk for MII nondisjunction and were associated with increased maternal age, but this pattern was not found among MI errors. Further work is needed to synthesize these results with those based on location of the events along nondisjoined chromosomes 21.
We do not provide consistent evidence that genomic features present at the site of recombination or differential hotspot usage are implicated in the nondisjunction of chromosome 21. However, altered patterns of recombination on 21q have long-been identified to be associated with an increased risk for chromosome 21 nondisjunction. Thus we believe that either the absence or altered physical placement of recombination may be more important as it relates to the risk for chromosome 21 nondisjunction. Moving forward we plan to take a genome-wide approach in efforts to identify genetic factors implicated in the altered patterns of recombination associated with chromosome 21 nondisjunction. Figure S1 Distribution of Hotspots along 21q. Hotspot counts for each of the 66 bins across 21q. (TIF)


s2fieldsofstudy: Biology Medicine

text:
BRG1 overexpression in smooth muscle cells promotes the development of thoracic aortic dissection

Background Here we investigated Brahma-related gene 1 (BRG1) expression in aortic smooth muscle cells (SMCs) and its role in the regulation of the pathological changes in aortic SMCs of thoracic arotic dissection (TAD). Methods BRG1, matrix metalloproteinase 2 (MMP2), and MMP9 mRNA and protein expression in human aortic specimens were examined by qPCR and western blot, respectively. The percentage of apoptotic and contractile SMCs in aortic specimens were determined by TUNEL assay and α-SMA immunohistochemical staining, respectively. The role of BRG1 in MMP2 and MMP9 expression, cell apoptosis, and phenotype transition in aortic SMCs were investigated using a human aortic SMC line via adenovirus mediated gene transfer. MMPs mRNA and protein levels were analyzed by qPCR and western blot, respectively. The percentage of apoptotic and contractile cells were determined through flow cytometry analysis. Results The expression level of BRG1 in the aortic walls (adventitia-removed) was significantly higher in the TAD than the normal group. BRG1 expression was positively correlated to expression of MMP2 and MMP9 and SMC apoptosis, but was negatively correlated to the percentage of contractile aortic SMCs in TAD specimens. In human aortic SMC line, BRG1 transfection led to significant upregulation of MMP2 and MMP9 expression and a concomitant increase in SMC apoptosis as well as a decrease in the percentage of contractile phenotype of cells. Conclusions BRG1 is significantly upregulated in the aortic SMCs of TAD, and its overexpression might promote the development of TAD by increasing MMP2 and MMP9 expression, inducing SMC apoptosis and the transition from contractile to synthetic phenotype.

Background
Thoracic aortic dissection (TAD) is a life-threatening aortic catastrophe with high mortality [1][2][3]. It was reported that approximately 70% of untreated TAD cases would die during the first week following the occurrence of dissection [1][2][3]. The normal aortic media is mainly composed of concentrically arranged vascular smooth muscle cells (SMCs) and extracellular matrix (ECM) rich in elastic fibers, and the interplay between SMCs and ECM plays an important role for the structural and functional integrity of the aortic wall [4,5]. Previous studies showed that overexpression of matrix metalloproteinase 2 (MMP2) and MMP9 in aortic SMCs might induce extracellular matrix degradation, apoptosis and phenotypic transition from the contractile to the synthetic type and that such pathological changes might result in instability of the aortic media, which are responsible for the occurrence of TAD [6][7][8][9][10]. It was reported that single gene mutation may cause aortic SMC dysfunction leading to the development of TAD [11,12]. Over 80% of TAD cases, however, were found to be sporadic [5], suggesting that aortic SMC dysfunction may be mainly attributable to abnormal gene expression.
Brahma-related gene 1 (BRG1) is an ATPase subunit of the SWI/SNF complex, which is required for the transcriptional regulation of specific gene expression by chromatin remodeling [13,14]. Previous studies showed that BRG1 plays an important role in the proliferation and differentiation of vascular cells [15][16][17]. Recently, it was found that BRG1 overexpression could induce cell apoptosis in rat mesenchymal stem cells and some human tumor cells [18][19][20]. Besides, it was reported that BRG1 could promote MMP2 and MMP9 expression in some human tumor cells [21,22]. However, it is unclear whether BRG1 is involved in the regulation of MMP (especially MMP2 and MMP9) expression, apoptosis and phenotypic transition in aortic SMC during development of TAD.
In light of previous studies, we hypothesized that abnormal expression of BRG1 in aortic SMC might be involved in the development of TAD. To test the hypothesis, we investigated BRG1 expression in aortic SMCs of TAD and explored its role in the pathological processes of TAD. In clinical samples of TAD, BRG1 expression is significantly increased in aortic SMCs of TAD as compared with the controls. Moreover, the expression level of BRG1 was positively correlated with MMP2 and MMP9 expression and apoptosis index, but was negatively correlated with the proportion of contractile aortic SMCs in the aortic media of TAD. In a human aortic SMC line, we demonstrated that BRG1 overexpression markedly enhanced the expression of MMP2 and MMP9 and promoted apoptosis and the transition from the contractile to the synthetic phenotype. Our data suggest that BRG1 overexpression in aortic SMCs may be involved in the critical pathological processes that leads to the development of TAD.

