Validating regulatory predictions from diverse bacteria with mutant fitness data

Shiori Sagawa
Department of Molecular and Cell Biology
University of California
BerkeleyCAUnited States of America

Environmental Genomics and Systems Biology
Lawrence Berkeley National Laboratory
BerkeleyCAUnited States of America

Morgan N Price *firstname.lastname@example.com
Environmental Genomics and Systems Biology
Lawrence Berkeley National Laboratory
BerkeleyCAUnited States of America

Adam M Deutschbauer
Environmental Genomics and Systems Biology
Lawrence Berkeley National Laboratory
BerkeleyCAUnited States of America

Adam P Arkin
Environmental Genomics and Systems Biology
Lawrence Berkeley National Laboratory
BerkeleyCAUnited States of America

Validating regulatory predictions from diverse bacteria with mutant fitness data
RESEARCH ARTICLE
Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomics predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.

Introduction

Gene regulation is a key mechanism used by bacteria to survive in varying environments. The major class of regulators in prokaryotes are transcription factors (TFs), which are proteins that increase or decrease the transcription of their target genes. Transcription factors target specific genes by binding to specific sites near those genes' promoters. Each transcription factor has a specific pattern of DNA sequences that it binds to, called a motif [1].

Since the typical bacterial genome encodes over 100 TFs whose targets and functions are not known [2], it is critical to study TFs at large scales. Experimentally, TF-target interactions have been predicted by using high-throughput co-expression data [3] or by using chromatin immunoprecipitation and related techniques to identify a TF's binding locations on the genome [4]. However, co-expression is indirect evidence of a regulatory relationship [1] and these predictions are incomplete and have high false positive rates [3]. For instance, the context likelihood of relatedness algorithm identified 11% of known interactions at a false positive rate of 40% in E. coli [3]. Location data also has several limitations. First, location data fails to detect some interactions due to inefficient cross-linking at some locations [1] or poor TF binding in tested conditions. Second, location data may not imply regulatory relationships, as it may detect binding that does not affect expression [5]. Lastly, while genome-wide coexpression is available for over 20 species [6,3,7], to our knowledge large-scale location data is only available for a few bacteria [4,8,9].

Alternatively, TF-target pairs have been predicted using comparative genomics. Comparative genomics methods rely on a key assumption that transcription factor binding sites are more conserved than other intergenic sequences. For example, a known motif for a TF can be used to identify targets of the TF's orthologs by searching for occurrences of the known motif [1], using tools such as Patser [10]. The identified genes are predicted to be regulated by an ortholog of the TF, especially if multiple orthologous targets have the site. Another approach is to predict new motifs and to search for their associated targets. In order to predict a motif, a training set, composed of upstream regions of potentially co-regulated genes, is collected from multiple orthologous genomes [1]. Then, a conserved sequence is identified in the training set using motif discovery algorithms such as MEME [11]. The motif is considered reliable if it is conserved across functionally-related genes in multiple genomes. A major challenge in this approach is the association of a motif to the correct TF [1]. There are many heuristics for predicting the TF-motif pairs [12], but experimental evidence can significantly improve the accuracy [1].

RegPrecise provides a manually-curated collection of comparative genomics predictions that associate TFs with motifs and targets [13]. These predictions are based on both known and newly predicted motifs. Training sets for motif predictions are mainly composed of genes in a functional pathway [14]. Most RegPrecise predictions lack experimental evidence.

Here, we consider the utility of mutant fitness data for statistically validating the computational predictions from RegPrecise. To collect the fitness data, transposon mutant strains, each of which has one transposon insertion in the genome, are grown in a pool of~40,000-500,000 strains in various conditions. The effect of mutating each gene on growth in each condition is then measured by quantifying the change in abundance of their mutant strains [15]. We used fitness data for most genes in 25 bacteria across many conditions ( [16]; http://fit.genomics.lbl. gov/). The fitness patterns of activators and repressors are expected to be positively and negatively correlated with the fitness patterns of their targets [17].

In this study, we show that many characterized transcription factors have strongly correlated fitness patterns with a small subset of their targets. As a consequence, we consider a TFmotif association from comparative genomics to be statistically validated if at least one target's fitness pattern is significantly correlated with the TF's pattern. In RegPrecise, we validated 158 TFs in 107 ortholog groups with a 3.0% false discovery rate (FDR). We also classified most of these TFs as activators (124) or repressors (28) based on the sign of their cofitness with their target genes. Thus, the mutant fitness data provides experimental support for the roles of over 100 TFs that were previously predicted by comparative genomics.

Results

Cofitness of transcription factors and their targets

Activating TF-target pairs are expected to have strong positive cofitness, where cofitness is the correlation between the fitness patterns of two genes. If a transposon is inserted into an activator, then the target gene's expression would be reduced. If a transposon is inserted into a target gene, then there would no longer be functional transcripts of the target gene. Since the amount of functional transcript of the target is reduced under both scenarios, the activator and its targets are expected to have positively correlated fitness patterns. In contrast, repressors are expected to be anti-cofit (have negative cofitness) with their targets. If a transposon is inserted into a repressor, then the target gene expression would be elevated. If a transposon is inserted into a target gene, then there would no longer be functional transcripts of the target gene. If elevated and reduced levels of functional target transcripts lead to opposite phenotypes, then the two fitness patterns are expected to be negatively correlated. Similarly, TF-target pairs are expected to have extreme ranks in cofitness and anti-cofitness. Cofitness rank is the rank of TF-target cofitness relative to the TF's cofitness with other genes in the genome, where the most positive cofitness has the top rank. Anti-cofitness rank is the ranking in reverse order.

In E. coli, transcription factors do tend to be highly cofit or anti-cofit with at least one of their experimentally-identified targets. For example, DcuR is an activating transcription factor that regulates genes involved in C4-dicarboxylate metabolism, and it has high cofitness with one of its targets, dctA. As shown in Fig 1A, dcuR and dctA are important for aerobic growth with dicarboxylates (malate or succinate) as the carbon source, as expected. More importantly, dcuR's fitness pattern closely matches the fitness pattern of dctA (Fig 1A), with cofitness of 0.97 and rank 2 out of 3,788 (Fig 1B and 1C). Other targets of DcuR have weak cofitness and ranks ( Fig 1C). For example, fumarate reductase (fdrABCD) is only expected to be on important under anaerobic conditions, and its expression can be induced by another regulator besides DcuR (namely Fnr), so it's not surprising that it has a different fitness pattern than dcuR. In contrast, treR, which represses genes involved in trehalose transport and degradation, has a fitness pattern that seems to be the opposite of the fitness pattern of its target, treB. As shown in Fig 1D, treR and its targets have strong phenotypes when trehalose is the carbon source, as expected. More importantly, treR and treB have strong negative cofitness of -0.59 and a top anti-cofitness rank of 1 ( Fig 1C). TreR's other target, treC, has weak cofitness and rank (Fig 1C  and 1D); even though treBC forms an operon, the fitness pattern of treC seems to be more complex because, aside from its role in trehalose utilization, TreC can modify maltose [18]. Gene fitness is a log 2 ratio that describes the change in abundance of strains with a transposon inside the gene over the course of pooled incubation [15]. Positive fitness suggests that the gene is detrimental to growth in the condition, while negative fitness suggests that the gene is important for growth. The labels above and below the heatmap describe the growth conditions. More broadly, we examined the cofitness of all TF-target pairs that have been identified experimentally in E. coli [19]. TFs that are both activators and repressors were excluded to avoid ambiguous cases, which left 44 activating TFs and 57 repressing TFs. As shown in Fig  2A, TF-target pairs are highly enriched in strong positive cofitness (or anti-cofitness) relative to shuffled pairs. For example, cofitness above 0.5 is enriched in activating TF-target pairs by 15-fold, while anti-cofitness under -0.5 is enriched in repressing TF-target pairs by over 21-fold. Fig 2B shows that cofitness ranks of 1-50 are enriched in activating pairs by 10-fold, while anti-cofitness ranks of 1-50 are enriched in repressors by 4-fold.

