Edinburgh Research Explorer...Abeer Fadda,1† Mark Ryten,2 Dorothea Droll,1 Federico Rojas,3...

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Transcriptome-wide analysis of trypanosome mRNA decayreveals complex degradation kinetics and suggests a role for co-transcriptional degradation in determining mRNA levels

Citation for published version:Fadda, A, Ryten, M, Droll, D, Rojas, F, Färber, V, Haanstra, JR, Merce, C, Bakker, BM, Matthews, K &Clayton, C 2014, 'Transcriptome-wide analysis of trypanosome mRNA decay reveals complex degradationkinetics and suggests a role for co-transcriptional degradation in determining mRNA levels', MolecularMicrobiology, vol. 94, no. 2, pp. 307-326. https://doi.org/10.1111/mmi.12764

Digital Object Identifier (DOI):10.1111/mmi.12764

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Published In:Molecular Microbiology

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https://doi.org/10.1111/mmi.12764https://doi.org/10.1111/mmi.12764https://www.research.ed.ac.uk/en/publications/5207f6fa-5679-45ab-b2c4-6b73a3168ad6

Transcriptome-wide analysis of trypanosome mRNA decayreveals complex degradation kinetics and suggests a role forco-transcriptional degradation in determining mRNA levels

Abeer Fadda,1† Mark Ryten,2 Dorothea Droll,1

Federico Rojas,3 Valentin Färber,1‡

Jurgen R. Haanstra,4,5 Clemetine Merce,1

Barbara M. Bakker,4,5 Keith Matthews3 andChristine Clayton1*1Zentrum für Molekulare Biologie der UniversitätHeidelberg (ZMBH), DKFZ-ZMBH Alliance, ImNeuenheimer Feld 282, 69120 Heidelberg, Germany.2Flat 1,172 Gloucester Terrace, London, UK.3Centre for Immunity, Infection and Evolution, Institutefor Immunology and infection Research, School ofBiological Sciences, Ashworth Laboratories, Universityof Edinburgh, West Mains Road, Edinburgh EH9 3JT,UK.4Department of Pediatrics and Systems Biology, Centrefor Energy Metabolism and Ageing, University MedicalCenter Groningen, University of Groningen, Groningen,The Netherlands.5Department of Molecular Cell Physiology, Faculty ofEarth and Life Sciences, VU University Amsterdam,Amsterdam, The Netherlands.

Summary

African trypanosomes are an excellent system forquantitative modelling of post-transcriptional mRNAcontrol. Transcription is constitutive and polycis-tronic; individual mRNAs are excised by trans splic-ing and polyadenylation. We here measure mRNAdecay kinetics in two life cycle stages, bloodstreamand procyclic forms, by transcription inhibition andRNASeq. Messenger RNAs with short half-lives tendto show initial fast degradation, followed by a slowerphase; they are often stabilized by depletion of the5′–3′ exoribonuclease XRNA. Many longer-livedmRNAs show initial slow degradation followed byrapid destruction: we suggest that the slow phasereflects gradual deadenylation. Developmentally

regulated mRNAs often show regulated decay, andswitch their decay pattern. Rates of mRNA decay aregood predictors of steady state levels for shortmRNAs, but mRNAs longer than 3 kb show unexpect-edly low abundances. Modelling shows that varia-tions in splicing and polyadenylation rates cancontribute to steady-state mRNA levels, but this iscompletely dependent on competition between pro-cessing and co-transcriptional mRNA precursordestruction.

Introduction

The rates of transcription, RNA splicing, localization anddecay all affect the levels of cellular mRNAs, while proteinlevels are additionally affected by the rates of translationand protein turnover. In mammalian cells, mRNA abun-dance makes a contribution of up to 40% to final proteinlevels (Schwanhäusser et al., 2011). Results from highthroughput techniques have revealed that decay rates arecritical in overall mRNA homeostasis; genes encodingproteins with similar functions share similar transcriptdecay rates, and the decay rates respond to externalstimuli (Shalem et al., 2008; Munchel et al., 2011; Rabaniet al., 2011; Schwanhäusser et al., 2011).

Traditionally, decay rates have been measured byextracting mRNA at various times after transcription shut-off. More recently, a method involving pulse-chase with4-thiouracil has enabled less invasive measurements, butthis method is limited to cell types permissive to the labeluptake and incorporation into newly synthesized RNA(e.g. Munchel et al., 2011; Rabani et al., 2011). Indepen-dently of the method used, transcriptome-wide decaystudies can provide insight into the specificity (Grigullet al., 2004; Mnaimneh et al., 2004; Houalla et al., 2006;Chekanova et al., 2007; Kiss and Andrulis, 2010) anddynamics (Gudipati et al., 2012; Harigaya and Parker,2012; Schneider et al., 2012; Deneke et al., 2013) of thedecay machinery. Some of these studies revealed thepresence of feedback loops between synthesis and decay(Shalem et al., 2008; Trcek et al., 2011; Sun et al., 2012;Haimovich et al., 2013). In many cases, decay kineticsare not exponential; this can be explained by models in

Accepted 19 August, 2014. *For correspondence. E-mail [email protected]; Tel. (+49) 6221 546876; Fax (+49) 6221546891. Present affiliations: †Sidra Medical and Research Center,P.O. Box 2699, Doha, Qatar; ‡Fresenius Kabi Deutschland GmbH,Bad Homburg, Germany.

Molecular Microbiology (2014) 94(2), 307–326 ■ doi:10.1111/mmi.12764First published online 15 September 2014

© 2014 The Authors. Molecular Microbiology published by John Wiley & Sons Ltd.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution andreproduction in any medium, provided the original work is properly cited.

mailto:[email protected]:[email protected]://creativecommons.org/licenses/by/3.0/

which the ‘age’ of an mRNA influences its decay rate(Deneke et al., 2013).

African trypanosomes are unicellular parasites with amode of gene expression that is uniquely suited to thestudy of post-transcriptional mRNA control mechanisms.Transcription is polycistronic, with no apparent regulationof RNA polymerase II initiation (Palenchar and Bellofatto,2006; Daniels et al., 2010). Mature mRNAs are transspliced, with addition of a capped spliced leadersequence at the 5′ end (Michaeli, 2011); polyadenylationat the 3′ end is determined by, and coupled to, the transsplicing of the next transcript downstream (Michaeli,2011). Measurements that focused on trans splicing of thetubulin mRNA (in an in vitro system) (Ullu et al., 1993) andthe PGKB mRNA (in vivo) both found precursor half-livesof about 2 min (Haanstra et al., 2008), which suggeststhat the mRNA may be spliced once the RNA polymeraseis 1–2 kb further downstream (Haanstra et al., 2008).Results for the HSP70 tandem repeat were consistentwith these estimates (Huang and van der Ploeg, 1991).However, it is not known whether this trans splicing rate isgenerally applicable.

