Because of their shorter generation times and larger census population sizes, pathogens are usually ahead in the evolutionary race with their hosts. The risks linked to pathogen adaptation are still exacerbated in agronomy, where plant and animal populations are not freely evolving but depend on breeders and growers, and are usually highly genetically homogeneous. As a consequence, the speed of pathogen adaptation is crucial for agriculture sustainability. Unraveling the time scale required for pathogens’ adaptation to their hosts would notably greatly improve our estimation of the risks of pathogen emergence, the efficiency of disease control strategies and the design of epidemiological surveillance schemes. However, the temporal scale of pathogen evolution has received much less attention than its spatial scale [1]. In their study of a wheat fungal disease, Suffert et al. [2] reached contrasting conclusions about the pathogen adaptation depending on the time scale (intra- or inter-annual) and on the host genotype (sympatric or allopatric) considered, questioning the experimental assessment of this important problem.

Suffert et al. [2] sampled two pairs of Zymoseptoria tritici (the causal agent of septoria leaf blotch) sub-populations in a bread wheat field plot, representing (i) isolates collected at the beginning or at the end of an epidemic in a single growing season (2009-2010 intra-annual sampling scale) and (ii) isolates collected from plant debris at the end of growing seasons in 2009 and in 2015 (inter-annual sampling scale). Then, they measured in controlled conditions two aggressiveness traits of the isolates of these four Z. tritici sub-populations, the latent period and the lesion size on leaves, on two wheat cultivars. One of the cultivars was considered as "sympatric" because it was at the source of the studied isolates and was predominant in the growing area before the experiment, whereas the other cultivar was considered as "allopatric" since it replaced the previous one and became predominant in the growing area during the sampling period.

On the sympatric host, at the intra-annual scale, they observed a marginally-significant decrease in latent period and a significant decrease of the between-isolate variance for this trait, which are consistent with a selection of pathogen variants with an enhanced aggressiveness. In contrast, at the inter-annual scale, no difference in the mean or variance of aggressiveness trait values was observed on the sympatric host, suggesting a lack of pathogen adaptation. They interpreted the contrast between observations at the two time scales as the consequence of a trade-off for the pathogen between a gain of aggressiveness after several generations of asexual reproduction at the intra-annual scale and a decrease of the probability to reproduce sexually and to be transmitted from one growing season to the next. Indeed, at the end of the growing season, the most aggressive isolates are located on the upper leaves of plants, where the pathogen density and hence probably also the probability to reproduce sexually, is lower. On the allopatric host, the conclusion about the pathogen stability at the inter-annual scale was somewhat different, since a significant increase in the mean lesion size was observed (isolates corresponding to the intra-annual scale were not checked on the allopatric host). This shows the possibility for the pathogen to evolve at the inter-annual scale, for a given aggressiveness trait and on a given host.

In conclusion, Suffert et al.’s [2] study emphasizes the importance of the experimental design in terms of sampling time scale and host genotype choice to analyze the pathogen adaptation to its host plants. It provides also an interesting scenario, at the crossroad of the pathogen’s reproduction regime, niche partitioning and epidemiological processes, to interpret these contrasted results. Pathogen adaptation to plant cultivars with major-effect resistance genes is usually fast, including in the wheat-Z. tritici system [3]. Therefore, this study will be of great help for future studies on pathogen adaptation to plant partial resistance genes and on strategies of deployment of such resistance at the landscape scale.

References
[1] Penczykowski RM, Laine A-L and Koskella B. 2016. Understanding the ecology and evolution of host–parasite interactions across scales. Evolutionary Applications, 9: 37–52. doi: 10.1111/eva.12294

[2] Suffert F, Goyeau H, Sache I, Carpentier F, Gelisse S, Morais D and Delestre G. 2017. Epidemiological trade-off between intra- and interannual scales in the evolution of aggressiveness in a local plant pathogen population. bioRxiv, 151068, ver. 3 of 12th November 2017. doi: 10.1101/151068

[3] Brown JKM, Chartrain L, Lasserre-Zuber P and Saintenac C. 2015. Genetics of resistance to Zymoseptoria tritici and applications to wheat breeding. Fungal Genetics and Biology, 79: 33–41. doi: 10.1016/j.fgb.2015.04.017

