text:
Toll-Like Receptor (TLR)-Associated Sequence Variants and Prostate Cancer Risk among Men of African Descent

BACKGROUND Recent advances demonstrate a relationship between chronic/recurrent inflammation and prostate cancer (PCA). Among inflammatory regulators, toll-like receptors (TLRs) play a critical role in innate immune responses. However, it remains unclear whether variant TLR genes influence PCA risk among men of African descent. Therefore, we evaluated the impact of 32 TLR-associated single nucleotide polymorphisms (SNPs) on PCA risk among African-Americans and Jamaicans. METHODS SNP profiles of 814 subjects were evaluated using Illumina’s Veracode genotyping platform. Single and combined effects of SNPs in relation to PCA risk were assessed using age-adjusted logistic regression and entropy-based multifactor dimensionality reduction (MDR) models. RESULTS Seven sequence variants detected in TLR6, TOLLIP, IRAK4, IRF3 were marginally related to PCA. However, none of these effects remained significant after adjusting for multiple hypothesis testing. Nevertheless, MDR modeling revealed a complex interaction between IRAK4 rs4251545 and TLR2 rs1898830 as a significant predictor of PCA risk among U.S. men (permutation testing p-value = 0.001). CONCLUSIONS MDR identified an interaction between IRAK4 and TLR2 as the best two factor model for predicting PCA risk among men of African descent. However, these findings require further assessment and validation.

INTRODUCTION
Prostate cancer (PCA) is one of the most frequently diagnosed cancers among western men of African origin and is the second leading cause of their cancer-related deaths [1,2]. In the United States (U.S.), African-American men are more likely to receive a PCA diagnosis and die from disease than any other racial or ethnic group. Between 2003 and 2007, the average annual PCA incidence and mortality rates among African-American men were 1.6 and 2.4 higher than Caucasian-American men, respectively. Moreover, several studies indicate similar PCA disparities between American men and Caribbean men [1,3,4]. Based on 2008 worldwide statistics, age-standardized death rates among Caribbean men were 2.65-fold higher than American men [4]. Ethnicity, age, and family history are a few risk factors that have been implicated in determining PCA risk. More recently, chronic inflammation has also been considered as an important contributor of PCA, yet the precise etiology of PCA still remains unknown [5][6][7].
This study examined the role of TLR signaling pathway sequence variants on PCA among men of African descent. We evaluated the effects of 32 TLR SNPs individually or jointly on PCA susceptibility among 814 African-American and Jamaican men. Main effects and complex interactions were assessed using conventional and bioinformatic techniques, including logistic regression and entropy-based multi-factor dimensionality reduction (MDR). Investigation of genetic susceptibilities detected within the TLR signaling pathway will provide a better understanding of their influence on PCA risk among men of African descent.

Population Description
The demographic and other pertinent characteristics of cases and controls for the entire study population and each study center are summarized in Table 1 and Supplemental Tables AB. Overall, men diagnosed with prostate cancer were 14 years older and had higher PSA levels than controls (P < 0.0001). Among controls, Jamaican men were about 9 years older and had higher PSA levels (P < 0.0001) and as well as higher median Gleason scores (P = 0.018) than U.S. men. As summarized in Table 1, there were significant differences in family history of PCA with respect to the following: (1) cases to controls from the total population (P = 0.316) ( Table 1), U.S. alone (P = 0.592) (Supplemental Table A), or Jamaica alone (P = 0.272) (Supplemental Table B), and (2) controls (P = 0.757) or cases (P = 0.830) comparing the two study centers (data not shown). Among African-Americans, the degree of West African ancestry did not vary by disease-status, as shown in Supplemental Table A [ [47][48][49]; however, no such data was collected for the Jamaican men.
Minor allele and genotype frequency among men of African descent from the U.S. and Jamaica Overall, the average minor allele frequency (MAF) for TLR-associated SNPs among disease-free U.S. and Jamaican men combined was 23.3% [standard deviation (SD) = 15.8], as shown in Supplemental Table C. When stratified by study site, the MAFs for U.S. and Jamaican men were 23.3% (SD = 16.1) and 21.9% (SD = 14.9), respectively. When the TLR-associated SNPs among the total population (i.e., U.S. and Jamaican men combined) were analyzed, 87.5% had minor allele frequencies ≥5%.

Association between TLR-associated sequence variants and prostate cancer risk
Three TLR-associated markers (TLR6 rs2381289, TOLLIP rs3168046, and TOLLIP rs5743899) were modestly linked with PCA susceptibility for the total population under the age-adjusted LR models, as shown in Table 2. In particular, possession of the TLR6 rs2381289 GA or TOLLIP rs5743899 AG+GG genotypes was marginally associated with a 1.46-1.49 fold increase in the risk of developing PCA. In contrast, TOLLIP rs3168046 AA carriers had a marginally significant 42% reduction in PCA susceptibility (OR age-adjusted = 0.58; 95%CI = 0.35, 0.98). However, none of these markers remained significant after adjusting for multiple hypothesis testing.
Upon stratification by study center, six of the 32 candidate TLR-associated SNPs were identified as modestly significant determinants of PCA risk after adjusting for age, as shown in Table 3. Within the U.S. population, a nominal 40-66% reduction in PCA susceptibility was observed among men who possessed one or more IRAK4 (rs4251545A and rs4251473A) or TLR6 rs5743818C minor alleles; however, the relationship was strongest for carriers of the IRAK4 rs4251545 AA genotype (OR age-adjusted = 0.34; 95%CI = 0.13, 0.91). Moreover, the TOLLIP rs5743899 locus, under the recessive genetic model (GG versus AG +AA), was modestly associated with a 1.14 fold increase in the risk of developing PCA (OR age-adjusted = 1.14; 95%CI = 1.12, 1.18). Among Jamaican men, IRF3 rs2304206, TLR6 rs2381289, and TLR6 rs5743818 were marginally associated with PCA risk. For instance, inheritance of the IRF3 rs2304206 GG genotype (OR age-adjusted = 0.32; 95%CI = 0.10, 0.98) was linked to a modestly significant 68% reduction in PCA susceptibility. On the other hand, there was a 1.10-2.0-fold increase in PCA risk associated with inheriting the TLR6 rs2381289 GA+AA (OR age-adjusted = 2.05; 95%CI = 1.10, 3.78) or TLR6 rs5743818 AC+CC GA (OR age-adjusted = 1.10; 95%CI = 1.06, 1.14) genotypes. Notably, additive genetic models for IRF3 rs2304206 and TLR6 rs2381289 were significantly related to prostate cancer risk, which is modestly suggestive of a significant dose-response effect in relation to the number of inherited minor alleles (P-trend ≥ 0.0193). These modest associations; however, did not persist after adjustments for multiple hypothesis testing.