Statistical approach

Applying the above observations, we formulated a method to statistically validate TF-motif predictions from comparative genomics. It is useful to test these predictions at the motif level for two reasons. First, the predictions are motif-based, and the association of a TF with a motif is a major source of uncertainty in comparative genomics predictions. Second, the transcription factor may be cofit with a subset of its targets, as we observed for the characterized regulators dcuR and treR. Fig 3 demonstrates this trend for several other characterized transcription factors from E. coli. For this reason, the method validates TF-motif predictions based on the predicted target that is most strongly cofit or anti-cofit with the TF.

The method uses cofitness rank in addition to cofitness, since the distribution of cofitness varies widely among TFs (Fig 3). In fact, the standard deviation of the cofitness distribution varies from 0.08 to 0.31 among the experimentally-characterized TFs in E. coli. As a result, it is

Fig 2. Distribution of cofitness and cofitness ranks in experimentally-identified TF-target pairs in E. coli. (A) The distribution of cofitness values for

activating TF-target pairs, repressing TF-target pairs, and shuffled pairs. The distributions were smoothed using gaussian kernel density estimates. (B) The distribution of cofitness ranks for activating TF-target pairs, repressing TF-target pairs, and shuffled pairs. On the right side, ranks for anti-cofitness are shown with negative numbers, so that the most anti-cofit pair has x = -1. The proportion of pairs within each window of 10 cofitness ranks is plotted.

Validating regulatory predictions from diverse bacteria with mutant fitness data PLOS ONE | https://doi.org/10.1371/journal.pone.0178258 May 24, 2017 important to look at the cofitness of the TF-target pairs relative to the TF's cofitness with other genes. For example, in Fig 3, the cofitness value of 0.61 for deoR and its most cofit target seems significant, but such a cofitness would be less convincing for metR. Of the 97 characterized TFs in E. coli that have significant phenotypes (at a false discovery rate under 5%; Price et al., 2016), 64 (66%) have highly-ranked cofitness or anti-cofitness (rank 1-50) with at least one target.

To test whether a TF-motif association is significantly supported by the fitness data, we use a hypothesis test that is based on the top target's rank. By transforming the top target's rank into a p value, we can distinguish statistically-validated cases from ambiguous cases. The null and alternate hypotheses state that the TF is not and is associated with the motif, respectively. Under the null hypothesis, any cofitness between the TF and a target is due to random chance; more precisely, the cofitness of the TF with its predicted targets is assumed to follow the same distribution as for a random sample of genes. So, our primary test's p value is the probability that a random sample of T genes would have a top (anti-)cofitness rank as extreme as the observed value, where T is the number of TF's targets not including the TF. Here, we use cofitness ranks that are corrected for a bias, in which closely located gene pairs tend to have slightly higher cofitness (see Methods). We also do a second test to ensure that the cofitness (or anticofitness) of the top target is statistically significant, after correcting for the number of targets. Intuitively, if a TF and its top target have weak cofitness, then we would not consider the TF's validation convincing, even if the rank is high. Finally, we only conduct these tests for TFs that have a significant phenotype in at least one condition (false discovery rate under 5%; [16]). Again, the TF's validation would not be convincing if the TF's phenotypes are very weak. Putting these three requirements together, we consider a TF to be statistically validated if it has a significant phenotype and if both the rank-based and cofitness-based tests give P 0.01.

Statistical validation of RegPrecise predictions

The hypothesis test was applied to the RegPrecise predictions, but since predictions are not available for 17 of the 25 bacteria with fitness data, we tested orthologous predictions for those organisms. An orthologous prediction is a pair of genes that are orthologous to a TF and a target in an original prediction, where the motif of the original prediction exists near the target's ortholog (see Methods). In RegPrecise, we tested 479 TFs and we statistically validated at least one target for 158 TFs in 107 ortholog groups at a false discovery rate of 3.0%. (The FDR was estimated from the expected false positive rate à cases tested / positive cases = 0.01 à 479 / 158 = 0.030.) The validated predictions are listed in S1 and S2 Tables. Validated TFs are involved in a variety of pathways, as summarized in

Sources of false positives

Although our statistical test is stringent enough to obtain a low rate of false positives due to random correlations, false positives could also arise from other sources. The most prominent source is polar effects, where an insertion in a gene leads to transcription termination and reduced expression of downstream genes. Polar effects on a gene would cause positive cofitness between the gene and its co-transcribed genes, leading to false validations by positive cofitness. In particular, if a TF is co-transcribed with another gene and does not regulate the operon, then the TF may be falsely validated due to polar effects. TF predictions that are potentially affected by polar effects are more likely to be validated due to positive cofitness than other predictions (36% vs 20%) and this difference is statistically significant (16% ± 8%, 95% CI). As a control, we also examined the rate of validation by anti-cofitness and found no effect of co-transcription (a difference of -3% ± 4%, 95% CI). Thus, a significant fraction of predictions that are potentially affected by polar effects may be falsely validated. However, analysis of known TFs in E. coli suggests that only a small subset of TFs experience significant polar effects. Of 14 pairs of E. coli TFs that have significant phenotypes and have a potential polar effect (have a co-transcribed adjacent gene that is not a target), five pairs had a cofitness rank of 1-50 (ompR-envZ, yehT-yehU, dcuR-dcuS, basR-basS, and uhpA-uhpB). However, since all five of these pairs are histidine kinase-response regulator pairs and are thus expected to be cofit, they do not necessarily indicate polar effects. Overall, for 65 of the positively validated TFs (50%), they or their orthologous TFs are validated by targets that are not in the same operon as the TF.

Secondly, complex regulatory networks could lead to false positives. For example, if a TF regulates another TF, the first TF may be cofit with the second TF. In this scenario, the motif for the first TF might well be misassigned to the second TF because the first TF's motif occurs upstream of the second TF, and the cofitness might appear to validate the second TF with the first TF as a target. Although the majority of bacterial TFs do not regulate other TFs, a significant minority do: in E. coli, 43% of characterized TFs regulate another TF [19]. Nevertheless, false positives due to regulation by another TF seem to be uncommon: of 21 characterized TFs in E. coli that are "validated" by the fitness data and our statistical approach, only one TF is more cofit or anti-cofit with another TF than with their best target (marR is more cofit with acrR, which regulates marR). Another issue is that the first TF might be cofit with its indirect targets (genes that are regulated by the second TF). If the motif for the second TF had, for some reason, been erroneously associated with the first TF, then our approach might (incorrectly) validate that association. Of 18 TFs that regulate other TFs and have significant phenotypes, there were 3 cases where the TF was more cofit with an indirect target than with the any direct target, and for two of these TFs (rcdA and stpA), the indirect target had a high rank (in the top 50).

Prediction of regulatory sign

The method can also be used to predict the sign of the regulation. If statistically validated due to cofitness, then the TF is most likely an activator. If statistically validated due to anti-cofitness, then it is most likely a repressor. For known transcription factors in E. coli, this approach is mostly accurate. For this analysis, we excluded TFs that are both activators and repressors, as either prediction would be accurate. (Of the characterized TFs in E. coli, 23% are activators, 36% are repressors, and 41% are both activators and repressors [19].) Of the 9 remaining TFs from E. coli that we "validated" by positive cofitness, 8 are activators. And all 3 of the remaining E. coli TFs that were "validated" by anti-cofitness are repressors. In addition, the misclassified regulator, PuuR, can be explained by polar effects. Lastly, there were 5 ambiguous cases (AcrR, CytR, DeoR, DsdC, GlcC), where the TF was "validated" for both cofitness and anticofitness. This seems to happen because two target genes have negatively-correlated phenotypes, so that the TF is cofit with one target and anti-cofit with the other.