The preprint by Bertels et al. [1] reports an interesting application of the well-accepted idea that positively selected traits (here variants) can appear several times independently; think about the textbook examples of flight capacity. Hence, the authors assume that reciprocally convergence implies positive selection. The methodology becomes then, in principle, straightforward as one can simply count variants in independent datasets to detect convergent mutations.
In this preprint, the authors have applied this counting strategy on 95 available sequence alignments of the env gene of HIV-1 [2,3] that corresponds to samples taken in different patients during the early phase of infection, at the very beginning of the onset of the immune system. They have compared the number and nature of the convergent mutations to a "neutral" model that assumes (a) a uniform distribution of mutations and (b) a substitution matrix estimated from the data. They show that there is an excess of convergent mutations when compared to the “neutral” expectations, especially for mutations that have arisen in 4+ patients. They also show that the gp41 gene is enriched in these convergent mutations. The authors then discuss in length the potential artifacts that could have given rise to the observed pattern.
I think that this preprint is remarkable in the proposed methodology. Samples are taken in different individuals, whose viral populations were founded by a single particle. Thus, there is no need for phylogenetic reconstruction of ancestral states that is the typical first step of trait convergent analyses. It simply becomes counting variants. This simple counting procedure needs nonetheless to be compared to a “neutral” expectation (a reference model), which includes the mutational process. In this article, the poor predictions of a specifically designed reference model is interpreted as an evidence for positive selection.
Whether the few mutations that are convergent in 4-7 samples out of 95 were selected or not is hard to assess with certainty. The authors have provided good evidence that they are, but only experimental validations will strengthen the claim. Nonetheless, beyond a definitive clue to the implication of selection on these particular mutations, I found the methodological strategy and the discussions on the potential biases highly stimulating. This article is an excellent starting point for further methodological developments that could be then followed by large-scale analyses of convergence in many different organisms and case studies.

References

[1] Bertels, F., Metzner, K. J., & Regoes R. R. (2018). Convergent evolution as an indicator for selection during acute HIV-1 infection. BioRxiv, 168260, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/168260
[2] Keele, B. F., Giorgi, E. E., Salazar-Gonzalez, J. F., Decker, J. M., Pham, K.T., Salazar, M. G., Sun, C., Grayson, T., Wang, S., Li, H. et al. (2008). Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection. Proc Natl Acad Sci USA 105: 7552–7557. doi: 10.1073/pnas.0802203105
[3] Li, H., Bar, K. J., Wang, S., Decker, J. M., Chen, Y., Sun, C., Salazar-Gonzalez, J.F., Salazar, M.G., Learn, G.H., Morgan, C. J. et al. (2010). High multiplicity infection by HIV-1 in men who have sex with men. PLoS Pathogens 6:e1000890. doi: 10.1371/journal.ppat.1000890

The search for physiological markers of health and survival in wild animal populations is attracting a great deal of interest. At present, there is no (and may never be) consensus on such a single, robust marker but of all the proposed physiological markers, telomere length is undoubtedly the most widely studied in the field of evolutionary ecology (Monaghan et al., 2022). 

Broadly speaking, telomeres are non-coding DNA sequences located at the end of chromosomes in eukaryotes, protecting genomic DNA against oxidative stress and various detrimental processes (e.g. DNA end-joining) and thus maintaining genome stability (Blackburn et al., 2015). However, in most somatic cells from the vast majority of the species, telomere sequences are not replicated and telomere length progressively declines with increased age (Remot et al., 2022). This shortening of telomere length upon a critical level is causally linked to cellular senescence and has been invoked as one of the primary causes of the aging process (López-Otín et al., 2023). Studies performed in both captive and wild populations of animals have further demonstrated that short telomeres (or telomere sequences with a fast attrition rate) are to some extent associated with an increased risk of mortality, even if the magnitude of this association largely differs between species and populations (Wilbourn et al., 2018).

The repeated observations of associations between telomere length and mortality risk have called for studies seeking to identify the ecological and biological factors that – beyond chronological age – shape the between-individual variability in telomere length. A wide spectrum of environmental stressors such as the level of exposure to pathogens or the degree of human disturbances has been proposed as possible modulators of telomere dynamics (see Chatelain et al., 2019). However, within species, the relative contribution of various ecological and biological factors on telomere length has been rarely quantified. In that context, the study of Criscuolo and colleagues (2023) constitutes a timely attempt to decipher the relative contribution of environmental and biological factors driving telomere length in nestlings (i.e. when individuals are between 15 and 35 days of age) from a wild population of little owls, Athene noctua.

