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We next wanted to gain mechanistic insights into the specific sequences that drive the co-binding between members of the Forkhead and Ets families that could potentially explain TF binding to non-canonical sites through cooperative binding. To this end, we used FOXO1 and ETS1 as a prototype Forkhead-Ets pair, and classified DNA sequences based on their level of cooperativity. Specifically, for each k-mer, we compared the relative affinities for ETS1 and FOXO1 obtained from their respective high-throughput SELEX (HT-SELEX) datasets (Fig. 2a), and defined their cooperativity-potential as the ratio of predicted relative affinities between FOXO1:ELK3 (ETS1 paralogue) and the mean predicted relative affinity for FOXO1 and ETS1 (see Methods section). We found that the cooperativity potential dropped with increasing FOXO1 binding affinity, while the relative affinity of ETS1 had little effect (Fig. 2b). These results indicated that the FOXO1-binding strength determines the level of cooperativity. This conclusion was further corroborated by comparing representative DNA-sequences classified as non-cooperative, cooperative and highly cooperative (ω-none, ω, and ω-high, respectively Fig. 2c), which only differed in their Forkhead binding region. Similarly, protein-binding microarray (PBM) data8 revealed higher affinity of Forkhead members for ω-none than for ω sequences, while weak binding was observed for ω-high (Supplementary Fig. 3a; see Methods section). Despite a low affinity for ω-high in PBM data, we observed higher median relative affinities for this k-mer in CAP-SELEX datasets with both Forkhead and Ets factors than in other datasets (Supplementary Fig. 3b; see Methods section). These results and the negative correlation between Forkhead motif matches and cooperativity (Fig. 2b) suggest that Forkhead TFs can bind to ω-none sequences on their own by recognizing a strong Forkhead binding site, while relying on allosteric interactions with Ets partners for recognizing ω (and possibly ω-high) sequences by forming a cooperatively bound complex. Notably, crystal structure data suggest an alternate spacing between Forkhead and Ets DNA-binding domains for ω-high (Supplementary Fig. 2)23,24.
Transcription factor (TF) binding data analyzed in this work were collected from in vitro and in vivo studies. Specifically, CAP-SELEX and HT-SELEX sequencing reads were retrieved from the European Nucleotide Archive, under accession entries PRJEB7934, PRJEB7934, and PRJEB20112. protein binding microarray (PBM) data were downloaded from the UniProbe database52. ChIP-seq peak datasets were collected from the ReMap2 database26.
where L is the length of the consensus sequence, corrected by the ambiguity of each nucleotide. For example, GAGCA has an L-value of 5, but RRGCA has an L-value of 4, as R (purine) can represent either G or A. Datasets and reference k-mers used are listed in Supp. Data 1.
For each tiled k-mer table in each dataset, we use a 10-fold cross validation scheme to randomly separate the table into 10 fixed groups of equal size, iteratively fitting L2-MLR models with nine out of ten groups, and then assessing coefficient of determination (R²) in the held-out group. This is done using scikit-learn54. As a summary statistic for each tiled k-mer table, we report the median R² of all held-out groups (Supplementary Data 1 and 4). As a quality control and to remove datasets with low variability and enrichment for mapped k-mers, at this stage we filter out datasets whose minimum testing R² value across all models for all tiled k-mer tables is lower than zero (i.e., model is worse than using the mean of all values as a single feature).
To examine the DNA binding affinity of Forkhead TFs for ω-none, ω, and ω-high, we used PBM data from the UniProbe database and compared E-scores for all 8-mers containing the patterns GTAAACA, AACAACA, and ACGCACC across all available Forkhead family members. The E-score threshold of 0.35 was used to define high-affinity sites. For CAP-SELEX datasets, 13-mers connected to those three sequences were compared by averaging the relative affinities of all 10-mers contained within those into a single value.
Comparing the similarity of ΔR2p values between all SELEX datasets requires alignment and assessment of similarity between binding models generated by k-mer tables of different length. To align such cases, we introduced an unbiased clustering scheme. Briefly, we applied a cubic spline interpolation to all shape profiles of a TF binding model to normalize them to 1000 points (function interp1d, from scikit-learn). Sometimes shape profiles can be mirrored and maximum ΔR2p values can be recovered in opposite positions across binding models (e.g., a TF binding model of length 9 with maximum ΔR2p value at position 3 contains its complementary model with maximum ΔR2p at position 7). To account for these cases, we inverted the shape profile if the improvement in maximum performance was located at positions after the respective profile mean (position 500).