Using this approach, we predicted the signs of validated TFs from RegPrecise: 124 activators, 28 repressors, and 6 ambiguous. In some cases, the sign suggested by the cofitness data conflicts the annotation in RegPrecise. For example, MalR, which is in the LacI family, is annotated as a repressor. To our knowledge, there is no experimental evidence of the regulational sign of MalR or its orthologs. However, malR's fitness pattern is strongly correlated with the fitness pattern of malT (Shew_1874) in Shewanella loihica with cofitness of 0.94 and rank of 1, as demonstrated in Fig 5. Similar fitness patterns are also seen in Shewanella sp. ANA-3 and Shewanella amazonensis. Predictions in all three organisms are not affected by polar effects. In addition, MalR binds over 300 basepairs upstream of the transcription start site of its target, malT [20], as is often observed with activators [1]. Thus, it seems likely that the regulational sign of MalR is misannotated in RegPrecise, and it is actually an activator, at least in the presence of maltose. Fitness data also suggest that SO4705 (from Shewanella oneidensis, Shewanella sp. ANA-3, Shewanella amazonensis), PckR (from Sinorhizobium meliloti), SdaR (from Shewanella oneidensis), and ModE2 (from Desulfovibrio vulgaris) are activators, contrary to their annotations. These TFs are not affected by polar effects. However, for SO4705, the binding sites are very close to or downstream of the transcription start sites of the targets [20], which suggests that SO4705 may yet be a repressor. Application of the approach to coexpression data To our knowledge, coexpression has not been used to statistically validate regulatory predictions in bacteria at a large scale. We tested our approach on coexpression data from E. coli and Shewanella oneidensis in the Many Microbe Microarrays Database [21]. First, we tested characterized TFs in E. coli with at least one target that's not co-transcribed with the TF. We "validated" these TFs based on their coexpression with a target in a different operon. Of 171 such TFs with coexpression data, 16 of them were validated by coexpression with a false discovery rate (FDR) of 10.6%. For comparison, of 89 TFs with a significant phenotype, 17 were validated by cofitness at a 5.2% FDR. Of the 89 TFs with both a significant phenotype and coexpression data, only four (dsdC, galS, malT, and puuR) are validated by both methods, which suggests that the two validation methods are complementary. Second, we tested our approach on TFs from Shewanella oneidensis that have predictions from RegPrecise. Of 50 TFs tested, we validated 5 TFs at 10.0% FDR.

Coexpression seems to give a weaker signal than cofitness, as indicated by the higher FDRs. TFs often lack strong coexpression with any of their targets. For example, of 13 characterized TFs in E. coli that were "validated" by cofitness but not by coexpression, 12 TFs have coexpression of magnitude under 0.5 with all targets. fucR has moderate coexpression (0.52) and coexpression rank (37) with its target fucA, but these were not strong enough for validation. Overall, although validation by coexpression has a higher FDR, coexpression can be used to validate predictions that are not validated by cofitness.

Discussion

Advantages and limitations of use of fitness data

Although the fitness data statistically validated a subset of the regulatory predictions, it has several limitations compared to coexpression analysis. First, fitness data shows a correlation for a smaller fraction of TF-target pairs than expression data does [17]. In particular, cofitness analysis seems to be less effective for repressors. Although the characterized regulatory relationships in E. coli are roughly equally split between activators and repressors, most of the regulators that we validated had positive cofitness with their validated targets (78%). It appears that activators are much more likely to be cofit with a target than repressors are.

Second, fitness data measures changes in gene expression indirectly through its effects on growth, while the expression data does so directly. Nevertheless, cofitness seems to provide a stronger signal than coexpression, albeit for only a subset of the TF-target pairs. This was illustrated by the lower false discovery rate for "validating" known regulatory relationships in E. coli by cofitness as compared to coexpression. Third, it is not possible to validate each TF-target pair by cofitness analysis because a TF's fitness pattern may be determined only by a small subset of targets. This makes it difficult to make novel TF-motif or TF-target predictions based solely on fitness data.

Fourth, given our statistical approach, we cannot validate TFs with over a certain number of targets. For example, for TFs with over 18 targets in E. coli (which has 3,789 genes with fitness data), even a (anti-)cofitness rank of 1 would yield p > 0.01. For this reason, the cofitness analysis provides more powerful evidence for local TFs, which represent the vast majority of TFs in bacteria [22,23].

Despite these limitations, there are some advantages to using the fitness data. First, cofitness analysis should be more robust than coexpression analysis if a TF and a non-target gene are coregulated by other factors. In this case, the two genes will be coexpressed, but they probably won't be cofit. Because the fitness data measures the effect of disabling each gene, regulation of the gene does not determine its fitness pattern. Second, cofitness analysis is advantageous for validating co-transcribed TF-target pairs. If a TF and its potential targets are co-transcribed and coexpressed, it is unclear whether the coexpression is caused by co-transcription or by a regulatory relationship. But in the absence of a regulatory relationship, there should not be any cofitness (although because of polar effects, this will not always be true in practice). More broadly, the above two points illustrate that fitness data may have an advantage in identifying direct regulatory relationships because it relies on mutants of the TF. Finally, fitness data is complementary to other large-scale data. For example, coexpression profiles and cofitness data seem to have complementary signals, as they have been shown to be poorly correlated [17]. Also, we found that in E. coli, there is little overlap between the characterized TFs that are "validated" by cofitness and by coexpression. The correlation may be poor because coexpression signals are expected to be weak for constitutively expressed TFs, while this is not expected for cofitness.

Fitness data may also be useful as a starting point for comparative genomics analyses. A significant fraction of putative TFs without comparative genomics predictions are cofit with at least one gene. For example, of 80 putative TFs (from the TF families in RegPrecise) that have no predictions in RegPrecise, 40 have significant phenotypes and of those, 24 have cofitness of over 0.8 or conserved cofitness of over 0.6, which are strong indications of a functional relationship [16]. Another 3 TFs had predictions that were not validated and have strong cofitness with other genes. The associations for these 27 TFs are listed in S3 Table. Although these putative pairs are not expected to be high-confidence at this stage, further analysis using comparative genomics could lead to high-confidence predictions.

Conclusions

Activating (or repressing) TFs tend to be cofit (or anti-cofit) with a subset of their targets. Based on this observation, we formulated a statistical method to validate TF-motif associations from comparative genomics predictions. The method was applied to RegPrecise predictions and 158 TFs in 107 ortholog groups were validated with a false discovery rate of 3.0%. Most of these TFs were predicted as activators (124) or repressors (28) based on the sign of the cofitness. Although the statistical test has a low rate of false positives due to random correlations, false positives could also arise from other sources, most notably polar effects. Nevertheless, validation by cofitness has several advantages over and is complementary to the use of other large-scale data.

Methods

Data

We obtained known TF-target pairs in E. coli from RegulonDB version 9.1, downloaded on April 7, 2015 [19]. Version 4.0 of RegPrecise was downloaded on April 29, 2016 [13]. In RegPrecise predictions, each ortholog group has a unique regulog ID. Fitness data was obtained from http://genomics.lbl.gov/supplemental/bigfit/ and data from April 29, 2016 was used [16]. Coexpression data was obtained from the Many Microbe Microarrays Database on July 7, 2015 [21].

Propagating regulatory predictions to orthologs

Predictions were propagated from each organism with RegPrecise predictions to each organism with fitness data. In order to propagate predictions from one organism to another, we ran ublast [24] between protein sequences of their genomes in both directions with thresholds E 10 −5 , identity ! 50%, and coverage ! 80%. A gene was considered a potential ortholog of another gene if they were best-scoring hits of each other. Orthologous TF-target pairs are pairs of genes where one gene is an ortholog of the TF and another is an ortholog of the target. If a TF of interest had multiple orthologs with predictions in RegPrecise, we chose just one bacterium to propagate from. We considered only bacteria that had at least one orthologous-TF pair (that is, there was at least one target with an ortholog in the bacterium of interest), and among those, we chose the bacterium whose TF was the most similar to the TF of interest (i.e., the highest bit score). Then, for each orthologous TF-target pair, we searched for the motif of the original TF around the target using Patser [10]. We identified the best-scoring hit found within 250 bp of the upstream end of each potential operon that the target ortholog belongs to, where potential operons were any series of adjacent genes that are on the same strand and less than 250 bp apart. A p-value for the motif hit was calculated by identifying the best motif hit for every gene in the genome and computing the fraction of genes that score at least as well as the target; genes in the same potential operon as the target ortholog were excluded. An orthologous TF-target pair was considered a propagated prediction if there is a motif hit with p 0.01. There were significant motif hits for 56% of orthologous TF-target pairs from RegPrecise.