In addition to chronological age, Criscuolo and colleagues (2023) analysed the effects of two environmental variables (i.e. cohort and habitat quality) as well as three life history traits (i.e. hatching rank, sex and body condition). Among these traits, sex was found to impact nestling’s telomere length with females carrying longer telomeres than males. Traditionally, the among-individuals variability in telomere length during the juvenile period is interpreted as a direct consequence of differences in growth allocation. Fast-growing individuals are typically supposed to undergo more cell divisions and a higher exposure to oxidative stress, which ultimately shortens telomeres (Monaghan & Ozanne, 2018). Whether - despite a slightly female-biased sexual size dimorphism - male little owls display a condensed period of fast growth that could explain their shorter telomere is yet to be determined. Future studies should also explore the consequences of these sex differences in telomere length in terms of mortality risk. In birds, it has been observed that telomere length during early life can predict lifespan (see Heidinger et al., 2012 in zebra finches, Taeniopygia guttata), suggesting that females little owls might live longer than their conspecific males. Yet, adult mortality is generally female-biased in birds (Liker & Székely, 2005) and whether little owls constitute an exception to this rule - possibly mediated by sex-specific telomere dynamics - remains to be explored.   

Quite surprisingly, the present study in little owls did not evidence any clear effect of environmental conditions on nestling’s telomere length, at both temporal and special scales. While a trend for a temporal effect was detected with telomere length being slightly shorter for nestling born the last year of the study (out of 4 years analysed), habitat quality (measured by the proportion of meadow and orchards in the nest environment) had absolutely no impact on nestling telomere length. Recently published studies in wild populations of vertebrates have highlighted the detrimental effects of harsh environmental conditions on telomere length (e.g. Dupoué et al., 2022 in common lizards, Zootoca vivipara), arguing for a key role of telomere dynamics in the emerging field of conservation physiology. While we can recognize the relevance of such an integrative approach, especially in the current context of climate change, the study by Criscuolo and colleagues (2023) reminds us that the relationships between environmental conditions and telomere dynamics are far from straightforward. Depending on the species and its life history, telomere length in early life could indeed capture very different environmental signals.

References

Blackburn, E. H., Epel, E. S., & Lin, J. (2015). Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection. Science, 350(6265), 1193-1198. https://doi.org/10.1126/science.aab3389
 
Chatelain, M., Drobniak, S. M., & Szulkin, M. (2019). The association between stressors and telomeres in non-human vertebrates: A meta-analysis. Ecology Letters, 23, 381-398. https://doi.org/10.1111/ele.13426
 
Criscuolo, F., Fache, I., Scaar, B., Zahn, S. & Bleu, J. (2023). Telomere length vary with sex, hatching rank and year of birth in little owls, Athene noctua. EcoEvoRxiv, ver.4, peer-reviewed and recommended by PCI Evol Biol. https://doi.org/10.32942/X2BS3S
 
Dupoué, A., Blaimont, P., Angelier, F., Ribout, C., Rozen-Rechels, D., Richard, M., & Le Galliard, J. F. (2022). Lizards from warm and declining populations are born with extremely short telomeres. Proceedings of the National Academy of Sciences, 119(33), 2201371119. https://doi.org/10.1073/pnas.2201371119
 
Heidinger, B. J., Blount, J. D., Boner, W., Griffiths, K., Metcalfe, N. B., & Monaghan, P. (2012). Telomere length in early life predicts lifespan. Proceedings of the National Academy of Sciences, 109(5), 1743-1748. https://doi.org/10.1073/pnas.1113306109
 
Liker, A., & Székely, T. (2005). Mortality costs of sexual selection and parental care in natural populations of birds. Evolution, 59(4), 890-897. https://doi.org/10.1111/j.0014-3820.2005.tb01762.x
 
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2023). Hallmarks of aging: An expanding universe. Cell, 186(2), 243-278. https://doi.org/10.1016/j.cell.2022.11.001
 
Monaghan, P., Olsson, M., Richardson, D. S., Verhulst, S., & Rogers, S. M. (2022). Integrating telomere biology into the ecology and evolution of natural populations: Progress and prospects. Molecular Ecology, 31(23), 5909-5916. https://doi.org/10.1111/mec.16768
 