Tissue collection
Aortic dissection specimens were obtained from 30 patients of acute type A TAD who underwent aortic replacement procedures at Changhai hospital in 2011. Aortic tissues from patients with hereditary connective tissue defects, such as Marfan's syndrome, traumatic aneurysms or luetic aortic aneurysms were excluded. Control aortic specimens were obtained from 12 donors. The subjects with TADs and controls showed no significantly difference in clinical features including age, gender, smoking status, hypertension or diabetes. During surgery, full-thickness aortic wall specimens were collected from patients who underwent surgical repair of TAD (ascending aortas above the sinuses of Valsalva). Control specimens from normal ascending aortas were obtained from 12 organ donors. The aortic tissue was carefully cleared of adventitia, fixed in formalin for immunohistochemical staining or frozen fresh for Western blot analysis and qPCR.
The present study was conducted in accordance with the World Medical Association Declaration of Helsinki and was approved by the Medical Ethics Committee of Changhai Hospital. All participants signed informed consents.

Cell culture
Human aortic SMCs (smooth muscle cells) line from healthy aortas was obtained from Cascade Biologics and was used up to passage 8. The cells were cultured according to supplier's instructions, and were grown in Medium 231 with smooth muscle growth supplements (Cascade Biologics).

Adenovirus mediated cell infection
Ad.BRG1 and Ad.Null were constructed using AdEasy system (Stratagene) according to user protocol. Human SMCs were infected with Ad.BRG1 or Ad.Null using a multiplicity of infection of 1:100 for 12 h at 37°C before further examination.

Immunohistochemistry
Sections (5 μm) of the specimens were incubated with mouse anti-human primary antibodies (i.e. anti-BRG1, α-SMA, MMP2 and MMP9, Santa Cruz) overnight at 4°C, followed by incubation with horseradish peroxidaseconjugated goat anti-mouse antibody (Santa Cruz) for 1 hr at 37°C. Immunodetection was performed with the EnVision TM Kit (Dako), using diaminobenzidine as the chromogen.

TUNEL staining
The TUNEL reaction was performed with an in situ cell detection kit (Roche Applied Science, Indianapolis, IN, USA), according to the manufacturer's instruction, to detect the apoptosis of media cells. TUNEL-positive nuclei (stained brown) were counted by Motic Images Advanced 3.2 in 10 random fields (×200), and then averaged.

Apoptosis assay
Cell apoptosis was analyzed by annexin V-FITC assay. Briefly, cells were stained with annexin V-FITC and propidium iodide using the ANNEXIN V-FITC Kit (Beckman) according to the manufacturer's protocol and subjected to flow cytometric analysis. Viable cells were not stained by annexin V or propidium iodide; early apoptotic cells were stained by annexin V but not propidium iodide whilst late apoptotic cells were stained by annexin V and propidium iodide.
Equal amounts of protein (50 μg) were separated by 10% SDS PAGE and then transferred to nitrocellulose membranes (NY, USA) by electroblotting. The membranes were blocked with 5% BSA in TBST (10 mM Tris-HCl, pH 8.0, 150 mM NaCl, and 0.05% Tween 20) for 1 hr, and then incubated with mouse anti-human primary antibodies at 4°C overnight before subsequent incubation with horseradish peroxidase-conjugated goat anti-mouse antibody (BD) for 1 hr at 37°C. Protein was visualized using enhanced chemiluminescence reagent (Santa Cruz). The expression level of target protein was analyzed using LabWork 4.0 program (UVP) and normalized to that of β-actin protein.
PCR was performed using a Fast Start Master SYBR Green Kit (Roche) on a LightCycler (Roche). The expression level of target gene mRNA was analyzed using RealQuant software (Roche) and normalized to that of GAPDH mRNA.

Statistical analysis
Statistical significance was tested using SPSS15.0 software. The correlation between the expressions of two genes was analyzed using Pearson's correlation analysis. Other data are presented as mean ± SEM from 4 independent experiments, using student t tests for 2-group comparison. A P value less than 0.05 is considered as statistically significant.