Cofitness bias correction

Pairs of closely located genes tend to have slightly higher cofitness values than gene pairs that are far apart. This bias might arise from the effect of chromosomal position on DNA copy number; the fitness values are normalized to this trend [15], but a small bias may remain. Since TFs are often near their targets, cofitness values of TF-target pairs were corrected to account for this bias. To estimate the bias for each genome, we sampled 1,000 gene pairs that are at most 20KB apart and 1,000 gene pairs that are at least 50KB apart or are on a different scaffold. The median cofitness value for each set was computed. If the closely located pairs have higher median cofitness than the pairs that are far apart, the bias was estimated to be the difference in the median fitness. Otherwise, the bias was estimated as 0. The median and maximum bias among all 25 bacteria were 0.02 and 0.07. We corrected for the bias in positively cofit TF-target pairs that are at most 20 kb apart by subtracting the bias from the original cofitness value. The cofitness rank was calculated based on this corrected cofitness.

Hypothesis testing

The rank-based p-value quantifies the probability of getting a cofitness rank or an anti-cofitness rank as extreme as the observed value, given the number of targets. For each transcription factor, the rank-based p-values for activators and repressors were calculated as follows:
r activator ¼ 2 Á ½1 À ð1 À R=ðN À 1ÞÞ T r repressor ¼ 2 Á ½1 À ð1 À R 0 =ðN À 1ÞÞ T
where N is the number of genes with fitness data in the genome, R is the top cofitness rank of the targets, R' is the top anti-cofitness rank, and T is the number of targets not including the TF. The above p-values were calculated assuming sampling with replacement. However, their values are very close to the corresponding probabilities assuming sampling without replacement since T<<N (N is 1,899 to 6,384). Since we tested for two signs, rank-based p-values include the factor of 2 for Bonferroni correction. In addition, to ensure thatthe value of cofitness is statistically significant, the p-values for the most positive and negative cofitness values were computed by using Fisher's transformation, from these z-scores: Only TFs with significant phenotypes [16] were tested. In most bacteria, a gene has a significant phenotype if |fitness|>0.5 and |t|>4 for some condition. These thresholds are adjusted upwards as needed to ensure that the FDR for detecting significant phenotypes is under 5% in each bacterium [16].
z ¼ 1 2 ffiffiffiffiffiffiffiffiffiffiffiffiffi M À 3 p ln 1 þ r 1 À r
Although our hypothesis tests consider both the rank and the value of the cofitness, the test on the rank is more important in practice. For example, if we test only for activating relationships, and consider the 97 characterized TFs in RegulonDB that have significant phenotypes, then 82 of them have significant cofitness with a target (factivator 0.01), but just 15 have a significant rank for the top target (ractivator 0.01). And all of those 15 TFs with a significant rank have significant cofitness as well. Our impression is that the fitness data has strong biological patterns that leads to significant correlations for genes that do not have a close functional relationship, but the genes with a highest cofitness rank are more likely to have a close relationship. Also note that, since these two hypothesis tests are correlated, we estimate the number of false positives and the FDR as if we were doing a single test with p 0.01.

Correctness of the hypothesis test

To show that our p values are on the correct scale, we randomly selected new "targets" for the 228 transcription factors from 8 organisms that had significant phenotypes and had predictions in RegPrecise. We maintained the same number of targets with fitness data for each TF, and we repeated the procedure 10 times to give 2,280 cases. We computed pactivator and prepressor and we asked how often the smaller of these two p-values were below 0.01 or 0.05. We observed p 0.01 in 9 of the 2,280 cases (0.4%) and P 0.05 in 131 cases (5.8%). This confirms that our p values are on the correct scale and that our estimates of the false discovery rate are reasonable.

Analysis of polar effects

A prediction whose TF is co-transcribed with a putative target is potentially affected by polar effects. Validation rates were compared between predictions with potential polar effects and others. Only TFs with a significant phenotype and at least one target with fitness data were considered. We computed the fraction of validated TFs in the two sets of predictions and used the two-proportion z-test to construct a 95% confidence interval for the difference.

Putative TFs and potential targets

Putative TFs were identified from genes that belong to the same TIGRFAMs as TFs in RegPrecise predictions [25]. (The list of putative TFs is not complete.) TIGRFAMs without DNA binding domains were manually filtered out in the analysis. Across the 25 genomes, we found 80 unpredicted putative TFs. We then sought to identify a potential target for each putative TF. We considered all genes that the putative TFs are not predicted to regulate in RegPrecise and the orthologous predictions. The potential target is the gene with the highest cofitness with the putative TF, out of genes that have conserved cofitness > 0.6 and conserved cofitness rank 10 with the putative TF, or that have cofitness > 0.8. (Conserved cofitness > 0.6 if the two genes have cofitness > 0.6 and an orthologous pair also has cofitness > 0.6.) Otherwise, a potential target was not identified for the putative TF.

Statistical tools

Statistical analyses were done with R version 3.1.1. Cofitness distributions were smoothed using the density function with default parameters. Code and an R image including the data are available at http://genomics.lbl.gov/supplemental/cofittf/ or at https://doi.org/10.6084/m9. figshare.5005004.v1.

Author Contributions

Fig 1 .
1Examples of fitness patterns of experimentally-identified TF-target pairs in E. coli. (A) Heatmap showing the fitness values of the activator dcuR and its targets across 162 experiments.

(B) Scatter plot of the fitness values of dcuR and dctA. Lines show x = 0, y = 0, and x = y. (C) Distribution of cofitness values for dcuR and treR. Each panel shows the cofitness of the transcription factor with 3,788 genes in the E. coli genome that have fitness data (x axis). The y axis is random. (D) Heatmap showing the fitness values of the treR repressor and its targets across 162 experiments.

Fig 3 .
3Distribution of cofitness values for four experimentally characterized transcription factors in E. coli. Each panel shows the cofitness of the transcription factor with all other 3,788 genes in the E. coli genome with fitness data (x axis). The y axis is random. https://doi.org/10.1371/journal.pone.0178258.g003 Validating regulatory predictions from diverse bacteria with mutant fitness data PLOS ONE | https://doi.org/10.1371/journal.pone.0178258 May 24, 2017

Fig 4.

Fig 5 .
5Fitness pattern of malR in Shewanella loihica. (A) Heatmap showing fitness values of malR and its targets across 160 experiments. The labels above and below the heatmap are conditions. (B) Distribution of malR's cofitness with all other 3,008 genes in the Shewanella loihica genome with fitness data (x axis). The y axis is random. https://doi.org/10.1371/journal.pone.0178258.g005 Validating regulatory predictions from diverse bacteria with mutant fitness data PLOS ONE | https://doi.org/10.1371/journal.pone.0178258 May 24, 2017

Formal analysis :
analysisSS.Funding acquisition: AMD APA.

where r is the (corrected) cofitness and M is the number of fitness values for each gene. The resulting p-values were corrected for testing both signs and for testing across multiple target genes by Bonferroni correction. Finally, the overall p-values for activators and repressors are calculated as follows:p activator ¼ maxðf activator ; r activator Þ p repressor ¼ maxðf repressor ; r repressor Þ where and f activator and f repressor are Fisher transformation p-values. We used a threshold of p 0.01.