Monaghan, P., & Ozanne, S. E. (2018). Somatic growth and telomere dynamics in vertebrates: Relationships, mechanisms and consequences. Phil. Trans. R. Soc. B, 373(1741), 20160446. https://doi.org/10.1098/rstb.2016.0446
 
Remot, F., Ronget, V., Froy, H., Rey, B., Gaillard, J., Nussey, D. H., & Lemaitre, J. (2022). Decline in telomere length with increasing age across nonhuman vertebrates: A meta‐analysis. Molecular Ecology, 31(23), 5917-5932. https://doi.org/10.1111/mec.16145
 
Wilbourn, R. V., Moatt, J. P., Froy, H., Walling, C. A., Nussey, D. H., & Boonekamp, J. J. (2018). The relationship between telomere length and mortality risk in non-model vertebrate systems: A meta-analysis. Phil. Trans. R. Soc. B, 373(1741), 20160447. https://doi.org/10.1098/rstb.2016.0447

The social amoeba Dictyostelium discoideum is an important model system for the study of cooperation and multicellularity as is has both unicellular and aggregative life phases. In the aggregative phase, which typically occurs when nutrients are limiting, individual cells eventually gather together to form a fruiting bodies whose spores may be dispersed to another, better, location and whose stalk cells, which support the spores, die. This extreme form of cooperation has been the focus of numerous studies that have revealed the importance genetic relatedness and kin selection (Hamilton 1964; Lehmann and Rousset 2014) in explaining the maintenance of this cooperative collective behavior (Strassmann et al. 2000; Kuzdzal-Fick et al. 2011; Strassmann and Queller 2011). However, much remains unknown with respect to how the interactions between individual cells, their neighbors, and their environment produce cooperative behavior at the scale of whole groups or collectives. In this preprint, Forget et al. (2021) describe how the D. discoideum system is crucial in this respect because it allows these cellular-level interactions to be studied in a systematic and tractable manner.
Spore bias, which is the tendency of a particular genotype or strain to disproportionately migrate to the spore instead of the stalk, is often used to define which strains are "cheaters" (positive spore bias) and which are "cooperative" (negative spore bias). Forget et al. (2021) note that spore bias depends on a number of stochastic factors including external drivers such as variation in environmental (or nutrient) quality and internal drivers like cell-cycle phase at the time of starvation. Spore bias is also affected by the social environment where the fraction of cheater strains in a spore may be limited by the ability of the remaining stalk cells to support the spore. The social environment can also affect cells through their differential responsiveness to the chemical factors that induce differentiation into stalk cells; responsiveness is partly a function of nutrient quality (Thompson and Kay 2000), which in turn can be a function of cell density. Thus, Forget et al. (2021) highlight a number of mechanisms that could generate frequency-dependent selection that would lead to the stable maintenance of multiple strains with different spore biases; in other words, both cheater and cooperative strains might stably coexist due to these cellular-level interactions.
The cellular-level interactions that Forget et al. (2021) highlight are particularly important because they pose a challenge evolutionary theory: some evolutionary models of social and collective behavior neglect or simplify these interactions. For example, Forget et al. (2021) note that the developmental, behavior, and environmental timescales relevant for Dictyostelium fruiting body formation all overlap. Evolutionary analyses often assume some of these timescales, for example developmental and behavior, are separate in order to simplify the analysis of any interactions. Thus, new theoretical work that allows these timescales to overlap may shed light on how cellular-level interactions can produce environmental, physiological, and behavioral feedbacks that drive the evolution of cooperation and other collective behaviors.

References

Forget, M., Adiba, S. and De Monte, S.(2021) Social conflicts in *Dictyostelium discoideum *: a matter of scales. HAL, hal-03088868, ver. 2 peer-reviewed and recommended by PCI Evolutionary Biology. https://hal.archives-ouvertes.fr/hal-03088868/

Hamilton, W. D. (1964). The genetical evolution of social behaviour. II. Journal of theoretical biology, 7(1), 17-52. doi: https://doi.org/10.1016/0022-5193(64)90039-6

Kuzdzal-Fick, J. J., Fox, S. A., Strassmann, J. E., and Queller, D. C. (2011). High relatedness is necessary and sufficient to maintain multicellularity in Dictyostelium. Science, 334(6062), 1548-1551. doi: https://doi.org/10.1126/science.1213272