BRG1 expression is upregulated in aortic SMCs of TAD specimens
In the present study, we first scanned the expression of BRG1 in TAD and normal aortic tissues by immunohistochemical analysis. It was found that BRG1 was mainly localized in the nucleus of aortic SMCs. Expression level of BRG1 protein was intense in TAD media, whereas it was low intense in the normal tissue. The number of BRG1 positive cells was significantly higher in TAD tissues than in normal aortic tissues ( Figure 1A). Through qRT-PCR and western blot, we further revealed that the expression level of BRG1 mRNA and protein was 2.8 and 2.0 fold higher in the aortic media of TAD tissues compared with the normal tissues, respectively The expression of BRG1 mRNA and protein in TAD and normal tissues was detected by qRT-PCR and western blot and normalized to that of GAPDH and β-actin, respectively. Each dot represents the relative expression level of BRG1 mRNA (B) and protein (C) of a tissue sample (n =30 for TAD, n =12 for normal tissues) with the line indicating the mean level; **, P <0.01 by paired t test.
( Figure 1B and C). These data demonstrate that BRG1 expression is significantly upregulated in aortic SMCs of TAD specimens.
BRG1 expression is positively correlated to the rate of apoptotic cells in aortic SMCs of TAD specimens Apoptosis is one of the mechanisms underlying aortic medial layer SMC loss. To investigate the effect of BRG1 on apoptosis of aortic SMCs, we analyzed the relationship between BRG1 expression and the apoptotic rate of aortic SMCs. As shown in Figure 2A, TUNEL-positive cells were hardly detectable in normal aortic tissues. In direct contrast, TUNEL-positive cells were numerous in the middle layer of TAD tissues. The number of TUNEL-positive cells in TAD tissues was significantly higher than that in the control group. Moreover, it was found that the expression of BRG1 was significantly positively correlated with the rate of TUNEL-positive cells in TAD tissues ( Figure 2B).

BRG1 expression is positively correlated to the expression of MMP2 and MMP9 in aortic SMCs of TAD specimens
High expression of MMP2 and MMP9 was reported to contribute to the development of TAD. Consistent with the previous studies, immunohistochemistry assay showed that the expressions of MMP2 and MMP9 in TAD tissues were significantly higher than in the normal tissues ( Figure 3A). To investigate the possible relationship between BRG1 and MMP2/9 expression, we performed correlation analysis of the western blot data and found that expression of BRG1 was significantly positively correlated with the expression of MMP2 (and MMP9) in TAD tissues ( Figure 3B), suggesting that BRG1 might increase the expression of MMP2 and MMP9 in aortic SMCs of TAD specimens.

BRG1 expression is negatively correlated to the rate of contractile SMCs in TAD specimens
Previous studies showed that aortic SMCs transited from contractile to synthetic phenotype cells led to reduce the contraction of aortic SMCs and thus weaken the aortic wall. α-SMA is widely used as a marker of the contractile phenotype of SMCs. Coinciding with the previous studies, immunohistochemistry assay showed that the expressions percentage of α-SMA positive cells in TAD tissues were significantly lower than that in the normal tissues ( Figure 4A). Moreover, it was found that the expression of BRG1 was significantly negatively related to the percentage of α-SMA-positive cells in TAD tissues ( Figure 4B). These data suggest that BRG1 might transit aortic SMCs from contractile to synthetic phenotype cells in TAD specimens.

Overexpression of BRG1 induces apoptosis of aortic SMCs
To determine the effect of BRG1 on the apoptosis of human aortic SMCs, the apoptosis of human aortic SMCs transfected Ad.BRG1 was detected by flow cytometry. It was found that the expression of BRG1 mRNA and protein was significantly increased in the aortic SMCs treated with Ad.BRG1 as compared to the cells treated with Ad.Null at 72 hr post-infection ( Figure 5A and B). Using annexin V-FITC assay, we found that in human aortic SMC line, the percentage of annexin V-FITC + /PI − and annexin V-FITC + /PI + in Ad.BRG1 infected cells were both significantly higher than those in Ad.Null infected cells ( Figure 5C). These data indicate that overexpression of BRG1 promotes apoptosis of human aortic SMCs.

Overexpression of BRG1 increases MMP2 and MMP9 expression in aortic SMCs
To investigate the effect of BRG1 on the MMP2 and MMP9 expression in aortic SMCs, MMP2 and MMP9 mRNA and protein levels in cultured human aortic SMCs treated with Ad.BRG1 were analyzed by qPCR and Western blotting. It was found that the expression of both MMP2 and MMP9 mRNA (or protein) in Ad.BRG1 infected cells was significantly higher than those in Ad.Null-infected cells ( Figure 6A and B), suggesting that overexpression of BRG1 increases MMP2 and MMP9 expression in aortic SMCs.

