Biological systems are organized hierarchically, from molecules to cells, organisms and 32
populations (1) (2) (3) (4) (5) . At the lowest level, macromolecules form cellular modules, such as the 33 translation machinery, or other metabolic pathways (4, 6-9). Different modules perform different 34 cellular functions, which together determine the fitness of the organism. Adaptive mutations 35
improve these modules, enabling organisms to consume new resources (10, 11), become 36 resistant to drugs ( there are mutations available to improve it (that is, the module is not at a local performance peak) 48 and if their fitness benefits are above ~1/N, the inverse of the population size (30-34). Module 49 adaptation may also stop before reaching this threshold, when a balance is reached between 50
fixations of mutations that improve the module and those that degrade it (31, 35-37). Regardless 51 of the reasons for why some modules are not improvable, in this regime, all improvable modules 52 keep adapting simultaneously (albeit at possibly different rates). 53
In many populations, particularly in microbes, beneficial mutations are common and 54 recombination is rare, so that the evolutionary fates of different adaptive mutations are not 55 independent (14, 19, 20, 23) . In this so-called concurrent mutations regime, adaptation is 56 primarily driven by mutations with large fitness effects, whereas the evolutionary fates of 57 mutations whose effects fall below a certain "emergent neutrality threshold" are largely 58 determined by the genetic backgrounds in which they arise (26, 38, 39). The emergent neutrality 59 threshold depends on the supply and the fitness effects of all adaptive mutations in the genome. 60
Thus, the rate of adaptation in any one module depends not only on the mutations in that module 61 but also on mutations in all other modules, through the emergent neutrality threshold. 62
Coupling of modules by the emergent neutrality threshold has important implications for the 63 evolutionary dynamics of individual modules. In particular, improvable modules accumulate 64 adaptive mutations only if such mutations arise frequently and provide fitness benefits above the 65 emergent neutrality threshold. In contrast, modules that are improvable only by rare mutations, or 66 mutations whose fitness benefits are too small, will not adapt (29). We refer to a failure of an 67 otherwise improvable module to accumulate adaptive mutations as "evolutionary stalling". 68
Evolutionary stalling can theoretically limit the power of natural selection to improve a module, but 69 whether stalling occurs in biological systems is unclear. several of its orthologs (64, 65). We then evolve these strains in rich media where rapid and 102 accurate translation is required for fast growth (66). We thus expect natural selection to favor 103 adaptive mutations in the TM. In addition to such TM-specific mutations, mutations that improve 104 the performance of other cellular modules could also be adaptive. We refer to such mutations as 105 "generic". We show the TM-specific and generic mutations compete against each other in our 106
populations. We then directly demonstrate that TM adaptation stalls in at least some populations 107 and estimate the genetic load caused by stalling. Finally, we characterize the genetic 108 mechanisms that contribute to evolutionary stalling in our populations. 109
We previously replaced the native EF-Tu in E. coli with its orthologs from Salmonella 112 typhimurium, Yersinia enterocolitica, Vibrio cholerae and Pseudomonas aeruginosa and one 113 reconstructed ancestral variant (64) ( Table 1 , Dataset S1). EF-Tu is encoded in E. coli by two 114 paralogous genes, tufA and tufB, with the majority of the EF-Tu molecules being expressed from 115 tufA (67). To replace all EF-Tu molecules in the cell, the tufB gene was deleted and the foreign 116 orthologs were integrated into the tufA locus (64). We also included the control strain in which the 117 tufB gene was deleted and the original E. coli tufA was left intact. We refer to the engineered 118 "founder" E. coli strains as E, S, Y, V, A and P by the first letter of the origin of their tuf genes 119 ( their respective founders significantly increased during evolution (P < 0.05, t-test after Benjamini-146
Hochberg correction; Fig. 1A , Dataset S3), with significantly larger increases observed in the V, 147
A, and P populations compared to E, S, and Y populations (ANOVA, P < 10 -16 ). Furthermore, 24 148 out of 30 E, S, and Y populations and 3 of the 30 V, A and P populations became more fit than 149
the control E strain with 95% confidence (Fig. 1B, Dataset S4 ). 150
Adaptation in these populations can be driven not only by mutations in the TM, but also by 151 mutations in other modules. However, evolutionary stalling in the TM can occur only if mutations 152