Two trypanosome life cycle stages are routinely cul-tured in the laboratory: the bloodstream form, which isequivalent to the form in mammalian blood, and the pro-cyclic form, equivalent to the form that grows in the midgutof the Tsetse vector. Most open reading frames have morethan one possible spliced leader addition site, and often,several polyadenylation sites are used; moreover, thechoice of both can vary between life cycle stages (Kolevet al., 2010; Nilsson et al., 2010; Siegel et al., 2010; 2011;Zhang et al., 2010). From RNASeq experiments, approxi-mately 400 genes were judged to be developmentallyregulated, with approximately equal numbers being pref-erentially expressed in each form (Siegel et al., 2010).Numerous studies have shown that developmental regu-lation of individual mRNAs is associated with differencesin their decay rates (Fernández-Moya and Estévez, 2010;Kramer and Carrington, 2011; Clayton, 2013). Oneexample, that of PGKB, has been subjected to quantita-tive modelling. The PGKB mRNA is very unstable inbloodstream forms, and stable in the procyclic form.Measurement and modelling of its synthesis and degra-dation kinetics indicated that the level of the PGKB mRNAwas predominantly controlled by the rates of transcriptionand degradation, and life cycle regulation was achievedby changes in the degradation rate (Haanstra et al.,2008). The extent to which differences in mRNA degrada-tion were responsible for developmental regulation on atranscriptome-wide scale was however unknown.

Degradation of trypanosome mRNAs follows the classiceukaryotic paradigm: the CAF1-NOT complex dead-enylates the mRNA (Erben et al., 2014), and this is fol-lowed by 5′–3′ degradation by XRNA (Li et al., 2006) or

3′–5′ degradation by the exosome (Clayton and Estevez,2010). The PAN2/PAN3 deadenylase also has a role indegradation of some mRNAs, and there is, in addition, adeadenylation-independent pathway that is initiated by 5′degradation (Schwede et al., 2009; Fadda et al., 2013).Studies of mRNA degradation currently rely on transcrip-tion inhibition. Unfortunately, pulse-labelling with 4-thio-uracil is not feasible: not only is there hardly any uptake,but there is also a bypass pathway for uridine synthesis(Ali et al., 2013) and a very strong flux of the nucleotideinto glycosylation of surface proteins and other pathways(Ali et al., 2012). We previously obtained approximateestimates of bloodstream-form trypanosome mRNA half-lives by inhibiting transcription for 30 min. We demon-strated that for most mRNAs, deadenylation precedesdegradation (Manful et al., 2011; Fadda et al., 2013) andalso found that the 5′–3′ exoribonuclease XRNA wasimportant mainly for degradation of very unstable mRNAs(Manful et al., 2011). This confirmed previous observa-tions made with individual mRNAs (Schwede et al., 2008;2009). The analyses were however limited by the use of asingle time-point for transcription inhibition, meaning thathalf-life estimates were very approximate and complexkinetics could not be detected. Also, procyclic trypano-somes were not studied.

The work described here comprises part of a study thataims to model trypanosome metabolic pathways fromgenome to metabolome. To allow quantitative modelling,as many parameters as possible must be measured intrypanosomes cultured under tightly defined conditions(Bakker et al., 2010). In this paper we describe a detailedanalysis of mRNA decay in both bloodstream form andprocyclic trypanosomes, including complex decay kinet-ics. We find extensive variations in mRNA decay, bothbetween mRNAs and between life cycle stages, but theseare usually insufficient to account for steady-state mRNAabundance. By modelling, we uncover a potentially impor-tant role for competition between mRNA processing andco-transcriptional RNA destruction in determining steady-state mRNA levels.

Results

Developmental regulation of steady-state mRNA levels

In this work we aimed to measure the half-lives of alltrypanosome mRNAs in both bloodstream and procyclicforms, and to obtain an overview of trans splicing kinetics.To measure mRNA decay we inhibited trans splicing andtranscription for various times, using Sinefungin toprevent splicing and Actinomycin D to stop transcription;to measure splicing we used Actinomycin D alone. Weprepared RNA, depleted rRNA by subtraction and thenmeasured relative RNA amounts with RNAseq. All

308 A. Fadda et al. ■

© 2014 The Authors. Molecular Microbiology published by John Wiley & Sons Ltd., Molecular Microbiology, 94, 307–326

samples are listed in Supplementary Table S1, whichincludes the raw data. Supplementary Table S2 summa-rizes the main results. Details are in subsequent Supple-mentary Tables. Most analyses concentrate on a non-redundant list of genes in which repeated copies havebeen removed (Siegel et al., 2010).

We first analysed steady state mRNA levels, comparingresults for rRNA-depleted mRNA from four bloodstreamform samples and four procyclic samples. Statistically sig-nificant differential expression was seen for 693 genes, ofwhich 430 transcripts were more abundant in bloodstreamforms (Supplementary Table S3, sheets 1 & 2). We com-pared the these results with previously published data forpoly(A) + mRNA (Siegel et al., 2010; Singh et al., 2014;Vasquez et al., 2014) (Supplementary Figs S1 and S2,and Supplementary Table S3, sheet 3). There were cleardifferences between the datasets, which could have beencaused by differences in the trypanosomes used, theculture conditions, the methods used for RNA preparationand sequencing, and the tools used for data extractionand analysis. This illustrates the fact that for quantitativework, it is vital to control all conditions and methods astightly as possible.

The comparisons enabled us to compile a list of the 450most reproducibly developmentally regulated mRNAs (atleast twofold regulated in at least 3 of the 4 availableRNA-Seq datasets, Supplementary Table S3, sheet 4).We grouped these according to manually annotated func-tional categories. Rather surprisingly, almost twice asmany mRNAs were reproducibly upregulated in blood-stream forms than were upregulated in procyclics; andoddly, genes with no known function accounted for all ofthe difference. Figure 1 shows just the upregulated genesthat could be functionally assigned. The regulation ofenergy metabolism was as expected – mitochondrialpathways present in procyclic forms, and reliance on gly-colysis in bloodstream forms (Bringaud et al., 2006).Bloodstream forms are also known to have a high require-ment for vesicular transport and GPI anchor biosynthesis(Overath and Engstler, 2004). Bloodstream-form-specificexpression of genes encoding cytoskeletal proteins,protein kinases and phosphatases, and RNA-binding pro-teins was less expected. Levels of RNA, however, do notalways correlate with levels of protein (Gunasekera et al.,2012; Urbaniak et al., 2013). Translation (Vasquez et al.,2014) and protein degradation are also important.

Our procyclic forms were grown with proline as theprincipal carbon source, with glucose only from the dilutedserum. In contrast, the procyclic forms used by others(Siegel et al., 2010; Vasquez et al., 2014) were grown inmedium with 5 mM glucose. Thirteen mRNAs reproduc-ibly showed at least a threefold greater increase in ourprocyclics [ribo- and poly(A) + datasets] than was seen inthe RNASeq data for glucose-grown cells (Supplementary

Table S3, sheet 3, columns O–R; names in bold in columnC). The increased expression of proline dehydrogenase(Tb927.7.210) has been reported previously (Lamouret al., 2005), and glutamine synthetase (Tb927.7.4970) isnext on the same pathway (Bringaud et al., 2006). ThemRNAs encoding glycosomal malate dehydrogenase(Tb927.10.15410), ubiquinol-cytochrome c reductase(Tb927.4.4990) and an aquaporin (Tb927.6.1520) arealso upregulated in proline-cultured cells. Specificallyincreased mRNAs encoding proteins of unknown functionwere Tb927.4.3500, Tb927.9.13200, Tb927.11.1690 andTb927.10.2360. This shows again that carbon sourceavailability affects expression levels in trypanosomes.