Table .
.List of transcription factors validated in original predictions. (TAB) S2Table. List of transcription factors validated in orthologous predictions. (TAB) S3 Table. Potential TF-target pairs identified by cofitness. (TAB)
PLOS ONE | https://doi.org/10.1371/journal.pone.0178258 May 24, 2017 9 / 14
PLOS ONE | https://doi.org/10.1371/journal.pone.0178258May 24, 2017

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Spontaneous Altruism by Chimpanzees and Young Children

Felix Warneken
Department of Developmental and Comparative Psychology
Max Planck Institute for Evolutionary Anthropology
LeipzigGermany

Brian Hare
Department of Developmental and Comparative Psychology
Max Planck Institute for Evolutionary Anthropology
LeipzigGermany

Alicia P Melis
Department of Developmental and Comparative Psychology
Max Planck Institute for Evolutionary Anthropology
LeipzigGermany

Daniel Hanus
Department of Developmental and Comparative Psychology
Max Planck Institute for Evolutionary Anthropology
LeipzigGermany

Michael Tomasello
Department of Developmental and Comparative Psychology
Max Planck Institute for Evolutionary Anthropology
LeipzigGermany

Spontaneous Altruism by Chimpanzees and Young Children

People often act on behalf of others. They do so without immediate personal gain, at cost to themselves, and even toward unfamiliar individuals. Many researchers have claimed that such altruism emanates from a species-unique psychology not found in humans' closest living evolutionary relatives, such as the chimpanzee. In favor of this view, the few experimental studies on altruism in chimpanzees have produced mostly negative results. In contrast, we report experimental evidence that chimpanzees perform basic forms of helping in the absence of rewards spontaneously and repeatedly toward humans and conspecifics. In two comparative studies, semi-free ranging chimpanzees helped an unfamiliar human to the same degree as did human infants, irrespective of being rewarded (experiment 1) or whether the helping was costly (experiment 2). In a third study, chimpanzees helped an unrelated conspecific gain access to food in a novel situation that required subjects to use a newly acquired skill on behalf of another individual. These results indicate that chimpanzees share crucial aspects of altruism with humans, suggesting that the roots of human altruism may go deeper than previous experimental evidence suggested. Citation: Warneken F, Hare B, Melis AP, Hanus D, Tomasello M (2007) Spontaneous altruism by chimpanzees and young children. PLoS Biol 5(7): e184.In the first experiment, we compared the helpfulness of chimpanzees and human infants toward unfamiliar individuals. Subjects were 36 semi-free ranging chimpanzees born in the wild who were tested by a human with whom they had virtually no prior interaction (no training, no feeding, no

Introduction

Individuals of many primate species perform behaviors that benefit other individuals. In particular, chimpanzees engage in behaviors such as food sharing, grooming, consolation, or coalition formation [1][2][3][4][5][6]. Most of these instances, however, can likely be explained by direct or indirect benefits for the actor through mechanisms such as mutualism or kin selection [7][8]. Humans, in contrast, occasionally help other individuals, even strangers, without immediate benefit for themselves, encompassing situations that make reciprocation unlikely [9][10].

Many researchers have claimed that such altruism is unique to humans, emanating from a species-unique psychology [10][11][12]. More specifically, chimpanzees (one of human's two closest living primate relatives) are often conceived as lacking the propensity to act altruistically. Only humans are supposed to develop altruistic behaviors during ontogeny, whereas chimpanzees might only be guided by self-interest. In favor of this view, in two recent experiments [12,13], chimpanzees showed no other-regarding tendencies when they could deliver food to another chimpanzee. Chimpanzees did not seem to care about the welfare of a conspecific in this foodretrieval context.

In contrast, observational studies have provided many examples of other-regarding, possibly altruistic behaviors [1][2][3][4][5][6]14]. In support of these observations, a recent experimental study [15] showed that human-raised chimpanzees provided unrewarded helping behaviors toward their human caregiver by fetching objects she was unsuccessfully reaching for, providing the first experimental evidence for altruistic helping in chimpanzees.

Current research thus suggests the possibility that chimpanzees are able and willing to help, but they display this behavior only in very restricted contexts. First, chimpanzees might help only a familiar human caregiver with whom they maintained a close relationship, based on a rearing history in which compliant behavior in other contexts had been reinforced [15]. Second, chimpanzees might help others only if costs are low, whereas humans display much more costly helping. In all experimental studies thus far, chimpanzees did not have to put much effort (energetic costs) into helping by pulling a mechanism or picking up an object. Third, it is still unclear from experiments whether chimpanzees will help another chimpanzee [16]. In previous experiments chimpanzees did not help when the beneficiary was a conspecific. It is possible that helping among chimpanzees is limited, because their relationships are often characterized by competition, especially over monopolizable food [17,18], whereas this constraint is lifted during interactions with humans who deliver food rather than compete over it. Alternatively, in these studies, chimpanzees might not have helped another chimpanzee because they were preoccupied with retrieving food for themselves, and the recipient did nothing to indicate any need for help. The current set of experiments was designed to address these issues. previous testing). In addition, 36 18-mo-old human infants (same age as in [15]) were tested by an unfamiliar adult in a similar context, allowing a direct quantitative comparison between species. The general idea of the testing situation was that an object was placed out of reach for the recipient, but within reach of the subject. Helping consisted of handing the object to the recipient. To assess the motivations underlying helping, we varied whether the recipient made an unsuccessful attempt to get the object (reaching versus no reaching) and whether he rewarded the subjects in exchange for the object (reward versus no reward). If subjects are responsive to the other's goal, they should hand the object more often in reaching than in no-reaching conditions. If they are primarily interested in their own immediate benefit, they should help more often in reward than in no-reward conditions.

In the chimpanzee experiment, after experimenter 1 and 2 (E1 and E2) struggled over a wooden stick to highlight its value for E1, E2 placed the stick in the hallway ( Figure 1A, Video S1). Then, according to the condition, E1 would either outstretch his arm through the bars towards the stick (Reaching) or look at the object but not reach for it (No Reaching). In addition, he would either hold a piece of banana in view of the subject, which he gave them in exchange for the stick (Reward) or not have any food available (No Reward). The same basic procedure was used to test infants' helping. As a reward for the infants, we used toy cubes that were needed to play a highly motivating novel game. The helping scenario was as follows: E1 sat at the desk, using a pen to write a letter. E2 walked toward her, snatched the pen out of her hand, and put it on a stool in front of the desk out of E19s reach ( Figure 1B, Video S2). As with the chimpanzees, E1 then performed the behaviors according to the four conditions. Each subject was individually administered ten consecutive trials in one of four between-subject conditions (Reach-Reward; Reach-No Reward; No Reach-Reward; No Reach-No Reward). Thus, in each of the four conditions, nine chimpanzees and nine human infants were tested for a comparison of 72 subjects total. Each trial lasted up to 60 s: During the first 30 s, E1 vocalized and focused on the object, whereas in the remaining 30 s, E1 in addition called the subject's name and alternated gaze between the target object and the subject.

We conducted a four-way mixed-models analysis of variance on the mean percentage of trials with helping, entering species, reach, and reward as between-subject and 1st versus 2nd half of session as within-subject factor. As displayed in Figure 2, helping occurred more often in reaching than in no-reaching conditions [F (1,64) ¼ 14.52, p , 0.001], independently of species and reward (no main effects, no interactions). Pairwise comparisons using independent sample t-tests confirmed that reaching was the only significant factor, also when analyzed separately by species. On an individual level, 12 of 18 chimpanzees and 16 of 18 infants tested in reaching conditions helped at least once. For infants as well as for chimpanzees, the determining factor as to whether help was provided was whether the experimenter unsuccessfully attempted to retrieve the object. This indicates that subjects were motivated to help the experimenter with his/her unachieved goal (seeing the other succeed might even be intrinsically motivating for them), but did not aim at retrieving a material reward for themselves. Rewarding their helping was unnecessary and did not even raise the rate of helping in either case.

The only species difference found was that the helping of human infants was faster. Differences in reaction times should obviously be interpreted with caution because of the dissimilar locomotor skills and room setups (chimpanzees were more mobile and occasionally climbed up the experimental room and then returned for helping). However, it is of potential interest that the human children helped more often during the first 30-s phase in which the experimenter focused on the object (mean M ¼ 77% of their helping acts; standard deviation SD ¼ 27), in contrast to chimpanzees with M ¼ 47%, SD ¼ 42, F (1,40) ¼ 8.05, p , .01 (no effect of condition). Thus, the great majority of infants' helping acts occurred before the experimenter addressed the subject, whereas chimpanzees needed these additional cues about half of the time.

Given the fact that children and even chimpanzees often displayed unrewarded acts of helping that did not significantly decrease over time, we were wondering if (1) they would continue to help when the costs of helping are slightly raised-namely, when they have to locomote some distance to retrieve the object for the other-and (2) whether we could replicate that rewarding was not necessary to have subjects help reliably across trials.