improving the TM compete against other types of mutations within the same population. To 153 determine whether both types of mutations occur in our populations, we conducted whole-154 population whole-genome sequencing at multiple timepoints throughout the evolution experiment. 155
This sequencing strategy allows us to directly observe competition dynamics between mutations 156 in different modules (18, 57, 73, 74). 157
We selected replicate populations 1 through 6 descended from each founder (a total of 36 158 populations), sampled each of them at 100-generation intervals (a total of 11 time points per 159 population) and sequenced the total genomic DNA extracted from these samples. We developed 160 a bioinformatics pipeline to identify de novo SNPs and small insertion/deletion events in this data 161 set (Methods). Then, we called a mutation adaptive if it satisfied two criteria: (i) its frequency 162 changed by more than 20% in a population; and (ii) it occurred in a "multi-hit" gene, i.e., a gene in 163 which two independent mutations passed the first criterion. We augmented our pipeline with the 164 manual identification of large-scale copy-number variants which could only be reliably detected 165
after they reached high frequency in a population (Methods and SI Appendix, Fig. S2 ). We 166
successfully validated 43 / 45 tested variants via Sanger sequencing (Methods, Dataset S5) 167
We first identified and broadly classified the targets of adaptation and documented their 168 competition dynamics across all populations, irrespectively of their founder. Our filtering 169 procedure yielded a total of 167 new putatively adaptive mutations in 28 multi-hit genes, with the 170 expected false discovery rate of 13.6%, along with an additional 11 manually-identified 171 chromosomal amplifications, all of which span the tufA locus (Methods, Dataset S6 and SI 172
Appendix, Figs. S2, S3). We classified putatively adaptive mutations as "TM-specific" if the genes 173
where they occurred are annotated as translation-related (Methods). We classified all other 174 mutations as "generic". We found that 38 out of 178 (21%) putatively adaptive mutations in 6 out 175 of 28 multi-hit genes were TM-specific (Dataset S6). This is significantly more than expected by 176 chance (P < 10 -4 , randomization test) since the 215 genes annotated as translation-related 177 comprise only 4.3% of the E. coli genome. All of the TM-specific mutations occurred in genes 178 whose only known function is translation-related, such as rpsF and rpsG, suggesting these 179 mutations arose in response to the initial defects in the TM. The set of TM-specific mutations is 180 robust with respect to our filtering criteria (SI Appendix, Fig. S4 ). 181 TM-specific mutations occurred in 17 out of 36 sequenced populations. Generic mutations were 182 also observed in all of these populations (SI Appendix, Fig. S3 ). Thus, whenever TM-specific 183 mutations occurred, generic mutations also occurred, such that the fate of TM-specific mutations 184 likely depended on the outcome of competition between mutations within and between modules 185 (Fig. 2) . As a result of this competition, only 14 out of 27 (52%) TM-specific mutations that arose 186 (excluding 11 tufA ampliciations) went to fixation, while the remaining 13 (48%) succumbed to 187 clonal interference ( Fig. 2 and SI Appendix, Fig. S3 ). In at least two of these 13 cases, a TM-188 specific mutation was outcompeted by expanding clones driven by generic mutations: in 189 population V6, a TM-specific mutation in fusA was outcompeted by a clone carrying generic 190 mutations in fimD, ftsI and hslO (Fig. 2) ; and in population P3, a TM-specific mutation in tufA was 191 outcompeted by a clone carrying generic mutations in amiC and trkH (Fig. 2) . We conclude that, 192
while TM-specific beneficial mutations are sufficiently common and their fitness effects are at 193 least sometimes large enough to successfully compete against generic mutations, clonal 194
interference reduces the power of natural selection to recover TM performance. 195 In this model, the fitness of a genotype is a decreasing concave function of its distance to a 206 phenotypic optimum. A key property of this model is that genotypes that are farther from the 207 optimum have access to more beneficial mutations with large effects (75). Motivated by Fisher's 208 model, we hypothesized that TM adaptation is more likely to stall in founders with initially less 209 severe TM-defects because they are presumably closer to their performance optimum. To test 210 this hypothesis, we examined how the TM-specific mutations are distributed among populations 211 derived from different founders. 212
We found a total of 5, 10, and 12 TM-specific mutations in V, A and P populations, respectively 213 (1.7, 2.0 and 0.8 mutations per population on average, Fig. 3A ). We also observed 3, 6 and 2 214 chromosomal amplifications spanning the tufA locus in these populations, respectively. In 215 contrast, we did not observe any TM-specific mutations in the E, S and Y populations (Fig. 3A) . 216