The heterogeneity of trans splicing and polyadenylationsites precludes transcriptome-wide rate measurement ofprocessing kinetics

In order to model gene expression, we needed an esti-mate of the variability in pre-mRNA splicing rates. In anattempt to obtain this, we treated cells with Actinomycin Dalone for the shortest feasible time, 5 min, and subjectedrRNA-depleted RNA to RNASeq. Since the three previ-ously measured splice times were in the range of 1–2 min,time intervals in that range would have been preferable,but were not technically feasible. For the splicing meas-urements we used two replicates for bloodstream formsand one for procyclic forms, together with one untreatedsample for each. Reads aligning to intergenic regionswithin a transcription unit come from un-spliced mRNA.Trans splicing involves trans esterification followed bydestruction of the intergenic RNA by exoribonucleases.Most trypanosome mRNAs, however, have multiple pos-sible splice sites (Kolev et al., 2010; Nilsson et al., 2010;Siegel et al., 2010; 2011; Zhang et al., 2010), whichmeans that many RNA sequences can be either inter-genic or within an mRNA. We collected all documentedsplice acceptor sites for both procyclic and bloodstreamforms from TriTrypDB, and merged them with our ownidentified sites. For each site, the read counts in the20mer upstream region were extracted, normalized to theread counts for the corresponding gene, and the ratiobetween untreated and Actinomycin D-treated sampleswas calculated. We then calculated the pre-mRNA half-lifefor each site, assuming exponential kinetics. If an openreading frame had more than one splice site, we chosethe splice site with the shortest half-life to infer splicingkinetics for that open reading frame. 3029 measurementsof 5′ splicing for bloodstream forms and 1554 for procyclicforms are shown in Supplementary Table S2 and dis-played in Supplementary Fig. S3. The measurementswere limited by the numbers of reads, as well as by theuse of only a single time interval. The median 5′ pre-mRNA half-life measured in both life stages was a little

Transcriptome-wide mRNA decay in trypanosomes 309


Mitochondrialpathway

Mitochondrial electrontransport

Oxidative phosphorylation

Citric acid cycle

Unknown function: 27Mitochondrial

transport& membrane

Mitochondrialbiogenesis

Mitochondrialtranslation

Mitochondrial RNA Glucose & glycerol

Pentose phosphatepathway

Amino acidmetabolism

Lipids &fatty acids

Sterols

Prenylation Nucleotides

Redox pathways

Enzyme & Folate

Ribosome Translation, tRNA

RNA processing Membrane protein

Protease or peptidase Chaperone

Ion channelor pump

PROCYCLIC FORM

BLOODSTREAM FORM

Cell cycle Protein phosphatase

Vesicular transport

GPI anchors& metabolism

Lysosome Glycan synthesis& metabolism

Chaperone

Cytoskeleton

Protein kinase,phosphatase,

signalling

RNA binding,zinc finger

RNA synthesis,degradation,

helicase

DNA synthesis,modification,repair, chromatin

Glucose &glycerol

Lipids & fatty acids

Nucleotides

Redox pathways Enzyme & folate

Phosphatase Protease, peptidase Ubiquitin & proteasome

Transporter Membrane

protein

GRESAG,LRRP,PAG

Unknown function: 160

129 genes

134 genes

Fig. 1. Developmentally regulated mRNAs.For each stage, genes that were upregulated in at least three of the four available RNASeq datasets were selected (Supplementary Table S3,sheet 4) and grouped according to manually assigned functional categories. Genes encoding proteins of no known function (27 for procyclicforms, 160 for bloodstream forms) are not shown. ‘Upregulated’ means at least twofold more abundant than in the other stage; for our currentdataset it also means significantly regulated.



over 3 min, and many of the measured half-times werebetween 1–2 min. Since these times are similar to thosemeasured for tubulin, HSP70 and PGK mRNAs, they maybe correct. However, there was no correlation in 5′ splic-ing times between the two life stages even for genessharing the same major acceptor site in the two stages(not shown). Also, many precursor half-lives were over4–5 min. Although this could reflect slow splicing, it couldalso indicate persistence of bicistronic RNAs, or of mono-cistronic mature mRNAs that had upstream trans splicesites. The un-spliced PGK precursor is degraded with ahalf-life of 8 min (Haanstra et al., 2008) so half-livesabove this are unlikely to reflect splicing, but may insteadreflect precursor degradation. It was thus apparent that formost mRNAs, the complexity of trypanosome mRNA splic-ing patterns precluded reliable kinetic analysis using thehigh-throughput method that we had adopted. Our resultstherefore could not be used to assess the kinetics ofsplicing. Polyadenylation sites are even more heteroge-neous so we made no attempt to analyse 3′-processingkinetics.

Despite the limitations in the results, they did suggestthat many trypanosome mRNAs might be spliced within afew minutes of synthesis. Based on this, we adopted aconservative splicing half-time of 2 ± 1.5 min in the mod-elling (below).

Trypanosome mRNAs show complex decay kinetics

We next analysed the data for parasites treated with bothSinefungin and Actinomycin D, using multiple time inter-vals and at least two replicates for each time point. Tonormalize the data we hybridized Northern blots with aprobe complementary to the spliced leader (see Methodsand Supplementary Fig. S4). The spliced leader provides agood measure of the number of mRNAs present, becauseit is present at the 5′-end of every mRNA. Unlike the poly(A)tail, it does not vary in length; and it will be removed byXRNA very fast after the mRNA has been decapped.

To calculate half-lives, we considered three possiblescenarios: simple exponential decay, or one of two non-exponential patterns (Deneke et al., 2013). The non-exponential models represent a multi-step degradationprocess in which different mRNA molecules are in differ-ent stages of degradation; hence the ‘age’ of the mRNApopulation (i.e. the length of time after transcription hasstopped) plays a role in the shape of overall decay curve(Deneke et al., 2013). In the first scenario, the mRNApopulation initially seems relatively stable but becomesincreasingly unstable over time: we call this the ‘slow-fast’scenario. For example, the actin mRNA shows little deg-radation over the first 10 minutes, but then the rateincreases (Fig. 2A). In the second scenario, part of theRNA is degraded very rapidly, but a fraction appears to be

much more stable (fast-slow scenario). An example of thisis shown in Fig. 2B. The model that fit the data best(smallest least-squares error) was used to calculate asingle transcript lifetime and half-life for each mRNA andthe resulting half-lives (Supplementary Table S4) wereused in all further analyses. The results are summarizedin Table 1. Sometimes the fit was always poor, especiallywhen there was extensive scatter at early time points(Supplementary Fig. S5A and B), while in other cases,moving from an exponential model to one of the othermodels clearly gave a better fit (Supplementary Fig. S5Cand D). As noted previously (Deneke et al., 2013), tran-scripts with short half-lives tended to fit the fast-slowmodel, while those with long half-lives tended to fit aslow-fast model (Fig. 2C and D).

Some mRNAs showed no significant degradation overthe incubation period, and for some, the abundance wastoo low for a half-life measurement. However, the use ofmultiple time points and complex kinetic models enabledus to estimate half-lives for most mRNAs. Bloodstreamtrypanosome mRNAs had a modal half-life of 9 min,median 12 min, and arithmetic mean 16 min (Fig. 3),similar to the numbers that we previously reported (Manfulet al., 2011). Procyclic trypanosomes are larger thanbloodstream forms, grow at 27°C, have about twice asmany mRNAs per cell and divide more slowly (Haanstraet al., 2008); correspondingly, their mRNAs had a modalhalf-life of 12 min, median of 20 min, and arithmetic meanof 34 min (Fig. 3, Supplementary Table S3 sheet 5).