Experiment 2

We re-tested all subjects that had helped at least once in the previous experiment (18 chimpanzees and 22 infants). The experimental setup was the same as in experiment 1, only that subjects had to put more physical effort in retrieving the object for the other. Chimpanzees had to climb up into a raceway 2.5 m above the hallway, and infants had to surmount an array of obstacles ( Figure 1C, 1D, Video S3, S4). Subjects were randomly assigned to one of two conditions (reaching or no reaching) and again tested in ten consecutive trials of 60 s each. To replicate the finding that rewards are not necessary for helping, no rewards were offered in either condition.

As shown in Figure 3, there was no difference between conditions in either species. A three-way mixed model analysis of variance on the mean percentage of trials characterized by helping, with species and condition as between-subject and 1st versus 2nd half of the test session

Author Summary

Debates about altruism are often based on the assumption that it is either unique to humans or else the human version differs from that of other animals in important ways. Thus, only humans are supposed to act on behalf of others, even toward genetically unrelated individuals, without personal gain, at a cost to themselves. Studies investigating such behaviors in nonhuman primates, especially our close relative the chimpanzee, form an important contribution to this debate. Here we present experimental evidence that chimpanzees act altruistically toward genetically unrelated conspecifics. In addition, in two comparative experiments, we found that both chimpanzees and human infants helped altruistically, regardless of any expectation of reward, even when some effort was required, and even when the recipient was an unfamiliar individual-all features previously thought to be unique to humans. The evolutionary roots of human altruism may thus go deeper than previously thought, reaching as far back as the last common ancestor of humans and chimpanzees.

as within-subject factor, revealed no significant main effects or interactions. This was confirmed by pairwise post-hoc comparisons using t-tests. Thus, chimpanzees and human infants again did not differ in the total amount of helping acts; but this time, there was also no difference between reaching and no reaching conditions. This was likely due to a carry-over effect from experiment 1 in which subjects had possibly learned that the experimenter wanted the object: The number of helping acts was highly correlated between studies: r(N ¼ 40) ¼ 0.68, p , 0.001. This correlation was apparent also when analyzed separately by species (chimpanzees:
r (N ¼ 18) ¼ 0.79, p , 0.001; infants: r (N ¼ 18) ¼ 0.57, p , 0.01).
Correlations held when the number of rewards received in experiment 1 was controlled. Thus, subjects who had helped more during the first experiment continued to help more in the second one, independently of whether the experimenter was now reaching for the object or not. With regard to latencies, infants again helped more often during a first 30-s phase than chimpanzees who appeared to require additional cues in order to help, with no effect of condition (infants: M ¼ 67% of helping acts, SD ¼ 29; chimpanzees: M ¼ 33% of helping acts, SD ¼ 26; F(1,30) ¼ 11.39, p , 0.01). In sum, the second experiment demonstrated that (1) helping was sustained even if the costs were raised, and (2) replicated the finding that rewarding was not necessary to elicit helping behaviors in either species.  Taken together, experiments 1 and 2 underscore the claim that altruistic motivations are already apparent early in human ontogeny, requiring little socialization [15,[19][20][21]. Moreover, altruistic motivations to help others do not seem to be unique to humans. Also, chimpanzees act on the behalf of others-even if this is an unfamiliar human and even if over repeated trials, they do not receive any immediate material benefit in return for their effort.

Despite the fact that subjects helped without prior or current rewarding by the same individual in a different situation, or a different individual in the same situation, it is conceivable that infants and chimpanzees had been rewarded in the past for the general behavior of handing over objects to humans. Therefore, an even more stringent test might be one with a novel task with no potential reward history at all. Human infants help in such novel tasks [15], but there is as yet no such experimental evidence with chimpanzees. Moreover, in the first two experiments reported above, chimpanzees helped a human, not a conspecific. Studies using humanchimpanzee interaction can reveal what behaviors chimpanzees are, in principle, capable of performing, but the crucial test-case for social behaviors is one in which they interact with a conspecific. Would chimpanzees also help other chimpanzees? Previous experimental results have yielded negative results [12,13]. But in these studies, subjects were preoccupied with attaining food for themselves, and the recipient was not actively trying to solve a problem. The test was thus more a test of generosity than of instrumental helping (also called ''targeted helping'' [22]). Therefore, we created a novel situation in which a chimpanzee individual would actively struggle with a problem, namely, trying to open a door to gain access to food, and the subject would then have the opportunity to help.

Experiment 3

Chimpanzees were faced with the problem that a door leading to a room with food was fixed with a chain (Figure 4, Video S5). A chain stretched from the target door to the subject room in which the subject was placed. The chain was attached to the bars with a peg, accessible only by the subject and not the recipient. Only if the subject released this chain from their room could the recipient enter. We tested nine chimpanzees from Ngamba Island who, during an individual pretest, had been skillful in manipulating the door mechanism (Protocol S1).

In the experimental condition, food was placed in the target room so that the recipient would try to open the target door and the subject could potentially help by releasing the chain. In a control condition, the food was not placed in the target room, but was placed instead behind a distracter door on the opposite side that could not be opened by either chimpanzee. If subjects were sensitive to the recipient's goal, they should produce the target behavior of releasing the chain more often in the experimental condition (when the recipient is trying to open the target door) than in the control condition (when the recipient ignores the target door).

Each subject performed both conditions of five consecutive trials each for a within-subject comparison. Three different chimpanzees served as recipients, and they were always the same for each subject in both conditions. Importantly, all were genetically unrelated group members, and the roles of recipient and subject were never reversed within a pair to exclude the possibility of simple short-term reciprocation in the same situation.

For each individual, we computed a mean score of target behaviors across the five trials of each condition. Preliminary inspection of the data indicated that it was irrelevant which condition was presented first. Because we had a directional hypothesis based upon experiments 1 and 2, one-tailed tests were used. As shown in Figure 5, chimpanzees released the chain more often in the experimental than in the control condition, paired sample t-test, t(8) ¼ 2.29, p ¼ 0.025. The same result was obtained using exact nonparametric statistics, Wilcoxon matched paired test, T þ ¼ 38, N ¼ 9, p ¼ 0.031. In addition, the frequency of target behaviors increased over  time in the experimental condition from 67% to 89% in the first two to the last two trials of a session and decreased in the control condition from 56% to 39% of the first to the last two trials, respectively (time x condition interaction, F (1,8) ¼ 12.25, p , 0.01). This result suggests that subjects over time discriminated better whether help was needed or not. On an individual level, eight of nine subjects helped over multiple experimental trials, seven of them more often in the experimental than in the control condition. Analysis of latencies revealed that subjects released the chain earlier in the experimental (M ¼ 20 s, SD ¼ 6 s) than in the control condition (M ¼ 32 s, SD ¼ 5 s; t(5) ¼ À2.93, p ¼ 0.03).

With regard to the behavior of the recipient, we could not program the chimpanzee recipient in a similar fashion as in experiments 1 and 2 with a human recipient performing predetermined behaviors. Therefore, we assessed whether the recipient actually acted according to condition, i.e., oriented toward the target door in the experimental and toward the distracter door in the control condition. In experimental trials, recipients always positioned themselves in front of the target door. In 47 percent of control trials, however, they did not at all times remain in front of the distracter door, but approached the target door, perhaps explaining why subjects occasionally released the chain in control trials. In fact, we found that the subject's behavior was associated with that of the recipient: Across both conditions, subjects were more likely to release the chain if the recipient approached the target door than when he remained solely at the distracter door r (N ¼ 83) ¼ 0.37, p , 0.01. This analysis corroborates the finding that subjects were attentive to the recipient's attempts. They were more likely to release the chain if the recipient was unsuccessfully trying to enter through that door. The mere presence of a social partner or food cannot explain these results because that did not vary between conditions. Also, the apparatus by itself was unlikely to elicit the subject's release of the chain, as additional baseline conditions showed (Protocol S1). Importantly, we also observed no begging or harassment by the subjects after they helped, ruling out the possibility that they opened the door only to have the recipients access the food and then try to coax it from them.

