Science - USA (2022-03-04)

disorder and diseases that involve abnormal
DA signaling like Parkinson’s disease.

REFERENCES AND NOTES

1. T. E. Scammell, E. Arrigoni, J. O. Lipton,Neuron 93 , 747– 765
   (2017).


2. C. B. Saper, T. C. Chou, T. E. Scammell,Trends Neurosci. 24 ,
   726 – 731 (2001).


3. L. Dahanet al.,Neuropsychopharmacology 32 , 1232– 1241
   (2007).


4. J. D. Miller, J. Farber, P. Gatz, H. Roffwarg, D. C. German,
   Brain Res. 273 , 133–141 (1983).


5. J. M. Monti, H. Jantos,Prog. Brain Res. 172 , 625–646 (2008).


6. A. Eban-Rothschild, G. Rothschild, W. J. Giardino, J. R. Jones,
   L. de Lecea,Nat. Neurosci. 19 , 1356–1366 (2016).


7. K. T. Beieret al.,Cell 162 , 622–634 (2015).


8. K. T. Beieret al.,Cell Rep. 26 , 159–167.e6 (2019).


9. F. Sunet al.,Cell 174 , 481–496.e19 (2018).


10. O. Yizharet al.,Nature 477 , 171–178 (2011).


11. Y. Oishi, M. Lazarus,Neurosci. Res. 118 , 66–73 (2017).


12. K. Dzirasaet al.,J. Neurosci. 26 , 10577–10589 (2006).


13. J. M. Monti, D. Monti,Sleep Med. Rev. 11 , 113–133 (2007).


14. M. M. S. Limaet al.,Behav. Brain Res. 188 , 406–411 (2008).


15. R. Kumar Yadav, B. N. Mallick,Neuropharmacology 193 ,
    108607 (2021).


16. O. A. Massecket al.,Neuron 81 , 1263–1273 (2014).


17. C. R. Burgess, G. Tse, L. Gillis, J. H. Peever,Sleep 33 ,
    1295 – 1304 (2010).


18. S. Nishinoet al.,J. Neurosci. 11 , 2666–2671 (1991).


19. E. Mignotet al.,J. Neurosci. 13 , 1057–1064 (1993).


20. J. Haraet al.,Neuron 30 , 345–354 (2001).


21. Y. Oishiet al.,J. Neurosci. 33 , 9743–9751 (2013).


22. T. E. Scammell, J. T. Willie, C. Guilleminault, J. M. Siegel,
    International Working Group on Rodent Models of Narcolepsy,
    Sleep 32 , 111–116 (2009).


23. M. Corsi-Cabreraet al.,J. Sleep Res. 25 , 576–582 (2016).


24. P. Maquetet al.,Nature 383 , 163–166 (1996).


25. J. P. Wisoret al.,J. Neurosci. 21 , 1787–1794 (2001).


26. S. Melettiet al.,J. Neurosci. 35 , 11583–11594 (2015).


27. C. R. Burgess, Y. Oishi, T. Mochizuki, J. H. Peever,
    T. E. Scammell,J. Neurosci. 33 , 9734–9742 (2013).


28. M. S. Reidet al.,Brain Res. 733 , 83–100 (1996).


29. M.Okura,J.Riehl,E.Mignot,S.Nishino,Neuropsychopharmacology
    23 , 528–538 (2000).


30. J. D. Miller, K. F. Faull, S. S. Bowersox, W. C. Dement,Brain Res.
    509 , 169–171 (1990).


31. E. Hasegawaet al.,Proc. Natl. Acad. Sci. U.S.A. 114 ,
    E3526–E3535 (2017).

ACKNOWLEDGMENTS
We thank T. Amano and T. Maejima for valuable discussions.
Funding:This work was supported by JSPS KAKENHI Grant-in-Aid
for Scientific Research (B) (JP 18H02595) (to T.S.); JSPS KAKENHI
Grant-in-Aid for Scientific Research (A) (JP 21H04796) (to T.S.);
JSPS KAKENHI Grant-in-Aid for Scientific Research on Innovative
Areas,“Willdynamics”(16H06401) (to T.S.); JSPS KAKENHI
grant no. JP19K22465 (to T.S.); JST CREST grant no. JPMJCR1655
Japan (to T.S.); AMED grant no. JP21zf0127005 (to T.S.); JSPS
KAKENHI Grant-in-Aid for Young Scientists (18K14846) (to E.H.);
Naito Memorial Science Grant/Research Grant 2017 (to E.H.);
SENSHIN Medical Research Foundation 2017 Young Researcher
Grant (to E.H.); and Takeda Science Foundation 2019 (to E.H.).
Author contributions:Conceptualization: E.H. and T.S.
Methodology: E.H., T.S., Y.C., A.M., K.S., and Y.L. Funding
acquisition: E.H. and T.S. Project administration: E.H. and T.S.
Supervision: T.S. Writing: E.H. and T.S.Competing interests:The
authors declare that they have no competing interests.Data and
materials availability:All data are available in the main text or
the supplementary materials.