RNASeq half-lives are similar to those found byother methods

We previously had estimated half-lives for bloodstreamforms by measuring the amount of mRNA remaining after30 min of transcription inhibition (Manful et al., 2011). Thecorrelation between our new and old datasets was bestfor mRNAs with longer half-lives (Supplementary Fig. S6).One likely reason for this is that the reliability of RNASeq

Table 1. Summary of half-life results.

Category Bloodstream Procyclic

Slow-fast 1903 2632Fast-slow 3074 1622Exponential 1495 1704Stable 92 149Not measured 480 993

Results given are for 6951 unique open reading frames. mRNAs thathad half-lives over 120 min (bloodstream forms) or 240 min (procyclicforms) were mostly fitted using the exponential model, but wereclassified as stable since the half-life could not be measured wellusing the time points that we had. ‘Not measured’ usually means thatthe numbers of counts were insufficient to calculate a half-life and/orthat the data could not be fitted with any of the three models.



decreases at low read densities, and unstable RNAs givevery few reads after 30 min of degradation.

The half-lives that we measured by RNA-Seq were inquite good agreement with previously reported half-livesmeasured by real-time PCR or Northern blotting, withcorrelation coefficients (for log-transformed data) of over0.8 (Fig. 2A and B; Fig. 4, Table 2). When we comparedthe RNASeq results with our previous measurementsmade using the same protocols for RNA synthesis inhibi-tion, differences in the kinetics were rather minor (Sup-plementary Fig. S7). RNA-Seq might be somewhat moresensitive at the later time-points, when RNA levelsdropped even below the levels that could be measuredreliably by q-PCR. Disagreements with literature values

(Table 2) could be due to differences in the cell cultureconditions as well as in methodology.

Changes in mRNA decay partially account fordevelopmental regulation

We next asked whether mRNAs encoding proteins withparticular functions showed similar half-lives. We groupedmRNAs into functional classes (Supplementary Table S2)and plotted half-lives for each class (Fig. 5). A one-wayANOVA showed significant life-time differences betweenthe different classes (P-value

stages: large amounts of mRNA are produced from only1–3 open reading frames (Manful et al., 2011). Although allRNA classes were overall more stable in the procyclicstage (T-test results in Supplementary Table S5), morepronounced shifts were apparent for functions related toprocyclic energy metabolism, such as amino acids, citricacid cycle, mitochondrial biogenesis and oxidative phos-phorylation. The small number of mRNAs involved in folateand sterol metabolism also showed greater increases inhalf-life than average. These results were consistent withthe idea that developmental regulation is, at least in part,mediated through changes in half-life.

To analyse the relationship further we plotted half-liferatios (bloodstream half-life/procyclic half-life) against theabundance ratios (bloodstream abundance/procyclicabundance). There was almost no correlation, except forvery strongly regulated mRNAs (Supplementary Fig. S8).Moreover, not all mRNAs with regulated turnover wereregulated in abundance. Restriction of the dataset to themRNAs that were rapidly spliced in both forms, or usingonly those mRNAs that had the same splice site in bothforms, did not increase the correlation (not shown).However, this analysis almost certainly under-estimatedthe contribution of mRNA turnover to developmental

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Fig. 3. Most trypanosome mRNAs havehalf-lives of less than 30 min.All mRNAs with measurable half-lives (stableones are excluded) were placed into classesaccording to half-life range. The numbers ofmRNAs in each class are plotted. All data arefor ribo-minus RNA preparations.

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regulation. We had to exclude not only all stable mRNAs,but also those for which abundance in one stage was toolow for the half-life to be measured. The latter included29 procyclic-form-specific and 172 bloodstream-form-specific mRNAs (Supplementary Table S3, sheet 3).Excluding these, we found 179 mRNAs in which mRNAdecay played a clear role in regulation (SupplementaryTable S3, sheet 6). We saw regulated mRNA decay notonly for components of the cytochrome oxidase (Mayhoet al., 2006) and ATP synthase (Brown et al., 2001) com-plexes (Table 2), but also for 67 other mitochondrialproteins, the AATP11 amino acid transporter, and 9 glyco-somal or cytosolic enzymes involved in procyclic energymetabolism. For bloodstream form-specific expression, wedetected regulation for the GPI-phospholipase C (Webbet al., 2005); the mRNAs encoding fructose bisphosphatealdolase and the alternative oxidase were relatively stablein procyclic forms but showed no decay at all during ourassay in bloodstream forms (Table 2).

Overall, more mRNAs fit a slow-fast model in procyclicthan in bloodstream forms (Table 1), and no fewer than1110 transcripts had different decay models in the two lifestages. 976 mRNAs switched from fast-slow in blood-

stream forms to slow-fast in procyclics. These were par-ticularly enriched for mitochondrial processes that aredevelopmentally upregulated in procyclics: mitochondrialtranslation, RNA processing, electron transport, biogen-esis and other pathways. Other classes that showed thisswitch were prenylation, RNA processing and the catch-all ‘enzyme’ group. The 134 that switched the other waywere mostly upregulated in bloodstream forms and wereenriched for GPI anchors and metabolism, vesiculartransport, calcium regulation and DNA synthesis, modifi-cation and repair.

Many mRNAs are less abundant than expected fromtheir half-lives

Since trypanosome mRNAs are constitutively transcribed,gene copy number and mRNA half-life are expected tomake a significant contribution to steady-state mRNAabundance. We therefore normalized mRNA copynumbers per cell to the gene copy number, then com-pared these values with the half-life. Results suggestedthat half-life contributes to, but cannot completely accountfor, the mRNA level (Fig. 6). High abundance was

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Fig. 5. Half-lives of mRNAs in particular functional classes.Unique coding regions were placed into functional classes based on their genome annotation, user comments and publications. The classesused are indicated in Supplementary Table S5. In this box-and-whisker plot the bottom and top of the box are the first and third quartiles, theband inside the box is the median, the whiskers represent the lowest datum still within 1.5 of the lower quartile, and the highest datum stillwithin 1.5 of the upper quartile and the circles represent outliers.



possible only if the mRNA was relatively stable: forexample, nearly all mRNAs present at more than 16copies/cell/gene in bloodstream forms or 32 copies percell in procyclics had half-lives longer than 30 min.