Analysis of gene-gene interactions using Multi-factor Dimensionality Reduction (MDR)
MDR modeling was used to efficiently assess and validate age-adjusted gene-gene interactions for the total population, U.S. men alone, and Jamaican men alone in relation to PCA risk. The top one-, two-, and three-way interaction models for the total population, involving U.S. and Jamaican men combined, displayed 100% cross validation consistency (CVC) values, 57-65% average testing accuracy (ATA) scores, and permutation p-values = 0.001. However, the three-way interaction among TLR6 rs2381289, TLR10 rs11096957, and IRF3 rs2304206 was selected as the best PCA predictor for men of African descent, since this model had the highest average testing accuracy (ATA = 0.6505). This three-way interaction was primarily driven by a synergistic relationship between TLR6 rs2381289 and IRF3 rs2304206 (data not shown).
CVC and ATA scores for all 1-, 2-, and 3-factor models among U.S. men were significant and characterized as 90-100% and 57-62%, respectively, as described in Table 4A-B. However, interaction between TLR2 rs1898830 and IRAK4 rs4251545 was chosen as the best PCA predictor, based on a higher average testing accuracy (ATA = 61.94%) and lower permutation testing value (P = 0.001) relative to the best three-factor model (P = 0.015). The two-way interaction, as shown in Figure 2, was highly synergistic since the joint information gain score (2.33%) exceeded the mutual information gain scores for TLR2 rs1898830 alone (0.04%) and IRAK4 rs4251545 alone (1.30%).
For the Jamaican population, the one-way model containing the TLR6 rs2381289 loci was the best PCA-related MDR model based on a 62.7% prediction accuracy score and significant permutation p-value (P = 0.0018). Although the two-and three-way models both had high CVC scores (≥ 80%), these models failed to reach statistical significance.

DISCUSSION
Dysregulation and genetic alterations in immune system function are linked to many cancers. In particular, it is estimated that approximately 20% of all human cancers, including prostate cancer (PCA), are associated with chronic inflammation [28,50]. TLR activation, a key initiator of inflammation and dysregulation of TLR-responsive pathways, has been associated with cancer susceptibility. The current study evaluated 32 TLR-associated sequence variants to determine their individual and joint modifying effects on PCA risk among 279 cases and 535 disease free men of African descent. Out of the 32 minor variant alleles, 7 were modestly associated with a 1.14-2.05-fold increase (TLR6 rs2381289, TOLLIP rs5743899) or 39-68% decrease (TOLLIP rs3168046, IRAK4 rs4251473, IRAK4 rs4251545, IRF3 rs2304206, TLR6 rs5743818) in the risk of developing prostate cancer either in the total population and/or the stratified analysis, after adjusting for age. Among U.S. men, there was a nominal 40-66% reduction in PCA susceptibility among those who possessed one or more IRAK4 (rs4251545 and rs4251473) or TLR6 rs5743818 minor alleles. We also found that the TOLLIP rs5743899 SNP under the recessive genetic model (GG versus AG+AA) was modestly associated with a 1.14 fold increase in the risk of developing PCA (OR age-adjusted = 1.14; 95%CI = 1.12, 1.18) among men of African descent from the U.S. The TOLLIP rs3168046 SNP was unique to the total population; whereas, 3 markers (IRAK4 rs4251473, IRAK4 rs4251545, TLR6 rs2381289) were unique to the U.S. men. Jamaican and U.S. sub-groups each had 1 SNP in common with the total population, namely TLR6 rs2381289 and TOLLIP rs5743899, respectively. Among Jamaicans, both the IRF3 rs2304206 and TLR6 rs2381289 loci were significant under the additive genetic model. However, only the TLR6 rs2381289 SNP for the Jamaican population remained statistically significant after adjusting for age and multiple hypothesis testing (Permutation p-value = 0.018).
Finally, we examined main effects and interactions of TLR-associated SNPs as predictors of prostate cancer using age-adjusted MDR and found that the best model varied depending on the composition of the study population. Among U.S. men of African descent, the best predictor of PCA risk was the two-factor interaction between IRAK4 rs4251545 and TLR2 rs1898830. Several investigators have evaluated the link between prostate cancer outcomes and toll-like receptor-associated (TLR1, TLR4, TLR6, TLR10, IRAK4) sequence variants. Collectively, 9 studies evaluated 14 out of the 32 SNPs considered in the current study with mixed findings [38][39][40][41][42][43][44][45][46]51]. In the Cancer of the Prostate Study (CAPS), inheritance of one or more TLR1 rs4833095 and TLR10 (rs11096955, rs11096957) minor alleles were associated with an increase in prostate cancer risk among Caucasians. However, the American Cancer Society Cancer Prevention II Nutrition Cohort (CPS-II) study reported protective effects [44] and null findings were observed for Caucasians of the Health Professionals Follow-up Study [45,46], PLCO Study [45] as well as a meta-analysis of 3142 cases and 2567 controls [45]. Consistent with the current study, five independent studies reported null findings in relation to prostate cancer risk and possession of TLR1 rs46224663 and TLR6 (rs5743814, rs5743810, rs1039559, rs3821985) variant alleles [44][45][46]51]. Inheritance of one or more minor alleles were associated with either an increase (TLR1 rs5743604, TLR10 rs4274855) [45,51] or decrease (TLR1 rs5743595, TLR1 rs2149356, TLR4 rs1927911) [43][44][45] in the risk of developing prostate cancer among Caucasians in four separate observational studies. However, these same markers were not significantly related to prostate cancer among men of African and European descent in the current and other independent studies [40,42,45,46]. The TLR4 rs1927911CC genotype was linked to an increase in prostate cancer risk in one small Korean case-control study [38]; whereas, another Korean study did not reveal a statistically significant relationship [41]. Discrepant findings for these two Korean studies may be attributed to differences in methods used for allelic discrimination. Utilization of RFLP-PCR method used in the former study [38] may not have the same capacity to discern between homozygous and heterozygous genotypes, relative to a more advance method used in the study performed by and coworkers [41]. Other explanations for differences in the directionality of prostate cancer risk estimates in the aforementioned TLR-related SNPs may include: inadequate statistical power to detect significant differences between prostate cancer cases and controls among Koreans [38,41]; failure to adjust for multiple hypothesis testing [38,39,[41][42][43][44][45][46]51]; and unknown environmental exposures/lifestyle factors (e.g., pathogens, environmental toxins, antioxidants) [38][39][40][41][42][43][44][45][46]51]. Variations in population composition may account for the mildly suggestive yet distinct risk alleles noted in the men of African descent from the U.S. (IRAK rs4251473, IRAK rs425154, TOLLIP rs5743899) and Jamaica (IRF3 rs2304206, TLR6 rs2381289) in the current study.
This study is the first to address a relationship between select polymorphic TLR-related genes and PCA in general (i.e., TLR2, IRF3) and more specifically among men of African descent [i.e., TLR1,TLR 2,TLR 4,TLR 6,TLR10, IRAK4, IRF3, and TOLLIP]. TLR6 rs2381289 and TLR2 rs1898830 PCA-related SNPs identified in this study are likely to affect transcriptional regulation of TLR genes. TLRs 2 and 6 are controlled by the master regulatory transcription factor p53 [76]. TLR-associated SNPs may alter p53 interactions with these TLR genes. Furthermore, the IRAK4 rs4251545 SNP, in silco, codes for alterations in mRNA splicing, which in turn may influence mRNA stability, IRAK4 kinase activity, as well as TLR signaling protein-protein interactions. Such alterations may lead to biochemical conditions that favor cell death, decreases in matrix metalloproteinases linked to cell migration, decreases in pro-inflammatory cytokines/chemokines and ultimately abrogation of tumor growth. However, further molecular biological studies are needed to determine the exact impact of the aforementioned SNPs on prostate tumor biology.
We are aware of both strengths and limitations of our approach. Although we observed nominal relationships between selected TLR-associated SNPs and PCA risk, we cannot rule out the possibility that anomalies within the innate immune pathway will influence disease prognosis. Consequently, future studies will enable us to evaluate the relationship between TLR SNPs and Gleason score, tumor stage, biochemical/disease recurrence, and overall/ disease specific mortality. Future multi-center pooled genetic studies with thousands of cases and controls may enable us to confirm and refine our small effect sizes. It is plausible that other targets immediately downstream of the TLRs in innate immune signaling pathways may play a role in PCA risk among men of African descent, as shown in Figure 1. Moreover, PCA susceptibility may also be influenced by polymorphisms of some genes even further down the TLR signaling pathway, including caspases (CASP 3, 7, 8, and 10), mitogen-activated protein kinases (MAPKs), interferon-regulatory factors (IRFs), interferons, inflammatory cytokines, and chemokines. This represents an area of active research within future collaborative studies in our lab.
Analysis of mRNA and protein levels of TLR-related SNPs and investigation of the relative expression and activity of downstream targets are needed to define the biological mechanisms that give rise to prostate cancer disparities among men of African and European descent. The genetic admixture among African-American men, as documented by other published reports, may modify the relationship between TLR SNPs and prostate cancer risk. For the current study, analyses were restricted to men with ≥25% West African Ancestry. Adjustment of our risk models for West African Ancestry ultimately had no significant bearing on calculated risk estimates among African-American men. Due to chance alone, it is estimated that 5% of the 744 SNP interactions among 32 TLR sequence variants will result in 37 significant relationships. However, we controlled for multiple hypothesis testing bias by adjusting our MDR findings with permutation testing. Given the low prediction accuracy (i.e., 61.9%) between TLR2 and IRAK4, our study findings require replication within independent study sets. However, recent simulation studies demonstrate that even modest disparities in genotype frequencies among study participants of independent study sets may interfere with the capacity to replicate complex interactions [52]. Consequently, to replicate our findings, it is critical that future replicate studies should have the same genetic architecture (i.e., ancestry identification markers and TLR SNPs) as the African-Americans in the current study. Caution is recommended in the interpretation of our study findings due to a modest marginal effect between TLR signaling sequence variants and PCA risk. However, enthusiasm for the relationship between PCA and the innate immune signaling pathway was slightly elevated in our exhaustive 2-and 3-way interactions. In particular, our exploratory analysis revealed a synergistic relationship between IRAK4 and TLR2 as significant PCA markers among men of African descent in the U.S. We speculate that genetic variations in TLR-related genes may influence the PCA risk by modulating cell survival, proliferation and/or inflammation. Mechanistic studies are needed in order to corroborate these findings and explore the functional consequences of TLR-related SNPs on PCA development. By combining our genetic variation analysis of TLR-related polymorphisms with biological studies, we hope to develop a level of understanding that will allow us to accurately predict and eventually offset the increased genetic risk factors for PCA that threaten men of African descent.