Overexpression of BRG1 promotes transistion of aortic SMCs from the contractile to the synthetic phenotype
To investigate the effect of BRG1 on the phenotypic transition of aortic SMCs, the expression of α-SMA was analyzed by qPCR and western blotting, and percentage of α-SMA-positive cells was analyzed by flow cytometry in cultured human aortic SMCs treated with Ad.BRG1. As shown in Figure 7A and B, the expression of α-SMA mRNA and protein was significantly lower in Ad.BRG1-infected cells than in Ad.Null-infected cells. In addition, the percentage of α-SMA-positively cell in Ad.BRG1 treated cells was significantly lower than that in Ad.Null treated cells ( Figure 7C). These results suggest that overexpression of BRG1 may promote the  transition of aortic SMCs from the contractile to the synthetic phenotype.

Discussion
Mounting evidence supports the notion that thoracic aortic dissection (TAD) is the result of biomechanical weakening of the aortic media [1][2][3]. Previous studies revealed that matrix metalloproteinase 2 (MMP2) and MMP9 overexpression in SMCs might induce extracellular matrix degradation (especially the elastic fibers), apoptosis and phenotypic transition of SMCs (from contractile to synthetic types), and hence is strongly associated with the development of TAD [6][7][8][9][10]. Here, we show for the first time that BRG1 is overexpressed in the aortic SMCs of TAD, and that its overexpression can upregulate MMP2 and MMP9 expression, induce cell apoptosis  and phenotypic transition in aortic SMCs. Our data suggest that BRG1 overexpression in aortic SMC contribute to the development of TAD.
It was reported that BRG1 is essential for the proliferation and differentiation in a variety of cell types including SMCs [15,17,[23][24][25][26]. However, the role of BRG1 in TAD has not been investigated. In the present study, we first examine the expression of BRG1 in aortic walls using immunohistochemical staining. It was found that BRG1 was mainly localized at the nucleus of SMCs in the aortic media. Through qPCR and western blot analysis of the adventitia-free aortic walls, we further revealed that the expression of BRG1 in TAD specimens was significantly higher than in normal controls, suggesting that BRG1 might be involved in the development of TAD.
Extracellular matrix (ECM) degradation is wildly regarded as a key pathological process in TAD formation [6,8]. Matrix metalloproteinase family is a group of protein enzymes responsible for maintaining the synthetic-lytic equilibrium of extracellular matrix [27,28], amongst which MMP2 and MMP9 are members essential for ECM (especially elastic fibers) degradation [29,30]. Ishii et al. showed that the expression level of MMP2 and MMP9 in SMCs of degenerated media was much higher than in other regions in TAD, suggesting that MMP2 and MMP9 overexpression in aortic SMCs is involved in TAD formation by promoting extracellular matrix degradation [31]. On the other hand, Ma et al. and Zhang et al. showed that BRG1 could induce MMP2 and MMP9 overexpression in some human tumor cells, respectively [15,21]. In the present study, we demonstrated that the expression level of BRG1 was positively correlated with that of MMP2 and MMP9 in aortic media of TAD. In addition, using a human aortic SMC line, we show that BRG1 overexpression could significantly increase the mRNA and protein expression of MMP2 and MMP9, indicating BRG1 could promote MMP2 and MMP9 expression in SMCs probably at the transcriptional level. These results suggest that BRG1 overexpression may contribute to the development of TAD via upregulation of MMP2 and MMP9 expression in aortic SMCs and subsequent enzymatic degradation of the ECM.
Aortic SMCs are responsible for the tensile strength and elasticity of the aortic wall, which are the characteristic biomechanical properties of aorta representing its capacity to withstand stress [5,11,12]. Conceivably, loss of aortic SMCs would impair the mechanical strength of the vessel wall, leading to dilation and even rupture of the aortic wall [5]. It was reported that the number of apoptotic aortic SMCs in TAD was significantly higher than that in normal, suggesting that aortic SMCs apoptosis is involved in the formation of TAD [6,7]. Previous studies showed that BRG1 overexpression could significantly increase the apoptotic rate of mesenchymal stem cells and some tumor cells [19,20]. Recently, Zhang et al. showed that BRG1 was upregulated in vascular SMC of patients with primary atherosclerosis and was involved in the pathophysiological process of atherosclerosis presumably by inducing cell apoptosis [32]. In the present study, we found that BRG1 is overexpressed in aortic SMC and its level was positively correlated with the percentage of apoptotic aortic SMCs in the aortic media of TAD. Using an aortic SMC line, we further demonstrated that BRG1 overexpression can significantly induce aortic SMCs apoptosis. Our results suggested that BRG1 overexpression may promote the occurrence of TAD by inducing aortic SMCs apoptosis.
SMCs in the normal aorta are mostly quiescent cells that harbor a unique repertoire of contractile proteins required for cell functions [33]. It was reported that in response to vascular injury, the aortic SMCs could transit from contractile to a synthetic phenotype with little contractile proteins [34,35]. Inamoto et al. and Huang et al. independently reported that the proportion of contractile aortic SMCs in the aortic media of TAD was significantly lower than in the normal aorta, suggesting that the transition of aortic SMCs from contractile to synthetic phenotype may play an important role in aortic SMCs contractile dysfunction in TAD [36,37]. In the present study, we found that the level of BRG1 was positively correlated with the percentage of aortic SMCs with synthetic phenotype in TAD aortic media. In cellular experiment with an aortic SMC line, BRG1 overexpression was found to promote the transition from contractile to synthetic phenotype. The results imply that overexpression of BRG1 might be involved in the development of TAD by promoting phenotypic transition of aortic SMCs. It was reported that BRG1 could up-regulate the stemness genes such as Oct4, Sox2, Sall4, and maintain the self-renewal capacity in embryonic stem cells. As we known, the synthetic phenotype of aortic SMCs was regarded as the dedifferentiated cells. In this sense, it would be interesting to see whether BRG1 promote phenotypic transition of aortic SMCs through its regulation on the expression of stemness genes in the future. However, our study had several limitations that should be mentioned. Low rate of proliferation is another important characteristic of contractile phenotype SMCs other than decreased production of MMPs, increased expression of SMC-specific contractile genes such as α-SMA and SM22α. Inflammatory cytokines such as IL-1β are another important factors regulating phenotype transition and induce expression of proinflammatory genes in SMCs. Whether BRG1 regulates SMC proliferation and the interaction between BRG1 expression and inflammatory states warrants further investigation. In addition, since the matrix degradation activity of the MMPs is also regulated by the presence of naturally present tissue inhibitors of metalloproteinases (TIMPs), examining the matrix degradation activity of the MMPs may better reflect the pathophysiologic role in the development and progression of the aortic disease than measuring MMP expression.
Besides, interactions among MMPs expression, cell phenotypes transition and apoptosis would be investigated in the future. On the other hand, although our clinical investigation and in vitro cellular experiment suggest that BRG1 overexpression play roles in the development of TAD, further studies using appropriate animal models is still warranted.