We previously developed a model for gene expressionin bloodstream trypanosomes, based on detailed meas-urements for the PGK and tubulin genes (Haanstra et al.,2008). We expanded this model in order to account for ournew results (Fig. 7A, Table 3). In the published model, thefirst step is transcription; we had set the overall rate (ini-tiation plus elongation) so that it was sufficient to producethe measured steady state level of mRNA (step 1 inFig. 7A). The next step (step 2 in Fig. 7A) is 5′ splicing,with a fixed, half-time of 1 min. Splicing competes withprecursor degradation rate (step 4 in Fig. 7A); for this, weused a half-life of ∼ 8.7 min, as determined for the PGKprecursor (Haanstra et al., 2008). Mature mRNA degra-dation (step 6 in Fig. 7A) includes degradation in both thenucleus and the cytoplasm. The model also includes dilu-tion of RNA species via parasite growth (specific growthrate, μ). To start the new analysis, we used this publishedmodel to calculate the expected amounts of mRNA atsteady state for various half-lives (grey squares, Fig. 6A).We found that they were towards the upper edge of thecloud of data points. This indicates that the publishedmodel predicts almost the maximum amount of mRNA

that can be obtained per gene in vivo. Notably, however,the levels of many mRNAs fell well below the estimatesfrom the published model. The first, and most obvious,explanation was that we had not allowed for the possibilitythat splicing rates might vary. We therefore went back toour measurements, and plotted the half-lives and abun-dances of mRNAs with relatively short measured 5′ pre-cursor half-lives (Fig. 6C and D): although the correlationwas marginally better than for the bulk population, thiscould be an artefact of the lower sample size. Returning tothe published model, we plotted the RNA abundances fora 5′ trans splicing reaction half-time of 10 min (greycircles, Fig. 6A). This still failed to account for the unex-pectedly low abundances of many mRNAs. Obviously,something was missing from the published model. OurmRNA decay rates (Fig. 7A, step 6) already included bothintra-nuclear and cytoplasmic degradation of the maturemRNA, so differences in nuclear export of mRNA couldnot explain the discrepancies between the model andreality. We identified transcription, polyadenylation andprecursor decay as possible variables.

Modelling predicts an important role forco-transcriptional mRNA decay

The first issue that we needed to address was the assump-tion that the transcription rate is constant within, andbetween, transcription units. It has previously been shownthat genes that encode mRNAs that persist after heatshock are located towards the end of transcription units(Kelly et al., 2012). This presumably enables synthesis ofthe mRNAs to continue for some time after stress-inducedshut-off of transcription initiation (Kelly et al., 2012). Thedistance of a gene from the transcription start site also borea relationship with cell cycle control (Kelly et al., 2012). Wetherefore considered the possibility that the position of thegene within the transcription unit was having an effect onthe mRNA abundance at steady state. To assess this, weplotted the half-life : abundance ratios for each geneacross the chromosomes. The plot for chromosome 10 is in

Fig. 6. Relationship between half-life and mRNA abundance.A. Relationship between the number of mRNAs per cell per gene and the half-life for 6367 non-redundant open reading frames in bloodstreamforms. All mRNAs with apparent half-lives of less than 2 min were excluded since (given the 5 min Sinefungin treatment) this is outside therange of the assay. The mRNAs that appeared stable have been arbitrarily assigned half-lives of 240 min. The Pearson correlation coefficient(R) was calculated from log-transformed data. The grey squares indicate the results of modelling steady-state mRNA abundance in thepublished model (Haanstra et al., 2008) assuming a splicing half-time of 1 min; the circles are for an assumed 5′-trans splicing half-time of10 min. The gene copy numbers were estimated from random shotgun sequences of genomic DNA.B. As for (A), but for 5831 non-redundant open reading frames in procyclic forms. The mRNAs that appeared stable have been arbitrarilyassigned half-lives of 360 min; their median number of transcripts per gene per cell is 9.C. As for (A) but including only mRNAs with precursor half-times of less than 3 min.D. As for (B) but including only mRNAs with precursor half-times of less than 4 min.E. As for (A), but including only the 600 longest (red) and shortest (blue) mRNAs. The length ranges and correlation coefficients are alsoshown for the two groups. The grey squares indicate the results of modelling steady-state mRNA abundance in the published model (Haanstraet al., 2008) assuming a splicing half-time of 1 min. Lengths were taken from TritrypDB; all mRNAs lacking annotated poly(A) sites wereexcluded.F. As for (B), but including only the 600 longest (red) and shortest (blue) mRNAs.

Table 3. The reactions in the model and their rate equations.

Rate For reaction Rate equation or value

v1 Transcription intoprecursor

0.24 min−1

v2 5′ trans-splicing ofunspliced precursor

k2 × [unsplicedprecursor]

v3 3′ poly-adenylation k3 × [5′splicedprecursor]v4 Degradation of unspliced

precursor(k4 + μ) × [unsplicedprecursor]

v5 Degradation of 5′ trans-spliced precursor

(k5 + μ) × [5′splicedprecursor]

v6 Degradation of maturemRNA

(k6 + μ) × [mRNA]



Supplementary Fig. S9. Although more detailed analysismight reveal some trend for particular mRNA classes, wefound no gradient within transcription units, and no differ-ences between transcription units. We therefore did notadd variations in transcription rate to the model.

Next, we added a step that was obviously missingfrom the published model: polyadenylation (step 3 inFig. 7A). This was assigned the same kinetics as fortrans splicing (Ullu et al., 1993). We assumed that it, liketrans splicing, was competing with precursor degradation

Fig. 7. The kinetic model for trypanosome gene expression.A. Cartoon showing the main aspects of the model. *Steps 3 and 5 are extensions compared to (Haanstra et al., 2008).B. Trypanosome mRNA behaviour was modelled for three different groups: 550 mRNAs of 0.4–1, 4408 of 1–3 and 551 of 3–20. Thisdistribution approximates to that seen in our dataset. For each set, half-lives were distributed as in Fig. 4 up to 120′. The amount of mRNA atsteady state was then modelled using the following parameters: Transcription rate 0.24 molecules cell−1 min−1; Splicing and polyadenylationhalf-lives were sampled from a normal distribution around 2 ± 1.5′. The constant for degradation of the 5′ spliced precursor k5 (0.08 min−1) wasmultiplied by the ratio (mRNA length in nt)/(600 nt). Shown is the mature mRNA half-life range of 5–60 min, since this includes mosttranscripts.C. Measured abundances of mRNAs of 0.4–1 kb with half-lives of 5–60 min were plotted over the results from the model. The regression lineformulae and correlation coefficients are for log2-transformed data. Median mRNAs/cell/gene are also indicated.D. As (C) but for 1–3 kb mRNAsE. As (D) but for mRNAs longer than 3 kb.



– in this case, degradation of a primary transcript thathas been 5′ trans spliced, but not yet polyadenylated(step 5 in Fig. 7A).

Meanwhile, we discovered that the correlation betweenhalf-life and abundance was much better for shortermRNAs than for longer ones (Fig. 6E and F andFig. S10A), and that the shortest mRNAs fitted the pub-lished model relatively well (Fig. 6E). Longer mRNAswere on average less abundant than shorter ones with thesame half-life (Supplementary Fig. S10B). We concludedthat at least one of the steps in the model must include anegative influence of mRNA length.

We now set up a simulation that incorporated variationsin processing, splicing and polyadenylation rates as wellas in mRNA decay, and that included a negative influenceof mRNA length. Splicing and polyadenylation were variedsomewhat conservatively such that the half-time for eachstep was 2 ± 1.5 min. To mimic length-dependent co-transcriptional destruction we multiplied kinetic constantk5 (degradation of the trans spliced precursor) by a factorof (mRNA length in nt)/600. (The number 600 was chosenempirically.) Our rationale for this was that a longer tran-script has more chance to be degraded before it is pro-cessed, since it takes longer for the polymerase to makea precursor that contains the downstream trans splicingsignal. In addition, slow (inefficient) polyadenylation ofan mRNA should also increase the likelihood of co-transcriptional destruction.