Study Population
Two independent case-control study sets with participants from the Prostate Cancer Clinical Outcome (PC 2 O) Study and the Prostate Cancer Case-Control Study were used in the current study (Table 1 Table B. The examination and inclusion criteria of all subjects have been described in detail previously [53,54].

Genetic Analysis of Variant TLR-Associated SNPs
De-identified germ-line DNA was obtained from incident PCA male cases (n = 279) and controls (n = 535). SNPs detected in TLRs (1, 2, 4, 6, and 10), IRAK4, TOLLIP, and IRF3 were genotyped using Illumina's GoldenGate genotyping assay system combined with Veracode Technology (Illumina, Inc., San Diego, CA). Allelic discrimination was performed using a BeadXpress Reader (Illumina, Inc., San Diego, CA) according to the manufacturer's instructions [51]. Quality control analyses and data management were performed using Golden Helix's SNP Variation Software 7.0 (Bozeman, MT). To ensure high quality data, SNPs were excluded if: genotype call rates were <95% (n = 1); genotypic distribution between controls deviated substantially from the HWE with a significance cutoff value of P ≤ 0.005 (n = 4); or the minor allele frequency was <1% (n = 5). Based on the above criteria, 32 TLR-related SNPs were examined among men of African descent, as listed in Supplemental Table A. Based on the above criteria, 32 TLR-related SNPs were examined among men of African descent, as listed in Supplemental Table A.

Ancestry Markers
Among the U.S. men, cases and controls were also genotyped with a set of 100 genomewide ancestry informative markers to correct for potential population stratification among our admixed population, as previously described [47,48]. Individual genetic ancestry (IA) was determined for each person using 100 AIMs for West African and European genetic ancestry. IA was estimated from the genotype data using the Bayesian Markov Chain-Monte Carlo (MCMC) method implemented in the program STRUCTURE 2.1, as detailed elsewhere [47,49]. Study participants were grouped from lowest to highest genetic West African ancestry, with scores ranging from 0-100%. These 100 markers were evaluated using DNA from self-identified African-Americans (Coriell Institute for Medical Research, n = 96), Yoruban West Africans (HapMap, n = 60), West Africans (Bantu and Nilo Saharan speakers, n = 72), Europeans (New York City, n = 24), and CEPH Europeans (HapMap Panel, n = 60), as previously reported [48]. Individuals with a West African ancestry (WAA) score ≥25% and available TLR genotype data were included in the final analysis.

Statistical Analysis for Single Gene Effects
Univariate and multivariate statistical analyses were used to examine the relationship between TLR SNPs among men of African descent and PCA risk. For each TLR SNP, frequency differences in TLR genotypes between cases and controls were tested using the Chi-square (χ 2 ) test of homogeneity. Odds ratios (OR) and corresponding 95% confidence intervals (CI) for PCA risk in association with TLR SNPs were estimated using unconditional multivariate logistic regression (LR) models after adjusting for age. LR analyses for genetic variants and PCA risk were conducted using the major or common genotype as the referent category. Chi-square test and LR analyses were performed using SAS 9.3 (SAS Institute Inc., Cary, NC) and SVS software (Golden Helix, Inc., Bozeman, MT). Adjustment for multiple hypothesis testing was achieved using false discovery rate (FDR). Statistically significant data was based on a p-value cut-off of 0.05.