SUPPLEMENTARY MATERIALS
science.org/doi/10.1126/science.abl6618
Materials and Methods
Figs. S1 to S11
Tables S1 to S6
References ( 32 – 37 )
MDAR Reproducibility Checklist
Movies S1 and S2

29 July 2021; accepted 14 January 2022
10.1126/science.abl6618

SYNTHETIC GENOMICS

Transcriptional neighborhoods regulate transcript

isoform lengths and expression levels

Aaron N. Brooks^1 †‡, Amanda L. Hughes^1 †, Sandra Clauder-Münster^1 , Leslie A. Mitchell^2 §,

Jef D. Boeke2,3, Lars M. Steinmetz1,4,5*

Sequence features of genes and their flanking regulatory regions are determinants of RNA transcript

isoform expression and have been used as context-independent plug-and-play modules in synthetic

biology. However, genetic context—including the adjacent transcriptional environment—also influences

transcript isoform expression levels and boundaries. We used synthetic yeast strains with stochastically

repositioned genes to systematically disentangle the effects of sequence and context. Profiling 120 million

full-length transcript molecules across 612 genomic perturbations, we observed sequence-independent

alterations to gene expression levels and transcript isoform boundaries that were influenced by neighboring

transcription. We identified features of transcriptional context that could predict these alterations and

used these features to engineer a synthetic circuit where transcript length was controlled by neighboring

transcription. This demonstrates how positional context can be leveraged in synthetic genome engineering.

G

ene regulatory sequence features such

as promoters and terminators are con-

sidered primary drivers of transcript iso-

form boundaries and expression levels

( 1 – 4 ). In synthetic biology, promoters

and terminators are assembled along with

coding DNA sequences (CDSs) into transcrip-

tional units (TUs). Promoters and terminators

are characterized and distributed as stand-

ardized parts. These are used analogously to

plug-and-play modules in electronics as if they

would function identically in any context ( 5 – 7 ).

However, this is not always the case. TUs in

eukaryotic genomes exhibit a high degree of

interdependence ( 8 ) as a result of the physical

effects of transcribing neighboring genes ( 9 – 11 ).

For example, transcriptional interference be-

tween neighboring genes can influence the

selection of isoform boundaries and impact

expression levels ( 12 – 14 ). Quantifying the ex-

tent to which isoform expression and boundaries

are driven by factors beyond DNA sequences

could improve rational genome design. How-

ever, this has been difficult to test systemat-

ically as existing technologies cannot achieve

the scale required to observe genes in many

alternative genetic and transcriptional contexts.

Synthetic yeast genomes create genetic diversity

To overcome this challenge, a synthetic yeast

genome (Sc2.0) was designed to encode a Cre-

dependent system known as synthetic chro-

mosome rearrangement and modification by

LoxP-mediated evolution (SCRaMbLE), which

can generate stochastic genomic rearrange-

ments on demand directly in its genome ( 15 ).

These rearrangements occur at 34 base pair

(bp)–loxPsym sites inserted 3 bp downstream

of the stop codon of all nonessential CDSs.

Upon induction of Cre recombinase, loxPsym

sites can recombine in either orientation,

producing duplications, deletions, inversions,

and translocation events (Fig. 1A). Because of

the placement of the loxPsym sites, rearranged

CDSs retain their native promoters but can be

decoupled from downstream sequences such

as 3′-untranslated regions (3′UTRs).

The synthetic yeast chromosome IXR (synIXR)

is 91,010 bp in length and contains 43 loxPsym-

flanked segments. After SCRaMbLE of synIXR,

64 strains containing 156 deletions, 89 inversions,

94 duplications, and 55 additional complex

rearrangements were isolated ( 16 ). Notably,

these strains do not suffer from gross growth

defects (table S1). Altogether, these SCRaMbLE

genomes harbor 612 novel (i.e., newly created)

junctions formed by juxtaposing genomic seg-

ments that are usually separated (Fig. 1A). As

some rearrangements occur more than once,

there are 363 distinct novel junctions. These

novel junctions represent several different types

of rearrangements, specifically new convergent

and tandem gene pairs; genes with alternative

3 ′UTRs; and complex juxtapositions of coding

sequences, noncoding RNAs (ncRNAs), regulatory

elements, and intergenic sequences (fig. S1A)

( 17 ). We used this genomic diversity to deter-

mine the relative contributions of DNA se-

quence features versus transcriptional context

in establishing RNA isoform boundaries and

expression levels.

We profiled the transcriptomes of 64 pre-

viously genotyped synIXR SCRaMbLE strains;

the parental strain (-SCRaMbLE, JS94), which

bears synIXR without rearrangements; and

1000 4 MARCH 2022•VOL 375 ISSUE 6584 science.orgSCIENCE

(^1) European Molecular Biology Laboratory (EMBL), Genome
Biology Unit, 69117 Heidelberg, Germany.^2 Institute for
Systems Genetics and Department of Biochemistry and
Molecular Pharmacology, NYU Langone Health, New York, NY
10016, USA.^3 Department of Biomedical Engineering, NYU
Tandon School of Engineering, Brooklyn, NY 11201, USA.
(^4) Stanford Genome Technology Center, Stanford University,
Palo Alto, CA 94304, USA.^5 Department of Genetics, School
of Medicine, Stanford University, Stanford, CA 94305, USA.
*Corresponding author. Email: [email protected]
†These authors contributed equally to this work.
‡Present address: Inscripta, Inc., Boulder, CO 80301, USA.
§Present address: Neochromosome, Inc., New York, NY 11101, USA.
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