We now used this extended model to simulate steady-state abundances for transcripts with varying lengths,half-lives, 5′ trans splicing and 3′ polyadenylation kinetics,using the known length distribution for the mRNAs and ahalf-life distribution based on our measurements. Theresults are shown in Fig. 7B. We then compared the simu-lated data for each length group with the real data(Fig. 7C–E). There was a good qualitative agreementbetween simulations and the data. In the simulations thegoodness-of-fit (R2) for the relation between mRNA half-life and abundance decreased with increasing transcriptlength, and in both the simulations and in the experimen-tal data, abundance was highest for the short transcriptsand lower for the longer ones. The real data, though, hadbigger variation (lower R or R2) than the simulations. Thismay hint at additional regulation, or a need to adjust theparameters further. For instance, compared with theextended model, in the real dataset the intermediatelength group (Fig. 7D) had more transcripts that werestable (half-life > 16′) and showed very low abundance.These might be mRNAs that are polyadenylated slowerthan the maximum half-time of 3.5 min that we allowed forin the model. (Unfortunately we could not model using thereal distribution of processing times because these couldnot be measured using the RNASeq approach.) Themodel also predicted a population of long, very unstable

transcripts with lower abundance than was seen in thedata (less than one mRNA in every two parasites)(Fig. 7E). These would be very unstable, long transcriptsthat are processed slowly. It is possible that this particularcombination of properties (long and unstable) is rare.

We also tested the effect of a theoretical length-dependent 3′ polyadenylation step – although it is difficultto imagine a mechanism for this (k3 decreasing withincreasing transcript length). This also resulted in thelinear relationship between mRNA half-life and abun-dance being poorer for longer transcripts but the effectwas not as pronounced as seen when we allowedfor co-transcriptional degradation (SupplementaryFig. S11A). Notably, if we also removed degradation of the5′ trans spliced precursor (step 5), we obtained a virtuallyperfect relation between half-life and abundance for alllengths (Supplementary Fig. S11B). This is a conse-quence of the steady-state assumption. Simulationsshowed that without degradation of the 5′ trans splicedprecursor, if transcription were induced from an ‘off’ state,slower 3′ polyadenylation lowered the mature mRNAabundance in the initial phase, but did not affect theabundance when the steady state was reached (notshown). If degradation of the un-spliced precursor wasremoved as well, a perfect correlation between abun-dance and half-life was obtained (SupplementaryFig. S11C). The modelling thus yielded the important con-clusion that length-dependent variations in processingcan only affect steady state mRNA abundance if process-ing is competing with degradation of the partly processedintermediates.

Discussion

At the start of this study it was already known that the rateof mRNA decay is an important determinant of trypano-some mRNA abundance, but detailed quantitative infor-mation, including time-courses for the degradation,was available only for a very small number of mRNAs(Table 2). We have now provided transcriptome-widedecay kinetics for both bloodstream and procyclic forms,and have developed a detailed model for gene expres-sion. We could confirm the important role of mRNA decayin determining steady state mRNA levels. We also foundthat a previously unrecognized step in processing has tobe present to explain the steady-state mRNA abundanceswe measured. This additional step is most likelyco-transcriptional degradation. Differences in mRNAmaturation rates can only affect mRNA abundance ifco-transcriptional degradation is also occurring.

The kinetics of mRNA decay fell into at least threedifferent patterns. The ‘fast-slow’ group of mRNAsshowed initial fast degradation, followed by a slowerphase. This is also seen, in bloodstream forms, for



mRNAs bearing the 3′-UTR of the mRNA encoding themajor surface protein EP procyclin; in that case, the rapiddegradation phase occurs without deadenylation and isdependent on the 5′–3′ exoribonuclease XRNA (Li et al.,2006; Schwede et al., 2009). Those EP mRNAs that arenot attacked in this way are deadenylated more slowly,then degraded by XRNA and the exosome (Irmer andClayton, 2001; Haile et al., 2003; Li et al., 2006; Schwedeet al., 2009). Depletion of the exosome caused a slightdelay in the initiation of EP mRNA degradation (Haileet al., 2003), suggesting some cross-talk between the 5′and 3′ pathways. To see if this pattern was general weexamined previous RNASeq results for parasites defec-tive in different steps of mRNA decay. The fast-to-slowmethod of decay was indeed the predominant one in thecase of mRNAs whose half-lives were increased afterXRNA depletion (Manful et al., 2011) (Fisher’s testP-value

trans splicing, but may in fact be slightly slower, since it isthought to occur after splicing has been completed (Ulluet al., 1993). Second, in considering the effects of tran-script length on degradation of the spliced precursor, wehave opted for a simple length-dependence, which largelyleaves transcripts < 600 nt unaffected by intermediate deg-radation. We could have changed the boundary chosen, ormade this dependence more complex. Third, the modelwas evaluated at steady state, but we cannot rule out thatpre-steady-state kinetics influenced the experimentalresults. If there are variations in the overall transcriptioninitiation rate during the cell cycle, some slowly processedmRNAs may never reach steady state.

The detailed mechanism of trypanosome co-transcriptional degradation is currently unknown. Co-transcriptional degradation certainly occurs in othersystems, although the details may not apply in trypano-somes. In Opisthokonts, the nuclear quality controlmachinery is triggered by failure of mRNA capping, and byfailure to splice or polyadenylate a terminated mRNA(Schmid and Jensen, 2010; 2013; Kilchert and Vasiljeva,2013). 5′->3′ degradation by Xrn2/Rat1 requires a free 5′end, and has mainly been studied in relation to transcrip-tion termination beyond polyadenylation sites (Schmid andJensen, 2010; 2013; Kilchert and Vasiljeva, 2013). Withtrypanosome polycistronic transcription, most mRNAshave no free 5′ end, since the mature 5′-end is created bytrans-esterification, and termination is relevant only for atiny proportion of genes. Polyadenylation might, however,fail after downstream trans splicing has been completed,and the resulting free 3′-ends should be attacked by thenuclear exosome (Cristodero and Clayton, 2007;Etheridge et al., 2009). There is, though, no reason to thinkthat this would be more efficient for long mRNAs than forshort ones and in fact, depletion of the exosome actuallycauses preferential loss of longer mRNAs (Fadda et al.,2013; Jha et al., 2014). The NOT deadenylation complex isassociated with RNA polymerase II in mammals, but it isthought to promote (not inhibit) elongation (Reese, 2013).Previous experiments that aimed at characterization ofRNA polymerase II subunits and transcription factors (e.g.Lee et al., 2009) were not designed for detection of pro-teins present in sub-stoichiometric amounts, but aftertandem affinity purification of the NOT complex, wedetected two peptides from a putative polymerase II elon-gation factor (Tb927.2.3580) (Färber et al., 2013).

Our data are best explained by the existence of anendonuclease that intermittently cleaves the associatedmRNA precursor. This is highly speculative since there is,so far as we know, no real precedent for such a degrada-tion process (Schmid and Jensen, 2010; 2013; Kilchertand Vasiljeva, 2013). To recognize the precursor specifi-cally, we would expect the endonuclease to be associatedeither with chromatin, or with the elongating RNA poly-

merase II complex. One consequence of such a mecha-nism would be that a decrease in the polymeraseelongation rate would increase the time that the precursoris accessible for digestion. Indeed, after depletion oftrypanosome CTR9, a probable transcription elongationfactor (Ouna et al., 2012), the abundances of longmRNAs were drastically decreased (Fadda et al., 2013;Jha et al., 2014). In animal cells and yeast, elongatingRNA polymerase II pauses, and even backtracks(Darzacq et al., 2007; Churchman and Weissman, 2011).We speculate that such pauses might, in trypanosomes,be a trigger for cleavage without detachment of the poly-merase, and could even be influenced by the DNAsequence. This would provide an additional means toregulate the formation of mRNA in the absence of anycontrol of transcription initiation.