Conclusions
In summary, we report here that BRG1 level is significantly upregulated in aortic SMCs of TAD as compared with the controls in human samples, and its overexpression in human aortic SMC line can promote MMP2 and MMP9 expression, apoptosis and phenotypic transition from contractile to synthetic type. The results suggest that BRG1 overexpression in aortic SMC may play a role in the development of TAD. Further experiments in animal models are warranted to establish the role of BRG1 in the pathophysiological processes of TAD formation, and targeting BRG1 might provide a novel therapy in the treatment of TAD.


s2fieldsofstudy: Biology Chemistry

text:
Tryptophan: ‘essential’ for the pathogenesis of irritable bowel syndrome?

IntroductionTryptophan is an ‘essential’ amino acid: humanscannot make it themselves, but must obtain it inthe form of food. In addition to being a buildingblock in the biosynthesis of proteins, this aromaticsubstance can be converted into a number of meta-bolites of great clinical importance. The best known isthe enzymatic conversion to serotonin. However, inrecent years, attention has increasingly focused on themedical signiﬁcance of the enzymatic oxidation oftryptophan to form indole compounds collectivelyknown as kynurenines. Both excessive kynurenineproduction and an accompanying deﬁciency of thesubstrate, tryptophan, may be the cause of a numberof pathological conditions. In addition, the oxidationitself may affect the body’s redox balance. In thisarticle, we hypothesize that tryptophan is ‘essential’inthepathogenesisofirritablebowelsyndrome (IBS).Indolamine dioxygenaseIndolamine dioxygenase (IDO) is the key enzymeinvolved in the conversion of tryptophan in the intes-tine. The enzyme catalyses oxidation of tryptophan tokynurenine in a reaction that produces peroxide andgives rise to highly reactive and potentially harmfuloxygen and hydroxyl radicals. The IDO reaction maytherefore contribute to what is generally calledoxidative stress [1,2]. Oxygen and hydroxyl radicalsformed as a result of the IDO reaction will acceleratethe oxidation of nitrogen monoxide (NO) to nitrite(NO

Introduction
Tryptophan is an 'essential' amino acid: humans cannot make it themselves, but must obtain it in the form of food. In addition to being a building block in the biosynthesis of proteins, this aromatic substance can be converted into a number of metabolites of great clinical importance. The best known is the enzymatic conversion to serotonin. However, in recent years, attention has increasingly focused on the medical significance of the enzymatic oxidation of tryptophan to form indole compounds collectively known as kynurenines. Both excessive kynurenine production and an accompanying deficiency of the substrate, tryptophan, may be the cause of a number of pathological conditions. In addition, the oxidation itself may affect the body's redox balance. In this article, we hypothesize that tryptophan is 'essential' in the pathogenesis of irritable bowel syndrome (IBS).