Lehmann, L., and Rousset, F. (2014). The genetical theory of social behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1642), 20130357. doi: https://doi.org/10.1098/rstb.2013.0357

Strassmann, J. E., and Queller, D. C. (2011). Evolution of cooperation and control of cheating in a social microbe. Proceedings of the National Academy of Sciences, 108(Supplement 2), 10855-10862. doi: https://doi.org/10.1073/pnas.1102451108

Strassmann, J. E., Zhu, Y., & Queller, D. C. (2000). Altruism and social cheating in the social amoeba Dictyostelium discoideum. Nature, 408(6815), 965-967. doi: https://doi.org/10.1038/35050087

Thompson, C. R., & Kay, R. R. (2000). Cell-fate choice in Dictyostelium: intrinsic biases modulate sensitivity to DIF signaling. Developmental biology, 227(1), 56-64. doi: https://doi.org/10.1006/dbio.2000.9877

Accurate information flow is central to living systems. The continuity of genomes through generations as well as the reproducible functioning and survival of the individual organisms require a faithful information transfer during replication, transcription and translation. The differential efficiency of natural selection against “mistakes” results in decreasing fidelity rates for replication, transcription and translation. At each level in the information flow chain (replication, transcription, translation), numerous complex molecular systems have evolved and been selected for preventing, identifying and, when possible, correcting or removing such “mistakes” arising during information transfer.

However, fidelity cannot be improved ad infinitum. First, because of the limits imposed by the physical nature of the processes of copying and recoding information over different molecular supports: all mechanisms ensuring fidelity during biological information transfer ultimately rely on chemical kinetics and thermodynamics. The more accurate a copying process is, the lower the synthesis rate and the higher the energetic cost of correcting errors. Second, because of the limits imposed by random genetic drift: natural selection cannot effectively act on an allele that contributes with a small differential advantage unless effective population size is large. If s <1/Ne (or s <1/(2Ne) in diploids) the allele frequency in the population is de facto subject to neutral drift processes.

In their preprint “Random genetic drift sets an upper limit on mRNA splicing accuracy in metazoans”, Bénitière, Necsulea and Duret explore the validity of this last mentioned “drift barrier” hypothesis for the case study of alternative splicing diversity in eukaryotes (Bénitière et al. 2022). Splicing refers to an ensemble of eukaryotic molecular processes mediated by a large number of proteins and ribonucleoproteins and involving nucleotide sequence recognition, that uses as a molecular substrate a precursor messenger RNA (mRNA), directly transcribed from the DNA, and produces a mature mRNA by removing introns and joining exons (Chow et al. 1977). Alternative splicing refers to the case in which different molecular species of mature mRNAs can be produced, either by cis-splicing processes acting on the same precursor mRNA, e.g. by varying the presence/absence of different exons or by varying the exon-exon boundaries, or by trans-splicing processes, joining exons from different precursor mRNA molecules.

The diversity of mRNA molecular species generated by alternative splicing enlarges the molecular phenotypic space that can be generated from the same genotype. In humans, alternative splicing occurs in around 95% of the ca. 20,000 genes, resulting in ca. 100,000 medium-to-high abundance transcripts (Pan et al. 2008). In multicellular organisms, the frequency of alternatively spliced mRNAs varies between tissues and across ontogeny, often in a switch-like pattern (Wang et al. 2008). In the molecular and cell biology community, it is commonly accepted that splice variants contribute with specific functions (Marasco and Kornblihtt 2023) although there exists a discussion around the functional nature of low-frequency splice variants (see for instance the debate between Tress et al. 2017 and Blencowe 2017). The origin, diversity, regulation and evolutionary advantage of alternative splicing constitutes thus a playground of the selectionist-neutralist debate, with one extreme considering that most splice variants are mere “mistakes” of the splicing process (Pickrell et al. 2010), and the other extreme considering that alternative splicing is at the core of complexity in multicellular organisms, as it increases the genome coding potential and allows for a large repertoire of cell types (Chen et al. 2014).