The simplicity of the trypanosome system has made itpossible to construct a mathematical model of mRNAcontrol which has revealed novel aspects and refined ourview of the likely contribution of mRNA processing anddegradation to mRNA abundance. When combined withribosome profiling to determine translation efficiency, ourmeasurements will enable us to mathematically modeltrypanosome gene expression from DNA to protein.

Experimental procedures

Sample preparation

Bloodstream and procyclic form trypanosomes of the Lister427 strain, expressing the tet repressor, were used; the blood-stream forms were the 2T1 line described in (Alsford et al.,2005), while procyclic cells expressed the tet repressor fromplasmid pHD2060. Bloodstream forms were cultured in HMI-9medium containing 20% FCS, to a density of 1.0–1.2 × 106

cells ml−1; procyclic forms were cultured in MEM-Pros to adensity of 2 × 106 (experiment 1) or 1.3 × 106 (experiment 2)cells ml−1. For the pre-mRNA decay assay, transcription wasinhibited briefly by incubating the cells with Actinomycin D(10 μg ml−1) and samples were harvested immediately(equivalent to a 5 min incubation); the bloodstream formexperiment was done twice; for procyclics a single experimentwas done. For the mRNA decay assay, cap methylation wasinhibited by adding 1 μg ml−1 Sinefungin for 5 min to preventsplicing (t0 samples), then Actinomycin D was added for up to120 min for bloodstream forms and 240 min for procyclics. Allsamples are listed in Supplementary Table S1. The parasiteswere harvested by centrifugation for 5 min (all times giveninclude the centrifugation time); RNA was extracted usingpeqGOLD-Trifast (peqLab) and depleted for rRNA usingRibominus Eukaryotic kit for RNA-seq (Invitrogen, no.10837-02 or -08). The RNA was then sequenced (Librarypreparation at the BioQuant facility, Heidelberg and HiSeq atEMBL). The first set of bloodstream-form decay samples wasmeasured using Illumina GAIIX and all remaining sampleswith Illumina HiSeq (Supplementary Table S1). All data areavailable at the NCBI Sequence read archive, with accessionnumber SRP042959, and a simplified version of the results



has been sent to TritrypDB. We cannot rule out the possibilitythat the Sinefungin orActinomycin D treatments influenced themRNA decay rates, especially after long incubation times, butat present alternative methods are not available.

RNA decay analysis by Northern blotting

To determine the overall mRNA decay rate, aliquots of thesequenced samples were separated by agarose gel electro-phoresis, blotted and probed with a spliced leader probe,using the rRNA signal for normalization, as previouslydescribed (Fadda et al., 2013). The measured signal wasused to fit an exponential decay curve with R (SupplementaryFig. S4) and the values corresponding to the curve were usedto normalize the RNAseq decay data.

Data processing

Reads were aligned to the T. brucei 927 reference genome,and read counts were extracted using Bowtie, SamTools andcustom-made PERL scripts as previously described (Faddaet al., 2013). Read counts per ORF were first normalized forthe total number of counts (RPM, reads per million reads). Inorder to build the libraries, the same amount of input RNAwas used for each sample; and similar numbers of readcounts were obtained for each library. After addition of tran-scription inhibitors, the proportions of the unstable mRNAs godown (giving a decrease in RPM) while the proportions of thestable RNAs go up (giving a decrease in RPM). The stablemRNAs seem to increase because the overall reduction inmRNA levels is not reflected in the RNASeq data. This neces-sitates the use of an external control to detect – and correctfor – the decline in mRNA quantities in the cell with time. Wecould not normalize using endogenous stable RNAs,because they were excluded during the ribo-minus and sizeselection steps of library preparation. To measure globalmRNA decay we therefore used Northern blots to assay thedecline of the spliced leader (SL) signal, as previouslydescribed (Fadda et al., 2013). Then, to account for thedecreasing total amounts of RNA during the decay assay, theRPM for each time point were multiplied by the correspondingSL signal at that time. ORFs with an average of less than 50reads at time 0 were excluded from the half-life analysis.Lastly, the read counts were calculated as a ratio to time point0 (t0). This format was used as input for a C++ code tocalculate half-lives. Using the equations described byDeneke et al., (2013) we allowed for three alternative decaypatterns: simple exponential, initially fast, then slower (fast-slow, equation 9 of Deneke et al., 2013) and initially slow,then fast (slow-fast, equation 10 of Deneke et al., 2013). Forthe calculations each measurement was treated as an inde-pendent data point. Differential expression was analysed byDESeq (Anders and Huber, 2010).

To measure splicing kinetics, read counts were extractedfor the 20mer upstream of all known splice acceptor sites foreach gene ( Kolev et al., 2010; Nilsson et al., 2010; Siegelet al., 2010; Zhang et al., 2010 and our data), as well as foreach coding region. If the read counts for the 20mer wereequal to, or more than, the length-normalized read counts incoding region, we assumed that the splice site was not being

used in our sample, and the data for the site were removed;we also removed sites if no reads existed in the 20mer regionin the control (untreated) sample. Finally, zero values in the20mer region in Actinomycin D samples were converted to 1read. Read counts per 20mer were normalized to the readcounts per coding region of the corresponding gene in eachsample. The ratio of the normalized 20mer counts in thetreated/untreated samples was used to calculate a pre-mRNA half-life based on an exponential model. While inter-preting the results, we attribute this to splicing, althoughstrictly speaking pre-mRNA degradation also contributes.Probability density plots were done using a kernel densityfunction contained in the R package ‘ggplot’.

To calculate gene copy numbers, we first did a control foruniformity of coverage. We mapped the reads from the refer-ence genome against itself using reads available from Dr A.Ivens (University of Edinburgh). Allowing each read to maponly once should yield a bell-shaped coverage plot. Next weallowed the reads to map up to 300 times and calculatedRPKM for each gene. We assumed that the modal RPKMvalue (M) corresponded to a gene copy number of 1, sincemost genes should be unique. Next, we aligned the reads fromsequenced, fragmented DNA from our lab strain, and mappedthem to the reference genome, allowing each read to align onlyonce. We again calculated the modal value (M′). If the numberof reads from a gene was M′, then that gene has the same copynumber as in the reference; if M′ has a value n × M, then thestrain has n times the number of copies in the reference.