Indolamine dioxygenase
Indolamine dioxygenase (IDO) is the key enzyme involved in the conversion of tryptophan in the intestine. The enzyme catalyses oxidation of tryptophan to kynurenine in a reaction that produces peroxide and gives rise to highly reactive and potentially harmful oxygen and hydroxyl radicals. The IDO reaction may therefore contribute to what is generally called oxidative stress [1,2]. Oxygen and hydroxyl radicals formed as a result of the IDO reaction will accelerate the oxidation of nitrogen monoxide (NO) to nitrite (NO 2 ) and of ferrous iron compounds to ferric iron compounds. The reactive oxygen products have an antimicrobial effect, but may also harm mitochondria and lead to increased production of inflammatory cytokines (TNF-a, IL-6) [3,4] and be causally related to fatigue and pain hypersensitivity [5,6].
The harmful effects of the IDO reaction may be counteracted by ascorbic acid (vitamin C) and by antioxidants that occur naturally in plants. Most antioxidants in plants are members of the main groups polyphenols, carotenoids and flavonoids. The best known are curcumin in turmeric, catechines in green tea, resveratrol in red wine and epigallocatechins in cocoa. In addition to phenol antioxidants, edible plants in the Brassica family, such as cabbage and broccoli, contain glucosinolates that are broken down in the stomach to thiocyanates. These compounds help to control the redox balance in the intestine, for example, by activating the redox-regulating transcription factor Nrf2 [7]. Glucosinolates also give rise to indole products that are identical to some of those formed by enzymatic breakdown of tryptophan in the intestine, for example, indole-3-carbinol and 3,3-di-indolylmethane [7].

Hypothesis
Increased production of peroxide and reactive oxygen as a result of IDO activation may conceivably disrupt the finely tuned interaction between oxidants and antioxidants, concurrently with a change in the tryptophan and kynurenine levels. Perhaps such disruptions in the redox and tryptophan-kynurenine balance, and alterations in levels of tryptophan and tryptophan metabolites can 'explain' the abdominal and systemic symptoms of IBS? If this is the case, tryptophan is indeed essential, also in IBS pathogenesis.

The kynurenine pathway
Up to the present, interest in tryptophan from a medical perspective has focused mainly on its role in serotonin metabolism. Serotonin is an important neurotransmitter in the enteric and central nervous system. In the bowel, it regulates secretion, motility and sensibility, while in the central nervous system it modulates mood, cognition and sleep. Neuropsychiatric problems have been attributed to too much or too little serotonin. For example, it has been demonstrated that a temporary tryptophan deficiency causes memory failure in healthy individuals [8]. In recent years, the focus has been more on tryptophan metabolism via kynurenine (the kynurenine pathway), where most of the tryptophan is catabolised by the IDO in the gastrointestinal tract. Tryptophan 'surplus' is catabolised in the liver, but by a different dioxygenase from IDO: tryptophan-2,3-dioxygenase. Only 1% of tryptophan intake is metabolised into serotonin, and the two metabolic pathways (serotonin and kynurenine) compete for the substrate (tryptophan) [9]. Increased consumption of tryptophan via the kynurenine pathway may result in too little tryptophan and serotonin in both the central nervous system and the gastrointestinal tract. It has been clearly shown to cause symptoms of anxiety and low mood in mice [10]. This may also be relevant for humans, because people vulnerable to stress may benefit from tryptophan supplements, for example, in the form of tryptophan-rich albumin or whey protein hydrolysates [11].
Most of the serotonin in the body (95%) is found to be in the gastrointestinal tract. Interestingly, studies have demonstrated that patients with IBS have low serotonin concentrations [12] and small numbers of serotonin-containing neuroendocrine cells in the small intestine [13]. Acting on mucosal and submocosal neurons in a paracrine fashion, serotonin is highly important for regulation of motility of the gastrointestinal tract. Impaired small intestinal motor function, as observed in patients with IBS [14], may thus be a consequence of too low serotonin levels within the intestinal wall. The end product of serotonin catabolism, 5-hydroxy indole acetic acid, is excreted in urine. In patients with IBS this excretion is low, indicating low production of serotonin as well [15]. Serotonin is inactivated by reuptake to enterocytes, neurons and platelets, and this serotonin transport function is reduced in patients with IBS, possibly a result of increased production of interferon gamma (INFg) [16,17].
Immunoactivation that results in increased production of INFg will also increase IDO activity [1]. IDO oxidation stimulated by tryptophan has proved to be of great significance for such diverse diseases as myocardial infarction, Alzheimer's disease and mental retardation [8,18,19], and tryptophan is used in some therapeutic combinations for cognitive impairment in the elderly [20]. Activation of the kynurenine pathway also has an immunomodulatory effect, and is of significance for the development of multiple allergies and autoimmune diseases [21,22]. Both local tryptophan deficiency and systemic metabolites from the kynurenine pathway are considered to play an important part in this interaction. Thus, local tryptophan depletion in and around infected mucosal cells may impair microbial growth [23]. Intestinal malabsorption may also contribute. Patients with IBS and chronic fatigue often have signs of low-grade malabsorption with deficiencies of iron, vitamin B12, folic acid and vitamin D, and we have previously demonstrated that about 30% of IBS patients have fat malabsorption [24]. Ledochowski et al. demonstrated as early as in 2001 that an excessively high fructose level in the colon may reduce the availability of tryptophan as a substrate for biosynthesis of serotonin, and that serotonin deficiency and depression may be associated with fructose malabsorption [25].