The differences in the observed rates of TM-specific mutations between founders are statistically 217 significant (P = 4×10 -11 , 2 -test). Consistent with Fisher's model, the number of TM-specific 218 mutations observed in a founder was negatively correlated with its initial fitness (r = -0.993, P = 219 7.8×10 -5 ). We also found consistent patterns in the distribution of fixed TM-specific mutations: at 220 least one of them fixed in each of the sequenced A and P populations ( Fig. 3A and SI Appendix, 221 Fig. S3 ), but only 3 out of 6 sequenced V populations fixed one or more TM-specific mutations, 222
assuming that all tufA amplifications present in the population at generation 1,000 are fixed (Fig.  223 3A and SI Appendix, Fig. S3 ). Thus, in the A and P genetic backgrounds, which have the most 224
severe TM defects, TM-specific beneficial mutations are abundant enough and have large-225 enough fitness effects that they drive adaptation. In the V founder, which has an intermediate TM 226
defect, TM-specific mutations also contribute to adaptation but to a lesser degree, suggesting that 227 they are less common and/or provide weaker benefits in this background. In the E, S and Y 228 founders whose TMs have only weak defects, TM-specific mutations are either not available or, if  229 they are available, they are so rare and/or so weak that they fail to cross our detection threshold. 230
If the supply and/or the fitness benefits of TM-specific mutations indeed increase with the 231 magnitude of the TM defect, the contribution of such mutations to adaptation should shrink as 232 populations accumulate TM-specific mutations. To test this hypothesis, we examined the 233 temporal distribution of fixed adaptive mutations in V, A and P populations. Out of the 14 TM-specific mutations that eventually fixed, 12 (86%) did so in the first selective 242
sweep. As a result, an average TM-specific beneficial mutation reached fixation by generation 243 300 ± 52, and only one (7%) reached fixation after generation 600 ( Fig. 3B and SI Appendix, Fig.  244 S5). In contrast, the average fixation time of generic mutations in the V, A and P populations was 245 600 ± 72 generations, and 9 of them (56%) fixed after the first selective sweep (Fig. 3B and SI 246
Appendix, Fig. S5 ). As a consequence, TM-specific mutations were overrepresented among 247 mutations that were fixed within the first 300 generations in the V, A and P populations (P = 248 0.014, Fisher's exact test). These numbers exclude 11 tufA amplifications, for which we do not 249 have temporal resolution. We observe similar trends if we consider the average allele frequencies 250 of mutations (Fig. 3C) . We conclude that the accumulation of TM-specific mutations in the A, P 251
and V populations has essentially ceased by the end of the evolution experiment, while the 252 accumulation of adaptive mutations in other modules continued (Figs. 3B, C) . In other words, 253
natural selection initially improved both the TM and other cellular modules, but after ~1 TM-254 specific mutation was fixed, the focus of selection shifted away from the TM. 255
Why did TM-specific mutations fix early in evolution? As mentioned above, TM genes occupy only 256 about 4.3% of the E. coli genome. It is therefore unlikely that TM-specific mutations arise at 257 higher rates than generic mutations. Instead, their early fixation suggests that their fitness 258 benefits must have been typically much larger than those of generic mutations. To test this 259 hypothesis, we estimated selection coefficients of mutations from the mutation frequency 260 trajectories. We found that mutations in the A and P populations that are detected by generation 261 300 provide an average selective advantage of 3.7% (n = 22), while mutations that are 262 detected after generation 300 provide an average selective advantage of 1.6% (n = 28, P = 263 0.0017, t-test). This approach likely under-estimates the effects of the strongest mutations 264 because we cannot accurately resolve selective sweeps that take ≲100 generations. To 265 complement this approach, we genetically reconstructed two TM-specific mutations in their 266 respective founder backgrounds and directly measured their fitness benefits (Methods, Dataset 267 S7 and SI Appendix, Fig. S7 ). We found that mutation A74G in the rpsF gene, which was 268 observed in the population A5, confers a 8.2 ± 0.4% fitness benefit to the A founder. Mutation 269 G331A in rpsG gene, which was found in populations P2, P3 and P5, confers a fitness benefit of 270
6.5 ± 0.5% to the P founder (Dataset S8). Consistent with our hypothesis, these fitness benefits 271 are much larger than the average estimated 1.6% fitness effect of late-arriving mutations (which 272 are mostly generic). Even more strikingly, either of the two reconstructed mutations provides a 273 larger fitness gain to its founder than the total fitness gains achieved by the E, S and Y 274 populations during the entire evolution experiment (Fig. 1A) . 275