General Discussion

Taken together, the current results indicate that the altruistic tendency seen in early human ontogeny did not evolve in humans de novo. The roots of human altruism may go deeper than previously thought, reaching as far back as the last common ancestor of humans and chimpanzees. Undoubtedly, humans are exceptional with respect to the breadth in which they help, displaying helping across diverse contexts already early in infancy [15,19,21]. But, as demonstrated here, humans are not unique, because chimpanzees have the capacity to use a newly acquired skill to help a conspecific as well. This helping occurs spontaneously and repeatedly, even in a novel situation when no reward is expected and no previous rewarding could have trained them to act accordingly.

Observational studies of behaviors such as food sharing or consolation in the wild provide ecological validation to the current finding that altruistic behaviors are within the behavioral repertoire of chimpanzees. However, we know of no systematic observational study that has specifically addressed altruism in the form of instrumental help as investigated in the current experiments (helping others who fail to achieve their goal). Perhaps importantly, our experimental setup removed possible constraints such as competition over food, constraints that are prevalent in the wild and might thus often preclude altruistic behaviors.

A crucial question for future research is to determine the proximate mechanisms by which these types of helping behaviors in chimpanzees are maintained as evolutionarily stable strategies. Do chimpanzees engage in reciprocal altruism, repaying an incurred cost in the future, possibly even in a different currency [2,4,[23][24][25][26]? Do they punish nonaltruistic individuals [9,[27][28]? To what extent are these helping behaviors driven by empathy with the emotional states of the other, as it has been shown in the case of human infants and adults [6,20]? It is possible that one crucial difference between modern humans and their ancestors lies in the mechanisms that amplified the basic form of altruism common to humans and chimpanzees. Human cultural groups might have created unique social mechanisms to preserve and foster altruism, such as sanctioning selfish behavior and internalizing social norms [11,28,29]. But they cultivated rather than implanted the propensity to act altruistically in the human psyche.

Materials and Methods

Experiment 1: Participants. We tested 36 semi-free ranging chimpanzees living at the Ngamba Island Chimpanzee Sanctuary in Uganda (21 females and 15 males aged 3 to 20 y, M ¼ 10 y). All of the chimpanzees were born in the wild, were unrelated, were orphans as a result of the illegal trade in chimpanzee bushmeat, and were confiscated from poachers. During the day, the chimpanzees were released to range freely in the 39 hectares of tropical forest on the island. In the evening, the chimpanzees returned to eat food provided by caregivers and slept in a large holding facility (4 m high and approximately 140 m 2 ) consisting of seven rooms (3 3 5m) with interconnecting raceways. Therefore, subjects could be tested in their indoor enclosure before being released into the forest each day. The subjects were never food deprived and water was available at all times throughout the tests. Subjects could choose to stop participat- ing at any time. Chimpanzees were mostly naive to empirical testing. One third of chimpanzees had participated in a social learning study 3 y prior to our experiment [30]. Most importantly, none of the subjects had previously been fed or tested by the first experimenter (BH).

In addition, 36 children of 18 mo of age participated (17.2-18.5 mo). This age-group was selected because it represents the earliest age at which robust instrumental helping has been demonstrated so far [15]. Children were all native German speakers and came from heterogeneous socioeconomic backgrounds. None of the children had previously participated in experiments on helping.

Experiment 1: Procedure with chimpanzees. The test area consisted of the subject room (3 3 5 m), a hallway, and the outside area of the holding facility, all segregated through metal bars. The first experimenter (E1) always sat outside the facility in front of a door leading to the hallway. In each trial, experimenter 2 (E2) approached E1 and started to struggle with him over a wooden stick (16 cm long; 4 cm in diameter). This was done to highlight its value for E1 in a context familiar to chimpanzees (competition over objects). After successfully pulling the stick out of E19s hand, E2 entered the hallway and shut the door. E2 put a piece of food in the hallway to position the chimpanzee at the starting location, placed the stick on the ground, and left toward the end of the hallway. The chimpanzees could easily reach the stick from the chimpanzee room through a hole in the bars, but this was impossible for E1 because he was located behind the door.

Once the stick was in place and the subject had taken the food, E1 would perform the following behaviors according to the four conditions (Reach-Reward; Reach-No Reward; No Reach-Reward; No Reach-No Reward): He either outstretched his arm toward the stick through the bars of the door and vocalized (Reaching) or sat in front of the door, looking at the object, but not reaching for it (No Reaching). In addition, he would either hold a piece of banana in view of the subject which he gave them in exchange for the stick (Reward) or did not have any food available (No Reward).

In all conditions, each trial lasted up to 60 s, divided up into two phases: During the first 30 s, E1 vocalized and focused primarily on the object. If the chimpanzees did not hand him the stick during this period, he then became louder by banging at the door, calling the subject's name and gaze-alternating between the stick and the subject for an additional 30 s. Trials ended after the subject handed the stick or after the allotted maximum time of 60 s. After the trial, E2 either picked up the stick from the hallway (if subjects had not retrieved it) or snatched it out of E19s hand (if subjects had helped).

At the beginning and the end of each session, a baseline was administered with the stick lying in front of the subject in the hallway but no other person present. Subjects very rarely took the stick and put it back outside the cage when no one else was there. Taken together, subjects took the stick in 39 of 72 baseline trials (26 pre-and 13 posttest trials) and placed it back outside only five times.

Experiment 1: Procedure with children. The same basic procedure was used to test children's helping behavior. Again, 36 subjects were individually tested in the four conditions with ten consecutive trials each, all performed within one testing session of approximately 20 min. Instead of food, we used toy cubes which were needed to play a novel game: Upon throwing the cube through an opening, it would slide down a transparent tube into a box and create a jingle sound. This game was introduced during the warm-up phase. Children were highly motivated to play the game throughout the test (e.g., when E1 offered them the toy cube as a reward, they threw it into the box in 57 of 58 instances).

Children were tested in a room of 4.5 3 4 m, accompanied by a parent who sat in a corner of the room and remained passive during the session. The room contained a desk, a stool located 0.5 min front of the desk, and the tube game placed at the wall opposite to the desk. E1 sat at the desk and E2 operated a camera in the corner of the room. The helping scenario was as follows: E1 sat at the desk, using a pen to write a letter. E2 walked towards her, snatched the pen out of her hand, used it shortly on her notepad and then put it on a stool out in front of the desk out of E19s reach. To locate the child in the starting position, she then placed a toy cube on the tube apparatus. After the child had thrown the cube down the tube, E1 performed the behaviors according to the four conditions: Reaching for the pen by bending over the desk or looking at it (Reaching versus No Reaching), either with a cube as a potential reward held in her hand or without (Reward versus No Reward). Again, in all conditions each of the ten consecutive trials lasted up to 60 s, split in two 30-s phases: (1) focusing on the object and vocalizing, and (2) addressing child through gaze-alternation and name calling.

If the child handed the pencil to E1, she used it on her sheet of paper and continued with the next trial. E1 never praised the child. In Reward conditions, children received a block in exchange for the pencil. Trials ended when the children handed the pen over or after a maximum of 60 s. If the child did not bring the pencil after 60 s, E2 walked to the stool, took the pencil for a quick note on her pad and placed it on the desk where E1 could reach it for the next trial. To prevent frustration, a short play period was interspersed if children did not bring the object for three consecutive trials. Experiment 2. Design and procedure. Chimpanzees were tested 1 mo and children on average 8 d after experiment 1. The major procedural difference to experiment 1 was that in the chimpanzee experiment, the stick was not placed next to the subject's starting position on the ground, but in a raceway 2.5 m above, where chimpanzees had to climb in order to retrieve it. Thus, after E2 had pulled the stick out of E19s hand, she placed a banana on the ground to get the chimpanzee in the starting position, put the stick up in the raceway and left. After that, E1 entered the hallway and stood underneath the raceway, either reaching or not reaching for the stick (contingent upon condition). Subjects were tested in ten consecutive trials of up to 60 s each. During the first 30 s, E1 would look at the chimpanzee, and during the last 30 s, start to alternate gaze and increase his effort to retrieve the stick through vocalizing, hand clapping, and name calling. Sessions lasted approximately 20 min. At the beginning and the end of each session, two baseline trials were administered: In a food-for-self baseline, we put a piece of banana up in the raceway to ensure that the chimpanzees would climb up to the raceway at all, which they always did. In a stick-baseline, we put the stick in the raceway with no other person present. Chimpanzees entered the raceway in 14 of 18 trials at the beginning and 10 of 18 trials at the end of the sessions, but transferred the stick into the hallway only four times altogether (two times before and two times after the session). Thus, without anybody else present, chimpanzees virtually never performed the target behavior.