Statistical Power for Single Gene Effects
We calculated the odds of developing PCA among carriers of at least one or more minor allele based on the average MAF 21.9-23.3% for the three study sets (i.e., U.S. men, Jamaican men, and U.S. and Jamaican men combined), a PCA disease prevalence of 0.740%, a significance level (α) of 5%, and 100% linkage disequilibrium (LD) between the casual and the predisposing variant. According to our sample size for the combined population (279 cases, 535 controls), U.S. (170 cases, 433 controls) and Jamaican men (109 cases, 102 controls), we had >80% power to detect odds ratios (ORs) of ≥1.4 ≥ 1.6, and ≥1.9 for PCA risk, respectively. Statistical power calculations were performed using Power for Genetic Association Version 2 Software, as described previously [55].

Analysis of Gene Interactions Using Multi-factor Dimensionality Reduction (MDR)
To evaluate the single-and joint-modifying effects of 32 candidate TLR-associated SNPs within a large dataset is computationally challenging. In order to overcome this problem, open source and freely available MDR 2.0 was used to detect and characterize all possible one-, two-, and three-way interaction models in relation to PCA (http://www.epistasis.org/) [56]. To reduce computation time needed to process thousands of SNP combinations in relation to PCA risk, we distributed MDR on a workstation with 12 hyper-threaded cores across two central processing units (total of 24 simultaneous threads of execution) and 24GB of RAM. Although MDR has been described elsewhere, for convenience we provide a brief summary. With MDR, reduced genetic information is reduced to a one-dimensional multilocus genotype variable [57,58]. Information from various disease loci were grouped and labeled as "high risk" or "low risk" based on whether or not the control ratio met or exceeded a particular threshold. Subsequently, the resulting one-dimensional multi-locus genotype variable was examined for its capability to categorize and predict disease outcome through cross-validation and permutation testing procedures. A 10-fold cross-validation was achieved by dividing the entire dataset into a training set and an independent testing set. The training set involved 9/10 ths of the data; the remaining 1/10 th , known as the independent testing set, was evaluated against the training set. Evaluation of each independent testing set predicted average testing accuracy values for each MDR model. The greatest cross validation consistency (i.e., CVC ≥ 8/10) and highest prediction accuracy [i.e., Average Testing Accuracy (ATA)] were selected as the best predictors of disease outcome. Sensitivity and specificity were determined as functions of true negatives (TN), false positives (FP), and false negatives (FN). Sensitivity, specificity and balanced accuracy values were calculated as follows: sensitivity = (TP)/(TP+FN); specificity = (TN)/(FP + TN); and balanced accuracy = (sensitivity + specificity)/2. ATAs were averaged across all 10 pieces of the data, in order to provide an estimate of the predictive ability of the loci in relation to the outcome of interest. We used cross-validation consistency (CVC) to determine the degree to which the same best MDR model is selected across the 10 divisions of the data. Models with a CVC of ≥8/10 using a 10-fold cross-validation were considered more carefully. CVCs and ATAs were calculated across 1,000 random seeds to ensure reproducibility in model selection. If the MDR model met the CVC criteria, we selected models that had the highest ATAs. Multiple hypothesis testing was controlled by CVC in combination with permutation testing. Permutation testing results ≤0.05, generated using random seed 500, were considered statistically significant. Age-group covariate effects were removed by integrating over-and under-sampling methods.

Visualization of Interaction Models Using Hierarchical Interaction Entropy Graphs
Hierarchical interaction entropy graphs, based on information theory, were used to visualize and interpret complex interactions among selected TLR SNPs and PCA risk [59][60][61][62]. With this approach, individual and all possible pairwise loci are assigned a joint or mutual information percentage score based on disease risk, respectively. Joint mutual information and mutual information gain scores are based on a number system, ranging from 0-100%. However, these scores rarely exceed 5-6%. When the pairwise or joint mutual information exceeds the mutual information gain scores, then the pairwise interaction is considered more informative in relation to prostate cancer risk when compared to each locus considered separately. Potential interactions are assessed using interaction entropy graphs, which uses a color-coding system to depict redundant or synergistic interactions. The distinction between the synergistic or redundant epistasis models is based on a color coding system. Within the entropy graph, lines depicted between SNP pairs that are color-coded red, orange, green, blue, and gold represent highly synergistic, moderately synergistic, moderately redundant, highly redundant, and neither synergistic/redundant pairwise interaction models, respectively. All entropy-based analyses were conducted using Orange software [63].  This graphical model describes the percent entropy as explained by each single TLR-related SNP or a combination of two loci within the U.S. population. Information gain or synergy is expressed as positive percent entropy. Redundant or missing information gain is expressed as negative percent entropy. A range from synergy (i.e. non-additive) to redundancy is represented using a schematic coloration in the visualization tool. The colors range from red representing a high degree of synergy (positive information gain), orange a lesser degree, and gold representing independence and a midway point between synergy and redundancy. Green represents redundancy. Study population characteristics among men of African descent from the U.S. and Jamaica.  Table 2 Relationship between TLR-associated SNPS and prostate cancer risk among men of African descent (total population).  Table 3 Relationship between TLR-associated SNPs and prostate cancer risk stratified by location.


extfieldsofstudy: Biology Medicine

text:
Regulated aggregative multicellularity in a close unicellular relative of metazoa

The evolution of metazoans from their unicellular ancestors was one of the most important events in the history of life. However, the cellular and genetic changes that ultimately led to the evolution of multicellularity are not known. In this study, we describe an aggregative multicellular stage in the protist Capsaspora owczarzaki, a close unicellular relative of metazoans. Remarkably, transition to the aggregative stage is associated with significant upregulation of orthologs of genes known to establish multicellularity and tissue architecture in metazoans. We further observe transitions in regulated alternative splicing during the C. owczarzaki life cycle, including the deployment of an exon network associated with signaling, a feature of splicing regulation so far only observed in metazoans. Our results reveal the existence of a highly regulated aggregative stage in C. owczarzaki and further suggest that features of aggregative behavior in an ancestral protist may had been co-opted to develop some multicellular properties currently seen in metazoans. DOI: http://dx.doi.org/10.7554/eLife.01287.001