To compare shape profiles of double and matched single TF datasets that have a common Forkhead TF member, we studied the ΔR2p values for FOXO1 and FOXI1, as the corresponding CAP-SELEX datasets are enriched in cluster 1 and most of their TF-pair combinations have an equivalent topology. To align TF binding models generated from CAP-SELEX and HT-SELEX, we used the consensus sequence motif of the Forkhead TF (listed in the reference k-mer) as an anchor point. Then, we maximized the number of matches between the Forkhead motif region and the reference consensus sequence across all composite motifs (FOXO1: RWMAAAC; FOXI1: RTMAAC). For ETS1, we used the GGAA pattern for alignment. HT-SELEX data for comparison was retrieved for FOXO1, FOXI1 and ETS1 using the available IDs in each case (Supplementary Data 4). Since these datasets capture short motifs, shape profiles can be generated using a single k-mer representing the consensus binding motif. Reference k-mers were used as in the CIS-BP database11. For aligning and comparing the profiles with the respective profiles for FOXO1, FOXI1 and ETS members we matched HT-SELEX k-mers to the respective composite k-mers reported for FOXO1 and ELK3 (ETS1 paralog), using the individual core motif for alignment, respectively.
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A vast number of chemical substances are released into the aquatic environment, leading to complex chemical mixtures in surface waters. Current water quality assessments, however, are based on the risk assessment of single substances. To consider potential mixture effects in water quality assessments, the North Rhine Westphalian State Agency for Nature, Environment and Consumer Protection (LANUV), Germany started a project assessing mixture toxicity in surface waters. This article summarises the mixture evaluation of chemical data collected by the Erftverband during a water sampling campaign in the Erft River in 2016/2017. Altogether, 153 substances were included in the analysis, of which 98 were detected. Two different approaches based on the concept of concentration addition were used to analyse the data. The results were compared to findings based on datasets from LANUV surveillance monitoring according to the EU Water Framework Directive.
Acute and chronic mixture risk calculations indicated risks for 32% and up to 90% of the samples, respectively. The greatest acute toxic pressure was identified for the aquatic flora due to continuous exposure to varying pesticides, whereas the greatest chronic mixture risk was identified for fish as result of a ubiquitous presence of the pharmaceuticals diclofenac and ibuprofen. Overall, only a limited number of substances significantly contributed to the calculated mixture risks. However, these substances varied seasonally and regionally. When mixture risks were calculated based on different datasets, the monitoring design markedly affected the outcome of the mixture risk assessment. Data gaps of both ecotoxicological and exposure data lead to high uncertainties in the mixture risk assessment.
In the present study, two CA-based risk assessment approaches were applied to datasets from the Erft River: (1) summation of Toxic Units (TU) and (2) summation of risk quotients (RQ) of the individual mixture components (c.f. Backhaus and Faust ). Two chemical datasets from the Erft River were evaluated, i.e. an extensive monitoring program from the Erftverband in 2016/2017 as well as data from the EU Water Framework Directive (WFD) surveillance monitoring undertaken by LANUV. The data were analysed for seasonal variability and spatial trends along the Erft River. Moreover, difference in mixture risks between the biological groups of algae, macrophytes, aquatic invertebrates and fish as well as substances potentially acting as drivers of mixture toxicity was identified. The results of both approaches are compared with regard to the information generated and with respect to the applicability to data from routine monitoring.
Analytical methods used for the surveillance monitoring are presented in Additional file 1: Table S2-1. This sampling site is located in between two sites of the monitoring program by the Erftverband. In 2016/2017, 15 samplings were performed at Eppinghoven. Five datasets with sampling dates matching the dates of the Erftverband monitoring program were selected for mixture evaluation. These samples were taken in May, late June, August, early November 2016 and January 2017, representing two dry and three wet conditions. In contrast to the Erftverband, the number of substances analysed varied between two and eight for most of the samplings. In November, a broader set of 58 substances were evaluated as part of the annual WFD surveillance monitoring. 2b1af7f3a8