In the children experiment, the same room setup as in the first child experiment was used, but subjects had to surmount an obstacle of two soft toy snakes lying across the room, often requiring the children to stabilize themselves with one hand at boxes aligned to the side, which served as a kind of handrail. Before testing, an obstacle control condition was administered to assure that the children are not afraid of the obstacle or locomotorically impaired. The child stood in the starting position while E1 was on the other end of the obstacle, encouraging the child to walk across the obstacles to take an attractive toy she was holding. This was repeated up to three times until the children passed the obstacles once. All children met this criterion. Again, infants were tested in ten consecutive trials of up to 60 s each, divided up in two 30-s phases (focusing on object; addressing the child). Experiment 3. Design and procedure. All subjects were tested in both the experimental and the control condition on two separate days, spaced 1 to 2 d apart. Four subjects started with the experimental condition and the other five subjects with the control condition. Each condition consisted of five consecutive trials, administered during a single session of approximately 20 to 30 min. At the beginning and the end of each session, a baseline condition was conducted in which neither a recipient nor food were present.

In all conditions, a chain stretched from the target door to the subject room in which the subject was placed. The chain was attached to the bars with a peg, accessible only by the subject and not the recipient. The subject room was mostly empty, containing only some straw and a rope attached to the bars, with which the subject could play during the trials. In the experimental and the control condition, a second chimpanzee was placed in the recipient room. In the baseline, no second chimpanzee was present. Trials ended after the subject released the peg or after the allotted maximum time of 60 s.

In the experimental condition, E1 first placed food (a piece of banana or watermelon) on the floor in the target room behind a tire, visible to the recipient but invisible to the subject. E1 then attached the chain with a peg between the bars of the helper room. At the same time, E2 and E3 distracted the subject by giving food in the corner of the helper room so that E1 could safely attach the peg.

In the control condition, the same procedure was used, with the crucial difference that food was placed not in the target room, but behind the distracter door on the opposite side. The distracter door could not be opened by either the recipient or the helper.

It was of course critical that the recipient would constantly make attempts to enter through the respective doors. If recipients did not orient towards the appropriate door, the trial was repeated. This was necessary only six out of 90 times (one experimental and five control trials). To ensure that recipients would continue to orient toward the distracter door in control trials, we interspersed motivation trials in which the distracter door could be slid open by the recipient and the food could therefore be accessed. Correspondingly, if recipients did not receive help for two consecutive experimental trials, we included a motivation trial in which food was placed in the target room and the door could be opened by the recipient.

The procedure of the baseline conditions was similar to that of the other conditions, only that no food was placed in either location and no recipient was present.

Figure 1 .
1Test Areas and Setup for Chimpanzees and Human Children Experiment 1 (A, B) and experiment 2 (C, D) are shown. S indicates the subject and R indicates the recipient. The target object (chimpanzees: stick; children: pencil) was placed out of reach for the recipient but was accessible by the subject. The measured target behavior was whether the subject helped by picking up the object and handing it to the recipient. doi:10.1371/journal.pbio.0050184.g001

Figure 2 .
2Experiment 1 Mean percentage of trials with target behavior (handing the out-of-reach object to the recipient) as a function of species and condition. Error bars represent the standard error of the mean (SEM). Each subject participated in one of the four conditions in a between-subject comparison. doi:10.1371/journal.pbio.0050184.g002

Figure 4 .
4Test Area and Setup in Experiment 3 Both the target and the distracter door were held shut by chains. The recipient (R) could not access either chain, but the subject (S) could release the chain of the target door. In the experimental condition, food was placed in the target room, so that the recipient would try to open the target door and the subject could help by releasing the target chain. In the control condition, food was placed in the distracter room, so that the recipient would try to open the distracter door. In this situation, it was irrelevant (with respect to the recipient's attempt to open the distracter door) whether the subject released the target chain. The target measure in both conditions was whether the subject released the target chain. doi:10.1371/journal.pbio.0050184.g004

Figure 3 .
3Experiment 2 Mean percentage of trials with target behavior (handing the out-of-reach object to the recipient) as a function of species and condition. Error bars represent SEM. Each subject participated in one of the two conditions in a between-subject comparison. doi:10.1371/journal.pbio.0050184.g003

Figure 5 .
5Experiment 3 Mean percentage of trials with target behavior (releasing the target chain) by condition. Error bars represent SEM. Each subject was tested in both conditions in a within-subject comparison. doi:10.1371/journal.pbio.0050184.g005
PLoS Biology | www.plosbiology.org July 2007 | Volume 5 | Issue 7 | e184
AcknowledgmentsWe are grateful to L. Ajarova, D. Cox, R. Ssunna, and the trustees and staff of Ngamba Island Chimpanzee Sanctuary for their enthusiasm, help, and support. In particular, we appreciate the hard work of the animal caregivers: M. Gum, P. Kibirege, I. Mujaasi, L. Mugisha, M. Musumba, G. Muyingo, A. Okello, R. Okello, and S. Nyandwi. We also appreciate permission from the Ugandan National Council for Science and Technology and the Uganda Wildlife Authority for allowing us to conduct our research in Uganda. We are grateful to the staff members in Leipzig: A. Loose, A. Misch, and C. Salomo for testing the infants as well as C. Schneider and S. Schü tte for coding of the data. Thanks to D. Stahl and R. Mundry for statistical advice and to A. Rosati for helpful comments on an earlier draft. We owe gratitude to the infants and their parents for their participation.Author contributions. FW, BH, APM, DH, and MT conceived and designed the experiments. FW, BH, APM, and DH performed the experiments. FW analyzed the data. FW, BH, and MT wrote the paper.Funding. The research of BH and APM is supported by a Sofja Kovalevskaja award received from The Alexander von Humboldt Foundation, and the German Federal Ministry for Education and Research.Competing interests. The authors have declared that no competing interests exist.
. S1 Protocol, 10.1371/journal.pbio.0050184.sd001(54KBDOCSupporting Materials and Methods Found. Protocol S1. Supporting Materials and Methods Found at doi:10.1371/journal.pbio.0050184.sd001 (54 KB DOC).

Experiment 1 Chimpanzee in condition Reaching/No Reward. S1 Video, 10.1371/journal.pbio.0050184.sv001(8.9MBMPGVideo S1. Experiment 1 Chimpanzee in condition Reaching/No Reward. Found at doi:10.1371/journal.pbio.0050184.sv001 (8.9 MB MPG).

Experiment 1 Child in condition Reaching/No Reward. S2 ; Video, Mb Mpg, 10.1371/journal.pbio.0050184.sv002Video S2. Experiment 1 Child in condition Reaching/No Reward. Found at doi:10.1371/journal.pbio.0050184.sv002 (9.5 MB MPG).

Experiment 2 Chimpanzee in condition No Reaching. S3 Video, 10.1371/journal.pbio.0050184sv003 (6.9 MB MPGVideo S3. Experiment 2 Chimpanzee in condition No Reaching. Found at doi:10.1371/journal.pbio.0050184.sv003 (6.9 MB MPG).

Experiment 2 Child in condition Reaching. S4 Video, 10.1371/journal.pbio.0050184.sv004(7.3MBMPGVideo S4. Experiment 2 Child in condition Reaching. Found at doi:10.1371/journal.pbio.0050184.sv004 (7.3 MB MPG).

Experiment 3 Chimpanzee helps in experimental condition. S5 Video, 10.1371/journal.pbio.0050184sv005 (4.4 MB MPGVideo S5. Experiment 3 Chimpanzee helps in experimental condition. Found at doi:10.1371/journal.pbio.0050184.sv005 (4.4 MB MPG).

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