Introduction
Living organisms emerged from the integration of multiple levels of organization. These levels were shaped by both physiochemical constraints and historical circumstances, the latter being more important in more complex systems (Jacob, 1977). Therefore, it is important to identify the phylogenetic inertia (sensu Burt, 2001) imposed by the raw starting material in order to properly understand major evolutionary transitions, such as the origin of metazoan multicellularity (Knoll, 2011). Examination of both the genetic repertoire (King, 2004;Ruiz-Trillo et al., 2007;Rokas, 2008) and the cell types present in the immediate unicellular relatives of metazoans can provide insights into this evolutionary transition, as they reveal the historical constraints in early metazoan evolution. In this regard, the analyses of unicellular holozoan genomes, that is choanoflagellates and filastereans, have shown that the genetic repertoire of the metazoan unicellular ancestor was much more complex than previously thought King et al., 2008;Sebé-Pedrós et al., 2010;.
Multicellularity has been independently acquired multiple times during the evolution of eukaryotes, in more than 20 different lineages including animals, plants, fungi, slime molds, green and brown algae, and several other eukaryotes (King, 2004;Parfrey and Lahr, 2013). Multicellular organisms evolved through two major strategies: aggregation of different cells or clonal division of a single cell. Multi-level selection theory has proposed that the most complex multicellular organisms likely arose through clonal development rather than by aggregation of genetically diverse cells, since intra-organismal eLife digest When living things made from many cells evolved from single-celled ancestors, it was a breakthrough in the history of life-and one that has occurred more than once. In fact, multicellular life has evolved independently at least 25 times, in groups as diverse as animals, fungi, plants, slime molds and seaweeds. There are broadly two ways to become multicellular. The most complex multicellular species, such as animals, will replicate a single cell, over and over, without separating the resultant cells. However, in species that are only occasionally multicellular, free-living cells tend instead to join together in one mass of many cells.
Evolution is constrained by its raw materials; so looking at the living relatives of a given species, or group, can lead to a better understanding of its evolution because its relatives contain clues about its ancestors. To gain insights into how animal multicellular life might have began; Sebé-Pedrós et al. studied the life cycle of the amoeboid organism Capsaspora owczarzaki. Found within the bodies of freshwater snails, this single-celled amoeba is a close relative of multicellular animals and could resemble one of their earliest ancestors.
At certain stages of the life cycle Sebé-Pedrós et al. noticed that individual amoebae gathered together to form a multicellular mass-something that had not been seen before in such a close relative of the animals. Moreover, the genes that 'switched on' when the amoebae began to aggregate are also found in animals; where, together with other genes, they control development and the formation of tissues. Sebé-Pedrós et al. suggest that the first multicellular animals could have recycled the genes that control the aggregation of single-celled species: in other words, genes that once controlled the changes that happen at different times in a life cycle, now control the changes that develop between different tissues at the same time.
Sebé-Pedrós et al. also observed that alternative splicing-a process that allows different proteins to be made from a single gene-occurs via two different mechanisms during the life cycle of Capsaspora. Most of the time Capsaspora employs a form of alternative splicing that is often seen in plants and fungi, and only rarely in animals; for the rest of the time it uses a form of alternative splicing similar to that used by animal cells.
The evolution of complex alternative splicing mechanisms is a hallmark feature of multicellular animals. The exploitation of two major forms of alternative splicing in Capsaspora could thus reflect an important transition during evolution that resulted in an increased diversity of proteins and in more complex gene regulation. Such a transition may ultimately have paved the way for the increased specialization of cell types seen in animals.
This glimpse into the possible transitions in gene regulation that contributed to the birth of multicellular animals indicates that the single-celled ancestor of the animals was likely more complex than previously thought. Future analyses of the animals' close relatives may further improve our understanding of how single-celled organisms became multicellular animals. DOI: 10.7554/eLife.01287.002 aggregative behaviors; for example, mesenchymal (O'Shea, 1987) and germ line cells (Savage and Danilchik, 1993) during development, sponge cells after cell dissociation (Wilson, 1907) and arthropod blood cells through active amoeboid movement (Loeb, 1903(Loeb, , 1921. To gain deeper insight into the possible transitions that arose during the emergence of metazoan multicellularity, we have performed a detailed examination of the life cycle and associated transcriptomic changes of Capsaspora owczarzaki, one of the closest known unicellular relatives of metazoans ( Figure 1). Isolated decades ago as an endosymbiont of the fresh-water snail Biomphalaria glabrata (Owczarzak et al., 1980), C. owczarzaki belongs to the clade Filasterea, the sister-group of Metazoa and choanoflagellates (Torruella et al., 2012). Filasterea also includes a free-living marine unicellular species known as Ministeria vibrans (Shalchian-Tabrizi et al., 2008).
We analyzed the C. owczarzaki life cycle and its regulation using electron microscopy, flow cytometry and high-throughput RNA sequencing (RNA-Seq). Through these analyses, we show that C. owczarzaki life cycle is tightly regulated at the level of gene expression and alternative splicing (AS). Moreover, we demonstrate the existence of an aggregative multicellular stage in C. owczarzaki in which many orthologs of genes important for metazoan clonal multicellularity are upregulated.