Calibrating decay models

The code for decay calibration is written in C++ and is pro-vided as Supplementary file S1. We used the equationsdescribed by Deneke et al., (2013) to model yeast mRNAdecay; that paper explains the mathematical details.Complex, non-exponential mRNA degradation is modelled bya Markov chain with 5 states; each state represents a differ-ent biochemical process of mRNA degradation, where themolecule is likely to be degraded at any state. The rate oftransition between states is assumed to be the same for allstates and is denoted as λ. The degradation rate from anystate is denoted as μ. In one variant of the complex model theinitial rate of degradation μ1 is assumed bigger than all sub-sequent rates, denoted ν. This is described by the parametricdensity Equation 9 (Deneke et al., 2013) and referred to hereas ‘fast-slow’ model. The other model variant assumes alldegradation rates except the last to be equal, and the lastrate μn ≡ ν > 0. This is described by the parametric densityEquation 10 (Deneke et al., 2013) and here referred to as‘slow-fast’ model. We calibrated and compared three models:the exponential model, ‘fast-slow’ and ‘slow-fast’. The calibra-tion procedure worked in several stages. In the first stage wecalibrated each model. The fitting procedure using non-linearleast squares which we used (Dennis et al., 1981) isextremely fast but suffers numerically from multiple localminima. These local minima are obtained from different ‘initialparameter guesses’. For the three-parameter models wechose to calibrate the model using 125 different startingpoints, lying on a mesh between 0 and 3.0 with 5 points peraxis. For each calibration we stored the residual model errorand the calibrated parameters. In the next stage we ran a



rejection/acceptance procedure for each of the parametricforms, and for each of a form’s 125 models. The rejectioncriteria were subjective, but were designed to reject modelsthat were completely implausible, or had collapsed to simplermodels. The rejection criteria for each model are found in thefunction includeTentativeSolution(). As an example, for a cali-brated model of Equation 9’s density we have parameters λ,μ, ν (Deneke et al., 2013). The model is rejected if λ or ν are0, or if any of the parameters have become infinite. The nextstage of calibration chooses the best fitting accepted modelfor each density type. We call the three best-fit models thethree ‘candidate’ models. The last stage of calibration selectsthe best-fit model from among the candidate models. The nullhypothesis is that the distribution is Exponential E. For acandidate model C ≠ E we look at the relative

D C ER C R E

R C R E,

.( ) >

( ) − ( )⋅ ( ) + ( )0 5

where R(M) is the residual sum of squares error from fittingwith model M. If D(C;E) > 5% we rejected the null hypothesisand accepted C. The choice of threshold is essentially arbi-trary. Deneke et al. (2013) used a boundary of 10%, we chose5% after visual examination of individual examples. Using thelower threshold leads to a lower proportion of curves beingdesignated as exponential. We did not favour either the Equa-tion 10 or Equation 9 density over each other and simply tookthe candidate from the two with the minimal residual. The ‘halflives’ for the mRNAs with non-exponential decay in all theTables are the time at which half of the mRNA is left (the valueof time T where 50% of the distribution lies above T, and 50%lies below T).

Modelling mRNA processing and decay

The PGK mRNA model developed in Haanstra et al. (2008)was initially used. Then we made a new, modified versionwhich applies to any transcript. The new model was extendedto include polyadenylation and length-dependent degradationof the trans spliced intermediate. Model simulations weredone, and extensions were made, in the open source soft-ware COPASI v.4.9.45 (Hoops et al., 2006). Figure 7A showsa graphical representation of the reactions in the model, andtheir rate equations are listed in Table 1.

The constants k in the equations relate to half-lives (HL) ofthe corresponding species via

k =( ) −ln min2 1

HL

For k4, k5 and k6 this relation has been explicitly included inthe model, which enables us to affect the k’s by changing thehalf-life through random sampling (see below).

k2 and k3 each have a default value of 0.41 (half-life of 1.7′)(as in Haanstra et al., 2008). We assumed k4 and k5 to beequal to each other and took a value of 0.08 min−1 which isbased on the previously measured half-life of the PGK pre-cursor after inhibition of both transcription and splicing byActinomycin D and Sinefungin (Haanstra et al., 2008). μ isthe specific growth rate and is 0.0019 min−1 (a populationdoubling time of 6 h).

The differential equations that describe the changes inspecies concentrations over time in the model are:

ddt

v kk

unsplicedprecursorunsplicedprecursor

[ ]= − × [ ]

− +( ) ×1 2

4 μ uunsplicedprecursor[ ]

ddt

k k[ ]′ = × [ ] −

× ′

5

52 3

splicedprecursorunsplicedprecursorspliceedprecursorsplicedprecursor

[ ] − +( )× ′[ ]

k55

μ

ddt

k kmRNA

splicedprecursor mRNA[ ]

= × ′[ ] − +( ) × [ ]3 65 μ

Concentrations are in molecules cell−1.To simulate effects that depend on the length of the mature

mRNA, the relevant constant was multiplied or divided by(length in nt)/(600 nt): For length-dependent degradation ofthe 5′ spliced intermediate, k5 was multiplied by length/600 sothat longer transcripts would be affected faster. For length-dependent polyadenylation, k3 was multiplied by 600/lengthso that polyadenylation would be slowed down for longertranscripts.

The model was used to simulate steady state abundancesfor individual transcripts (i.e. with their own values for thekinetic constants). To this end, 5510 transcripts were simu-lated by

• Sampling the half-life of the un-spliced precursor in the 5′trans splicing reaction from a normal distribution with amean of 2′ and a standard deviation of 1.5′ (affects k2),

• Sampling the half-life of the 5′ spliced precursor in the 3′poly-adenylation reaction from a normal distribution witha mean of 2′ and a standard deviation of 1.5′ (affects k3;only used when k3 was not multiplied by a length-dependent factor), and

• Sampling the length of a transcript from a uniform dis-tribution for short (551 transcripts in the range 400–1000 nt), intermediate (4408 transcripts in the range1000–3000 nt) and long (551 transcripts in the range3000–20000 nt) transcripts

For each length range, the half-life of the mature mRNA(affecting k6) was sampled from a uniform distribution withinthe half-life range in each bin in Fig. 3, except that for thefirst bin, the half-lives ranged between 1–5′. The number oftranscripts in the length range for each bin was taken pro-portionally to the distribution of lengths of the overalldataset (10% short, 80% intermediate and 10% longtranscripts).

In some cases, the combination of parameters did notresult in a steady state and the final number of solutions wastherefore lower than the number of simulations.

Acknowledgements

We thank Janine Philipp for making the SL-hybridizedNorthern blots for bloodstream forms, and Ute Leibfried fortechnical assistance. We also thank Dr Carlus Deneke (MaxPlanck Institute of Colloids and Interfaces, Potsdam) foruseful discussions, and Alasdair Ivens (University of Edin-burgh) for the TREU927 shotgun reads.



Funding

This work was supported by the collaborative Sysmo grant‘The silicon trypanosome’, with funds from the Bundesminis-terium für Bildung und Forschung, BBSRC (BB/I004602/1),and the Netherlands Organization for Scientific Research(NWO). Work in Edinburgh was also funded by the WellcomeTrust (088293; 095831). DD was supported by the DeutscheForschungsgemeinshaft, Grant no CL112/17-1, and BMB isfunded by a Rosalind Franklin Fellowship from the Universityof Groningen.

Author contributions

Abeer Fadda did most of the data analysis. Mark Ryten wrotethe programme to fit the different decay models to the data andderive half-lives. Dorothea Droll, Federico Rojas and ValentinFärber were responsible for parasites, RNA samples andNorthern blots. Clementine Merce did the gene copy numbercalculations. Keith Matthews supervised the bloodstream-form work. Jurgen Haanstra was responsible for the model-ling, with supervision from Barbara Bakker. Christine Claytonwas responsible for overall supervision and coordinatingwriting of the paper.

Conflict of interest

The authors declare no conflict of interest.

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