Microbial IDO activation?
A high kynurenine/tryptophan ratio is the result of a high level of activity in the kynurenine pathway, and this may be the closest one comes to a biomarker for IBS [26,27]. But this ratio is also high in a number of other inflammatory conditions and types of cancer where there is a high INFg, either locally or systemically [26,28]. Low tryptophan and a high kynurenine/ tryptophan ratio are therefore not specific to IBS, but may indicate an underlying low-grade inflammatory process. This immunoactivation and subsequent high IDO activity may conceivably have a microbial cause. Brottveit et al. recently described an INFg increase in duodenal biopsies after intake of gluten-containing bread by gluten-sensitive patients without coeliac disease [29]. The mechanisms and consequences of this INFg increase have not been determined. Gluten is only partly digestible by human enzymes, and remains semi-digested and unabsorbed in the small intestine. Undigested gluten peptides may be directly toxic [30], antimicrobial and immunomodulatory [31]. INFg activation is an important part of the innate immune system and may be induced by microbes [32,33]. As soon as the undigested gluten peptides reach the large intestine, they become food for the intestinal bacteria [34], and the question is whether it is this gluten-stimulated intestinal flora that causes IBS-like symptoms in patients with noncoeliac gluten-sensitivity. Our hypothesis is that INFg activation and subsequent IDO oxidation of tryptophan is a pathogenetic mechanism of IBS and that the initial stimulus may be of microbial origin also in the case of non-coeliac gluten-sensitivity.

Does tryptophan act via aryl hydrocarbon receptor?
The receptor protein aryl hydrocarbon receptor (AhR) has been preserved in phylogenesis of vertebrates for several hundred million years. It used to be believed that its primary significance was in the inactivation of environmental toxins such as xenobiotics, by stimulating the activity of cytochrome P (CYP) enzymes. However, activated AhR also functions as an intracellular transcription factor that governs a number of vital functions [35] and is necessary inter alia for normal intestinal immune function. Of particular interest to us is the fact that AhR regulates the barrier function of the intestinal mucous membrane, apparently in a system whereby the mucous membrane 'tastes' the contents of the intestine and regulates its defences accordingly. AhR activation of lymphoid cells produces cytokines (IL-22), which regulate the immune system and development of tolerance. These innate lymphoid cells (ILC) form part of the innate immune system. In other words, the 'old' receptor (AhR) acts on cells that are ready to function already at birth, and we are clearly dealing with an innate immune system here. A number of studies have revealed that AhR ligands regulate the number of intraepithelial and regulatory T cells, cytotoxic T cells, antibody-producing B cells and mast cells. Failure of this immune system may result in autoimmunity and allergy [36].
Since AhR is an innate receptor in a number of cells, it is reasonable to assume that endogenous ligands may existand they do. New AhR ligands are constantly being found, and many of them actually prove to be tryptophan metabolites [37]. INFginduced tryptophan degradation may induce antimicrobial and immunomodulatory effects in epithelial cells [31]. Little research has been conducted on the importance of the intestinal flora in this regard, but the microbes can metabolise tryptophan and give rise to AhR ligands, which in turn affect the mucous membrane defence. The lactic acid bacteria (i.e., Lactobacillus reuteri in the intestine and L. acidophilus in the vagina) eat tryptophan as a source of energy and produce indole-3-aldehyde (IAld), an AhR ligand that activates ILC and releases IL-22, which stimulates the production of antimicrobial peptides, curbs the growth of Candida albicans, but boosts the growth of lactic acid bacteria. Given the choice, bacteria eat sugar rather than amino acidsand without tryptophan in their 'diet' they do not produce IAldwhich will mean less lactobacilli and more Candida [33], which is typical of IBS.
It was also shown recently that kynurenine activates mast cells via AhR [38] and that AhR is upregulated in allergic rhinitis [39] and can be inhibited by another (non-toxic) tryptophan metabolite (abbreviated to ITE) [40]. These metabolites present very interesting possibilities for developing new treatments for allergies. However, there are a number of AhR ligands that are much more readily available and that may be equally interesting: the Brassica family, for example, contains tryptophan derivatives (glucosinolates) which convert gastric acid into potent AhR ligands that contribute to a favourable microbial environment in the stomach [41]. A relevant question is therefore: How much and in what manner is this regulatory system affected by our daily diet?