Results and discussion
Under initial culture conditions ('Materials and methods'), C. owczarzaki differentiates into an amoeba that crawls over substrate (Video 1), surveying its environment with its filopodia . At this stage, active DNA replication occurs (with >10% of the cells in S-phase) and, within 48 hr, the cells enter an exponential growth phase (Figures 2 and 3). Subsequently, the cells start to detach from the surface and begin to retract their filopodia and encyst ( Figure 3B,C). After 8 days, no attached amoebas remain and growth is stabilized (Figures 2 and 3). This cystic stage may represent a dispersal resistance form. Strikingly, we observe an alternative path to this process involving the active formation of cell aggregates (Videos 2 and 3). In these aggregates, the cells attach to each other and produce cohesive extracellular material ( Figure 3D) until a compact cell aggregate, in which cells no longer bear filopodia, is formed ( Figure 3E). Transmission electron microscopy demonstrates the presence of a thick, unstructured, extracellular material within the aggregates that appears to prevent direct contact between cells ( Figure 3F). Clusters of cells appear to occur at random under normal culture conditions.
To investigate whether C. owczarzaki cell clusters are formed through aggregation or clonal division, we first mixed two differentially stained populations of cells ('Materials and methods') and induced aggregate formation, resulting in dual-colored cell clusters ( Figure 4A). This indicates that cell clusters are not composed of daughter cells resulting from successive cell divisions, which would result in single-color cell clusters, but instead by aggregation of multiple cells. We also observed that aggregates could form efficiently even when cell division was blocked by two different inhibitors, hydroxyurea and aphidicolin ( Figure 4B). Finally, by flow cytometry, we observed that the proliferation rate of aggregative cells (Figure 4-figure supplement 1) is extremely low (compared with the observations in Figure 2). Overall, these results show that C. owczarzaki cell clusters form by active cell aggregation, not by clonal cell division. This observation represents the first reported case of aggregative multicellularity in a close unicellular relative of Metazoa. The aggregative multicellularity observed in C. owczarzaki adds an additional cell behavior to those already known among extant close unicellular relatives of Metazoa (i.e., choanoflagellates, ichthyosporeans, and filastereans [Torruella et al., 2012]). We can then infer that multiple cell types and behaviors (including aggregative behavior, flagellar motility, amoeboid movement, clonal colony formation, etc) were most likely present among the unicellular ancestors of metazoans. This range of cell behaviors may have provided the basis for the evolution of the diverse cell types seen in animals (Arendt, 2008;Arendt et al., 2009). Interestingly, each one of the three known unicellular lineages closely related to Metazoa (choanoflagellates, ichthyosporeans and filastereans) has some kind of simple multicellularity. Moreover, the tight regulation observed in C. owczarzaki (see below) emphasizes that a regulated temporal cell differentiation was already in place among unicellular ancestor of animals. This reinforces the view that, during the transition to animal multicellularity, ancestral premetazoan cell types may have been integrated into a single multicellular entity by means of controlling cell differentiation spatially, rather than temporally (Mikhailov et al., 2009). An alternative explanation is that some of the cell behaviors observed in extant unicellular relatives of Metazoa may had evolved independently in some particular unicellular holozoan lineages or species, and do not represent ancestral states. The limited taxon sampling in many of these poorly studied lineages makes it difficult to reliably assess whether these are derived or ancestral characters and this situation is especially dramatic in the case of filastereans, in which only two species have been described so far (C. owczarzaki and the free-living M. vibrans).
To investigate the molecular composition and regulation of the distinct life cycle stages of C. owczarzaki, we isolated filopodiated amoebae, aggregates, and cysts, and analyzed their transcriptomes using RNA-Seq. Of 8637 annotated genes, 4486 showed statistically significant differential regulation ('Materials and methods') in one or more pair-wise comparisons between life cycle stages, including 1354 changes between filopodial and aggregate stages, 3227 between filopodial and cystic stages, and 3096 between aggregate and cystic stages. Moreover, when performing one-versus-all comparisons, each cell stage had a specific transcriptomic profile ( Figure 5A), indicating tight regulation at the level of gene expression. Using both pairwise and one-versus-all comparisons, we identified significantly enriched gene ontology (GO) categories ( Figures 5 and 6) and Pfam protein domains ( Figure 7) in each set of differentially expressed (both up and down-regulated) genes (p<0.01 for each significant category; Fisher's exact test). Genes upregulated in the filopodial stage were enriched in signalling functions, such as tyrosine kinase activity and G-protein-coupled receptor activity, as well as in transcription factors, especially of the Basic Leucine Zipper Domain (bZIP) superfamily ( Figures 5 and 6). Genes involved in protein synthesis and DNA replication were also significantly upregulated, consistent with the rapid cell proliferation at this stage observed by flow cytometry (Figure 2), and further suggesting a high metabolic rate.
When compared to filopodial and aggregative cells, cystic cells showed significant downregulation of genes associated with myosin transport, translation, DNA replication and metabolic activities (especially mitochondrial energy production). However, genes involved in vesicle transport and autophagy were significantly upregulated at this stage ( Figure 5). These differences may reflect recycling of intracellular components triggered by starvation or other adverse conditions, as has been observed under conditions of adaptive cell survival in other eukaryotes (Kiel, 2010). Protein domains involved in the ubiquitin pathway (e.g., UQ_con, zf-RING2 and Cullin domains) and in synaptic cell-cell communication, such as SNARE, synaptobrevin and syntaxin, as well specific transcription factor families (e.g., bHLH transcription factors), were also significantly upregulated in the cystic cells ( Figure 7). Altogether, these results suggest that major cytosolic rearrangement and protein turnover occur at the cystic stage.  Remarkably, the aggregative stage showed strong upregulation of the components of the integrin adhesome and associated signalling and cell-adhesion proteins ( Figures 5 and 8A,B), such as the LamininG domain-containing protein CAOG_07351 (which contains a N-terminal signal peptide sequence and therefore is likely to be secreted) ( Figure 8C), the IPP complex (ILK-PINCH-Parvin) signalling module, G-protein α-13 (Gong et al., 2010), several cytoplasmatic tyrosine kinases (Hamazaki et al., 1998) and two receptor tyrosine kinases (which possess extracellular DERM [Lewandowska et al., 1991] and fibronectin_3 domains, known to interact with integrins [ Figure 8C]). These observations strongly suggest that the integrin adhesome and the likely associated tyrosine kinase signalling genes play an important role in the formation of the C. owzarzaki aggregates. Furthermore, we also observed upregulation of genes involved in mitosis and in the tubulin cytoskeleton (e.g., kinesins) at the aggregative stage ( Figure 5). These results indicate that a molecular repertoire associated with animal multicellularity, could function either in aggregative or in clonal multicellularity and in different phylogenetic contexts, in line with previous hypotheses (Newman, 2012).
A hallmark feature of the evolution of metazoan multicellularity and cell type diversity is the expansion of AS complexity and regulation through exon skipping, which has entailed the formation of cell type-specific networks of co-regulated exons belonging to functionally related or pathway-specific genes . In contrast, differential intron retention is the most widespread form of AS found in non-metazoan eukaryotic species (McGuire et al., 2008). To assess the extent to which these forms of AS may contribute to gene regulation in C. owczarzaki, we systematically mapped reads from each life cycle stage to a comprehensive set of intron-exon and exon-exon junctions (i.e., formed by exon/intron inclusion and skipping) to score their differential usage. Of 25,677 introns with sufficient RNA-Seq read coverage across the three life cycle stages, 2986 (11.6%) showed ≥20% PSI (Percent Spliced In, percent of transcripts from a given gene in which the intron sequence is present) in at least one stage, and approximately a third of genes had at least one such intron retention event. Moreover, we observed marked differences in the extent to which detected intron retention is differentially regulated between the different life cycle stages ( Figure 9A). In particular, 797 retained introns (in 441 genes) and 259 retained introns (in 232 genes) display differential PSI (dPSI) values of 25% or more in the filopodial and cystic stages compared to the other stages, respectively ( Figure 9B). In contrast, no retained introns were found to be differentially spliced at the aggregative stage. Most (12 out of 15, 80%) of the analyzed cases of differentially retained introns were validated by RT-PCR ( Figure 9C and GO enrichment analysis for the two sets of differentially retained introns showed distinct gene function enrichment (e.g., protein kinase activity and intracellular targeting in the filopodial stage, and histone modification and myosin complex in the cystic stage) (Figure 6), implying that regulated intron retention plays different roles at these stages. A low fraction of read-through introns (with length-multiples of three and no inframe stop codons) were found among the two sets of differentially retained introns, suggesting that most if not all of these retained introns act by reducing the levels of spliced mRNAs exported from the nucleus and translated into protein, as has been observed previously for regulated retained introns in metazoan species (Yap et al., 2012). Moreover, we observe that multiple introns belonging to a gene can be retained in a stage-specific manner. For instance, >73% and >29% of multi-intronic genes with one differentially retained intron had at least one additional differentially retained intron at the filopodial or cystic-specific stages, respectively, and 22% and 5% of genes at these stages showed evidence of high retention for all introns in the same genes. Furthermore, RT-PCR analyses and mate information from paired-end read analyses suggested that multiple intron retention events often occur in a combinatorial manner (Figure 9-figure supplement 1), thereby increasing the potential impact of intron retentions on mRNA regulation. All of the above observations were highly consistent across three biological replicates ( Figure 9B), and not observed for neighbouring genes, ruling out contamination of genomic DNA.
We analyzed different features of differentially retained introns that may account for their stagespecific regulation. First, we compared intron lengths. While filopodial-specific differentially retained introns have a similar length distribution to constitutive (PSI less than 2% across all stages) introns, cystic stage-specific introns were significantly longer (p=1.7e −14 Wilcoxon rank sum test) ( Figure 9D). In line with this observation, the average level of intron retention increased steadily with intron length only in the cystic stage ( Figure 9E). Furthermore, cystic stage-specific retained introns harbored significantly weaker canonical 5´ and 3´ splice site signals than other intron sets (p<0.0013 Wilcoxon rank sum test for all comparisons). Collectively, these data suggest that differential intron retention in the cystic stage may be associated with suboptimal introns (i.e., long and with weak splice sites) that are more efficiently spliced in the other cell stages. In the case of the filopodial-specific differentially retained introns, analyses of sequence motif enrichment with MEME show enrichment of a long T/Grich motif that highly resembles a recently identified consensus binding site for Elav-like protein in mammals (Ince-Dunn et al., 2012) (Figure 10). Interestingly, the single ortholog for this gene in  C. owczarzaki shows a highly-regulated expression pattern, with lowest expression in the filopodial stage ( Figure 10C). Therefore, it is tempting to speculate that Elav-like protein may negatively regulate filopodial-specific intron retention of some introns. Experimental depletion of Elav-like protein in C. owczarzaki will require the development of RNAi or gene-targeting methods in this species before this hypothesis can be tested.
Next, we investigated differential exon splicing and identified 191 cassette exons with PSIs <95% in at least one life cycle stage, 39 of which display PSIs <85%. 29 of these exons showed a ≥15% PSI difference in pairwise comparisons between the cell stages, with lower PSIs typically associated with the filopodial stage (Supplementary file 1); RT-PCR assays confirmed skipping for 7 out of 8 tested cases ( Figure 11A, and Figure 11-figure supplement 1). Most (∼60%) of these exons maintain an open reading frame when skipped. In contrast to previous reports demonstrating that differentiallyregulated exons are significantly under represented in modular, folded domains in metazoans (Romero et al., 2006;Ellis et al., 2012), two thirds of the differently regulated exons in C. owczarzaki overlap annotated domains (Supplementary file 1). Furthermore, genes with differential exon Orange represents enrichment in the aggregative stage, blue in the cystic stage, and green in the filopodial stage, with color intensity proportional to enrichment significance. The node size is proportional to the number of genes associated to the GO category, and the width of the edges is proportional to the number of genes shared between GO categories. Groups of functionally related GOs are manually circled and assigned a label. DOI: 10.7554/eLife.01287.011 skipping are statistically significantly enriched in protein kinase activity, impacting both tyrosine and serine/threonine kinases ( Figure 11B). This observation strongly suggests a role for coordinated exon skipping in the modulation of cell signaling in C. owczarzaki. To our knowledge, this represents the first example of a regulated exon network linked to a specific biological function in a unicellular organism.
In summary, our results offer new insight into the origin of metazoan multicellularity. In particular, the observation of an aggregative multicellular stage in C. owczarzaki represents the first example of such cellular behavior in a close unicellular relative of metazoans. This observation therefore adds to the repertoire of reported complex cellular behaviors among extant unicellular relatives of metazoansincluding clonal colony formation in choanoflagellates and sporangia formation by hypertrophic syncytial growth in ichthyosporeans (Dayel et al., 2011;, thus expanding the potential starting 'raw material' available for the evolution of animal multicellularity. We note that the current evolutionary framework on the opisthokonts, based on phylogenomic analyses (Steenkamp et al., 2006;Ruiz-Trillo et al., 2008;Shalchian-Tabrizi et al., 2008;Torruella et al., 2012;Paps et al., 2013), discards the possibility that C. owczarzaki (or choanoflagellates) derives from a more complex Furthermore, we show that the complex, metazoan-like genetic 'toolkit' of C. owczarzaki (Sebé-Pedrós et al., 2010Suga et al., 2012) is dynamically deployed during its highly-regulated life cycle, with upregulation of integrin adhesome and signalling genes linked to multicellularity in metazoans during the aggregative stage. Extensive differential AS between the C. owczarzaki life cycle stages likely further contributes to the dynamic gene regulation observed in this species, with differential intron retention likely acting as an important mechanism in the control of transcript levels between life cycle stages, probably through triggering non-sense mediated decay (NMD). Our discovery of an exon network associated with tyrosine kinase genes in C. owczarzaki further adds to the metazoan-like features of this species. Together with genes resembling those that function in metazoan multicellular processes, the emergence of an exon network that functions in conjunction with differentially-regulated intron retention may have provided a degree of proteomic and regulatory complexity that was key in the evolution of cell type complexity in metazoans (Nilsen and Graveley, 2010). Based on the collective results from our investigation of C. owczarzaki, it is intriguing to consider that the integration of regulatory innovations involving differential expression and splicing of metazoan-like genes set the stage for the evolution of cell specialization in the common ancestors of metazoans and C. owczarzaki.