In their manuscript, Bénitière, Necsulea and Duret set the cursor towards the neutralist end of the gradient and test the hypothesis of whether the high alternative splice rate in “complex” organisms corresponds to a high rate of splicing “mistakes”, arising from the limit imposed by the drift barrier effect on the power of natural selection to increase accuracy (Bush et al. 2017). In their preprint, the authors convincingly show that in metazoans a fraction of the variation of alternative splicing rate is explained by variation in proxies of population size, so that species with smaller Ne display higher alternative splice rates. They communicate further that abundant splice variants tend to preserve the reading frame more often than low-frequency splice variants, and that the nucleotide splice signals in abundant splice variants display stronger evidence of purifying selection than those in low-frequency splice variants. From all the evidence presented in the manuscript, the authors interpret that “variation in alternative splicing rate is entirely driven by variation in the efficacy of selection against splicing errors”.

The authors honestly present some of the limitations of the data used for the analyses, regarding i) the quality of the proxies used for Ne (i.e. body length, longevity and dN/dS ratio); ii) the heterogeneous nature of the RNA sequencing datasets (full organisms, organs or tissues; different life stages, sexes or conditions); and iii) mostly short RNA reads that do not fully span individual introns. Further, data from bacteria do not verify the herein communicated trends, as it has been shown that bacterial species with low population sizes do not display higher transcription error rates (Traverse and Ochman 2016). Finally, it will be extremely interesting to introduce a larger evolutionary perspective on alternative splicing rates encompassing unicellular eukaryotes, in which an intriguing interplay between alternative splicing and gene duplication has been communicated (Hurtig et al. 2020).

The manuscript from Bénitière, Necsulea and Duret makes a significant advance to our understanding of the diversity, the origin and the physiology of post-transcriptional and post-translational mechanisms by emphasising the fundamental role of non-adaptive evolutionary processes and the upper limits to splicing accuracy set by genetic drift.

References

Bénitière F, Necsulea A, Duret L. 2023. Random genetic drift sets an upper limit on mRNA splicing accuracy in metazoans. bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.12.09.519597 

Blencowe BJ. 2017. The Relationship between Alternative Splicing and Proteomic Complexity. Trends Biochem Sci 42:407–408. https://doi.org/10.1016/j.tibs.2017.04.001

Bush SJ, Chen L, Tovar-Corona JM, Urrutia AO. 2017. Alternative splicing and the evolution of phenotypic novelty. Philos Trans R Soc Lond B Biol Sci 372:20150474. https://doi.org/10.1098/rstb.2015.0474

Chen L, Bush SJ, Tovar-Corona JM, Castillo-Morales A, Urrutia AO. 2014. Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity. Mol Biol Evol 31:1402–1413. https://doi.org/10.1093/molbev/msu083

Chow LT, Gelinas RE, Broker TR, Roberts RJ. 1977. An amazing sequence arrangement at the 5’ ends of adenovirus 2 messenger RNA. Cell 12:1–8. https://doi.org/10.1016/0092-8674(77)90180-5

Hurtig JE, Kim M, Orlando-Coronel LJ, Ewan J, Foreman M, Notice L-A, Steiger MA, van Hoof A. 2020. Origin, conservation, and loss of alternative splicing events that diversify the proteome in Saccharomycotina budding yeasts. RNA 26:1464–1480. https://doi.org/10.1261/rna.075655.120

Marasco LE, Kornblihtt AR. 2023. The physiology of alternative splicing. Nat Rev Mol Cell Biol 24:242–254. https://doi.org/10.1038/s41580-022-00545-z

Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ. 2008. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40:1413–1415. https://doi.org/10.1038/ng.259

Pickrell JK, Pai AA, Gilad Y, Pritchard JK. 2010. Noisy splicing drives mRNA isoform diversity in human cells. PLoS Genet 6:e1001236. https://doi.org/10.1371/journal.pgen.1001236

Traverse CC, Ochman H. 2016. Conserved rates and patterns of transcription errors across bacterial growth states and lifestyles. Proc Natl Acad Sci U S A 113:3311–3316. https://doi.org/10.1073/pnas.1525329113

Tress ML, Abascal F, Valencia A. 2017. Alternative Splicing May Not Be the Key to Proteome Complexity. Trends Biochem Sci 42:98–110. https://doi.org/10.1016/j.tibs.2016.08.008

Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB. 2008. Alternative isoform regulation in human tissue transcriptomes. Nature 456:470–476. https://doi.org/10.1038/nature07509