Therapeutic possibilities for IBS
If IBS and associated health problems like fibromyalgia and fatigue [42] are an IDO-stimulated consequence of an abnormal intestinal flora, one might imagine that the problems could be treated with antibiotics. Interestingly, we sometimes see astonishing results due to antibiotics, but in our experience, unfortunately, the patients subsequently become sicker than ever. We suspect that the guilty microbes are facultative anaerobes, and that they are mucousproducing and possibly 'conceal themselves' in a biofilm of mucus [43]. Lipopolysaccharide (LPS) from Gram-negative bacteria may stimulate IDO [44], but LPS and CD14 in blood appear to be normal (unpublished data), even though the LPS receptor TLR4 may appear upregulated in the colon of a subgroup of IBS patients [45]. We therefore believe that the undesirable intestinal flora is not Gram-negative.
When antibiotics do not work, we must find other options. Dietary treatment (low FODMAP diet) to 'starve' the bacteria has proved useful [46], but hardly curative. The alternative, bacteriotherapy, is increasingly in fashion. Following the publication of a study of such therapy for patients with post-giardiasis IBS [47], we have noticed great interest in faecal bacteriotherapy (a variant of faecal microbiota transplantations). The effect was transitory, unfortunately, but perhaps better, nonetheless, than the effect of rifaximin, a non-absorbable antibiotic that was recently described in the New England Journal of Medicine [48]. However, getting the installed flora to establish themselves in a new host is a major problem that may be due to changes in the environment of the host's 'fermentation chamber'the coecum. The contents of the coecum should normally be slightly acidic, with a pH of between 5 and 6, and the acidity in the coecum is related to the redox potential [49]. If the redox potential is not low enough (not sufficiently anoxic), there will be excessive growth of facultative anaerobic bacteria, and the fermentation process will be incomplete [50]. With too high oxygen tension in the coecum, reactive oxygen species will be generated, which can oxidise NO to NO 2 leaving an NO deficiency, while NO 2 (and other oxidation products) may become 'food' for a non-benign bacterial flora [51]. If the host coecum does not have the correct redox potential, this may be sufficient to prevent new flora establishing themselvesuntil something is first done with the host redox environment. This is a challenge that has not yet been solved.
Exogenic addition of tryptophan may increase levels of serotonin and benign AhR ligands. However, this gives rise to the fear that one may be 'adding fuel to the flames,' because tryptophan oxidation will give rise to more reactive oxygen compounds. This is where the combination with antioxidants comes in. If we can curb the oxidative stress with antioxidants, the favourable effects of increased levels of serotonin and AhR ligands may make the overall effect of administering tryptophan positive. Thiocyanates formed by the breakdown in the stomach of glucosinolates in Brassica plants may be of special interest because they activate the Nrf2 receptor and have a regulatory effect on the redox level [7]. A diet high in Brassica vegetables is associated with reduced risk of chronic diseases [7], and constitutes an uninvestigated possibility of reducing pain and pain hypersensitivity in IBS and fibromyalgia [5,6]. In the past, we probably consumed much larger quantities than we do now, because hybridisation of Brassica plants has led to a reduction in their glucosinolate levels [52].

Concluding remarks
IBS may be related to disrupted tryptophan metabolism with increased oxidative stress and deficiencies of tryptophan and tryptophan-derived AhR ligands [36]. The cause may be dysbiosis [53] due to high intake of carbohydrates [46]. If the microbes get their preferred food (sugar), they do not produce AhR ligands that strengthen the defences of and barrier presented by the mucous membranes. Without these AhR ligands, the ratio between the lactic acid bacteria and C. albicans is reduced and the immunobalance skewed in favour of the development of IBS, allergies and autoimmune diseases. When we say 'You AhR what you eat' [54], it means that a number of disorders, including IBS, fibromyalgia and fatigue, may be indirect consequences of diet, mediated by the essential amino acid tryptophan and its microbial metabolites. So perhaps it is more correct to say 'You AhR what your microbiota eat?' Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.