Materials and methods
Scanning electron microscopy C. owczarzaki cells of the corresponding stage were fixed for 1 hr with 2.5% glutaraldehyde (Sigma-Aldrich, St. Louis, MO, USA), and for another hour with 1% osmium tetroxide (Sigma-Aldrich), followed by dehydration in a graded ethanol series (25%, 50%, 70%, 99%) for 15 min per step, followed by three 15-min rinses in 100% ethanol. Samples were critical-point dried in liquid CO 2 using a BAL-TEC CPD 030 critical-point drying apparatus. They were subsequently glued to SEM stubs with colloidal silver, sputter-coated with gold-palladium, and examined with a Hitachi S-3500N (Hitachi High-Technologies Europe GmbH, Krefeld, Germany).

Transmission electron microscopy
Cell aggregates were loaded into the copper tubes and immediately cryoimmobilized using a Self-Pressurized Freezing System (EM SPF) (Leica-Microsystems, Vienna, Austria). The cells were then stored in liquid nitrogen until further use. Peeled copper tubes were freeze-substituted in anhydrous acetone containing 2% osmium tetroxide and 0.1% uranyl acetate at −90°C for 72 hr and warmed to room temperature, following a 2°C increase per hour in five consecutive steps (−60°C, −30°C, 0°C, 4°C, and room temperature) with a total of 8 hr at each temperature and using an EM AFS (Leica-Microsystems, Vienna). After several acetone rinses, samples were infiltrated with Epon resin during 7 days and embedded in resin and polymerised at 60°C during 48 hr. Ultrathin sections were obtained using a Leica Ultracut UC6 ultramicrotome (Leica-Microsystems) and mounting on Formvar-coated copper grids. The sections were stained with 2% uranyl acetate in water and lead citrate, and were observed in a Tecnai Spirit 120 kv electron microscope (FEI Company, Eindhoven, Netherlands) equipped with a Megaview III CCD camera. Cell culture conditions C. owczarzaki cells were grown axenically in 5-ml flasks with ATCC medium 1034 (modified PYNFH medium) in a 23°C incubator. Three biological replicates (three independent cell lines) were generated by subculturing from a single-founding cell and grown for 2 months. Adherent filopodiated cells were obtained by initiating a new 1/100 sub-culture (from an approximately 5 × 10 6 cells/ml initial culture) and, after 3-4 days, cells were scratched from the substrate. Aggregate formation was induced by initiating a new 1/250 sub-culture (from an approximately 5 × 10 6 cells/ml initial culture) and by gentle agitation at 60 rpm during 4-5 days. Finally, floating cystic cells were obtained from a 14-day-old culture, starting from a new 1/100 sub-culture (from an approximately 5 × 10 6 cells/ml initial culture).