Our observations so far can be broadly interpreted in two ways. One explanation is that the TMs 276 in E, S and Y founders are unimprovable by natural selection because they are at their local 277 performance peaks and that TMs in the V, A and P founders become unimprovable after 278 acquiring ~1 TM-specific mutation. The second interpretation is that natural selection fails to 279 improve the TMs in E, S and Y founders because TM-specific mutations in these backgrounds 280 are too rare and/or too weak to drive adaptation, i.e., TM adaptation is stalled. At the same time, 281 TM adaptation stalls after the fixation of ~1 TM-specific mutation in the V, A and P populations. 282
We present three lines of evidence that TM adaptation stalls while the module is still improvable. 283 First, recall that TM-specific mutations fixed in at most three out of six sequenced V populations, 284
which strongly suggests that TMs in at least three remaining V populations (V2, V4, V6) are still 285 improvable by TM-specific mutations (SI Appendix, Fig. S3 ). Second, we observed that in at least 286 three populations (V5, A6, P3), fixation of one TM-specific mutation was followed by the rise of a 287 second TM-specific mutation, which was eventually outcompeted by a clone carrying generic 288 mutations (see population P3 in Fig. 2 and the other populations in SI Appendix, Fig. S3 ). This 289 observation suggests that the TMs remained improvable in these populations as well. Third, 290 adaptive mutations have been observed in the translation-related rpsD and rpsE genes in the 291 long-term evolution experiment in E. coli whose founder has a wild-type TM (57). If the wild-type 292 TM is improvable, it is likely that the TM in our E, S, and Y strains-which carry mild defects-are 293 also improvable. It is possible to imagine a structure of epistasis between mutations, such that 294 each of these observations is consistent with the hypothesis that all TMs are at their local 295 performance peaks. However, when considered together, these observations are more 296 parsimoniously explained by the alternative hypothesis: at least some of the TMs in our 297 experiment are not at their local performance peaks; instead, TM-specific mutations fail to keep 298 accumulating in these populations because they are outcompeted by stronger beneficial 299 mutations that improve other cellular modules, i.e., the TM exhibits evolutionary stalling. 300
Evolutionary stalling prevents a module from reaching its local performance peak and thereby 301 imposes a genetic load, i.e., the organism carrying a stalled module suffers a fitness cost relative 302
to an organism whose module performance is optimal. It is difficult to measure these loads in our 303
populations directly because we do not know how far below their local optima they are. However, 304
we can estimate these loads if we assume that strains with the locally optimal TMs are at least as 305 fit as the E strain. Note that the E strain itself carries a 4% fitness cost relative to the wild-type E. 306
coli due to a defect in the TM. 13 out of 20 A and P populations remained significantly less fit than 307 the control E strain, with the average fitness cost of about 0.5% (Fig. 1B) . However, TM 308 adaptation in all of these populations stalled before the end of the evolution experiment. Thus, 309 these populations suffer an average genetic load of at least ~0.5% due to residual TM defects. In 310 fact, the genetic loads could be much higher if the initial TM defects present in the founders have 311 not been alleviated by mutations outside of the canonically annotated TM. Under this assumption, 312 the fact that we did not observe any TM-specific mutations in population V4 would imply that it still 313 carries a ~19% genetic load imposed by the original TM defect in the V founder. By the same 314 logic, all Y populations still carry a ~3% genetic load. These estimates suggest that the power of 315 natural selection to improve an individual module can be severely limited by evolutionary stalling. 316
Genetic mechanisms underlying evolutionary stalling in the TM 317 Next, we asked what genetic mechanisms contributed to evolutionary stalling in the TM. We 318 begin with the observation that TM adaptation is stalled or stopped in some founders (E, S, and 319 Y) and initially not stalled in others (V, A and P). This observation indicates that founders have 320 access to different pools of beneficial mutations, i.e., evolutionary stalling is caused by "historical 321
contingency" epistasis (76). To further characterize the extent of historical contingency on 322 evolution in our experiment, we examined the distribution of beneficial TM-specific and generic 323 mutations across populations descended from different founders. 324
We found that TM-specific mutations were not uniformly distributed among V, A and P 325
populations. Specifically, 4 out of 7 classes of TM-specific mutations arose in a single founder 326 (Fig. 4A) . For example, we detected six independent mutations in the rpsG gene, which encodes 327 the ribosomal protein S7, and all of these mutations occurred in the P founder (P < 10 -4 , 328 randomization test, Methods). Similarly, all four mutations in the rpsF gene, which encodes the 329 ribosomal protein S6, occurred in the A founder (P < 10 -4 ). We have shown above that adaptive 330 alleles at these loci confer strong fitness benefits in their respective founder backgrounds. The distribution of some generic mutations among founders was also non-uniform. We found that 338 7 out of 22 classes of generic mutations occurred in fewer founders than expected by chance 339 (Fig. 4B, Methods) . For example, we detected five independent mutations in the ybeD gene, 340 which encodes a protein with an unknown function, and all these mutations occurred in the V 341 founder (P < 10 -4 ). Similarly, all three mutations in the alaA gene, which encodes a glutamate-342 pyruvate aminotransferase, occurred in the A founder (P < 10 -4 ). To corroborate these statistical 343 observations, we reconstructed the T93G mutation in the ybeD gene in all six founder strains and 344 directly measured its fitness effects (Dataset S8). As expected, this mutation confers a 5.9% ± 345 0.4% fitness benefit in the V founder. In contrast, it is strongly deleterious in the P founder and 346
indistinguishable from neutral in the remaining founders (SI Appendix, Fig. S6 ). Thus, historical 347 contingency epistasis not only opens up or closes down certain adaptive pathways within the TM 348 but also changes the availability of at least some beneficial mutations outside of it. 349
We next turn our attention to the genetic mechanisms for the onset of evolutionary stalling in the 350 V, A and P founders. Historical contingency could play a role here too if mutations that 351 accumulate early in evolution alter the identities and fitness effects of subsequent beneficial 352 mutations (40, 57). Additionally, another mechanism, known as "modular epistasis" (40) or 353 "coupon collecting" (57), could also operate if fixation of a mutation depletes the pool of further 354 beneficial mutations in the same module, without changing the effects of mutations in other 355 modules (for example, one beneficial loss-of-function substitution eliminates the benefits of other 356 loss-of-function mutations in the same gene). Modular epistasis could lead to evolutionary stalling 357 in the TM because the most frequent TM-specific mutations with the largest fitness benefits tend  358  to fix earlier thereby enriching the pool of available TM-specific mutations for rare and weak  359 mutations which are more likely to fall below the emergent neutrality threshold (57). If repeated 360 mutations in a given gene occur unusually often in the same population (under-dispersion), this 361 signals historical-contingency epistasis with unknown mutations present in that population (57). 362
On the other hand, unexpectedly even distribution of mutations in a given gene across different 363
populations signals the presence of modular epistasis (57). 364
To detect historical contingency and modular epistasis, we examined distribution of mutations 365 across populations (tufA amplifications were excluded from this analysis; see Methods). We find 366 that TM-specific mutations collectively are overdispersed (P = 0.0018). That is, TM-specific 367 mutations at any given locus usually arise only once per population. The distribution of generic 368 mutations collectively is consistent with the random expectation. However, mutations in some 369
individual genes show opposing tendencies. Specifically, mutations in genes trkH and fimD are 370 overdispersed (corrected P < 10 -4 ), while mutations in icd and ydcI are underdispersed (corrected 371 P = 0.004 and < 10 -4 ). Taken 
The fitness of an organism depends on the performance of many molecular modules inside cells. 384
While natural selection favors genotypes with better-performing modules, it may be difficult for it 385 to improve multiple modules simultaneously, particularly when recombination rates are low and 386 many adaptive mutations in different modules are available. In this regime, natural selection is 387 expected to focus on those modules where large effect mutations occur frequently, while 388
improvements in other modules are expected to stall. Here we have documented such 389 evolutionary stalling in the TM module during laboratory adaptation of E. coli. 390
We found that populations whose TMs were initially mildly perturbed (incurring ≲ 3% fitness cost) 391
adapted by acquiring mutations that did not directly affect the TM. Populations whose TM had a 392 moderately severe defect (incurring ~19% fitness cost) discovered TM-specific mutations, but 393 clonal interference often prevented their fixation. Populations whose TMs were initially severely 394 perturbed (incurring ~35% fitness cost) rapidly discovered and fixed TM-specific beneficial 395
mutations. However, after ~1 TM-specific mutation fixed, further accumulation of mutations in the 396 TM essentially stalled, while mutations in other modules continued to accumulate despite the fact 397 that the TMs remained improvable in at least some of our populations. 398
We estimated that the genetic load imposed by evolutionary stalling in the TM ranges from 0.5% 399 to 19% in our populations. These estimates are inherently uncertain because we do not know the 400 functional and fitness effects of all fixed mutations. The true genetic loads may be higher than 401 0.5% because some of the fitness gains in the A and P populations are probably caused by 402 improvements in other cellular modules and because the optimal TM probably provides fitness 403 higher than that of the E strain (since the E strain itself has a TM defect). On the other hand, true 404 genetic loads may be lower than 19% because TM performance may be improved by some 405 mutations in genes not annotated as translation-related. 406
In our analyses, we defined the TM module operationally as a set of 215 genes in the E. coli 407 genome that are annotated as translation-related. However, there may be other genes that 408 contribute to translation that are not annotated as such. A more fundamental problem is that, 409
while the core of the translation machinery is well defined, its periphery may in reality be quite 410 amorphous. In other words, a model of a cell where modules are discrete and clearly delineated 411
is an abstraction. For example, a recent study found that a genetic defect in the DNA replication 412 module can be alleviated by mutations outside of it (54). Similarly, we found that populations with 413 different initial tufA defects acquire mutations in statistically distinct sets of genes that are not 414 annotated as translation-related, suggesting that some of these genes may nevertheless be 415 important for translation. Notwithstanding, our results show that the focus of natural selection can 416 transition sharply from one set of genes-those encoding the core of the translation machinery 417
where the initial defect occurred-to functionally more distant parts of the cell such as genes trkH 418
and fimD that are involved with potassium transport and fimbriae production respectively. 419
As long as the environment remains constant, the supply of beneficial mutations in an adapting 420 population is gradually being depleted and their fitness effects typically decrease (40, 58-60, 77, 421 78), thereby lowering the effective neutrality threshold. These changes should in turn allow for 422 less frequent mutations with smaller effects to contribute to adaptation, and adaptation in 423 previously stalled modules may resume. While we did not observe resumption of adaptive 424 evolution in the TM during the duration of this experiment, we find evidence for a transition from 425 stalling to adaptation in trkH and fimD genes. Mutations in these two genes appear to be 426 beneficial in all our genetic backgrounds (Fig. 4) In this work, we identified several TM-specific adaptive mutations, but their biochemical and 459
physiological effects are at this point unknown. However, the fact that 11 chromosomal 460 amplifications and 12 noncoding or synonymous events occurred in the tufA operon (which 461
consists of tufA, fusA, rpsG and rpsL) suggests that some of the TM-specific mutations are 462 beneficial because they adjust EF-Tu abundance in the cell. This would be consistent with 463
previous evolution experiments (47, 84, 85). Directly measuring the phenotypic effects of the TM-464 specific mutations described here is an important avenue for future work. 465
Overall, our results highlight the fact that it is impossible to fully understand the evolution of a 466 cellular module in isolation from the genome where it is encoded and the population-level 467
processes that govern evolution. The ability of natural selection to improve any one module 468 depends on the population size, the rate of recombination, the supply and the fitness effects of all 469 beneficial mutations in the genome and on how these quantities change as populations adapt. 470
Further theoretical work and empirical measurements integrated across multiple levels of 471 biological organization are required for us to understand adaptive evolution of modular biological 472 systems. 473 474
The details of the experimental model, media and growth conditions, fitness and growth 477 measurements, genome sequencing and all statistical analyses are provided in the SI Appendix. 478 479 Competing Interests 505 The authors declare that they do not have any competing interests. 506 507 508