Compound and Conditioned Likelihood Ratio Behavior within a Probabilistic Genotyping Context

In cases where multiple questioned individuals are separately supported as contributors to a mixed DNA profile, guidance documents recommend performing a comparison to see if there is support for their joint contribution. Anecdotal observations suggest the summed log of the individual likelihood ratios (LR), termed the simple LR product, should be roughly equivalent to or less than the log(LR) for the joint likelihood ratio, termed the compound LR. To assist casework analysts in evaluating statistical weights applied to a case at hand, this study assessed how consistently compound LRs conform to an additive behavior when compared to the simple LR product counterparts. Two-, three-, and four-person DNA mixture data, of various mixture proportions and DNA inputs, were interpreted by STRmix® version 2.8 Probabilistic Genotyping Software. Relative magnitudes of LR increases were found to be dependent on both template level and mixture composition. The distribution of log(LR) differences between all compound/simple LR comparisons was ~−2.7 to ~28.3. This level of information gain was similar to that for compound LR comparisons, with and without interpretation conditioning (~−3.2 to ~27.7). In both scenarios, the probability density peaked at approximately 0.5, indicating the information gain from constrained genotype combinations has a comparable impact on the outcome of LR calculations whether the restriction is applied before or after interpretation.

Introduction
Likelihood ratios (LRs) have been in use in forensic DNA typing for several decades now as a weight-of-evidence statistic for DNA mixture interpretation [1][2][3][4][5][6][7]. The construction of a sub-source LR necessarily requires a consideration of which propositions best represent the inclusionary (prosecution) and alternative (exclusionary/defense) theories about the source of the DNA evidence based on the relevant case information [8][9][10][11]. When the inclusionary proposition includes one individual as a contributor to the mixture and the alternative proposition replaces that individual with an unknown, unrelated contributor, the LR propositions for such a calculation, hereafter referred to as a "simple LR", can be represented with the following shorthand notation (shown below for a two-person mixture), where "ID" represents a questioned individual and "U" represents an unknown, unrelated mixture contributor: This LR assesses how well individual ID1 explains the data. When there are multiple individuals in question and the presence of at least one of those individuals in the evidence can be reasonably postulated, an LR for the remaining individuals, hereafter referred to as a "conditioned LR", may be calculated [12][13][14][15]. Basic properties of conditioned LRs in the forensic DNA context have been previously discussed in the literature. Generally, when the simple LRs for multiple individuals are greater than 1, conditioning on one or more individuals increases the magnitude of the LR for the remaining individuals, if all individuals are true contributors to the mixture [16]. The higher value from a conditioned LR is due to the reduction in ambiguity that results from a decrease in the possible genotype combinations in the mixture. This basic concept is represented below as: Unconditioned LR for ID1 LR for ID1, Conditioned on ID2 where ID1 and ID2 represent two different individuals.
On the other hand, the most appropriate proposition pair in the context of a given case may place all questioned individuals in the inclusionary proposition versus an equal number of unknown, unrelated contributors in the alternative proposition. The propositions for this calculation, hereafter referred to as a "compound LR", are represented below (with the same two-person mixture as an example): A criticism by the recent NISTIR 8351-DRAFT [17] speaks to the impact of specific propositions chosen for the calculated LR value: "This fact should encourage more effort to standardize development of propositions . . . " Numerous publications on proposition formulation and LR reporting [10][11][12]15,18] provide valuable guidance. While simple, conditioned, and compound likelihood ratios are described in detail, the relative quantitative properties are not well represented in the literature. In particular, it has anecdotally been observed that when simple LRs (>1) for different individuals in a given case are multiplied together (i.e., their exponents are added), the product, hereafter referred to as the "simple LR product", is typically less than or equal to the corresponding compound LR for the combination of those same individuals. This additive behavior is represented below, using the same two-person mixture from Equations (1) and (3): Simple LR product for ID1 and ID2 Compound LR for ID1 and ID2 A related question is whether a compound LR increases when adding a conditioned contributor, as represented below with a three-person mixture: Exploring the robustness of these quantitative relationships is helpful both to individual casework analysts making decisions about setting propositions for compound LRs as well as standardization bodies that require empirical support for guidelines related to these LRs.
Presented here is a systematic assessment of compound and conditioned LR calculations aimed at establishing LR magnitude expectations. One of the primary objectives of this study was to determine how consistently compound and conditioned LRs conformed to an additive behavior across a variety of mixture proportions as well as a wide range of DNA inputs. Beyond the question of whether a compound or conditioned LR is equal to or greater in magnitude than its simple LR product/unconditioned counterpart, the study sought also to address the questions of how large the increase is and whether the relative magnitude of the increase can be predicted based on the properties of the mixture. Finally, the study assessed whether any parallels can be drawn between the magnitudes of compound and conditioned LRs. Such information can assist the casework analyst in evaluating the statistical weights of the LRs calculated in a case at hand. For example, in sexual assault or touch DNA cases where the prosecution's case posits multiple suspects as DNA mixture contributors, empirically supported expectations of compound and/or conditioned LR magnitude would allow an analyst to confidently provide information on whether those multiple suspects can explain the mixture data when considered together, instead of considering them in isolation. Per the authors of the NISTIR 8351-DRAFT, such information is currently insufficient in the literature.

Construction, Amplification, Capillary Electrophoresis, and Analysis of Ground Truth DNA Mixtures
Buccal cell DNA was collected with informed consent from healthy, unrelated laboratory volunteers and extracted using the PrepFiler™ DNA Extraction Kit (Life Technologies, South San Francisco, CA, USA).
Because two-person mixtures are the easiest for STRmix ® to analyze, the composition of the two-person donor sets was not directed at achieving a given maximum allele count (see Table 1 and Supplementary Table S5). Meanwhile, the three-and four-person mixture donors were selected so that one set that had a maximum allele count consistent with N-1 contributors-, i.e., a maximum of four detected alleles per locus for the three-person mixtures and a maximum of six detected alleles per locus for the four-person mixtures-and one set that had a maximum allele count consistent with N contributors (5-6 alleles per locus for the three-person mixtures and 7-8 alleles per locus for the four-person mixtures). Mixtures at a variety of DNA input amounts and mixture ratios were constructed by quantifying extraction yield for each donor with Quantifiler™ Trio DNA Quantification Kit (Life Technologies) and combining the amounts of template indicated in Table 2. Each template series for a given mixture was made by diluting a single high-level mixture. Particular attention was focused on the inclusion of mixtures with high major to minor contributor ratios (e.g., 99:1, 100:100:4, 100:100:100:6), since these extreme ratios are occasionally encountered in casework and the PCAST review of forensic science [19,20] indicated that more empirical support is needed to demonstrate reliable software performance under these conditions: " . . . the studies involve few mixtures in which a sample is present at an extremely low ratio. By expanding these empirical studies, it should be possible to test validity and reliability across a broader range". The mixtures were amplified with the GlobalFiler™ PCR Amplification Kit (Life Technologies), either singly or in duplicate as indicated, with a 28-cycle thermal cycling protocol on the ProFlex (Life Technologies) and electrophoresed on an Applied Biosystems™ 3500 Genetic Analyzer using POP-6 polymer (Life Technologies). Replicate amplifications were performed for any mixture with a minimum minor mixture proportion less than 25% in order to test a feature of STRmix unrelated to this study; however, since these replicates are independent data points, they were all included in this study, and each was independently interpreted with STRmix.
The capillary electrophoretic data from the amplified mixtures were subsequently analyzed in GeneMapper ID-X v1.6 (Life Technologies, South San Francisco, CA, USA) at channel-specific analytical thresholds of 51 RFU (blue), 71 RFU (green), 35 RFU (yellow), 41 RFU (red), and 61 RFU (purple) and analyzed with STRmix v2.8 (Institute of Environmental Science and Research, Auckland, New Zealand) using laboratory-validated settings, which include stutter models for −1/+1, −2/+2, −0.5/+0.5, and −1.5 STR repeats. Upon completion of interpretation, the targeted mixture proportions were confirmed to be consistent with the mixture proportions from the STRmix Interpretation Report for the highest template amount for each mixture from Table 2 (data not shown).
For this study, both unconditioned and conditioned analyses were performed, with the conditioned contributors consisting of each combination of single and multiple mixture donors up to a total number of N-1. The "MCMC Accepts" settings used were the default MCMC accepts of 10,000 burn-in/50,000 post burn-in per chain unless stated otherwise, where the accepts were increased by a factor of 20 to 200,000 burn-in and 1,000,000 post burn-in per chain.

Compound LR Calculations
Compound LR calculations were performed for all combinations of true contributors to the 197 mixtures in Table 2 using STRmix v2.8 [21]; this amounts to a total of 1775 compound LRs calculated (see Supplementary Tables S1-S4 for raw LR data). Table 3 is a summary of the proposition sets used for these calculations. Note that all combinations of two contributors were tested for the three-person mixtures, and all combinations of two and three contributors were tested for the four-person mixtures. One-sided 99.9% lower credible intervals, termed "Highest Posterior Density" (HPD), were calculated for all LRs unless otherwise indicated. NIST databases for African American, Caucasian, and Hispanic populations [22] were used for the calculations. Equal proportion population-stratified LRs were reported as the primary weight-of-evidence statistics. Table 3. Propositions for simple LR product v. compound LR comparisons. Note that the designations "C1", "C2", etc. are generic and represent more than one combination of mixture contributors. For instance, under "Two true contributors", C1 and C2 in proposition set (2) represent the three different combinations of two true contributors in a three-person mixture that could be paired together.

Compound LR comparison propositions (4 true contributors)
Simple LR product propositions Compound LR propositions Simple LRs for all contributors to the mixtures in Table 2 were calculated, and the log(LR) were plotted to compare the magnitude of each compound LR to its corresponding simple LR product for each contributor combination.

Conditioned LR Calculations
LR calculations were performed using STRmix v2.8 for all combinations of nonconditioned true contributors to the 197 mixtures in Table 2, given a particular conditioned interpretation; this amounts to a total of 4686 conditioned LRs calculated (see Supplementary Tables S1-S4 for raw LR data). Table 4 summarizes the proposition sets used for these calculations. Table 4. Propositions for unconditioned v. conditioned LR comparison. Note that this comparison applies to compound LRs as well as simple LRs; for instance, for the three-person mixtures, both the simple and 2-person compound LRs were compared to their conditioned counterparts.

Unconditioned propositions
Conditioned propositions

Unconditioned propositions
Conditioned propositions

C1+C2+C3+C4 Unk+C2+C3+C4
Corresponding unconditioned LR calculations were performed, and the log(LR) were plotted comparing the magnitude of each conditioned LR to its corresponding unconditioned LR for each contributor combination.

Compound LR Calculations: 2-and 3-Person Mixtures
The results of the compound LR calculations show that the additive behavior of compound LRs largely holds over the entire per-contributor DNA input range of~5 pg-2.5 ng for 2-and 3-person mixtures (see Figure 1), with a majority above the black line of equivalence (~92% and~88%, respectively). Furthermore, the majority (100% for 2-person and 94% for 3-person) of the compound LRs that are less than their simple LR product counterparts, were within one order of magnitude from the line of equivalence.
In terms of the trends observed for the various mixture proportions, the compound LRs with the largest difference are those with the most ambiguity in the genotype combinations (i.e., the mixtures with the most even mixture proportions). The 2-person mixtures are so well resolved that only the higher level 1:1 mixtures, with total DNA input levels of 100 pg, 200 pg, 400 pg and 800 pg, show a substantial increase in the compound LR compared to the simple LR product, whereas the 3-person compound LRs show significant increase over the simple LR product for most mixture proportions.

Conditioned LR Calculations: 2-and 3-Person Mixtures
LRs for true contributors to the 2-and 3-person mixtures interpreted with conditioning were generally equal to or higher than their unconditioned counterparts (see Figure 2). This effect is expected due to the simplification imposed upon the interpretation by fixing one or more contributor genotypes. Comparing these plots to the compound LR plots, the same trends in terms of the mixture proportions most affected by conditioning are apparent; the mixtures with the most ambiguity in the genotype combinations (i.e., the mixtures with the most even mixture proportions) tend to increase the most with conditioning.

Compound LR Calculations: 2-and 3-Person Mixtures
The results of the compound LR calculations show that the additive behavior of compound LRs largely holds over the entire per-contributor DNA input range of ~5 pg-2.5 ng for 2-and 3-person mixtures (see Figure 1), with a majority above the black line of equivalence (~92% and ~88%, respectively). Furthermore, the majority (100% for 2-person and 94% for 3-person) of the compound LRs that are less than their simple LR product counterparts, were within one order of magnitude from the line of equivalence. In terms of the trends observed for the various mixture proportions, the compound LRs with the largest difference are those with the most ambiguity in the genotype combinations (i.e., the mixtures with the most even mixture proportions). The 2-person mixtures are so well resolved that only the higher level 1:1 mixtures, with total DNA input levels of 100 pg, 200 pg, 400 pg and 800 pg, show a substantial increase in the compound

Compound LR Calculations: 4-Person Mixtures
The compound LR plots for the 4-person mixtures also show that the additive behavior generally holds over the entire per-contributor DNA input range of~5 pg-2 ng, as well as the same trends in upward deviation from the line of equivalence with increasing ambiguity of genotype combinations. However, many false negative compound LRs (defined as LRs of 0) are also apparent, which increase in frequency with the number of true contributors placed in the numerator of the LR (see Figure 3).
A critical observation in understanding the root cause of these false negative compound LRs is where they fall on the x axis compared to the rest of the data set. The false negatives occur at relatively low log(simple LR product) values, but do not primarily occur near the origin. Because LR magnitude correlates with DNA mixture input, this simple LR product range corresponds to mixtures that are relatively low level but not the lowest level in the data set. For instance, among the Donor Group 1 4-person mixtures, the false negatives in Figure 3c had modest total DNA input ranges of~250-638 pg (~6-250 pg on a per-contributor basis); the Donor Group 2 false negatives had a similar total DNA input range of~200-625 pg (~5-350 pg on a per-contributor basis). In terms of the complexity of the interpretation (and the corresponding length of the plausible genotype combination list generated by STRmix), these mixtures ranked among the most complicated, because the contributors' input amounts are all in the stochastic range-high enough to allow full allelic detection of some contributors but low enough that allelic dropout is a realistic (and in some cases expected) possibility for all contributors.
Concomitant with the false negative compound LRs for the 4-person mixtures was an effect on the magnitude of the difference between the point estimate LR and HPD LR, which can be viewed as a secondary diagnostic of the issue. The 2-and 3-person plots of HPD LR v. point estimate LR (see Appendix A Figure A1) were as expected, given that the HPD is intended to be a lower bound on normal LR variation; a log-scale drop of 1-2 units from point estimate to HPD was observed for the compound LRs, the same as observed for the simple LRs also shown in the plots. However, very large differences between the point estimate LR and HPD LR (>5 log units, in some cases >10 log units) were observed in the 4-person HPD v. point estimate plots of the compound LRs, with the outlying data points increasing along with the number of contributors in the LR numerator (Appendix A Figure A2a). LR compared to the simple LR product, whereas the 3-person compound LRs show significant increase over the simple LR product for most mixture proportions.

Conditioned LR Calculations: 2-and 3-Person Mixtures
LRs for true contributors to the 2-and 3-person mixtures interpreted with conditioning were generally equal to or higher than their unconditioned counterparts (see Figure 2). This effect is expected due to the simplification imposed upon the interpretation by fixing one or more contributor genotypes. Comparing these plots to the compound LR plots, the same trends in terms of the mixture proportions most affected by conditioning are apparent; the mixtures with the most ambiguity in the genotype combinations (i.e., the mixtures with the most even mixture proportions) tend to increase the most with conditioning.

Compound LR Calculations: 4-Person Mixtures
The compound LR plots for the 4-person mixtures also show that the additive behavior generally holds over the entire per-contributor DNA input range of ~5 pg-2 ng, as well as the same trends in upward deviation from the line of equivalence with increasing ambiguity of genotype combinations. However, many false negative compound LRs (defined as LRs of 0) are also apparent, which increase in frequency with the number of true contributors placed in the numerator of the LR (see Figure 3). A critical observation in understanding the root cause of these false negative compound LRs is where they fall on the x axis compared to the rest of the data set. The false negatives occur at relatively low log(simple LR product) values, but do not primarily occur near the origin. Because LR magnitude correlates with DNA mixture input, this simple LR product range corresponds to mixtures that are relatively low level but not the lowest level in the data set. For instance, among the Donor Group 1 4-person mixtures, the false negatives in Figure 3c had modest total DNA input ranges of ~250 pg-638 pg (~6 pg-250 pg on a per-contributor basis); the Donor Group 2 false negatives had a similar total DNA input range of ~200 pg-625 pg (~5 pg-350 pg on a per-contributor basis). In terms of the complexity of the interpretation (and the corresponding length of the plausible genotype combination list generated by STRmix), these mixtures ranked among the most complicated, because the contributors' input amounts are all in the stochastic range-high enough to allow full allelic detection of some contributors but low enough that allelic dropout is a realistic (and in some cases expected) possibility for all contributors.
Concomitant with the false negative compound LRs for the 4-person mixtures was an effect on the magnitude of the difference between the point estimate LR and HPD LR, which can be viewed as a secondary diagnostic of the issue. The 2-and 3-person plots of The increased complexity of the problematic 4-person mixture data, relative to the remaining 4-person data, explains both where the false negative compound LRs emerge in terms of x axis position and frequency, as well as why they are accompanied by large differences between the point estimate and HPD LRs. With a very complex interpretation and a long list of plausible genotype combinations comes a substantial diffusion of probability among the options. Because obtaining a non-zero LR requires that the interpretation places all of the true contributors together in the mixture, with a longer list of plausible options comes an increasingly remote opportunity for the true genotype combinations to be selected at all loci over the course of the interpretation. Hence, the sample effectively hits a "complexity ceiling". In other cases, even if a non-zero LR is avoided, the same diffusion of probability leads to the very large drop in HPD, as very small genotype combination weights can lead to excessively low resampled weights from the HPD calculation process.

Optimization of Markov Chain Monte Carlo (MCMC) Accept Settings for 4-Person Compound LRs
If the complexity ceiling is the issue causing the false negative LRs and large differences between the point estimate LR and HPD LR, then more thoroughly exploring the probability space with increased MCMC accepts is a reasonable solution that has been suggested in the relevant literature [23,24]. In order to preliminarily test a MCMC accept increase, we reanalyzed all of the problematic 4-person mixtures with the MCMC burn-in and post burn-in accepts set to 100,000 and 500,000 per chain, which is a 10-fold increase over the default settings of 10,000 burn-in and 50,000 post burn-in accepts per chain for STRmix v2.8. These altered settings produced non-zero LRs for every problematic mixture with the exception of a 250 pg 4:3:2:1 mixture. In this case, the LR with all four contributors in the LR numerator was 0, as was the LR with the three lower-level contributors in the numerator (at input levels of 75 pg, 50 pg, and 25 pg), indicating that fitting these low-level contributors together was the source of the issue. Upon performing 10 replicate analyses and 4-person compound LR calculations for this mixture, 3 of the 10 recalculations returned LRs of 0, indicating that further adjustment of the MCMC accepts was warranted.
To optimize the MCMC accepts to completely eliminate the observation of false negative compound LRs, the variations in burn-in and post burn-in accepts shown in Table 5 were tested on the 250 pg 4:3:2:1 sample. We observed that while increasing the post burn-in accepts 10-fold alone was more effective in preventing false negative LRs than increasing the burn-in accepts 10-fold alone, false negatives were still observed after both isolated setting adjustments. There was at least one false negative compound LR with a 20x increase in burn-in or post burn-in, when adjusted in isolation. However, no false negative compound LRs were observed with a 15x, 20x or 50x increase to both burn-in and post burn-in accepts. In order to achieve a balance between eliminating false negative compound LRs and avoiding unnecessary increases in runtime, we elected to move forward with the 20x MCMC accept increase (i.e., to 200,000 burn-in/1 million post burn-in per chain) for the remainder of the study.

Repeated Interpretation/Compound LR Calculation at 20x MCMC Accepts
All of the 4-person interpretations and calculations from Figure 3 were performed with the adjusted 20x MCMC accepts setting in place (see Figure 4). No LRs of 0 were observed, and although some large differences between point estimate and HPD LR were still observed, their incidence and magnitude were greatly reduced (see Appendix A Figure A2).

Conditioned LR Calculations: 4-Person Mixtures
Observations of false negative LRs occurred for the conditioned 4-person interpretations with the default number of MCMC accepts, similarly to the compound 4-person LRs (see Figure 5). A prominent difference with the conditioned 4-person LR plot is that the false negatives occurred along both axes; that is, in some instances, unconditioned compound LRs were zero and the corresponding conditioned LRs were non-zero, whereas in other cases unconditioned compound LRs were non-zero and the corresponding conditioned LRs were zero.

Repeated Interpretation/Compound LR Calculation at 20x MCMC Accepts
All of the 4-person interpretations and calculations from Figure 3 were performed with the adjusted 20x MCMC accepts setting in place (see Figure 4). No LRs of 0 were observed, and although some large differences between point estimate and HPD LR were still observed, their incidence and magnitude were greatly reduced (see Appendix A Figure A2).

Conditioned LR Calculations: 4-Person Mixtures
Observations of false negative LRs occurred for the conditioned 4-person interpretations with the default number of MCMC accepts, similarly to the compound 4person LRs (see Figure 5). A prominent difference with the conditioned 4-person LR plot Another difference in the conditioned 4-person LR plot is the reversal of the trend observed for increasing numbers of contributors in the LR numerator; whereas false negatives increased along with the number of contributors in the numerator, false negatives decreased with increased amounts of conditioning. This effect would be expected, given that increased conditioning correlates with decreased mixture complexity.

Conditioned LR Calculations: 4-Person Mixtures at 20x Increased MCMC Accepts
The interpretations and LR calculations from Figure 5 were performed again at 20x MCMC accepts. As with the compound LR plots, the increased accepts effectively addressed the false negative LRs observed with the default accepts; however, they were not completely eliminated (see Figure 6). Examining the EPG data for the conditioned interpretations with false negative LRs at 20x MCMC accepts (see Appendix A Figure A3) demonstrates a biological cause for these false negatives. In both cases, a very low level contributor produced unexpectedly high peak data at one locus, given the targeted mixture proportions, and the high peak data was produced for an allele exclusive to that low level contributor.
is that the false negatives occurred along both axes; that is, in some instances, unconditioned compound LRs were zero and the corresponding conditioned LRs were non-zero, whereas in other cases unconditioned compound LRs were non-zero and the corresponding conditioned LRs were zero. Another difference in the conditioned 4-person LR plot is the reversal of the trend observed for increasing numbers of contributors in the LR numerator; whereas false negatives increased along with the number of contributors in the numerator, false negatives decreased with increased amounts of conditioning. This effect would be expected, given that increased conditioning correlates with decreased mixture complexity.

Conditioned LR Calculations: 4-Person Mixtures at 20x Increased MCMC Accepts
The interpretations and LR calculations from Figure 5 were performed again at 20x MCMC accepts. As with the compound LR plots, the increased accepts effectively addressed the false negative LRs observed with the default accepts; however, they were not completely eliminated (see Figure 6). Examining the EPG data for the conditioned interpretations with false negative LRs at 20x MCMC accepts (see Appendix A Figure A3) demonstrates a biological cause for these false negatives. In both cases, a very low level Under these amplification conditions, STRmix heavily favored sharing the low level contributor peak. Further exacerbating the drive toward an incorrect inference was the fact that in both cases, the low level contributor in question was conditioned upon. Since data at other loci were consistent with an overall low contribution to the mixture, sharing of the peak appeared to be the most consistent with the data, despite this not being the ground truth.
Since the reason for the false negative LRs from the conditioned interpretations is an aberrant PCR amplification, the issue would be unlikely to be effectively addressed by increasing MCMC accepts. In other words, it is the unexpected height of the low level contributor peaks, not the length of the genotype combination list that is causing genotype combinations inconsistent with ground truth to be favored by the software.
Genes 2022, 13, x FOR PEER REVIEW 13 of 18 contributor produced unexpectedly high peak data at one locus, given the targeted mixture proportions, and the high peak data was produced for an allele exclusive to that low level contributor. Under these amplification conditions, STRmix heavily favored sharing the low level contributor peak. Further exacerbating the drive toward an incorrect inference was the fact that in both cases, the low level contributor in question was conditioned upon. Since data at other loci were consistent with an overall low contribution to the mixture, sharing of the peak appeared to be the most consistent with the data, despite this not being the ground truth.
Since the reason for the false negative LRs from the conditioned interpretations is an aberrant PCR amplification, the issue would be unlikely to be effectively addressed by increasing MCMC accepts. In other words, it is the unexpected height of the low level contributor peaks, not the length of the genotype combination list that is causing genotype combinations inconsistent with ground truth to be favored by the software.

Discussion
The data indicate that compound and/or conditioned LRs can be expected to be greater than simple LR products in a vast majority of cases, whether the mixture in question is at a low or high level of DNA input. Furthermore, the typical amount of increase was dependent on both the DNA input level as well as the mixture composition.

Discussion
The data indicate that compound and/or conditioned LRs can be expected to be greater than simple LR products in a vast majority of cases, whether the mixture in question is at a low or high level of DNA input. Furthermore, the typical amount of increase was dependent on both the DNA input level as well as the mixture composition.
The spread of the data around the equivalence line in each plot is small near the origin, then expands in the middle of the x-axis range before contracting again near the upper boundary of the x-axis range. This indicates that maximum information gain from compound LR calculation and/or conditioning occurred at input amounts in the middle of the studied input range, which on a per-contributor basis is~1 ng for our dataset. Such a maximum is sensible, given that low-level mixtures contain less information in general, and high-level mixtures are more definitively analyzed and so have less information to gain from compound LR calculation or conditioning than mixtures at a moderate template levels.
The impact of mixture composition on the magnitude of LR change for both the compound and conditioned LRs is most apparent when the constraints placed on the genotype combinations relevant to the LR calculations are maximized; that is, when N true contributors are placed in the numerator of an N-contributor mixture and when N-1 contributors are conditioned upon in the interpretation of an N-contributor mixture. Figure 1a,c and Figure 2a,c, for the 2-and 3-person mixtures, are the most striking in this regard; mixtures with contributors in the same proportions have regular, distinct datapoint groupings, and the trends for the mixtures with more inherent ambiguity (e.g., 1:1, 1:1:1 and 100:100:4) are translated further upward than the trends for the more easily resolved mixture proportions. The corresponding Figures 4c and 6c, for the 4-person mixtures, have less clear demarcation of the datapoint groupings for the ambiguous mixture proportions, which is not surprising given their level of complexity.
Because not every compound LR was higher than its simple LR counterpart, our expectations about compound LR magnitude are most straightforwardly defined in terms of upper and lower bounds. The distribution of log(LR) differences between all compound LRs in this study and their corresponding simple LR products ranged from a minimum of −2.7 to a maximum of~28.3, with the probability density peaking at approximately 0.5 (see Figure 7a). Figures 1a,c and 2a,c, for the 2-and 3-person mixtures, are the most striking in this regard; mixtures with contributors in the same proportions have regular, distinct datapoint groupings, and the trends for the mixtures with more inherent ambiguity (e.g., 1:1, 1:1:1 and 100:100:4) are translated further upward than the trends for the more easily resolved mixture proportions. The corresponding Figures 4c and 6c, for the 4-person mixtures, have less clear demarcation of the datapoint groupings for the ambiguous mixture proportions, which is not surprising given their level of complexity.
Because not every compound LR was higher than its simple LR counterpart, our expectations about compound LR magnitude are most straightforwardly defined in terms of upper and lower bounds. The distribution of log(LR) differences between all compound LRs in this study and their corresponding simple LR products ranged from a minimum of ~−2.7 to a maximum of ~28.3, with the probability density peaking at approximately 0.5 (see Figure 7a).
Furthermore, in a similar fashion to the compound/LR product plots, the conditioned/unconditioned LR plots show that conditioning does not always lead to a higher LR. The distribution of log(LR) differences between conditioned LRs and their corresponding unconditioned counterparts ranged from a minimum of ~−3.2 to a maximum of ~27.7, with the probability density again peaking at approximately 0.5 (see Figure 7b). The similarity of the log(LR) difference distributions for the compound/simple and conditioned/unconditioned comparisons (see Figure 7c) points to parallel mechanisms of action. In fact, because the weights produced by STRmix for the various genotype combinations can be characterized as either unconditional or conditional probabilities, some connections can be drawn between the effects of compound LRs and conditioned LRs.
For instance, if we let Pr(A) be the weight assigned to a genotype combination including contributor A, we could also let Pr(A|B) be the weight assigned to the same genotype combination if the interpretation were conditioned on contributor B. Alternatively, if we place both contributor A and contributor B in the numerator of the LR for a compound LR calculation, we could let Pr(A,B) be the weight assigned to the combination of contributor A and contributor B together. However, we could also deconstruct Pr(A,B) into Pr(B) * Pr(A|B). Furthermore, in a similar fashion to the compound/LR product plots, the conditioned/unconditioned LR plots show that conditioning does not always lead to a higher LR. The distribution of log(LR) differences between conditioned LRs and their corresponding unconditioned counterparts ranged from a minimum of~−3.2 to a maximum of~27.7, with the probability density again peaking at approximately 0.5 (see Figure 7b).
The similarity of the log(LR) difference distributions for the compound/simple and conditioned/unconditioned comparisons (see Figure 7c) points to parallel mechanisms of action. In fact, because the weights produced by STRmix for the various genotype combinations can be characterized as either unconditional or conditional probabilities, some connections can be drawn between the effects of compound LRs and conditioned LRs.
For instance, if we let Pr(A) be the weight assigned to a genotype combination including contributor A, we could also let Pr(A|B) be the weight assigned to the same genotype combination if the interpretation were conditioned on contributor B. Alternatively, if we place both contributor A and contributor B in the numerator of the LR for a compound LR calculation, we could let Pr(A,B) be the weight assigned to the combination of contributor A and contributor B together. However, we could also deconstruct Pr(A,B) into Pr(B) * Pr(A|B).
Since the same type of conditional probabilities come into play in calculating both compound LRs and LRs based on conditioned interpretations, this explains why the information gain from performing these types of calculations is similar. In fact, it could be said that calculating a compound LR is performing a type of conditioning, the difference being that the conditioning is applied at the point of LR calculation rather than at the point of interpretation.

Conclusions
The relative quantitative results of this LR study provide a basis for expecting additive behavior of LR exponents at all DNA input amounts. While not all of the compound LRs were greater than their simple LR product equivalents, a vast majority were equal or greater, and trends in the magnitude of the effect were seen in relation to both template DNA amount and mixture composition. We also determined that the substantial increases in analysis complexity inherent to higher-order mixture interpretations may necessitate increased MCMC accepts in order to avoid false negative compound LRs. In addition, the similarity of the log(LR) distributions for compound LRs and conditioned LRs relative to their simple LR product and unconditioned counterparts, respectively, derives from the similar information gain that occurs from either conditioning during the interpretation or the LR calculation of a compound LR.

Acknowledgments:
The authors would like to thank the two anonymous peer reviewers for their valuable constructive feedback. The quality of the finished product was substantially enhanced as a result of their contributions.

Conflicts of Interest:
The authors declare no conflict of interest. valuable constructive feedback. The quality of the finished product was substantially enhanced as a result of their contributions.

Conflicts of Interest:
The authors declare no conflict of interest.  Funding: This research received no external funding.

Acknowledgments:
The authors would like to thank the two anonymous peer reviewers for their valuable constructive feedback. The quality of the finished product was substantially enhanced as a result of their contributions.

Conflicts of Interest:
The authors declare no conflict of interest.    Figure A3. EPG data for conditioned mixtures with false negative LRs at 20x MCMC iterations. In both cases (see (a,b) below), unexpectedly high amplifications of peaks exclusive to one minor contributor at these loci led to incorrect inferences about peak sharing, which were exacerbated by conditioning. The black dotted line in each figure indicates the approximate expected height of the observed peak given exclusive attribution to the ground-truth contributor, based on per-contributor summary statistics in the STRmix Interpretation Report. This demonstrates visually why the software disfavored attributing the full height of each peak to its single true contributor.


s2fieldsofstudy: Biology Medicine

text:
Resistance Profile of Mycobacterial Strains Causing Cluster Pulmonary Infection in Bayelsa State, Nigeria

_____________________________________________________________________________________________________ ABSTRACT Aim: The aim of this study was to profile the anti-microbial resistant of Mycobacterial strains causing cluster pulmonary infections in Bayelsa State, Nigeria. Study Design: Cross sectional facility based. Place and Duration: This study was carried out in all the in tuberculosis centers in all the eight local government area in Bayelsa State, Nigeria between January 2019 to February 2021. Drug resistant profiling by GeneXpert shows 8(7.8) strain mutation at the rpoB gene only while the resistant profiling of the isolated on Lowenstein Jensen solid media had 14(13.7) strain with mutation on genes responsible for first and second line drug resistant. Two non-tuberculosis Mycobacterium species which are Mycobacteriode abscessus subsp. abscessus st and Mycobacterium Kansasii strain FDAARGOS_1534 where isolated among Tuberculosis patients, both are multidrug-resistant strains. The Mycobacterium tuberculosis strains in circulation causing cluster TB infection in Yenagoa especially and other parts of Bayelsa State, Nigeria are MG003 and R2092 strains, but the most predominant strain that is traced to cluster TB infection is MG003. The extraction of MTB gene from a pure culture of a Lowenstein Jensen selected media reduced the possibility of contamination and enhanced the reliability of gene sequencing and Bioinformatics analysis which includes comparing strains of MTB with the once in the National center for Biotechnology information (BLAST) data base. Conclusion: The resistance profiling of MTB reveals that most strains were resistant to rifampicin. This means that there were more mutation on the rifampicin activation gene (rpoB) than any other gene. Most cases of multi drug resistance is associated with rifampicin resistance while cases of extensive drug resistance is not common.

INTRODUCTION
Tuberculosis is a disease condition caused by Mycobacterium tuberculosis, it is transmitted from one individual to another through aerosolized inhaled droplets. It majorly affects the lungs but can also affect other parts of the body. After the inhalation of TB bacilli it is captured by macrophages, they can evade the immune cells and remain dormant for a long period of time. They could become active when the environment is favorable, because of the peculiarity of MTB virulent nature. After infection the organism is quickly phagocytized by professional alveolar macrophages which in most cases kill the entering bacteria. If the bacilli survives this first line of defense, it starts actively replicating in macrophages and diffuse to nearby cells including epithelial and endothelial cells. it is important to complete the treatment within the expected period which is not less than six months [1]. This is important because MTB has both fast and slow growing strains. However there has being frequent resistance to the first line drugs which is occasioned by relapses and the spread of resistant strains. Therefore, the main aim of this study is to obtain the resistance profile of tuberculosis and non-tuberculosis Mycobacterial strains causing cluster pulmonary infection in Bayelsa State, Nigeria.
Antimicrobial resistance is a major treat in the management of infections caused by bacteria. The danger posed by drug resistant mycobacterium tuberculosis is already a global concern. The drug resistant profiling of mycobacterium species requires the isolation of the organism and susceptible testing to both first and second line tuberculosis drugs. The first line drugs are rifampicin (RMP), isoniazid (INH), ethambutol (EM) and pyrazinamide (PZA). The second line drugs are amikacin, kanamycin, capreomycin and fluoroquinoline [2].
Mycobacterium tuberculosis (TB) resistant, which ranges from mono drug resistance TB (DR-TB), multiple drug resistance (MDR-TB) to extensive drug resistance (XDR-TB) is a leading challenge in the management of tuberculosis in Bayelsa State, Nigeria. Genexpert method of diagnosis can only identify MTB strains that are resistant to rifampicin and in some cases produce indeterminate resistant results. it is therefore important to carry out a more advanced and systematic resistant profiling that has the ability to detect anti-microbial resistance of tuberculosis [3].

MATERIALS AND METHODS
A cross sectional facility bases study was carried out in some approved tuberculosis treatment center in Bayelsa State. These facilities were General Hospital Sagbama, Cottage Hospital Okologba Kolokuma/Opocuma, Tuberculosis and leprosy control center Igbogene Yenagoa, Federal medical center (FMC) Yenegoa, Comprenhensive Health Centre Ekeremor, FMC Otuoke Ogbia, General Hospital Amasoma Southern Ijaw, Genaeral Hospital Nembe and General Hospital Brass. The State has an estimated population of 1.7 million people. It is located between 4 '15' north and 5 '23' south latitude and 5 '22' west longitude 6 '45' east. It has boundary with Delta State in the north, Rivers State in the east, and the Atlantic Ocean in the west and south part.
Ethical clearance was gotten from Bayelsa State Ministry of Health before the commencement of this research. Informed and writing Consent were obtained from each subject before collecting demographic information, data and sputum samples.This study were carried out in accordance with the Declaration of Helsinki (ethical considerations).A questionnaire were issued to each of the participants in other to obtain information about their age, gender and address.
A total number of 102 sputum Samples were collected from TB patients who had previously been diagnosed with tuberculosis. Both untreated (new) and treated (old) cases were considered. A 50ml capacities falcon tubes were used to collect a minimum of 2ml sputum samples for TB culture, while a wide mouth sputum cup was used for GeneXpert. Each participant produce sputum into a falcon tube and a wide mouth sputum cup.
The GeneXpert MTB/Rif machine was used for the identification and probing of resistance to Rifampicin. Sputum samples were processed according to the operating procedures for GeneXpert MTB/RIF assay as Line Probe Assay (MTBDRplus assay) method were used for resistant profiling of isolates gotten from MTB positive Lowenstein Jensen solid culture media. Genomic of Mycobacterial culture was extracted by incubating the colonies dissolved in 300 L of molecular biology grade water for 20 minutes at 95 ∘ C in water bath followed by 15 minutes in ultrasonic bath and centrifugation for 5 minutes at 12000 rpm. Polymerase chain reaction (PCR) and hybridization were performed following manufacturer's recommendations (Hain Lifescience, Nehren, Germany). Colorimetric method (using streptavidin conjugated with alkaline phosphatase and substrate buffer) was used to detect hybridized amplicons. The strip containing hybridized amplicons were interpreted following manufacturer's instructions [5].
Drug resistance (R) and drug Susceptibility (S) were determined by the presence of mutation or no mutation. The resistance profiling of all the isolated was carried out with MTBDRplus assay. This molecular technique targets the genes that were responsible for drug resistance of both the first and second line drugs. If there are indication of mutation on either rpoB, KatG, inhA, gyr, rrs & eis gene it were considered as drug resistant, reported as R. If there was no indication of mutation it were represented as S meaning no mutation.

16S
rRNA Sequencing of isolated Mycobacterium species was done using the BigDye Terminator kit on on a 3510 ABI sequencer by Inqaba Biotechnological, Pretoria South Africa. The sequencing was done at a final volume of 10ul, the components included 0.25 ul BigDye® terminator v1.1/v3.1, 2.25ul of 5 x BigDye sequencing buffer, 10uM Primer PCR primer, and 2-10ng PCR template per 100bp. The sequencing condition were as follows 32 cycles of 96°C for 10s, 55°C for 5s and 60°C for 4min and phylogenic analysis was carried.
Statistical Package for Social Science (SPSS) version 21 was the statistical tool used to analyse data after collation with Microsoft excel spread sheet. Frequency, percentage and prevalence were calculated. Chi square was used to measure association between variables. Mann Whitney test and kruskal Wallis were used to compare difference between two and more than two variables respectively. All tests of significance was at 0.05 alpha level. Results were presented on tables.

RESULTS
Subjects who are positive to rifampicin resistance tuberculosis while using the GeneXpert method across the Local Government Area are shown in Table 1. The results on Table 1 shows Rifampicin resistant TB among males and female in each of the Local Government Area using GeneXpert technique. Yenagoa L.G.A had the highest rifampicin resistance case but not statistically significant. Females had more cases of rifampicin resistance than males.
In this research GeneXpert confirm 8 MTB strains that has mutation on the rpoB gene ( Rifampicin resistance as a result of mutation on rpoB gene detection using Line Probe Assay (MTBDRplus assay) technique is shown on Table 3. Rifampicin resistance profiling of all the culture isolates using the line probe assay technique are shown in Table 3. The Mycobacterial species and strains are identified by gene sequencing and phylogenetic analysis. The strains identified in this research were lineage 4(Euro-American) strains.    Table 4. Mycobacterium tuberculosis resistant and gene mutation S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15

S1-S15= isolates; R=resistance; S=susceptible
The isolates confirmed and harvested from the Lowenstein Jensen culture media are represented as S1-S15 in Table 4. The results gotten from the profiling of isolates gene whose mutation decides drug resistance is shown on Table 4.

DISCUSSION
GeneXpert molecular technique was used to detect MTB resistance to rifampicin, Majority of the isolated strains are resistant to rifampicin. The Line Probe Assay technique also reveals that most of isolates from Lowenstein Jensen culture media had mutation on the rpoB gene. The second most common form of mutation occurred at the katG gene, further confirming that most cases of MDR-TB cases starts from rifampicin resistance. GeneXpert molecular technique was only able to detected mutation at the rpoB gene, it cannot offer an insight into the genetic characterization of MTB. It also lack the ability to identify non tuberculosis mycobacterium specie but has being proven to be more sensitive and specific than ZN staining technique. When MTB is resistant to rifampicin, the chance of being resistant to the first line TB drug becomes high. Line Probe Assay provides the possibility of detecting resistance to all the first and second line tuberculosis drugs. GeneXpert technique had being considered as a gold standard for TB management in some hospitals across Bayelsa State because of unavailability of an accredited facility for TB culture. This situation possesses a greater risk because the physiologic and genetic characterization of Mycobacterium is an unavoidable tool in the management of multiple drug resistant tuberculosis which is currently the highest treat in TB program across Bayelsa State. The possibility for the identification and prediction of extensive drug resistant MTB depend on the genetic fingerprinting of all mycobacterium isolates in Bayelsa State, Nigeria.
The ability of the Genxpert molecular system to identify mutation on the rpoB gene which is the receptor for rifampicin was a major mile stone in the fight against drug resistant tuberculosis. Yenagoa has been identified as the highest local government with the highest TB prevalence rate in Bayelsa state, this also agrees with study carried out by Obioma and kpomasirichi, [3].
Prolong use of drug is one of the major reasons while MTB is resistant to rifampicin. GeneXpert being a molecular technique is said to be more sensitive and specific than ZN AFB staining technique. Therefore its diagnostic value is very important, several journals such as (4) Calabar, [4] Nasarawa and [6] Adamawa all in Nigeria have shown that individuals within 31-40 years age interval tend to have more positive TB cases than persons within other age intervals.
The isolation of Mycobacterium Kansasii (MBK) and Mycobacteriode abscessus (Table 3) Table 3. Gene mutation on the rifampicin resistance determinant site is more common than any other form of mutation that is likely to occur with MTB genome. A total of 13.7% of isolates where resistant to rifampicin, using the MTBDRplus assay. Therefore resistance to rifampicin is the first point of concern in the genetic profiling of resistant genes of MTB Various genetic profile studies in different part of the world had reveal great diversity in the genetic evolution of mycobacterium. A study in Ethiopia had revealed the existence of extensive mycobacterium drug resistance. This was achieved as a result of the phylogenic and spoligotyping profile of various isolates of Mycobacterium tuberculosis [5]. Drug resistant pattern of occurrence has also been linked to clusters of Mycobacterium tuberculosis strain that is predominant in some certain areas [11]. We were able to observe in this study that the cluster of Mycobacterium tuberculosis strain MGOO3 is the most common strain associated with MTB drug resistant in Bayelsa State. Progress has been made in understanding of the molecular mechanisms that determine the epidemiological success of certain M. tuberculosis lineages. However, the molecular basis for the high epidemiological suitability of the M strain remains unclear [5].
The genetic resistant profiling of the isolated strains includes the observation of nucleotide or amino acid changes within the gate keeper genes. Mutation profiling using the MTBDRplus assay was able to show gene mutation that has the ability to confer resistance to first and second line TB drugs. KatG gene mutation is the determinant factor for the effectiveness of isoniazid, KatG gene mutation is not as common as rpoB gene mutation. Total of 2.9% of isolates had mutation on the KatG gene, while 2.0% had mutation on the inhA gene. MTB resistance to isoniazid depends on the mutation of both KatG and inhA gene therefore those isolates that had mutations on KatG and inhA gene are resistant to isoniazid Table 4. It is important to note that certain drugs had been recommended for treatment by the National Tuberculosis and leprosy control program. Therefore, for a more precise contribution to knowledge this research is focus on the gene mutation which will lead to resistant of the approved first and second line drugs. Studies had also revealed that when there is mutation on rpoB gene, there is always a high possibility of having mutation on KatG or inhA gene [12].
Flouroquinolones are major second line drugs, activation of flouroquinolones is determined by gyr gene. In this research the genetic profiling of gyr gene reveals no mutation or amino acid changes. Therefore flouroquinolones are highly recommended for management of MDR-TB cases in Bayelsa State. This further confirms the need for the use of flouroquinolones as a major second line anti tuberculosis agent. There is also no mutation on the rrs gene of all the isolates in this research. This shows that all the isolates are sensitive to other second line drugs like Amikasin, Kanamycin and caporemycin. 1.0% of the isolate had mutation on the eis gene which is responsible for the activation of kanamycin and Amikasin. Therefore 1.0% of the isolate will not respond to kanamycin and Amikasin Table 4.

CONCLUSION
Genexpert technique is the most common technique uses for multi-drug resistance tuberculosis diagnosis across Bayelsa State because of unavailability of an accredited facility for TB culture, this situation poses a greater risk because the phylogenic and genetic characterization of mycobacterium is an unavoidable tool in the management of multiple drug resistant tuberculosis which is currently the highest treat in TB program across Bayelsa State. The possibility to identify and predict extensive drug resistant MTB depend on the genetic fingerprinting of all mycobacterium isolate in Bayelsa State. The most predominate multidrug resistant strain of mycobacterium circulating in Bayelsa State is mycobacterium tuberculosis MG003. In this research this strain had been identified as the cause of a cluster infection in Amarata community of yenagoa local government area in Bayelsa State. The second most predominate MTB strain is Mycobacterium tuberculosis R2092 while the non-tuberculosis species causing pulmonary multi drug resistance are Mycobacteriode abscessus and Mycobacterium kansasii.

CONSENT
Written consent has been collected and preserved by the authors.

ETHICAL APPROVAL
Ethical approval was gotten from Bayelsa State Ministry of Health Yenagoa.


s2fieldsofstudy: Biology Chemistry

text:
Protein interaction, cytotoxic, transcriptomic and proteomic responses to structurally distinct EPAC1 activators in HUVECs

The N-acylsulfonamide derivative, I942, represents the first non-cyclic nucleotide partial agonist of EPAC1. This was soon followed by the identification of the I942 analogues, PW0381, PW0521 and PWO577 and a series of benzofuran oxoacetic acid EPAC1 activators, SY006, SY007 and SY009. Protein interaction, cytotoxicity and EPAC1 activation assays applied here identify PWO577 and SY007 as being effective EPAC1 binders that are well tolerated in HUVECs at concentrations greater than 100 μM and up to 48 h incubation and are effective activators of transfected EPAC1 in U2OS cells. Using RNAseq in HUVECs we show that PWO577 and SY007 regulate approximately 11,000 shared genes, with only few differential gene changes being “off-target”. The genes significantly regulated by both PWO577 and SY007 included a subset of genes normally associated with endothelial activation, including ICAM1, MMP1 and CCL2. Of these, only the expression of MMP1 was markedly increased at the protein level, as determined by LC–MS-based proteomics. Both PWO577 and SY007 suppressed IL-6-induced STAT3 activation and associated downstream gene expression, including inhibition of SOCS3, STAT3, IL6ST and JAK3 genes. Together these results demonstrate the utility of structurally distinct, specific and non-toxic EPAC1 activators. Future modifications will be aimed at eliminating the few noted off-target effects.

Results
Effects of EPAC1 activators on protein stability. Nonspecific interactions and associated proteindestabilising properties of small molecule ligands can lead to false-positive hits in screening studies. To address this concern for our previously identified EPAC1 activators, we tested I942 20 and the identified I942 analogues, PWO381, PWO521 and PWO577 21 (Fig. 1B), plus the SY series compounds SY006, SY007 and SY009 19 ( Fig. 1C), for their ability to interact with and effect protein stability of the EPAC1 cyclic nucleotide-binding domain (CNBD), using thermal shift assays (TSA) 26 . TSA monitors protein thermal denaturation in the presence of a potential ligand using an environmentally sensitive fluorescent dye, such as SYPRO Orange. Increasing temperature leads to protein unfolding and exposure of hydrophobic regions that SYPRO Orange can bind to, promoting an increase in fluorescence signal 26 . The resulting melt curves provide information on ligand binding, which usually stabilises the protein and shifts the melting temperature (Tm) towards higher values, but also on potential destabilising properties of the compound. At low temperatures, proteins should be natively folded, which is accompanied by a minimal SYPRO Orange signal. If high fluorescence values are observed instead, it means that the test compound might be denaturing.
Recombinant GST-EPAC1-CNBD protein, test ligand dilution series, and SYPRO Orange dye, were combined in a PCR-compatible microplate. Cyclic AMP was also used in addition to the test EPAC ligands, as a protein-binding and protein-stabilising control, while the EPAC1 antagonist, ESI-09, was employed as a proteindenaturing control as previously described 27 (Fig. 1A). Vehicle controls (DMSO) were included as well to account for potential effects of the ligand solvent (Fig. 1A). As shown in Fig. 1A, cyclic AMP causes a dose-dependent shift in GST-EPAC1-CNBD Tm towards higher values, which means it increases the thermal stability of the protein by binding to it specifically, as expected. At the same time, high concentrations of ESI-09 (100 µM, and to a lesser extent, 50 µM) were found to distort the shape of the melt curve and induce protein unfolding at low temperatures, which is manifested by high fluorescence signal in this region, confirming the denaturing properties of this EPAC inhibitor at high concentrations, as previously described 27,28 . Thermal profiles for I942, PWO381, PWO521 and PWO577 (Fig. 1B) and SY007 (Fig. 1C) retain correct shapes and low fluorescence at low temperatures, demonstrating that none of the compounds negatively affects the stability of GST-EPAC1-CNBD. All test compounds induce a dose-dependent increase in the Tm value, resembling the stabilising effect of the endogenous ligand, cyclic AMP. All Tm values at 100 µM ligand concentration were compared using one-way ANOVA with Tukey's post hoc test to detect statistically significant differences (Fig. 1D). Consistent with previous 8-NBD-cAMP binding assay data 21 , PWO381, PWO521 and PWO577 are characterised by improved binding affinity, as 100 µM concentrations of these compounds induce a significant increase in ∆Tm of 9 °C, 9.1 °C and 10.5 °C, respectively, compared to cyclic AMP (Fig. 1D). SY007 and SY009 produced a comparable increase in Tm to cyclic AMP at 100 µM, but SY006 was observed to exert denaturing properties on GST-EPAC1-CNBD at this concentration (Fig. 1C).

Effects of EPAC1 activators on cell cytotoxicity.
Having determined the impact of EPAC1 activators on the stability of the EPAC1 CNBD, we next explored whether the compounds displayed unwanted cytotoxic effects in HUVECs, which we routinely use for inflammatory assays 22 . To do this, we employed a multiplex method utilizing three fluorescent viability probes, Alamar Blue, CFDA-AM and Neutral Red as described in Material and Methods. Before investigating the cytotoxicity profiles of I942 and lead compounds, their autofluorescence at all three assay wavelengths was tested to rule out the possibility of any of them interfering with the readings (results not shown). HUVECs were exposed to I942, PWO381, PWO521, PWO577, SY006, SY007 and SY009 with a concentration range of 3.9-500 µM, for 24 h and then IC 50 values were calculated and compared between the compounds, for Alamar Blue, CFDA-AM and Neutral Red assays, respectively. Statistical differences between these values were determined using one-way ANOVA with Tukey's post hoc test (Fig. 2)  125-100 µM range) plus diluent (DMSO; concentrations as indicated in graph) were prepared and combined with 2 µM GST-EPAC1-CNBD and 10 × SYPRO Orange dye, in a PCR-compatible microplate. Plates were incubated overnight at 4 °C, and then subjected to a standard temperature gradient programme using a real-time PCR machine, with a temperature range of 11-80 °C, ramping up in 0.5 °C increments, with a 30-s hold at each temperature Changes in fluorescence of SYPRO Orange then were monitored. A 20-70 °C range is shown in the graphs. Presented data is an average of three independent experiments. (B) Thermal shift assays (TSA) were carried out on a dilution series of I942 (3.125-100 µM range) and selected chemical analogues, as described in the legend to (A). (C) TSAs were carried out on a dilution series of "SY" series analogues (3.125-100 µM range) and selected chemical analogues, as described in the legend to (A). (D) Tm values for 100 μM ligand concentration from the TSAs carried out in (A-C) are displayed as a bar graph. Significant differences in Tm values relative to cyclic AMP are indicated, **p < 0.01 and ***p < 0.001, or ns, for no significant difference (n = 3-5).  www.nature.com/scientificreports/ than either PWO381, PWO577 or SY007. The results from the Neutral Red and CFDA-AM cytotoxicity assay ( Fig. 2) again follow the same trend as the Alamar Blue assay and show that exposure to PWO381, PWO577 and SY007 is better tolerated in HUVECs than treatment with the other test ligands (Fig. 2). Overall, the presented results confirm lower cytotoxicity of PWO381, PWO577 and SY007, relative to I942, PWO521, SY006 and SY009. SY007 was therefore carried forward for further assays due to its tight interactions with EPAC1, lack of protein denaturing and tolerance in cytotoxicity assays. PWO577 was also carried forward as a representative from the PWO series due to its improved ability to activate EPAC1 in vitro, over PWO381, as demonstrated previously 21 .
Transcriptomic and proteomic responses to EPAC1 activators. PWO577 and SY007 were next tested for their ability to activate EPAC1 in U2OS cells expressing EPAC1 using immunoprecipitation with an activation-selective antibody and Rap1 activation assays (Fig. 3). This demonstrated that although both compounds fully engaged comparably with EPAC1, as demonstrated by immunoprecipitation (Fig. 3A), the levels of stimulated Rap1 activation induced by PWO577 and SY007 (Fig. 3B) were not significantly comparable to those achieved with D-007, indicating partial agonist effects of PWO577 and SY007 towards Rap1 (Fig. 3). Given that PWO577 and SY007 exert similar levels of EPAC1 activation in cells, we next applied RNAseq to examine whether the transcriptomic response to PWO577 and SY007 was also similar ( Fig. 4; Supplementary Data). For this, HUVECs were stimulated for 16 h in the presence or absence of EPAC1 activators then total RNA was extracted from cells; this was used for RNA library preparation and subsequently sequenced using the Illumina platform, as described in "Materials and methods". The gene expression level is estimated by the abundance of transcripts (count of sequencing) that mapped to genome or exon locations. Comparison of the abundance of transcripts from each treatment grouped demonstrated that the majority of 11,139 transcripts identified were regulated by both PWO577 and SY007 ( Fig. 4A; Supplementary Data), indicating that both EPAC1 activators exert similar transcriptomic responses in HUVECs. However, 250 transcripts appeared to be regulated specifically by PWO577 and a further 219 by SY007. It could be concluded that these changes represent "off-target" effects, but it cannot be ruled out that the 2 ligands may activate EPAC1 in different manners. After gene expression quantification, statistical analysis of the expression data was carried out to screen for genes whose expression levels are significantly different in the different treatment conditions (Fig. 4B,C; . Only a few "off target" significant gene changes were identified for PWO577 and SY007 however proteomic analysis indicated that these "off target" genes were not translated into significant changes in protein expression in these cells ( Fig. 5; Supplementary Data). Moreover, many of the "on-target" gene changes induced by treatment by both PWO577 and SY007, were not translated into changes in protein expression (e.g., CCL2; Fig. 5) and the majority of gene changes that were translated into changes in protein expression, were small and didn't breach the twofold barrier for acceptance (e.g., ICAM1; Fig. 5). In fact, only MMP1 was regulated by both PWO577 and SY007 to produce a significant upregulation in protein expression that was greater than twofold (Fig. 5). These results suggest that while PWO577 and SY007 exert significant effects on gene activity in HUVECs, the effects on translation of target genes are modest. It should also be noted that re-analysis of previously published RNAseq data 29 from D-007 stimulated HUVECs ( Fig. 4D; Supplementary Data), demonstrated that ICAM1 was also significantly upregulated following D-007 treatment at all time points tested (Fig. 4D). Whereas some of the gene changes that were upregulated in response to PWO577 and SY007 (e.g., MMP1 and SERPINB2), were downregulated in response to D-007 treatment for 48 h (Fig. 4D). Moreover, certain "off-target" actions, including upregulation of FOSB and ATF3, which was associated with SY007 treatment, were also upregulated by 2 h D-007 treatment (Fig. 4D). The reason for the similarities and differences between D-007 treatment, and treatment with either PWO577 or SY007, is not yet known but may be due to differences in cell responses to either a full agonist (D-007) or partial agonists (PWO577 and SY007) and the resulting consequences for the dynamics of EPAC1 activation in HUVECs.

Inhibition of IL-6 signalling by EPAC1 activators.
We have previously shown that I942 effectively stimulates EPAC1 to promote up-regulation of the SOCS3 gene and suppression of IL-6-induced STAT3 activation and VCAM1 expression in HUVECs 22 . It is IL-6 receptor "trans-signalling" 30 that is thought to underlie the pro-inflammatory actions of IL-6 in a variety of diseases, including atherosclerosis 31 . During trans-signalling, IL-6 binds to soluble forms of IL-6R (sIL-6R) allowing activation of gp130, leading to receptor clustering and www.nature.com/scientificreports/ activation of the JAK-STAT3 and ERK, MAPK and PI3K signalling pathways. Of these, it is activated STAT3 that then homodimerises and translocates to the nucleus, where it acts as a transcription factor for the induction of pro-inflammatory IL-6-responsive genes [32][33][34] .
Here we use RNAseq and KEGG pathway analysis to show that in HUVECs, IL-6 stimulation leads to the regulation of more than 11,000 genes ( Fig. 6; Supplementary Data), many of which are involved in cytokine action, cancer and inflammatory pathways, including Rap1, PI3K, MAPK and JAK-STAT signalling (indicated by arrows in Fig. 6B). Moreover, many of the genes regulated by IL-6 are also regulated by PWO577 and SY007 (Fig. 6B). In terms of gene regulatory pathways, we show that 100 µM D-007, PWO577 or SY007 significantly inhibited IL-6-stimulated STAT3 activation (Fig. 7A). Moreover, PWO577 and SY007 also inhibited downstream IL-6 gene targets in HUVECs, including SOCS3, IL6ST, STAT3 and JAK3 ( Fig. 7B; Supplementary Data). Interestingly, neither PWO577 or SY007 were able to down-regulate IL-6-induced ICAM1 and CCL2 gene expression, consistent with the ability of the two EPAC1 activators to directly upregulate the expression of these genes (Fig. 5B), although it is likely that this does not lead to a sizeable upregulation of the protein products of these genes as shown by proteomics in Fig. 5. were preincubated for 10 min with 100 μM of EPAC1 activators, PWO577, SY007 or D-007. Active EPAC1 was then isolated from cells by immunoprecipitation with the activation selective EPAC1 (DE3) antibody and detected by immunoblotting with the same antibody, as described in "Materials and methods". Significant increases in active EPAC1 levels relative to D-007-treated cells are indicated; **p < 0.01 and ***p < 0.001 or ns, for no significant difference (n = 5-7). (B) U2OS cells transfected with EPAC1 were preincubated for 10 min with 100 μM of EPAC1 activators, PWO577, SY007 or D-007. Active Rap1 was then isolated from cell lysates, immunoblotted and compared to total Rap1 levels, as described in "Materials and methods". Data are presented as means ± SEM (n = 5-7). Significant increases in active Rap1 levels in comparison to D-007-treated cells treated cells are indicated, p < 0.001 (n = 5-7).

Discussion
To address concerns about false-positive hits from screens, resulting from non-specific protein-denaturing properties of compounds on EPAC1 27 , we tested the effects of I942 and the three identified lead analogues, PWO381, PWO521 and PWO577, on EPAC1 stability in vitro. TSA experiments were carried out and thermal denaturation profiles of recombinant GST-EPAC1-CNBD in the presence of increasing compound concentrations were analysed (Fig. 1). Importantly, no distortions of the protein melt curves, which are characteristic for protein-denaturing agents, such high concentrations of ESI-09 27 , were observed at low temperatures in the presence of test compounds. It can therefore be concluded that I942 and selected analogues do not negatively affect the natively folded state of recombinant GST-EPAC1-CNBD and that protein-destabilising properties of these compounds can be ruled out with high probability. Moreover, I942, PWO381, PWO521 and PWO577 induced a dose-dependent protein stabilisation, a typical indicator of ligand binding, which manifests itself in a shift of Tm . "On"-and "Off "-target Gene Regulation by PWO577 and SY007 in HUVECs. To identify gene regulation by EPAC1 activators, confluent HUVECs were stimulated for 16 h with PWO577 and SY007. Total RNA was then extracted and processed for RNAseq and plotted as a Venn diagram (A) or volcano plots (B,C), as described in "Materials and methods". In the upper panel of (B,C) the data represents gene expression regulated by both PWO577 and SY007, encompassing 11,139 shared gene expression changes, whereas the lower panel demonstrates genes regulated solely by either PWO577 (B) or SY007 (C), representing either 250 or 219 gene expression changes, respectively. Significant gene expression changes (p > 0.05) are shown in light grey with changes in gene expression greater than 2-fold indicated either red (increase in gene expression) or green (decrease in gene expression). Non-significant gene changes are presented in black. In (D), previously published data 29 has been re-analysed to demonstrate changes in gene expression promoted by a time-course of D-007 treatment. Named genes represent those that were also regulated by either PWO577 or SY007.  www.nature.com/scientificreports/ towards higher values. This confirmed the protein-ligand interactions and relative binding affinities observed previously using 8-NBD-cAMP competition assays [19][20][21] and assured us that none of the three newly identified EPAC1 ligands was a false-positive hit. For a reliable and comprehensive cytotoxicity assessment, it is strongly recommended that more than one type of assay is used 25 . The method we selected meets this requirement, as it comprises Alamar Blue, CFDA-AM and Neutral Red assays, which test for different viability and cytotoxicity indicators, including effects on metabolic activity, membrane integrity or lysosomal function. In general, from the N-acylsulfonamide series, compounds PWO381 and PWO577 were significantly better tolerated by HUVECs than I942 and PWO521. In the case of benzofuran oxoacetic acid ligands, SY006 and SY009 were shown to be significantly more toxic than SY007. Cytotoxic effects of PWO381, PWO577 and SY007 were detected, but only at very high concentrations, way above those normally used in activity assays 19 . In summary, the presented in vitro cytotoxicity experiments allowed for the selection of compounds with good cytotoxicity profiles from both EPAC1 ligand series. In the N-acylsulfonamide class, PWO381 and PWO577 were found to be best tolerated by HUVECs, whereas, among the benzofuran oxoacetic acid derivatives (SY series), compound SY007 was significantly less toxic than SY006 and SY009. In combination with their binding properties and demonstrated in-cell activities, this makes PWO577 and SY007 promising candidates for further drug development and in-depth activity studies in cellulae. www.nature.com/scientificreports/ From RNAseq and proteomic experiments we found that PWO577 and SY007 exerted qualitatively similar effects on gene expression, leading to upregulation of a subset of MMP1 at the mRNA and protein level (Figs. 4 and 5). Beyond this, many of the induced gene expression changes detected by RNAseq led to only modest changes in protein expression (e.g., ICAM1) or were absent from proteomic analysis (e.g., CCL2). Given that PWO577 and SY007 treatment led to many more changes in protein expression (Fig. 5), beyond those identified by RNseq (Fig. 4), implies that EPAC1 activation may also promote post-transcriptional mechanisms of gene regulation, in addition to the transcriptional changes identified here. Another point to consider, is that RNAseq and proteomic experiments were carried out using the same time points. This may explain why the results don't map exactly to each other, since changes in RNA levels normally precede changes in protein translation. This will need to be investigated in future.
EPAC1 has emerged as an important factor in the regulation of the pro-inflammatory IL-6 trans-signalling pathway in VECs [35][36][37][38] , including promotion of SOCS3 induction and blockade of IL-6 JAK/STAT3 signalling,  Figure 7. Impact of PWO577 and SY007 on IL-6 Signalling in HUVECs. (A) HUVECs were stimulated for the indicated times with IL-6 (5 ng/ml) plus sIL-6Rα (25 ng/ml) in the presence or absence of the indicated concentrations or D-007, PWO577 or SY007. Cell extracts were then immunoblotted with antibodies to pSTAT3 (Tyr705) or total STAT3. Densitometric values from 3 separate immunoblots, at 100 μM ligand concertation, are shown in the bar graph in the lower panel with significant changes STAT3 phosphorylation, relative to cells stimulated with IL-6/sIL-6Rα alone, being indicated: **p < 0.01 and ***p < 0.01 (n = 3). (B) HUVECs were stimulated with IL-6/sIL-6Rα, as described above, in the presence or absence of either PWO577 or SY007, as indicated. RNAseq was then performed, as described in "Materials and methods", and the resulting gene expression changes shown as volcano plots. Significant gene changes (p < 0.05) are shown in light grey whereas non-significant gene changes are shown in black. Significant changes in gene induction associated with IL-6 signalling are shown in red. www.nature.com/scientificreports/ suppression of the expression of the pro-inflammatory cell adhesion molecule, VCAM1 39 . Moreover, EPAC1 protects the heart from cytokine-induced cardiac dysfunction, at least in part, through the inhibition of the JAK-STAT pathway 40 , and decreases foam cell formation 41 , although contradictory data to this point has recently been published 42 . Here we show that PWO577 and SY007 suppresses STAT3 activation by IL-6 and downstream gene expression, including SOCS3, IL6ST, STAT3 and JAK3 (Fig. 7). This probably arises from early upregulation of SOCS3 and inhibition of JAK signalling to STAT3 as demonstrated for IL-6 signalling in HUVECs 22 .
Notably, PWO577 and SY007 were not able to inhibit CCL2 and ICAM1 induction by IL-6 ( Fig. 7). This is probably because inhibition of STAT3 by PWO577 and SY007 is insufficient to block induction of these two genes. Indeed, for ICAM1 we have previously shown that inhibition of ERK and PI3K induces expression of ICAM1 in I942-stimulated HUVECs 22 . A similar situation might underly the regulation of CCL2, but this remains to be determined, moreover upregulation of CCL2 mRNA may necessarily lead directly to up-regulation of CCL2 protein, as shown in Figs. 4 and 5. It is tempting to speculate that wider gene expression changes induced by PWO577 and SY007 here might involve activation of the AP1/Jun transcription factor complex as described for I942 43,44 . This is a particularly attractive idea of one study suggesting that AP1/Jun transcription factors play a key role in the control of transcriptional networks in human aortic endothelial cells by co-binding enhancers with VEC-specific genes 45 . Whether these mechanisms are further controlled by EPAC1 activation remains to be determined.
In summary, we have identified PWO577 and SY007 as EPAC1 activators that are specific, non-toxic and inhibit IL-6 activation in HUVECs. Further modifications to the compounds will be guided by these results to eliminate the few noted off target effects identified. Chemical synthesis. Compounds I942, PW0381, PW0521 and PWO577 were synthesised as previously described (compounds 3, 25g, 25q, 25n, respectively, in 21 ). Compounds SY006, SY007 and SY009 were synthesised as previously described 19 . Cell culture. Cryopreserved primary human umbilical vein endothelial cells (HUVECs) as well as dedicated medium and growth supplements, were bought from (PromoCell, Heidelberg, Germany). Cells were cultured in the growth medium, comprising Endothelial Cell Growth Basal Medium supplemented with Endothelial Cell Growth Medium SingleQuots Supplements (excluding antibiotics), in 75 cm 2 , tissue culture treated flasks, at 37 °C, in a humidified atmosphere containing 5% (v/v) CO 2 . HUVECs were used for experiments at no older than passage 6.

Materials
Human bone osteosarcoma epithelial cells (U2OS) stably transfected to express FLAG-tagged EPAC1 were a gift from Professor Holger Rehmann (Flensburg University of Applied Sciences, Germany). Cells were cultured in 75 cm 2 , tissue culture treated flasks containing complete medium, comprising Dulbecco's modified Eagle's medium (DMEM), with 4.5 g/l d-glucose and phenol red, supplemented with 10% (v/v) foetal bovine serum (FBS), 1% (v/v) GlutaMAX and 1% (v/v) Penicillin-Streptomycin (all from ThermoFisher Scientific), at 37 °C in a humidified atmosphere containing 5% (v/v) CO 2 . To ensure selection of stable transfectants, 2 mg/l puromycin was also added to the complete medium.
Thermal shift assays. Recombinant GST-EPAC1-CNBD (2 μM), test ligand dilutions (3.1-100 μM) or 0.0006% to 0.02% (v/v) DMSO dilutions (diluent for ligands), were combined with 1 × (v/v) SYPRO Orange dye in PCR-compatible microplates, which were then stored, protected from light, overnight at 4 °C. Plates were then subjected to a standard temperature gradient programme using a real-time PCR machine (Applied Biosystems StepOnePlus Real-time PCR Instrument, ThermoFisher Scientific). The applied temperature range was 11-80 °C, ramping up by 0.5 °C increments, with a 30-s hold at each temperature. Changes in the fluorescence of SYPRO Orange dye were detected using a filter set for a ROX reporter dye (ex/em maxima 580/621 nm). Melting temperatures (Tm) were generated from melt curves by fitting a selected range of acquired data to a Boltzmann equation, using the first derivative approach 26  www.nature.com/scientificreports/ Cytotoxicity assays. The method used here is a modified version of the protocol developed by Dayeh et al. 46 , which allows the simultaneous use of three different fluorescence-based cytotoxicity assays, using the same set of cells: Alamar Blue assay, 5-carboxyfluorescein diacetate acetoxymethyl ester (CFDA-AM) assay, and Neutral Red assay. This allows the simultaneous determination of the impact of each compound on cell metabolic activity, plasma membrane integrity, and lysosomal function. The half-maximal effective concentration (EC 50 ) parameter was adopted as a measure of cytotoxic potential of test compounds in each assay. To obtain EC 50 values from the dose-response data, curve fitting was performed using nonlinear regression in GraphPad Prism 8 software.
EPAC1 and Rap1 activation assays. U2OS  . The blocking step was followed by overnight incubation with primary antibody diluted 1:1000 in the blocking buffer at 4 °C, followed by incubation with horseradish peroxidase-conjugated anti-rabbit secondary antibody diluted in 5% (w/v) non-fat dry milk in 1 × TBS-T for 1 h at room temperature. Membranes were then washed three times with 15 ml of 1 × TBS-T and then incubated for 5 min with SuperSignal West Pico PLUS Chemiluminescent Substrate (ThermoFisher Scientific). Images were acquired using a Fusion FX7 camera platform (Vilber, Collégien, France) on the chemiluminescence setting. Signal intensities were measured densitometrically, using ImageJ software (National Institutes of Health, Bethesda, USA), and normalised to the signal obtained from a known housekeeping protein in the same sample.
RNA sequencing. HUVEC cells were grown on 6-well plates until they had achieved 70-80% confluence.
Cells were then pre-incubated for 30 min in the presence or absence of 100 μM PWO577 or SY007 for 30 min, following treatment with IL6 (5 ng/ml) plus IL6Rα (25 ng/ml) for 16 h. Total RNA was isolated from cells using RNeasy Mini Kit (Qiagen, Manchester, UK), according to the manufacturer's protocol. RNA concentration was determined using NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Paisley, UK). RNA sample quality control, RNA library preparation, library quality control, Illumina library sequencing, data quality control and bioinformatics analysis were then carried out by Novogene UK (Cambridge, UK). In brief, raw reads of FASTQ format were firstly processed through in-house Perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing poly-N, and low-quality reads from raw data 47 . All the downstream analyses were based on the clean data with high quality. Paired-end clean reads were aligned to the human genome using Hisat2 v2.0.5 48 . Then, the abundance of each transcript was quantified using FeatureCounts v1.5.0-p3 49 . Differentially expressed gene (DEGs) analysis was performed using the DESeq2 package 50 and gene expression was normalized using relative-log-expression (RLE). Genes with an adjusted p-value < 0.05 were assigned as being differentially expressed. Metascape (http:// metas cape. org) was employed to perform gene enrichment and functional annotation analyses by sourcing the Kyoto Encyclopaedia of Genes and Genomes (KEGG) Pathway 51,52 . The profileR package 53 was used to test the statistical enrichment of differential expression genes in KEGG pathways.
Proteomic analysis. HUVEC cells were grown on 10 cm dishes until they had achieved 70-80% confluence. Then, the cells were treated with 100 µM of PWO577 or SY007 for 16 h. Cells were then washed with PBS, scrapped into 1 ml of PBS and centrifuged 5 min at 800 rpm. Cell pellets were collected and frozen at − 80 °C. Proteomic analysis was then carried out by DC Biosciences Ltd (Dundee, UK). Briefly, peptides were labelled with TMT 10-plex reagents according to the instructions provided by the manufacturer (Pierce, Rockford Statistical analysis. One-way analysis of variance (ANOVA) with Tukey's or Dunnett's post hoc test were performed using GraphPad Prism 9 software (GraphPad Software, San Diego, USA).

Data availability
All data generated or analysed during this study are included in this published article and its supplementary information file.