Aggregation experiments
Two different groups of cells (from two different starting cultures, 5 ml flasks with a cell density of 10 6 cells/ml, consisting exclusively of adherent filopodial cells) were stained either with 75 nM (in PBS1x) Lysotracker Green DND-26 (Life Technologies, Carlsbad, CA, USA) or with 1 μM (in PBS1x) Chromeo Live Cell Mitochondrial Staining Kit (Active Motif Inc, Carlsbad, CA, USA). The cells were stained for 30 min at 23°C. After staining, 1/3 of the cells from the two differentially stained populations were mixed in a new culture flask and the remaining 2/3 of cells for each staining were kept as a control. All three cultures were grown for 2 hr and then the aggregate formation was induced (see above, 'Results and Discussion'). After 8 hr, aggregates were visualized in poly-L-lysine covered (Sigma-Aldrich, St. Louis, MO) glass-bottom plates in a Leica TCS SP5 confocal microscope (Leica-Microsystems).
C. owczarzaki cell division was blocked using 100 mM hydroxyurea (Sigma-Aldrich) or 25 μg/ml aphidicolin (Sigma-Aldrich). The effect of both drugs was evaluated by following cultures treated with each drug during 7 days, using Neubauer chamber. The cells were cultured in 16-multiwell plates and with an initial density of 5 × 10 4 cells/ml. Once these conditions were established, different cultures were treated with each drug for 1 day and, then, aggregate formation was induced (see above). 2 days later, the formation of aggregates was visualized in poly-L-lysine covered (Sigma-Aldrich) glass-bottom plates in a Leica TCS SP5 confocal microscope (Leica-Microsystems).

RNA-Seq and analysis
C. owczarzaki cells were grown in 5-ml flasks with ATCC medium 1034 (modified PYNFH medium) in a 23°C incubator. Total RNA from each cell stage (and from three biological replicates from each stage) was extracted using Trizol reagent (Life Technologies). Nine libraries were sequenced over 2 lanes HiSeq 2000 instrument (Illumina, San Diego, CA, USA), generating a total of 197M 76-base paired reads. Reads were aligned to the reference genome using Tophat  with default options and specifying that this was a strand-specific sequencing, rendering an average mapping of 90%. Significant differential expression was calculated by performing pairwise comparisons with DESeq (Anders and Huber, 2010) (threshold 1e-05), EdgeR (Robinson et al., 2010) (threshold 1e-05), CuffDiff  (threshold 1e-05) and NOISeq (Tarazona et al., 2012) (threshold 0.8) and only genes that appear to be significant at least in three out of the four methods were considered as differentially expressed. Quality control analyses of the data were performed using cummeRbund R package . These include count vs dispersion plot to estimate over-dispersion, density plot to assess the distributions of FPKM scores across samples and squared coefficient of variation plot to check for cross-replicate variability.
A gene ontology of C. owczarzaki's 8637 genes was generated using Blast2GO (Conesa et al., 2005) and GO enrichments of the different lists of differentially expressed genes (see above) were analyzed using Ontologizer (Bauer et al., 2008) using the Topology-Weighted method. A p-value threshold of 0.01 was used. The results from Ontologizer were loaded into Enrichment map cytoscape plug-in (Merico et al., 2010) to generate a network visualization. Pfam domains of all genes were analyzed using Pfamscan v26 with default Gathering Threshold, and counts were generated using custom Perl scripts. Fisher's exact tests were performed using custom R scripts and a p-value threshold of 0.01 was used.

Alternative splicing analysis
Exon skipping and intron retention were analyzed as previously described (Curtis et al., 2012;Han et al., 2013). In short, for exon-skipping analyses, multifasta libraries of exon-exon junctions were built by combining all forward annotated splicing donors and acceptors. A minimum of eight base pairs was required at each boundary to assure specificity. Next, the number of effective mappable positions was calculated for each exon-exon junction, as previously described (Labbé et al., 2012;Barbosa-Morais et al., 2012). Then, RNA-seq reads (previously trimmed to 50 nucleotides and combining each three replicates to increase read depth) were aligned to these sequences using Bowtie, with−m 1 −v 2 parameters (single mapping and two or fewer mismatches). Percentage of exon inclusion was calculated and a minimal read coverage was required, as previously described (Khare et al., 2012). For intron retention, a similar approach was taken for each contiguous intron-exon and exonexon junction, and percentage of intron inclusion (PSI, Percent Spliced In, the percentage of transcript for a given gene that contain the intron) was calculated as previously described (Curtis et al., 2012). For comparisons among cellular stages, only events with enough read coverage in the three samples were considered (either (i) ≥10 reads in the exon-exon junction or (ii) ≥10 reads in one intron-exon junction and ≥5 in the other), and introns showing >95% inclusion in the three samples were discarded. To assess whether differentially retained introns in the same genes were included in a coordinated or in a combinatorial manner, mate information of read pairs was used. If each end of a read mapped to two different intron retention events, each end may be providing support for retention of both introns or splicing of both introns (coordinated regulation), or retention of one and splicing of another (combinatorial intron retention). For the 555 pairs of retained introns that had read mate information, 196 (35.3%) showed evidence for combinatorial regulation. Finally, for sequence motif enrichment analyses, full intron sequences were compared using MEME (Bailey et al., 2009).

RT-PCR
To validate AS analysis predictions, the three stages were induced (RNA-Seq and analysis sections) and RNA was extracted using Trizol reagent (Life Technologies). To eliminate genomic DNA, total RNA was treated with DNAse I (Roche, Basel, Switzerland) and purified using RNeasy columns (Qiagen, Venlo, Netherlands). For each stage, cDNA was produced from 1 μg of total RNA using SuperScript III reverse transcriptase (Life Technologies). Pairs of primers of similar melting temperature (60°C) and spanning the putative alternatively spliced segments were designed using Geneious software. PCR was performed using ExpandTaq polymerase (Roche).

Flow cytometry
C. owczarzaki cells were grown for 10 to 15 days, sampling every day from both the supernatant (to obtain floating cells, which after day 7 are completely encysted) and the scratched flask (to obtain filopodial adherent cells). Thus, two samples were obtained daily, for floating and adherent cells. For DNA-content analysis, a sample was fixed using 70% ethanol and stored at −20°C for one month. The samples were subsequently fixed and stained with Propidium Iodide (as described in Darzynkiewicz and Huang, 2004) and DNA content estimated using FACScalibur flow cytometer (Becton Dickinson, Franklin Lakes, NJ, USA). For cell counting, 1 ml of fresh sample (one from the supernatant and one from the flask surface) was mixed in a BD Trucount Tube (Becton Dickinson), with a known number of beads, so absolute cell number counts could be calculated, using an LSR Fortessa flow cytometer (Becton Dickinson). Two replicate experiments (R1 and R2) were performed independently in order to confidently establish growth dynamics. Two measures were calculated from the DNA-content analysis. First, the proliferation rate, which indicates the proportion of number of cells in S and G2/M phases vs the number of cells in G0/G1. Second, the percentage of cells in S-phase.

Major dataset
The following dataset was generated: